FAQs - Climate TRACE

The emissions map displays emissions from 1.8 million sources (such as power plants, manufacturing units, landfills, etc) and is a convenient way to explore Climate TRACE emissions data for the sectors and geographies of interest to you. These data can be explored using 4 parameters:

Sector/ subsector: You can choose the emissions sector/subsector of interest, such as “agriculture” (an emissions sector) or “cement” (an emissions subsector under “manufacturing”).

Gas: You can select greenhouse gases (CO2, CH4 and N2O) and view emissions in either CO2-equivalents or in metric tonnes per gas, and view non-GHG air emissions (Sulfur Dioxide, Nitrogen Oxides, Particulate Matter, Carbon Monoxide) for many types of assets. You can also see the data confidence for every source.

Geography: You can view emissions sources by selecting a specific country, state/province, county/district or city.

Time period: You can view monthly emissions for any year between 2021-2024. For historical data at the country level, please visit the “Country Inventory” page.

CO2e - Carbon dioxide equivalent. You can view this in 100-year or 20-year CO2 equivalent. For more on what this means, check out our blog post here.

BT or B Tonnes - Billion metric tonnes

MT or M Tonnes - Million metric tonnes

tCO2 - tonnes of carbon dioxide

Each greenhouse gas has a different impact on the climate – some remain in the atmosphere for a shorter time, but trap more heat. 

Global warming potential (GWP) is a metric developed to make it easier to compare the climate change effects of different GHGs. Carbon dioxide — the most abundant GHG — is the reference gas, so its GWP is always 1 on all time scales. Other gases such as methane and nitrous oxide trap significantly more heat than CO2. Their GWPs over timescales of 100 years and 20 years are expressed in “carbon dioxide equivalents (CO2e)”. This is important because even though methane and nitrous oxide emissions are smaller in quantity than CO2, their impact on the climate is significant over the near-term due to their GWP; using CO2e as the unit ensures that their impact is not minimized in decision-making. You can read more about global warming potential and the 20 versus 100 year timescales in this blog post.

Asset type: This refers to the type of source you are looking at. For example, a coal or gas power plant, a blast furnace or electric arc furnace steel plant. You can find more about this in the sector specific methodology documentation.

Capacity: This is a measure of the size of the source, primarily useful when validating and comparing emissions estimates. It is measured different ways in different sectors, but is often something that measures or helps approximate the maximum potential emissions this source could cause if it ran flat out. For example, power plants are measured in their maximum potential production of electricity per hour. Whereas road transport emissions are measured by the total length of roads within a particular urban area.

Activity: This refers to the amount of total emitting activity that occurred at a source in a given year, and can be a measure of total production or use. For example, a petrochemical plant with an activity of “420,000” produced those many metric tonnes of ethylene in the selected year.

Descriptions of what these mean for specific sectors and their units are included in the data download. You can also find more information about how these are calculated in the sector-specific methodology documentation.

Take a look at our ‘Sector’ page to see which sectors are included in our inventory and how we measure their emissions.

Climate TRACE has gathered emissions estimates for all major emitting sectors to provide a comprehensive global database of emissions. But for some smaller sectors we are not yet fully independent or granular, and are simply republishing high-level estimates from elsewhere. For example, we don’t yet produce granular, source-level estimates for heat plants, battery manufacture and rare earth mining. If you are interested in such estimates or know experts in these areas, we encourage you to get in touch!

In most cases, the geolocation information for our emitting sources use publicly available data sources and are verified using satellite data. In a few cases like oil-and-gas-production, due to data license restrictions, we obscure the exact location of an emitting source.

Let us know! Although our database is getting closer and closer to being globally complete, it’s not quite there yet. If you know of any source of anthropogenic (human-caused) emissions we don’t know about yet, anywhere on the planet, we’d love to know about it so we can add it! Please reach out to us via our contact page and share anything you know about the missing source. We’ll investigate to cross-verify and then put it on our to-do list to add.

Let us know! Climate TRACE methods have varying levels of uncertainty. You can see from the “confidence” value whether it is a source we have been able to verify against multiple high-quality methods (higher confidence) or are still particularly interested in feedback (lower confidence). If you know of any information that can be used to measure this source more accurately, we’d love to know.  You can share feedback with us by using the “share feedback” link that is available on all the cards that pop up when you click on an emissions source. Please note that to prevent misinformation, we can only rely on information we are able to independently verify. So anything you can provide about why you believe the emissions are different, and your sources, would be very helpful in our ability to investigate this claim and consider editing the database.

