The datasets comprise greenhouse gas (GHG) emission factors (Factors) for 1,016 U.S. commodities as defined by the 2017 version of the North American Industry Classification System (NAICS). The Factors are based on GHG data for 2022. Factors are given for all NAICS-defined commodities at the 6-digit level except for electricity, government, and households. Each record consists of three factor types as in the previous releases: Supply Chain Emissions without Margins (SEF), Margins of Supply Chain Emissions (MEF), and Supply Chain Emissions with Margins (SEF+MEF). One set of Factors provides kg carbon dioxide equivalents (CO2e) per 2022 U.S. dollar (USD) for all GHGs combined using 100-yr global warming potentials from IPCC 5th report (AR5) to calculate the equivalents. In this dataset there is one SEF, MEF and SEF+MEF per commodity. The other dataset of Factors provides kg of each unique GHG emitted per 2022 dollar per commodity without the CO2e calculation. The dollar in the denominator of all factors uses purchaser prices. See the supporting file 'Aboutv1.3SupplyChainGHGEmissionFactors.docx' for complete documentation of this dataset.

The EFDB is a database containing various parameters used to calculate anthropogenic greenhouse gas emissions from sources and removals by sinks. While it specifically includes “emission factors,” it also covers a range of other relevant parameters. For simplicity, however, the term “Emission Factor” (or “EF”) is sometimes applied broadly to refer to any parameter within the database.

EFDB at present contains the IPCC default data (Revised 1996 IPCC Guidelines, IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, IPCC Good Practice Guidance for Land Use, Land-Use Change and Forestry, 2006 IPCC Guidelines for National Greenhouse Gas Inventories and 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands), and data from peer-reviewed journals and other publications including National Inventory Reports (NIRs).

This datasets contains a range of Greenhouse Gas (GHG) emission factors (e.g. electricity, gas, waste, fuel, refrigerants) relevant to organisations' operations and used to calculate GHG emission (including CO2), in GHG inventory dashboards and reports.

Monthly average hourly CO2, NOx, and SO2 emission factors for each U.S. eGRID subregion. This project utilized GridViewTM, an electric grid dispatch software package, to estimate hourly emission factors for all of the eGRID subregions in the continental United States. These factors took into account electricity imports and exports across the eGRID subregion boundary, and included estimated transmission and distribution (T) losses. Emission types accounted for included carbon dioxide (CO2), nitrogen oxides (NOx), and sulfur dioxide (SO2).Data reported as part of this project include hourly average, minimum, and maximum emission factors by month; that is, the average, minimum, and maximum emission factor for the same hour of each day in a month. Please note that the data are reported in lbs/MWh, where the MWh value reported is site electricity use (the actual electricity used at the building) and the pounds of emissions reported are the emissions created at the generator to meet the building load, including transmission and distribution losses. The demand profiles used to generate the data pertain to the following years: eastern interconnect - 2005; Electricity Reliability Council of Texas (ERCOT) - 2008; Western Electricity Coordinating Council (WECC) - 2008.

Annual summaries of facility and retail seller of electricity GHG emissions. MassDEP calculations of annual Retail Seller GHG emission factors.

Tables presenting supply chain and margin emission factors and data quality scores for US commodities and industries calculated from USEEIO models at two levels of commodity/industry categorization, detail and summary, for both industries and commodity, and annually from 2010-2016. See the EPA report for full details on emission factor preparation. These factors were produced by knitting the GenerateEmissionFactorsDataset.Rmd file in RStudio using the supply-chain-factors code, v1.1.1 at https://github.com/USEPA/supply-chain-factors/releases/tag/v1.1.1. This dataset is associated with the following publication: Ingwersen, W., and M. Li. Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities. U.S. Environmental Protection Agency, Washington, DC, USA, 2020.

This table contains quarterly and annual figures on the Dutch emissions of carbon dioxide (CO2) and other greenhouse gases (nitrous oxide (N2O), methane (CH4) and fluorinated gases. These are the emission figures broken down into six climate sectors (Industry, Power, Mobility, Built Environment, Agriculture, LULUCF), as used within the Dutch Climate Agreement, and in accordance with internationally established IPCC regulations. The IPCC (Intergovernmental Panel on Climate Change) is an organization of the United Nations that prepares reports on scientific knowledge about climate change.

