# Welcome

Welcome to Tracenable Documentation

Explore platform guides, dataset documentations, and step-by-step tutorials designed to help you confidently use our data in your research, product development, and decision-making.

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What is Tracenable?

{% columns %} {% column %} Tracenable is an open platform that enables direct access to traceable corporate financial and ESG data. Data is compiled through our human-in-the-loop architecture, which combines AI data extraction with expert validation to ensure quality and transparency. {% endcolumn %} {% column %} The platform makes hundreds of metrics available across thousands of global companies. You can access the data instantly through: * **APIs** – integrate metrics into your workflows, products, or research tools. * **CSV downloads** – export datasets for analysis in spreadsheets, BI tools, or custom pipelines. {% endcolumn %} {% endcolumns %}

Key Platform Features

Tracenable’s tools are built for simplicity, accessibility, and trust, helping you work with corporate data efficiently and confidently.

Company ScreenerExplore our company coverage and access company-level dashboards showcasing traceable financial and ESG data.https://tracenable.com/company-screener/files/2VqtPbtQ4hk4FO4TOsT4
Data ExporterBuild, preview, and export customized financial and ESG datasets (CSV/XSLX) in just a few clicks.https://tracenable.com/dataset-exporter/files/Q94uivDLOJuKQBdy4UMF
Disclosure SearchAccess and search millions of corporate disclosures from global companies.https://tracenable.com/disclosure-search/files/S5SyxoY6r1hEYAkCBhL2

Get Started with the Basics!

{% columns %} {% column %} {% content-ref url="/pages/lrN7sGPXQ2VdvfLHIunr" %} [Overview](/platform/overview) {% endcontent-ref %} {% endcolumn %} {% column %} {% content-ref url="/pages/UxcGru7oGR6KmaNn8qli" %} [Key Concepts](/platform/key-concepts) {% endcontent-ref %} {% endcolumn %} {% endcolumns %} Need some help? [Chat with us](mailto:hello@tracenable.com) Create an account: [Sign up](https://tracenable.com/signup) # Overview The Overview page introduces each panel of the Tracenable platform. Learn what every panel does, how it fits into your workflow, and where to start when building datasets or exploring company data. ## Introduction The Tracenable platform is designed to make corporate data discoverable, customizable, and traceable. This overview introduces the core tools available to you and explains how they fit into the process of building datasets, exploring disclosures, and managing company selections. *** ## Company Screener The Company Screener is your entry point into Tracenable’s coverage universe. It lists all companies we track, along with identifiers and security metadata. A search bar and intuitive filters make it easy to find the entities you need. When you click on a company in the screener, you are taken directly to that company’s dashboard, where you can explore detailed metrics, disclosures, and analytics for that entity. Why it matters: The screener is also the starting point for building custom datasets. When exporting data in CSV or Excel, this is where you add or remove companies from your universe.
{% hint style="warning" %} **Can’t find some companies?** Our coverage spans \~95% of global market capitalization, and we actively expand it. Let us know if something is missing and we’ll work to add it. {% endhint %} {% hint style="success" %} **Have a reference index or a list of identifiers (ISINs, tickers, etc.)?** If you’re working with a reference index (e.g. FTSE All-World, MSCI World, S\&P500, DAX40...) or identifiers, share it with us, we’ll map the companies on your behalf and build a ready-to-use universe for you. {% endhint %} *** ## Data Exporter Use the Data Exporter to create datasets tailored to your needs. Pick the companies, metrics, and time periods you need, preview coverage, and download a ready-to-use CSV or Excel spreasheets in seconds. Instant pricing ensures you only pay for what you use, while previews help you evaluate data coverage before exporting. This makes dataset creation both cost-efficient and transparent.
For detailed instructions on how to use the Data Exporter, see our step-by-step guide on exporting CSV datasets: {% content-ref url="/pages/pIDCUfpFOu9DKcAbVX0K" %} [Export Data in CSV/Excel](/platform/export-data-in-csv-excel) {% endcontent-ref %} *** ## Disclosure Search The Disclosure Search gives you direct access to the original corporate sources behind the data. Explore annual reports, sustainability disclosures, spreadsheets, and webpages - all indexed and searchable in one place. Traceability is central to Tracenable. Disclosure Search ensures you can always validate metrics against primary sources. Searches are scoped to a single company and emphasize the most recent disclosures for speed and relevance.
Tracenable uses Elasticsearch’s simple query string syntax, so you can refine results with powerful operators: * `+` for AND (e.g. `climate +emissions`) * `|` for OR (e.g. `climate | energy`) * `-` to exclude (e.g. `climate -risk`) * `"` for exact phrases (e.g. `"net zero"`) * `*` for prefix (e.g. `sustainab*`) * `( )` for grouping (e.g. `(climate | energy) +data`) * `~` for fuzzy match (e.g. `sustainble~1`) {% hint style="info" %} **Looking for more?** * Want to search across multiple companies or entire industries? Contact us and we’ll help you expand your scope. * Need access to older or historical reports? Contact us to unlock archived disclosures. * Want to download corporate disclosures? Contact us to discuss bulk or individual access. {% endhint %} *** ## Company Universes The Universe panel helps you organize groups of companies into reusable sets. A universe is simply a saved selection that you can apply across data exports and other features. Universes are especially useful if you work with the same group of companies repeatedly, or if you manage multiple sets of companies for tasks like portfolio management or market research. Instead of rebuilding your selection each time, you can save it once and reuse it across data exports and other features. Within this panel, you can: * **Create** new universes from scratch or based on existing lists. * **Manage** universes by renaming them or adding descriptions for clarity. * **Delete** universes you no longer need.
*** ## Saved Datasets The Saved Datasets panel keeps a copy of every dataset you export with the Data Exporter. This way, you can always come back to previous datasets without needing to rebuild them from scratch. Saved datasets make it easy to revisit past work, share consistent results with colleagues, or re-export data when needed.
# Key Concepts Tracenable uses a few core concepts throughout the platform. Understanding them will help you unlock the platform’s full potential. ## Company Universe A **company universe** is the set of companies you select for analysis or export. * It can be built interactively in the [Company Screener](/platform/overview#company-screener), or defined through a reference list you share with us. That list can be an existing index (e.g., MSCI World, S\&P 500, DAX 40) or any custom list containing company- or security-level identifiers (e.g., ISINs, SEDOLs, FIGIs, tickers). * Once defined, the same universe can be reused across datasets and exports. * Universes give you control over *which companies* you are analyzing, whether it’s a small peer group or the entire Tracenable coverage. *** ## Dimensions and Metrics Tracenable datasets follow a dimensional model. * **Dimensions** are the ways you can analyze or slice the data. Each dataset is defined by one or more dimensions. * **Metrics** are the most granular layer: each one represents a unique combination of dimension values that defines a specific data point. **Example: GHG Emissions Dataset** The GHG dataset is structured around three dimensions: * **Level:** Total / Categories * **Scope:** Scope 1 / Scope 2 / Scope 3 * **Type:** Absolute / Revenue Intensity A metric is created by setting one value for each dimension. For example: * **Total Scope 1 (Absolute):** Level = Total • Scope = Scope 1 • Type = Absolute * **Scope 3 by Category (Absolute):** Level = Categories • Scope = Scope 3 • Type = Absolute (this yields values across the 15 Scope 3 categories reported by the company). The dimensional model is a transparent way to structure data: it removes ambiguity in naming and makes metric definitions predictable. # Company Universes 101 The following pages explains everything you need to know about company universes, including how to create, edit, and manage them so you can tailor Tracenable to your needs. A **company universe** is a set of companies you select for analysis or export. Universes give you control over *which companies* you want to work with, whether that’s a peer group, an index, or the entire Tracenable coverage. Once created, a universe can be reused across different datasets and workflows, so you don’t need to start from scratch every time. On the following pages, you’ll find step-by-step guidance for managing universes: * [**Create a Universe**](/platform/company-universes-101/create-a-new-universe) – start fresh from the Universe Panel or the Company Screener. * [**Add or Remove Companies** ](/platform/company-universes-101/add-remove-companies-to-from-a-universe)– adjust your universe composition directly from the screener. * [**View Companies in a Universe**](/platform/company-universes-101/view-companies-in-a-universe) – filter the company screener to see which companies belong to a given universe. * [**Rename a Universe**](/platform/company-universes-101/rename-a-universe) – update the name and description of your universes to keep things organized. * [**Delete a Universe**](/platform/company-universes-101/delete-a-universe) – remove a universe when it’s no longer needed. # Create a New Universe This page explains how to create a new company universe in Tracenable. There are two ways to create a universe: ### **Option 1 – From the Universe Panel** {% stepper %} {% step %} #### Open the Universe Panel
{% endstep %} {% step %} #### Create the Universe In the panel, click the **+ New Universe** button. A modal will appear, prompting you to give it a name and (optionally) a description:
Click **Create Universe** to save the newly created universe. {% hint style="warning" %} When you create a universe from the Universe Panel, it starts out empty by default. To add companies to it, see the [section below](https://app.gitbook.com/o/VvoGLAuwzQNub9ZaLt0R/s/CaiZtb2FdhVao9WNSdyl/~/changes/16/platform/company-universes/~/page#add-remove-companies-from-a-universe). {% endhint %} {% endstep %} {% endstepper %} ### **Option 2 – From the Company Screener** {% stepper %} {% step %} #### **Open the Company Screener**
{% endstep %} {% step %} #### Select the companies you want to include Simply click the rows of the companies you want to include in your Universe. They will become highlighted.
{% endstep %} {% step %} #### Create the universe Click the **Add \[XX] to Universe** button at the bottom of the table
A modal will display a list of your existing company universes:
In the search bar, type the name of the new universe you wish to create instead of selecting an existing one.
Click the **Create \[Name of Universe]** button to save the universe. It will contain the companies you have selected in step 2. {% endstep %} {% endstepper %} # Add/Remove Companies to/from a Universe This page explains how to add and/or remove companies to/from an existing universe in Tracenable. Follow the four steps below to add and/or remove companies to/from an existing universe: {% stepper %} {% step %} #### **Open the Company Screener**
{% endstep %} {% step %} #### Search & filter companies (optional) To find the companies you're interested in, you can: * **Search:** Use the search bar to look for specific company names or tickers. * **Filters:** Leverage filters to narrow down the list. You can filter by Country, Sector, Industry, Revenues, and more.
{% endstep %} {% step %} #### Select companies you want to include Simply click the rows of the companies you want to include in your Universe. They will become highlighted.
{% endstep %} {% step %} #### Add the selected companies to a universe With your companies selected, click on the **Add XX to Universe** button (where XX is the number of companies you've selected). This action will reveal a list of your existing universes. You can then simply add the companies selected to a given Universe by simply clicking on its name.

Screenshot showing the button to add companies to a universe

{% hint style="danger" %} To remove companies from an existing universe, select them in the table and click the **Remove XX from Universe** button instead. {% endhint %} {% endstep %} {% endstepper %} # View Companies in a Universe This page explains how to filter the Company Screener to see which companies belong to an existing company universe. Follow the two steps below to view the companies populating an existing universe: {% stepper %} {% step %} #### **Open the Company Screener**
{% endstep %} {% step %} #### **Use the Universe filter to select the universe you want to review**
Once you've selected the universe of interest, the companies that populate this universe will be filtered in the table. {% endstep %} {% endstepper %} # Rename a Universe This page explains how to rename a company universe in Tracenable. Follow the two steps below to rename an existing universe: {% stepper %} {% step %} ### Open the Universe Panel
{% endstep %} {% step %} ### Rename the universe of interest Navigate the list of existing universe and locate the one you want to update. Click the pencil icon next to its name, then enter a new name (and optionally a description) in the modal that appears.
Click the **Update Universe** button. Your changes will be saved. {% endstep %} {% endstepper %} ## Delete a Universe To delete a universe, # Delete a Universe This page explains how to delete a company universe in Tracenable. Follow the two steps below to rename an existing universe: {% stepper %} {% step %} #### Open the Universe Panel
{% endstep %} {% step %} #### Delete the Universe Navigate the list of existing universe and locate the one you want to delete. Click the **Delete Universe** button.
The platform will prompt you to confirm the action. Once confirmed, the deletion is irreversible. {% endstep %} {% endstepper %} # Export Data in CSV/Excel Learn how to build, preview, and export custom datasets with your chosen dataset, companies, and years, complete with coverage and cost transparency. Exporting data in CSV or Excel is one of the most powerful features of Tracenable. It allows you to build a custom dataset tailored to your exact needs by selecting: * The dataset you want * The companies you are interested in * The reporting years Before you confirm your purchase, the platform displays data coverage and a transparent cost estimate, so you know exactly what you will receive. This guide walks you through the full process, from selecting a dataset to downloading your file, and explains coverage, quotes, and saved datasets along the way. {% stepper %} {% step %} ### Open the Data Exporter From the main navigation panel, click on Dataset Exporter.
You will see a list of available Datasets, organized by financial and ESG categories.
Each Dataset represents a group of related sustainability or performance topics. For example: * Select GHG Emissions if you’re interested in carbon footprint data. * Select Waste if you want data on hazardous, non-hazardous, and radioactive waste. 👉 Think of datasets as pre-structured data packages designed around specific themes. {% endstep %} {% step %} ### Select Your Dataset Click on the Dataset you want to export.
Each Dataset already contains the relevant structured data fields associated with that topic. You do not need to manually select individual metrics. The Dataset defines the scope of the export. Once selected, the platform prepares the available data structure for your chosen companies and years. {% endstep %} {% step %} ### Select Years Next, choose the reporting years you want the data for. Simply click the years, for example 2022, 2023, or 2024. You can select a single year or multiple years depending on your analysis needs.
{% endstep %} {% step %} ### Select Companies You can directly select the companies you want to include in your export. Use the company selection interface to: * Search for specific companies by name * Add companies individually * Select multiple companies at once
There is no need to create or select a universe beforehand. You can build your company selection directly within the Dataset Exporter. #### Using Filters To refine your selection, you can apply filters such as: * Industry * Country * Revenue * Market capitalization * Other available company attributes
If you have previously created a Universe, you can use the filters to narrow down specifically to companies within that Universe. This allows you to leverage existing portfolio or benchmark groupings while still maintaining full flexibility in your selection. This approach combines the convenience of saved Universes with the flexibility of direct company filtering. {% endstep %} {% step %} ### Review Coverage & Quote Once you’ve set metrics, companies, and years, the platform will show you: * **Data Coverage** – The percentage of data currently available compared to the total possible data points based on your selection. For example: if you select 10 companies and 1 year, and coverage shows 50%, it means data is currently available for 5 of those 10 companies for that year within the selected dataset. * **Cost Estimate** – Tracenable calculates the cost automatically based on your selection. The quote clearly reflects the number of data requests generated by your chosen dataset, companies, and years.
{% endstep %} {% step %} ### Checkout Once you have reviewed the coverage and credit cost of your selection, you can proceed to Checkout. Tracenable operates on a credit-based system: * If you have sufficient credits in your balance, you can confirm the purchase and download your dataset immediately. * If you do not have enough credits, you will need to top up your credit balance before completing the purchase. The required number of credits is displayed clearly before confirmation, ensuring full transparency. {% endstep %} {% step %} ### Access & Download Your Dataset Once you've confirmed the download: * **Processing:** The Tracenable platform will start processing and building your dataset. This might take a few moments depending on the size and complexity. * **Saved datasets:** Your dataset isn't a one-time download. It will be stored permanently in the **Saved Datasets** tab of the platform. * **Download options:** Navigate to Saved Datasets, find your dataset, and download it in various formats, including CSV and XLSX (Excel). {% endstep %} {% endstepper %} {% hint style="success" %} **Takeaways** The Data Export feature ensures you: * Get exactly the data you need, no more and no less. * Always see coverage before purchase, so there are no surprises. * Pay only for what you use, with instant quotes. * Keep permanent access to your datasets for future use. {% endhint %} # API overview Programmatically access Tracenable's corporate datasets with our REST API. Learn how to authenticate, search for companies, and retrieve verified data for specific reporting periods. The Tracenable API provides programmatic access to our comprehensive corporate datasets. Built on REST principles, it enables you to integrate verified company data directly into your applications, research models, and internal workflows. {% hint style="info" %} The Tracenable API is currently in **Beta**. Access is limited to **Company** (Search/List) and **GHG Emissions** endpoints. We are actively expanding the API and will release additional datasets and endpoints in the coming weeks.\ \ We welcome your feedback to help shape the product. Please send any comments, suggestions, or feature requests to . {% endhint %} You can use the API to identify companies and retrieve their full datasets for specific reporting periods. ### Base URL All API requests should be made to the following base URL: ```http https://tracenable.com/api/v1 ``` ### Authentication The API uses Bearer Authentication. To access the API, you must include your API key in the `Authorization` header of each request. To generate an API key, please visit [the API key page](https://tracenable.com/settings/api-key). #### Header Format: ```http Authorization: Bearer ``` {% hint style="warning" %} Your API keys carry the same privileges as your user account. Keep them secure and never expose them in client-side code {% endhint %} #### Test Mode You can explore the API and test your integration without consuming any credits by using our dedicated test token. Requests made with this token return mock data and are free of charge. ```http Authorization: Bearer sk_test_tracenable ``` ### Core Concepts The API is designed around two primary actions: identifying companies and retrieving their data. 1. **Company Identification**: Use the Search or List endpoints to resolve a company's unique identifiers (such as `company_id`, ISIN, or LEI) using their name or ticker symbol. 2. **Data Retrieval**: Once you have identified a company, you can retrieve its full dataset for a specific reporting period (year). Unlike traditional APIs that might require fetching individual metrics, a single call to our endpoints retrieves all available data points for that company and year. ### Response Codes & Data Availability * **200 OK**: The request was successful, and data was returned. * **204 No Content**: The company and reporting period exist, but the company did not publish any data for that specific year (e.g., they released a report but it did not contain the requested data). * **404 Not Found**: The company or reporting period could not be found, or the relevant documents have not yet been processed by our platform. ### Pricing & Credits The Tracenable API operates on a usage-based credit system. * **Pay-As-You-Go**: You only pay for the data you successfully retrieve. * **Cached Data**: Once you have purchased data for a specific company and year, subsequent requests for that same data (returning a `200 OK`) are charged at a significantly reduced "cached" rate. * **No Expiration**: Unused credits never expire. You can monitor the cost of each call via the custom response headers included in every successful response: * `x-credit-cost`: The cost of the specific request. * `x-credit-balance`: Your account's current balance. Each endpoint's documentation provides a breakdown of specific costs: * [Ghg Emissions](/api/ghg-emissions) – *View pricing for absolute and intensity datasets.* * [Company](/api/company) – *View pricing for search and list operations.* To top up your balance, please visit the [Billing Settings](https://tracenable.com/settings/billing). # Ghg Emissions Greenhouse Gas Emissions data ## GHG Emissions (Absolute) > Dataset covering corporate greenhouse gas emissions across Scopes 1, 2, and 3, reported in absolute terms (tCO2e), with detailed breakdowns by emission categories as per GHG Protocol.\ > \ > \ > Pricing (depending on the response received):\ > \- 200 (First access to the data): 50\ > \- 200 (Cached data): 1\ > \- 204 (Company did not publish data for the requested year): 1\ > \- Other responses (40x, 50x): Free of charge
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If not provided, the first matching ticker will be used."},"required":false,"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used.","name":"mic_code","in":"query"},{"schema":{"type":"string","format":"uuid","description":"The UUID of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The UUID of the company. If provided, this takes precedence over ticker and mic.","name":"company_id","in":"query"},{"schema":{"type":"string","minLength":12,"maxLength":12,"pattern":"^[A-Z]{2}[A-Z0-9]{9}[0-9]$","description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic.","name":"isin","in":"query"},{"schema":{"type":"string","minLength":20,"maxLength":20,"pattern":"^[A-Z0-9]{18}[0-9]{2}$","description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic.","name":"lei","in":"query"},{"schema":{"type":"integer","description":"The reporting period for which to retrieve GHG emissions. Accepts an absolute year (1990..2026) or a negative offset (-20..-1) where -1 resolves to the most recent reporting period available for the company, -2 to the second most recent, etc."},"required":true,"description":"The reporting period for which to retrieve GHG emissions. Accepts an absolute year (1990..2026) or a negative offset (-20..-1) where -1 resolves to the most recent reporting period available for the company, -2 to the second most recent, etc.","name":"reporting_period","in":"query"}],"responses":{"200":{"description":"Greenhouse Gas (GHG) Emissions - Absolute Values","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"application/json":{"schema":{"type":"array","items":{"type":"object","properties":{"year_of_disclosure":{"type":"integer","description":"Year in which the data point was disclosed."},"reporting_period":{"type":"integer","description":"Period for which the data point was measured, assessed, or is applicable."},"metric":{"type":"string","enum":["Total Scope 1","Total Scope 2","Total Scope 3","Categories of Scope 1","Categories of Scope 2","Categories of Scope 3","Total Scope 1 Revenue Intensity","Total Scope 2 Revenue Intensity","Total Scope 3 Revenue Intensity","Categories of Scope 1 Revenue Intensity","Categories of Scope 2 Revenue Intensity","Categories of Scope 3 Revenue Intensity"],"description":"The specific measurement or data points requested."},"level":{"type":"string","enum":["Total","Categories"],"description":"The level of emissions data, distinguishing between Total and Categories."},"type":{"type":"string","enum":["Absolute","Revenue Intensity"],"description":"The type of emissions data, indicating whether it is an intensity or absolute value."},"scope":{"type":"array","items":{"type":"string","enum":["Scope 1","Scope 2","Scope 3"]},"description":"GHG emission scopes (1, 2, and/or 3) included in the value."},"emissions_categories":{"type":"array","items":{"type":"string","enum":["Scope 1 - Direct GHG Releases","Scope 1 - Flaring","Scope 1 - Fugitive","Scope 1 - Mobile Combustion","Scope 1 - Other/Unspecified Sources","Scope 1 - Process","Scope 1 - Stationary Combustion","Scope 1 - Total","Scope 1 - Total Combustion","Scope 1 - Unspecified Combustion","Scope 1 - Venting","Scope 2 - Cooling","Scope 2 - Electricity","Scope 2 - Heat","Scope 2 - Other/Unspecified Sources","Scope 2 - Steam","Scope 2 - Total","Scope 3 - Business Travel (Cat. 6)","Scope 3 - Capital Goods (Cat. 2)","Scope 3 - Downstream Leased Assets (Cat. 13)","Scope 3 - Downstream Transportation and Distribution (Cat. 9)","Scope 3 - Employee Commuting (Cat. 7)","Scope 3 - End-of-Life Treatment of Sold Products (Cat. 12)","Scope 3 - Franchises (Cat. 14)","Scope 3 - Fuel- and Energy-Related Services (Cat. 3)","Scope 3 - Investments (Cat. 15)","Scope 3 - Other Downstream Sources","Scope 3 - Other Upstream Sources","Scope 3 - Other/Unspecified Sources","Scope 3 - Processing of Sold Products (Cat. 10)","Scope 3 - Purchased Goods and Services (Cat. 1)","Scope 3 - Total","Scope 3 - Total Downstream (Cat. 9+10+11+12+13+14+15)","Scope 3 - Total Transportation and Distribution","Scope 3 - Total Upstream (Cat. 1+2+3+4+5+6+7+8)","Scope 3 - Unspecified Transportation and Distribution","Scope 3 - Upstream Leased Assets (Cat. 8)","Scope 3 - Upstream Transportation and Distribution (Cat. 4)","Scope 3 - Use of Sold Products (Cat. 11)","Scope 3 - Waste Generated in Operations (Cat. 5)"]},"description":"Categories of GHG emissions."},"value":{"type":"number","description":"Amount of GHG emissions or intensity."},"unit":{"type":"string","enum":["Metric Tonnes of CO2 equivalent (mtCO2e)","Metric Tonnes of CO2 equivalent (mtCO2e) per million USD of revenue"],"description":"The unit of measurement for the value, indicating the scale or dimension."},"method":{"type":"string","enum":["Location-based","Market-based","Not Specified"],"description":"Approach used to calculate Scope 2 and occasionally Scope 3 GHG emissions, distinguishing between 'Location-Based' (reflecting grid averages), 'Market-Based' (reflecting the specific electricity profile purchased), and 'Not Specified' methods."},"company_id":{"type":"string","format":"uuid","description":"Tracenable's internal company identifier."},"traceability_source_url":{"type":"string","format":"uri","description":"URL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes."},"document_id":{"type":"string","format":"uuid","description":"Unique document identifier to see the source of the data point"}},"required":["year_of_disclosure","reporting_period","metric","level","type","scope","value","unit","company_id","traceability_source_url","document_id"]}}}}},"204":{"description":"No data available - the company did not publish data for the requested year.","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"400":{"description":"Bad Request","content":{"text/plain":{"schema":{"type":"string"}}}},"401":{"description":"Unauthorized","content":{"text/plain":{"schema":{"type":"string"}}}},"402":{"description":"Insufficient credits","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"text/plain":{"schema":{"type":"string"}}}},"403":{"description":"Subscription required","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"404":{"description":"No data found for the provided parameters","content":{"text/plain":{"schema":{"type":"string"}}}},"500":{"description":"Internal Server Error","content":{"text/plain":{"schema":{"type":"string"}}}}}}}}} ```` ## GHG Emissions (Revenue Intensity) > Dataset covering corporate greenhouse gas emissions across Scopes 1, 2, and 3, expressed as revenue-normalized intensity metrics (tCO2e per USD million), with detailed breakdowns by emission categories as per GHG Protocol.\ > \ > \ > Pricing (depending on the response received):\ > \- 200 (First access to the data): 50\ > \- 200 (Cached data): 1\ > \- 204 (Company did not publish data for the requested year): 1\ > \- Other responses (40x, 50x): Free of charge
````json {"openapi":"3.0.0","info":{"title":"Tracenable API","version":"1.0.0"},"tags":[{"name":"ghg-emissions","description":"Greenhouse Gas Emissions data"}],"servers":[{"url":"https://tracenable.com","description":"Tracenable Production Server"}],"security":[{"BearerAuth":[]}],"components":{"securitySchemes":{"BearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"JWT","description":"JWT bearer token used for authentication.\n\n**Test token (no charges apply; responses contain mock data):**\n```\nBearer sk_test_tracenable\n```"}}},"paths":{"/api/v1/ghg-emissions/intensity":{"get":{"summary":"GHG Emissions (Revenue Intensity)","description":"Dataset covering corporate greenhouse gas emissions across Scopes 1, 2, and 3, expressed as revenue-normalized intensity metrics (tCO2e per USD million), with detailed breakdowns by emission categories as per GHG Protocol.\n\n\n Pricing (depending on the response received):\n- 200 (First access to the data): 50\n- 200 (Cached data): 1\n- 204 (Company did not publish data for the requested year): 1\n- Other responses (40x, 50x): Free of charge\n","operationId":"getGhgEmissionsIntensity","tags":["ghg-emissions"],"parameters":[{"schema":{"type":"string","minLength":1,"maxLength":15,"description":"The stock ticker of the company"},"required":false,"description":"The stock ticker of the company","name":"ticker","in":"query"},{"schema":{"type":"string","enum":["XAMM","XMLI","ALXP","XDUB","XNCO","XMAU","VPXB","FNLT","AQSG","XKUW","XWAR","XTRN","XAMS","XDHA","XZAG","XPRM","XKRX","NOTC","FSME","MTAA","XSHG","XSAU","XLUX","XBOG","SCLE","XSES","XJSE","XXXX","GROW","XNSE","OTCM","XTAI","BJSE","RUSX","XFRA","XHEL","MISX","XNCM","XEQY","XRIS","ZBUL","XDFM","HAMN","XMAI","XNSA","WBDM","XMAE","XBER","XLJM","XCAI","XBUE","XHKG","XNYS","MERK","AIXK","XOAS","ENAX","XADS","XLJU","XTKS","XBOM","XKLS","ARCX","XKAZ","XBRN","XPHS","XKAR","NSME","XSAT","XZIM","XTSE","XNMS","XBIL","XMUN","XIDX","BATS","XNGS","ALXB","ENXL","XSHE","XBAR","XNZE","XBSE","XLIS","XHAM","XLIT","AQST","XCSE","XSGO","XSTO","XNAI","XSTC","SSME","XCNQ","XLIM","DSME","XBKK","XCOL","XGUA","XMAL","DSMD","XASE","ROCO","XDUS","XBAH","HSTC","XBUD","FNIS","XSTU","XCYS","XMAD","XLON","XBRA","XKOS","EXGM","XCAN","XICE","XBRU","ABUL","XTAE","XTSX","XCAS","BVMF","XMEX","XSWX","XHNX","FNLV","XATH","XETR","XPRA","XPAR","HMOD","XNGM","XBEL","XOSL","XMUS","XTAL","XASX","WBAH","NEOE","XLAT"],"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used."},"required":false,"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used.","name":"mic_code","in":"query"},{"schema":{"type":"string","format":"uuid","description":"The UUID of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The UUID of the company. If provided, this takes precedence over ticker and mic.","name":"company_id","in":"query"},{"schema":{"type":"string","minLength":12,"maxLength":12,"pattern":"^[A-Z]{2}[A-Z0-9]{9}[0-9]$","description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The International Securities Identification Number (ISIN) of the company. 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Enables direct access to the audit trail for validation and transparency purposes."},"document_id":{"type":"string","format":"uuid","description":"Unique document identifier to see the source of the data point"}},"required":["year_of_disclosure","reporting_period","metric","level","type","scope","value","unit","company_id","traceability_source_url","document_id"]}}}}},"204":{"description":"No data available - the company did not publish data for the requested year.","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"400":{"description":"Bad Request","content":{"text/plain":{"schema":{"type":"string"}}}},"401":{"description":"Unauthorized","content":{"text/plain":{"schema":{"type":"string"}}}},"402":{"description":"Insufficient credits","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"text/plain":{"schema":{"type":"string"}}}},"403":{"description":"Subscription required - the user does not have an active subscription.","content":{}},"404":{"description":"No data found for the provided parameters","content":{"text/plain":{"schema":{"type":"string"}}}},"500":{"description":"Internal Server Error","content":{"text/plain":{"schema":{"type":"string"}}}}}}}}} ```` # Eu Taxonomy EU Taxonomy data ## EU Taxonomy > Dataset covering EU Taxonomy metrics, detailing alignment and eligibility metrics across turnover, OPEX, and CAPEX, offering both aggregate and precise activity-level insights.\ > \ > \ > Pricing (depending on the response received):\ > \- 200 (First access to the data): 75\ > \- 200 (Cached data): 1\ > \- 204 (Company did not publish data for the requested year): 1\ > \- Other responses (40x, 50x): Free of charge
