{ "@context": "https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld", "@type": "dcat:Catalog", "conformsTo": "https://project-open-data.cio.gov/v1.1/schema", "describedBy": "https://project-open-data.cio.gov/v1.1/schema/catalog.json", "dataset": [ { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=66dad9817da847b385d3b2323ce1be57&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/imf-dataviz::forest-and-carbon", "title": "Forest and Carbon", "description": "

Source: Food and Agriculture Organization of the United Nations (FAO), 2022. FAO, 2022 FAOSTAT Land, Inputs and Sustainability, Land Use https://www.fao.org/faostat/en/#data/RL, Rome, FAO; IMF staff calculations.

Category: Mitigation

Data series: 
Metadata:

The FAOSTAT Land Use domain contains data on twenty-one land use categories. The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). 
In terms of the 2030 SDG Agenda statistical processes, FAO land use classes - Land Area and Forest Land provide inputs into the computations of SDG indicator 15.1.1.

Methodology:

Data on land area, forest area and carbon stocks in forests for the years 1992-2020 have been sourced from FAOSTAT. The methodology adopted by FAO for the compilation of land cover datasets can be seen at-
https://www.fao.org/faostat/en/#data/RL. For some of the countries that were formed during 1992-2020, the shares as in the year of formation have been used to impute the values for the previous years using the values of the originating country.
The following three indicators/indices have been compiled to enable a macro-view of changes in the forests post the ratification of the UN Framework Convention for Climate Change (UNFCCC).
1.\tShare of forest area: The indicator can be considered as identical to global SDG indicator 15.1.1 "Forest area as a proportion of total land area".
2.\tIndex of forest extent: The index shows the magnitude of the forest area of a given year with reference to the base year 1992, that is depicted as 100. 
3.\tIndex of carbon stocks in forests: The index shows the magnitude of the carbon stocks in living biomass in forests of a given year with reference to the base year 1992, that is depicted as 100. 

The indices and the indicators have also been compiled and presented by region and sub-region according to the M49 and the World Economic Outlook Classifications.  

The “World” estimates do not include emissions of selected small countries.

Disclaimer:

Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.
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The IMF-adapted ND-GAIN index is an adaptation of the original index, adjusted by IMF staff to replace the Doing Business (DB) Index, used as source data in the original ND-GAIN, because the DB database has been discontinued by the World Bank in 2020 and it is no longer allowed in IMF work. 

The IMF-adapted ND-GAIN is an interim solution offered by IMF staff until the ND-GAIN compilers will review the methodology and replace the DB index.

Sources: ND-GAIN; Findex - The Global Findex Database 2021; Worldwide Governance Indicators; IMF staff calculations. 

Category: Adaptation

Data series: 

Metadata:

The IMF-adapted ND-GAIN Country Index uses 75 data sources to form 45 core indicators that reflect the vulnerability and readiness of 192 countries from 2015 to 2021. As the original indicator, a country's IMF-adapted ND-GAIN score is composed of a Readiness score and a Vulnerability score.   

The Readiness score is measured using three sub-components – Economic, Governance and Social. In the original ND-GAIN database, the Economic score is built on the DB index, while in the IMF-adapted ND-GAIN, the DB Index is replaced with a composite index built using the arithmetic mean of “Borrowed from a financial institution (% age 15+)” from The Global Financial Index database (FINDEX_BFI) and “Government effectiveness” from the Worldwide Governance Indicators database (WGI_GE).   The Vulnerability, Social and Governance scores do not contain any DB inputs and, hence, have been sourced from the original ND-GAIN database.  

Methodology:

The procedure for data conversion to index is the same as the original ND-GAIN and follows three steps:  

Step 1. Select and collect data from the sources (called “raw” data), or compute indicators from underlying data. Some data errors (i.e., tabulation errors coming from the source) are identified and corrected at this stage. If some form of transformation is needed (e.g., expressing the measure in appropriate units, log transformation to better represent the real sensitivity of the measure etc.) it happens also at this stage.   

Step 2. At times some years of data could be missing for one or more countries; sometimes, all years of data are missing for a country. In the first instance, linear interpolation is adopted to make up for the missing data. In the second instance, the indicator is labeled as "missing" for that country, which means the indicator will not be considered in the averaging process.   

Step 3. This step can be carried out after of before Step 2 above. Select baseline minimum and maximum values for the raw data. These encompass all or most of the observed range of values across countries, but in some cases the distribution of the observed raw data is highly skewed. In this case, ND-GAIN selects the 90-percentile value if the distribution is right skewed, or 10-percentile value if the distribution is left skewed, as the baseline maximum or minimum.  

Based on this procedure, the IMF–Adapted ND-GAIN Index is derived as follows: 
i. Replace the original Economic score with a composite index based on the average of WGI_GE and cubic root of FINDEX_BFI1,  as follows:

IMF-Adapted Economic = ½ · (WGI_GE) + ½ · (FINDEX_BFI)1/3             (1)  

The IMF-adapted Readiness and overall IMF-adapted ND-GAIN scores are then derived as:  

IMF-Adapted ND-GAIN Readiness = 1/3 · ( IMF-Adapted Economic + Governance + Social)  
IMF-Adapted ND-GAIN = ½·( IMF-Adapted ND-GAIN Readiness+ND-GAIN Vulnerability)  

ii. In case of missing data for one of the indicators in (1), IMF-Adapted ND-GAIN Economic would be based on the value of the available indicator. In case none of the two indicators is available, the IMF-Adapted Economic score would not be produced but the IMF-Adapted ND-GAIN Readiness would be computed as average of the Governance and Social scores. This approach, that replicates the approach used to derive the original ND-GAIN indexes in case of missing data, ensures that the proposed indicator has the same coverage as the original ND-GAIN database.  


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Category: Mitigation,Transition to a Low-Carbon Economy
 
Data series: 
Sources: Department of Economic and Social Affairs/United Nations. 2025. United Nations Comtrade database https://comtrade.un.org; International Monetary Fund (IMF) Direction of Trade Statistics (DOTS) https://data.imf.org/en/datasets/IMF.STA:IMTS; World Economic Outlook (WEO) Database https://www.imf.org/en/Publications/WEO/Issues/2025/10/14/world-economic-outlook-october-2025; IMF staff calculations.
 
Metadata:
Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.
 
Methodology:
Bilateral trade in low carbon technology products are estimated by aggregating, by partner country, HS 6-digit commodities identified as low carbon technology products based on Pigato, Miria A., Simon J. Black, Damien Dussaux, Zhimin Mao, Miles McKenna, Ryan Rafaty, and Simon Touboul. 2020. Technology Transfer and Innovation for Low-Carbon Development. International Development in Focus. Washington, DC: World Bank, and IMF research. Total goods exports and imports by partner country are estimated by aggregating all commodities.
 