Data available from: 1st quarter of 2019

Status of the figures: The figures for the four quarters of 2024 have a provisional status, just like the annual figure for 2024 and the first two quarters of 2025. The four quarters of 2019, 2020, 2021, 2022 and 2023 and their annual figures have a final status. In order to obtain coherent and consistent time series, the complete data set is recalculated annually (including those with a final status, in accordance with IPCC regulations), so that the latest insights, especially with regard to the emission factors, can be included in the recalculation. The annual recalculation takes place when the 4th quarter of the most recent reporting year is published (mid-March).

Changes as of 10 September 2025: The figures for the second quarter of 2025 are included. The figures for the first quarter of 2025 and the annual figures for 2024 (and the four underlying quarters) have also been slightly revised.

When will new figures be published? New preliminary quarterly figures are published two and a half months after the end of the quarter.

The issue ofclimate change is the focus of international community. CO2 released from the combustion of fossil fuel contribute more than 58% to the total greenhouse gas (GHGs) emissions, which is the main source of GHGs emissions. It is also the main focus of GHGs emissions reduction in every country. Therefore, the accuracy of CO2 emission accounting of fossil fuel combustion is the basis for the formulation and implementation of emission reduction policies. The difference between different accounting methods mainly lies in the choice of emission factors. The national greenhouse gas inventory report is an official document submitted to the United Nations by the States Parties to the Paris Agreement. It consists of the national inventory report (NIR) and common reporting format (CRF). In this study, the greenhouse gas inventory report submitted by the forty-four annex I countries was collated. And the dataset of CO2 emission factors of fossil fuels combustion by Sectoral-approach in annex I countries from 1990 to 2016 was obtained and classified. The dataset is an xlsx file which contains the data from different countries and aggregated data. It is convenient for data retrieval, filtering, comparison and analysis. It can also be used as the basic data for subsequent greenhouse gas accounting.

Smoke emission factors (EFs) have been developed for a variety of wildland fuels beginning in the late 1960s. Many of these EFs have been presented in a variety of outlets and there is no centralized repository containing many of the EFs developed in the 1970s and 1980s. This data publication contains a compilation of emission factors for a variety of smoke components which have been presented in refereed as well as gray literature (literature that has not been published commercially or is not generally accessible) from the late 1960s through 2011. Included in this data publication is a list of all smoke emissions related literature found during this same time period (including part of 2012), and any that were funded by the USDA Forest Service are included in the data publication download.This data publication was created to provide a simple tool that can be used to locate potential emission factors of interest to the user. Sorting by fuel type, region, and fire type are possible.

Original metadata date was 05/22/2014. Minor metadata updates on 12/13/2016.

The purpose of this page is to describe the organization of the greenhouse gas inventory report and to indicate where to find the online material. To learn more about Canada’s official greenhouse gas inventory, visit the Main page: https://www.canada.ca/ghg-inventory Contact us: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/contact-team.html Documents available online: The National Inventory Report (NIR) comprises three parts. Part 1 of the NIR includes the Executive Summary and Chapters 1 to 8. Part 2 consists of Annexes 1 to 7. Part 3 includes Annexes 8 to 13. The full report can be found at the following address: https://publications.gc.ca/site/eng/9.506002/publication.html. Part 2 and Part 3 data files can be accessed by clicking on the “Explore” button below, then “Go to resource”. Description of the content of each folder: A-IPCC Sector: Contains various greenhouse gas (GHG) emissions files by Intergovernmental Panel on Climate Change (IPCC) sector and by gas, for all years, for Canada and for provinces and territories. B-Economic Sector: Contains various GHG emissions files by Economic sectors, for all years for Canada and for the latest year for provinces and territories. In the EN_GHG_Econ_Canada file, a tab containing the relationship between IPCC sector and Economic sector is also provided. Emissions are also presented by gas for Canada and for the provinces and territories. C-Tables Electricity Canada Provinces Territories: Contains summary and GHG intensity tables for Electricity in Canada. D-Emission Factors: Contains files with information on emission factors. E-LULUCF: Contains time-series estimates for the Land Use, Land-Use Change and Forestry (LULUCF) sector, geomatics files containing estimates attributed to spatial units, and a multi-year forestry improvement plan. F-Agriculture: Contains time-series estimates for the Agriculture sector and geomatics files containing estimates attributed to spatial units. G-Additional NIR Annexes: Contains Annex 1 (key categories), Annex 2 (uncertainty), Annex 3 (methodologies), Annex 4 (sectoral and reference approaches, and national energy balance), Annex 5 (completeness), Annex 7 (ozone and aerosol precursors) and Annex 8 (rounding protocol) of the National Inventory Report (NIR).