````json {"openapi":"3.0.0","info":{"title":"Tracenable API","version":"1.0.0"},"tags":[{"name":"eu-taxonomy","description":"EU Taxonomy data"}],"servers":[{"url":"https://tracenable.com","description":"Tracenable Production Server"}],"security":[{"BearerAuth":[]}],"components":{"securitySchemes":{"BearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"JWT","description":"JWT bearer token used for authentication.\n\n**Test token (no charges apply; responses contain mock data):**\n```\nBearer sk_test_tracenable\n```"}}},"paths":{"/api/v1/eu-taxonomy":{"get":{"summary":"EU Taxonomy","description":"Dataset covering EU Taxonomy metrics, detailing alignment and eligibility metrics across turnover, OPEX, and CAPEX, offering both aggregate and precise activity-level insights.\n\n\n Pricing (depending on the response received):\n- 200 (First access to the data): 75\n- 200 (Cached data): 1\n- 204 (Company did not publish data for the requested year): 1\n- Other responses (40x, 50x): Free of charge\n","operationId":"getEuTaxonomy","tags":["eu-taxonomy"],"parameters":[{"schema":{"type":"string","minLength":1,"maxLength":15,"description":"The stock ticker of the company"},"required":false,"description":"The stock ticker of the company","name":"ticker","in":"query"},{"schema":{"type":"string","enum":["XAMM","XMLI","ALXP","XDUB","XNCO","XMAU","VPXB","FNLT","AQSG","XKUW","XWAR","XTRN","XAMS","XDHA","XZAG","XPRM","XKRX","NOTC","FSME","MTAA","XSHG","XSAU","XLUX","XBOG","SCLE","XSES","XJSE","XXXX","GROW","XNSE","OTCM","XTAI","BJSE","RUSX","XFRA","XHEL","MISX","XNCM","XEQY","XRIS","ZBUL","XDFM","HAMN","XMAI","XNSA","WBDM","XMAE","XBER","XLJM","XCAI","XBUE","XHKG","XNYS","MERK","AIXK","XOAS","ENAX","XADS","XLJU","XTKS","XBOM","XKLS","ARCX","XKAZ","XBRN","XPHS","XKAR","NSME","XSAT","XZIM","XTSE","XNMS","XBIL","XMUN","XIDX","BATS","XNGS","ALXB","ENXL","XSHE","XBAR","XNZE","XBSE","XLIS","XHAM","XLIT","AQST","XCSE","XSGO","XSTO","XNAI","XSTC","SSME","XCNQ","XLIM","DSME","XBKK","XCOL","XGUA","XMAL","DSMD","XASE","ROCO","XDUS","XBAH","HSTC","XBUD","FNIS","XSTU","XCYS","XMAD","XLON","XBRA","XKOS","EXGM","XCAN","XICE","XBRU","ABUL","XTAE","XTSX","XCAS","BVMF","XMEX","XSWX","XHNX","FNLV","XATH","XETR","XPRA","XPAR","HMOD","XNGM","XBEL","XOSL","XMUS","XTAL","XASX","WBAH","NEOE","XLAT"],"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used."},"required":false,"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used.","name":"mic_code","in":"query"},{"schema":{"type":"string","format":"uuid","description":"The UUID of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The UUID of the company. If provided, this takes precedence over ticker and mic.","name":"company_id","in":"query"},{"schema":{"type":"string","minLength":12,"maxLength":12,"pattern":"^[A-Z]{2}[A-Z0-9]{9}[0-9]$","description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic.","name":"isin","in":"query"},{"schema":{"type":"string","minLength":20,"maxLength":20,"pattern":"^[A-Z0-9]{18}[0-9]{2}$","description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic.","name":"lei","in":"query"},{"schema":{"type":"integer","description":"The reporting period for which to retrieve EU Taxonomy data. Accepts an absolute year (2021..2026) or a negative offset (-5..-1) where -1 resolves to the most recent reporting period available for the company, -2 to the second most recent, etc."},"required":true,"description":"The reporting period for which to retrieve EU Taxonomy data. Accepts an absolute year (2021..2026) or a negative offset (-5..-1) where -1 resolves to the most recent reporting period available for the company, -2 to the second most recent, etc.","name":"reporting_period","in":"query"}],"responses":{"200":{"description":"EU Taxonomy","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"application/json":{"schema":{"type":"array","items":{"type":"object","properties":{"year_of_disclosure":{"type":"integer","description":"Year in which the data point was disclosed."},"reporting_period":{"type":"integer","description":"Period for which the data point was measured, assessed, or is applicable."},"metric":{"type":"string","enum":["Total CAPEX (Denominator)","Total OPEX (Denominator)","Total Turnover (Denominator)","Total Taxonomy-Eligible CAPEX","Total Taxonomy-Eligible OPEX","Total Taxonomy-Eligible Turnover","Total Taxonomy-Aligned CAPEX","Total Taxonomy-Aligned OPEX","Total Taxonomy-Aligned Turnover","Activity-level Taxonomy-Eligible CAPEX","Activity-level Taxonomy-Eligible OPEX","Activity-level Taxonomy-Eligible Turnover","Activity-level Taxonomy-Aligned CAPEX","Activity-level Taxonomy-Aligned OPEX","Activity-level Taxonomy-Aligned Turnover"],"description":"The specific measurement or data points requested."},"level":{"type":"string","enum":["Total","Activities"],"description":"Level of data distinguishing between Total or Activities."},"kpi":{"type":"string","enum":["Turnover","CAPEX","OPEX"],"description":"Key Performance Indicator (KPI) requested (turnover, OPEX or CAPEX)."},"screening_criteria":{"type":"string","enum":["Eligible","Aligned","Denominator"],"description":"EU Taxonomy screening criteria (Aligned, Eligible or Denominator)."},"activities":{"type":"array","items":{"type":"string"},"description":"Economic activities that meet the EU Taxonomy's criteria for environmentally sustainable classifications."},"relative_value":{"type":"number","description":"Proportion of turnover, OPEX, or CAPEX."},"relative_value_unit":{"type":"string","enum":["Percentage of Total CAPEX","Percentage of Total OPEX","Percentage of Total Turnover"],"description":"Unit of measurement of the relative value."},"absolute_value":{"type":"number","description":"Amount of turnover, OPEX, or CAPEX in absolute terms."},"absolute_value_ccy":{"type":"string","enum":["AED","AUD","BRL","CAD","CHF","CLP","CNY","COP","CZK","DKK","EUR","EGP","HKD","HUF","GBP","IDR","INR","ILS","JPY","KZT","MYR","MXN","NOK","NZD","PHP","PLN","RON","RUB","SAR","SEK","SGD","THB","TRY","TWD","USD","ZAR","ZAC","KRW","ILA","QAR","PEN","ARS","VND","PKR","ISK","KWD","LKR","KES","BDT","GEL","BGN","MAD","NAD","NGN","HRK","BHD","ZWG","BWP","ZWL"],"description":"Currency in which the absolute value is reported."},"scope_of_disclosure":{"type":"string","enum":["Operational Control","Financial Control","Equity Share","Other"],"description":"Oganizational boundaries used for data consolidation."},"pct_contribution_to_ccm":{"type":"number","description":"Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Climate Change Mitigation (CCM) EO."},"pct_contribution_to_cca":{"type":"number","description":"Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Climate Change Adaptation (CCA) EO."},"pct_contribution_to_wtr":{"type":"number","description":"Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Water and Marine Resources (WTR) EO."},"pct_contribution_to_ce":{"type":"number","description":"Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Circular Economy (CE) EO."},"pct_contribution_to_ppc":{"type":"number","description":"Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Pollution Prevention and Control (PPC) EO."},"pct_contribution_to_bio":{"type":"number","description":"Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Biodiversity and Ecosystems (BIO) EO."},"activities_contribution_type":{"type":"string","enum":["Enabling","Transitional","Substantial","Not Specified"],"description":"Contribution type of the activity(ies) according to the Taxonomy classification (Enabling, Transitional or Substantial). When the contribution type cannot be clearly determined, the value is set to \"Not Specified\"."},"not_assessed_activities_non_material":{"type":"number","description":"Proportion of turnover/OPEX/CAPEX from the activity(ies) that were not assessed for taxonomy alignment/eligibility because they were deemed non-material by the reporting company."},"company_id":{"type":"string","format":"uuid","description":"Tracenable's internal company identifier."},"document_id":{"type":"string","format":"uuid","description":"Unique document identifier to see the source of the data point."},"traceability_source_url":{"type":"string","format":"uri","description":"URL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes."}},"required":["year_of_disclosure","reporting_period","metric","level","kpi","screening_criteria","activities","relative_value","relative_value_unit","company_id","document_id","traceability_source_url"]}}}}},"204":{"description":"No data available - the company did not publish EU Taxonomy data for the requested year.","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"400":{"description":"Bad Request","content":{"text/plain":{"schema":{"type":"string"}}}},"401":{"description":"Unauthorized","content":{"text/plain":{"schema":{"type":"string"}}}},"402":{"description":"Insufficient credits","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"text/plain":{"schema":{"type":"string"}}}},"403":{"description":"Subscription required","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"404":{"description":"No data found for the provided parameters","content":{"text/plain":{"schema":{"type":"string"}}}},"500":{"description":"Internal Server Error","content":{"text/plain":{"schema":{"type":"string"}}}}}}}}} ```` # Climate Targets Climate Targets data ## Climate Targets > Dataset on corporate greenhouse gas reduction targets, with detailed insights into baseline emissions, target types and timelines, and progress achieved to date.\ > \ > \ > Pricing (depending on the response received):\ > \- 200 (First access to the data): 100\ > \- 200 (Cached data): 1\ > \- 204 (Company did not publish data for the requested year): 1\ > \- Other responses (40x, 50x): Free of charge
````json {"openapi":"3.0.0","info":{"title":"Tracenable API","version":"1.0.0"},"tags":[{"name":"climate-targets","description":"Climate Targets data"}],"servers":[{"url":"https://tracenable.com","description":"Tracenable Production Server"}],"security":[{"BearerAuth":[]}],"components":{"securitySchemes":{"BearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"JWT","description":"JWT bearer token used for authentication.\n\n**Test token (no charges apply; responses contain mock data):**\n```\nBearer sk_test_tracenable\n```"}}},"paths":{"/api/v1/climate-targets":{"get":{"summary":"Climate Targets","description":"Dataset on corporate greenhouse gas reduction targets, with detailed insights into baseline emissions, target types and timelines, and progress achieved to date.\n\n\n Pricing (depending on the response received):\n- 200 (First access to the data): 100\n- 200 (Cached data): 1\n- 204 (Company did not publish data for the requested year): 1\n- Other responses (40x, 50x): Free of charge\n","operationId":"getClimateTargets","tags":["climate-targets"],"parameters":[{"schema":{"type":"string","minLength":1,"maxLength":15,"description":"The stock ticker of the company"},"required":false,"description":"The stock ticker of the company","name":"ticker","in":"query"},{"schema":{"type":"string","enum":["XAMM","XMLI","ALXP","XDUB","XNCO","XMAU","VPXB","FNLT","AQSG","XKUW","XWAR","XTRN","XAMS","XDHA","XZAG","XPRM","XKRX","NOTC","FSME","MTAA","XSHG","XSAU","XLUX","XBOG","SCLE","XSES","XJSE","XXXX","GROW","XNSE","OTCM","XTAI","BJSE","RUSX","XFRA","XHEL","MISX","XNCM","XEQY","XRIS","ZBUL","XDFM","HAMN","XMAI","XNSA","WBDM","XMAE","XBER","XLJM","XCAI","XBUE","XHKG","XNYS","MERK","AIXK","XOAS","ENAX","XADS","XLJU","XTKS","XBOM","XKLS","ARCX","XKAZ","XBRN","XPHS","XKAR","NSME","XSAT","XZIM","XTSE","XNMS","XBIL","XMUN","XIDX","BATS","XNGS","ALXB","ENXL","XSHE","XBAR","XNZE","XBSE","XLIS","XHAM","XLIT","AQST","XCSE","XSGO","XSTO","XNAI","XSTC","SSME","XCNQ","XLIM","DSME","XBKK","XCOL","XGUA","XMAL","DSMD","XASE","ROCO","XDUS","XBAH","HSTC","XBUD","FNIS","XSTU","XCYS","XMAD","XLON","XBRA","XKOS","EXGM","XCAN","XICE","XBRU","ABUL","XTAE","XTSX","XCAS","BVMF","XMEX","XSWX","XHNX","FNLV","XATH","XETR","XPRA","XPAR","HMOD","XNGM","XBEL","XOSL","XMUS","XTAL","XASX","WBAH","NEOE","XLAT"],"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used."},"required":false,"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used.","name":"mic_code","in":"query"},{"schema":{"type":"string","format":"uuid","description":"The UUID of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The UUID of the company. If provided, this takes precedence over ticker and mic.","name":"company_id","in":"query"},{"schema":{"type":"string","minLength":12,"maxLength":12,"pattern":"^[A-Z]{2}[A-Z0-9]{9}[0-9]$","description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic.","name":"isin","in":"query"},{"schema":{"type":"string","minLength":20,"maxLength":20,"pattern":"^[A-Z0-9]{18}[0-9]{2}$","description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic.","name":"lei","in":"query"}],"responses":{"200":{"description":"Climate Targets","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"application/json":{"schema":{"type":"array","items":{"type":"object","properties":{"metric":{"type":"string","enum":["GHG Emissions Reduction Targets"],"description":"The specific measurement or data points requested."},"target_type":{"type":"string","enum":["Intensity-based Target","Absolute-based Target"],"description":"Type of GHG emissions reduction target set by the company (absolute or intensity-based)."},"year_of_disclosure":{"type":"integer","description":"Year in which the data point was disclosed."},"scope_of_target":{"type":"array","items":{"type":"string","enum":["Scope 1 - Total","Scope 1 - Total Combustion","Scope 1 - Stationary Combustion","Scope 1 - Mobile Combustion","Scope 1 - Unspecified Combustion","Scope 1 - Process","Scope 1 - Direct GHG Releases","Scope 1 - Fugitive","Scope 1 - Venting","Scope 1 - Flaring","Scope 1 - Other/Unspecified Sources","Scope 2 - Total","Scope 2 - Electricity","Scope 2 - Heat","Scope 2 - Steam","Scope 2 - Cooling","Scope 2 - Other/Unspecified Sources","Scope 3 - Total","Scope 3 - Total Upstream (Cat. 1+2+3+4+5+6+7+8)","Scope 3 - Total Downstream (Cat. 9+10+11+12+13+14+15)","Scope 3 - Purchased Goods and Services (Cat. 1)","Scope 3 - Capital Goods (Cat. 2)","Scope 3 - Fuel- and Energy-Related Services (Cat. 3)","Scope 3 - Total Transportation and Distribution","Scope 3 - Upstream Transportation and Distribution (Cat. 4)","Scope 3 - Downstream Transportation and Distribution (Cat. 9)","Scope 3 - Unspecified Transportation and Distribution","Scope 3 - Waste Generated in Operations (Cat. 5)","Scope 3 - Business Travel (Cat. 6)","Scope 3 - Employee Commuting (Cat. 7)","Scope 3 - Upstream Leased Assets (Cat. 8)","Scope 3 - Processing of Sold Products (Cat. 10)","Scope 3 - Use of Sold Products (Cat. 11)","Scope 3 - End-of-Life Treatment of Sold Products (Cat. 12)","Scope 3 - Downstream Leased Assets (Cat. 13)","Scope 3 - Franchises (Cat. 14)","Scope 3 - Investments (Cat. 15)","Scope 3 - Other Upstream Sources","Scope 3 - Other Downstream Sources","Scope 3 - Other/Unspecified Sources"]},"description":"GHG emission scopes (1, 2, and/or 3) included in the reduction target."},"method":{"type":"string","enum":["Market-based","Location-based"],"description":"Approach used to calculate Scope 2 and occasionally Scope 3 GHG emissions, distinguishing between 'Location-Based' (reflecting grid averages), 'Market-Based' (reflecting the specific electricity profile purchased), and 'Not Specified' methods."},"intensity_category":{"type":"string","enum":["Product/Material","Other","Distance","Surface","Employee","Monetary","Energy"],"description":"High-level grouping classifying the denominators that companies report when setting targets to reduce greenhouse gas (GHG) emissions. These categories serve to standardize and simplify comparisons of GHG intensity across different companies, sectors, industries, and time periods."},"intensity_sub_category":{"type":"string","enum":["Metals and Mining Materials","Building and Construction Materials","Chemicals Compounds","Livestock Commodities","Crops Commodities","Packaging Materials","Processed Food and Beverages","Non-Renewable Energy Sources","Renewable Energy Sources","Energy Usages","Machinery and Equipments","Apparel and Textiles","Other Consumer Goods","Durable Goods","Hospitality and Retail Operations","Healthcare Operations","Transport Operations","Manufacture or Production Operations","Business and Service Operations","Utility Operations","Insurance Operations","Real Estate Operations","IT and Digital Operations","Other","Revenue and Profit Metrics","Asset and Investment Metrics","Procurement and Purchasing Metrics","Portfolio and Financial Management Metrics"],"description":"More specific classification within an intensity category that further refines the denominator used to scale GHG emissions. Sub-categories provide a detailed grouping based on the type of material, product, or operational activity."},"intensity_metric":{"type":"string","description":"Specific denominator that a company reports to measure the intensity of its GHG emissions. It reflects the exact operational or financial measure, such as the amount of energy consumed or revenue generated, and is used to normalize emissions data."},"baseline_year":{"type":"integer","description":"Reference year against which a company's GHG reduction targets are measured."},"baseline_value":{"type":"number","description":"Quantified GHG emissions value (absolute or intensity) recorded in the baseline year."},"baseline_unit":{"type":"string","description":"Unit of measurement used for the baseline value."},"year_target_was_set":{"type":"integer","description":"Year in which the GHG reduction target was formally established by the company."},"target_year":{"type":"integer","description":"Future year by which the company aims to achieve its GHG reduction target."},"target_value":{"type":"number","description":"Quantified GHG reduction or performance goal set by the company."},"target_unit":{"type":"string","description":"Unit of measurement used for the target value."},"target_unit_direction":{"type":"string","enum":["Absolute increase from baseline","Absolute emissions in target year (milestone)","Absolute emissions in achievement year (milestone)","Absolute decrease from baseline","Percentage increase from baseline","Percentage of the target value","Percentage decrease from baseline","Percentage decrease from Business-as-Usual","Percentage increase from Business-as-Usual","Percentage reduction of the target value","Absolute increase from Business-as-Usual","Absolute decrease from Business-as-Usual"],"description":"Direction of the target compared to the baseline or other reference points (e.g., absolute decrease from baseline, percentage increase from baseline)."},"achievement_year":{"type":"integer","description":"Year in which the company's progress towards the GHG target is evaluated."},"achievement_value":{"type":"number","description":"Quantified progress made towards the GHG target as of the achievement year."},"achievement_unit":{"type":"string","description":"Unit of measurement used for the achievement value."},"achievement_unit_direction":{"type":"string","enum":["Absolute increase from baseline","Absolute emissions in target year (milestone)","Absolute emissions in achievement year (milestone)","Absolute decrease from baseline","Percentage increase from baseline","Percentage of the target value","Percentage decrease from baseline","Percentage decrease from Business-as-Usual","Percentage increase from Business-as-Usual","Percentage reduction of the target value","Absolute increase from Business-as-Usual","Absolute decrease from Business-as-Usual","Not Specified"],"description":"Direction of progress compared to the baseline or other reference points (e.g., absolute emissions in achievement year, percentage decrease from baseline)."},"incomplete_boundaries":{"type":"string","enum":["True"],"description":"Indicates whether the reported data covers only a limited portion of the company's operational or organizational boundaries."},"company_id":{"type":"string","format":"uuid","description":"Tracenable's internal company identifier."},"document_ids":{"type":"array","items":{"type":"string","format":"uuid"},"description":"Tracenable's internal source document identifiers."},"traceability_source_url":{"type":"string","format":"uri","description":"URL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes."}},"required":["metric","target_type","year_of_disclosure","scope_of_target","target_year","target_value","target_unit","target_unit_direction","company_id","document_ids","traceability_source_url"]}}}}},"204":{"description":"No data available - the company did not publish any climate targets data.","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"400":{"description":"Bad Request","content":{"text/plain":{"schema":{"type":"string"}}}},"401":{"description":"Unauthorized","content":{"text/plain":{"schema":{"type":"string"}}}},"402":{"description":"Insufficient credits","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"text/plain":{"schema":{"type":"string"}}}},"403":{"description":"Subscription required","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"404":{"description":"No data found for the provided parameters","content":{"text/plain":{"schema":{"type":"string"}}}},"500":{"description":"Internal Server Error","content":{"text/plain":{"schema":{"type":"string"}}}}}}}}} ```` # Company Company information and metadata ## Search Companies > Search for companies by name or ticker symbol. Returns a list of companies matching the search query.\ > A maximum of 10 results will be returned. The most relevant results are returned first.\ > \ > \ > Pricing (depending on the response received):\ > \- 200 : 1\ > \- Other responses (40x, 50x): Free of charge
````json {"openapi":"3.0.0","info":{"title":"Tracenable API","version":"1.0.0"},"tags":[{"name":"company","description":"Company information and metadata"}],"servers":[{"url":"https://tracenable.com","description":"Tracenable Production Server"}],"security":[{"BearerAuth":[]}],"components":{"securitySchemes":{"BearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"JWT","description":"JWT bearer token used for authentication.\n\n**Test token (no charges apply; responses contain mock data):**\n```\nBearer sk_test_tracenable\n```"}}},"paths":{"/api/v1/companies/search":{"get":{"summary":"Search Companies","description":"Search for companies by name or ticker symbol. Returns a list of companies matching the search query.\nA maximum of 10 results will be returned. The most relevant results are returned first.\n\n\n Pricing (depending on the response received):\n- 200 : 1\n- Other responses (40x, 50x): Free of charge\n","tags":["company"],"operationId":"searchCompanies","parameters":[{"schema":{"type":"string","minLength":1,"maxLength":100,"description":"Search query for company name or ticker symbol"},"required":true,"description":"Search query for company name or ticker symbol","name":"query","in":"query"}],"responses":{"200":{"description":"List of companies matching the search query","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"application/json":{"schema":{"type":"array","items":{"type":"object","properties":{"company_id":{"type":"string","format":"uuid","description":"Unique identifier for the company"},"company_name":{"type":"string","description":"Full name of the company"},"ticker":{"type":"string","nullable":true,"description":"Ticker symbol of the company"},"mic_code":{"type":"string","nullable":true,"enum":["XAMM","XMLI","ALXP","XDUB","XNCO","XMAU","VPXB","FNLT","AQSG","XKUW","XWAR","XTRN","XAMS","XDHA","XZAG","XPRM","XKRX","NOTC","FSME","MTAA","XSHG","XSAU","XLUX","XBOG","SCLE","XSES","XJSE","XXXX","GROW","XNSE","OTCM","XTAI","BJSE","RUSX","XFRA","XHEL","MISX","XNCM","XEQY","XRIS","ZBUL","XDFM","HAMN","XMAI","XNSA","WBDM","XMAE","XBER","XLJM","XCAI","XBUE","XHKG","XNYS","MERK","AIXK","XOAS","ENAX","XADS","XLJU","XTKS","XBOM","XKLS","ARCX","XKAZ","XBRN","XPHS","XKAR","NSME","XSAT","XZIM","XTSE","XNMS","XBIL","XMUN","XIDX","BATS","XNGS","ALXB","ENXL","XSHE","XBAR","XNZE","XBSE","XLIS","XHAM","XLIT","AQST","XCSE","XSGO","XSTO","XNAI","XSTC","SSME","XCNQ","XLIM","DSME","XBKK","XCOL","XGUA","XMAL","DSMD","XASE","ROCO","XDUS","XBAH","HSTC","XBUD","FNIS","XSTU","XCYS","XMAD","XLON","XBRA","XKOS","EXGM","XCAN","XICE","XBRU","ABUL","XTAE","XTSX","XCAS","BVMF","XMEX","XSWX","XHNX","FNLV","XATH","XETR","XPRA","XPAR","HMOD","XNGM","XBEL","XOSL","XMUS","XTAL","XASX","WBAH","NEOE","XLAT",null],"description":"Market Identifier Code (MIC) of the company"},"country":{"type":"string","nullable":true,"enum":["Afghanistan","Åland","Albania","Algeria","American Samoa","Andorra","Angola","Anguilla","Antarctica","Antigua and Barbuda","Argentina","Armenia","Aruba","Australia","Austria","Azerbaijan","Bahamas","Bahrain","Bangladesh","Barbados","Belarus","Belgium","Belize","Benin","Bermuda","Bhutan","Bolivia","Bonaire, Sint Eustatius, and Saba","Bosnia and Herzegovina","Botswana","Bouvet Island","Brazil","British Indian Ocean Territory","British Virgin Islands","Brunei","Bulgaria","Burkina Faso","Burundi","Cabo Verde","Cambodia","Cameroon","Canada","Cayman Islands","Central African Republic","Chad","Chile","China","China (Mainland)","Christmas Island","Cocos (Keeling) Islands","Colombia","Comoros","Congo Republic","Cook Islands","Costa Rica","Croatia","Cuba","Curacao","Cyprus","Czechia","Denmark","Djibouti","Dominica","Dominican Republic","DR Congo","Ecuador","Egypt","El Salvador","Equatorial Guinea","Eritrea","Estonia","Eswatini","Ethiopia","Falkland Islands","Faroe Islands","Fiji","Finland","France","French Guiana","French Polynesia","French Southern Territories","Gabon","Georgia","Germany","Ghana","Gibraltar","Greece","Greenland","Grenada","Guadeloupe","Guam","Guatemala","Guernsey","Guinea-Bissau","Guinea","Guyana","Haiti","Heard and McDonald Islands","Honduras","Hong Kong","Hungary","Iceland","India","Indonesia","Iran","Iraq","Ireland","Isle of Man","Israel","Italy","Ivory Coast","Jamaica","Japan","Jersey","Jordan","Kazakhstan","Kenya","Kiribati","Kosovo","Kuwait","Kyrgyzstan","Laos","Latvia","Lebanon","Lesotho","Liberia","Libya","Liechtenstein","Lithuania","Luxembourg","Macau","Macedonia (the former Yugoslav Republic of)","Madagascar","Malawi","Malaysia","Maldives","Mali","Malta","Marshall Islands","Martinique","Mauritania","Mauritius","Mayotte","Mexico","Micronesia","Moldova","Monaco","Mongolia","Montenegro","Montserrat","Morocco","Mozambique","Myanmar","Namibia","Nauru","Nepal","New Caledonia","New Zealand","Nicaragua","Niger","Nigeria","Niue","Norfolk Island","North Korea","North Macedonia","Northern Mariana Islands","Norway","Oman","Pakistan","Palau","Palestine","Panama","Papua New Guinea","Paraguay","Peru","Philippines","Pitcairn Islands","Poland","Portugal","Puerto Rico","Qatar","Reunion","Romania","Russia","Rwanda","Saint Barthélemy","Saint Helena","Saint Lucia","Saint Martin","Saint Pierre and Miquelon","Samoa","San Marino","São Tomé and Príncipe","Saudi Arabia","Senegal","Serbia","Seychelles","Sierra Leone","Singapore","Sint Maarten","Slovakia","Slovenia","Solomon Islands","Somalia","South Africa","South Georgia and South Sandwich Islands","South Korea","South Sudan","Spain","Sri Lanka","St Kitts and Nevis","St Vincent and Grenadines","Sudan","Suriname","Svalbard and Jan Mayen","Sweden","Switzerland","Syria","Taiwan","Tajikistan","Tanzania","Thailand","Gambia","Netherlands","Timor-Leste","Togo","Tokelau","Tonga","Trinidad and Tobago","Tunisia","Turkey","Turkmenistan","Turks and Caicos Islands","Tuvalu","U.S. Outlying Islands","U.S. Virgin Islands","Uganda","Ukraine","United Arab Emirates","United Kingdom","United States","Uruguay","Uzbekistan","Vanuatu","Vatican City","Venezuela","Vietnam","Wallis and Futuna","Western Sahara","Yemen","Zambia","Zimbabwe",null],"description":"Country where the company is headquartered"}},"required":["company_id","company_name","ticker","mic_code","country"]}}}}},"400":{"description":"Bad Request","content":{"text/plain":{"schema":{"type":"string"}}}},"401":{"description":"Unauthorized","content":{"text/plain":{"schema":{"type":"string"}}}},"402":{"description":"Insufficient credits","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"text/plain":{"schema":{"type":"string"}}}},"403":{"description":"Subscription required - the user does not have an active subscription.","content":{}},"500":{"description":"Internal Server Error","content":{"text/plain":{"schema":{"type":"string"}}}}}}}}} ```` ## List Companies > Retrieve a list of all available companies.\ > \ > \ > Pricing (depending on the response received):\ > \- 200 : 100\ > \- Other responses (40x, 50x): Free of charge
````json {"openapi":"3.0.0","info":{"title":"Tracenable API","version":"1.0.0"},"tags":[{"name":"company","description":"Company information and metadata"}],"servers":[{"url":"https://tracenable.com","description":"Tracenable Production Server"}],"security":[{"BearerAuth":[]}],"components":{"securitySchemes":{"BearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"JWT","description":"JWT bearer token used for authentication.\n\n**Test token (no charges apply; responses contain mock data):**\n```\nBearer sk_test_tracenable\n```"}}},"paths":{"/api/v1/companies/list":{"get":{"summary":"List Companies","description":"Retrieve a list of all available companies.\n\n\n Pricing (depending on the response received):\n- 200 : 100\n- Other responses (40x, 50x): Free of charge\n","tags":["company"],"operationId":"listCompanies","responses":{"200":{"description":"List of available companies","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"application/json":{"schema":{"type":"array","items":{"type":"object","properties":{"company_id":{"type":"string","format":"uuid","description":"Unique identifier for the company"},"company_name":{"type":"string","description":"Full name of the company"},"ticker":{"type":"string","nullable":true,"description":"Ticker symbol of the company"},"mic_code":{"type":"string","nullable":true,"enum":["XAMM","XMLI","ALXP","XDUB","XNCO","XMAU","VPXB","FNLT","AQSG","XKUW","XWAR","XTRN","XAMS","XDHA","XZAG","XPRM","XKRX","NOTC","FSME","MTAA","XSHG","XSAU","XLUX","XBOG","SCLE","XSES","XJSE","XXXX","GROW","XNSE","OTCM","XTAI","BJSE","RUSX","XFRA","XHEL","MISX","XNCM","XEQY","XRIS","ZBUL","XDFM","HAMN","XMAI","XNSA","WBDM","XMAE","XBER","XLJM","XCAI","XBUE","XHKG","XNYS","MERK","AIXK","XOAS","ENAX","XADS","XLJU","XTKS","XBOM","XKLS","ARCX","XKAZ","XBRN","XPHS","XKAR","NSME","XSAT","XZIM","XTSE","XNMS","XBIL","XMUN","XIDX","BATS","XNGS","ALXB","ENXL","XSHE","XBAR","XNZE","XBSE","XLIS","XHAM","XLIT","AQST","XCSE","XSGO","XSTO","XNAI","XSTC","SSME","XCNQ","XLIM","DSME","XBKK","XCOL","XGUA","XMAL","DSMD","XASE","ROCO","XDUS","XBAH","HSTC","XBUD","FNIS","XSTU","XCYS","XMAD","XLON","XBRA","XKOS","EXGM","XCAN","XICE","XBRU","ABUL","XTAE","XTSX","XCAS","BVMF","XMEX","XSWX","XHNX","FNLV","XATH","XETR","XPRA","XPAR","HMOD","XNGM","XBEL","XOSL","XMUS","XTAL","XASX","WBAH","NEOE","XLAT",null],"description":"Market Identifier Code (MIC) of the company"},"country":{"type":"string","nullable":true,"enum":["Afghanistan","Åland","Albania","Algeria","American Samoa","Andorra","Angola","Anguilla","Antarctica","Antigua and Barbuda","Argentina","Armenia","Aruba","Australia","Austria","Azerbaijan","Bahamas","Bahrain","Bangladesh","Barbados","Belarus","Belgium","Belize","Benin","Bermuda","Bhutan","Bolivia","Bonaire, Sint Eustatius, and Saba","Bosnia and Herzegovina","Botswana","Bouvet Island","Brazil","British Indian Ocean Territory","British Virgin Islands","Brunei","Bulgaria","Burkina Faso","Burundi","Cabo Verde","Cambodia","Cameroon","Canada","Cayman Islands","Central African Republic","Chad","Chile","China","China (Mainland)","Christmas Island","Cocos (Keeling) Islands","Colombia","Comoros","Congo Republic","Cook Islands","Costa Rica","Croatia","Cuba","Curacao","Cyprus","Czechia","Denmark","Djibouti","Dominica","Dominican Republic","DR Congo","Ecuador","Egypt","El Salvador","Equatorial 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Islands","Martinique","Mauritania","Mauritius","Mayotte","Mexico","Micronesia","Moldova","Monaco","Mongolia","Montenegro","Montserrat","Morocco","Mozambique","Myanmar","Namibia","Nauru","Nepal","New Caledonia","New Zealand","Nicaragua","Niger","Nigeria","Niue","Norfolk Island","North Korea","North Macedonia","Northern Mariana Islands","Norway","Oman","Pakistan","Palau","Palestine","Panama","Papua New Guinea","Paraguay","Peru","Philippines","Pitcairn Islands","Poland","Portugal","Puerto Rico","Qatar","Reunion","Romania","Russia","Rwanda","Saint Barthélemy","Saint Helena","Saint Lucia","Saint Martin","Saint Pierre and Miquelon","Samoa","San Marino","São Tomé and Príncipe","Saudi Arabia","Senegal","Serbia","Seychelles","Sierra Leone","Singapore","Sint Maarten","Slovakia","Slovenia","Solomon Islands","Somalia","South Africa","South Georgia and South Sandwich Islands","South Korea","South Sudan","Spain","Sri Lanka","St Kitts and Nevis","St Vincent and Grenadines","Sudan","Suriname","Svalbard and Jan Mayen","Sweden","Switzerland","Syria","Taiwan","Tajikistan","Tanzania","Thailand","Gambia","Netherlands","Timor-Leste","Togo","Tokelau","Tonga","Trinidad and Tobago","Tunisia","Turkey","Turkmenistan","Turks and Caicos Islands","Tuvalu","U.S. Outlying Islands","U.S. Virgin Islands","Uganda","Ukraine","United Arab Emirates","United Kingdom","United States","Uruguay","Uzbekistan","Vanuatu","Vatican City","Venezuela","Vietnam","Wallis and Futuna","Western Sahara","Yemen","Zambia","Zimbabwe",null],"description":"Country where the company is headquartered"}},"required":["company_id","company_name","ticker","mic_code","country"]}}}}},"400":{"description":"Bad Request","content":{"text/plain":{"schema":{"type":"string"}}}},"401":{"description":"Unauthorized","content":{"text/plain":{"schema":{"type":"string"}}}},"402":{"description":"Insufficient credits","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"text/plain":{"schema":{"type":"string"}}}},"403":{"description":"Subscription required - the user does not have an active subscription.","content":{}},"500":{"description":"Internal Server Error","content":{"text/plain":{"schema":{"type":"string"}}}}}}}}} ```` # Revenues Corporate revenue data ## Revenues > Dataset covering corporate total revenues as reported in official company disclosures, standardized across currencies and fiscal years.\ > \ > \ > Pricing (depending on the response received):\ > \- 200 (First access to the data): 10\ > \- 200 (Cached data): 1\ > \- 204 (Company did not publish data for the requested year): 1\ > \- Other responses (40x, 50x): Free of charge
````json {"openapi":"3.0.0","info":{"title":"Tracenable API","version":"1.0.0"},"tags":[{"name":"revenues","description":"Corporate revenue data"}],"servers":[{"url":"https://tracenable.com","description":"Tracenable Production Server"}],"security":[{"BearerAuth":[]}],"components":{"securitySchemes":{"BearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"JWT","description":"JWT bearer token used for authentication.\n\n**Test token (no charges apply; responses contain mock data):**\n```\nBearer sk_test_tracenable\n```"}}},"paths":{"/api/v1/revenues":{"get":{"summary":"Revenues","description":"Dataset covering corporate total revenues as reported in official company disclosures, standardized across currencies and fiscal years.\n\n\n Pricing (depending on the response received):\n- 200 (First access to the data): 10\n- 200 (Cached data): 1\n- 204 (Company did not publish data for the requested year): 1\n- Other responses (40x, 50x): Free of charge\n","operationId":"getRevenues","tags":["revenues"],"parameters":[{"schema":{"type":"string","minLength":1,"maxLength":15,"description":"The stock ticker of the company"},"required":false,"description":"The stock ticker of the company","name":"ticker","in":"query"},{"schema":{"type":"string","enum":["XAMM","XMLI","ALXP","XDUB","XNCO","XMAU","VPXB","FNLT","AQSG","XKUW","XWAR","XTRN","XAMS","XDHA","XZAG","XPRM","XKRX","NOTC","FSME","MTAA","XSHG","XSAU","XLUX","XBOG","SCLE","XSES","XJSE","XXXX","GROW","XNSE","OTCM","XTAI","BJSE","RUSX","XFRA","XHEL","MISX","XNCM","XEQY","XRIS","ZBUL","XDFM","HAMN","XMAI","XNSA","WBDM","XMAE","XBER","XLJM","XCAI","XBUE","XHKG","XNYS","MERK","AIXK","XOAS","ENAX","XADS","XLJU","XTKS","XBOM","XKLS","ARCX","XKAZ","XBRN","XPHS","XKAR","NSME","XSAT","XZIM","XTSE","XNMS","XBIL","XMUN","XIDX","BATS","XNGS","ALXB","ENXL","XSHE","XBAR","XNZE","XBSE","XLIS","XHAM","XLIT","AQST","XCSE","XSGO","XSTO","XNAI","XSTC","SSME","XCNQ","XLIM","DSME","XBKK","XCOL","XGUA","XMAL","DSMD","XASE","ROCO","XDUS","XBAH","HSTC","XBUD","FNIS","XSTU","XCYS","XMAD","XLON","XBRA","XKOS","EXGM","XCAN","XICE","XBRU","ABUL","XTAE","XTSX","XCAS","BVMF","XMEX","XSWX","XHNX","FNLV","XATH","XETR","XPRA","XPAR","HMOD","XNGM","XBEL","XOSL","XMUS","XTAL","XASX","WBAH","NEOE","XLAT"],"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used."},"required":false,"description":"The Market Identifier Code (MIC) of the exchange where the company is listed (e.g., XNYS for New York Stock Exchange). If not provided, the first matching ticker will be used.","name":"mic_code","in":"query"},{"schema":{"type":"string","format":"uuid","description":"The UUID of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The UUID of the company. If provided, this takes precedence over ticker and mic.","name":"company_id","in":"query"},{"schema":{"type":"string","minLength":12,"maxLength":12,"pattern":"^[A-Z]{2}[A-Z0-9]{9}[0-9]$","description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The International Securities Identification Number (ISIN) of the company. If provided, this takes precedence over ticker and mic.","name":"isin","in":"query"},{"schema":{"type":"string","minLength":20,"maxLength":20,"pattern":"^[A-Z0-9]{18}[0-9]{2}$","description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic."},"required":false,"description":"The Legal Entity Identifier (LEI) of the company. If provided, this takes precedence over ticker and mic.","name":"lei","in":"query"},{"schema":{"type":"integer","description":"The reporting period for which to retrieve revenue data. Accepts an absolute year (2020..2026) or a negative offset (-6..-1) where -1 resolves to the most recent reporting period available for the company, -2 to the second most recent, etc."},"required":true,"description":"The reporting period for which to retrieve revenue data. Accepts an absolute year (2020..2026) or a negative offset (-6..-1) where -1 resolves to the most recent reporting period available for the company, -2 to the second most recent, etc.","name":"reporting_period","in":"query"}],"responses":{"200":{"description":"Revenues","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"application/json":{"schema":{"type":"array","items":{"type":"object","properties":{"year_of_disclosure":{"type":"integer","description":"Year in which the data point was disclosed."},"reporting_period":{"type":"integer","description":"Period for which the data point was measured, assessed, or is applicable."},"metric":{"type":"string","enum":["Total Revenue"],"description":"The specific measurement or data point requested."},"value":{"type":"integer","description":"Total revenue generated by the company during the reporting period, expressed as an integer in USD."},"ccy":{"type":"string","enum":["USD"],"description":"The currency in which the revenue value is reported. Always normalized to USD"},"company_id":{"type":"string","format":"uuid","description":"Tracenable's internal company identifier."},"document_id":{"type":"string","format":"uuid","description":"Unique document identifier to see the source of the data point."},"traceability_source_url":{"type":"string","format":"uri","description":"URL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes."}},"required":["year_of_disclosure","reporting_period","metric","value","ccy","company_id","document_id","traceability_source_url"]}}}}},"400":{"description":"Bad Request","content":{"text/plain":{"schema":{"type":"string"}}}},"401":{"description":"Unauthorized","content":{"text/plain":{"schema":{"type":"string"}}}},"402":{"description":"Insufficient credits","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{"text/plain":{"schema":{"type":"string"}}}},"403":{"description":"Subscription required","headers":{"X-Credit-Cost":{"description":"Number of credits consumed for this request.","schema":{"type":"integer"}},"X-Credit-Balance":{"description":"Number of remaining credits after this request.","schema":{"type":"integer"}}},"content":{}},"404":{"description":"No data found for the provided parameters","content":{"text/plain":{"schema":{"type":"string"}}}},"500":{"description":"Internal Server Error","content":{"text/plain":{"schema":{"type":"string"}}}}}}}}} ```` # Introduction Get introduced to Tracenable’s Waste Dataset, including its scope, key characteristics, and unique value, and get redirected to detailed pages to deepen your knowledge. ## Overview The Waste Dataset captures how companies worldwide manage and report waste, covering more than 4,000 global firms across diverse industries. It provides detailed disclosures on hazardous and non-hazardous waste streams, disposal methods, and recovery rates, offering both absolute and intensity-based measures. By applying rigorous standardization to waste categories and treatment practices, the dataset transforms fragmented corporate disclosures into comparable, decision-ready information. This enables robust cross-company benchmarking, sectoral analysis, and long-term tracking of corporate performance against regulatory requirements and circular economy objectives. *** ## Data Characteristics
:building: Company Coverage4000
:globe: Geographical CoverageGlobal
:shapes: Sectoral CoverageAll Sectors
:calendar-range: Data Historical RangeFrom 2021 to 2024
:reflect-vertical: Median Data History2 years
:magnifying-glass-waveform: Data Traceability Rate100%
:chart-simple: Data FrequencyAnnual
:repeat: Average Reporting Lag3 months
:bring-forward: Data FormatMost Recent/Point-in-Time
*** ## What Makes Tracenable’s Waste Data Unique Tracenable's waste management dataset sets the market benchmark for precision, standardization, reliability, and integrity. Learn why our data stands apart:
:arrow-down-triangle-square: Comprehensive Standardization When company waste data do not align with standard waste reporting frameworks, our team of environmental engineers meticulously maps the reported data to the correct waste categories and disposal methods. This guarantees uniformity and comparability across our dataset, bridging the gap created by diverse reporting formats.