Bilateral trade balances in low carbon technology products are calculated as low carbon technology products exports to a given partner country less low carbon technology products imports from a given partner country. A positive trade balance means an economy has a surplus in low carbon technology products with a given partner country, while a negative trade balance means an economy has a deficit in low carbon technology products with a given partner country.
 
Total trade in low carbon technology products by partner country is calculated as the sum of low carbon technology products exports to a given country and low carbon technology products imports from a given country.
 
Methodology Attachment 
 
Low Carbon Technology Harmonized System Codes
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Imports of low carbon technology products comprise all low carbon technology products entering the national territory. A relatively high share of low carbon technology products imports indicates that an economy purchases a significant share of low carbon technology products from other economies. 
Exports of low carbon technology products comprise all low carbon technology products leaving the national territory. A relatively high share of low carbon technology products exports indicates that an economy produces and sells a significant share of low carbon technology products to other economies. 
An economy’s trade balance in low carbon technology products is the difference between its exports and imports of low carbon technology products.
 
Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, low carbon technology products), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in low carbon technology products, while a value of less than one indicates a relative disadvantage.
 
Sources: Department of Economic and Social Affairs/United Nations. 2025. United Nations Comtrade database https://comtrade.un.org; International Monetary Fund (IMF) Direction of Trade Statistics (DOTS) https://data.imf.org/en/datasets/IMF.STA:IMTS; World Economic Outlook (WEO) Database https://www.imf.org/en/Publications/WEO/Issues/2025/10/14/world-economic-outlook-october-2025;
IMF staff calculations.
 
Category: Mitigation,Transition to a Low-Carbon Economy
 
 
Data series: 
  • Comparative advantage in low carbon technology products
  • Exports of low carbon technology products
  • Exports of low carbon technology products as percent of GDP
  • Exports of low carbon technology products as share of total exports
  • Imports of low carbon technology products
  • Imports of low carbon technology products as percent of GDP
  • Imports of low carbon technology products as share of total imports
  • Total trade in low carbon technology products
  • Total trade in low carbon technology products as percent of GDP
  • Trade balance in low carbon technology products
  • Trade balance in low carbon technology products as percent of GDP
     
Metadata:
 Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.
 
 
Methodology:
 Low carbon technology products are estimated by aggregating HS 6-digit commodities identified as low carbon technology products based on Pigato, Miria A., Simon J. Black, Damien Dussaux, Zhimin Mao, Miles McKenna, Ryan Rafaty, and Simon Touboul. 2020. Technology Transfer and Innovation for Low-Carbon Development. International Development in Focus. Washington, DC: World Bank, and IMF research. 
 
Trade balance in low carbon technology products is calculated as low carbon technology products exports less low carbon technology products imports. A positive trade balance means an economy has a surplus in low carbon technology products, while a negative trade balance means an economy has a deficit in low carbon technology products.
Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are low carbon technology products to the proportion of global exports that are low carbon technology products. 
Total trade in low carbon technology products is calculated as the sum of low carbon technology products exports and low carbon technology products imports. 
National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in low carbon technology products, which is important for understanding how these technologies can be transferred between economies.
 
Methodology Attachment 
 
Low Carbon Technology Harmonized System Codes
 
 
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The data has been sourced from the International Renewable Energy Agency (https://pxweb.irena.org/pxweb/en/IRENASTAT). 

The indicators on energy transition have been formulated to help users understand the progress in the adoption of renewable energy sources vis-à-vis the increasing energy requirements.

 

Sources: International Renewable Energy Agency (IRENA) (2024), Renewable Energy Statistics 2024, https://pxweb.irena.org/pxweb/en/IRENASTAT; IMF Staff Calculations.
 

 
Category: Mitigation,Transition to a Low-Carbon Economy 
 
 
Data series: 
 
Metadata:
 
 
Electricity generation:  The gross electricity produced by electricity plants, combined heat and power plants (CHP) and the distribution generators measured at the output terminals of generation. It includes on-grid and off-grid generation, and it also includes the electricity self-consumed in energy industries; not only the electricity fed into the grid (net electricity generation). The indicator is expressed in the Dashboard in Gigawatt hours (GWh).
 
 
Electricity Installed Capacity: The maximum active power that can be supplied continuously (i.e., throughout a prolonged period in a day with the whole plant running) at the point of outlet (i.e. after taking the power supplies for the station auxiliaries and allowing for the losses in those transformers considered integral to the station). This assumes no restriction of interconnection to the network. It does not include overload capacity that can only be sustained for a short period of time (e.g., internal combustion engines momentarily running above their rated capacity). For most countries and technologies, the data on installed capacity on the Dashboard reflects the capacity installed and connected at the end of the calendar year and are expressed in Mega Watts (MW). 
 
The renewable power capacity data shown in these tables represents the maximum net generating capacity of power plants and other installations that use renewable energy sources to produce electricity. For most countries and technologies, the data reflects the capacity installed and connected at the end of the calendar year. Pumped storage is included in total capacity but excluded from total generation. The capacity data are presented in megawatts (MW) and the generation data are presented in gigawatt-hours (GWh). All the data are rounded to the nearest one MW/GWh, with figures between zero and 0.5 shown as a 0.
 
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Change in mean sea levels, in millimeters, are estimated based on measurements of sea level from satellite radar altimeters. Time-series information is presented from 1992-12-17 to 2025-02-18, with 3/4 data points for every month. The estimates are provided for 24 regions across the world, along with a global estimate.

 
Source: National Oceanic and Atmospheric Administration (NOAA). 2020. Sea Level Rise. Laboratory for Satellite Altimetry. https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/LSA_SLR_timeseries.php.
 
 
Category: Climate and Weather
 
 
Data series: 
Metadata:
Data is available since 1958 and are acquired from the National Oceanic and Atmospheric Association Global Monitoring Laboratory.
 
 
Methodology:
Estimates of sea level rise are based on measurements from satellite radar altimeters. Altimetry satellites basically determine the distance from the satellite to a target surface by measuring the satellite-to-surface round-trip time of a radar pulse. Plots and time series are available for TOPEX/Poseidon (T/P), Jason-1, Jason-2, and Jason-3, which have monitored the same ground track since 1992.