In the context of EU and Global Covenant of Mayors for Climate and Energy, the JRC provides energy related GHG emission factors. This dataset provides updated CoM emission factors for local energy use and local electricity generation.

Three types of emission factors can be found in this dataset, following two approaches: an activity-based (IPCC) approach and a life-cycle (LC) approach. In the activity-based approach, (i) an emission factor is provided for CO2 emissions (in tonnes of CO2/MWh) only, and (ii) another for GHG emissions including CO2, CH4 and N2O (in tonnes of CO2-eq/MWh); in the LC approach (iii) an emission factor is provided accounting for GHG emissions, namely CO2, N2O and CH4 (in tonnes of CO2-eq/MWh), including upstream (supply chain) emissions.

Further details on the data and methodology used to calculate the emission factors presented in this version can be found in Bastos, Monforti-Ferrario and Melica (2024).

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.

Provide the national electricity emission factor and a comparison table of Taiwan Power Company's emission factor. The national electricity emission factor is based on the historical statistics announced by the Ministry of Economic Affairs, Energy Bureau, Energy Industry Greenhouse Gas Reduction Information Network (http://www.eigic-estc.com.tw/Main/Contents.aspx?id16&id26&id327).

🙋‍♂️ Introduction

ExioML is the first ML-ready benchmark dataset in eco-economic research, designed for global sectoral sustainability analysis. It addresses significant research gaps by leveraging the high-quality, open-source EE-MRIO dataset ExioBase 3.8.2. ExioML covers 163 sectors across 49 regions from 1995 to 2022, overcoming data inaccessibility issues. The dataset includes both factor accounting in tabular format and footprint networks in graph structure.

We demonstrate a GHG emission regression task using a factor accounting table, comparing the performance of shallow and deep models. The results show a low Mean Squared Error (MSE), quantifying sectoral GHG emissions in terms of value-added, employment, and energy consumption, validating the dataset's usability. The footprint network in ExioML, inherent in the multi-dimensional MRIO framework, enables tracking resource flow between international sectors.

ExioML offers promising research opportunities, such as predicting embodied emissions through international trade, estimating regional sustainability transitions, and analyzing the topological changes in global trading networks over time. It reduces barriers and intensive data pre-processing for ML researchers, facilitates the integration of ML and eco-economic research, and provides new perspectives for sound climate policy and global sustainable development.

📊 Dataset

ExioML supports graph and tabular structure learning algorithms through the Footprint Network and Factor Accounting table. The dataset includes the following factors in PxP and IxI:

- Region (Categorical feature)
- Sector (Categorical feature)
- Value Added [M.EUR] (Numerical feature)
- Employment [1000 p.] (Numerical feature)
- GHG emissions [kg CO2 eq.] (Numerical feature)
- Energy Carrier Net Total [TJ] (Numerical feature)
- Year (Numerical feature)

☁️ Factor Accounting

The Factor Accounting table shares common features with the Footprint Network and summarizes the total heterogeneous characteristics of various sectors.

🚞 Footprint Network

The Footprint Network models the high-dimensional global trading network, capturing its economic, social, and environmental impacts. This network is structured as a directed graph, where directionality represents sectoral input-output relationships, delineating sectors by their roles as sources (exporting) and targets (importing). The basic element in the ExioML Footprint Network is international trade across different sectors with features such as value-added, emission amount, and energy input. The Footprint Network helps identify critical sectors and paths for sustainability management and optimization. The Footprint Network is hosted on Zenodo.

🔗 Code and Data Availability

The ExioML development toolkit in Python and the regression model used for validation are available on the GitHub repository: (https://github.com/YVNMINC/ExioML). The complete ExioML dataset is hosted by Zenodo: (https://zenodo.org/records/10604610).