When you download each sector's folder and unzip the folder, you will find a CSV file titled "emissions-source_[subsector]_ownership.csv". This file contains the company ownership information. We have tried our best to provide ownership data where available but in many cases it is not available/not updated.

Each emissions source in the ownership file has a unique identifying number. You can use that number to find the same emissions source in the file that contains emissions estimates.

Due to the large volume of requests, Climate TRACE is not always able to provide individualized technical support. However, we do have a small team which offers free technical support and additional analysis for governments that are interested in using the data to implement emission reduction projects.

Climate TRACE identifies the most significant gaps in emissions reporting within current greenhouse gas inventories and develops models to estimate those emissions gaps. Climate TRACE emissions data is generated on a sector-by-sector basis by the relevant Climate TRACE sector lead (listed here) and additional contributed organizations they collaborate with. 

Each Climate TRACE sector uses a methodology that is specialized and relevant to each particular sector to estimate emissions. The most common method is using computer vision models to combine one predictor variable (such as satellite imagery) and one high-quality source of ground truth data (such as verified, calibrated sensors located at individual facilities).  Computer vision models are a form of artificial intelligence that, unlike generative AI, is relatively easy to verify and not prone to hallucination. In addition, Climate TRACE also uses a number of other statistical approaches such as disaggregating total production within a country to all the facilities, and modeling emissions using IPCC National GHG Inventory Guidelines.

More and more Climate TRACE teams are also moving towards comparing cross-verifying results from modeling different experts, using multiple different methods, to ensure maximum accuracy. In artificial intelligence, this is called an “ensemble model”.

We’ve published detailed documentation on our methodologies, peer reviewed research that underlies those methodologies, and the key datasets and assumptions for the Climate TRACE inventory. You can find those on our data downloads page.

For a few sectors like electricity-generation, internal and domestic-shipping, and road-transportation, please consult the methodology documentation for those sectors. For all other sectors and pollutants, we leveraged country-level emissions estimates from the Community Emissions Data System (CEDS) and the Emissions Database for Global Atmospheric Research (EDGAR) datasets that estimate GHGs and non-GHG pollutants at the country and sectoral level. The approach assumes a linear relationship between emissions of GHGs and non-GHGs, i.e. for every X tonnes of greenhouse gas emitted, Y tonnes of pollutant is emitted. These ratios are estimated at a country-sector-fuel level, and then applied across Climate TRACE assets – essentially utilizing CEDS/EDGAR emissions estimates but rescaled and distributed based on Climate TRACE activity data.

Overall, these estimates are considered ‘Tier 0’, as facilities within countries do not differ in terms of mitigation and reduction approaches; however, as Climate TRACE’s effort in air quality continues, we plan to update many parts of this dataset with higher quality estimates.

Climate TRACE estimates emissions data for the years 2015-2023 at the country level, and we’ve projected emissions for 2024, at a monthly scale. Source-level emissions estimates are available for some sectors from 2015, and for all other sectors from 2021 onwards, also at a monthly scale. 

Emissions in 2024 were calculated in a couple of ways:

• For some sectors, GHG emissions up to June or July 2024 were estimated or forecasted by the sector lead, and were projected through the rest of 2024 by forward filling 2023 emissions for the same month.

• For all other sectors, emissions were projected through 2024 by forward filling emissions for the same month from the previous year.

Please consult the sector-specific methodology document for more information.

All years of data are available via our downloads page. You can also view annual country-level totals from 2015-2024 by using our country inventory tool. Our source-level emissions map includes data from 2021 to 2024 for cities and subnational regions at a monthly scale.

Yes! Climate TRACE provides extremely detailed emissions data for nearly every major emitting source, down to the individual power plant, ship, oil rig, and manufacturing facility.

For some industries and sectors, this is easier than others. For example, with remote sensing, it’s much easier to monitor a stationary source like a power plant than it is to monitor something that constantly moves, like a ship.

This year, our emissions inventory grew from 350 million emitting assets to 660 million emitting assets, which we’ve aggregated to 1.8 million emission sources. We have summarized the results into human-readable format here on our emissions map and you can download data by sector and country.