:bullseye-arrow: Accuracy in Every MetricOur advanced cross-source data precision matching algorithm ensures that the most accurate data is always delivered. For instance, an exact figure like 15,245 metric tons of waste is prioritized over a rounded figure like 15,000 metric tons, reflecting our dedication to precision and detail.
:shield-check: Unbiased Data IntegrityOur approach is grounded in delivering waste data exactly as reported by companies, without making inferences or estimates for undisclosed data. This strict adherence to factual reporting ensures the integrity of the data you receive, providing an unaltered and accurate view of corporate waste management.
:magnifying-glass-waveform: End-to-End Data TraceabilityEvery data point we provide is directly traceable to its original source, down to the page numbers and exact coordinates within source documents. This level of detail ensures you have access to the most reliable and verifiable waste data available, equipping you with data you can trust completely.
*** ## Deep Dive into the Waste Dataset On the following pages, you’ll find all the resources needed to fully understand and apply the Waste Dataset: * [**Definitions & Terminology**](/waste-management/definitions-and-terminology) – Key terms and concepts used in the dataset * [**Data Dimensions & Metrics**](/waste-management/data-dimensions-and-metrics) – Breakdown of waste metrics and how they are structured * [**References & Standards**](/waste-management/references-and-standards) – Alignment with global reporting frameworks and regulations * [**Data Collection Methodology** ](/waste-management/data-collection-methodology)– How the dataset is built and validated * [**Data Sources**](/waste-management/data-collection-methodology/data-sources) – Origin and type of corporate disclosures collected * [**Standardization Guidelines**](/waste-management/data-collection-methodology/standardization-guidelines) – Rules applied to ensure comparability * [**Calculation Logic**](/waste-management/data-collection-methodology/calculation-logic) – Methods for deriving standardized metrics * [**Quality Assurance** ](/waste-management/data-collection-methodology/quality-assurance)– Checks and processes ensuring data integrity * [**Data Dictionary** ](/waste-management/data-dictionary)– Complete reference of fields, units, and definitions *** # Definitions & Terminology Understand what qualifies as waste, how it is classified, the three main waste types (hazardous, non-hazardous, and radioactive), and the two management methods (recovery and disposal). ## What is Waste Data? Waste data refers to the figures related to anything that a reporting company discards, intends to discard, or is required to discard. Tracenable’s waste dataset focuses on corporate solid waste management, and excludes: * Wastewater and gaseous effluents, and * Waste types explicitly excluded under Article 2 of the EU Waste Framework Directive ([Directive 2008/98/EC](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A02008L0098-20240218)) *** ## Types of Waste Waste can be classified in various ways. One common approach is by its source—for instance: garden waste, household waste, or industrial waste. However, at a more fundamental level, waste falls into three main categories: * Hazardous waste * Non-hazardous waste * Radioactive waste ### Hazardous Waste Hazardous waste refers to any waste that contains or exhibits [**hazardous characteristics**](#user-content-fn-1)[^1], as defined by regulations in the jurisdiction where the waste is generated. {% hint style="info" %} Hazardous characteristics are properties that render waste potentially harmful to human health, other living organisms, or the environment. {% endhint %} This type of waste can originate from a variety of sources. Common examples include: * **Infectious waste** from healthcare facilities * **Used oils** from industrial or automotive processes * **Waste containing heavy metals**, such as mercury or lead These materials require special handling, treatment, and disposal to prevent adverse impacts. ### Non-Hazardous Waste Non-hazardous waste includes all waste that **does not exhibit hazardous characteristics** and is not classified as hazardous under applicable regulations. Like hazardous waste, non-hazardous waste is produced from a wide range of sources. Common examples include: * **Kitchen waste** from households or food services * **Garden waste** such as grass clippings and branches * **Plastic waste** from packaging and consumer products While generally less dangerous, non-hazardous waste still requires proper management to minimize environmental impact. ### Radioactive Waste Radioactive waste consists of materials that contain radioactive substances and emit ionizing radiation above legally defined thresholds. Due to its significant health and environmental hazards, radioactive waste is subject to strict handling, storage, and disposal protocols under both national and international regulations. It is commonly generated from: * **Nuclear power production and fuel reprocessing** * **Medical procedures** using radioactive isotopes * **Industrial and scientific activities** involving radioactive materials {% hint style="info" %} Radioactive waste is treated as a distinct category—separate from hazardous and non-hazardous waste—due to its unique risks. Its classification and oversight fall under specialized national nuclear authorities and international regulatory frameworks. {% endhint %} In the context of sustainable finance, Radioactive Waste Generated is also a reportable metric under the mandatory Principal Adverse Impact (PAI) indicators defined by the Sustainable Finance Disclosure Regulation (SFDR). This emphasizes the importance of monitoring and disclosing radioactive waste in investment decision-making. *** ## Waste Management Waste management encompasses the collection, transport, recovery (including sorting), and disposal of waste, along with the supervision of these operations and the after-care of disposal sites. It also includes any actions taken in the role of a dealer or broker. At Tracenable, we focus specifically on the recovery and disposal phases of waste management. Accordingly, waste is managed through two fundamental pathways: * **Recovery** (or diversion from disposal) * **Disposal** (or direction to disposal) ### Recovery Operations (Diversion from disposal) Recovery refers to any operation where waste serves a useful purpose by replacing other materials that would otherwise have been used for a specific function—either within the company’s own operations or elsewhere in the economy. Waste may also be prepared to fulfil such a function. Waste diverted from disposal includes materials managed through recovery methods such as: * **Recycling** * **Reusing** * **Composting** These practices help conserve resources and reduce environmental impact by minimizing the need for virgin materials. ### Disposal Operations (Direction to disposal) Disposal refers to any operation that is not classified as recovery, even if it results in some byproduct recovery such as energy or materials. Waste directed to disposal includes materials managed through disposal methods such as: * **Landfilling** * **Incineration** * **Combustion** These processes are typically the final step for waste that cannot be viably recovered or repurposed, and they require careful oversight due to their environmental implications. *** [^1]: **Hazardous characteristics** are properties that render waste potentially harmful to human health, other living organisms, or the environment. # Data Dimensions & Metrics Learn how waste data is structured in Tracenable’s dimensional model, with two core dimensions (Waste Type and Management Method), and explore the full set of metrics derived from their combinations. ## How Dimensions and Metrics Work Tracenable datasets follow a dimensional model. * **Dimensions** are the attributes you can use to analyze or slice the data. Each dataset is defined by one or more dimensions. * **Metrics** are the most granular layer: each one represents a unique combination of dimension values that defines a specific data point. This dimensional model provides a transparent and predictable way to structure data. It removes ambiguity in naming, ensures consistency across datasets, and makes it easier to understand how each metric is constructed. *** ## Dimensions in the Waste Dataset All waste data in Tracenable is organized along two core dimensions: **Waste Type** and **Waste Management Method**. These dimensions define how waste is categorized and reported, and every metric in the dataset is derived from their combinations. * ***Waste Type*** classifies waste according to its inherent characteristics and regulatory definitions. It includes the following values: * `Total (All Types)` * `Hazardous` * `Non-Hazardous` * `Radioactive` * `Unclassified` * ***Waste Management Method*** indicates how the waste is treated once generated. It includes the following values: * `Generated (All Methods)` * `Recovered` * `Disposed` * `Unclassified` {% hint style="info" %} Including **Unclassified** values in both dimensions ensures that all disclosed figures are captured, even when companies provide limited detail. Instead of discarding incomplete data or forcing it into categories that may not be accurate, Tracenable assigns it to the Unclassified bucket. This approach maintains transparency about data limitations, avoids introducing bias, and ensures that all reported waste is still included in aggregate calculations. {% endhint %} *** ## Metrics in the Waste Dataset Metrics in the Waste Dataset are defined by specific combinations of the two core dimensions: **Waste Type** and **Waste Management Method**. This structure makes it clear whether a metric refers to a particular waste stream (for example, *Hazardous Waste Recovered*) or to a broader total (for example, *Total Waste Generated*). | Waste Type | Waste Management Method | Metric | | ----------------- | ----------------------- | ----------------------------------- | | Hazardous | Recovered | Hazardous Waste Recovered | | Hazardous | Disposed | Hazardous Waste Disposed | | Non-hazardous | Recovered | Non-hazardous Waste Recovered | | Non-hazardous | Disposed | Non-hazardous Waste Disposed | | Radioactive | Recovered | Radioactive Waste Recovered | | Radioactive | Disposed | Radioactive Waste Disposed | | Total (All Types) | Generated (All Methods) | Total Waste Generated | | Hazardous | Generated (All Methods) | Total Hazardous Waste Generated | | Non-hazardous | Generated (All Methods) | Total Non-hazardous Waste Generated | | Radioactive | Generated (All Methods) | Total Radioactive Waste Generated | | Total (All Types) | Recovered | Total Waste Recovered | | Total (All Types) | Disposed | Total Waste Disposed | {% hint style="warning" %} **Notice that “Unclassified” values are missing from the table?** When a data point is assigned the *Unclassified* option in either dimension, we do not deliver it as a standalone metric in the catalog. This is because such cases reflect incomplete or uncertain company disclosures, and the resulting metrics would lack comparability across companies. However, Unclassified values are still fully accounted for when calculating totals to ensure data completeness. A detailed explanation of how Unclassified values are handled can be found in the [Calculation Logic](/waste-management/data-collection-methodology/calculation-logic) page. {% endhint %} *** # References & Standards Discover the foundational references and reporting standards that shape Tracenable’s Waste Dataset. ## Foundational References These are the **authoritative sources** we rely on to define terms, set classification rules, and resolve edge cases. When sources conflict, we prioritize jurisdictional applicability first, then the most specific guidance. ### **EU Waste Framework Directive** (Directive 2008/98/EC) * **Why it matters:** Establishes legal definitions (e.g., *waste*, *hazardous waste*), the waste hierarchy, R/D operation codes (recovery vs disposal), and extended producer responsibility used widely across the EU and referenced by other regimes. * **What we adopt:** * Hazardous vs non-hazardous classification and hazardous properties (HP codes). * Method mapping: EU R-operations → “Recovered”; D-operations → “Disposed.” * Hierarchy language to inform treatment method labelling and quality checks. ### U.S. Resource Conservation and Recovery Act (RCRA) (EPA overview) * **Why it matters:** Governs U.S. waste generation, handling, and disposal; sets strict hazardous waste controls (listed vs characteristic wastes). * **What we adopt:** * Alignment for U.S. issuers on hazardous determinations and treatment method definitions. * Crosswalks between RCRA categories and our Waste Type + Waste Management Method dimensions. ### Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and Their Disposal * **Why it matters:** Global reference for hazardous waste classification and transboundary controls; influences national lists/annexes. * **What we adopt:** * Supporting reference for hazardous category interpretation when company disclosures cite Basel annexes. * Terminology consistency for international reporters. ### IAEA Safety Standards (radioactive waste) * **Why it matters:** Radioactive waste is governed separately; we include it as a distinct Waste Type with unique regulatory context. * **What we adopt:** * Use of “radioactive waste” as a discrete category; we do not re-map it into hazardous/non-hazardous. * Handling notes for disclosures citing national nuclear authorities. *** ## Related Reporting Frameworks & Standards Unlike greenhouse gas emissions reporting, which benefits from a globally recognized standard like the GHG Protocol, waste reporting remains decentralized, with no single dominant global framework. However, several key standards provide clear guidance for companies seeking to quantify, manage, and disclose their waste-related impacts. Note that these frameworks **do not define our ground truth**, but they guide field design, coverage expectations, and help users connect our metrics to reporting and investment workflows. ### GRI 306: Waste 2020 * **Relevance:** Most widely adopted corporate waste disclosure framework. * **How we align:** Field structure mirrors GRI emphasis on waste generated and management (diverted vs directed to disposal); supports mapping to GRI 306-3/4/5. ### ESRS E5: Resource Use and Circular Economy (CSRD) * **Relevance:** EU-mandated disclosures on waste generation, hazardous waste, and resource recovery. * **How we align:** Our Recovered/Disposed method dimension and hazardous split support ESRS E5 datapoints and KPIs. ### SFDR Principal Adverse Impacts (PAIs) * **Relevance:** Requires investors to report tonnes of hazardous waste and radioactive waste generated by investee companies. * **How we align:** Directly supported by our Total Hazardous Waste Generated and Total Radioactive Waste Generated metrics. ### SASB (now under IFRS Foundation) * **Relevance:** Industry-specific metrics (e.g., hazardous waste intensity, treatment disclosures) used by many public filers. * **How we align:** Sector tagging enables users to filter our metrics for SASB-relevant topics across industries. ### TNFD (Taskforce on Nature-related Financial Disclosures) * **Relevance:** Positions waste as a pressure on nature; calls for disclosure of hazardous and non-hazardous waste and management routes. * **How we align:** Our dimension model (Type × Method) maps cleanly to TNFD disclosure elements and supports nature-risk analysis. {% hint style="success" %} ### Takeaway: * You can **map our metrics directly** to GRI 306, ESRS E5, SFDR PAIs, SASB industry metrics, and TNFD disclosures. * You get **jurisdiction-aware definitions** that reflect how companies actually report. * You retain **full traceability** to the underlying source, enabling audit-grade use in compliance, benchmarking, and investment workflows. {% endhint %} *** # Data Collection Methodology Learn how Tracenable collects, standardizes, and validates corporate waste data through a five-step human-in-the-loop methodology, with links to detailed subpages on sources, standardization, and QAs. ## Introduction The value of waste data lies not just in its availability, but in its clarity, comparability, and traceability. At Tracenable, we designed a data collection methodology that combines rigorous research, comprehensive sourcing, and advanced human–AI workflows to produce corporate waste metrics that are both granular and broadly applicable. Our approach is built around four principles: define with authority, collect comprehensively, standardize precisely, and validate rigorously. *** ## Our Five-Step Waste Data Collection Approach {% stepper %} {% step %} ### Defining the Schema through Research We start by grounding our work in foundational references such as the EU Waste Framework Directive, the U.S. RCRA, and the Basel Convention. From there, we study voluntary frameworks like GRI 306, ESRS E5, and SFDR to understand disclosure expectations. This theoretical research is paired with empirical research: analyzing how companies actually report waste in practice across industries and regions. By combining both, we design a data schema that strikes the right balance: as granular as possible, but general enough to apply across thousands of companies worldwide. {% endstep %} {% step %} ### Comprehensive Collection of Disclosures Corporate waste data can appear in many places: sustainability reports, regulatory filings, standalone data spreadsheets, or hidden on a webpage deep in a company’s site. Our infrastructure is designed to capture all of it. Through automated web monitoring and targeted expert retrieval, we ensure that no disclosure is overlooked. This comprehensive approach minimizes blind spots and provides the broadest possible coverage of corporate waste data globally. {% endstep %} {% step %} ### Converting Disclosures into Structured Data Waste disclosures come in many formats: PDFs, Excel annexes, HTML tables, and narrative text. Our AI-driven pipelines first convert raw files into a unified structure (e.g., PDF to markdown). From there: * Computer vision parses tables and figures. * NLP models identify waste-related passages, detect units, and extract values. * Classification rules map waste into hazardous, non-hazardous, radioactive, or unclassified types, and into recovered vs disposed methods. The result: machine-readable, standardized data points that preserve traceability to the original disclosure. {% endstep %} {% step %} ### Data Human-in-the-Loop Validation AI brings speed and scalability, but human expertise ensures accuracy and context. Each extracted data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate waste data, with arbitration applied where discrepancies remain. This process allows us to: * Correct errors where AI misclassifies complex waste categories. * Preserve context from narrative disclosures. * Continuously improve our models through feedback. The outcome is audit-grade waste data that users can trust. {% endstep %} {% step %} ### Rigorous Quality Assurance Finally, our Waste dataset undergoes multi-layered quality checks: * Automated tests catch obvious anomalies (negative values, implausible spikes, inconsistent units). * Machine learning models detect statistical outliers through unsupervised methods and unusual time-series patterns. * Manual audits ensure nothing slips through the cracks. This combination of automation and human oversight guarantees that every waste metric delivered is reliable, comparable, and ready for use in compliance, benchmarking, and research. {% endstep %} {% endstepper %} *** ## Learn More To explore the methodology in detail, visit: * [**Data Sources**](/waste-management/data-collection-methodology/data-sources) - Where waste data comes from and how it is collected. * [**Standardization Guidelines** ](/waste-management/data-collection-methodology/standardization-guidelines)- How disclosures are normalized into consistent waste types and treatment methods. * [**Calculation Logic**](/waste-management/data-collection-methodology/calculation-logic) - How missing values are inferred and totals are computed using transparent accounting rules. * [**Quality Assurance**](/waste-management/data-collection-methodology/quality-assurance) - The validations and controls that safeguard data integrity. *** # Data Sources See where Tracenable’s waste data comes from. Learn which corporate disclosures, registries, and web sources we capture, and how every metric is fully traceable back to its origin. ## Introduction The reliability of waste data starts with the quality of its sources. At Tracenable, we collect information from a broad range of corporate and official channels, ensuring that every data point is traceable back to its origin. Our goal is simple: provide users with complete, transparent, and verifiable evidence of how companies disclose their waste performance. *** ## Where We Collect Data We capture waste disclosures wherever companies report them, across all common formats: * **Corporate reports** – Sustainability reports, annual reports, integrated reports, proxy statements. * **Regulatory filings** – Documents filed under mandatory disclosure regimes (e.g., CSRD, SEC, or national registries). * **Web disclosures** – Corporate webpages, environmental policy pages, or dedicated sustainability microsites. * **Data annexes and spreadsheets** – Often attached to sustainability reports or published as standalone datasets. * **Press releases and news articles** – Only when originating directly from the company. * **Government registries** – Authoritative third-party repositories of company-submitted waste data. No matter the format (PDF, HTML, Excel, or XML/XBRL) we normalize disclosures into a structured, machine-readable format without losing traceability to the original file. *** ## End-to-End Traceability Every data point in the Waste dataset includes a direct link to its original source, allowing users to audit disclosures in context. Links open the exact report, page, or section cited. Metadata such as publication date and reporting period are also captured to preserve the full reporting trail. This approach ensures transparency: users can always see *what a company reported, when, and where*. *** ## Coverage Strategy Our coverage is global and demand-driven. We monitor thousands of companies across sectors and geographies, prioritizing based on client requests. If your use case requires extended coverage, we can adapt our sourcing to include additional companies, jurisdictions, or disclosure types. This flexibility ensures that Tracenable’s Waste dataset reflects not only today’s mandatory reporting landscape, but also the evolving needs of users. *** # Standardization Guidelines Learn how Tracenable standardizes waste data by preserving company-reported classifications and applying consistent mapping rules for management methods to ensure comparability across companies. ## Why Standardization Matters Corporate energy disclosures are inconsistent. Companies report in different units, apply varied definitions of renewability, and disclose inflows and outflows in ways that differ across jurisdictions. This fragmentation makes it nearly impossible to compare energy performance reliably across firms, sectors, or regions. Tracenable resolves this challenge by applying a systematic normalization process. All energy data points are transformed into a consistent structure, ensuring that company disclosures are comparable, auditable, and aligned with recognized statistical and reporting frameworks *** ## The Challenge: Lack of Standardization While some waste types are universally classified (for example, organic solvents are always hazardous) others vary depending on context. For instance, e-waste may be hazardous or non-hazardous depending on whether it contains heavy metals or other hazardous substances. Whether a particular waste is classified as hazardous depends on three key factors: * **Composition** – whether the waste contains hazardous substances * **Concentration limits** – the levels of hazardous constituents present * **Particle size** – for example, glass powder may be hazardous, while glass packaging waste is typically non-hazardous These assessments are determined by local regulations, which differ significantly across jurisdictions. As a result, the same waste type may be classified differently depending on where the company operates. This regulatory variation creates serious comparability gaps in waste reporting. *** ## Tracenable’s Standardization Principles ### **Guideline 1.** Normalize Units to Metric Tonnes Companies use various units to report weight, such as kg, g, lb, or short tons. All are converted to **metric tonnes (t)** for consistency. **Example:** A company reports *500,000 lb of hazardous waste disposed.* Converted to metric tonnes (1 lb = 0.000453592 t), the normalized value equals *226.8 t.* This ensures comparability across international reporting standards and waste categories. ### **Guideline 2. Preserve Company-Reported Waste Type Classifications** * When a company clearly identifies a waste stream as hazardous or non-hazardous, we retain that classification as reported. * This approach respects jurisdictional rules: hazardousness is determined by the regulations applicable to the reporting company. * If the hazardousness of a reported waste type is not specified or cannot be confidently determined, Tracenable refers to the **foundational references** (e.g., EU WFD, RCRA, Basel) for authoritative classification. {% hint style="success" %} When waste type classification is clearly reported, Tracenable preserves the company’s designation rather than applying external mappings. Waste type classifications therefore remain faithful to company disclosures and jurisdictional rules. {% endhint %} ### **Guideline 3. Apply a Standardized Mapping for Waste Management Methods** * Waste management methods are often described inconsistently across disclosures (e.g., “thermal treatment,” “energy recovery,” “waste-to-energy”). * To ensure comparability, we standardize all reported methods into two categories: **Recovered** or **Disposed**. * This mapping follows the recovery/disposal operations defined in the foundational references, and is applied even when company terminology differs. In some cases, we may also reclassify company-reported methods to preserve consistency. For example, treating energy recovery as Disposed even when companies classify it as Recovered. Where classification cannot be determined with confidence, we retain the disclosure as reported rather than forcing a mapping. {% hint style="success" %} Waste management methods are standardized globally wherever possible, enabling reliable benchmarking across companies and industries. When companies provide only partial disclosures (e.g., “total waste recovered” without specifying the recovery method), we still preserve the information as reported. {% endhint %} {% hint style="info" %} **Looking for detailed mappings?** Tracenable has compiled an extensive internal reference of “as reported” waste types and management methods, along with their standardized mappings to our dataset dimensions. To protect the intellectual property behind our research, this resource is not published publicly. However, we’re happy to share it privately upon request. Just reach out, and we’ll provide access. {% endhint %} *** # Calculation Logic Learn how Tracenable infers missing waste metrics using clear bottom-up and top-down accounting rules, ensuring data is complete, consistent, and transparent. ## Introduction Companies often disclose waste data in fragments. One company might report only hazardous waste, another might report a total without any breakdown, and a third might split waste into recovered and disposed but leave out radioactive waste entirely. On their own, these disclosures are incomplete and inconsistent, making them difficult to compare. To address this, Tracenable applies a transparent accounting system. This system uses simple but powerful rules to fill in the gaps when companies do not report every possible waste metric. Every calculation is flagged, traceable, and auditable, so users always know what is reported and what is inferred. The goal is not to “guess” missing numbers, but to apply structured logic based on the relationships between waste types and management methods. *** ## Hierarchical Relationships Waste metrics are structured hierarchically, meaning **parents are made up of children**. * ***Total Waste Generated*** is the sum of hazardous, non-hazardous, radioactive, and unclassified waste. It is also the sum of waste that is recovered, disposed, or unclassified by method. * ***Hazardous Waste Generated*** is the parent of hazardous waste that is recovered, disposed, or unclassified by method. * ***Total Waste Recovered*** is the parent of hazardous, non-hazardous, radioactive, and unclassified waste recovered. Because of these relationships, we can often calculate missing metrics. For example: * If a company reports hazardous and non-hazardous waste but not the total, we can add them together. * If a company reports its total waste generated and the portion of hazardous waste but not the non-hazardous portion, we can sometimes calculate it by subtracting the other parts. This brings us to the two key accounting rules: ### Rule 1: Bottom-up Computation (Sum of Children) When a **parent metric is missing**, but some of its **children are reported**, we calculate the parent as the **sum of available children**. **Examples** * ***Total Waste Generated** = Hazardous Waste Generated + Non-Hazardous Waste Generated + Radioactive Waste Generated + Waste Generated with Unclassified Type* * ***Hazardous Waste Generated** = Hazardous Waste Recovered + Hazardous Waste Disposed + Hazardous Waste with Unclassified Management Method* * ***Total Waste Recovered** = Hazardous Waste Recovered + Non-Hazardous Waste Recovered + Radioactive Waste Recovered + Waste Recovered with Unclassified Type* This approach ensures that totals are always calculated whenever partial data is available, without discarding any company-reported values. ### Rule 2: Top-Down Computation (Subtraction) Sometimes the situation is reversed: the **parent is reported**, but one of the children is missing. In this case, we can calculate the missing child by subtracting the sum of the known children from the parent. **Examples** * ***Total Waste Recovered** = Total Waste Generated − Total Waste Disposed − Waste with Unclassified Management Method* * ***Non-Hazardous Waste Generated** = Total Waste Generated − Hazardous Waste Generated − Radioactive Waste Generated − Waste Generated with Unclassified Type* * ***Hazardous Waste Recovered** = Hazardous Waste Generated − Hazardous Waste Disposed − Hazardous Waste with Unclassified Management Method* {% hint style="warning" %} **When do we apply top-down computations?** The top-down computation only work if the most important pieces of the puzzle are already available. For example, to calculate *Non-Hazardous Waste Generated*, we must at least know *Hazardous Waste Generated and Total Waste Generated*. Without these two pieces, the subtraction would leave too much uncertainty.We therefore distinguish between: * **Mandatory children** → the core information that must be present for the subtraction to make sense (e.g., Hazardous Waste for type-level splits, Disposed Waste for method-level splits). * **Non-mandatory children** → categories like *Radioactive* or *Unclassified*. If they are reported, they are included in the subtraction. If they are not, they are treated as zero so that the calculation can still proceed. This ensures that every top-down computation is both logical and reliable, without creating misleading values. {% endhint %} {% hint style="success" %} **Takeaway** * You can compare companies on equal terms, even if they report different levels of detail * You can trust that no data has been invented or forced into categories. * You can always trace a number back to its source and see whether it was reported or computed. {% endhint %} *** # Quality Assurance Discover how Tracenable validates waste data through automated checks, statistical tests, and human review to deliver audit-grade reliability. ## Introduction High-quality waste data depends on more than just good collection and standardization: it requires rigorous validation. At Tracenable, we combine automated testing, statistical analysis, and expert human review to ensure that every waste metric meets the highest standards of accuracy, consistency, and reliability. Our Quality Assurance (QA) process is multi-layered, designed to detect errors, catch anomalies, and confirm that each data point is both faithful to the original disclosure and fit for use in compliance, benchmarking, and research. *** ## Automated Validation Checks The first layer of QA relies on automated rules that run across all waste metrics. These checks are designed to quickly spot issues that should never occur in valid data, such as: * **Impossible values** – negative or implausibly large waste quantities. * **Unit inconsistencies** – figures reported in mismatched or conflicting units across years. * **Structural errors** – totals that do not match the sum of their components. These rules ensure that obvious errors are flagged immediately and never propagate into the dataset. *** ## Statistical and Machine Learning Tests Beyond simple rules, we apply more advanced techniques to identify subtle anomalies: * **Time-series consistency checks** – highlight sudden spikes or drops in reported waste generation. * **Outlier detection** – identify company disclosures that deviate significantly from industry norms. * **Distribution analysis** – verify that waste metrics follow expected statistical patterns across sectors. These methods help us flag values that may be technically valid but require closer review. *** ## Human-in-the-Loop Review Not all issues can be resolved automatically. Our QA process therefore includes a human-in-the-loop review, where trained analysts validate flagged data points: * **Contextual review** – analysts check values against the original disclosure to confirm interpretation. * **Dual validation** – two independent reviewers may assess the same data point. * **Arbitration** – discrepancies between analysts are escalated to senior analysts for final decision. This ensures that ambiguous or complex waste disclosures are interpreted correctly, and that every value remains fully traceable to its source. *** ## Continuous Improvement Each QA outcome feeds back into our systems: * Automated rules are updated when new error patterns are identified. * Machine learning models are retrained to improve anomaly detection. * Documentation is refined to capture new edge cases and classification challenges. This iterative loop ensures that the Waste Dataset becomes more robust over time. *** # Data Dictionary Explore Tracenable’s Waste Management Data Dictionary, featuring standardized fields for waste metrics, disclosure details, and source traceability.
AttributeType/FormatDescriptionExample
isinAlphanumeric StringInternational Securities Identification Number (ISIN) of the primary publicly traded financial instrument associated with the company.US42704L1044
leiAlphanumeric StringLegal Entity Identifier (LEI)549300TP80QLITMSBP82
figiAlphanumeric StringFinancial Instrument Global Identifier (FIGI) of the primary publicly traded financial instrument associated with the company.BBG00WNPK2F5
tickerStringTicker symbol of the primary publicly traded financial instrument associated with the company.HRI
mic_codeStringMarket Identifier Code (MIC) of the primary publicly traded financial instrument associated with the company.XNGS
exchangeStringStock exchange of the primary publicly traded financial instrument associated with the company.NASDAQ
permidNumerical StringPermanent Identifier4295900057
company_nameStringLegal name of the company.HERC HOLDINGS INC
countryCategorical StringCountry where the company's headquarters are located.United States
sectorStringSector in which the company operates.Technology
industryStringIndustry classification of the company.Software - Application
year_of_disclosureYear (YYYY)Year in which the data point was disclosed.2023
reporting_periodYear (YYYY)Period for which the data point was measured, assessed, or is applicable.2023
metricStringThe specific measurement or data points requested.Total Hazardous Waste Generated
hazardousnessCategorical StringThe classification of waste as Hazardous, Non-Hazardous, or radioactive.Hazardous
treatment_methodCategorical StringThe method used to manage the waste, including Disposed or Recovered.Disposed
valueNumerical FloatAmount of waste.12000000
unitCategorical StringThe unit of measurement for the value, indicating the scale or dimension.Metric Tonnes
incomplete_boundariesBoolean (True or Not Specified)Indicates whether the reported data covers only a limited portion of the company's operational or organizational boundaries.Not Specified
source_namesArray of StringsNames of the sources from which the reported data was obtained.['Corporate Responsibility Report']
company_idStringTracenable's internal company identifier.dfb6bd7b-facf-4d0f-86a2-f1eedf191946
document_idStringTracenable's internal source document identifier.4a32b902-ae42-4692-ae96-b42c99fdd2f9
traceability_source_urlStringURL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes.https://platform.tracenable.com/source-trace?data-request-id-hash=lVy8DxwhOk8X&project=waste-management
# Introduction Get introduced to Tracenable’s EU Taxonomy Dataset, including its scope, key characteristics, and unique value, and get redirected to detailed pages to deepen your knowledge. ## Overview Tracenable’s EU Taxonomy Dataset delivers structured, standardized, and traceable data on how companies report under the EU’s sustainable finance regulation. Covering over 1,800 companies across industries, the dataset translates complex corporate disclosures into a consistent, comparable format. The dataset captures both activity-level and company-level reporting, organized across three dimensions: Level, KPI, and Eligibility Category. It provides metrics for turnover, CAPEX, and OPEX, classified by taxonomy eligibility and alignment with the EU’s six environmental objectives. This structure enables reliable benchmarking, supports regulatory compliance, and allows users to track corporate progress toward the EU’s environmental goals. *** ## Data Characteristics
:building: Company Coverage1800
:globe: Geographical CoverageEurope
:shapes: Sectoral CoverageAll Sectors
:calendar-range: Data Historical RangeFrom 2021 to 2024
:reflect-vertical: Median Data History2 years
:magnifying-glass-waveform: Data Traceability Rate100%
:chart-simple: Data FrequencyAnnual
:repeat: Average Reporting Lag3 months
:bring-forward: Data FormatPoint-in-Time
*** ## What Makes Tracenable’s EU Taxonomy Data Unique Tracenable's EU Taxonomy dataset sets the market benchmark for precision, standardization, reliability, and integrity. Learn why our data stands apart:
:octagon-check: Fully Compliant with the Latest Standards Our EU Taxonomy data is meticulously standardized to align with the Commission Delegated Regulation (EU) 2023/2486, capturing both activity-level details and aggregate metrics across turnover, CAPEX, and OPEX. This rigorous standardization ensures seamless regulatory reporting and cross-company comparability.
:shield-check: Unbiased Data IntegrityWe perform comprehensive validation of taxonomy metrics through cross-reference checks and mathematical relationship verification across all environmental objectives. This ensures data completeness and accuracy while maintaining the integrity of company-reported figures without estimations.
:bullseye-arrow: Precision in Every FigureOur advanced cross-source data precision matching algorithm ensures delivery of the most granular taxonomy metrics. For instance, we capture exact activity-level figures like 1,671,540 EUR rather than rounded-sm aggregates, enabling detailed compliance analysis and accurate regulatory reporting.
:magnifying-glass-waveform: End-to-End Data TraceabilityEvery taxonomy data point is linked to its original source document with exact page references and coordinates, complemented by documented calculation methodologies. This transparency enables confident verification of taxonomy metrics and supports regulatory compliance processes.