 
Disclaimer:
Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.
", "keyword": [ "Sea levels", "Climate Indicator", "Global Warming" ], "issued": "2021-03-04T04:26:48.000Z", "modified": "2025-07-01T13:58:09.158Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "climatedata_Admin", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "https://www.imf.org/external/terms.htm", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/datasets/b84a7e25159b4c65ba62d3f82c605855_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_3_3_melted_new/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/kml?layers=0" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/filegdb?layers=0" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/featureCollection?layers=0" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/excel?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/geoPackage?layers=0" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b84a7e25159b4c65ba62d3f82c605855/sqlite?layers=0" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=8e2772e0b65f4e33a80183ce9583d062&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/8e2772e0b65f4e33a80183ce9583d062_0", "title": "Green Bonds", "description": "

ESG debt instruments, including green bonds, social bonds, sustainability bonds, and sustainability-linked bonds, are fixed-income securities designed to sustain or improve the condition of the environment or society or governance practices. 

Green Bonds are fixed income instruments where the proceeds will be exclusively directed to finance or re-finance, in part or in full, new and/or existing green projects. Social bonds have use of proceeds that are dedicated to projects with positive social outcomes. Sustainability bonds have a mix of green and social use of proceeds. Sustainability linked bonds (SLB) are financial instruments where financial and structural characteristics are linked to achieving performance objectives that improve the condition of the environment or society. SLBs are not use-of proceed bonds. They typically include key performance indicators which are structurally connected to the issuer’s goal achievement.  

Sources: LSEG. Accessed on 2025-02-24; IMF staff calculations. 

Category: Climate Finance  

Data series:  

The following data series are available by debt instruments:  

Metadata:  

The source dataset is based on LSEG (formerly Refinitiv), which contain bond-by-bond issuances for Green Bonds, Social Bonds, Sustainability Bonds, and Sustainability-Linked Bonds starting from 2006 to 2024. Bonds by type encompass investment grade, high-yield, and not-rated bonds, commercial papers, certificates of deposit, and sukuks. By issuer type, bonds encompass government, corporate, agency, non-US munis, and other gov/supra bonds. 

Methodology:  

The data are aggregated by country of incorporations, use of proceeds, type of currency and type of issuers (nonfinancial corporations, other financial corporations, banks, state owned entities, sovereign, state and local governments and international organizations). Sovereign green bonds are green bonds issued by central governments and central banks. Compilation of the indicator is based on the methodology used by London Stock Exchange Group, and divergences may be observed when compared to data from other providers. 

", "keyword": [ "Green Bonds", "Green Finance", "Climate Change" ], "issued": "2021-02-27T17:23:57.000Z", "modified": "2025-05-19T17:34:53.692Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "climatedata_Admin", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "https://www.imf.org/external/terms.htm", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/datasets/8e2772e0b65f4e33a80183ce9583d062_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_10_Green_Finance/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://climatedata.imf.org/api/download/v1/items/8e2772e0b65f4e33a80183ce9583d062/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/8e2772e0b65f4e33a80183ce9583d062/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://climatedata.imf.org/api/download/v1/items/8e2772e0b65f4e33a80183ce9583d062/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://climatedata.imf.org/api/download/v1/items/8e2772e0b65f4e33a80183ce9583d062/kml?layers=0" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=4063314923d74187be9596f10d034914&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/4063314923d74187be9596f10d034914_0", "title": "Annual Surface Temperature Change", "description": "
Estimates of changes in the mean surface temperature are presented, in Degree Celsius, for the years 1961-2024 by country and for World.

Source:Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from:https://www.fao.org/faostat/en/#data/ET.
Category:Climate and Weather
Data series:
Metadata:

Statistics are acquired from the FAO. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).
Methodology:
The time series temperature change at a point is calculated as a weighted average of the GISTEMP data over all stations within a given radius, with the closest stations weighted most heavily. The details of the method adopted by FAO for estimating Annual country level and global temperature change are available at –https://fenixservices.fao.org/faostat/static/documents/ET/ET_e.pdf


Disclaimer:
Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.
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Source: The Emergency Events Database (EM-DAT) , Centre for Research on the Epidemiology of Disasters (CRED) / Université catholique de Louvain (UCLouvain), Brussels, Belgium – www.emdat.be.

Category: Climate and Weather

Data series: 
Metadata:

EM-DAT: The International Disasters Database - Centre for Research on the Epidemiology of Disasters (CRED), part of the University of Louvain (UCLouvain) www.emdat.be, Brussels, Belgium. 

Only climate related disasters (Wildfire, Storm, Landslide, Flood, Extreme Temperature, and Drought) are covered. See the CID Glossary for the definitions. 

EM-DAT records country level human and economic losses for disasters with at least one of the following criteria: 

i.          Killed ten (10) or more people  

ii.         Affected hundred (100) or more people  

iii.        Led to declaration of a state of emergency 

iv.        Led to call for international assistance   

 

The reported total number of deaths “Total Deaths” includes confirmed fatalities directly imputed to the disaster plus missing people whose whereabouts since the disaster are unknown and so they are presumed dead based on official figures. “People Affected” is the total of injured, affected, and homeless people. Injured includes the number of people with physical injuries, trauma, or illness requiring immediate medical assistance due to the disaster. Affected includes the number of people requiring immediate assistance due to the disaster. Homeless includes the number of people requiring shelter due to their house being destroyed or heavily damaged during the disaster. Disaster intensity is calculated by summing “Total Deaths” and 30% of the “People Affected”, and then dividing the result by the total population. For each disaster and its corresponding sources, the population referred to in these statistics and the apportionment between injured, affected, homeless, and the total is checked by CRED staff members. Nonetheless, it is important to note that these are estimates based on certain assumptions, which have their limitations. For details on the criteria and underlying assumptions, please visit https://doc.emdat.be/docs/data-structure-and-content/impact-variables/human/.  

 

Methodology:

Global climate related disasters are stacked to show the trends in climate related physical risk factors.
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Price App PPT", "description": "{{description}}", "keyword": [], "issued": "2025-01-29T14:14:32.000Z", "modified": "2025-01-29T14:14:38.000Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "climatedata_Admin", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/documents/4620310b71b44916a51e29903c221c5c" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=4685105440144e7e87f65076a08ec195", "landingPage": "https://climatedata.imf.org/documents/4685105440144e7e87f65076a08ec195", "title": "Note on Emissions Intensities Multipliers", "description": "{{description}}", "keyword": [], "issued": "2025-01-29T14:12:29.000Z", "modified": "2025-01-29T14:12:38.000Z", "publisher": { "name": 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Annual country-level estimates of CO₂ emissions embodied in production and final demand for 66 countries are presented for the years 1995-2018.
Annual country-level estimates of CO₂ emissions embodied in gross exports and gross imports for 66 countries are presented for the years 1995-2020.