At a high level, machine learning (ML) is defined as a branch of artificial intelligence (AI) and computer science that enables software applications to more accurately predict outcomes using in situ historical data. In general, ML works by trying many, many different algorithms that can use data one actually has access to (“features”) to predict data you would like to have access to (“target variables”). It then selects the algorithm that does so most accurately. The definition of an accurate algorithm is the one that can most accurately predict target variables it hasn’t “seen”, only by looking at the features. To measure this, you must have access to accurate, high-quality data on many examples of the target variable (“training data”).

In the case of Climate TRACE, the target variable we are estimating is emissions. To estimate it, we combine many features, but particularly satellite imagery. And to gauge the accuracy of different algorithms, we use training data which are verified, high-quality, on-site measurements of many individual facilities. We then weed out any algorithms which did not pass quality control–for example if they were inaccurate, or had a high risk of potential bias. We then run any algorithms that do pass quality control globally, and publish the results including data about our confidence and certainty that a given result is accurate. 

One surprising finding of Climate TRACE is that, while we do use data from satellites that directly spot CO2 and methane emissions to predict the level of CO2 and methane coming out of individual assets, we often discover that some other method or “feature” is actually even more accurate. Often this is because emissions disperse in the wind, whereas sometimes other signals can be pinpointed to an exact location with higher certainty. Thus, we have learned to not assume in advance which features will be most accurate, but instead to combine many different features and use whatever actually produces the most accurate results in practice.

Today that includes a long and growing list of features, from extremely high temperatures at steel plants, to fan blades spinning on a power plant, to lidar imagery of trees, to automated identification systems (AIS) tracking the movement of ships, to visual markers of flaring data in oil and gas extraction. 

Other than independence, the primary value of using ML to estimate emissions is that direct monitoring systems are cost-prohibitive for most countries and operators. An important element of such models is, therefore, obtaining globally representative ground truth data to avoid causing out-of-sample bias. Thus, in addition to collecting existing emissions measurements, Climate TRACE has invested heavily in gathering high-quality ground truth data in a globally representative mix of countries, biomes, weather conditions, times of day, and equipment types. 

We are always collecting more ground truth data and features. If you know of data that can be used to help predict emissions and validate those predictions, we’d love to hear from you!

Climate TRACE was built on collaboration. If you like the idea of working together to make the world’s emissions transparent, we want to hear from you! Whether you’re a researcher who would like to contribute data and analysis; a decision-maker interested in diving deeper into our data; or a journalist looking to shine a light on emissions trends; or a potential funder of our nonprofit work, we’d love to talk! Please reach out to us here.

We built the Climate TRACE website with the goal of helping users have easy access to emissions data in a comprehensive and accessible way. There are several options on the website to get started with reviewing the data:

• Emissions map: where you can see individual sources of emissions globally by country/state/county/city, sector, year, and gas. You can also download the data based on your selection.

• Country inventory: where you can see countries’ overall emissions inventories by sector, year, and gas. You can also view global data by sector. Users can download the data based on your selection.

• Comparison tool: where you can compare emissions based on a custom selection of location, sector, year, and gas.

• Data Downloads: where you can download Climate TRACE data in bulk and analyze the data using the tools you are most familiar with. You can also access the data via our beta API via this page.

Climate TRACE also offers free analytical support for governments interested in using Climate TRACE data for a project that is likely to significantly reduce emissions. If this sounds like you, please contact us.

Nearly all of Climate TRACE emissions data are available for download via the downloads page.  In a handful of sectors, the Climate TRACE dataset includes more granular, geospatial data than is available in the download packages and our API due to the sheer volume of information. Specifically, that includes: 

• Land use raster data for global forestry at 1km-by-1km distribution, 

• Global synthetic fertilizer application raster data at 5km-by-5km resolution, 

• Rice cultivation raster data at 10m-by-10m resolution,

• Enteric fermentation and manure left on soils in cattle pastures raster data available at a 10km-by-10km resolution

• Cropland fires raster data available at at 11km-by-11-km resolution,

• Onsite fuel usage in residential and non-residential buildings raster data available at a 1km-by-1km resolution,

• Emissions from individual ship voyages, 

• Road transportation emissions at a road-segment level.

Due to the large file size, these versions of the data are available only upon request. Before making a request for the data, please first examine the download packages that aggregate the above raster data to administrative areas (counties/districts) and determine if those might satisfy your needs. If you are interested in the geospatial data, please contact us to let us know which sector you’re interested in, and arrange a file transfer.