*** ## Deep Dive into the EU Taxonomy Dataset On the following pages, you’ll find all the resources needed to fully understand and apply the EU Taxonomy Dataset: * [**Definitions & Terminology**](/eu-taxonomy/definitions-and-terminology) – Key terms and concepts used in the dataset * [**Data Dimensions & Metrics**](/eu-taxonomy/data-dimensions-and-metrics) – Breakdown of EU Taxonomy metrics and how they are structured * [**References & Standards**](/eu-taxonomy/references-and-standards) – Alignment with global reporting frameworks and regulations * [**Data Collection Methodology** ](/eu-taxonomy/data-collection-methodology)– How the dataset is built and validated * [**Data Sources**](/eu-taxonomy/data-collection-methodology/data-sources) – Origin and type of corporate disclosures collected * [**Standardization Guidelines**](/eu-taxonomy/data-collection-methodology/standardization-guidelines) – Rules applied to ensure comparability * [**Calculation Logic**](/eu-taxonomy/data-collection-methodology/calculation-logic-up-to-2025) – Methods for deriving standardized metrics * [**Quality Assurance** ](/eu-taxonomy/data-collection-methodology/quality-assurance)– Checks and processes ensuring data integrity * [**Data Dictionary** ](/eu-taxonomy/data-dictionary)– Complete reference of fields, units, and definitions *** # Definitions & Terminology EU Taxonomy explained: environmental objectives, eligible vs. non-eligible activities, alignment criteria, and KPIs. ## Introduction to EU Taxonomy ### What is the EU Taxonomy? The EU Taxonomy is the European Union’s official classification system for economic activities. Think of it as a dictionary that defines what counts as *environmentally sustainable* economic activity. Its purpose is simple: to create a common language that allows companies, investors, and policymakers to assess environmental performance in the same way. {% hint style="info" %} #### Why does this matter? Without such a framework, each company could define “green” in its own way, making it impossible to compare results. The EU Taxonomy closes this gap by setting clear rules for: * **Which activities** can be considered sustainable. * **What criteria** they must meet. * **How results** must be disclosed. {% endhint %} From 2022 onward, large EU companies and financial institutions are legally required to check their activities against the Taxonomy and publish the results. This improves transparency and helps channel investments toward activities that truly support Europe’s climate and environmental goals. ### The Environmental Objectives The EU Taxonomy is built around six environmental objectives: * Climate change mitigation (CCM) * Climate change adaptation (CCA) * Sustainable use and protection of water and marine resources (WTR) * Transition to a circular economy (CE) * Pollution prevention and control (PPC) * Protection and restoration of biodiversity and ecosystems (BIO) For each objective, the European Commission has published a **list of economic activities** that may contribute substantially to it. Each listed activity comes with defined [**technical screening criteria (TSC)**](#user-content-fn-1)[^1] that set the conditions it must meet for contribution. When reporting under the EU Taxonomy, companies must first check whether their activities appear in one of these official lists. *** ## How Activities Are Classified To understand the classification system better, let us look at the schematic diagram depicted below:

Activities classification as per the EU Taxonomy

As illustrated above, the EU Taxonomy classifies economic activities into two broad categories: ### **Taxonomy-eligible activities** Activities that are explicitly listed in the EU Taxonomy Regulation as potentially contributing to at least one environmental objective. {% hint style="warning" %} Eligibility means the activity is in scope and has defined [technical screening criteria (TSC)](#user-content-fn-1)[^1], but it may or may not meet those criteria. {% endhint %} Eligible activities can therefore be classified as either aligned (meeting all criteria) or not aligned (failing one or more criteria). #### **Taxonomy-eligible and aligned activities** * Activities that are listed in the Taxonomy *and* meet all the conditions: * [Technical screening criteria (TSC)](#user-content-fn-2)[^2] * [Do No Significant Harm (DNSH)](#user-content-fn-3)[^3] * [Minimum social safeguards (MSS)](#user-content-fn-4)[^4] * These activities are considered “sustainable". #### **Taxonomy-eligible but not aligned activities** * Activities that are listed in the Taxonomy but fail one or more conditions. * For example, a renewable energy project that harms biodiversity may be eligible but not aligned. {% hint style="info" %} From 2026 onwards, taxonomy-eligible but not aligned activities are no longer required to be explicitly reported. {% endhint %} ### **Taxonomy non-eligible activities** These are activities not yet listed in the Taxonomy. {% hint style="warning" %} Non-eligible does not mean unsustainable. It only means the activity is not currently defined under the regulation. {% endhint %} {% hint style="info" %} From 2026 onwards, non-eligible activities are no longer required to be explicitly reported. {% endhint %} {% hint style="info" %} ### A note on Materiality Before assessing eligibility and alignment, companies must first determine which activities are financially material. An activity is generally considered non-material where its cumulative Turnover, CAPEX, or OPEX represents less than 10% of the respective KPI denominator. Non-material activities are not required to undergo a full eligibility and alignment assessment. However, they are not omitted from reporting entirely; companies must still disclose their combined share as a single percentage of the relevant KPI denominator. Only material activities proceed to the full eligibility and alignment assessment described above. {% endhint %} {% hint style="success" %} In short: * **Eligible = on the list** * **Aligned = on the list *****and***** meets all criteria** * **Non-Eligible = not on the list** * **Non-material = not assessed for eligibility or alignment.** {% endhint %} *** ## Key Performance Indicators (KPIs) Once activities are classified, companies must report how much of their business falls into each bucket using three mandatory **Key Performance Indicators (KPIs)**: * **Revenue/Turnover** – Proportion of revenue from eligible, aligned, or non-eligible activities (in other words, *what share of sales comes from eligible or aligned activities*). * **Capital Expenditure (CAPEX)** – proportion of CAPEX related to eligible, aligned, or non-eligible activities (*how much investment goes into eligible or aligned activities*). * **Operational Expenditure (OPEX)** – proportion of OPEX related to eligible, aligned, or non-eligible activities (*how much day-to-day spending supports eligible or aligned activities*). These KPIs convert the classification into measurable, comparable numbers making disclosures easier to compare across firms and over time. *** ## How the EU Taxonomy Works The Taxonomy uses a step-by-step approach to classify economic activities: {% stepper %} {% step %} ### Determine materiality Companies first identify which activities are financially material, as described above. Non-material activities are set aside and disclosed as a single aggregated percentage. Only material activities proceed to the steps below. {% endstep %} {% step %} ### Identify Eligible Activities within KPIs Companies begin by reviewing their turnover, capital expenditure (CAPEX), and operating expenditure (OPEX) to see which activities appear on the official EU Taxonomy lists linked to the six environmental objectives. * If an activity is listed, it is **taxonomy-eligible.** * If not listed, it is taxonomy non-eligible. **Example**: A power utility earns $100 million in revenue. $30 million comes from renewable wind farms (an activity listed in the Taxonomy), while $70 million comes from coal power plants (not listed). In this case: * $30 million of turnover is classified as eligible. * $70 million of turnover is classified as non-eligible. {% endstep %} {% step %} ### Assess Alignment Eligible activities are then tested against three conditions: * Meet the **technical screening criteria (TSC)**, * **Do no significant harm (DNSH)** to other objectives, and * Comply with **minimum social safeguards (MSS)**. Activities that satisfy all three are considered **taxonomy-aligned**, while those that fail one or more are considered taxonomy-eligible but not aligned. {% endstep %} {% step %} ### **Determine Substantial Contribution to Environmental Objectives** Once aligned activities are identified, companies must indicate which environmental objective(s) these **aligned** activities contribute to. The contribution is expressed as a percentage of the relevant KPI denominator (turnover, CAPEX, or OPEX), showing not just whether an activity is aligned, but also how much of the company's business supports each environmental goal. **Example**: Suppose a wind power generation project is taxonomy-aligned under Climate Change Mitigation (CCM). If the project generates $30 million out of a total $100 million in turnover, the substantial contribution is reported as: * CCM: 30% of total turnover aligned * CCA, WTR, CE, PPC, BIO: 0% {% endstep %} {% endstepper %} *** [^1]: Detailed thresholds set by the European Commission that define when an economic activity makes a *substantial contribution* to an environmental objective. These criteria are usually quantitative (e.g., emission limits, energy efficiency benchmarks) and vary by activity. [^2]: Detailed thresholds set by the European Commission that define when an economic activity makes a *substantial contribution* to an environmental objective. These criteria are usually quantitative (e.g., emission limits, energy efficiency benchmarks) and vary by activity. [^3]: A safeguard principle that requires an activity contributing to one environmental objective not to cause serious negative impacts on the others. For example, a renewable energy project must not significantly harm biodiversity or water resources. DNSH checks are activity-specific and ensure that “sustainable” activities do not simply shift environmental burdens elsewhere. [^4]: Baseline requirements ensuring that activities respect human rights and good governance. They reference international standards such as the OECD Guidelines for Multinational Enterprises, the UN Guiding Principles on Business and Human Rights, and ILO conventions. Compliance with MSS ensures that environmentally sustainable activities also uphold social and labor protections. # Data Dimensions & Metrics Discover how Tracenable’s dimensional model structures EU Taxonomy data into metrics measuring eligibility and alignment of KPIs: turnover, CAPEX, OPEX. ## How Dimensions and Metrics Work Tracenable datasets follow a dimensional model. * **Dimensions** are the attributes you can use to analyze or slice the data. Each dataset is defined by one or more dimensions. * **Metrics** are the most granular layer: each one represents a unique combination of dimension values that defines a specific data point. This dimensional model provides a transparent and predictable way to structure data. It removes ambiguity in naming, ensures consistency across datasets, and makes it easier to understand how each metric is constructed. *** ## Dimensions in EU Taxonomy Data The EU Taxonomy data is organized along three core dimensions. These dimensions define how EU Taxonomy data is categorized and reported, and every metric in the dataset is derived from their combinations. * ***Level**** -*** Identifies whether the disclosure applies to the company’s total business or is broken down by specific economic activities. * `Total` * `Activities` * ***KPI**** -*** Represents the key performance indicators (KPIs) required by the EU Taxonomy, used to quantify how a company’s business activities align with sustainability criteria. * `Turnover` * `CAPEX` * `OPEX` * ***Eligibility Category*** or ***Screening Criteria**** -*** Defines the sustainability classification of economic activities under the EU Taxonomy. * `Aligned` * `Eligible` * `Denominator` *** ## Metrics in the EU Taxonomy Dataset EU Taxonomy metrics are derived by combining the three dimensions: **Level**, **KPI**, and **Eligibility Category / Screening Criteria**. Each metric represents a standardized data point that shows how a company’s activities align with the regulation.
LevelKPIEligibility Category / Screening CriteriaMetric
TotalTurnoverAlignedTotal Taxonomy-Aligned Turnover
TotalTurnoverEligibleTotal Taxonomy-Eligible Turnover
TotalTurnoverDenominatorTotal Turnover (Denominator)
TotalCAPEXAlignedTotal Taxonomy-Aligned CAPEX
TotalCAPEXEligibleTotal Taxonomy-Eligible CAPEX
TotalCAPEXDenominatorTotal CAPEX (Denominator)
TotalOPEXAlignedTotal Taxonomy-Aligned OPEX
TotalOPEXEligibleTotal Taxonomy-Eligible OPEX
TotalOPEXDenominatorTotal OPEX (Denominator)
ActivitiesTurnoverAlignedActivity-level Taxonomy-Aligned Turnover
ActivitiesTurnoverEligibleActivity-level Taxonomy-Eligible Turnover
ActivitiesCAPEXAlignedActivity-level Taxonomy-Aligned CAPEX
ActivitiesCAPEXEligibleActivity-level Taxonomy-Eligible CAPEX
ActivitiesOPEXAlignedActivity-level Taxonomy-Aligned OPEX
ActivitiesOPEXEligibleActivity-level Taxonomy-Eligible OPEX
*** # References & Standards Discover the regulations and reporting standards, including the EU Taxonomy Regulation and delegated acts, that shape Tracenable’s EU Taxonomy dataset. ## Foundational References These are the authoritative sources we rely on to define terms, set classification rules, and resolve edge cases. The EU Taxonomy dataset draws from a network of EU laws and standards. At its center is the EU Taxonomy Regulation, the authoritative source for defining sustainability criteria, classification methods, and mandatory disclosures. ### EU Taxonomy Regulation (Regulation (EU) 2020/852) The EU Taxonomy Regulation is the foundational law. It: * Defines what qualifies as an environmentally sustainable activity. * Establishes the six environmental objectives. * Sets the four conditions: substantial contribution, Do No Significant Harm (DNSH), minimum safeguards, and compliance with technical screening criteria (TSC). * Creates the legal obligation for large companies and financial market participants to disclose taxonomy alignment. {% hint style="info" %} In short, the regulation sets the rules of the game: it defines the objectives and criteria but leaves room for delegated acts to spell out the details. {% endhint %} ### Delegated Acts Delegated acts are implementing regulations adopted by the European Commission that make the EU Taxonomy operational. They translate the high-level framework of Regulation (EU) 2020/852 into practical rules, criteria, and disclosure requirements. #### Article 8 Delegated Act (Regulation (EU) 2021/2178) This act implements Article 8 of the Taxonomy Regulation, which requires companies to disclose their taxonomy KPIs. It: * Defines the three mandatory KPIs: turnover, CAPEX, OPEX. * Provides methodology for calculating eligibility vs. alignment. * Introduces standardized templates for disclosure. * Turns Article 8’s legal requirement into consistent, comparable reporting. #### Climate Delegated Act (Regulation (EU) 2021/2139) Defines the technical screening criteria (TSC) for activities that contribute to climate change mitigation and adaptation, covering sectors such as energy, manufacturing, transport, and buildings. #### Complementary Climate Delegated Act (Regulation (EU) 2022/1214) Introduces certain nuclear and natural gas activities as transitional solutions for climate mitigation, but only under strict conditions such as emissions limits, time-bound transition plans, and safeguards to ensure they support, rather than delay, the transition to renewable energy. #### Environmental Delegated Act (Regulation (EU) 2023/2486) Expands the taxonomy by defining TSC for the remaining four environmental objectives: sustainable water use, circular economy, pollution prevention, and biodiversity protection. *** ## Related Reporting Frameworks & Standards The EU Taxonomy is not standalone. It is the cornerstone of the EU Sustainable Finance Framework and complements several other regulations: ### **Sustainable Finance Disclosure Regulation (SFDR, Regulation (EU) 2019/2088)** Requires financial market participants to disclose how sustainable their products are. Funds marketed as sustainable — Article 8 (“light green”) and Article 9 (“dark green”) products — must state the share of investments aligned with the EU Taxonomy. ### **Non-Financial Reporting Directive (NFRD, Directive 2014/95/EU)** and **Corporate Sustainability Reporting Directive (CSRD, Directive (EU) 2022/2464)** NFRD first required large listed companies to disclose sustainability information, including taxonomy eligibility and alignment. Its successor, CSRD, greatly expands this obligation to thousands more companies and embeds taxonomy KPIs (turnover, CAPEX, OPEX) directly into the new **European Sustainability Reporting Standards (ESRS)**. ### **European Green Bond Regulation (2023)** Establishes the **EU Green Bond Standard**, requiring that bond proceeds are allocated primarily to taxonomy-aligned activities, reinforcing the taxonomy as the reference point for green capital markets. ### **EU Climate Benchmarks Regulation (Regulation (EU) 2019/2089)** Ensures that EU Climate Transition and Paris-aligned benchmarks use taxonomy definitions when determining which activities and investments qualify as consistent with climate goals. {% hint style="info" %} In practice: the **taxonomy provides the criteria**, while SFDR, CSRD, Green Bonds, and Benchmarks are the **disclosure and market instruments** that apply those criteria. {% endhint %} {% hint style="success" %} ### Takeaway With Tracenable’s EU Taxonomy dataset, you get: * **Jurisdiction-anchored definitions** based directly on the EU Taxonomy Regulation and its delegated acts. * **Standardized KPIs and eligibility categories** that reflect how companies are required to report under Article 8 and CSRD. * **Full traceability to original disclosures**, ensuring audit-grade use in compliance, benchmarking, and investment workflows. {% endhint %} *** # Data Collection Methodology Learn how Tracenable collects, standardizes, and validates EU Taxonomy data through a five-step human-in-the-loop methodology, with links to detailed subpages on sources, standardization, and QAs. ## Introduction The value of EU Taxonomy data lies not just in its availability, but in its clarity, comparability, and traceability. At Tracenable, we designed a data collection methodology that combines rigorous research, comprehensive sourcing, and advanced human–AI workflows to produce EU Taxonomy metrics that are both granular and broadly applicable. Our approach is built around four principles: define with authority, collect comprehensively, standardize precisely, and validate rigorously. *** ## Our Five-Step Data Collection Approach {% stepper %} {% step %} ### Defining the Schema through Research Our EU Taxonomy data is meticulously standardized to align with the Commission Delegated Regulation (EU) 2023/2486, capturing both activity-level details and aggregate metrics across turnover, CAPEX, and OPEX. This rigorous standardization ensures seamless regulatory compliance and cross-company comparability. {% endstep %} {% step %} ### Comprehensive Collection of Disclosures EU Taxonomy data can appear in many places: annual reports, sustainability reports, regulatory filings (e.g., SFDR/CSRD templates), investor presentations, standalone data spreadsheets, or hidden on a webpage deep in a company’s site. Our infrastructure is designed to capture all of it. Through automated web monitoring and targeted expert retrieval, we ensure that no disclosure is overlooked. This comprehensive approach minimizes blind spots and provides the broadest possible coverage of EU Taxonomy reporting worldwide. {% endstep %} {% step %} ### Converting Disclosures into Structured Data EU Taxonomy disclosures vary widely in format: from PDFs and Excel annexes to HTML tables or embedded text within narrative sections. Our AI-driven pipelines convert these raw files into a unified, machine-readable structure (e.g., PDF to markdown). From there: * Computer vision extracts and parses tables and figures. * NLP models detect taxonomy-related passages, extract KPI values, and identify eligibility classifications. * Classification rules map disclosures into aligned, eligible but not aligned, non-eligible, and combined categories under each KPI. The result: machine-readable, standardized data points that preserve traceability to the original disclosure. {% endstep %} {% step %} ### Data Human-in-the-Loop Validation AI brings speed and scalability, but human expertise ensures accuracy and context. Each extracted data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate taxonomy data, with arbitration applied where discrepancies remain. This process allows us to: * Correct AI misclassifications when disclosures are complex or ambiguous. * Preserve context from narrative disclosures. * Continuously improve our models through feedback. The outcome is audit-grade EU Taxonomy data that users can trust. {% endstep %} {% step %} ### Rigorous Quality Assurance Finally, the EU Taxonomy dataset undergoes multi-layered quality checks: * Automated tests flag anomalies (e.g., KPIs not summing correctly, negative percentages, implausible trends). * Machine learning models detect outliers across time series and peer groups. * Manual audits ensure completeness and resolve edge cases. This combination of automation and human oversight guarantees that every metric delivered is reliable, comparable, and decision-ready for use in compliance, benchmarking, and research. {% endstep %} {% endstepper %} *** ## Learn More To explore the methodology in detail, visit: * [**Data Sources**](/eu-taxonomy/data-collection-methodology/data-sources) – Where EU Taxonomy disclosures come from and how they are collected. * [**Standardization Guidelines** ](/eu-taxonomy/data-collection-methodology/standardization-guidelines)– How activities, KPIs, and eligibility categories are normalized for consistency. * [**Calculation Logic**](/eu-taxonomy/data-collection-methodology/calculation-logic-up-to-2025) – How derived values are computed using transparent rules. * [**Quality Assurance**](/eu-taxonomy/data-collection-methodology/quality-assurance) - The validations and controls that safeguard data integrity. *** # Data Sources See where Tracenable’s EU Taxonomy data comes from. Learn which corporate disclosures, registries, and web sources we capture, and how every metric is fully traceable back to its origin. ## Introduction The reliability of EU Taxonomy data starts with the quality of its sources. At Tracenable, we collect information from a broad range of corporate and official channels, ensuring that every data point is traceable back to its origin. Our goal is simple: provide users with complete, transparent, and verifiable evidence of how companies disclose their EU Taxonomy performance. *** ## Where We Collect Data We capture EU Taxonomy disclosures wherever companies report them, across all common formats: * **Corporate reports** – Sustainability reports, annual reports, integrated reports, proxy statements. * **Regulatory filings** – Documents filed under mandatory disclosure regimes (e.g., CSRD or national registries). * **Web disclosures** – Corporate webpages, environmental policy pages, or dedicated sustainability microsites. * **Data annexes and spreadsheets** – Often attached to sustainability reports or published as standalone datasets. * **Press releases and news articles** – Only when originating directly from the company. * **Government registries** – Authoritative third-party repositories of company-submitted data. No matter the format (PDF, HTML, Excel, or XML/XBRL) we normalize disclosures into a structured, machine-readable format without losing traceability to the original file. *** ## End-to-End Traceability Every data point in the EU Taxonomy dataset includes a direct link to its original source, allowing users to audit disclosures in context. Links open the exact report, page, or section cited. Metadata such as publication date and reporting period are also captured to preserve the full reporting trail. This approach ensures transparency: users can always see *what a company reported, when, and where*. *** ## Coverage Strategy Tracenable’s EU Taxonomy dataset focuses on **non-financial undertakings**, in line with the regulation’s scope. * **Mandatory reporters under CSRD**: We cover all publicly listed companies in the EU that are required to report EU Taxonomy KPIs. * **Voluntary reporters**: We also capture companies outside mandatory scope that choose to disclose EU Taxonomy data. * **Global reach**: Coverage extends beyond the EU to include multinational groups that report on their EU operations or publish EU Taxonomy-aligned disclosures. Our goal is to achieve the **largest industry coverage** of EU Taxonomy disclosures, ensuring users have access to the most complete dataset available. *** # Standardization Guidelines Learn how Tracenable standardizes EU Taxonomy disclosures by reconciling templates, mapping activities, and unifying KPI terms. ## Why Standardization Matters EU Taxonomy disclosures are governed by a single framework, but in practice companies report in different ways. Companies may: * Use different reporting templates depending on the reporting year. * Report activities with inconsistent detail (activity codes, descriptions, or NACE codes). * Use varying terminology for the same KPIs (e.g., “revenues,” “net sales,” or “business volume” for *turnover*). Without harmonization, these variations would make it difficult to compare disclosures across firms or reconcile metrics within the dataset. Tracenable’s standardization rules bridge these gaps, while retaining traceability to the original company reports. *** ## Tracenable’s Standardization Guidelines ### Guideline 1: Reconciling Reporting Templates Three official EU Taxonomy templates are in use, and they capture eligibility and alignment differently: * **2020–2022 template** * Substantial contribution to environmental objectives is expressed as quantitative percentages at both activity and total level, for **both eligible and aligned activities**. * A dedicated "taxonomy-aligned proportion" column captures aligned (A1) values separately. * No materiality concept applies, and the `not_assessed_activities_non_material` field is absent. * **2023-2025 template** * Substantial contribution to environmental objectives, for **both eligible and aligned activities**, are expressed as qualitative flags at the activity level (e.g., “Yes (Y),” “No (N),” “Eligible (EL)”), with quantitative values only at the total level. * The standalone "aligned proportion" column is replaced by distinct rows for A1 (aligned) and A2 (eligible but not aligned). * No materiality concept applies, and the `not_assessed_activities_non_material` field is absent. * **2026 onward template** * Substantial contribution is reported exclusively for **aligned** activities, expressed as quantitative percentages at both activity and total level. * The materiality concept is introduced, making the `not_assessed_activities_non_material` field a mandatory disclosure. * The values of the eligibility criteria (or screening criteria) dimension is reduced from `A+B`, `A`, `A1`, `A2` and `B` to three values: `Aligned` (A1), `Eligible` (A) and `Denominator` (A+B). #### Tracenable’s Reconciliation Rules To ensure consistency across reporting years, Tracenable translates all disclosures, regardless of the template used, into the structure of the 2026 template. For example: * When companies report only qualitative flags for substantial contribution to environmental objectives in the 2023-2025 template, Tracenable standardizes these into equivalent percentages (e.g., if aligned turnover = 2% and the flag is “Yes” for CCM, the percentage contribution to CCM is recorded as 2%). * The A2 and B rows are removed in the 2026 template. As a result, an 2020-2022 template disclosure of "10% eligible" and "8% aligned proportion" is translated as follows: 8% is recorded under `Aligned` and 10% under `Eligible`, with the 2% not-aligned portion implicit within `Eligible`. * For companies reporting under the older templates, no materiality assessment was required and no `not_assessed_activities_non_material` value exists. In these cases, the field is marked as "not defined". This reconciliation ensures that all disclosures, old or new, fit into a single, consistent framework for analysis. *** ### Guideline 2: Harmonizing Percentage Contribution Formats Companies use two different ways to report percentages that describe how much of an activity contributes to an environmental objective. This can make disclosures look different even when they mean the same thing. * **Percentage-of-value format** – the percentage is expressed as a share of the relative KPI.\ *Example:* If relative turnover = 2% and the company reports “100% contribution to climate change mitigation,” this means the full 2% is linked to that objective. * **Direct-percentage format** – the percentage is reported directly as the final share of the relative KPI.\ *Example:* If relative turnover = 2% and the company reports “2% contribution to climate change mitigation,” this represents the same situation as above, just written differently. Tracenable standardizes both approaches into a single format so they can be compared consistently. In both examples above, the contribution is recorded as 2%. *** ### Guideline 3: Mapping Activities to Taxonomy-Defined Options Company-reported activities are not always expressed in official EU Taxonomy terms. Disclosures may include: * Activity numbers without objectives. * Descriptions that partially or loosely match Taxonomy activities. * NACE codes without clear activity descriptions. Tracenable applies structured mapping rules to reconcile these disclosures with the official activity list: * Perfect matches are mapped directly. * Semi-matches use NACE codes and context. * Ambiguous or unmatched cases are categorized under *Other activities* to preserve data without forcing an inaccurate mapping. This ensures that every activity is anchored to the Taxonomy framework, with uncertain cases handled transparently. *** ### Guideline 4: Unifying KPI Terminology Even for core KPIs (turnover, CAPEX, and OPEX) companies often use alternate terms that can create confusion. Tracenable maintains a mapping guide to ensure these terms are consistently standardized to the correct EU Taxonomy KPI. | EU Taxonomy KPI Names | Alternate KPI Names | | -------------------------------- | -------------------------------------------------------------------------------- | | **Turnover** | Revenues, Sales, Gross sales , Gross rental income, Business volume | | **OPEX (Operating Expenditure)** | Business expenses, Investments in working capital, Investments in current assets | | **CAPEX (Capital Expenditure)** | Investments, Investments in fixed assets | This ensures that all disclosures roll into the official EU Taxonomy KPIs, regardless of the terminology used in reports. {% hint style="success" %} ### Takeaway Tracenable’s standardization rules make sure EU Taxonomy data speaks the same language across companies and years. Whether reports use old or new templates, vague activity descriptions, or alternative KPI terms, everything is harmonized into a single structure that’s easy to compare and analyze. {% endhint %} *** # Calculation Logic (Up to 2025) See how Tracenable transforms incomplete EU Taxonomy disclosures into complete, reconciled metrics through structured accounting rules. {% hint style="danger" %} This article covers the calculation logic applicable to the 2020–2022 and 2023–2025 reporting templates. While the 2026 onward template is now in effect, companies with financial years beginning before 1 January 2026 may still report under the 2023–2025 template, making this guidance relevant during the transitional period. {% endhint %} The EU Taxonomy dataset relies on consistent rules to transform raw company disclosures into complete, standardized metrics. Not every company reports every required data point, and disclosure practices vary widely. To address these gaps, Tracenable applies a clear accounting logic that respects the hierarchy of EU Taxonomy dimensions. This ensures that users always have a full, reliable set of metrics for each KPI and reporting year. *** ## Hierarchical Structure of Dimensions The EU Taxonomy dimensions are organized around two key hierarchies: * ***Level*** hierarchy * **Activity-level** – disclosure broken down by specific economic activities. * **Total-level** – aggregated across all activities of the company. {% hint style="success" %} Totals can be derived from the sum of activities when missing. {% endhint %} * ***Eligibility Category*** (or Screening criteria) hierarchy * **A+B (Eligible and Non-Eligible)** * **A (Eligible)** * **A1 (Aligned)** * **A2 (Eligible but not Aligned)** * **B (Non-Eligible)** {% hint style="success" %} Higher-level categories (A, A+B) can be derived from the sum of their subcategories (A1, A2, B). {% endhint %} {% hint style="info" %} These hierarchies define the structure of the dataset. The accounting rules build on this structure to fill gaps, derive missing values, and ensure consistent reporting across companies. {% endhint %} *** ## What We Deliver For each KPI (Turnover, CAPEX, OPEX), Tracenable delivers up to **eight metrics** per company: * `A activity-level` * `A1 activity-level` * `A2 activity-level` * `A total` * `A1 total` * `A2 total` * `B total` * `A+B total` Together, these metrics provide a complete view of a company’s reported and computed taxonomy performance. *** ## Accounting Rules Tracenable applies structured accounting rules to ensure that every KPI is complete, consistent, and reconcilable. These rules respect both the **Level hierarchy** (Activities → Total) and the **Eligibility Category hierarchy** (A → A1 + A2; A+B = A + B). ### Rule 1: Compute Missing Parents from Children (Bottom Up) Higher-level (parent) values are derived directly from their children. For example: * *A activity = A1 activity + A2 activity* * *Total (A+B) = Total A + Total B* * *Total A = Total A1 + Total A2* * *Total A = Sum of activity A* * *Total A1 = Sum of activity A1* * *Total A2 = Sum of activity A2* {% hint style="success" %} This guarantees that roll-up values (e.g., totals) are always available, even when companies only disclose granular activity-level data. It ensures consistency across levels and prevents gaps in reporting. {% endhint %} ### Rule 2: Infer Missing Values from Related Metrics (Top Down) Missing values are inferred using known relationships between categories and totals— but **only when at least one side of the equation is a company-reported value (not already computed by accounting rules)**. This avoids circular calculations and ensures that inferred values remain anchored in real disclosures. For example: * *Total B = (A+B) – Total A* (if Total A is reported, not computed) * *Total A = (A+B) – Total B* (if Total B is reported, not computed) * *Total A1 = Total A – Total A2* (if both Total A and Total A2 are reported) * *Total A2 = Total A – Total A1* (if both Total A and Total A1 are reported) {% hint style="success" %} By enforcing this condition, Tracenable avoids compounding errors and ensures that every inferred value is tethered to at least one direct company disclosure. This makes the dataset more reliable, transparent, and audit-grade, since users can always trace key metrics back to reported figures. {% endhint %} ### Rule 3: Compute or Infer Absolute Values When only percentages (relative values) are disclosed, absolute values are computed from totals. For example: * *Absolute A1 activity = relative A1 activity × absolute (A+B)* * *Absolute Total A1 = relative Total A1 × absolute (A+B)* * *Absolute (A+B) = absolute A + absolute B* * *Absolute (A+B) = constituent absolute ÷ constituent relative* {% hint style="success" %} This rule translates percentage-based disclosures into absolute figures, allowing users to compare across companies and sectors. It increases the analytical utility of the dataset by ensuring both relative and absolute values are available. {% endhint %} ### Rule 4: Apply Boundary Defaults Logical defaults are applied when disclosures are explicitly zero or not specified. For example: * *Total A1 = 0% if Total A = 0% with absolute value = 0 or not specified* * *Total B = 100% if Total A = 0% with absolute value = 0 or not specified* {% hint style="success" %} These defaults prevent missing or zero disclosures from creating inconsistencies. They provide a reliable baseline when companies report minimal information. {% endhint %} ### Rule 5: Establish Baseline Totals When nothing is reported at the top level, the dataset still ensures coverage by setting a baseline. For example: * *If (A+B) is missing, compute (A+B) = 100%*. {% hint style="success" %} This ensures that all company disclosures can be anchored in a complete taxonomy structure, even when top-level totals are absent. It preserves comparability across firms. {% endhint %} {% hint style="success" %} ## Takeaway By applying these rules, Tracenable ensures that the EU Taxonomy dataset is: * **Complete** – every KPI has totals and subcategories, even when disclosures are partial. * **Consistent** – activity-level and total-level values always reconcile across eligibility categories. * **Comparable** – disclosures from different companies and sectors are normalized into the same structure. * **Traceable** – all computed values remain anchored in reported data, ensuring audit-grade reliability. The result is a dataset that remains robust, transparent and decision-ready, even when company disclosures are incomplete or inconsistent. {% endhint %} *** # Calculation Logic (2026 onward) See how Tracenable transforms incomplete EU Taxonomy disclosures into complete, reconciled metrics through structured accounting rules. {% hint style="danger" %} This article covers the calculation logic applicable to the 2026 onward reporting template. {% endhint %} The EU Taxonomy dataset relies on consistent rules to transform raw company disclosures into complete, standardized metrics. Not every company reports every required data point, and disclosure practices vary widely. To address these gaps, Tracenable applies a clear accounting logic that respects the hierarchy of EU Taxonomy dimensions. This ensures that users always have a full, reliable set of metrics for each KPI and reporting year. *** ## Hierarchical Structure of Dimensions The EU Taxonomy dimensions are organized around two key hierarchies: * ***Level*** hierarchy * **Activity-level** – disclosure broken down by specific economic activities. * **Total-level** – aggregated across all activities of the company. * ***Eligibility Category*** (or Screening criteria) hierarchy * **Denominator** * **Eligible** * **Aligned** {% hint style="info" %} These hierarchies define the structure of the dataset. The accounting rules build on this structure to fill gaps, derive missing values, and ensure consistent reporting across companies. {% endhint %} {% hint style="warning" %} #### **A note on Denominator composition** In the older templates, the Denominator (A+B) could be fully decomposed: A+B = A + B, and A = A1 + A2, meaning every part of the denominator was explicitly accounted for. Under the 2026 onward template, this is no longer possible. The non-eligible (B) and eligible but not aligned (A2) categories are no longer standalone disclosures, and the non-material portion is reported only as an aggregated percentage without further breakdown. It is therefore unclear whether the non-material portion contains non-eligible activities or not. As a result, Tracenable does not attempt to derive or impute B, and the Denominator should not be assumed to decompose fully from reported figures alone. {% endhint %} *** ## What We Deliver For each KPI (Turnover, CAPEX, OPEX), Tracenable delivers up to **five metrics** per company: * `Eligible activity-level` * `Aligned activity-level` * `Eligible total` * `Aligned total` * `Denomintor` Together, these metrics provide a complete view of a company’s reported and computed taxonomy performance under the 2026 onward framework. *** ## Accounting Rules Tracenable applies structured accounting rules to ensure that every KPI is complete, consistent, and reconcilable. These rules respect both the **Level hierarchy** (Activities → Total) and the **Eligibility Category hierarchy** (Aligned ⊂ Eligible ⊂ Denominator). ### Rule 1: Compute Missing Parents from Children (Bottom Up) Higher-level (parent) values are derived directly from their children. For example: * *Total Eligible = Sum of Eligible activities* * *Total Aligned = Sum of Aligned activities* {% hint style="success" %} This guarantees that roll-up values (e.g., totals) are always available, even when companies only disclose granular activity-level data. It ensures consistency across levels and prevents gaps in reporting. {% endhint %} ### Rule 2: Compute or Infer Absolute Values When only percentages (relative values) are disclosed, absolute values are computed from totals. For example: * *Absolute Aligned activity = relative Aligned activity × absolute Denominator* * *Absolute Total Aligned = relative Total Aligned × absolute Denominator* * *Absolute Denominator = constituent absolute ÷ constituent relative* {% hint style="success" %} This rule translates percentage-based disclosures into absolute figures, allowing users to compare across companies and sectors. It increases the analytical utility of the dataset by ensuring both relative and absolute values are available. {% endhint %} {% hint style="success" %} #### **Takeaway** By applying these rules, Tracenable ensures that the EU Taxonomy dataset is: * **Complete** — every KPI has totals and activity-level values, even when disclosures are partial. * **Consistent** — activity-level and total-level values always reconcile within the reported hierarchy. * **Comparable** — disclosures are normalized into the same structure across companies and reporting years. * **Traceable** — all computed values remain anchored in reported data, ensuring audit-grade reliability. Where the framework itself introduces ambiguity, such as the composition of the Denominator, Tracenable preserves that ambiguity transparently rather than masking it with assumptions. {% endhint %} *** # Quality Assurance Discover how Tracenable validates EU Taxonomy data through automated checks, statistical tests, and human review to deliver audit-grade reliability. ## Introduction High-quality EU Taxonomy data depends on more than just good collection and standardization: it requires rigorous validation. At Tracenable, we combine automated testing, statistical analysis, and expert human review to ensure that every EU Taxonomy metric meets the highest standards of accuracy, consistency, and reliability. Our Quality Assurance (QA) process is multi-layered, designed to detect errors, catch anomalies, and confirm that each data point is both faithful to the original disclosure and fit for use in compliance, benchmarking, and research. *** ## Automated Validation Checks The first layer of QA relies on automated rules that run across all EU Taxonomy metrics. These checks are designed to quickly spot issues that should never occur in valid data, such as: * **Impossible values** – for instance, reporting more than 100% taxonomy-eligible turnover, CAPEX, or OPEX, or disclosing absolute values that exceed total company revenues or expenditures. * **Unit inconsistencies** – figures reported in mismatched or conflicting units across years. * **Structural errors** – totals that do not match the sum of their components. These rules ensure that obvious errors are flagged immediately and never propagate into the dataset. *** ## Statistical and Machine Learning Tests Beyond simple rules, we apply more advanced techniques to identify subtle anomalies: * **Time-series consistency checks** – highlight sudden spikes or drops in reported EU Taxonomy metrics. * **Outlier detection** – identify company disclosures that deviate significantly from industry norms. * **Distribution analysis** – verify that the metrics follow expected statistical patterns across sectors. These methods help us flag values that may be technically valid but require closer review. *** ## Human-in-the-Loop Review Not all issues can be resolved automatically. Our QA process therefore includes a human-in-the-loop review, where trained analysts validate flagged data points: * **Contextual review** – analysts check values against the original disclosure to confirm interpretation. * **Dual validation** – two independent reviewers may assess the same data point. * **Arbitration** – discrepancies between analysts are escalated to senior analysts for final decision. This ensures that ambiguous or complex disclosures are interpreted correctly, and that every value remains fully traceable to its source. *** ## Continuous Improvement Each QA outcome feeds back into our systems: * Automated rules are updated when new error patterns are identified. * Machine learning models are retrained to improve anomaly detection. * Documentation is refined to capture new edge cases and classification challenges. This iterative loop ensures that the EU Taxonomy Dataset becomes more robust over time. *** # Data Dictionary Explore Tracenable’s EU Taxonomy Data Dictionary, featuring standardized fields for taxonomy-eligible and taxonomy-aligned metrics, disclosure details, and source traceability. | Attribute | Type/Format | Description | Example | | ---------------------------------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | | isin | Alphanumeric String | International Securities Identification Number (ISIN) of the primary publicly traded financial instrument associated with the company. | US42704L1044 | | lei | Alphanumeric String | Legal Entity Identifier (LEI) | 549300TP80QLITMSBP82 | | figi | Alphanumeric String | Financial Instrument Global Identifier (FIGI) of the primary publicly traded financial instrument associated with the company. | BBG00WNPK2F5 | | ticker | String | Ticker symbol of the primary publicly traded financial instrument associated with the company. | HRI | | mic\_code | String | Market Identifier Code (MIC) of the primary publicly traded financial instrument associated with the company. | XNGS | | exchange | String | Stock exchange of the primary publicly traded financial instrument associated with the company. | NASDAQ | | permid | Numerical String | Permanent Identifier | 4295900057 | | company\_name | String | Legal name of the company. | HERC HOLDINGS INC | | country | Categorical String | Country where the company's headquarters are located. | United States | | sector | String | Sector in which the company operates. | Technology | | industry | String | Industry classification of the company. | Software - Application | | year\_of\_disclosure | Year (YYYY) | Year in which the data point was disclosed. | 2023 | | reporting\_period | Year (YYYY) | Period for which the data point was measured, assessed, or is applicable. | 2023 | | metric | String | The specific measurement or data points requested. | Total Taxonomy-Aligned Turnover | | level | Categorical String | Level of data distinguishing between Total or Activities. | Total | | kpi | Categorical String | Key Performance Indicator (KPI) requested (turnover, OPEX or CAPEX). | Turnover | | screening\_criteria | Categorical String | EU Taxonomy screening criteria (Aligned, Eligible or Denominator). | Aligned | | activities | Array of Strings | Economic activities that meet the EU Taxonomy's criteria for environmentally sustainable classifications. | 4.9 CCM/CCA - Transmission and distribution of electricity | | relative\_value | Numerical Float | Proportion of turnover, OPEX, or CAPEX. | 0.156 | | relative\_value\_unit | Categorical String | Unit of measurement of the relative value. | Percentage of Total Turnover | | absolute\_value | Numerical Float | Amount of turnover, OPEX, or CAPEX in absolute terms. | 3978900000 | | absolute\_value\_ccy | Categorical String | Currency in which the absolute value is reported. | EUR | | scope\_of\_disclosure | Categorical String | Oganizational boundaries used for data consolidation. | Operational Control | | pct\_contribution\_to\_ccm | Numerical Float | Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Climate Change Mitigation (CCM) EO. | 0.156 | | pct\_contribution\_to\_cca | Numerical Float | Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Climate Change Adaptation (CCA) EO. | 0 | | pct\_contribution\_to\_wtr | Numerical Float | Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Water and Marine Resources (WTR) EO. | 0 | | pct\_contribution\_to\_ce | Numerical Float | Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Circular Economy (CE) EO. | 0 | | pct\_contribution\_to\_ppc | Numerical Float | Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Pollution Prevention and Control (PPC) EO. | 0 | | pct\_contribution\_to\_bio | Numerical Float | Proportion of eligible/aligned turnover/OPEX/CAPEX from the activity(ies) contributing to the Biodiversity and Ecosystems (BIO) EO. | 0 | | activities\_contribution\_type | Categorical String | Contribution type of the activity(ies) according to the Taxonomy classification (Enabling, Transitional or Substantial). When the contribution type cannot be clearly determined, the value is set to "Not Specified". | Enabling | | not\_assessed\_activities\_non\_material | Numerical Float | Proportion of turnover/OPEX/CAPEX from the activity(ies) that were not assessed for taxonomy alignment/eligibility because they were deemed non-material by the reporting company. | 0.05 | | source\_names | Array of Strings | Names of the sources from which the reported data was obtained. | Corporate Responsibility Report | | company\_id | String | Tracenable's internal company identifier. | dfb6bd7b-facf-4d0f-86a2-f1eedf191946 | | document\_id | String | Unique document identifier to see the source of the data point. | 4a32b902-ae42-4692-ae96-b42c99fdd2f9 | | traceability\_source\_url | String | URL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes. | | # Introduction Get introduced to Tracenable’s Greenhouse Gas (GHG) Emissions Dataset, including its scope, key characteristics, and unique value, and get redirected to detailed pages to deepen your knowledge. ## Overview The Greenhouse Gas (GHG) Emissions Dataset captures how companies worldwide disclose and report greenhouse gas emissions, covering more than 5,000 global firms across all major industries. It provides detailed disclosures on Scope 1 (direct), Scope 2 (indirect energy), and Scope 3 (value chain) emissions, with both absolute and intensity-based measures to support benchmarking analyses. By applying rigorous standardization to reporting boundaries, units, and methodologies, the dataset transforms fragmented corporate climate disclosures into comparable, decision-ready information. This enables reliable cross-company benchmarking, sector and portfolio carbon footprint, and long-term tracking of corporate progress against net-zero targets, climate regulations, and global sustainability frameworks. *** ## Data Characteristics
:building: Company Coverage5000
:globe: Geographical CoverageGlobal
:shapes: Sectoral CoverageAll Sectors
:calendar-range: Data Historical RangeFrom 2014 to 2024
:reflect-vertical: Median Data History5 years
:magnifying-glass-waveform: Data Traceability Rate100%
:chart-simple: Data FrequencyAnnual
:repeat: Average Reporting Lag3 months
:bring-forward: Data FormatMost Recent/Point-in-Time
*** ## What makes Tracenable's Greenhouse Gas (GHG) Emissions Data Unique Tracenable's Greenhouse Gas (GHG) Emissions data product sets the market benchmark for precision, standardization, reliability, and integrity. Learn why our data stands apart:
:arrow-down-triangle-square: Uncompromised StandardizationOur team of environmental engineers meticulously maps reported emissions data to GHG Protocol standards, including detailed categorization of all 15 Scope 3 categories and proper classification of Scope 1 and 2 categories. This guarantees uniformity and comparability across our dataset, bridging the gap created by diverse reporting formats.