💡 Additional Information

More details about the dataset are available in our paper: *ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability*, accepted by the ICLR 2024 Climate Change AI workshop: (https://arxiv.org/abs/2406.09046).

📄 Citation

@article{guo2024exioml,
 title={ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability},
 author={Guo, Yanming and Guan, Charles and Ma, Jin},
 journal={arXiv preprint arXiv:2406.09046},
 year={2024}
}

🌟 Reference

Stadler, Konstantin, et al. "EXIOBASE 3." Zenodo. Retrieved March 22 (2021): 2023.

With the publication of the latest version of ISO 14064-1, the National Carbon Neutrality Program of Costa Rica included measurement uncertainty as a mandatory requirement for the reporting of greenhouse gas (GHG) inventories as an essential parameter to have precise and reliable results. However, technical gaps remain for an optimal implementation of this requirement, including a lack of information regarding uncertainties in the official database of Costa Rican emission factors. The present article sought to fill the gap of uncertainty information for 22 emission factors from this database, providing uncertainty values through the collection of input information, use of expert criteria, fitting of probability distributions, and the application of the Monte Carlo simulation method. Emission factors were classified into three groups according to their estimation methods and their information sources. Five probability distributions were chosen and fitted to the input data based on their previous application in the field. Standard uncertainties and 95% confidence intervals were estimated for each emission factor as the standard deviations and differences between the 2.5% and 97.5% percentiles of their simulated data. As expected, most of the standard uncertainties were estimated between 15% and 50% of the value of the emission factor, and confidence intervals tended to asymmetry as the standard uncertainties or the number of input data for the emission factor estimation increased. High consistency was found between these results and values reported in other studies. These results are critical to complement the official database of Costa Rican emission factors and for national users to estimate the uncertainties of their greenhouse gas inventories, easing to comply with national environmental policies by adapting to international requirements in the fight against climate change. Additionally, improvement opportunities were identified to update the emission factors from livestock enteric fermentation, manure management, waste treatments, and non-energy use of lubricants, whose estimations are based on outdated references and methodologies. An opportunity to improve and reduce the remarkably high uncertainties for emission factors associated with the biological treatment of solid waste through studies adapted to the specific characteristics of tropical countries like Costa Rica was also pointed out.

Emissions factors have long been the fundamental tool in developing national, regional, state, and local emissions inventories for air quality management decisions and in developing emissions control strategies. More recently, emissions factors have been applied in determining site-specific applicability and emissions limitations in operating permits by federal, state, local, and tribal agencies, consultants, and industry. AP-42 is a compendium of EPA recommended emissions factors.

Monetary emission factors to estimate the carbon footprint associated to purchases in research laboratories. Emissions embodied in goods and services can be estimated by measuring physical or monetary flows. Physical estimates are more accurate if emission factors and inventories for each product exist. This is not the case for the sheer amount of goods and services purchased in research laboratories. To make the problem tractable, goods were classified according to the French system for accountability in research (NACRES), to which we manually associated monetary emission factors (EFs) in kg CO2e/euro and for which monetary inventories are readily available through the laboratory accountability. All euro values correspond to year 2019. The emissions of good i can calculated as e(i) = p(i)* EF(i), with p(i) its tax-free price in euro. EFs were estimated using the three approaches sketched in Fig. 1 of the associated publication: i) an environmentally extended input-output (EEIO) method that we will call in the following macro and note the resulting EFs EFmacro; ii) a process-based method that we call micro EFmicro (or LCA for life cycle assessment); and iii) an intermediate approach based on the carbon footprint of selected companies of the research sector, that we call meso. The detailed description of the files is in the tab README for each file and in the accompanying publication.