Anyone. Climate TRACE is a free tool that anyone can use. Our data are available to all under a Creative Commons 4.0 License, which means all users are free to copy, modify and distribute Climate TRACE data in any format for any purpose including commercial use, as long as you acknowledge Climate TRACE. Please read our terms of use for more information.  

Climate TRACE data are already being used by policymakers for verification of self-reported emissions and data-driven climate action planning, by multinational companies to make supply-chain procurement decisions, by researchers for building decarbonization pathway models, and by climate start-ups and consulting firms to help their clients meet decarbonization targets. You can read about some of these use cases. We also welcome feedback from people who are already using our data, please use the survey form located at the bottom of this page to tell us how you're using the data.

Since some source_ids changed from our previous year to this year, we created a crosswalk to help you find the asset you are looking for! Please reach out to us via our contact form.

The sector-to-subsector mapping is available in our data downloads page along with the emissions-sources files in the “detailed-data-schema”. It is also available via our API. For access to our beta API, refer to API reference page.

No, Climate TRACE primarily focuses on emissions that can be physically observed in the real world, i.e. Scope 1 emissions (i.e. direct emissions occurring at a source). 

However, we do provide some information. First, for some sectors such as heavy industries, Scope 2 emissions (indirect emissions from the consumption of electricity) have been provided in the “other” data columns. 

Further, while Climate TRACE does not provide Scope 3 emissions for downstream industries, data users can use the source-level emissions data to calculate their own Scope 3 supply chain emissions. 

Take a look at this blog to see how source-level data can provide visibility on supply chain emissions, and how some of Climate TRACE’s partners are already using the data to decarbonize supply chains. If you are a government or company that could use scope 2 or scope 3 emissions data in a project with a high chance of significantly reducing emissions, we may be able to support you with additional custom analysis. If so, you can contact us here.

No. Climate TRACE does not track emissions from individuals nor do we provide any personal data such as individuals’ names, addresses or other information that relates to an identifiable person. Our focus is on large emission sources such as manufacturing units, power plants, mines, and cargo ships. While we do track emissions from cars and buildings, these are aggregated up to the level of a city or county, so an individual’s privacy is never at any risk of violation. And we have no plans to change this, ever. We are people too and take seriously that there is no need to invade people’s privacy to fight climate change.

We also protect your privacy while you visit and use our site. All website browsing activities are anonymized and we do not use any tracking cookies. The only information we collect about you is when you sign up for our newsletter or get in touch with us. You can read our privacy policy here.

There is general consensus about the big picture when it comes to greenhouse gas estimates. The total amount of greenhouse gas emissions in the atmosphere is well measured and not in question. Climate TRACE estimates of the global total emitted from all human activities also broadly agree with existing global data sources like EDGAR, Global Carbon Project, and Carbon Monitor. In most cases, Climate TRACE data also broadly match both national inventory data and (where available) corporate inventory data. However, when digging into the details — specific emissions sectors in specific countries, or gases other than CO2, there are differences.

There are many reasons why: 

(a) Many national inventories have not been updated in a long time. When comparing the latest national inventories to Climate TRACE data from the same year, they are often similar.

(b) Many inventories are not comprehensive. For example, many national inventories exclude some entire sectors. Climate TRACE makes every effort to measure all sectors for all countries. When Climate TRACE can clearly verify through satellite imagery or other means that a “missing” industry is in reality present in a country, we are more confident in our results. 

(c) Many inventories use a sampling approach. For example, we are particularly impressed by Carbon Monitor and EDGAR’s extensive work in obtaining detailed data for some facilities or industries, and use it to extrapolate reasonable estimates about others worldwide. But when Climate TRACE has the opportunity to directly monitor a facility or country instead of assuming, we prioritize this over extrapolation-based estimates.     

(d) Varying definitions of where in an inventory to put different emission sources. (For example, some inventories include heat or co-generation plants under ‘electricity’ and others under ‘industry’.) One reason Climate TRACE makes every effort to monitor each individual facility is that it’s making it easier to reconcile different inventories, by separating differences of definitions from genuine different views on what’s happening.   

(d) Different emission factors. Like nearly all inventories, in many cases Climate TRACE often measures emitting activity and multiplies by emissions factors to estimate emissions. Currently Climate TRACE is using IPCC standard emissions factors except in a few cases where we have specific highly reliable verification that other factors are more accurate. But there is some uncertainty of these emissions factors, as displayed in our uncertain and confidence data.