:bullseye-arrow: Precision in Every FigureOur advanced cross-source data precision matching algorithm ensures that the most accurate data is always delivered. For instance, an exact figure like 1,542,450 tCO2e is prioritized over a rounded figure like 1,500,000 tCO2e, reflecting our dedication to precision and detail.
:shield-check: Unbiased Data IntegrityOur approach is grounded in delivering emissions data exactly as reported by companies, without making inferences or estimates for undisclosed data. This strict adherence to factual reporting ensures the integrity of the data you receive, providing an unaltered and accurate view of corporate emissions.
:magnifying-glass-waveform: End-to-End Data TraceabilityEvery emissions data point is directly traceable to its original source, with exact page references and calculation methodologies documented. This level of detail enables immediate verification of emission figures and supports compliance with regulatory reporting requirements.
:octagon-check: Full-Scope Boundary VerificationWe tag emission figures that do not cover a company's entire organizational or operational boundaries with an 'Incomplete Boundaries' attribute. This attribute enhances transparency and ensures the comparability of our data by keeping you informed of any potential limitations.
*** ## Deep Dive into the Greenhouse Gas (GHG) Emissions Dataset On the following pages, you’ll find all the resources needed to fully understand and apply the Greenhouse Gas (GHG) Emissions Dataset: * [**Definitions & Terminology**](/ghg-emissions/definitions-and-terminology) – Key terms and concepts used in the dataset * [**Data Dimensions & Metrics**](/ghg-emissions/data-dimensions-and-metrics) – Breakdown of Greenhouse Gas (GHG) Emissions metrics and how they are structured * [**References & Standards**](/ghg-emissions/references-and-standards) – Alignment with global reporting frameworks and regulations * [**Data Collection Methodology** ](/ghg-emissions/data-collection-and-methodology)– How the dataset is built and validated * [**Data Sources**](/ghg-emissions/data-collection-and-methodology/data-sources) – Origin and type of corporate disclosures collected * [**Standardization Guidelines**](/ghg-emissions/data-collection-and-methodology/standardization-guidelines) – Rules applied to ensure comparability * [**Calculation Logic**](/ghg-emissions/data-collection-and-methodology/calculation-logic) – Methods for deriving standardized metrics * [**Quality Assurance** ](/ghg-emissions/data-collection-and-methodology/quality-assurance)– Checks and processes ensuring data integrity * [**Data Dictionary** ](/ghg-emissions/data-dictionary)– Complete reference of fields, units, and definitions *** # Definitions & Terminology Explore greenhouse gas emissions: gases covered by the GHG Protocol and how they are classified into Scopes 1, 2, and 3. ## What Are Greenhouse Gas (GHG) Emissions? Greenhouse Gas (GHG) emissions are gases released into the atmosphere that trap heat and contribute to climate change. These emissions are produced when companies carry out everyday activities: such as burning fuel for energy, running industrial processes, or transporting goods. When companies talk about their “GHG emissions,” they are referring to the amount of these gases generated by their operations or value chain. Tracenable follows the **Greenhouse Gas Protocol (GHG Protocol)**, the global standard for measuring and reporting these emissions. It requires companies to account for seven gases as defined in the Kyoto Protocol: * Carbon dioxide (CO₂) * Methane (CH₄) * Nitrous oxide (N₂O) * Hydrofluorocarbons (HFCs) * Perfluorocarbons (PFCs) * Sulfur hexafluoride (SF₆) * Nitrogen trifluoride (NF₃) #### What’s Not Included in GHG Emissions To stay aligned with the GHG Protocol, Tracenable’s dataset excludes: * **Biogenic CO₂ emissions** – These are reported separately and not counted in Scope 1 totals. (Biogenic CH₄ and N₂O, however, remain part of Scope 1 process emissions.) * **Air pollutants** – Gases such as NOₓ, SOₓ, CO, particulate matter (PM), and volatile organic compounds (VOCs). These affect local air quality and health but are not greenhouse gases and are tracked under different reporting frameworks. {% hint style="success" %} In short: GHG data covers only gases with global warming potential, as defined by international climate agreements and operationalized by the GHG Protocol. Tracenable’s dataset strictly follows this scope for comparability and global alignment. {% endhint %} *** ## Scopes of Emissions The GHG Protocol classifies emissions into **three scopes** based on where they occur in relation to company operations. Think of them as three “boundaries” that help identify what a company is responsible for. ### Scope 1 – Direct Emissions Emissions from **sources that a company owns or directly controls**, including: #### **Stationary Combustion** * Emissions from burning fuels in on-site equipment, such as boilers, furnaces, or generators. * *Example: A manufacturing plant burning natural gas in industrial furnaces.* #### **Process Emissions** * Emissions from chemical or physical processes that are not related to fuel combustion. * *Example: A cement company reporting CO₂ emissions from clinker production.* #### **Mobile Combustion** * Emissions from fuel burned in company-owned vehicles or mobile equipment, such as trucks, ships, or aircraft. * *Example: A logistics company reporting diesel use from its delivery truck fleet.* #### **Direct Releases** Gases released intentionally or unintentionally into the atmosphere. This includes: * **Fugitive emissions** (e.g., leaks from pipelines, tanks, or wells) * **Refrigerant emissions** (e.g., leakage from air conditioning and refrigeration units) * **Venting emissions** (e.g., direct release of natural gas during oil extraction) * **Flaring emissions** (e.g., burning of natural gas during oil production) ### Scope 2 – Indirect Energy Emissions Emissions from the generation of **purchased or acquired energy** consumed by the company including electricity, heat, steam, and cooling. {% hint style="info" %} These occur at the energy provider’s facilities but are attributed to the company because of its energy use. {% endhint %} #### **Electricity** * Emissions from purchased electricity used to run buildings, facilities, and equipment. * *Example: An office sourcing power from a grid that relies on fossil fuels.* #### **Heat** * Emissions from purchased heat for industrial processes or building climate control. * *Example: A commercial building using district heating supplied by an external provider.* #### **Steam** * Emissions from purchased steam used in production or heating. * *Example: A paper mill purchasing steam for its manufacturing process.* #### **Cooling** * Emissions from purchased chilled water or cooled air supplied by third-party providers. * *Example: A data center relying on external cooling services.* {% hint style="info" %} #### **The GHG Protocol requires reporting of Scope 2 emissions under two methods:** * **Location-based method**: Reflects the average emissions intensity of the grid where the energy is consumed. It uses published grid emission factors and does not account for specific energy purchasing decisions. \ *Purpose:* Shows the environmental impact based on the regional energy mix (e.g., fossil-heavy vs. renewable-heavy grids). * **Market-based method**: Reflects emissions based on specific contractual arrangements or instruments — such as renewable energy certificates (RECs), power purchase agreements (PPAs), or supplier-specific emissions factors. \ *Purpose:* Allows companies to demonstrate their choice to purchase lower-emission electricity or participate in green energy programs. {% endhint %} {% hint style="success" %} Tracenable captures and distinguishes both values (when disclosed), ensuring consistency with GHG Protocol requirements. {% endhint %} ### Scope 3 – Other Indirect (Value Chain) Emissions All other indirect emissions across the value chain, often the largest part of a company’s footprint. It includes: #### **Upstream Scope 3 Emissions** {% hint style="info" %} Emissions generated **before a company’s operations**, linked to the production and delivery of inputs it relies on. {% endhint %} * **Purchased goods and services** - emissions from producing raw materials or services a company buys. * **Capital goods** - emissions from manufacturing long-term assets like buildings, vehicles, or machinery. * **Fuel and energy-related activities** - emissions from fuel supply chains, not already counted in Scope 1 or 2. * **Transportation and distribution (upstream)** - emissions from moving inputs to the company. * **Waste generated in operations** - emissions from treatment and disposal of waste from company facilities. * **Business travel** - emissions from employee flights, trains, and other work-related travel. * **Employee commuting** - emissions from daily transport between employees’ homes and workplaces. * **Leased assets (upstream)** - emissions from assets used but not owned by the company. #### **Downstream Scope 3 Emissions** {% hint style="info" %} Emissions generated **after a company’s operations**, linked to how its products and services are distributed, used, and disposed of. {% endhint %} * **Transportation and distribution (downstream)** - emissions from moving products to customers. * **Processing of sold products** - emissions from customers transforming sold products into other goods. * **Use of sold products** - emissions from consumers using the company’s products (e.g., fuel use in cars). * **End-of-life treatment of sold products** - emissions from disposal, recycling, or waste treatment after use. * **Leased assets (downstream)** - emissions from assets owned by the company but leased to others. * **Franchises** - emissions from operations of franchisees not directly controlled by the company. * **Investments** - emissions associated with investments in other businesses. *** # Data Dimensions & Metrics Learn how Tracenable’s dimensional model structures GHG emissions data, with metrics for total and category-level Scope 1, 2, and 3 aligned to the GHG Protocol. ## How Dimensions and Metrics Work Tracenable datasets follow a dimensional model. * **Dimensions** are the attributes you can use to analyze or slice the data. Each dataset is defined by one or more dimensions. * **Metrics** are the most granular layer: each one represents a unique combination of dimension values that defines a specific data point. This dimensional model provides a transparent and predictable way to structure data. It removes ambiguity in naming, ensures consistency across datasets, and makes it easier to understand how each metric is constructed. *** ## Dimensions in GHG Emissions Data All greenhouse gas (GHG) emissions data in Tracenable is organized along three core dimensions: **Level**, **Scope**, and **Type**. These dimensions define how emissions are categorized and reported, and every metric in the dataset is derived from their combinations. * ***Level*** indicates whether data is reported in aggregate or broken down into categories: * `Total` - Consolidated values for Scope 1, Scope 2, or Scope 3. * `Categories` - Granular breakdowns within each scope, such as stationary combustion (Scope 1), purchased electricity (Scope 2), or business travel (Scope 3). * ***Scope*** classifies emissions according to the Greenhouse Gas Protocol: * `Scope 1` - Direct emissions from company-owned or controlled operations. * `Scope 2` - Indirect emissions from purchased electricity, heat, steam, or cooling. * `Scope 3` - Other indirect emissions across the value chain, both upstream and downstream. * ***Type*** defines how emissions are measured: * `Absolute` - Total greenhouse gases emitted, expressed in metric tons of CO₂ equivalent (tCO₂e). * `Revenue Intensity` - Emissions normalized by company revenue, allowing fairer comparisons across companies of different sizes and industries. {% hint style="success" %} Including all three dimensions ensures that metrics are both comprehensive and flexible, making it possible to move seamlessly between high-level totals and detailed category-level insights. {% endhint %} *** ## Metrics in the GHG Emissions Dataset Metrics in the dataset are generated by combining the three dimensions: **Level**, **Scope**, and **Type**. This structure provides clarity on whether a metric refers to a consolidated total (e.g., *Total Scope 1*) or a more detailed breakdown (e.g., *Categories of Scope 3*).
LevelScopeTypeMetric
TotalScope 1AbsoluteTotal Scope 1
TotalScope 2AbsoluteTotal Scope 2
TotalScope 3AbsoluteTotal Scope 3
CategoriesScope 1AbsoluteCategories of Scope 1
CategoriesScope 2AbsoluteCategories of Scope 2
CategoriesScope 3AbsoluteCategories of Scope 3
TotalScope 1Revenue IntensityTotal Scope 1 Revenue Intensity
TotalScope 2Revenue IntensityTotal Scope 2 Revenue Intensity
TotalScope 3Revenue IntensityTotal Scope 3 Revenue Intensity
CategoriesScope 1Revenue IntensityCategories of Scope 1 Revenue Intensity
CategoriesScope 2Revenue IntensityCategories of Scope 2 Revenue Intensity
CategoriesScope 3Revenue IntensityCategories of Scope 3 Revenue Intensity
{% hint style="info" %} For ***Categories of Scope X*** metrics, each data point is complemented by an ***Emissions Categories*** attribute. This specifies the exact source of emissions within that Scope, such as: * stationary combustion or mobile combustion for Scope 1, * purchased electricity or steam for Scope 2, and * purchased goods or product use for Scope 3. This attribute ensures clarity and context at the category level, ensuring users can see exactly which part of a company’s emissions the metric represents. {% endhint %} *** # References & Standards Learn how Tracenable’s GHG Emissions dataset aligns with the GHG Protocol, EU CSRD, SEC, GRI, CDP, TCFD, SASB, and PCAF for global comparability. ## Foundational References These are the authoritative sources we rely on to define terms, set classification rules, and resolve edge cases. ### [**Greenhouse Gas Protocol (WRI/WBCSD Corporate Standard)**](https://ghgprotocol.org/sites/default/files/standards/ghg-protocol-revised.pdf) * **Why it matters**: The GHG Protocol is the globally recognized framework for measuring and reporting GHG emissions. It defines Scope 1, Scope 2, and Scope 3 categories, sets organizational boundary rules, and provides calculation guidance used by regulators, companies, and investors worldwide. * **What we adopt:** * Core scope definitions (Scope 1 direct, Scope 2 energy-indirect, Scope 3 value chain). * Coverage of all seven Kyoto gases expressed in CO₂-equivalent (tCO₂e). * Category mapping for Scope 1, 2, and 3 to ensure alignment with official definitions and company disclosure practices. * Guidance on organizational boundaries and Scope 2 accounting (location-based and market-based). *** ## Related Reporting Frameworks & Standards While the GHG Protocol provides the universal foundation for measuring and reporting emissions, additional frameworks build on its principles to address regulatory, voluntary, and industry-specific needs. Tracenable bridges these standards by mapping disclosures back to the GHG Protocol’s core definitions while supporting compliance and comparability. ### Regulatory Frameworks #### [**EU Corporate Sustainability Reporting Directive (CSRD) – ESRS E1 Climate Change**](https://www.efrag.org/sites/default/files/media/document/2024-08/ESRS%20E1%20Delegated-act-2023-5303-annex-1_en.pdf) * **Why it matters:** CSRD mandates climate disclosures for thousands of EU companies, covering Scope 1, Scope 2 (with both location- and market-based methods), and Scope 3 with category-level detail. * **How we align:** We align with ESRS E1 disclosure fields by capturing gross Scope 1, 2, and 3 emissions including Scope 2 method splits and Scope 3 category breakdowns, all standardized in line with the GHG Protocol and ISO 14064-1:2018. #### [**U.S. SEC Climate Disclosure Rule (2024)**](https://www.sec.gov/files/rules/final/2024/33-11275.pdf) * **Why it matters:** The SEC rule requires U.S. registrants to disclose Scope 1 and Scope 2 emissions in annual filings, with assurance for large filers. Scope 3 is voluntary but often reported for investor expectations. * **How we align:** * Gross reporting of Scope 1 and 2 emissions in metric tons CO₂e (excluding offsets). * Capture of Scope 3 emissions when disclosed, even if not mandatory under SEC rules. * Traceability and assurance-ready data to meet SEC’s audit requirements. ### Voluntary Global Frameworks #### [**GRI 305: Emissions (2016)**](https://www.globalreporting.org/publications/documents/english/gri-305-emissions-2016/) * **Why it matters:** A widely adopted disclosure standard requiring Scope 1, 2, and 3 totals, category detail, and intensity metrics. * **How we align:** Our dataset provides Scope 1, 2, and 3 totals, Scope 3 categories, Scope 2 method splits, and intensity values mapped directly to GRI 305 indicators. #### [**TCFD (Task Force on Climate-related Financial Disclosures)**](https://assets.bbhub.io/company/sites/60/2021/10/FINAL-2017-TCFD-Report.pdf) * **Why it matters:** A global framework for climate risk reporting, embedded in many regulatory regimes, requiring disclosure of Scope 1, 2, and (where material) Scope 3 emissions. * **How we align:** We provide complete Scope 1, 2, and (where available) Scope 3 data that supports TCFD-aligned reporting and comparability. #### [**CDP (Carbon Disclosure Project)**](https://assets.ctfassets.net/v7uy4j80khf8/7AC4SpiM7JnJs3x7NMqOhp/8c7e9197c678d486a82b011193a0d4d3/Greenhouse_Gas_Emissions_Tools_and_Datasets_for_Cities_Full_Report__May_2024_.pdf) * **Why it matters:** A widely used voluntary disclosure system requiring Scope 1, 2, and 3 emissions at both total and category levels, with Scope 2 reported by method. * **How we align:** Our dataset captures Scope 1, 2, and 3 totals and categories, and clearly distinguishes location- vs. market-based Scope 2, aligned with CDP questionnaires. ### Sector- and Finance-Specific Frameworks #### [**SASB Standards (now IFRS Foundation)**](https://sasb.ifrs.org/standards/) * **Why it matters:** Require industry-specific disclosure of GHG emissions, including Scope 1, Scope 2, and in some cases Scope 3, along with intensity metrics (e.g., emissions per unit of production or revenue). These requirements vary by sector, making them highly relevant for industry benchmarking. * **How we align:** Tracenable’s dataset provides scope-level totals and intensity metrics, with sector tagging to support SASB-aligned, industry-specific comparisons. #### [**PCAF (Partnership for Carbon Accounting Financials)**](https://carbonaccountingfinancials.com/files/downloads/PCAF-Global-GHG-Standard.pdf) * **Relevance:** The leading global standard for measuring and disclosing financed emissions across asset classes, complementing the GHG Protocol by providing detailed methodologies for financial institutions. * **How we align:** We integrate PCAF categories and accounting methods into our Scope 3 Category 15 (Investments), enabling consistency with financial-sector best practices. {% hint style="success" %} ## Takeaway: * Tracenable’s GHG dataset is built on the GHG Protocol and mapped to major regulatory frameworks (EU CSRD, U.S. SEC rule). * It is fully compatible with leading voluntary standards (GRI, TCFD, CDP). * It extends to sector-specific and finance-specific standards (SASB, PCAF) for industry relevance. **The result**: jurisdiction-aware coverage of gross Scope 1, 2, and 3 emissions with clear mapping to global standards, full traceability to company disclosures, and audit-ready reliability for compliance, benchmarking, and investment analysis. {% endhint %} *** # Data Collection & Methodology Learn how Tracenable collects, standardizes, and validates corporate Greenhouse Gas (GHG) Emissions data through a five-step human-in-the-loop methodology. ## Introduction Accurate, comparable, and traceable Greenhouse Gas (GHG) Emissions data requires more than simply aggregating figures. It requires structured methodology, reliable sourcing, and careful standardization. Tracenable’s approach combines automation, human expertise, and adherence to global reporting frameworks to deliver decision-ready emissions data you can trust. *** ## Our Five-Step GHG Emissions Data Collection & Standardization Approach {% stepper %} {% step %} ### Defining the Schema through Research We start with a rigorous review of foundational references, most importantly the GHG Protocol, which defines the core elements of emissions reporting: Scope 1, Scope 2, and Scope 3 classifications, boundary-setting rules, and the seven recognized greenhouse gases (CO₂, CH₄, N₂O, etc.). Building on this foundation, we incorporate requirements from leading regulatory frameworks such as the EU CSRD (ESRS E1), as well as voluntary standards like GRI 305, CDP, SASB, and TCFD. This ensures our schema reflects both global best practices and the disclosure formats companies are expected to follow. Finally, we complement these standards with empirical research, studying how companies actually report emissions across sectors and regions. This combined approach allows us to design a schema that is granular enough to capture detail, yet flexible enough to apply consistently across thousands of companies worldwide. {% endstep %} {% step %} ### Capturing GHG Emissions Disclosures at Scale Corporate Greenhouse Gas (GHG) Emissions data can appear in many places: annual reports, sustainability reports, regulatory filings, standalone data spreadsheets, or hidden on a webpage deep in a company’s site. Our infrastructure is designed to capture all of it. Through automated web monitoring and targeted expert retrieval, we ensure that no disclosure is overlooked. This comprehensive approach minimizes blind spots and provides the broadest possible coverage of corporate Greenhouse Gas (GHG) Emissions data globally. {% endstep %} {% step %} ### Extracting and Converting Disclosures into Structured Data Greenhouse Gas (GHG) Emissions disclosures come in many formats: PDFs, Excel annexes, HTML tables, and narrative text. Our AI-driven pipelines first convert raw files into a unified structure (e.g., PDF to markdown). From there: * Computer vision to extract and parse tables, figures, and graphical emissions data. * Natural language processing (NLP) to detect emissions-related text, identify Scope and category, and extract quantitative values and units. * Classification rules to map disclosures into Scope 1, Scope 2, or Scope 3, and to identify whether metrics are absolute or relative measures. The result: machine-readable, standardized data points that preserve traceability to the original disclosure. {% endstep %} {% step %} ### Data Human-in-the-Loop Validation AI brings speed and scalability, but human expertise ensures accuracy and context. Each extracted GHG data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate GHG emissions data, with arbitration applied where discrepancies remain. This process allows us to: * Correct errors where AI may misclassify scope categories or emission types. * Preserve context from narrative disclosures, such as Scope 2 accounting methods or Scope 3 category definitions. * Continuously improve our extraction models through analyst feedback. The outcome is audit-grade GHG emissions data that users can trust. {% endstep %} {% step %} ### Rigorous Quality Assurance Finally, our GHG Emissions dataset undergoes multi-layered quality checks: * Automated tests catch obvious anomalies (negative values, implausible spikes, inconsistent units). * Machine learning models detect statistical outliers through unsupervised methods and unusual time-series patterns. * Manual audits ensure nothing slips through the cracks. This combination of automation and human oversight guarantees that every Greenhouse Gas (GHG) Emissions metric delivered is reliable, comparable, and ready for use in compliance, benchmarking, and research. {% endstep %} {% endstepper %} *** ## Learn More To explore the methodology in detail, visit: * [**Data Sources**](/ghg-emissions/data-collection-and-methodology/data-sources) - Where Greenhouse Gas (GHG) Emissions data comes from and how it is collected. * [**Standardization Guidelines** ](/ghg-emissions/data-collection-and-methodology/standardization-guidelines)- How disclosures are normalized into consistent Greenhouse Gas (GHG) Emissions dataset. * [**Calculation Logic**](/ghg-emissions/data-collection-and-methodology/calculation-logic) - How missing values are inferred and totals are computed using transparent accounting rules. * [**Quality Assurance**](/ghg-emissions/data-collection-and-methodology/quality-assurance) - The validations and controls that safeguard data integrity. *** # Data Sources See where Tracenable’s GHG emissions data comes from. Learn which corporate disclosures, registries, and web sources we capture, and how every metric is fully traceable back to its origin. ## Introduction The reliability of our Greenhouse Gas (GHG) Emissions data starts with the quality of its sources. At Tracenable, we collect information from a broad range of corporate and official channels, ensuring that every data point is traceable back to its origin. Our goal is simple: provide users with complete, transparent, and verifiable evidence of how companies disclose their Greenhouse Gas (GHG) Emissions performance. *** ## Where We Collect Data We capture Greenhouse Gas (GHG) Emissions disclosures wherever companies report them, across all common formats: * **Corporate reports** - Sustainability reports, annual reports, integrated reports, proxy statements. * **Regulatory filings** - Documents filed under mandatory disclosure regimes (e.g., CSRD, SEC, or national registries). * **Web disclosures** - Corporate webpages, environmental policy pages, or dedicated sustainability microsites. * **Data annexes and spreadsheets** - Often attached to sustainability reports or published as standalone datasets. * **Press releases and news articles** - Only when originating directly from the company. * **Government registries** - Authoritative third-party repositories of company-submitted Greenhouse Gas (GHG) Emissions data. No matter the format (PDF, HTML, Excel, or XML/XBRL) we normalize disclosures into a structured, machine-readable format without losing traceability to the original file. *** ## End-to-End Traceability Every data point in the Greenhouse Gas (GHG) Emissions dataset includes a direct link to its original source, allowing users to audit disclosures in context. Links open the exact report, page, or section cited. Metadata such as publication date and reporting period are also captured to preserve the full reporting trail. This approach ensures transparency: users can always see *what a company reported, when, and where*. *** ## Coverage Strategy Our coverage is global and demand-driven. We monitor thousands of companies across sectors and geographies, prioritizing based on client requests. If your use case requires extended coverage, we can adapt our sourcing to include additional companies, jurisdictions, or disclosure types. This flexibility ensures that Tracenable’s Greenhouse Gas (GHG) Emission dataset reflects not only today’s mandatory reporting landscape, but also the evolving needs of users. *** # Standardization Guidelines Learn how Tracenable standardizes GHG emissions data by mapping disclosures to GHG Protocol scopes and sources, clarifying Scope 2 methods, and ensuring comparability. ## Why Standardization Matters Corporate Greenhouse Gas (GHG) Emissions disclosures vary widely. Some companies publish only total emissions, others break data down by detailed sources, and many use inconsistent or ambiguous terminology. Without harmonization, these differences make it difficult to: * Compare emissions across companies and industries. * Aggregate data for portfolio- or sector-level analysis. * Align corporate disclosures with GHG Protocol, CSRD (ESRS E1) or voluntary frameworks (GRI, CDP, TCFD). Tracenable’ addresses this by applying clear standardization rules, ensuring all Greenhouse Gas (GHG) Emissions data fits into a single, comparable structure while retaining traceability to the original company report. *** ## **Tracenable's Standardization Guidelines** ### **Guideline 1:** Normalize Units to Metric Tonnes of CO₂e Emissions may appear in units such as kg CO₂e, g CO₂e, or short tons. All are converted to **metric tonnes of CO₂e (tCO₂e)** following GHG Protocol standards. **Example:** A value of *100,000 short tons CO₂e* converts to *90,718.5 tCO₂e* (1 short ton = 0.907185 metric tonnes). This guarantees that emissions are expressed on a unified global scale. ### **Guideline 2: Map Reported Data to GHG Protocol-Defined Scopes and Sources** Companies don’t always follow standard language when reporting emissions. For example: * A disclosure may use vague terms like “direct” (Scope 1) or “indirect" (Scope 2 or 3). * Some combine categories (e.g., “Scope 1+2”) instead of reporting them separately. * Others list raw emission sources like “vehicle fleet” or “air travel” without specifying the scope. Tracenable resolves this by mapping all reported values to the **GHG Protocol’s standardized framework of scopes and source categories**: * **Scopes**: All emissions are classified into Scope 1 (direct), Scope 2 (indirect energy), or Scope 3 (value chain). Combined totals are retained only when separate scope-level data is not disclosed. * **Sources**: Within each scope, company terms are mapped to the correct standard categories. For example: * “Natural gas boilers” → Scope 1 - Stationary Combustion. * “Vehicle fleet” → Scope 1 - Mobile Combustion. * “Purchased power” → Scope 2 - Electricity. * “Business travel by air and rail” → Scope 3 - Business Travel (Category 6) This ensures consistency across disclosures and enables reliable apples-to-apples comparisons. {% hint style="success" %} When reported values cannot be confidently mapped to a standard category due to limited detail or ambiguous terminology, Tracenable assigns them to a dedicated “**Unmapped**” category within each Scope. This approach ensures no information is discarded, keeps the dataset complete, and highlights where reporting gaps or data quality issues may affect comparability. {% endhint %} {% hint style="warning" %} #### Looking for detailed mapping guides? Tracenable maintains internal mapping references for Scope 1, 2, and 3 categories. These guides reconcile “as reported” terminology with standardized definitions, based on the GHG Protocol and related frameworks. While not published publicly, they can be shared privately upon request. {% endhint %} ### Guideline 3: **Map Reported Methods to Market-Based or Location-Based** Scope 2 and some categories of Scope 3 emissions may be reported with unclear or inconsistent terminology, or without a method specified. For instance: * A disclosure might reference *“grid average factors”* → mapped to **location-based**. * Another may provide values *“before RECs” and “after RECs”* → mapped to **location-based vs. market-based**. * No method specified at all. Tracenable standardizes all such cases into the three recognized categories — **location-based, market-based, or not specified** — and preserves both values when available. This ensures clarity, avoids double counting, and allows for transparent comparison across companies and years. {% hint style="success" %} #### Takeaway: Tracenable’s standardization rules ensure that GHG emissions data is: * **Consistent** – all disclosures are mapped to GHG Protocol scopes and source categories. * **Comparable** – variations in terminology and reporting styles are harmonized into a single structure. * **Transparent** – original company terms and methods remain traceable, even when standardized. * **Reliable** – Scope 2 methods are clearly distinguished (location-based vs. market-based), avoiding double counting. * **Complete** – no information is lost, even when company disclosures cannot be fully standardized. {% endhint %} *** # Calculation Logic Discover how Tracenable applies transparent accounting logic to standardize Greenhouse Gas (GHG) Emissions data across Scope 1, Scope 2, and Scope 3 for consistency and traceability. ## **Introduction** Companies disclose Greenhouse Gas (GHG) Emissions data in many different ways. Some provide only consolidated totals for Scope 1, Scope 2, or Scope 3, while others break data down into categories but omit the total. Many publish a mix of both, and approaches often shift across reporting years, creating inconsistencies that make it difficult to compare performance across peers or track progress over time. Tracenable’s GHG dataset applies **hierarchical accounting rules** to ensure that emissions data is consistently aggregated across scopes and categories. These rules reflect the structure of the GHG Protocol and allow users to move seamlessly between granular source-level data and consolidated totals. *** ## Hierarchical Structure of GHG Emissions Data The GHG emissions dataset follows a hierarchical structure based on the Greenhouse Gas Protocol. This hierarchy reflects how emissions are classified and aggregated across **scopes** and their underlying **categories**. **Scope 1 – Direct emissions** * *Scope 1 Total* * Stationary combustion * Mobile combustion * Process emissions * Direct GHG releases * Fugitive emissions * Refrigerant emissions * Venting emissions * Flaring emissions **Scope 2 – Indirect energy emissions** * *Scope 2 Total* * Electricity * Heat * Steam * Cooling **Scope 3 – Value chain emissions** * *Scope 3 Total* * Category 1: Purchased goods and services * Category 2: Capital goods * Category 3: Fuel- and energy-related activities * Category 4: Upstream transportation and distribution * Category 5: Waste generated in operations * Category 6: Business travel * Category 7: Employee commuting * Category 8: Upstream leased assets * Category 9: Downstream transportation and distribution * Category 10: Processing of sold products * Category 11: Use of sold products * Category 12: End-of-life treatment of sold products * Category 13: Downstream leased assets * Category 14: Franchises * Category 15: Investments {% hint style="success" %} Totals (Scope 1, Scope 2, Scope 3) can be derived from the sum of their respective categories when missing. {% endhint %} This hierarchical organization provides the foundation for Tracenable’s accounting rules, which ensure that emissions data is always **complete, consistent, and reconcilable** across different reporting practices. *** ## Accounting Rules ### **Rule 1: Bottom-Up Computation (Sum of Children)** When a total for Scope 1, Scope 2, or Scope 3 is not reported but category-level values are available, Tracenable computes it as the sum of whichever child categories are disclosed: * Total Scope 1 = Sum of categories of Scope 1 * Total Scope 2 = Sum of categories of Scope 2 * Total Scope 3 = Sum of categories of Scope 3 {% hint style="warning" %} Companies do not need to report every possible category for the total to be calculated. For example: If a company reports only stationary combustion and mobile combustion under Scope 1, the Scope 1 total is derived from just those two categories. {% endhint %} **Example** If a company reports: * Stationary combustion = 1,200 tCO₂e * Mobile combustion = 800 tCO₂e * Process emissions = 500 tCO₂e But provides no Scope 1 total, Tracenable computes: **Scope 1 Total = 2,500 tCO₂e** {% hint style="success" %} This approach ensures that totals are always available, even when companies report only partial category-level data. It avoids gaps and makes the dataset usable across all firms, regardless of disclosure practices. {% endhint %} #### **Dual Representation** This accounting system preserves both detail and comparability: * **Granularity preserved** – Users can analyze emissions at the most detailed level available (e.g., Scope 3 Category 6 – Business Travel). * **Comparability enabled** – All disclosures, whether granular or aggregated, roll up into consistent Scope 1, Scope 2, and Scope 3 totals. This ensures fair comparisons across companies, even when reporting practices differ. {% hint style="info" %} #### Note on Consistency Checks When both category-level data and a reported total are available, Tracenable performs an internal check to ensure they are consistent within defined tolerance levels. If consistent, the reported total is retained; if not, the discrepancy is flagged for review. This guarantees transparency while maintaining trust in reported values. {% endhint %} {% hint style="success" %} ## Takeaway: By applying this structured accounting logic, Tracenable ensures that the GHG emissions dataset is: * **Complete** – scope-level totals are always available, even if companies report only a subset of categories. * **Consistent** – child categories and parent totals always reconcile within a clear hierarchy. * **Comparable** – different reporting styles are harmonized into a single framework. * **Traceable** – all computed values remain anchored in reported disclosures, ensuring audit-grade reliability. The result is a dataset that balances detail with usability, giving users both granular insights and high-level comparability. {% endhint %} *** # Quality Assurance Discover how Tracenable validates Greenhouse Gas (GHG) Emissions data through automated checks, statistical tests, and human review to deliver audit-grade reliability. ## Introduction High-quality GHG emissions data depends on more than just good collection and standardization: it requires rigorous validation. At Tracenable, we combine automated testing, statistical analysis, and expert human review to ensure that every waste metric meets the highest standards of accuracy, consistency, and reliability. Our Quality Assurance (QA) process is multi-layered, designed to detect errors, catch anomalies, and confirm that each data point is both faithful to the original disclosure and fit for use in compliance, benchmarking, and research. *** ## Automated Validation Checks The first layer of QA relies on automated rules that run across all GHG Emissions metrics. These checks are designed to quickly spot issues that should never occur in valid data, such as: * **Impossible values** – negative or implausibly large emission quantities. * **Unit inconsistencies** – figures reported in mismatched or conflicting units across years. * **Structural errors** – totals that do not match the sum of their components. These rules ensure that obvious errors are flagged immediately and never propagate into the dataset. *** ## Statistical and Machine Learning Tests Beyond simple rules, we apply more advanced techniques to identify subtle anomalies: * **Time-series consistency checks** – highlight sudden spikes or drops in reported GHG Emissions data. * **Outlier detection** – identify company disclosures that deviate significantly from industry norms. * **Distribution analysis** – verify that GHG Emissions metrics follow expected statistical patterns across sectors. These methods help us flag values that may be technically valid but require closer review. *** ## Human-in-the-Loop Review Not all issues can be resolved automatically. Our QA process therefore includes a human-in-the-loop review, where trained analysts validate flagged data points: * **Contextual review** – analysts check values against the original disclosure to confirm interpretation. * **Dual validation** – two independent reviewers may assess the same data point. * **Arbitration** – discrepancies between analysts are escalated to senior analysts for final decision. This ensures that ambiguous or complex disclosures are interpreted correctly, and that every value remains fully traceable to its source. *** ## Continuous Improvement Each QA outcome feeds back into our systems: * Automated rules are updated when new error patterns are identified. * Machine learning models are retrained to improve anomaly detection. * Documentation is refined to capture new edge cases and classification challenges. This iterative loop ensures that the Greenhouse Gas (GHG) Emissions Dataset grows more robust and reliable over time. *** # Data Dictionary GHG Emissions data dictionary: definitions of metrics, scopes, categories, units, and attributes in Tracenable’s dataset, complete with formats and real company examples.
