The datasets are comprised of 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 representing 2019. 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 (SupplyChainGHGEmissionFactors_v1.2_NAICS_CO2e_USD2021.csv) provides kg carbon dioxide equivalents (CO2e) per USD for all GHGs combined using 100 yr global warming potentials from the 4th IPPC Assessment report to calculate the equivalents. In this dataset there is one SEF, MEF and SEF+MEF per commodity. The other dataset of Factors (SupplyChainGHGEmissionFactors_v1.2_NAICS_byGHG_USD2021.csv) provides kg of each unique GHG emitted per dollar per commodity without the CO2e calculation. The dollar (USD) in the denominator of all factors uses purchaser prices in 2021 USD. See the supporting file 'Aboutthe2019v1.2SupplyChainGHGEmissionFactors.pdf' for complete documentation of this dataset.

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.

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 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.

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 quarter 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 11 June 2025: The figures for the first quarter of 2025 are included. When will new figures be published? New preliminary quarterly figures are published two and a half months after the end of the quarter.

This document contains agriculture emission factors collected between 2000 and 2020 from published datasets that was housed on the Standard Assessment of Agricultural Mitigation Potential and Livelihoods (SAMPLES) website managed by the former CGIAR Research Program Climate Change, Agriculture and Food Security (CCAFS). Users were able to download site-specific emission factors and associated agroecological data for use in greenhouse gas accounting or inventories and find links and contact information to access full datasets for use in biogeochemical modeling. It was also encouraged that anyone conducting greenhouse gas (GHG) measurements from agricultural systems to upload their data to the database. Information in this database is freely available to the public for use in research or other purposes based on the CCAFS Data Ownership and Sharing Agreement. SAMPLES was a digital platform launched in 2014 by CCAFS, SAMPLES aimed to address the dearth of reliable information about GHG emissions from agriculture in tropical countries. SAMPLES scientists work with developing countries to improve data on agricultural GHG emissions and mitigation potentials for smallholder agriculture. CCAFS coordinated SAMPLES in collaboration with CGIAR centers and scientists, and global and regional partners. Methodology: SAMPLES database is based on IPCC Emission Factor Database input guidance and the MAGGnet database input spreadsheet, with thanks to Mark Liebig and the Global Research Alliance on Agricultural Greenhouse Gases.

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.

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 national electricity consumption (also referred to as National and European Emission Factors for Electricity - NEEFE).

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, namely 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).

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.

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).

🙋‍♂️ 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.

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.

All of HydroQuébec’s data on direct GHG emission factors from energy sources on Québec’s import and export markets. Updated yearly.

Method Data from public sources outside Hydro-Québec is extracted and compiled. The direct GHG emission factors are presented for each energy source on an external market from which Québec can import energy or to which it can export energy.

Generating stations powered by renewable energy sources do not produce direct GHG emissions.

Sources of GHG emissions by generating stations powered by fossil fuels:

Ontario: Values calculated retroactively based on the following sites of the Independent Electricity System Operator (IESO): https://www.ieso.ca/-/media/Files/IESO/Document-Library/gas-phase-out/Decarbonization-and-Ontarios-Electricity-System.ashx and https://www.ieso.ca/en/Corporate-IESO/Media/Year-End-Data#yearenddata New York State, New England, MISO and PJM: https://www.epa.gov/egrid

The GHG emission values from electricity generation in New York State, New England and by MISO and PJM are calculated based on GHG emissions from generating stations that are published in the Environmental Protection Agency’s eGRID database (https://www.epa.gov/egrid). The GHG emission values from electricity generation in New Brunswick and Ontario come from the federal government (https://publications.gc.ca/collections/collection_2023/eccc/En81-4-2021-3-eng.pdf) and IESO, respectively. All these values differ from the recommended values by the Regulation to amend the Regulation respecting mandatory reporting of certain emissions of contaminants into the atmosphere (in French only).

Renseignements additionnels

Geographic coverage: Northeastern North America Initial distribution: 2022‑10‑17 Notices and conditions of use: The information provided represents raw data. It is without a guarantee of quality and subject to change without notice.

A searchable database of emission factors from open burning, open detonation, and static firing of military ordnance and rocket motors has been developed and made available. Sampling and analytical results since 2010 have been compiled from seven campaigns in the USA and Canada at four different sites. Various ordnance types were used for multiple test scenarios varying location, soil covered/uncovered, metal-cased and uncased, and net explosive weight. Target compounds include, variously, particulate matter, metals, energetics, volatile organic compounds, and others. Data were primarily derived from unmanned aerial sampling methods using ordnance charge sizes and procedures representative of operational demilitarization operations. The database includes 1,212 emission factors for 39 different types of munitions and an array of pollutants where open burns accounted for 514 scenarios, static firing for 83, and open detonation for 614. The database will be of use for risk evaluations, environmental reporting requirements, and demilitarization operations.

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.

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.

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.