CO₂ emissions from fuel combustion embodied in domestic final demand, production, and trade are in million tons of CO₂ equivalent.


Sources: OECD (2021), Trade in embodied CO2 (TeCO₂) Database, https://www.oecd.org/sti/ind/carbondioxideemissionsembodiedininternationaltrade.htm. 
International Monetary Fund (IMF). 2022. Direction of Trade Statistics (DOTS). https://data.imf.org/dot.

Category: Greenhouse Gas (GHG) Emissions

Data series: 
Metadata:

Total value of merchandise exports and imports by trade partners are taken from DOTS. 
CO₂ emissions from fuel combustion embodied in gross exports and gross imports in respect of the trade by 66 countries with the rest of the World have been taken from the OECD’s compilation of indicators on Carbon dioxide emissions embodied in international trade (2021 ed.) which combines the Input-Output Database and Trade in embodied CO₂ (TeCO₂) Database.

Methodology:

The methodology used in the estimation of CO₂ emissions embodied in international trade and domestic final demand for the years 1995-2018 is discussed in Yamano, N. and J. Guilhoto (2020), https://doi.org/10.1787/8f2963b8-en. 

Estimates for the years after 2018 have been compiled by the IMF by trending forward the time series of CO₂ emissions embodied in gross imports and gross exports using annual estimates of value of merchandise exports and imports by trading partner taken from the IMF’s Direction of Trade Statistics database.

References:

Yamano, N. and J. Guilhoto (2020), "CO₂ emissions embodied in international trade and domestic final demand: Methodology and results using the OECD Inter-Country Input-Output Database", OECD Science, Technology and Industry Working Papers, No. 2020/11, OECD Publishing, Paris, https://doi.org/10.1787/8f2963b8-en. 




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Annual country-level estimates for 66 countries for the three indicators are presented by industry for 45 industries, for the years 1995-2018.

CO₂ emissions from fuel consumption are in millions of metric tons of CO₂.
CO₂ emissions intensities are in metric tons of CO₂ emissions per $1 million USD of output.
CO₂ emissions multipliers are in metric tons of CO₂ emissions per $1 million USD of output.

Sources: OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio; OECD (2021), Trade in embodied CO₂ (TeCO2) Database, https://www.oecd.org/sti/ind/carbondioxideemissionsembodiedininternationaltrade.htm; Organisation for Economic Co-operation and Development (OECD). 2021. Input-Output Tables (IOTs) (https://oe.cd/i-o).

Category: Greenhouse Gas (GHG) Emissions

Data series: 
Metadata:

Input-Output tables and Carbon Emissions for 66 Countries and 45 industries have been taken from the OECD’s compilation of indicators on “Carbon dioxide emissions embodied in international trade” (2021 ed.) which combines the Input-Output Database and Trade in embodied CO₂ (TeCO2) Database. 
In this release of TeCO2 sourced from OECD, emissions from fuels used for international aviation and maritime transport (i.e. aviation and marine bunkers) are also considered.
The data series “CO₂ emissions, emission intensities; emission multipliers” was earlier referred to as “Carbon emissions from fuel combustion per unit of output” in the previous vintage of the Climate Change Indicator Dashboard.

Methodology:

CO₂ emission intensities are calculated by dividing the CO₂ emissions from fuel consumption by output from the OECD Inter-Country Input-Output (ICIO) Tables and multiplying the result by 1 million for scaling purposes. CO₂ emission multipliers are calculated by multiplying the Leontief inverse (also known as output multipliers matrix) from the OECD Inter-Country Input-Output (ICIO) Tables by the CO₂ emission intensities.

Disclaimer:

Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.

Note on CO2 Emissions, Intensities, and Multipliers, June 2022
Update of the CO₂ emissions by industry - April 2022
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These are subdivided into four types of taxes: taxes on energy (including fuel for transport); taxes on transport (excluding fuel for transport), taxes on pollution and taxes on resources.

Sources: Organisation for Economic Co-operation and Development (2020), Environmentally Related Tax Revenue, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=ERTR. Accessed on 2023-06-17; International Monetary Fund (IMF), Statistics Department (Government Finance Division) Questionnaire.

Category: Mitigation

Data series: 
Methodology:

Environmental taxes are a subset of taxes, as defined in the 2008 System of National Accounts and the Government Finance Statistics Manual 2014, whose tax base is a physical unit (or a proxy of it) of something that has a proven, specific, negative impact on the environment.
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The selection of key indicators from the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) climate scenarios comprises Primary Energy Mix, Fossil Fuel Prices, Final Energy Mix, Emissions and CCS, Shadow Carbon Price, and Mean Surface Temperature. 

Sources: NGFS (2023), Scenarios Portal; and IIASA (2023), NGFS Phase 4 Scenario Explorer.

Category: Transition to a Low-Carbon Economy

Metadata
The framework of the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) allows to simulate, in a forward-looking fashion, the dynamics within and between the energy, land-use, economy, and climate systems. Consistent with that framework, the NGFS explores a set of seven climate scenarios, which are characterized by their overall level of physical and transition risk. The scenarios in the current Phase IV (NGFS climate scenarios data set) are Low Demand, Net Zero 2050, Below 2°C, Delayed Transition, Nationally Determined Contributions (NDCs), Current Policies, and Fragmented World. Each NGFS scenario explores a different set of assumptions for how climate policy, emissions, and temperatures evolve. To reflect the uncertainty inherent to modeling climate-related macroeconomic and financial risks, the NGFS scenarios use different models, over and above the range of scenarios. These integrated assessment models (IAMs) are, by their acronyms: GCAM, MESSAGEix-GLOBIOM, and REMIND-MAgPIE. For details please see the NGFS climate scenarios presentation the Climate scenarios technical documentation, and the User guide for data access.

Methodology
The database of key indicators is curated by the IMF in collaboration with the NGFS. The full data set can be found at the original sources. The license of the NGFS applies. 

The transition pathways for the NGFS scenarios have been generated with three well-established integrated assessment models, namely GCAM, MESSAGEix-GLOBIOM and REMIND-MAgPIE. These models combine macro-economic, agriculture and land-use, energy, water and climate systems into a common numerical framework that enables the analysis of the complex and non-linear dynamics in and between these components. The IAM results have been downscaled to the level of 140 countries. 

For details please see the NGFS climate scenarios presentation, the Climate scenarios technical documentation, and the User guide for data access. 

The table that follows give the correspondence of the variables in the IMF Climate Change Indicators Dashboard and the NGFS climate scenarios database.