AttributeType/FormatDescriptionExample
isinAlphanumeric StringInternational Securities Identification Number (ISIN) of the primary publicly traded financial instrument associated with the company.US42704L1044
leiAlphanumeric StringLegal Entity Identifier (LEI)549300TP80QLITMSBP82
figiAlphanumeric StringFinancial Instrument Global Identifier (FIGI) of the primary publicly traded financial instrument associated with the company.BBG00WNPK2F5
tickerStringTicker symbol of the primary publicly traded financial instrument associated with the company.HRI
mic_codeStringMarket Identifier Code (MIC) of the primary publicly traded financial instrument associated with the company.XNGS
exchangeStringStock exchange of the primary publicly traded financial instrument associated with the company.NASDAQ
permidNumerical StringPermanent Identifier4295900057
company_nameStringLegal name of the company.HERC HOLDINGS INC
countryCategorical StringCountry where the company's headquarters are located.United States
sectorStringSector in which the company operates.Technology
industryStringIndustry classification of the company.Software - Application
year_of_disclosureYear (YYYY)Year in which the data point was disclosed.2023
reporting_periodYear (YYYY)Period for which the data point was measured, assessed, or is applicable.2023
metricStringThe specific measurement or data points requested.Categories of Scope 2
levelCategorical StringThe level of emissions data, distinguishing between Total and Categories.Total
typeCategorical StringThe type of emissions data, indicating whether it is an intensity or absolute value.Absolute
scopeArray of StringsGHG emission scopes (1, 2, and/or 3) included in the value.['Scope 1']
emissions_categoriesArray of StringsCategories of GHG emissions.['Scope 2 - Cooling', 'Scope 2 - Electricity', 'Scope 2 - Heat', 'Scope 2 - Steam']
valueNumerical FloatAmount of GHG emissions or intensity.12000000
unitCategorical StringThe unit of measurement for the value, indicating the scale or dimension.Metric Tonnes of CO2 equivalent (mtCO2e)
methodCategorical StringApproach used to calculate Scope 2 and occasionally Scope 3 GHG emissions, distinguishing between 'Location-Based' (reflecting grid averages), 'Market-Based' (reflecting the specific electricity profile purchased), 'Not Specified', and 'Not Applicable' methods.Market-based
incomplete_boundariesBoolean (True or Not Specified)Indicates whether the reported data covers only a limited portion of the company's operational or organizational boundaries.Not Specified
source_namesArray of StringsNames of the sources from which the reported data was obtained.['Corporate Responsibility Report']
company_idStringTracenable's internal company identifier.dfb6bd7b-facf-4d0f-86a2-f1eedf191946
document_idStringTracenable's internal source document identifier.4a32b902-ae42-4692-ae96-b42c99fdd2f9
traceability_source_urlStringURL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes.https://platform.tracenable.com/source-trace?data-request-id-hash=AF-CkCtAvJnG&project=ghg-emissions
# Introduction Get introduced to Tracenable’s Energy Dataset, including its scope, key characteristics, and unique value, and get redirected to detailed pages to deepen your knowledge. ## Overview The Energy dataset provides comprehensive coverage of corporate energy management across more than 5,000 global companies, offering a standardized view of how organizations consume, produce, and distribute energy. It captures detailed information on energy types such as electricity and heat, with breakdowns by technologies (e.g., solar PV, hydropower, combined heat and power) and energy sources (e.g., biofuels, coal, natural gas, renewables). With thorough information on renewability and rigorous standardization of every energy metric, the dataset enables precise benchmarking, informed strategic planning, and strategic insights across sectors. *** ## Data Characteristics
:building: Company Coverage5000
:globe: Geographical CoverageGlobal
:shapes: Sectoral CoverageAll Sectors
:calendar-range: Data Historical RangeFrom 2021 to 2024
:reflect-vertical: Median Data History2 years
:magnifying-glass-waveform: Data Traceability Rate100%
:chart-simple: Data FrequencyAnnual
:repeat: Average Reporting Lag3 months
:bring-forward: Data FormatMost Recent/Point-in-Time
*** ## What Makes Tracenable’s Energy Data Unique Tracenable's energy management dataset sets the market benchmark for precision, standardization, reliability, and integrity. Learn why our data stands apart:
:arrow-down-triangle-square: Comprehensive Standardization When company energy data do not align with standard energy reporting frameworks, our team of environmental engineers meticulously maps the reported figures to the correct energy types and flow categories. This guarantees uniformity and comparability across our dataset, bridging the gap created by diverse reporting formats.
:bullseye-arrow: Accuracy in Every MetricOur advanced cross-source data precision matching algorithm ensures that the most accurate energy metrics are always delivered. For instance, an exact figure like 12,510,545 Joules is prioritized over a rounded figure like 12mio, reflecting our dedication to precision and detail.
:shield-check: Unbiased Data IntegrityOur approach is grounded in delivering energy data exactly as reported by companies, without making inferences or estimates for undisclosed data. This strict adherence to factual reporting ensures the integrity of the data you receive, providing an unaltered and accurate view of corporate emissions.
:magnifying-glass-waveform: End-to-End Data TraceabilityEvery energy data point is directly traceable to its original source, complete with page references and calculation methodologies. This level of detail ensures the reliability and verifiability of our data, giving you complete confidence in our energy dataset.
:arrows-maximize:Full-Scope Boundary VerificationWe tag energy figures that do not cover a company's entire operational boundaries with an 'Incomplete Boundaries' attribute. This transparency ensures that any potential limitations are clearly communicated, enhancing the comparability of our energy data.
*** ## Deep Dive into the Energy Dataset On the following pages, you’ll find all the resources needed to fully understand and apply the Energy Dataset: * [**Definitions & Terminology** ](broken://pages/gV8LCMykrBOzd0E41baD)– Key terms and concepts used in the dataset * [**Data Dimensions & Metrics**](/energy/data-dimensions-and-metrics) – Breakdown of energy metrics and how they are structured * [**References & Standards**](/energy/references-and-standards) – Alignment with global reporting frameworks and regulations * [**Data Collection Methodology**](/energy/data-collection-methodology) – How the dataset is built and validated * [**Data Sources**](/energy/data-collection-methodology/data-sources) – Origin and type of corporate disclosures collected * [**Standardization Guidelines** ](/energy/data-collection-methodology/standardization-guidelines)– Rules applied to ensure comparability * [**Calculation Logic**](/energy/data-collection-methodology/calculation-logic) – Methods for deriving standardized metrics * [**Quality Assurance** ](/energy/data-collection-methodology/quality-assurance)– Checks and processes ensuring data integrity * [**Data Dictionary** ](/energy/data-dictionary)– Complete reference of fields, units, and definitions *** # Definitions & Terminology Understand what qualifies as energy data, its classifications, main energy types (total, electricity, heat), energy flow type, and renewability. This article introduces the conceptual foundation of the **Energy** dataset. It explains what “energy” means in corporate sustainability reporting and how it is classified.\ It serves as an entry point for understanding the key ideas that shape energy accounting and reporting across industries and regulations. *** ## **What Energy Means** Every organization depends on energy to operate: to power buildings, produce goods, transport materials, support digital infrastructure and so on. In scientific terms, **energy is the capacity to do work**: it enables movement, heating, lighting, and every form of activity in the physical world. In sustainability reporting, **it represents the total amount of energy a company consumes, produces, purchases or distributes over a specific period**, typically one fiscal year. Yet, energy is more than an environmental metric; it is the backbone of the global economy. Virtually every economic activity is an act of energy transformation. The correlation between global energy consumption and GDP has historically been close to one, reflecting how deeply energy flows shape productivity, industrial output, and societal development. Understanding energy data, therefore, isn’t only about environmental responsibility; it’s about grasping the material basis of economic growth. By tracking how organizations consume and produce energy, we gain insight into both their climate impact and their role in the broader economic system. ### What is Energy Data Energy data captures a company’s quantitative information about how it uses and manages energy: including **consumption, production, purchases, exports, reserves,** and **production capacities.** By tracking this data, companies, investors, and regulators can: * Assess how resource-intensive operations are, * Evaluate efficiency and reduction efforts, * Measure dependence on renewable versus fossil energy sources, * Connect energy use directly to greenhouse gas (GHG) emissions and broader climate impacts, and * Understand how energy consumption underpins economic activity and productivity across industries. Energy data, therefore, provides the foundation for climate performance analysis. It connects what companies *do* (their activities) to their *environmental footprint*. *** ## **How Energy Is Classified** Corporate energy reporting doesn’t just measure how much energy is used: it explains **what kind of energy**, **where it came from**, and **how it was used**.\ This conceptual structure reflects the principles of energy accounting and the logic behind international frameworks such as the *Greenhouse Gas Protocol*, *GRI 302: Energy*, and *ISO 50001*. Energy data is classified along **five core dimensions**: | Dimension | Conceptual Question | What It Describes | | ------------------------------- | -------------------------------------------------- | ---------------------------------------------------------- | | **Energy Type** | What kind of energy is it? | The physical form — e.g., electricity, heat, fuel | | **Energy Inflow** | Where does the energy come from? | Whether it is produced internally or purchased externally | | **Energy Outflow** | How is the energy used or distributed? | Whether it is consumed, sold, or stored | | **Energy Renewability** | What is the nature of the source? | Whether it is renewable, non-renewable, or a mix of both | | **Energy Source or Technology** | How or from what is the energy generated/produced? | The underlying source, such as solar, natural gas, or coal | Each dimension adds a layer of context, allowing analysts to understand not just *how much* energy a company uses, but *what kind of energy system it operates*. *** ## Energy Dimensions in Detail ### **Energy Type: The Form of Energy** Energy can take many forms, and distinguishing between them helps clarify what part of a company’s operations is being measured. Common energy types include: * **Electricity** - Electrical power used in buildings, machinery, or data centers. * **Heat** - Thermal energy used for heating or industrial processes (e.g., district heating steam, chilled water). * **Raw resources** - Primary energy sources such as fuel, natural gas, or other physical resources that can consumed directly. *Example:*\ A cement manufacturer may consume 200 GJ of electricity and 500 GJ of district heating.\ Here, electricity and heat represent two different **energy types**, both contributing to the company’s total energy use. ### **Energy Inflow: Where Energy Comes From** Energy enters a company’s system in two main ways: * **Purchased Energy** - Bought from external suppliers (e.g., electricity from the grid). * **Produced Energy** - Generated internally by the company (e.g., solar power, cogeneration). Understanding inflows helps evaluate **energy dependence and autonomy**. For instance, whether a company relies heavily on the public grid or generates its own energy on-site. *Example:*\ A logistics company purchases 10,000 MWh of grid electricity and produces 2,000 MWh from rooftop solar panels. ### **Energy Outflow: Where Energy Goes** Outflow tracks what happens to energy once it enters the organization: how it is used, sold, or stored. * **Consumption/Use** - Energy used in operations (e.g., to power offices). * **Sold/Exported** - Energy delivered to third parties (e.g., excess solar power sold to the grid). * **Reserves/Storage** - Energy stored for later use (e.g., batteries, fuel tanks). {% hint style="info" %} The **energy balance principle** connects inflows and outflows: Total Inflow = Total Outflow\ Purchased + Produced = Consumed + Sold + Stored {% endhint %} *Example:*\ An industrial plant generates 100 MWh of electricity, consumes 90 MWh, and sells 10 MWh.\ The inflow (100 MWh produced) equals the outflow (90 + 10 MWh). {% hint style="info" %} #### Energy Production Capacities In addition to tracking actual energy flows, Tracenable also captures **energy production capacities**: the *maximum potential* output their energy systems can deliver. Production capacity represents how much energy infrastructure (e.g., power plants, solar farms, cogeneration units) could theoretically produce if operated at full load. It is typically expressed as **installed capacity**, often measured in **megawatts (MW)** for electricity or **gigajoules per hour (GJ/h)** for thermal systems. Unlike inflows and outflows, **production capacity does not reflect actual energy produced or consumed**, but rather the *capability* of a company’s assets. It helps analysts understand the scale and potential of a company’s energy generation systems — for example, comparing renewable versus non-renewable installed capacities across years. {% endhint %} ### **Energy Renewability: The Nature of the Energy Source** Not all energy is created equal. This dimension captures whether the source is **renewable**, **non-renewable**, or a mix of both. * **Renewable** – Renewable energy comes from sources that naturally replenish on a human timescale, such as solar, wind, hydropower, geothermal, and bioenergy. * **Non-Renewable** – Non-renewable energy is derived from finite fossil fuel resources, including coal, oil, and natural gas. This classification is essential for understanding a company’s transition to cleaner energy systems. ### **Sources and Technologies — How Energy Is Generated** This dimension identifies how and from what energy is generated, providing the most granular level of detail for analysis and reporting. It combines two complementary aspects: * **Energy Sources** - The natural resources from which energy originates, such as biofuels, biomass, biogas, coal, crude oil, natural gas, or uranium. These can be renewable or non-renewable depending on their regeneration potential and environmental impact. * **Energy Technologies** - The systems or methods used to generate, harness, or utilize energy, such as solar photovoltaic, wind turbines, hydropower, geothermal systems, nuclear plants, cogeneration units, fuel cells, or waste heat recovery systems. This allows for rich analytical insights, such as comparing the share of wind energy versus fossil fuel energy within total consumption. *** # Data Dimensions & Metrics Learn how Energy data is structured in Tracenable’s dimensional model, with four core dimensions, and explore the full set of metrics derived from their combinations. ## How Dimensions and Metrics Work Tracenable datasets follow a dimensional model. * **Dimensions** are the attributes you can use to analyze or slice the data. Each dataset is defined by one or more dimensions. * **Metrics** are the most granular layer: each one represents a unique combination of dimension values that defines a specific data point. This dimensional model provides a transparent and predictable way to structure data. It removes ambiguity in naming, ensures consistency across datasets, and makes it easier to understand how each metric is constructed. *** ## Dimensions in the Energy Dataset All energy data in Tracenable is organized along four core dimensions: **Level, Energy Type, Flow Type, and Renewability.** These dimensions define how energy is categorized and reported, and every metric in the dataset is derived from their combinations. * ***Level*** indicates whether the metric reflects an aggregate total or a breakdown by individual energy sources. It includes the following values: * `Total` * `Sources` * ***Energy Type*** distinguishes between the forms of energy reported. It includes the following values: * `Total Energy (All Types)` * `Electricity` * `Heat` * ***Flow Type*** describes the pathways through which energy interacts with company operations. It includes the following values: * `Produced` * `Purchased` * `Consumed` * `Exported` * `Reserves` * `Production Capacities` * ***Renewability*** classifies energy according to whether it is renewable or non-renewable. It includes the following values: * `Total` * `Renewable` * `Non-renewable` *** ## Metrics in the Energy Dataset Metrics in the energy dataset are defined by specific combinations of the four core dimensions: **Level, Energy Type, Flow Type,** and **Renewability.** This structure makes it clear whether a metric refers to a specific aspect of corporate energy use (for example, *Electricity Consumed from Renewable Sources*) or to a broader total (for example, *Total Energy Produced*). | Level | Energy Type | Flow Type | Renewability | Metric | | ------- | ----------- | --------------------- | ------------- | ---------------------------------------------------------- | | Total | Energy | Produced | Total | Total Energy Produced | | Total | Energy | Purchased | Total | Total Energy Purchased | | Total | Energy | Consumed | Total | Total Energy Consumed | | Total | Energy | Exported | Total | Total Energy Exported | | Total | Energy | Reserves | Total | Total Energy Reserves | | Total | Energy | Production Capacities | Total | Total Energy Production Capacities | | Total | Electricity | Produced | Total | Total Electricity Produced | | Total | Electricity | Purchased | Total | Total Electricity Purchased | | Total | Electricity | Consumed | Total | Total Electricity Consumed | | Total | Electricity | Exported | Total | Total Electricity Exported | | Total | Electricity | Reserves | Total | Total Electricity Reserves | | Total | Electricity | Production Capacities | Total | Total Electricity Production Capacities | | Total | Heat | Produced | Total | Total Heat Produced | | Total | Heat | Purchased | Total | Total Heat Purchased | | Total | Heat | Consumed | Total | Total Heat Consumed | | Total | Heat | Exported | Total | Total Heat Exported | | Total | Heat | Reserves | Total | Total Heat Reserves | | Total | Heat | Production Capacities | Total | Total Heat Production Capacities | | Total | Energy | Produced | Renewable | Total Renewable Energy Produced | | Total | Energy | Purchased | Renewable | Total Renewable Energy Purchased | | Total | Energy | Consumed | Renewable | Total Renewable Energy Consumed | | Total | Energy | Exported | Renewable | Total Renewable Energy Exported | | Total | Energy | Reserves | Renewable | Total Renewable Energy Reserves | | Total | Energy | Production Capacities | Renewable | Total Renewable Energy Production Capacities | | Total | Electricity | Produced | Renewable | Total Renewable Electricity Produced | | Total | Electricity | Purchased | Renewable | Total Renewable Electricity Purchased | | Total | Electricity | Consumed | Renewable | Total Renewable Electricity Consumed | | Total | Electricity | Exported | Renewable | Total Renewable Electricity Exported | | Total | Electricity | Reserves | Renewable | Total Renewable Electricity Reserves | | Total | Electricity | Production Capacities | Renewable | Total Renewable Electricity Production Capacities | | Total | Heat | Produced | Renewable | Total Renewable Heat Produced | | Total | Heat | Purchased | Renewable | Total Renewable Heat Purchased | | Total | Heat | Consumed | Renewable | Total Renewable Heat Consumed | | Total | Heat | Exported | Renewable | Total Renewable Heat Exported | | Total | Heat | Reserves | Renewable | Total Renewable Heat Reserves | | Total | Heat | Production Capacities | Renewable | Total Renewable Heat Production Capacities | | Total | Energy | Produced | Non-Renewable | Total Non-Renewable Energy Produced | | Total | Energy | Purchased | Non-Renewable | Total Non-Renewable Energy Purchased | | Total | Energy | Consumed | Non-Renewable | Total Non-Renewable Energy Consumed | | Total | Energy | Exported | Non-Renewable | Total Non-Renewable Energy Exported | | Total | Energy | Reserves | Non-Renewable | Total Non-Renewable Energy Reserves | | Total | Energy | Production Capacities | Non-Renewable | Total Non-Renewable Energy Production Capacities | | Total | Electricity | Produced | Non-Renewable | Total Non-Renewable Electricity Produced | | Total | Electricity | Purchased | Non-Renewable | Total Non-Renewable Electricity Purchased | | Total | Electricity | Consumed | Non-Renewable | Total Non-Renewable Electricity Consumed | | Total | Electricity | Exported | Non-Renewable | Total Non-Renewable Electricity Exported | | Total | Electricity | Reserves | Non-Renewable | Total Non-Renewable Electricity Reserves | | Total | Electricity | Production Capacities | Non-Renewable | Total Non-Renewable Electricity Production Capacities | | Total | Heat | Produced | Non-Renewable | Total Non-Renewable Heat Produced | | Total | Heat | Purchased | Non-Renewable | Total Non-Renewable Heat Purchased | | Total | Heat | Consumed | Non-Renewable | Total Non-Renewable Heat Consumed | | Total | Heat | Exported | Non-Renewable | Total Non-Renewable Heat Exported | | Total | Heat | Reserves | Non-Renewable | Total Non-Renewable Heat Reserves | | Total | Heat | Production Capacities | Non-Renewable | Total Non-Renewable Heat Production Capacities | | Sources | Energy | Produced | Total | Sources of Energy Produced | | Sources | Energy | Purchased | Total | Sources of Energy Purchased | | Sources | Energy | Consumed | Total | Sources of Energy Consumed | | Sources | Energy | Exported | Total | Sources of Energy Exported | | Sources | Energy | Reserves | Total | Sources of Energy Reserves | | Sources | Energy | Production Capacities | Total | Sources of Energy Production Capacities | | Sources | Electricity | Produced | Total | Sources of Electricity Produced | | Sources | Electricity | Purchased | Total | Sources of Electricity Purchased | | Sources | Electricity | Consumed | Total | Sources of Electricity Consumed | | Sources | Electricity | Exported | Total | Sources of Electricity Exported | | Sources | Electricity | Reserves | Total | Sources of Electricity Reserves | | Sources | Electricity | Production Capacities | Total | Sources of Electricity Production Capacities | | Sources | Heat | Produced | Total | Sources of Heat Produced | | Sources | Heat | Purchased | Total | Sources of Heat Purchased | | Sources | Heat | Consumed | Total | Sources of Heat Consumed | | Sources | Heat | Exported | Total | Sources of Heat Exported | | Sources | Heat | Reserves | Total | Sources of Heat Reserves | | Sources | Heat | Production Capacities | Total | Sources of Heat Production Capacities | | Sources | Energy | Produced | Renewable | Sources of Renewable Energy Produced | | Sources | Energy | Purchased | Renewable | Sources of Renewable Energy Purchased | | Sources | Energy | Consumed | Renewable | Sources of Renewable Energy Consumed | | Sources | Energy | Exported | Renewable | Sources of Renewable Energy Exported | | Sources | Energy | Reserves | Renewable | Sources of Renewable Energy Reserves | | Sources | Energy | Production Capacities | Renewable | Sources of Renewable Energy Production Capacities | | Sources | Electricity | Produced | Renewable | Sources of Renewable Electricity Produced | | Sources | Electricity | Purchased | Renewable | Sources of Renewable Electricity Purchased | | Sources | Electricity | Consumed | Renewable | Sources of Renewable Electricity Consumed | | Sources | Electricity | Exported | Renewable | Sources of Renewable Electricity Exported | | Sources | Electricity | Reserves | Renewable | Sources of Renewable Electricity Reserves | | Sources | Electricity | Production Capacities | Renewable | Sources of Renewable Electricity Production Capacities | | Sources | Heat | Produced | Renewable | Sources of Renewable Heat Produced | | Sources | Heat | Purchased | Renewable | Sources of Renewable Heat Purchased | | Sources | Heat | Consumed | Renewable | Sources of Renewable Heat Consumed | | Sources | Heat | Exported | Renewable | Sources of Renewable Heat Exported | | Sources | Heat | Reserves | Renewable | Sources of Renewable Heat Reserves | | Sources | Heat | Production Capacities | Renewable | Sources of Renewable Heat Production Capacities | | Sources | Energy | Produced | Non-Renewable | Sources of Non-Renewable Energy Produced | | Sources | Energy | Purchased | Non-Renewable | Sources of Non-Renewable Energy Purchased | | Sources | Energy | Consumed | Non-Renewable | Sources of Non-Renewable Energy Consumed | | Sources | Energy | Exported | Non-Renewable | Sources of Non-Renewable Energy Exported | | Sources | Energy | Reserves | Non-Renewable | Sources of Non-Renewable Energy Reserves | | Sources | Energy | Production Capacities | Non-Renewable | Sources of Non-Renewable Energy Production Capacities | | Sources | Electricity | Produced | Non-Renewable | Sources of Non-Renewable Electricity Produced | | Sources | Electricity | Purchased | Non-Renewable | Sources of Non-Renewable Electricity Purchased | | Sources | Electricity | Consumed | Non-Renewable | Sources of Non-Renewable Electricity Consumed | | Sources | Electricity | Exported | Non-Renewable | Sources of Non-Renewable Electricity Exported | | Sources | Electricity | Reserves | Non-Renewable | Sources of Non-Renewable Electricity Reserves | | Sources | Electricity | Production Capacities | Non-Renewable | Sources of Non-Renewable Electricity Production Capacities | | Sources | Heat | Produced | Non-Renewable | Sources of Non-Renewable Heat Produced | | Sources | Heat | Purchased | Non-Renewable | Sources of Non-Renewable Heat Purchased | | Sources | Heat | Consumed | Non-Renewable | Sources of Non-Renewable Heat Consumed | | Sources | Heat | Exported | Non-Renewable | Sources of Non-Renewable Heat Exported | | Sources | Heat | Reserves | Non-Renewable | Sources of Non-Renewable Heat Reserves | | Sources | Heat | Production Capacities | Non-Renewable | Sources of Non-Renewable Heat Production Capacities | *** # References & Standards Discover the foundational references and reporting standards that shape Tracenable’s Energy Dataset. ## Foundational References These are the **authoritative sources** we rely on to define terms, set classification rules, and resolve edge cases. ### UN International Recommendations for Energy Statistics (IRES) & Standard International Energy Classification (SIEC) * **Why it matters:** Provides internationally agreed definitions, classifications, and measurement units for energy sources and flows. Through the Standard International Energy Product Classification (SIEC), it ensures consistency and comparability of energy statistics across countries and sectors. * **What we adopt:** * Apply IRES/SIEC categories (e.g., coal, fuel oils, natural gas, biofuels, electricity) to define the **Sources** dimension in our dataset. * Key energy flows such as **Energy Produced**, **Energy Purchased (Imported)**, and **Energy Exported** are mapped to the **Flow Type** dimension ### EU Renewable Energy Directive (2018/2001/EU, “RED II”) * **Why it matters:** Provides a legal definition of energy from renewable sources, including wind, solar, biomass, hydro, and geothermal, establishing a consistent framework for reporting. * **What we adopt:** * Use the Directive’s definitions to classify energy as **Renewable** or **Non-renewable** within our **Renewability** dimension, ensuring consistency and comparability in energy source reporting. ### EU Energy Efficiency Directive (2012/27/EU) * **Why it matters:** Establishes the legal framework in the EU for measuring energy use and efficiency, including standardized definitions for **energy consumption**. * **What we adopt:** * Align our **Total Energy Consumption** metrics with the Directive’s concept of **final energy consumption.** ### US EIA Energy Glossary (U.S. Energy Information Administration) * **Why it matters:** Provides widely recognized definitions and conventions for energy terms, supporting consistency in reporting and analysis across U.S. companies and international comparability. * **What we adopt:** * Standardize our energy sources and units definitions according to EIA conventions, ensuring consistency for both U.S. and global data. *** ## Related Reporting Frameworks & Standards Unlike greenhouse gas emissions reporting, which benefits from a globally recognized standard like the GHG Protocol, energy reporting remains decentralized, with no single dominant global framework. However, several key standards provide clear guidance for companies seeking to quantify, manage, and disclose their energy-related impacts. Note that these frameworks **do not define our ground truth**, but they guide field design, coverage expectations, and help users connect our metrics to reporting and investment workflows. ### **GRI 302: Energy 2016** * **Relevance:** A widely adopted corporate energy disclosure standard that specifies disclosures on energy management, including total fuel and electricity consumption by type (renewable vs non-renewable) * **How we align:** Our dataset mirrors GRI 302-1 structure, capturing total energy consumption across the organization, along with detailed breakdowns for electricity and heat, both consumed and exported. ### **ESRS E1: Climate Change (CSRD)** * **Relevance**: EU-mandated standard requiring disclosure of energy use and mix alongside greenhouse gas emissions. ESRS E1-5 explicitly demands total energy consumption and its breakdown by source (renewable vs non-renewable) * **How we align:** Our dataset captures total energy metrics, with clear distinction between renewable and non-renewable sources, mapped across all energy flows (produced, purchased, consumed, exported), allowing direct alignment with ESRS E1 requirements. ### **SASB / ISSB** (now under IFRS Foundation) * **Relevance**: SASB (now under IFRS) includes industry-specific “Energy Management” metrics (common for manufacturing, services, and infrastructure sectors). These typically require *total energy consumed*, *% grid electricity*, and *% renewable energy.* * **How we align:** Our dataset is structured with dimensions for **energy source, renewability, and flow type** (including **produced** and **purchased**). This enables alignment with SASB/ISSB requirements. ### SFDR Principal Adverse Impacts (PAIs) * **Relevance:** Requires investors to report *Share of non-renewable energy* (or conversely, share of renewables) generated by investee companies. * **How we align:** Directly supported by our **Total Renewable Energy Consumed** and **Total** Total **Non-Renewable Energy Consumed** metrics. ### CDP Climate Questionnaire * **Relevance:** CDP requires disclosure of total energy use and energy mix, linking it directly to GHG emissions and transition strategies. * **How we align:** Our dimension model (Type × Method) maps cleanly to TNFD disclosure elements and supports nature-risk analysis. {% hint style="success" %} ### Takeaway: * You can **map our metrics directly** to GRI 302, ESRS E1, SFDR PAIs, SASB industry metrics, and CDP disclosures. * You retain **full traceability** to the underlying source, enabling audit-grade use in compliance, benchmarking, and investment workflows. {% endhint %} *** # Data Collection Methodology Learn how Tracenable collects, standardizes, and validates corporate energ data through a five-step human-in-the-loop methodology, with links to detailed subpages on sources, standardization, and QAs. ## Introduction The value of energy data lies not just in its availability, but in its clarity, comparability, and traceability. At Tracenable, we designed a data collection methodology that combines rigorous research, comprehensive sourcing, and advanced human–AI workflows to produce corporate energy metrics that are both granular and broadly applicable. Our approach is built around four principles: define with authority, collect comprehensively, standardize precisely, and validate rigorously. *** ## Our Five-Step Energy Data Collection Approach {% stepper %} {% step %} ### Defining the Schema through Research We start by grounding our work in foundational references such as the UN International Recommendations for Energy Statistics (IRES) & Standard International Energy Classification (SIEC). From there, we study voluntary frameworks like GRI 302, ESRS E1, and SASB to understand disclosure expectations. This theoretical research is paired with empirical research: analyzing how companies actually report energy data in practice across industries and regions. By combining both, we design a data schema that strikes the right balance: as granular as possible, but general enough to apply across thousands of companies worldwide. {% endstep %} {% step %} ### Comprehensive Collection of Disclosures Corporate energy data can appear in many places: sustainability reports, regulatory filings, standalone data spreadsheets, or hidden on a webpage deep in a company’s site. Our infrastructure is designed to capture all of it. Through automated web monitoring and targeted expert retrieval, we ensure that no disclosure is overlooked. This comprehensive approach minimizes blind spots and provides the broadest possible coverage of corporate energy data globally. {% endstep %} {% step %} ### Converting Disclosures into Structured Data Energy data disclosures come in many formats: PDFs, Excel annexes, HTML tables, and narrative text. Our AI-driven pipelines first convert raw files into a unified structure (e.g., PDF to markdown). From there: * Computer vision parses tables and figures. * NLP models identify energy-related passages, detect units, and extract values. * Classification rules map energy data across four dimensions: source, energy type, flow type, and renewability The result: machine-readable, standardized data points that preserve traceability to the original disclosure. {% endstep %} {% step %} ### Data Human-in-the-Loop Validation AI brings speed and scalability, but human expertise ensures accuracy and context. Each extracted data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate energy data, with arbitration applied where discrepancies remain. This process allows us to: * Correct errors where AI misclassifies complex energy dimensions. * Preserve context from narrative disclosures. * Continuously improve our models through feedback. The outcome is audit-grade energy data that users can trust. {% endstep %} {% step %} ### Rigorous Quality Assurance Finally, our Energy Dataset undergoes multi-layered quality checks: * Automated tests catch obvious anomalies (negative values, implausible spikes, inconsistent units). * Machine learning models detect statistical outliers through unsupervised methods and unusual time-series patterns. * Manual audits ensure nothing slips through the cracks. This combination of automation and human oversight guarantees that every energy metric delivered is reliable, comparable, and ready for use in compliance, benchmarking, and research. {% endstep %} {% endstepper %} *** ## Learn More To explore the methodology in detail, visit: * [**Data Sources**](/energy/data-collection-methodology/data-sources) - Where energy data comes from and how it is collected. * [**Standardization Guidelines** ](/energy/data-collection-methodology/standardization-guidelines)- How disclosures are normalized into consistent energy sources, energy type, flow type, and renewability. * [**Calculation Logic**](/energy/data-collection-methodology/calculation-logic) - How missing values are inferred and totals are computed using transparent accounting rules. * [**Quality Assurance**](/energy/data-collection-methodology/quality-assurance) - The validations and controls that safeguard data integrity. # Data Sources See where Tracenable’s energy data comes from. Learn which corporate disclosures, registries, and web sources we capture, and how every metric is fully traceable back to its origin. ## Introduction The reliability of energy data starts with the quality of its sources. At Tracenable, we collect information from a broad range of corporate and official channels, ensuring that every data point is traceable back to its origin. Our goal is simple: provide users with complete, transparent, and verifiable evidence of how companies disclose their energy data. *** ## Where We Collect Data We capture energy disclosures wherever companies report them, across all common formats: * **Corporate reports** – Sustainability reports, annual reports, integrated reports, proxy statements. * **Regulatory filings** – Documents filed under mandatory disclosure regimes (e.g., CSRD, SEC, or national registries). * **Web disclosures** – Corporate webpages, environmental policy pages, or dedicated sustainability microsites. * **Data annexes and spreadsheets** – Often attached to sustainability reports or published as standalone datasets. * **Press releases and news articles** – Only when originating directly from the company. * **Government registries** – Authoritative third-party repositories of company-submitted energy data. No matter the format (PDF, HTML, Excel, or XML/XBRL) we normalize disclosures into a structured, machine-readable format without losing traceability to the original file. *** ## End-to-End Traceability Every data point in the Energy Dataset includes a direct link to its original source, allowing users to audit disclosures in context. Links open the exact report, page, or section cited. Metadata such as publication date and reporting period are also captured to preserve the full reporting trail. This approach ensures transparency: users can always see *what a company reported, when, and where*. *** ## Coverage Strategy Our coverage is global and demand-driven. We monitor thousands of companies across sectors and geographies, prioritizing based on client requests. If your use case requires extended coverage, we can adapt our sourcing to include additional companies, jurisdictions, or disclosure types. This flexibility ensures that Tracenable’s Energy Dataset reflects not only today’s mandatory reporting landscape, but also the evolving needs of users. # Standardization Guidelines Learn how Tracenable standardizes corporate energy data through consistent mapping and classification rules, ensuring energy reporting remains accurate, comparable, and aligned with global standards. ## The Challenge: Lack of Standardization Energy reporting varies widely across firms, sectors, and jurisdictions: * **Units of Measure:** Companies report in multiple physical units (kWh, liters, barrels, joules, tons of oil equivalent). * **Classification of Energy Flows**: Purchased, produced, consumed, or exported energy may be defined inconsistently. * **Renewability Definitions**: Some firms classify sources differently (e.g., biomass as renewable or not, depending on the jurisdiction). * **Aggregation Rules**: Some report only totals, others break down by source, technology, or flow type. These discrepancies lead to major comparability gaps in corporate energy reporting and prevents meaningful cross-company benchmarking. To address this, Tracenable applies a unified standardization logic that reconciles these inconsistencies, translating diverse corporate disclosures into a common energy accounting structure. *** ## Tracenable’s Standardization Principles ### **Guideline 1: Normalize Units into a Standard Energy Unit** * All values are converted into a single energy unit: **joules (J)**. Whether reported in energy units (kWh, BTU) or physical units (liters, cubic meters), values are standardized using source-specific conversion factors. {% hint style="success" %} This ensures comparability across disclosures, regardless of the original reporting unit. {% endhint %} ### **Guideline 2: Map Energy Disclosure to Four Standardized Dimensions** All company-reported energy disclosure is normalized into four standardized dimensions to ensure consistency, comparability, and traceability across companies and jurisdictions: * **Energy Source:** Identifies the specific origin of the energy (e.g., coal, crude oil, natural gas, biomass, solar, wind, hydropower, or Total). * **Energy Type:** Distinguishes the form in which energy is reported, such as total energy, electricity, or heat. * **Flow Type:** Describes the pathway of energy relative to company operations: produced, purchased, consumed, sold/exported, held in reserves, or reported as production capacity. * **Renewability** – Indicates whether the energy is renewable, non-renewable, or reported only as a total category, based on company disclosures or the inherent nature of the source. {% hint style="info" %} For example, If a company reports “Fossil Electricity purchased from the grid,” the data is mapped as: * Energy Source: Electricity (unspecified sources) * Energy Type: Electricity * Flow Type: Purchased * Renewability: Non-Renewable {% endhint %} ### **Guideline 3: Preserve Company-Reported Renewability Classifications** * When a company explicitly classifies an energy source as renewable or non-renewable, we retain that classification as reported. This approach respects jurisdictional definitions and sector-specific reporting practices, i.e., renewability status is determined by the frameworks applicable to the reporting company. * If renewability is not specified or cannot be confidently determined, Tracenable applies default mappings based on the intrinsic characteristics of the energy source (e.g., natural gas is classified as *Non-Renewable*). * If a company classification or source-based mapping is not available, Tracenable does not guess. Instead, the energy is placed in a total category “Total Renewability” to keep the data clear and reliable. {% hint style="success" %} Renewability classifications remain faithful to company disclosures whenever available. Default rules are applied only to fill gaps where no clear designation is provided. {% endhint %} ### **Guideline 4: Preserve Company-Reported Energy Sources Granularity** * When a company provides a detailed breakdown of energy sources (e.g., distinguishing between coal, natural gas, biomass, or solar), Tracenable preserves that granularity as reported. This ensures that company-level distinctions are not lost and that disclosures remain true to the original reporting. * If a company does not specify energy sources in detail, Tracenable applies standardized source categories. For example, if a company reports “fuels” without specifying further, the data is mapped to the appropriate aggregate category "Unknown Fuel Source . {% hint style="success" %} Energy source classifications remain as detailed as possible, while still ensuring comparability and consistency when only partial information is available. {% endhint %} ### **Guideline 5: Preserve Energy Flow Subcategories and Derive Flow Type Totals** Companies often disclose energy flows in parts rather than as unified totals. For instance, they may report separately the amounts of energy produced for internal consumption and those produced for sale. In such cases, Tracenable retains the reported subcategories while also deriving the corresponding aggregate. {% hint style="success" %} In the background, we internally split the flows into inflows and outflows to derive the standardized flow type, without altering the company-reported values. {% endhint %} **Example:** If a company reports 400 GJ produced for consumption and 100 GJ produced for sale, Tracenable internally treats the 400 GJ as a Produced inflow and a Consumed outflow, and the 100 GJ as a Produced inflow and an Exported outflow. This internal inflow/outflow split is used only to derive the standardized **Flow Type** dimension. Both reported values are retained, and the totals are aggregated as follows: * Total Energy Produced = 500 GJ * Total Energy Consumed = 400 GJ * Total Energy Sold/Exported = 100 GJ {% hint style="success" %} #### Takeaway: Corporate energy disclosures vary widely in format, scope, and interpretation. Without a common standard, meaningful comparison and aggregation are impossible. Tracenable’s energy standardization framework addresses this challenge by: * **Harmonizing units** into a single universal energy measure (joules). * **Aligning all disclosures** under a consistent structure of standardized dimensions: Source, Type, Flow, and Renewability. * **Respecting company-reported classifications** while filling unavoidable data gaps through transparent, rule-based logic. * **Preserving reported granularity** to maintain traceability and analytical richness. By applying these principles, Tracenable transforms fragmented, inconsistent disclosures into a unified dataset that is **comparable across companies, sectors, and jurisdictions**—providing a clear, credible foundation for sustainability and climate analysis. {% endhint %} *** # Calculation Logic Learn how Tracenable infers missing energy metrics using clear bottom-up and top-down accounting rules, ensuring data is complete, consistent, and transparent. ## Introduction Companies often disclose energy data in fragments. One company might report only *non-renewable energy consumed*, another might provide a *total energy consumption* figure without any breakdown, while a third might disclose *energy produced* but omit what portion was consumed or exported. On their own, these disclosures are incomplete and inconsistent, making them difficult to compare. To address this, Tracenable applies a transparent accounting system that reconstructs the relationships between reported metrics to form a consistent, comparable dataset. This system does not “guess” or “predict” missing numbers. Instead, it uses structured logic based on how energy data behaves across its analytical dimensions. Every calculated value is flagged, traceable, and auditable, so users always know what has been reported, derived, or inferred. This approach ensures that even when companies report partial information, Tracenable can produce a complete, verifiable picture of their energy profile, without making assumptions or altering the original data. *** ## The Foundation: Hierarchical Relationships Energy data is inherently hierarchical. Every aggregated figure can be broken down into smaller parts, or child metrics, and those parts can often be summed back up into parent metrics. For example * *Total Energy Consumed* can be calculated as the sum of *Renewable Energy Consumed* and *Non-Renewable Energy Consumed*. * *Total Energy Consumed* can also be expressed as the sum of *Electricity Consumed*, *Heat Consumed*, *Raw Resources Consumed*, and *Other Energy Types Consumed.* * Alternatively, it can be derived from the sum of all company-reported energy sources (e.g., *natural gas + diesel + biomass + electricity*). This hierarchical logic enables Tracenable to validate totals, fill gaps, and standardize disclosures across companies, even when the original data is incomplete. *** ## Core Rules of Calculation To maintain consistency and reliability, Tracenable’s accounting logic is built on two simple but powerful computational rules. ### Rule 1: Bottom-up Computation (Sum of Children) When the parent metric of a dimension is missing but its components are disclosed, the parent can be calculated as the sum of all available children. **Examples** * *Total Energy Renewability = Renewable Energy + Non-Renewable Energy* * *Total Energy Type = Electricity + Heat + Other Energy Types* * *Total Energy Level = Sum of all company-reported energy sources* {% hint style="success" %} This ensures that totals are constructed whenever sufficient detail exists, without discarding any company-reported information. {% endhint %} ### Rule 2: Top-Down Computation (Subtraction) Sometimes, the situation is reversed: the parent is disclosed, but one of the children is missing. In these cases, the missing value can be inferred by subtraction, but only if the parent and all other children of that dimension are known. **Examples** * Renewable Energy = *Total Energy Renewability* − *Non-Renewable* * Non-Renewable Energy = *Total Energy Renewability* − *Renewable* * Produced Energy = *Total Energy Inflow* − *Purchased Energy* * Purchased Energy = *Total Energy Inflow* − *Produced Energy* {% hint style="info" %} Top-down logic is only applied to dimensions with unambiguous additive relationships, namely: * Renewability (Renewable + Non-Renewable = Total) * Inflow (Produced + Purchased = Total Inflow) Other dimensions, such as Energy Source or Energy Type, are unsuitable for top-down inference because their aggregate is made up of three or more options (e.g., solar, wind, hydro for sources; electricity, heat, fuel for types), which prevents a unique and reliable subtraction. {% endhint %} This careful application ensures that every computed value is both logical and reliable, avoiding the risk of introducing misleading data. {% hint style="success" %} Together, these two rules (bottom-up addition and top-down subtraction) define the mathematical backbone of Tracenable’s Directed Acyclic Graph (DAG) (see next section). Every computation within the Energy dataset follows these same principles: the DAG uses *Rule 1* to aggregate metrics upward whenever possible, and applies *Rule 2* selectively when complete parent–child relationships allow for reliable subtraction. This ensures that the hierarchical graph described in the next section behaves consistently with the accounting logic introduced here. {% endhint %} *** ## The Graph Model: How Energy Data Is Structured Behind the scenes, Tracenable represents all energy metrics as part of a Directed Acyclic Graph (DAG), a hierarchical structure that applies the same bottom-up and top-down rules described in the previous section. The DAG defines how every metric connects to others through five hierarchical dimensions: 1. **Energy Type** – electricity, heat, raw resources (e.g. fuel) or total energy 2. **Inflow** – how energy enters the system (produced, purchased, or total inflow). 3. **Outflow** – how energy exits or is used (consumed, sold, reserved, or total outflow). 4. **Renewability** – renewable, non-renewable, or total. 5. **Source or Technology** – the specific origin or generation method (e.g., solar, hydropower, diesel, or unknown). Each metric in the Energy dataset corresponds to a unique combination of these five dimensions. And within any given metric, every dimension has a **Dimensional Resolution**, meaning it can be either: * **Specific** – when the dimension refers to a particular value (e.g., Renewability = *Renewable*, Inflow = *Produced*, Energy type = *Electricity*), or * **Aggregated** – when the dimension represents the total across all its specific values (e.g., Renewability = *Total* (Renewable + Non-Renewable), Inflow = Total (Produced + Purchased). ### Depth Hierarchy The combination of specific and aggregated dimensions defines the position (or depth) of each metric in the DAG: * A metric with all five dimensions ***specific*** sits at the deepest level (Depth 6): it is the most granular form of energy data. * A metric with all five dimensions *aggregated* sits at the root (Depth 1): representing *Total Energy*, the sum of all energy outflows from all inflows, across all types, renewability classes, and sources or technologies. This concept of Dimensional Resolution ensures that all energy data, whether reported in fragments or fully detailed, fits coherently within a single, logical structure, allowing Tracenable to calculate totals, identify gaps, and maintain perfect traceability from the most granular disclosures to the highest-level aggregates.