This dataset contains forecasted emissions factors for electricity generation and transportation. These factors are used to convert activity data to particulate matter 2.5 (PM2.5) emissions and metric ton of carbon dioxide equivalent (mTCO2e). This dataset can be applied to "Forecast of Emissions and PM 2.5 Reductions from City Actions" and the "Forecast of Citywide Emissions" dataset to convert from activity data to emissions. For any additional detail please refer to section 6 of the New York City Climate Budgeting Technical Appendices (https://www.nyc.gov/assets/omb/downloads/pdf/exec24-nyccbta.pdf). This dataset is going to be updated once a year during the Executive Budget. You can find the complete collection of Climate Budget data by clicking here.

The Carbon Base is a public database, managed by ADEME, of emission factors necessary for carrying out carbon accounting exercises. (An emission factor is a ratio of greenhouse gas emissions related to an object, material, or service). ADEME gives the following description on its site: > Today, it is the reference database of Article 75 of the Grenelle II Act and is fully consistent with Article L1341-3 of the Transport Code and the default values of the European Emissions Trading System. > Administered by ADEME, its governance is multi-stakeholder: 14 members make up it such as the Ministry of Ecological and Solidarity Transition (MTES), the Mouvement des entreprises de France (MEDEF), the Climate Action Network (RAC), the Association of Professionals in Climate Council (APCC), etc. Its enrichment is open to all through the possibility of external contributions. ### Access to the official base The legal framework is as follows: Article L312-1-1** of the Code of Relations between the Public and Administration (CRPA) stipulates that administrations are obliged to publish online “*data, regularly updated, whose publication is of economic, social, health or environmental interest*”. * Article D323-2-1** of the RCAP states that, if the administration wishes to restrict the possible re-use of the data, it may choose one of the following licenses: “Open License” or “Open Database License”. If it wishes to impose another licence, it must be the subject of prior approval (Article D323-2-2). * Article L300-4** of the CRPA adds that the data must be published “in an open standard, easily reusable and usable by an automated processing system*”. Until 28 May 2020, the “Base Carbone” files could only be viewed online one by one (after creating a user account). This did not allow automated processing. The complete download of the database was restricted (subject to the supply of a Kbis and the signing of a licence not approved within the meaning of Article D323-2-2). The ADEME offered free download, under the Open License, only one extract of 858 lines (version 14.0). This extract represented only 6 % of the base, and was not up to date with the latest version. Since 28 May 2020, ADEME has been broadcasting Base Carbone v18.0 on the same site, in trilingual version and with v18.0 data, without requiring the creation of a user account or the signing of a specific license. Unfortunately, the license chosen is not indicated on this page. It is nevertheless from this dataset that we have to leave for any new analysis. ### Description of the proposed game Here we keep a work of artisan consolidation of the complete base, which was useful before the publication by ADEME dated May 28, 2020 of the database in open data: * the game is distributed in two formats: * a format XLSX, for easy opening in Excel, for the general public, * a format CSV, with encoding UTF-8, separator virgul, for automated use in a standard format, for computer scientists, * the data are those of the version 17.0 (14 November 2019) which is therefore no longer the latest version, * column names are chosen from the official extract from the Carbon Base proposed for download, * only fields in French are taken up, saving time, * the “Element Type” field is not completed because it does not understand its definition. ### Documentation The best available documentation is the one written and disseminated by ADEME, for example from the User Manual page of the official website. Reading this excellent documentation is essential to fully understand the meaning of the data. In addition, the official website contains additional information on how to calculate emission factors.

The monthly Greenhouse Gas (GHG) emission data represents Montgomery County Facilities and Fleet by month beginning July 2019. Facilities: The Facilities GHG data represents physical structures used by County residents and County staff who provide services for County residents. Examples include recreation, libraries, theater and arts, health and human services, liquor retail, courthouses, general services, maintenance facilities, correctional facilities, police stations, fire stations, volunteer fire stations, garages, parking lots, bus shelters and park & ride locations. Facilities use the following fuel sources: grid electricity, natural gas, propane and diesel fuel. Facilities GHG data DOES NOT include Montgomery County Public Schools, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC). Fleet: The Fleet GHG data represents Montgomery County vehicles used by County staff who provide services for County residents. Examples include mass transit buses, snowplows, liquor trucks, light duty trucks, police cars, fire engines and fire service equipment, etc. Each County vehicle use different fuel sources (i.e. diesel, mobil diesel, compressed natural gas, unleaded and E-85). Fleet GHG data DOES NOT include Montgomery County Public School buses, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC) vehicles. GHG Calculation Method: Facilities and Fleet fuel sources are converted into one common unit of energy- 1 Million British thermal units (MMBtu) which are then used with emissions factors and 100-year global warming potential (GWP) to calculate GHG emissions into one common unit of measure- Metric Tons of CO2 Equivalent (MTCO2e). For more information go to: • How to Calculate GHG emissions at https://www.youtube.com/watch?v=zq5wTjvLqnY&t=186s • Emissions & Generation Resource Integrated Database (eGRID) at https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid • Emission Factors for GHG Inventories at https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf Update Frequency : Monthly