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n

Series

\n 		\n

IMF Variable

\n 		\n

NGFS Variable

\n
\n

Primary Energy

\n 		\n

Coal

\n 		\n

Primary Energy|Coal

\n
\n

 

\n 		\n

Oil

\n 		\n

Primary Energy|Oil

\n
\n

 

\n 		\n

Gas

\n 		\n

Primary Energy|Gas

\n
\n

 

\n 		\n

Biomass

\n 		\n

Primary Energy|Biomass

\n
\n

 

\n 		\n

Hydro

\n 		\n

Primary Energy|Hydro

\n
\n

 

\n 		\n

Wind

\n 		\n

Primary Energy|Wind

\n
\n

 

\n 		\n

Geothermal

\n 		\n

Primary Energy|Geothermal

\n
\n

 

\n 		\n

Solar

\n 		\n

Primary Energy|Solar

\n
\n

 

\n 		\n

Nuclear

\n 		\n

Primary Energy|Nuclear

\n
\n

Energy Prices

\n 		\n

Price: Coal

\n 		\n

Price|Primary Energy|Coal

\n
\n

 

\n 		\n

Price: Oil

\n 		\n

Price|Primary Energy|Oil

\n
\n

 

\n 		\n

Price: Gas

\n 		\n

Price|Primary Energy|Gas

\n
\n

Final Energy

\n 		\n

Electricity

\n 		\n

Final Energy|Electricity

\n
\n

 

\n 		\n

Gases

\n 		\n

Final Energy|Gases

\n
\n

 

\n 		\n

Heat

\n 		\n

Final Energy|Heat

\n
\n

 

\n 		\n

Hydrogen

\n 		\n

Final Energy|Hydrogen

\n
\n

 

\n 		\n

Liquids

\n 		\n

Final Energy|Liquids

\n
\n

 

\n 		\n

Solids

\n 		\n

Final Energy|Solids

\n
\n

Emissions and CCS1)

\n 		\n

Energy and industrial processes\n CO2 emissions

\n 		\n

= Emissions|CO2 – Emissions|CO2|LULUCF\n Direct+Indirect

\n
\n

 

\n 		\n

LULUCF CO2 emissions

\n 		\n

Emissions|CO2|LULUCF\n Direct+Indirect

\n
\n

 

\n 		\n

Total non-CO2 emissions

\n 		\n

Emissions|Total Non-CO2

\n
\n

 

\n 		\n

Fossil energy CCS

\n 		\n

Carbon Sequestration|CCS|Fossil

\n
\n

 

\n 		\n

Bioenergy with CCS

\n 		\n

Carbon\n Sequestration|CCS|Biomass

\n
\n

 

\n 		\n

Industrial processes CCS

\n 		\n

Carbon\n Sequestration|CCS|Industrial Processes

\n
\n

Carbon Price

\n 		\n

Price: Carbon

\n 		\n

Price|Carbon

\n
\n

Surface Temperature2)

\n 		\n

5th percentile

\n 		\n

Country\n Temperature|Downscaling|5.0th Percentile

\n
\n

 

\n 		\n

50th percentile

\n 		\n

Country\n Temperature|Downscaling|50.0th Percentile

\n
\n

 

\n 		\n

95th percentile

\n 		\n

Country\n Temperature|Downscaling|95.0th Percentile

\n

1)World emissions are IMF staff\ncalculations. 2)Postprocessed results. World mean surface\ntemperature relative to pre-industrial levels; variable names “AR6 climate\ndiagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|5.0th Percentile”, “50.0th\nPercentile”, and “95.0th Percentile”, respectively.



Data series: 
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Potential national income loss from climate risks can be computed using simple damage functions that estimate damages based on the temperature outcomes inferred from the emissions trajectories projected by the transition scenarios. Potential national income benefit from avoided climate damages can be computed by contrasting the damages estimates based on the temperature outcomes from the transition scenarios with the policy, or mitigation, costs from climate action needed to meet a particular temperature outcome.

Sources: Network for Greening the Financial System (2023), Scenarios Portal; and International Institute for Applied Systems Analysis (2023), NGFS Phase 4 Scenario Explorer; IMF Staff Calculations.

Category: Transition to a Low-Carbon Economy

Metadata
The framework of the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) allows to simulate, in a forward-looking fashion, the dynamics within and between the energy, land-use, economy, and climate systems. Consistent with that framework, the NGFS explores a set of seven climate scenarios, which are characterized by their overall level of physical and transition risk. The scenarios in the current Phase IV (NGFS climate scenarios data set) are Low Demand, Net Zero 2050, Below 2°C, Delayed Transition, Nationally Determined Contributions (NDCs), Current Policies, and Fragmented World. Each NGFS scenario explores a different set of assumptions for how climate policy, emissions, and temperatures evolve. To reflect the uncertainty inherent to modeling climate-related macroeconomic and financial risks, the NGFS scenarios use different models, over and above the range of scenarios. These integrated assessment models (IAMs) are, by their acronyms: GCAM, MESSAGEix-GLOBIOM, and REMIND-MAgPIE. 

GDP losses and benefits are derived based on the National Institute Global Econometric Model (NiGEM). NiGEM consists of individual country models for the major economies, which are linked together through trade in goods and services and integrated capital markets. Country level data (or country aggregates, whenever country level disaggregation is not present) for GDP, population, primary energy consumption by fuel type, useful energy and carbon taxes from the IAM output is used as an input into the NiGEM scenarios. 
Climate scenarios within NiGEM can be broadly categorized into physical and transition events. While the effects of physical and transition shocks alongside policy decisions are contemporaneous, the scenarios in NiGEM can be run in a “stacked” manner, where each scenario uses the information provided by the previous scenario as its starting point. This allows for decomposition of shocks and their effects. Results are available for three scenarios: Net Zero 2050, Delayed Transition, and Current Policies. For details please see the NGFS climate scenarios presentation, the Climate scenarios technical documentation, and the User guide for data access.

Methodology
The NGFS climate scenarios database contains information on mitigation policy costs, business confidence losses, chronic climate damages, and acute climate damages. Mitigation policy costs reflect transition risk in a narrow sense and is measured against the Current Policies scenario (for which it is zero). Business confidence losses result from unanticipated policy changes, and only in the Delayed Transition scenario. GDP losses from chronic risks arise from an increase in global mean temperature. Estimates of the macroeconomic impact of acute risks are based on physical risk modelling covering different hazards. 