DepthDescriptionExample of Reported Metric
6None of the dimensions are aggregated / All dimensions are specificsRenewable Electricity Produced from Hydropower for Consumption
5One dimension are aggregated / Four dimensions are specificsRenewable Electricity Produced for Consumption
4Two dimensions are aggregated / Three dimensions are specificsElectricity Produced for Consumption
3Three dimensions are aggregated / Two dimensions are specificsTotal Renewable Heat
2Four dimensions are aggregated / One dimension is specificTotal Energy Consumed
1All dimensions are aggregated / None dimensions are specificsTotal Energy
### Computational Logic with the Hierarchical DAG Each step upward in the DAG aggregates all compatible child metrics one level below, collapsing any one dimension while keeping others equal and constant (ceteris paribus). In other words, for two metrics to be summed together, they must share four identical dimensions, and the fifth dimension must be the one being aggregated. This ensures that only comparable metrics are summed. For example, two energy values may be summed if they refer to the same energy type, inflow, outflow, and renewability, while differing only in source or technology. The resulting total then represents the aggregate for that dimension (e.g., *Total Sources*). While most relationships in the DAG are additive (bottom-up sums), certain parent–child pairs, specifically along the *Renewability* and *Inflow* dimensions, can also operate in reverse using the top-down subtraction rule outlined in Section 2. Together, this bidirectional logic creates a robust, traceable network of relationships that can reconstruct a company’s entire energy profile from the bottom up, or, where possible, verify it from the top down. #### **Illustrative Example:** Suppose a company reports only two values: * *Renewable Electricity Produced from Hydropower:* 200 GJ, and * *Non-Renewable Electricity Purchased:* 300 GJ, both of which are used for consumption during the year. #### **Reported values (Depth 6):** Both reported values above sit at depth 6, since all five dimensions are specific.
Energy (GJ)TypeInflowOutflowRenewabilitySource/Technology
200ElectricityProducedConsumedRenewableHydropower
300ElectricityPurchasedConsumedNon-RenewableUnknown
From these two detailed metrics, the DAG systematically aggregates upward across multiple depths — first across single dimensions (Depth 5), then across combinations (Depth 4–2), until reaching the top-level *Total Energy* node (Depth 1). At every level, the same logical conditions apply: only metrics with compatible dimensions can be aggregated, and when necessary, top-down subtraction ensures the integrity of renewability or inflow balances. #### **Depth 5 - Aggregations of Depth-6 Metrics** Metrics from depth 6 are aggregated upward to depth 5 by collapsing one additional dimension at a time while keeping all others equal and constant (ceteris paribus). From the two reported values above, we can compute the following parent metrics:
Energy (GJ)Energy TypeInflowOutflowRenewabilitySource / TechnologyAggregated Dimension
200ElectricityProducedConsumedRenewableTotal SourcesSource
300ElectricityPurchasedConsumedNon-RenewableTotal SourcesSource
200ElectricityProducedConsumedTotal RenewabilityHydropowerRenewability
300ElectricityPurchasedConsumedTotal RenewabilityUnknownRenewability
200ElectricityTotal InflowConsumedRenewableHydropowerInflow
300ElectricityTotal InflowConsumedNon-RenewableUnknownInflow
200ElectricityProducedTotal OutflowRenewableHydropowerOutflow
300ElectricityPurchasedTotal OutflowNon-RenewableUnknownOutflow
200Total EnergyProducedConsumedRenewableHydropowerEnergy Type
300Total EnergyPurchasedConsumedNon-RenewableUnknownEnergy Type
At this level, every metric combines four specific dimensions and one aggregated dimension. This step produces a richer set of intermediate totals, bridging between detailed source-specific disclosures and broader operational aggregates. Each row represents a new parent node at depth 5, one level closer to full aggregation. #### **Depth 4 – Aggregations of Depth-5 Metrics** Metrics from depth 5 are aggregated upward to depth 4 by collapsing one additional dimension at a time, while keeping the remaining four equal and constant (ceteris paribus). From the depth-5 metrics above, we can compute the following parent metrics (note: a few metrics only are showcased for illustration purposes):
Energy (GJ)Energy TypeInflowOutflowRenewabilitySource / Technology
200ElectricityProducedConsumedTotal RenewabilityTotal Sources
300ElectricityPurchasedConsumedTotal RenewabilityTotal Sources
200ElectricityTotal InflowConsumedRenewableTotal Sources
300ElectricityTotal InflowConsumedNon-RenewableTotal Sources
200Total EnergyProducedConsumedRenewableTotal Sources
300Total EnergyPurchasedConsumedNon-RenewableTotal Sources
200Total EnergyProducedConsumedTotal RenewabilityTotal Sources
300Total EnergyPurchasedConsumedTotal RenewabilityTotal Sources
At this stage, broader categories emerge; bridging the gap between detailed disclosures and operational summaries. Each metric at depth 4 therefore combines three specific dimensions and two aggregated ones, resulting in totals that describe higher-level patterns such as *Total Energy Produced and Consumed (Renewable, Total Sources)* or *Electricity Consumed (Total Renewability, Total Sources)*. **Depth 3 – Aggregations of Depth-4 Metrics** Metrics from depth 4 are then aggregated again, collapsing one more dimension at a time. By this point, most dimensions are aggregated, leaving only two still specific. From the depth-54metrics above, we can compute the following parent metrics (note: a few metrics only are showcased for illustration purposes): | Energy (GJ) | Energy Type | Inflow | Outflow | Renewability | Source / Technology | | ----------- | ------------ | ------------ | ------------- | ------------------ | ------------------- | | 200 | Electricity | Total Inflow | Total Outflow | Renewable | Total Sources | | 300 | Electricity | Total Inflow | Total Outflow | Non-Renewable | Total Sources | | 500 | Electricity | Total Inflow | Total Outflow | Total Renewability | Total Sources | | 200 | Total Energy | Produced | Total Outflow | Renewable | Total Sources | | 300 | Total Energy | Purchased | Total Outflow | Non-Renewable | Total Sources | | 500 | Total Energy | Total Inflow | Total Outflow | Total Renewability | Total Sources | Depth 3 introduces familiar operational totals such as Total *Electricity Consumed*, *Total Renewable Energy Produced*, or *Total Heat Purchased*. #### Depth 2 – Aggregations of Depth-3 Metrics At depth 2, four of the five dimensions are aggregated, leaving typically only one still specific. This level yields totals such as *Total Energy Consumed, Total Renewable Energy, Total Energy Produced;* metrics that represent a company’s complete energy flow for a specific form or direction of energy.
Energy (GJ)Energy TypeInflowOutflowRenewabilitySource / Technology
500ElectricityTotal InflowTotal OutflowTotal RenewabilityTotal Sources
500Total EnergyTotal InflowConsumedTotal RenewabilityTotal Sources
500Total EnergyProducedTotal OutflowTotal RenewabilityTotal Sources
#### Depth 1 – Aggregations of Depth-2 Metrics Finally, all remaining dimensions are aggregated into the single top-level metric: Total Energy. This node represents the sum of all energy outflows (consumed, sold, reserved) from all inflows (produced, purchased), across all energy types, renewability categories, and technologies.
Energy (GJ)Energy TypeInflowOutflowRenewabilitySource / Technology
500Total EnergyTotal InflowTotal OutflowTotal RenewabilityTotal Sources
The root node acts as the apex of the entire Directed Acyclic Graph. {% hint style="success" %} #### Summary: Why the Hierarchical DAG Matters By structuring all energy metrics within a hierarchical DAG, Tracenable guarantees that every number - no matter how aggregated - remains traceable, logical, and reproducible. This approach delivers four key benefits: 1. **Completeness** – All available data contributes to a coherent system; totals can be derived even when reports are fragmented. 2. **Consistency** – All metrics follow the same additive and hierarchical rules across dimensions. 3. **Traceability** – Each aggregated value can be decomposed back to the underlying company-reported metrics. 4. **Comparability** – Standardized structures allow apples-to-apples benchmarking across companies, sectors, and reporting years. \ Tracenable’s calculation logic therefore turns heterogeneous, partial energy disclosures into a unified, audit-ready dataset—one where every total has context, every relationship is mathematically sound, and every computation is transparent. {% endhint %} *** # Quality Assurance Discover how Tracenable validates energy data through automated checks, statistical tests, and human review to deliver audit-grade reliability. ## Introduction High-quality energy data depends on more than just good collection and standardization: it requires rigorous validation. At Tracenable, we combine automated testing, statistical analysis, and expert human review to ensure that every energy metric meets the highest standards of accuracy, consistency, and reliability. Our Quality Assurance (QA) process is multi-layered, designed to detect errors, catch anomalies, and confirm that each data point is both faithful to the original disclosure and fit for use in compliance, benchmarking, and research. *** ## Automated Validation Checks The first layer of QA relies on automated rules that run across all energy metrics. These checks are designed to quickly spot issues that should never occur in valid data, such as: * **Impossible values** – negative or implausibly large energy metric quantities. * **Unit inconsistencies** – figures reported in mismatched or conflicting units across years. * **Structural errors** – totals that do not match the sum of their components. These rules ensure that obvious errors are flagged immediately and never propagate into the dataset. *** ## Statistical and Machine Learning Tests Beyond simple rules, we apply more advanced techniques to identify subtle anomalies: * **Time-series consistency checks** – highlight sudden spikes or drops in reported energy values. * **Outlier detection** – identify company disclosures that deviate significantly from industry norms. * **Distribution analysis** – verify that energy metrics follow expected statistical patterns across sectors. These methods help us flag values that may be technically valid but require closer review. *** ## Human-in-the-Loop Review Not all issues can be resolved automatically. Our QA process therefore includes a human-in-the-loop review, where trained analysts validate flagged data points: * **Contextual review** – analysts check values against the original disclosure to confirm interpretation. * **Dual validation** – two independent reviewers may assess the same data point. * **Arbitration** – discrepancies between analysts are escalated to senior analysts for final decision. This ensures that ambiguous or complex energy disclosures are interpreted correctly, and that every value remains fully traceable to its source. *** ## Continuous Improvement Each QA outcome feeds back into our systems: * Automated rules are updated when new error patterns are identified. * Machine learning models are retrained to improve anomaly detection. * Documentation is refined to capture new edge cases and classification challenges. This iterative loop ensures that the Energy Dataset becomes more robust over time. *** # Data Dictionary
AttributeType/FormatDescriptionExample
isinAlphanumeric StringInternational Securities Identification Number (ISIN) of the primary publicly traded financial instrument associated with the company.US42704L1044
leiAlphanumeric StringLegal Entity Identifier (LEI)549300TP80QLITMSBP82
figiAlphanumeric StringFinancial Instrument Global Identifier (FIGI) of the primary publicly traded financial instrument associated with the company.BBG00WNPK2F5
tickerStringTicker symbol of the primary publicly traded financial instrument associated with the company.HRI
mic_codeStringMarket Identifier Code (MIC) of the primary publicly traded financial instrument associated with the company.XNGS
exchangeStringStock exchange of the primary publicly traded financial instrument associated with the company.NASDAQ
permidNumerical StringPermanent Identifier4295900057
company_nameStringLegal name of the company.HERC HOLDINGS INC
countryCategorical StringCountry where the company's headquarters are located.United States
sectorStringSector in which the company operates.Technology
industryStringIndustry classification of the company.Software - Application
year_of_disclosureYear (YYYY)Year in which the data point was disclosed.2023
reporting_periodYear (YYYY)Period for which the data point was measured, assessed, or is applicable.2023
metricStringThe specific measurement or data points requested.Sources of Renewable Electricity Produced
levelCategorical StringThe level of the data, whether Total or Energy Source/Technology break down.Sources
energy_typeCategorical StringEnergy type, whether Total Energy, Electricity or HeatTotal Energy
flow_typeCategorical StringThe type of energy flow, whether Produced, Purchased, Consumption, Exported, Reserves or Production CapacitiesProduced
renewabilityCategorical StringThe renewability of the energy, whether renewable, non-renewable or total.Non-renewable
energy_sourcesArray of StringsSources of energy.['Chilled Water', 'Hot Water']
valueNumerical FloatAmount of Energy.12000000
unitCategorical StringThe unit of measurement for the value, indicating the scale or dimension.Joules
incomplete_boundariesBoolean (True or Not Specified)Indicates whether the reported data covers only a limited portion of the company's operational or organizational boundaries.Not Specified
source_namesArray of StringsNames of the sources from which the reported data was obtained.['Corporate Responsibility Report']
company_idStringTracenable's internal company identifier.dfb6bd7b-facf-4d0f-86a2-f1eedf191946
document_idStringTracenable's internal source document identifier.4a32b902-ae42-4692-ae96-b42c99fdd2f9
traceability_source_urlString (URL)URL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes.https://platform.tracenable.com/source-trace?data-request-id-hash=L-dBVPN_qQmw&project=energy
# Introduction Get introduced to Tracenable’s Climate Targets dataset, including its scope, key characteristics, and unique value, and get redirected to detailed pages to deepen your knowledge. ## Overview The Climate Targets dataset captures how over 5,000 companies worldwide disclose and set greenhouse gas (GHG) reduction targets: both absolute (total emissions reduction) and intensity-based (emissions per unit of output). It provides a harmonized view of corporate climate ambition across industries and regions, enabling consistent measurement and comparison. The dataset standardizes disclosures on target scopes: Scope 1 (direct), Scope 2 (indirect energy), and Scope 3 (value chain) emissions, along with baseline and target years, reduction magnitudes, and intensity denominators. Through rigorous normalization of metrics, time horizons, and boundaries, fragmented corporate targets are transformed into comparable, time-bound, and data-backed commitments. This dataset focuses on **quantified GHG reduction targets**, enabling robust analysis of corporate ambition and measurable progress toward global climate objectives. It supports comparability across companies and facilitates long-term tracking of decarbonization performance. {% hint style="warning" %} Broader corporate climate claims & commitments, such as net-zero and carbon neutrality, are captured in a separate dataset: Climate Commitments. {% endhint %} *** ## Data Characteristics
:building: Company Coverage5000
:globe: Geographical CoverageGlobal
:shapes: Sectoral CoverageAll Sectors
:calendar-range: Data Historical RangeFrom 2019 to 2024
:reflect-vertical: Median Data History2.5 years
:magnifying-glass-waveform: Data Traceability Rate100%
:chart-simple: Data FrequencyAnnual
:repeat: Average Reporting Lag3 months
:bring-forward: Data FormatMost Recent
*** ## What makes Tracenable's Climate Targets Data Unique Tracenable's Climate Targets data product sets the market benchmark for precision, standardization, reliability, and integrity. Learn why our data stands apart:
:award: Beyond Market StandardTracenable's GHG emissions reduction target data offers greater detail and standardization than SBTi or CDP data - not only in terms of units and directionality coverage, but every data point includes information on baseline figures and achievements against targets whenever available, facilitating immediate application for calculating temperature alignment scores and other critical environmental assessments.
:puzzle: Maximized CompletenessOur advanced algorithms synthesize climate targets across multiple sources to create the most complete picture possible. When a company reports target details in different documents, such as baseline in annual reports and progress updates on their website, we integrate all data points to provide continuous, up-to-date tracking of commitments and achievements.
:bullseye-arrow: Precision in Every FigureOur cross-source validation ensures precise tracking of climate commitments and achievements. For instance, we capture exact baseline emissions of 1,542,450 tCO2e rather than rounded-sm figures, while maintaining clear links between targets, baselines, and achievement metrics.
:arrow-down-triangle-square: Exceptionally Granular & Fully StandardizedWe standardize intensity-based targets using over 200 'as reported' metrics mapped to 28 comparable categories. This unique approach enables meaningful comparison of targets across companies, sectors, and time periods, while preserving original reporting detail.
:magnifying-glass-chart: End-to-End Data TraceabilityEvery climate target and achievement metric is traceable to its original source with exact page references and documentation of calculation methodologies. This transparency enables confident verification of corporate climate commitments and progress claims.
:octagon-check: Full-Scope Boundary VerificationWe tag GHG emissions reduction targets that do not cover a company's entire organizational or operational boundaries with an 'Incomplete Boundaries' attribute. This attribute enhances transparency and ensures the comparability of our data by keeping you informed of any potential limitations.
*** ## Deep Dive into the Climate Targets Dataset On the following pages, you’ll find all the resources needed to fully understand and apply the Climate Targets dataset: * [**Definitions & Terminology**](/climate-targets/definitions-and-terminology) – Key terms and concepts used in the dataset * [**Data Dimensions & Metrics**](/climate-targets/data-dimensions-and-metrics) – Breakdown of Climate Targets metrics and how they are structured * [**References & Standards**](/climate-targets/references-and-standards) – Alignment with global reporting frameworks and regulations * [**Data Collection Methodology** ](/climate-targets/data-collection-methodology)– How the dataset is built and validated * [**Data Sources**](/climate-targets/data-collection-methodology/data-sources) – Origin and type of corporate disclosures collected * [**Standardization Guidelines**](/climate-targets/data-collection-methodology/standardization-guidelines) – Rules applied to ensure comparability * [**Calculation Logic**](/climate-targets/data-collection-methodology/calculation-logic) – Methods for deriving standardized metrics * [**Quality Assurance** ](/climate-targets/data-collection-methodology/quality-assurance)– Checks and processes ensuring data integrity * [**Data Dictionary** ](/climate-targets/data-dictionary)– Complete reference of fields, units, and definitions *** # Definitions & Terminology Understand core terms in climate target reporting, including GHG Protocol-defined emissions scopes, reduction targets, baselines, absolute vs. intensity targets, and GHG target components. ## **What are Climate Targets?** A climate target is a measurable goal that a company sets to reduce its **greenhouse gas (GHG) emissions** over a defined period. In simple terms, it answers the question: > *“By how much, and by when, does a company plan to reduce its emissions?”* Climate targets turn ambition into action. They translate corporate climate ambition into quantifiable outcomes: turning short and long-term visions into trackable progress. #### **Why Climate Targets Matter** Setting and disclosing climate targets makes corporate climate action measurable, comparable, and accountable. Companies set these targets to: * Drive internal planning and investments toward lower-emission operations. * Meet growing regulatory and investor expectations for climate transparency. * Track and demonstrate progress toward global goals such as the Paris Agreement. * Enable consistent comparison of climate ambition and performance across peers and industries. Without standardized targets, it becomes difficult to measure real progress or benchmark how effectively companies are reducing emissions. *** ## What are Greenhouse Gas (GHG) Emissions? Greenhouse Gas (GHG) emissions are gases released into the atmosphere that trap heat and contribute to climate change. These emissions are produced when companies carry out everyday activities, such as burning fuel for energy, running industrial processes, or transporting goods. When companies talk about their “GHG emissions,” they are referring to the amount of these gases generated by their operations or value chain. Tracenable follows the **Greenhouse Gas Protocol (GHG Protocol)**, the global standard for measuring and reporting these emissions. It requires companies to account for seven gases: * Carbon dioxide (CO₂) * Methane (CH₄) * Nitrous oxide (N₂O) * Hydrofluorocarbons (HFCs) * Perfluorocarbons (PFCs) * Sulfur hexafluoride (SF₆) * Nitrogen trifluoride (NF₃) {% hint style="info" %} #### What’s Not Included in GHG Emissions? Under the Greenhouse Gas Protocol, GHG reporting focuses only on gases with global warming potential (GWP), and excludes: * **Biogenic CO₂ emissions** – These are reported separately and not counted in Scope 1 totals. (Biogenic CH₄ and N₂O, however, remain part of Scope 1 process emissions.) * **Air pollutants** – Gases such as NOₓ, SOₓ, CO, particulate matter (PM), and volatile organic compounds (VOCs). These affect local air quality and health but are not greenhouse gases and are tracked under different reporting frameworks. {% endhint %} *** ## Scopes of Emissions When companies set GHG reduction targets, they must first define *which emissions those targets cover.* The GHG Protocol classifies emissions into **three scopes** based on where they occur in relation to company operations. Think of them as three “boundaries” that help identify what a company is responsible for. ### Scope 1 – Direct Emissions Emissions from **sources that a company owns or directly controls**, including: #### **Stationary Combustion** * Emissions from burning fuels in on-site equipment, such as boilers, furnaces, or generators. * *Example: A manufacturing plant burning natural gas in industrial furnaces.* #### **Process Emissions** * Emissions from chemical or physical processes that are not related to fuel combustion. * *Example: A cement company reporting CO₂ emissions from clinker production.* #### **Mobile Combustion** * Emissions from fuel burned in company-owned vehicles or mobile equipment, such as trucks, ships, or aircraft. * *Example: A logistics company reporting diesel use from its delivery truck fleet.* #### **Direct Releases** Gases released intentionally or unintentionally into the atmosphere. This includes: * **Fugitive emissions** (e.g., leaks from pipelines, tanks, or wells) * **Refrigerant emissions** (e.g., leakage from air conditioning and refrigeration units) * **Venting emissions** (e.g., direct release of natural gas during oil extraction) * **Flaring emissions** (e.g., burning of natural gas during oil production) ### Scope 2 – Indirect Energy Emissions Emissions from the generation of **purchased or acquired energy** consumed by the company, including electricity, heat, steam, and cooling. {% hint style="info" %} These occur at the energy provider’s facilities but are attributed to the company because of its energy use. {% endhint %} #### **Electricity** * Emissions from purchased electricity used to run buildings, facilities, and equipment. * *Example: An office sourcing power from a grid that relies on fossil fuels.* #### **Heat** * Emissions from purchased heat for industrial processes or building climate control. * *Example: A commercial building using district heating supplied by an external provider.* #### **Steam** * Emissions from purchased steam used in production or heating. * *Example: A paper mill purchasing steam for its manufacturing process.* #### **Cooling** * Emissions from purchased chilled water or cooled air supplied by third-party providers. * *Example: A data center relying on external cooling services.* {% hint style="warning" %} #### **The GHG Protocol requires reporting of Scope 2 emissions under two methods:** * **Location-based method**: Reflects the average emissions intensity of the grid where the energy is consumed. It uses published grid emission factors and does not account for specific energy purchasing decisions. \ *Purpose:* Shows the environmental impact based on the regional energy mix (e.g., fossil-heavy vs. renewable-heavy grids). * **Market-based method**: Reflects emissions based on specific contractual arrangements or instruments — such as renewable energy certificates (RECs), power purchase agreements (PPAs), or supplier-specific emissions factors. \ *Purpose:* Allows companies to demonstrate their choice to purchase lower-emission electricity or participate in green energy programs. {% endhint %} {% hint style="success" %} Tracenable captures and distinguishes both values (when disclosed), ensuring consistency with GHG Protocol requirements. {% endhint %} ### Scope 3 – Other Indirect (Value Chain) Emissions All other indirect emissions across the value chain, often the largest part of a company’s footprint. It includes: #### **Upstream Scope 3 Emissions** {% hint style="info" %} Emissions generated **before a company’s operations**, linked to the production and delivery of inputs it relies on. {% endhint %} * **Purchased goods and services** - emissions from producing raw materials or services a company buys. * **Capital goods** - emissions from manufacturing long-term assets like buildings, vehicles, or machinery. * **Fuel and energy-related activities** - emissions from fuel supply chains, not already counted in Scope 1 or 2. * **Transportation and distribution (upstream)** - emissions from moving inputs to the company. * **Waste generated in operations** - emissions from treatment and disposal of waste from company facilities. * **Business travel** - emissions from employee flights, trains, and other work-related travel. * **Employee commuting** - emissions from daily transport between employees’ homes and workplaces. * **Leased assets (upstream)** - emissions from assets used but not owned by the company. #### **Downstream Scope 3 Emissions** {% hint style="info" %} Emissions generated **after a company’s operations**, linked to how its products and services are distributed, used, and disposed of. {% endhint %} * **Transportation and distribution (downstream)** - emissions from moving products to customers. * **Processing of sold products** - emissions from customers transforming sold products into other goods. * **Use of sold products** - emissions from consumers using the company’s products (e.g., fuel use in cars). * **End-of-life treatment of sold products** - emissions from disposal, recycling, or waste treatment after use. * **Leased assets (downstream)** - emissions from assets owned by the company but leased to others. * **Franchises** - emissions from operations of franchisees not directly controlled by the company. * **Investments** - emissions associated with investments in other businesses. *** ## **What are GHG Reduction Targets?** Once a company understands the sources and boundaries of its emissions across Scopes 1, 2, and 3, the next step is to define *how much it plans to reduce them.* This is where GHG reduction targets come in. A GHG reduction target is a measurable goal that defines *how much* a company intends to lower its greenhouse gas emissions from a given baseline and within what timeframe. **For example**: *“Reduce total Scope 1+2 emissions by 40% by 2030 from a 2020 baseline.”* Targets translate climate ambition into specific, trackable outcomes. They differ from broader climate commitments in one key way: * **Targets** are *quantified and time-bound*. * **Commitments** (such as “net zero” or “climate neutrality”) are broader intentions that may or may not include measurable goals. ### **Types of GHG Reduction Targets** Climate targets generally fall into **two main categories**, based on how emissions reductions are measured: #### **1. Absolute Targets** Absolute targets measure total emissions reductions in physical terms: for example, total metric tons of CO₂e reduced over time. They focus on the *overall decline* in emissions, regardless of changes in the company's size or output. **Example:** *“Reduce total Scope 1 and 2 emissions by 50% by 2030 from a 2020 baseline.*” This approach is ideal for organisations committed to making significant, system-wide emissions reductions. #### **2. Intensity-Based Targets** Intensity targets measure emissions relative to a unit of activity, such as output, revenue, or energy consumed. They show how efficiently a company operates, even if total emissions rise with growth. **Example:** *“Reduce Scope 3 emissions intensity by 30% per unit of production by 2030 compared to 2019.”* This approach is particularly effective for rapidly growing companies, as it allows them to balance business expansion with emissions reduction efforts. *** ## **Components of a GHG Reduction Target** Every GHG reduction target has three key components that together define its intent, scope, and measurability: 1. **The Baseline Component** 2. **The Target Component** 3. **The Achievement (or Progress) Component** Understanding these elements helps interpret how companies plan, measure, and track their decarbonization goals. ### 1. Baseline Component The **baseline** defines the starting point from which reductions are measured. It establishes the company’s emissions level before reduction actions begin and serves as the reference for calculating progress. **Attributes include:** * **Baseline Year:** The specific year against which reductions are measured (e.g., 2019, 2020). * **Baseline Value:** The total GHG emissions (absolute or intensity) in that year, expressed in metric tonnes of CO₂ equivalent (tCO₂e) or an intensity ratio. * **Baseline Unit:** The measurement format used, either absolute (tCO₂e) or intensity-based (tCO₂e per unit of activity such as revenue, production, or energy consumed). * **Scope Coverage:** Which emissions scopes (1, 2, and/or 3) and sources the baseline represents. * **Intensity Metric**: Specific denominator that a company reports to measure the intensity of its GHG emissions. It reflects the exact operational or financial measure, such as the amount of energy consumed or revenue generated, and is used to normalize emissions data. **Example:**\ A manufacturing company reports a 2020 baseline of 100,000 tCO₂e covering Scope 1 and Scope 2 emissions. The baseline anchors the target, ensuring reductions are measurable and comparable over time. ### 2. Target Component The **target** defines the company’s intended level of reduction, the timeframe for achieving it, and how success will be measured. It translates ambition into a quantifiable, time-bound goal. **Attributes include:** * **Target Type:** Whether the target is absolute (e.g., total tonnes reduced) or intensity-based (e.g., per unit of revenue or production). * **Target Value:** The magnitude of reduction aimed for, expressed as a percentage decrease or an absolute emission value. * **Target Unit:** The unit in which the goal is expressed, such as percentage (%) or metric tonnes CO₂e. * **Target Unit Direction**: Direction of the target compared to the baseline (e.g., absolute decrease from baseline, percentage increase from baseline). * **Target Year:** The year by which the company intends to achieve its goal (e.g., 2030). **Example:**\ “Reduce total Scope 1 and 2 emissions by 50% by 2030 from a 2020 baseline.” The target component is what transforms a baseline into a measurable plan for emissions reduction. ### 3. Achievement (or Progress) Component The **achievement** (also referred to as progress over time) component tracks the extent to which the target has been realised at any point between the baseline and the target year. It shows whether the company is on track, behind, or ahead of its stated goal. **Attributes include:** * **Achievement Year:** The year for which progress is being reported (e.g., 2023). * **Achievement Value:** Quantified progress made towards the GHG target as of the achievement year (e.g., 20% or 20,000 tCO2e). * **Achievement Unit**: The measurement unit used to express the achievement value, consistent with the units used for the baseline and target (e.g., tCO₂e or %). * **Achievement Direction:** Indicates whether the progress represents a decrease or an increase relative to the baseline. **Example:**\ From the earlier example, if the company reports 70,000 tCO₂e in 2023 against its 2020 baseline of 100,000 tCO₂e, it has achieved a **30% reduction** toward its 50% target. Tracking achievements over time helps assess performance, credibility, and the pace of decarbonization. {% hint style="success" %} #### **Putting It All Together** A complete GHG reduction target connects these three components in a single logical framework with each element providing context for the others: the baseline defines where the company began, the target defines where it wants to go, and the achievements show how far it has come. Together, they make GHG reduction targets measurable, comparable, and actionable across industries. {% endhint %} *** # Data Dimensions & Metrics Discover how Tracenable structures Climate Targets data with one core GHG reduction metric, standardized under the GHG Protocol and SBTi. ## How Dimensions and Metrics Work Tracenable datasets are usually built on a **dimensional model**, where *dimensions* describe how data can be analyzed (for example, by scope or energy type) and *metrics* represent the quantitative values being measured, each formed from the combination of multiple dimensions. However, the **Climate Targets** dataset is structured differently. Unlike datasets that track continuous activities over time, this dataset captures **discrete corporate goals**. Each record already contains all the context required to describe a company’s greenhouse gas (GHG) emissions reduction target. *** ## **Why This Dataset Has No Dimensions** Because every target includes its own **scope coverage, baseline, target year, and reduction magnitude**, the dataset does not rely on separate analytical dimensions.\ All relevant attributes are embedded directly within each record, making the structure simple, self-contained, and easy to interpret. *** ## **Metric in the Climate Targets Dataset: GHG Emissions Reduction Target** The sole metric in Tracenable's Climate Target dataset is the ***Greenhouse Gas (GHG) Emissions Reduction Target***. It represents the targeted reduction of a company's GHG emissions (scope 1, 2, 3 and their GHG Protocol-defined sources/categories), either as an absolute figure or adjusted according to an intensity metric, from a designated baseline year to the target year.