(e) Lastly, no global inventory will ever be as accurate as an on-site, high-quality, properly calibrated CO2 or methane sensor directly at the source of emissions. At the individual facility level, if verified data are available from direct on-site sensors, measured by reputable third parties, they will often be more reliable than Climate TRACE estimates. The biggest value of Climate TRACE is that the vast majority of emitting facilities worldwide do not have such sensors, and/or do not make it available to the public.

You can find comparisons to other data sources in the methodology documentation for each emissions sector, under the “results” section.

Individual methodologies and related research papers publish accuracy metrics for cases where reliable ground truth data are available from which to calculate accuracy. But summarizing “how accurate is Climate TRACE data overall?” is actually much harder than it sounds. Why? Because to quantitatively measure how accurate a dataset is, you need to know exactly what the true answer is. And if there were any data source that knew the exact accurate emissions of every emitting facility on the entire planet, there would be no need for Climate TRACE.

However, in practice we know that some parts of the dataset are the same accuracy as existing methods (for example, when we re-use IPCC emissions factors), others are more accurate (for example, when we can see power plants turning on more often than is reported), and still others are much more accurate (for example, when existing inventories exclude a facility or whole sector entirely, but we can clearly see it from space). 

In general we know of relatively few examples of Climate TRACE data being less accurate than existing datasets. Why? Because as a coalition, any time we find evidence that any relevant dataset produced by anyone is measuring anything more accurately than Climate TRACE already is, our default workflow is to try to incorporate those better data into the coalition, rather than sticking with a less accurate method. Like all AI projects, the more we incorporate additional data sources and methods in this way, the more our accuracy will continue to increase over time.  

But our confidence still varies, and is generally lower in fields where no highly certain methods have yet been found (such as underground soil carbon in agriculture). So we make transparent everything we know about the confidence and uncertainty of our estimates for each asset, including detailed metadata on our confidence about different component parts of the estimate. Our confidence and uncertainty metrics are largely patterned off the approaches used by the International Panel on Climate Change (IPCC) with minor modifications. We also encourage you to read through our methodology to understand the underlying data sources we use, the models, and their relative advantages and limitations.

All users interested in accessing data using an API (Application Programming Interface) can do so via the beta version of Climate TRACE API. The API supports search of the emitting sources by sector, owner, and location. Please read our API reference page for more information and access.

As of the current (beta) release, the API supports search for emitting assets by sector, owner, and location. The API also supports queries to get emissions and other emitting sources details. Unfortunately, as this is a beta release, we cannot guarantee availability or stability of the Climate TRACE API, so please keep volume of use in mind, and use it with caution in production settings.

We’d love to learn more about how you hope to use the API. Please share your work and ideas or ask technical questions about the API on our Contact page.

For access to our beta API, refer to our API reference page. 

We currently have asset-level ownership information available for 38% of global emissions. The following sectors have ownership data available currently, either partially or fully: 

• Aluminum

• Bauxite mining

• Coal mining

• Copper mining

• Cement

• Domestic and International aviation

• Electricity generation

• Enteric fermentation and manure management (cattle operations)

• Iron mining

• Iron and steel

• Oil and gas production 

• Oil and gas transport

• Oil and gas refining

• Petrochemicals steam cracking

We are currently working to increase ownership data coverage and plan to include more data in the next release.

When you download each sector's folder and unzip the folder, you will find a CSV file titled "emissions-source_[subsector]_ownership.csv". This file contains the company ownership information. We have tried our best to provide ownership data where available but in many cases it is not available/not updated.

Each emissions source in the ownership file has a unique identifying number. You can use that number to find the same emissions source in the separate file that contains emissions estimates.

We have done extensive research and analysis to provide the best and most up-to-date information on ownership, but do not have comprehensive ownership information available yet. The ownership information we do have includes data on direct ownership and ultimate parent ownership. That data is openly available via the downloads page.

We have also mapped Climate TRACE assets to entities’ identifiers, which includes Legal Entity Identifiers (LEI) and other identifier systems. Climate TRACE provides information on the full chain of ownership – from the direct owners, through intermediate owners, up to the ultimate parents – as well as companies’ mailing addresses, websites, and name variants (other languages, abbreviations, long-form names, nicknames, etc.), where possible. This information is available upon request, so please contact us for more information. Please note that the data may have limitations, given that we don't yet have complete ownership information for all assets. In other cases, where ownership information is available, the associated company/sector may not have an LEI.