Acute risks are modeled independent of the input IAM. Results are available at the original sources for four hazards: droughts impacting on crop yields, tropical cyclones directly damaging assets, heatwaves affecting productivity and demand, and riverine floods directly damaging assets too. Apart from floods acute risks are the result of randomized stochastic output, yielding 60th to 99th percentile GDP impacts. In accordance with the presentation of the scenario results by the NGFS, the 90th percentile has been chosen as the representative confidence bound. That way, the results are focusing on tail risk. While the choice of the percentile will lead to marked differences for the GDP losses indicator, its influence on the GDP benefits indicator is muted due to comparing like-with-like. Further, the sum of the impacts from the four hazards is taken as the acute physical risk measure; see what follows for the methodology in deriving the net benefits. 

Net benefits can be calculated by comparing the impact of stronger climate action to the reference scenario, the Current Policies scenario: Net Benefit  =  100  *  (GDP[Policy scenario] / GDP[Current Policies]  –  1). 

GDP in either scenario can be inferred from the hypothetical baseline with no transition nor physical risk and the percentage losses due to mitigation policy (MP), business confidence (BC), chronic climate (CC), and acute climate (AC): 

GDP  =  Baseline  *  (1  +  (MP + BC + CC + AC) / 100). 

Plugging this into the above equation one finds after some algebra: 

Net Benefit  =  (MP[Policy scenario] – MP[Current Policies]  +  BC[Policy scenario] – BC[Current Policies]  +  CC[Policy scenario] – CC[Current Policies]  +  AC[Policy scenario] – AC[Current Policies])  / (1  +  (MP + BC + CC + AC)[Current Policies] / 100). 

Obviously, MP[Current Policies] = BC[Current Policies] = BC[Net Zero 2050] = 0. 

In order to achieve consistency in aggregation of the four components to the total benefit, the denominator is kept fixed, while for the individual contributions only one component at a time, MP, BC, CC, or AC, is used in the numerator. 

Results are presented for the 49 countries, five geographic regions covering the remainder of countries, and a global and European total. The coverage of the five remainder regions refers to the country classification of emerging market and developing economies in the IMF’s World Economic Outlook.

Data series: 

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Source: ICE Data Services, Orbis (Bureau Van Dijk), Network for Greening the Financial System (NGFS); IMF staff calculations.

Category: Transition to a Low-Carbon Economy

Data series: 
Metadata:

Data from the private data vendor ICE Data Services are used for firm level emissions. ICE Data Services provides company-level data on emissions disclosures, estimated emissions, emission targets, and model-based emission projections up to 2050. The Carbon Cost to Revenues and Carbon Cost to Assets in Disclosing Firms indicators leverage firm-level emissions disclosures and projections of these firms for the period 2025-2050. The sample is restricted to firms whose emissions disclosures are deemed complete. Data for a firm are considered “Complete” when at least 95% of a company’s worldwide Scope 1 and 2 emissions within an appropriately chosen reporting boundary are covered by their reporting. 
 
Firm identifiers (ISIN or LEI codes) from ICE Data Services are mapped to Orbis dataset for the compilation of the indicators. Orbis is a dataset compiled and maintained by Bureau Van Dijk and owned by Moody’s Analytics. The Carbon Cost to Revenues and Carbon Cost to Assets in Disclosing Firms indicators are calculated using the information on company revenues, assets, country of incorporation, and ISIC core sector provided by ICE.
 
The Network for Greening the Financial System (NGFS) produces model-based simulation of the joint climate-economic systems under different scenarios. The Carbon Cost to Revenues and Carbon Cost to Assets in Disclosing Firms indicators are calculated using NGFS Phase 3 scenarios from the simulation of the model REMIND-MAgPIE 3.0-4.4, downloaded by IMF staff in December 2023.

Methodology:

Carbon Tax to Revenues/Assets in Disclosing Firms indicators are calculated at the level of ISIC Section (letter code), ISIC Division (2 digits), and WEO country grouping, but not crossing the sectoral and geographic dimensions. In addition, the indicators are also calculated for both Scope 1 and Scope 2 emissions, and for each projection year between 2025-2050. 
 
Specifically, for each of the sectors or geographies, the indicator is defined as total emissions in each scenario/year times the corresponding carbon price in that scenario/year from the NGFS phase 3 scenarios, summed over all companies, and divided by the total revenues or assets of all companies in that sector/geography in the base year.
 
The underlying assumption when calculating these indicators based on Scope 1 emissions is that companies will be directly and fully taxed at the assumed carbon tax rate under the given scenario. As estimates of future revenues are not available, results are derived in comparison to the 2021 revenues to scale the carbon tax burden on the firms. However, current revenues may not fully reflect future revenues. To address this, a second version of the estimates is calculated by relaxing the constant revenues and assets assumption. For this set of indicators, firms’ revenues and assets are adjusted by a growth factor by considering past growth trends (based on five years sector-country average growth rate pre-covid) and WEO growth assumptions.
 
As for Scope 2 emissions, results are based on full carbon taxation of Scope 2 indirect emissions. This is interpreted as an upper bound to the potential impact in a scenario in which either the energy producing sector fully passes through these costs downstream to higher energy prices, or companies get taxed directly on their purchases of energy. Scope 2 Market Based emissions are used in the calculations as this allows the reduction in the accounting of indirect emissions that arises from the purchase of “green certificates” or other instruments that companies can use to offset their carbon footprint.
 
To guarantee the preservation of a reasonable degree of confidentiality/ representativeness of the underlying company-level information, results are not calculated in those instances in which either the number of disclosing firms in each sector/geography is strictly less than 10 (9 or lower), or the share of either assets or revenues (whichever is applicable) of the top 2 firms exceeds 85%.


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The indicators are created from ICE Data Services and per license agreement they should not be used for commercial purposes.

IMF Copyright and Usage", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/datasets/b3c440134b334a869d39e7e6ff792f81_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_16_2/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b3c440134b334a869d39e7e6ff792f81/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b3c440134b334a869d39e7e6ff792f81/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b3c440134b334a869d39e7e6ff792f81/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://climatedata.imf.org/api/download/v1/items/b3c440134b334a869d39e7e6ff792f81/kml?layers=0" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=72d6bea354a543eaa5c2e16bbf3639d3&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/72d6bea354a543eaa5c2e16bbf3639d3_0", "title": "Revenues/Assets at Risk in Disclosing Firms", "description": "

Source: ICE Data Services, Orbis (Bureau Van Dijk), Network for Greening the Financial System (NGFS); IMF staff calculations.

Category: Transition to a Low-Carbon Economy

Data series: 
Metadata:

The data from the private data vendor ICE Data Services are used for firm level emissions. ICE Data Services provides company-level data on emissions disclosures, estimated emissions, emission targets, and model-based emission projections up to 2050. The Revenues/Assets at Risk in Disclosing Firms indicators leverage on firm-level emissions disclosures and projections of these firms for the period 20252050. The sample is restricted to firms whose emissions disclosures are deemed complete. Data for a firm are considered “Complete” when at least 95% of a company’s worldwide Scope 1 and 2 emissions within an appropriately chosen reporting boundary are covered by their reporting. 
 