Each target includes key details such as: * **Scope coverage** (Scope 1, 2, 3 and their GHG Protocol-defined sources) * **Target type** (Absolute or Intensity-based) * **Reduction magnitude** (Percentage or absolute value) * **Baseline and base year** (The reference point from which reductions are measured) * **Target year** (The future year by which the reduction is to be achieved) * **Achievement and achievement year** (Track actual progress toward the target and the year in which that progress is reported) {% hint style="success" %} #### **Takeaway:** The **Climate Targets** dataset focuses on one clear metric, *GHG Emissions Reduction Target,* enriched with contextual attributes. Grounded in the principles of the **Greenhouse Gas (GHG) Protocol** and the **Science-Based Targets Initiative (SBTi)**, this streamlined structure ensures consistent and comparable analysis of corporate decarbonization goals across companies, sectors, and geographies. {% endhint %} *** # References & Standards Learn how Tracenable’s Climate Targets dataset aligns with the GHG Protocol, Paris Agreement, SBTi, EU CSRD, GRI, CDP, TCFD, SASB, and PCAF for global comparability. ## Foundational References These are the authoritative sources we rely on to define terms, set classification rules, and resolve edge cases. ### [**Greenhouse Gas Protocol (WRI/WBCSD Corporate Standard)**](https://ghgprotocol.org/sites/default/files/standards/ghg-protocol-revised.pdf) * **Why it matters**: The GHG Protocol is the globally recognized framework for measuring and reporting GHG emissions. It defines Scope 1, Scope 2, and Scope 3 categories, sets organizational boundary rules, and provides calculation guidance used by regulators, companies, and investors worldwide. * **What we adopt:** * Core scope definitions (Scope 1 direct, Scope 2 energy-indirect, Scope 3 value chain). * Coverage of all seven Kyoto gases expressed in CO₂-equivalent (tCO₂e). * Category mapping for Scope 1, 2, and 3 to ensure alignment with official definitions and company disclosure practices. * Guidance on organizational boundaries and Scope 2 accounting (location-based and market-based). *** ## Related Reporting Frameworks & Standards While the GHG Protocol defines how emissions are measured, several global frameworks expand upon it to guide how companies set, disclose, and evaluate their GHG reduction targets.\ Tracenable bridges these standards by mapping corporate climate targets to the GHG Protocol’s foundational concepts, ensuring each target is comparable, time-bound, and aligned with recognized global frameworks for corporate decarbonization. ### Regulatory Frameworks #### [**UN Paris Agreement (2015)**](https://unfccc.int/sites/default/files/english_paris_agreement.pdf) * **Why it matters:** The Paris Agreement is the cornerstone of global climate ambition. It commits countries (and by extension, corporations) to limit global warming to well below 2°C and pursue 1.5°C. Corporate GHG reduction targets often reference this alignment, framing their goals in terms of “Paris-aligned” or “1.5°C-aligned” pathways. * **What we adopt:** * Context for capturing both absolute and intensity-based reduction targets in line with global decarbonization pathways. * Tracking of baseline and target years to measure company alignment with the Paris Agreement’s temperature goals. #### [**EU Corporate Sustainability Reporting Directive (CSRD) – ESRS E1 Climate Change**](https://www.efrag.org/sites/default/files/media/document/2024-08/ESRS%20E1%20Delegated-act-2023-5303-annex-1_en.pdf) * **Why it matters:** The CSRD introduces mandatory climate disclosures across Europe, including GHG reduction targets, progress, and transition plans, under the ESRS E1 standard. * **How we align:** * Alignment with ESRS E1 requirements for target-setting, including baseline year, target year, and reduction magnitude. * Standardization of scope coverage (1, 2, and 3) and inclusion of both absolute and intensity-based targets. ### Voluntary Global Frameworks #### [Science-Based Targets initiative (SBTi)](https://sciencebasedtargets.org/) * **Why it matters:** The SBTi provides the leading global methodology for setting corporate GHG reduction targets in line with climate science. It defines what constitutes a “science-based target” and sets sector-specific pathways for 1.5°C alignment. * **What we adopt:** * Standardized capture of baseline, target years, and reduction magnitudes to assess consistency with SBTi criteria. * Recognition of both near-term and long-term science-based targets. #### [**GRI 305: Emissions (2016)**](https://www.globalreporting.org/publications/documents/english/gri-305-emissions-2016/) * **Why it matters:** GRI 305 remains one of the most widely adopted frameworks for corporate GHG disclosure and includes clear expectations for reporting emissions reduction targets and progress. * **How we align:** * Direct mapping of disclosed targets and performance data to GRI 305-5 (“Reduction of GHG emissions”). * Consistent expression of reduction magnitudes and intensities in line with GRI reporting definitions. #### [**TCFD (Task Force on Climate-related Financial Disclosures)**](https://assets.bbhub.io/company/sites/60/2021/10/FINAL-2017-TCFD-Report.pdf) * **Why it matters:** TCFD requires companies to disclose climate targets and performance against them as part of broader climate risk and governance reporting. * **How we align:** * Capture of both short-term and long-term emissions reduction targets within a standardized framework. * Enables TCFD-aligned reporting through traceable metrics that show progress toward stated goals. #### [**CDP (Carbon Disclosure Project)**](https://assets.ctfassets.net/v7uy4j80khf8/7AC4SpiM7JnJs3x7NMqOhp/8c7e9197c678d486a82b011193a0d4d3/Greenhouse_Gas_Emissions_Tools_and_Datasets_for_Cities_Full_Report__May_2024_.pdf) * **Why it matters:** CDP collects self-reported data from companies on emissions, reduction targets, and progress as part of its annual questionnaire. * **How we align:** * Integration of company-disclosed targets, baseline data, and achievement updates aligned with CDP fields. * Consistent normalization of absolute and intensity targets for comparability across respondents. ### Sector- and Finance-Specific Frameworks #### [**SASB Standards (now IFRS Foundation)**](https://sasb.ifrs.org/standards/) * **Why it matters:** SASB standards require industry-specific climate targets and metrics that enable investors to assess performance in a comparable way. * **How we align:** * Mapping of reduction targets and progress metrics to sector-specific SASB requirements. * Harmonization with IFRS Sustainability Disclosure Standards (IFRS S1 and S2). #### [**PCAF (Partnership for Carbon Accounting Financials)**](https://carbonaccountingfinancials.com/files/downloads/PCAF-Global-GHG-Standard.pdf) * **Why it matters:** PCAF complements the GHG Protocol by providing a framework for measuring financed emissions and related targets in the financial sector. * **How we align:** * Capture of GHG reduction targets related to investments and portfolio emissions (Scope 3, Category 15). * Alignment with PCAF guidance for financial institutions tracking financed emissions reduction goals. {% hint style="success" %} #### Takeaway: * Tracenable’s **Climate Targets** dataset is built on the **GHG Protocol** and aligned with key regulatory frameworks, including the **Paris Agreement** and **EU CSRD (ESRS E1)**. * It is fully compatible with leading voluntary standards such as the **SBTi**, **GRI 305**, **TCFD**, and **CDP**. * It extends to sector- and finance-specific frameworks, including **SASB/IFRS** and **PCAF**, ensuring relevance across industries and asset classes. **The result:** globally consistent coverage of corporate GHG reduction targets, with standardized baselines, years, and reduction magnitudes, providing full traceability to company disclosures and audit-ready comparability for compliance, benchmarking, and investment analysis. {% endhint %} *** # Data Collection Methodology Learn how Tracenable collects, standardizes, and validates corporate Climate Targets data through a five-step human-in-the-loop methodology. ## Introduction Accurate, comparable, and traceable Climate Targets data requires more than simply aggregating figures. It requires structured methodology, reliable sourcing, and careful standardization. Tracenable’s approach combines automation, human expertise, and adherence to global reporting frameworks to deliver decision-ready emissions data you can trust. *** ## Our Five-Step Climate Targets Data Collection & Standardization Approach {% stepper %} {% step %} ### Defining the Schema through Research We begin with a rigorous review of foundational frameworks, most importantly the Greenhouse Gas (GHG) Protocol, which provides the global foundation for defining emissions scopes (Scope 1, Scope 2, and Scope 3) and establishing consistent reporting boundaries: the same boundaries used in setting corporate climate targets. Building on this base, we incorporate guidance from leading regulatory frameworks such as the EU CSRD (ESRS E1) along with voluntary standards including the Science-Based Targets initiative (SBTi), GRI 305, CDP, SASB, and TCFD. These references ensure that the schema reflects both the methodological rigor of global standards and the practical formats companies use when setting and disclosing GHG reduction targets. Finally, we complement this theoretical foundation with empirical research, analyzing how companies actually set and report their climate targets: covering baseline and target years, scope coverage, reduction magnitude, and intensity metrics. This combined approach ensures that our schema captures the real-world diversity of corporate target disclosures while maintaining consistency, comparability, and scientific alignment across all sectors and geographies. {% endstep %} {% step %} ### Capturing Climate Targets Disclosures at Scale Corporate climate targets data can appear in many places: annual reports, sustainability reports, regulatory filings, standalone data spreadsheets, or hidden on a webpage deep in a company’s site. Our infrastructure is designed to capture all of it. Through automated web monitoring and targeted expert retrieval, we ensure that no disclosure is overlooked. This comprehensive approach minimizes blind spots and provides the broadest possible coverage of corporate climate targets data globally. {% endstep %} {% step %} ### Extracting and Converting Disclosures into Structured Data Climate targets disclosures come in many formats: PDFs, Excel annexes, HTML tables, and narrative text. Our AI-driven pipelines first convert raw files into a unified structure (e.g., PDF to markdown). From there: * Computer vision to extract and parse tables, figures, and graphical emissions data. * Natural language processing (NLP) to detect emissions reduction targets-related text, identify Scope and category, and extract quantitative values and units. * Classification rules to map disclosures into Scope 1, Scope 2, or Scope 3, and to identify whether targets are absolute or intensity-based measures. The result: machine-readable, standardized data points that preserve traceability to the original disclosure. {% endstep %} {% step %} ### Data Human-in-the-Loop Validation AI brings speed and scalability, but human expertise ensures accuracy and context. Each extracted GHG reduction target data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate climate targets data, with arbitration applied where discrepancies remain. This process allows us to: * Correct errors where AI may misclassify scope categories or emission types. * Preserve context from narrative disclosures, such as Scope 2 accounting methods or Scope 3 category definitions. * Continuously improve our extraction models through analyst feedback. The outcome is audit-grade GHG emissions reduction targets data that users can trust. {% endstep %} {% step %} ### Rigorous Quality Assurance Finally, our climate targets dataset undergoes multi-layered quality checks: * Automated tests catch obvious anomalies (negative values, implausible spikes, inconsistent units). * Machine learning models detect statistical outliers through unsupervised methods and unusual time-series patterns. * Manual audits ensure nothing slips through the cracks. This combination of automation and human oversight guarantees that every Greenhouse Gas (GHG) Emissions reduction target delivered is reliable, comparable, and ready for use in compliance, benchmarking, and research. {% endstep %} {% endstepper %} *** ## Learn More To explore the methodology in detail, visit: * [**Data Sources**](/climate-targets/data-collection-methodology/data-sources) - Where Climate Targets data comes from and how it is collected. * [**Standardization Guidelines** ](/climate-targets/data-collection-methodology/standardization-guidelines)- How disclosures are normalized into consistent Climate Targets dataset. * [**Calculation Logic**](/climate-targets/data-collection-methodology/calculation-logic) - How missing values are inferred and totals are computed using transparent accounting rules. * [**Quality Assurance**](/climate-targets/data-collection-methodology/quality-assurance) - The validations and controls that safeguard data integrity. *** # Data Sources See where Tracenable’s Climate Targets data comes from. Learn which corporate disclosures, registries, and web sources we capture, and how every data point is fully traceable back to its origin. ## Introduction The reliability of Climate Targets data starts with the quality of its sources. At Tracenable, we collect information from a broad range of corporate and official channels, ensuring that every data point is traceable back to its origin. Our goal is simple: provide users with complete, transparent, and verifiable evidence of how companies disclose their waste performance. *** ## Where We Collect Data We capture climate target disclosures wherever companies report them, across all common formats: * **Corporate reports** – Sustainability reports, annual reports, integrated reports, proxy statements. * **Regulatory filings** – Documents filed under mandatory disclosure regimes (e.g., CSRD, SEC, or national registries). * **Web disclosures** – Corporate webpages, environmental policy pages, or dedicated sustainability microsites. * **Data annexes and spreadsheets** – Often attached to sustainability reports or published as standalone datasets. * **Press releases and news articles** – Only when originating directly from the company. * **Government registries** – Authoritative third-party repositories of company-submitted climate targets data. No matter the format (PDF, HTML, Excel, or XML/XBRL) we normalize disclosures into a structured, machine-readable format without losing traceability to the original file. *** ## End-to-End Traceability Every data point in the Climate Targets dataset includes a direct link to its original source, allowing users to audit disclosures in context. Links open the exact report, page, or section cited. Metadata such as publication date and reporting period are also captured to preserve the full reporting trail. This approach ensures transparency: users can always see *what a company reported, when, and where*. *** ## Coverage Strategy Our coverage is global and demand-driven. We monitor thousands of companies across sectors and geographies, prioritizing based on client requests. If your use case requires extended coverage, we can adapt our sourcing to include additional companies, jurisdictions, or disclosure types. This flexibility ensures that Tracenable’s Climate Targets dataset reflects not only today’s mandatory reporting landscape, but also the evolving needs of users. *** # Standardization Guidelines Learn how Tracenable standardizes corporate GHG reduction targets for consistency, comparability, and alignment with GHG Protocol and SBTi. ## **Why Standardization Matters** Corporate GHG reduction targets are reported in many different formats and levels of detail, making comparison and analysis extremely difficult. Some companies refer vaguely to “operational” or “supply chain” emissions without specifying which GHG Protocol scopes these include. Others disclose Scope 2 targets but omit whether they use market-based or location-based accounting. Intensity targets add further variation, with different terms used for the same concept (for example, “per revenue,” “per sales,” or “per turnover"). Beyond terminology, companies also express reduction goals using inconsistent units and structures: * Percentage reductions (e.g., “reduce emissions by 40% by 2030”) * Absolute reductions (e.g., “cut 10,000 tCO₂e by 2030”) * Milestone values (e.g., “achieve 1,000 tCO₂e by 2030”) Without a consistent framework, it becomes nearly impossible to: * Determine which emissions and scopes each target actually covers. * Compare ambition across companies, sectors, or regions. * Aggregate or benchmark targets at portfolio or global levels. Tracenable addresses these challenges through a transparent, rules-based **standardization system** that harmonizes every aspect of corporate GHG targets: from terminology and accounting methods to units and reduction magnitudes. *** ## **Tracenable's Standardization Rules** ### **Rule 1: Map Reported Targets to GHG Protocol-Defined Scopes and Sources** Companies often use inconsistent terminology when describing what their climate targets cover. Examples include: * A target may use vague terms like “direct” (Scope 1) , “indirect" (Scope 2 or 3), or "operational footprint" (Scope 1+2, usually). * Others list raw emission sources like “vehicle fleet” or “air travel” without specifying the scope. Tracenable resolves this by mapping all reported targets to the **GHG Protocol’s standardized framework of scopes and source categories**: * **Scopes**: All targets are classified into Scope 1 (direct), Scope 2 (indirect energy), or Scope 3 (value chain). * **Sources**: Within each scope, company terms are mapped to the correct standard categories. For example: * “Natural gas boilers” → Scope 1 - Stationary Combustion. * “Vehicle fleet” → Scope 1 - Mobile Combustion. * “Purchased power” → Scope 2 - Electricity. * “Business travel by air and rail” → Scope 3 - Business Travel (Category 6) This mapping ensures consistency across disclosures and enables reliable apples-to-apples comparisons. {% hint style="success" %} When reported targets cannot be confidently mapped to a standard category due to limited detail or ambiguous terminology, Tracenable assigns them to a dedicated “**Unmapped**” category within each Scope. This approach ensures no information is discarded, keeps the dataset complete, and highlights where reporting gaps or data quality issues may affect comparability. {% endhint %} {% hint style="warning" %} #### Looking for detailed mapping guides? Tracenable maintains internal mapping references for Scope 1, 2, and 3 categories. These guides reconcile “as reported” terminology with standardized definitions, based on the GHG Protocol and related frameworks. While not published publicly, they can be shared privately upon request. {% endhint %} ### **Rule 2: Standardize Scope 2 Accounting Methods (Location- vs. Market-Based)** Many companies disclose Scope 2 (and some categories of Scope 3) reduction targets without specifying which accounting method is used. For example: * A disclosure might reference “grid average factors” → *mapped to location-based.* * Another may provide values “before RECs” and “after RECs” → *mapped to location-based vs. market-based.* * Or, no method may be specified at all. Because the same reduction can appear very different depending on the method, clarity here is critical. This ambiguity can also create apparent “duplicates,” where two Scope 2 targets from the same company, with identical baseline and target years, seem inconsistent or overlapping when, in fact, they are calculated under different methods. Tracenable standardizes all such cases into the three recognized categories (**location-based**, **market-based**, or **not specified**) and preserves both values when available. This ensures clarity, prevents double-counting, and enables transparent comparison of Scope 2 targets across companies and reporting years. ### Rule 3: Normalize Units to Metric Tonnes of CO₂e Companies often report GHG emissions in different physical units: short tons, kilograms, or grams. To ensure consistency, all values are converted to **metric tonnes of CO₂ equivalent (tCO₂e)** using standard conversion factors. **Example:** * Consider a company that reports a 2019 baseline of **100,000 short tons of CO₂e**. Applying the standard conversion factor (1 short ton = 0.907185 metric tonnes), the normalized baseline becomes **90,718.5 tCO₂e**. This ensures that all company targets are expressed in a uniform unit of measurement. ### Rule 4: Normalize Target Values and Directions The core normalization process focuses on expressing all targets as **percentage reductions from baselines**, wherever possible. **Example 1 – Absolute Reduction Target:** * Consider a company that aims to reduce its Scope 1 emissions from **50,000 tCO₂e in 2021** to **25,000 tCO₂e by 2030**. This translates to a **50% reduction from baseline**. {% hint style="warning" %} **However, not all targets provide enough information to calculate a percentage reduction.**\ Some companies define goals as absolute milestones rather than relative reductions, and without information about the baseline, our standardization pipelines cannot normalize these absolute milestones into percentage reductions from baseline. {% endhint %} **Example 2 – Absolute Milestone Target:** * Another company might report, “achieve 1,000 tCO₂e Scope 1 emissions by 2030,” without specifying a baseline. Because the starting value is unknown, a percentage reduction cannot be computed, and the original value is retained as an absolute milestone target. This approach ensures that targets are expressed in a standardized form when possible, and otherwise preserved faithfully as reported. ### Rule 5: Normalize Achievement Values and Directions The same principle applies to progress or achievement values, which represent interim performance toward a company’s target. Wherever possible, achievement data is expressed as a **percentage reduction from the baseline**, ensuring that progress and targets can be directly compared. **Example:** * Consider a company targeting a **50% reduction in Scope 2 emissions by 2030** from a **2020 baseline of 100,000 tCO₂e**.\ If in 2023, the company reports **70,000 tCO₂e**, this represents a **30% reduction from baseline**, showing measurable progress toward the goal. However, when necessary information is missing, such as the baseline value or the achievement-year emissions, normalization cannot be applied.\ \ In such cases, the reported figure is retained in its original absolute format, ensuring completeness and transparency without introducing assumptions. {% hint style="warning" %} #### Note: Standardization depends on complete and consistent data. When required information, such as a baseline, is missing, the dataset **preserves the original company-reported figures** rather than estimating or excluding them. These values remain fully traceable within the data structure, ensuring completeness and transparency even when normalization cannot be applied. {% endhint %} {% hint style="success" %} #### **Takeaway:** Tracenable’s Climate Targets standardization rules ensure that: * Targets are mapped to **GHG Protocol–defined scopes and sources**. * Scope 2 methods are standardized (**market-based**, **location-based**, or **unspecified**). * Values are normalized into comparable units (e.g., **tCO₂e**, **% reduction**). * Different target formats (**absolute**, **relative**, or **milestone**) are made analytically equivalent. The result is a consistent, transparent, and globally comparable dataset that preserves the integrity of each company’s disclosure while making climate targets truly measurable across firms and sectors. Tracenable’s approach aligns with the **Greenhouse Gas (GHG) Protocol** and leading initiatives such as the **Science-Based Targets Initiative (SBTi)**, ensuring methodological rigour and alignment with global climate reporting standards. {% endhint %} *** # Calculation Logic Discover how Tracenable reconciles corporate climate targets across disclosures to deliver consistent, complete, and up-to-date GHG reduction data. ## **Purpose and Context** Corporate climate targets are not static. They evolve as companies refine their strategies, improve data accuracy, or adjust ambitions. A single company may disclose different or partial target details across multiple years, filings, or documents. For instance: * In one year, a company’s sustainability report may state a “70% reduction in emissions by 2030.” * The next year, the same company might phrase it as “reduce emissions to one-third of 2020 levels by 2030.” * In some cases, earlier disclosures provide full details (scopes, baselines, and metrics), while later ones only restate part of the target or omit it entirely. Because of these variations, Tracenable applies a **cross-source calculation and reconciliation approach** to ensure that each target record in the dataset is as **complete, accurate, and up-to-date** as possible. *** ## **Our Cross-Source Approach** ### 1. Consolidating Information Across Multiple Disclosures Each corporate target is built using data drawn from multiple verified sources such as annual reports, sustainability disclosures, CDP submissions (disclosed on the companies' website), or regulatory filings. When the same target appears in multiple documents, the system merges these disclosures into a single, unified record. **Example:** * *Report A (2021)*: “Reduce GHG emissions by 50% by 2030.”\ *(No baseline year or scope coverage specified.)* * *Report B (2022)*: “Reduce Scope 1 and 2 emissions by 50% by 2030, from a 2019 baseline.”\ *(Adds missing baseline and scope information.)* → Tracenable combines these disclosures into one enriched record: > **Target:** 50% reduction in Scope 1 + 2 emissions by 2030, baseline 2019. {% hint style="success" %} This ensures that each target reflects the most complete and contextually accurate version of the information disclosed, even when details are scattered across multiple documents. {% endhint %} ### 2. Preserving the Most Complete and Current Version If multiple disclosures exist for the same target, preference is given to the **most recent document** that provides **the fullest information** — covering all three components (target, baseline, and progress) and their associated attributes such as scope coverage, baseline and progress values, and reference years. When newer disclosures confirm that a target remains active but omit certain details, Tracenable retains verified information from earlier versions to maintain completeness and comparability. **Example:** * *Report A (2020)*: “Reduce Scope 3 emissions by 35% by 2035 from a 2018 baseline.” * *Report B (2022)*: “Our Scope 3 target remains active and aligned with science-based pathways.” *(no baseline or target year restated)* * *Report C (2023)*: “Expand coverage to include Scope 1 and 2 under the same 2035 target.” → Tracenable merges these disclosures into a single, updated record: > **Target:** 35% reduction in Scope 1 + 2 + 3 emissions by 2035 from a 2018 baseline. {% hint style="success" %} This example shows how the latest version (2023) is prioritized for its expanded scope, while verified details from earlier years (such as baseline and reduction magnitude) are preserved to ensure continuity and analytical completeness. {% endhint %} ### 3. Harmonizing Inconsistent Expressions of Magnitude Companies may express the same target (or progress against the target) using different mathematical forms, for example, “reduce by 65%,” “cut by two-thirds,” or “divide by three.” Tracenable converts all such variations into standardized percentage terms while retaining the original phrasing for traceability. **Example:** * *Source A*: “Reduce emissions by 65% by 2030 from 2020.” * *Source B*: “Cut emissions to one-third of 2020 levels by 2030.” → Both statements represent the same goal and are harmonized as: > **Target:** 65% reduction in emissions by 2030 (baseline 2020). {% hint style="success" %} This allows users to interpret targets consistently while maintaining visibility into how each company originally reported its goal. {% endhint %} ### 4. Validating Target Continuity and Revisions Some companies revise or replace targets altogether. For instance, upgrading from a 30% to a 50% reduction goal, or shifting from absolute to intensity-based measurement. Tracenable’s reconciliation process ensures that only the **latest applicable version** of each target is retained, while prior versions remain traceable through document references. **Example:** * *2020*: “Reduce absolute emissions by 30% by 2030.” * *2022*: “Increase ambition to 50% absolute reduction by 2030.” → The dataset retains the 50% reduction target as the active record, while retaining earlier versions for historical context. {% hint style="success" %} This approach prevents duplication while accurately representing evolving corporate ambition. {% endhint %} {% hint style="success" %} #### **Outcome: Consistency, Completeness, and Currency** Through this cross-source approach, every Climate Target record in Tracenable’s dataset is: * **Comprehensive** – integrates data from all verified disclosures, ensuring that no reported detail is lost. * **Current** – reflects the most recent and applicable version of each company’s target. * **Consistent** – harmonized across wording, units, and reduction types to enable true comparability. * **Traceable** – retains links to every contributing source document for full auditability. Together, these principles ensure that Tracenable’s Climate Targets dataset delivers the most **accurate, up-to-date, and verifiable view of global corporate decarbonization goals** available. {% endhint %} *** # Quality Assurance Discover how Tracenable validates Climate Targets data through automated checks, statistical tests, and human review to deliver audit-grade reliability. ## Introduction High-quality Climate Targets data depends on more than just good collection and standardization: it requires rigorous validation. At Tracenable, we combine automated testing, statistical analysis, and expert human review to ensure that every data point meets the highest standards of accuracy, consistency, and reliability. Our Quality Assurance (QA) process is multi-layered, designed to detect errors, catch anomalies, and confirm that each data point is both faithful to the original disclosure and fit for use in compliance, benchmarking, and research. *** ## Automated Validation Checks The first layer of QA relies on automated rules that run across all climate target data points. These checks are designed to quickly spot issues that should never occur in valid data, such as: * **Impossible values** – negative or unrealistic reduction percentages (e.g., targets above 100% or below 0%). * **Date inconsistencies** – target years that precede the baseline year, or implausibly distant time horizons. * **Structural errors** – missing key attributes such as baseline year, reduction magnitude, or target coverage. These rules ensure that obvious errors are flagged immediately and never propagate into the dataset. *** ## Statistical and Machine Learning Tests Beyond simple rules, we apply more advanced techniques to identify subtle anomalies: * **Time-series consistency checks** – Flag abrupt changes in target ambition, such as sudden increases or decreases in reduction percentages or target years. * **Outlier detection** – Identify targets that deviate significantly from industry or regional benchmarks (e.g., unusually short time horizons or extreme reduction rates). * **Distribution analysis** – Verify that disclosed targets follow expected patterns across sectors and geographies, ensuring realism and comparability. These methods help us flag values that may be technically valid but require closer review. *** ## Human-in-the-Loop Review Not all issues can be resolved automatically. Our QA process therefore includes a human-in-the-loop review, where trained analysts validate flagged data points: * **Contextual review** – analysts check values against the original disclosure to confirm interpretation. * **Dual validation** – two independent reviewers may assess the same data point. * **Arbitration** – discrepancies between analysts are escalated to senior analysts for final decision. This ensures that ambiguous or complex climate targets disclosures are interpreted correctly, and that every value remains fully traceable to its source. *** ## Continuous Improvement Each QA outcome feeds back into our systems: * Automated rules are updated when new error patterns are identified. * Machine learning models are retrained to improve anomaly detection. * Documentation is refined to capture new edge cases and classification challenges. This iterative loop ensures that the Climate Targets dataset becomes more robust over time. *** # Data Dictionary Explore Tracenable’s Climate Targets data dictionary for complete field definitions of GHG reduction targets, baselines, target years, GHG Protocol scopes, and intensity metrics.
AttributeType/FormatDescriptionExample
isinAlphanumeric StringInternational Securities Identification Number (ISIN) of the primary publicly traded financial instrument associated with the company.US42704L1044
leiAlphanumeric StringLegal Entity Identifier (LEI)549300TP80QLITMSBP82
figiAlphanumeric StringFinancial Instrument Global Identifier (FIGI) of the primary publicly traded financial instrument associated with the company.BBG00WNPK2F5
tickerStringTicker symbol of the primary publicly traded financial instrument associated with the company.HRI
mic_codeStringMarket Identifier Code (MIC) of the primary publicly traded financial instrument associated with the company.XNGS
exchangeStringStock exchange of the primary publicly traded financial instrument associated with the company.NASDAQ
permidNumerical StringPermanent Identifier4295900057
company_nameStringLegal name of the company.HERC HOLDINGS INC
countryCategorical StringCountry where the company's headquarters are located.United States
sectorStringSector in which the company operates.Technology
industryStringIndustry classification of the company.Software - Application
metricStringThe specific measurement or data points requested.GHG Emissions Reduction Targets
target_typeCategorical StringType of GHG emissions reduction target set by the company (absolute or intensity-based).Intensity-based Target
year_of_disclosureYear (YYYY)Year in which the data point was disclosed.2023
scope_of_targetArray of StringsGHG emission scopes (1, 2, and/or 3) included in the reduction target.[“Scope 1”, “Scope 2”]
methodCategorical StringApproach used to calculate Scope 2 and occasionally Scope 3 GHG emissions, distinguishing between 'Location-Based' (reflecting grid averages), 'Market-Based' (reflecting the specific electricity profile purchased), 'Not Defined', and 'Not Applicable' methods.Market-based
intensity_categoryCategorical StringHigh-level grouping classifying the denominators that companies report when setting targets to reduce greenhouse gas (GHG) emissions. These categories serve to standardize and simplify comparisons of GHG intensity across different companies, sectors, industries, and time periods.Energy
intensity_sub_categoryCategorical StringMore specific classification within an intensity category that further refines the denominator used to scale GHG emissions. Sub-categories provide a detailed grouping based on the type of material, product, or operational activity.Non-Renewable Energy Sources
intensity_metricStringSpecific denominator that a company reports to measure the intensity of its GHG emissions. It reflects the exact operational or financial measure, such as the amount of energy consumed or revenue generated, and is used to normalize emissions data.Revenue
baseline_yearYear (YYYY)Reference year against which a company's GHG reduction targets are measured.2019
baseline_valueNumerical FloatQuantified GHG emissions value (absolute or intensity) recorded in the baseline year.0.0000583
baseline_unitStringUnit of measurement used for the baseline value.Metric Tonnes of CO2 equivalent (mtCO2e) per US Dollar (USD) of Revenue
year_target_was_setYear (YYYY)Year in which the GHG reduction target was formally established by the company.2020
target_yearYear (YYYY)Future year by which the company aims to achieve its GHG reduction target.2030
target_valueNumerical FloatQuantified GHG reduction or performance goal set by the company.0.25
target_unitStringUnit of measurement used for the target value.Percentage (%)
target_unit_directionCategorical StringDirection of the target compared to the baseline or other reference points (e.g., absolute decrease from baseline, percentage increase from baseline).Percentage decrease from baseline
achievement_yearYear (YYYY)Year in which the company's progress towards the GHG target is evaluated.2022
achievement_valueNumerical FloatQuantified progress made towards the GHG target as of the achievement year.17%
achievement_unitStringUnit of measurement used for the achievement value.Percentage (%)
achievement_unit_directionCategorical StringDirection of progress compared to the baseline or other reference points (e.g., absolute emissions in achievement year, percentage decrease from baseline).Percentage decrease from baseline
incomplete_boundariesBoolean (True or Not Specified)Indicates whether the reported data covers only a limited portion of the company's operational or organizational boundaries.Not Specified
source_namesArray of StringsNames of the sources from which the reported data was obtained.['Corporate Responsibility Report']
company_idStringTracenable's internal company identifier.dfb6bd7b-facf-4d0f-86a2-f1eedf191946
document_idsArray of StringsTracenable's internal source document identifiers.["4a32b902-ae42-4692-ae96-b42c99fdd2f9", "2af6f9a5-16e1-4de1-95ec-a309563e9e1a"]
traceability_source_urlStringURL linking to the Tracenable platform, where each data point can be verified. Enables direct access to the audit trail for validation and transparency purposes.https://platform.tracenable.com/source-trace?data-request-id-hash=EIBotf8DhT5c&project=climate-targets