Many organizations quantify greenhouse emissions in their value chain. Emissions from purchased goods and services and capital goods, referred to as Scope 3 emissions in the Greenhouse Gas Protocol Scope 3 Accounting and Reporting Standard, represent a significant emissions source for many organizations. To assist in quantifying these emissions, we have developed a comprehensive set of supply chain emission factors covering all categories of goods and services in the US economy. These factors are intended for quantifying emissions from purchased goods and services using the spend-based method defined in the Greenhouse Gas Protocol Technical Guidance for Calculating Scope 3 Emissions. The factors were prepared using USEEIO models, which are a life cycle models of goods and services in the US economy. The supply chain emission factors are presented in units of kilogram emissions per US dollar of purchases for a category of goods and services with a defined life cycle scope. Sets of factors covering all sectors of the economy are provided for years from 2010 to 2016 with two levels of sector aggregation. The factors are provided for both industries and commodities, where commodities are equivalent to a category of good or service, and industries are producers of one or more commodities. A set of five data quality scores covering data reliability, temporal, geographical and technological correlation and completeness of data collection is provided along with each factor. The factors presented are as follows: 1. Supply Chain Emission Factors without Margins: emissions associated with cradle to factory gate 2. Margins of Supply Chain Emission Factors: emissions associated with factory gate to shelf, which includes emissions from transportation, wholesale and retail as well as adjustments for price markups 3. Supply Chain Emission Factors with Margins: emissions associated with cradle to shelf (equal to the sum of the above two factors) End users of products will likely find the Supply Chain Emission Factors with Margins most appropriate for their use. Organizations purchasing intermediate products at the factory gate will likely find the Supply Chain Emission Factors without Margins to be most appropriate. See the Executive Summary of the associated report for an example calculation using the factors. All factors are associated with limitations and variations in underlying data quality. We encourage the reader to carefully read the report to understand the differences across these sets, underlying assumptions in their calculation, their limitations to decide if they are appropriate for their intended use. If the reader deems the factors are appropriate, this report along with the factor data quality scores will aid in selection of factors best fit for their intended use. This dataset is associated with the following publication: Ingwersen, W., and M. Li. Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities. U.S. Environmental Protection Agency, Washington, DC, USA, 2020.

Thailand Carbon Dioxide Emission per Electricity Generation data was reported at 0.399 kg/kWh in 2024. This records a decrease from the previous number of 0.400 kg/kWh for 2023. Thailand Carbon Dioxide Emission per Electricity Generation data is updated yearly, averaging 0.560 kg/kWh from Dec 1994 (Median) to 2024, with 31 observations. The data reached an all-time high of 0.656 kg/kWh in 1997 and a record low of 0.399 kg/kWh in 2024. Thailand Carbon Dioxide Emission per Electricity Generation data remains active status in CEIC and is reported by Energy Policy and Planning Office, Ministry of Energy. The data is categorized under Global Database’s Thailand – Table TH.RB020: Carbon Dioxide Emissions Statistics.

We publish Canada’s greenhouse gas (GHG) and air pollutant emissions projections annually. These projections help measure progress in reducing emissions and combating climate change. GHG projections are presented for various scenarios. Air pollutant emissions projections reflect our efforts to reduce air pollution. Site Contents: • current_projections_actuelles: Contains the latest projections from Canada's First Biennial Transparency Report (2024). • previous_projections_precedentes: Includes projections reported since 2017. From 2021, a file named “combined-table-tableau-combiné.xlsx” is also included in the top folder. This file contains a summary of all data tables included in the “GHG – GES” and "Energy - Énergie" sub-folders Key Folders: • GHG – GES (introduced in 2018): Contains GHG and air pollutant emissions data. From 2021, includes LULUCF net GHG fluxes and accounting contributions. From 2024, includes net GHG flux historical estimates and projections from provinces and territories by land category. • Energy – Énergie (introduced in 2018): Includes energy and macroeconomic data. From 2021, includes emission factors for flaring, venting, and fugitive emissions for the Oil and Gas sector. From 2022, includes sub-folders with results for the reference case and additional measures scenarios. From 2023, includes emissions per capita by province/territory and for Canada. From 2024, includes a document outlining the calculation of electricity grid intensities with and without biogenic carbon dioxide emissions. Additional Sub-Folders: • Reference Scenario de reference (introduced in 2022): Reflects the current Reference Case scenario. • AM Scenario AMS (introduced in 2022): Reflects the current Additional Measures scenario. From 2023, includes macroeconomic assumptions. • Description-Tables-Tableaux-de-description (introduced in 2024): This folder includes the data tables used to develop the data visualizations that are available on our website (https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/projections.html). The data presented in this folder can also be found in the other two folders, and is included to make the data presented in the visualizations more accessible.