Firm identifiers (ISIN or LEI codes) from ICE are mapped to Orbis dataset for the compilation of the indicators. Orbis is a dataset compiled and maintained by Bureau Van Dijk and owned by Moody’s Analytics. The Revenues/Assets at Risk in Disclosing Firms indicators are calculated using the information on company revenues, assets, country of incorporation, and ISIC core sector provided by ICE.
The Network for Greening the Financial System (NGFS) produces model-based simulation of the joint climate-economic systems under different scenarios. The Revenues and Assets at Risk in Disclosing Firms indicators are calculated using NGFS Phase 3 output from the simulation of the model REMIND-MAgPIE 3.0-4.4  downloaded by IMF staff in December 2023.

Methodology:

The Revenues/Assets at Risk in Disclosing Firms indicators are calculated at the level of ISIC Section (letter code), ISIC Division (2 digits), and WEO country grouping, but not crossing the sectoral and geographic dimensions. These indicators are only calculated for Scope 1 emissions. 
 
Specifically, for each of the sectors or geographies, the indicator is defined as the share of total revenues or assets in each sector or geography in firms at risk. In turn, firms at risk as defined as those disclosing firms for which, in at least one of the projection years 2025-2050, the total emissions in each scenario/year times the corresponding carbon price in that scenario/year from the NGFS phase 3 scenarios exceeds 10% of the revenues for that firm.
 
The underlying assumption when calculating these indicators based on Scope 1 emissions is that companies will be directly and fully taxed at the posted carbon tax rate under the assumed scenario. As estimates of future revenues are not available, firms at risk are identified in comparison to the 2021 revenues to scale the carbon tax burden on the firms. However, current revenues may not fully reflect future revenues. To address this, a second version of the estimates is calculated by relaxing the constant revenues and assets assumption. For this set of indicators, firms’ revenues and assets are adjusted by a growth factor by considering past growth trends (based on five years sector-country average growth rate pre-covid) and WEO growth assumptions.
 
To guarantee the preservation of a reasonable degree of confidentiality/ representativeness of the underlying company-level information, results are not calculated in those instances in which either the number of disclosing firms in each sector/geography is strictly less than 10 (9 or lower), or the share of either assets or revenues (whichever is applicable) of the top 2 firms exceeds 85%.

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The indicators are created from ICE Data Services and per license agreement they should not be used for commercial purposes.

IMF Copyright and Usage", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/datasets/72d6bea354a543eaa5c2e16bbf3639d3_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_16_1/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://climatedata.imf.org/api/download/v1/items/72d6bea354a543eaa5c2e16bbf3639d3/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/72d6bea354a543eaa5c2e16bbf3639d3/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://climatedata.imf.org/api/download/v1/items/72d6bea354a543eaa5c2e16bbf3639d3/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://climatedata.imf.org/api/download/v1/items/72d6bea354a543eaa5c2e16bbf3639d3/kml?layers=0" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=d48cfd2124954fb0900cef95f2db2724&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/d48cfd2124954fb0900cef95f2db2724_0", "title": "Fossil Fuel Subsidies", "description": "


Undercharging for fuels is disaggregated\ninto explicit and implicit subsidies. Explicit subsidies measure the amount\nthat the financial cost to supply a fuel (i.e., the supply cost) exceeds the price\npaid by the fuel user. Implicit subsidies measure the difference between a\nfuel’s full social cost and the price paid by the fuel user, exclusive of any\nexplicit subsidy. A fuel’s full social cost includes both supply costs and negative\nexternalities, which are costs imposed on society due to consuming the fuel and\nprimarily include local air pollution, climate change, and broader\nexternalities related to driving.

\n\n

It should be noted that the concept of “subsidies”\nused here differs from the definition of subsidies in macroeconomic statistics.

Sources: Black, Simon; Liu, Antung A.; Parry, Ian; Vernon, Nate. 2023. IMF Fossil Fuel Subsidies Data: 2023 Update. International Monetary Fund. ISBN: 9798400249006/1018-5941. IMF staff estimations.

Category: Mitigation

Data series: 

Methodology:

The calculations follow two steps for each fuel source and use (e.g., industrial coal use): (i) estimation of country-level externalities by fuel on societal costs of associated local air pollution, greenhouse gas emissions, congestion and road accidents, and (ii) calculation of country-level subsidies based on support given to producers and the gap between retail prices and the socially optimal price.

Subsidies are disaggregated into explicit and implicit subsidies, where explicit refers to subsidies caused by the supply costs being greater than the retail prices, whereas implicit subsidies reflect subsidies caused by the efficient price (incorporating the cost of negative externalities of fossil fuel use and foregone consumption tax revenues),  being greater than the retail price exclusive of explicit subsidies.

A full description of the methodology and associated data is provided in the Working Papers titled Still Not Getting Energy Prices Right: A Global and Country Update of Fossil Fuel Subsidies and IMF Fossil Fuel Subsidies Data: 2023 Update, and on the IMF’s energy subsidy website. 

Disclaimer: 

The subsidy amounts are estimates using the available data. See the associated Working Paper for additional caveats.
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Sources: OECD (2022), OECD FDI Statistics Database, https://stats.oecd.org/. OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio. OECD (2018), OECD Analytical AMNE Database, https://www.oecd.org/sti/ind/analytical-amne-database.htm. IMF staff calculations.

Category: Greenhouse Gas (GHG) Emissions

Data series:
Metadata:

CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs). Source: IMF CO₂ emissions multipliers; OECD Input-Output tables, ISIC Rev.4; OECD FDI by country and economic activity_BMD4 and historical BMD3 series; OECD Capital formation by activity ISIC rev4; and IMF calculations.

CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs) to final demand ratio. Source: IMF CO₂ emissions in Gross Fixed capital formation of Direct Investment in resident operating units (non-SPEs); IMF CO₂ emissions multipliers; OECD Output tables, ISIC Rev.4.; and IMF calculations.

CO₂ emissions per unit of output for foreign controlled multinational enterprises. Source: IEA CO₂ emission; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations.

CO₂ emissions per unit of output of domestic controlled enterprises. Source: IEA CO₂ emissions; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations. 

CO₂ emissions in output of foreign controlled multinational enterprises. Source: IMF CO₂ emission intensities of output of foreign controlled multinational enterprises; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations.

CO₂ emissions in exports of foreign controlled multinational enterprises. Source: IMF CO₂ emission intensities of output of foreign controlled multinational enterprises; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations.

CO₂ emissions in output of domestic controlled enterprises. Source: IMF CO₂ emission intensities of output of domestic controlled enterprises; OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms)., ISIC rev4.; and IMF calculations.

CO₂ emissions in exports of domestic controlled enterprises. Source: IMF CO₂ emission intensities of output of domestic controlled enterprises; OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms)., ISIC rev4.; and IMF calculations.

Methodology:

CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs): CO₂ emission multipliers (reflecting both direct and indirect CO₂ emissions including CO₂ emissions from fuel combustion and CO₂ emissions embodied in goods and services used as inputs during the production process) were multiplied by the output used in gross fixed capital formation to obtain CO₂ emissions. The CO₂ emissions obtained were apportioned to direct investment using the share of direct investment in resident operating units (non-SPEs) to gross fixed capital formation.

CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs) to final demand ratio: Estimates of CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs) are divided by final demand.

CO₂ emissions per unit of output of foreign controlled multinational enterprises: Direct CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) were multiplied by calculated output multipliers of foreign controlled multinational enterprises.

CO₂ emissions per unit of output of domestic controlled enterprises: Direct CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) were multiplied by calculated output multipliers of domestic controlled enterprises.

CO₂ emissions in output of foreign controlled multinational enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of foreign controlled multinational enterprises were multiplied by final demand of products of foreign controlled multinational enterprises.

CO₂ emissions in exports of foreign controlled multinational enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of foreign controlled multinational enterprises were multiplied by exports for final use of foreign controlled multinational enterprises.

CO₂ emissions in output of domestic controlled enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of domestic controlled enterprises were multiplied by final demand of products of domestic owned enterprises.

CO₂ emissions in exports of domestic controlled enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of domestic controlled enterprises were multiplied by exports for final use of domestic owned enterprises.

Methodology Attachment
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Sources: OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio; International Monetary Fund (IMF), Statistics Department Questionnaire; IMF staff calculations.

Category: Climate Finance

Data series:
Metadata:

For relevant literature see Guan, Rong, Haitao Zheng, Jie Hu, Qi Fang, and Ruoen Ren. 2017. “The Higher Carbon Intensity of Loans, the Higher Non-Performing Loan Ratio: The Case of China.” Sustainability 9 (4) (April 22): 667. https://dx.doi.org/10.3390/su9040667.

Methodology:

The IMF has developed the Carbon Footprint of Bank Loans (CFBL) indicator for selected countries. CFBL indicator requires (i) deposit takers’ domestic loans by industry data, and (ii) the estimation of carbon emission factors (CEFs) by industry.

The IMF has conducted a data collection exercise to obtain deposit takers’ domestic loans by industry data. 

The CEFs are calculated based on (i) direct metric tons of carbon emissions from fuel consumption per million $US of output by country and industry (CO2 emission intensities), and (ii) direct and indirect carbon emissions from fuel consumption per million $US of output by country (CO2 emission multipliers). The output multipliers and carbon emission intensities for 66 countries and 45 industries are sourced from the OECD Input-Output Database. Direct and indirect carbon emission factors are calculated by multiplying the Leontief inverse (also known as input-output multipliers) from the OECD World Input-Output Table by the carbon emissions from fuel consumption intensities.

CFBL indicator is obtained by multiplying domestic loans to a specific industry by their corresponding carbon emission factors, summing over all industries and dividing the final result by total domestic loans. For a limited number of countries, updated CFBL information until 2018 will be posted in due course. 

CFBL is an experimental indicator. The index requires a nuanced reading. For instance, a sharp increase in the share of a brown industry in the deposit takers’ loans portfolio may create a negative impact on this indicator in the short term, but longer term results could diverge significantly if these loans were allocated for transition to low carbon environment or for continuing unsustainable brown activities. 

The emission coefficients applied to loans related to the emissions of the industry and not the emissions resulting from the consumption of the goods the industry produces. 

Also, the estimation methodology has a number of limitations. 

First, class level ISIC data could be more appropriate for the CFBL estimation, as it offers more detailed information to evaluate carbon footprint by industry. However, carbon emission factors are not available at this granularity. Also, the ISIC structure is not fully aligned with the needs of climate finance.

Second, the granularity of the deposit takers’ domestic loans by industry data availability is not fully consistent across jurisdictions. It is not possible to obtain the loans by industry data at the same level of granularity from all participating countries. 

Third, the country coverage is limited as carbon intensity factors are available for only 66 countries. 

Fourth, input-output multipliers have limiting assumptions. Input-output multipliers are static (i.e., assume that there is a fixed input structure and fixed ratios for production for each industry) and do not take into account supply-side constraints or budget constraints. Please see additional information in this link.

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It is\nproduced based on the INFORM Risk with IMF staff calculations.

Climate-driven Hazard & Exposure reflects the probability of\nphysical exposure associated with specific climate-driven hazards. 

Vulnerability represents economic, political and social\ncharacteristics of the community that can be destabilized in case of a hazard\nevent. 

\n\n\n\n\n\n

Lack of coping capacity relates to the ability of a country to\ncope with disasters in terms of formal, organized activities and the effort of\nthe country’s government as well as the existing infrastructure which\ncontribute to the reduction of disaster risk. 

The data are updated annually based on August data release by INFORM Risk.


Sources: Disaster Risk Management Knowledge Centre (DRMKC). 2022. INFORM Risk Index. European Commission. https://drmkc.jrc.ec.europa.eu/inform-index/ INFORM-Risk; IMF staff calculations.


Category: Adaptation

Data series: 
Methodology:

Climate-driven INFORM Risk Indicator is produced based on\nthe INFORM Risk with IMF staff adjustments to focus on climate-driven\nrisks only. INFORM Risk has three dimensions: hazard & exposure,\nvulnerability and lack of coping capacity. The hazard & exposure dimension\nhas been adjusted by IMF staff to include climate related components only\n(i.e., flood, tropical cyclone, and drought). This results in a Climate-driven\nINFORM Risk Indicator. Vulnerability and lack of coping capacity dimensions are\nbased on the INFORM Risk with no adjustments.

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Sources: International Monetary Fund (IMF), Statistics Department. 2021. Government Finance Statistics (GFS) Database. https://data.imf.org/?sk=a0867067-d23c-4ebc-ad23-d3b015045405; International Monetary Fund (IMF), Statistics Department (Government Finance Division) Questionnaire.

Category: Mitigation

Data series: 
Methodology:

Government expenditures on a specified set of activities including pollution abatement, protection of biodiversity landscape, waste and wastewater management, within the framework of the Classification of Functions of Government (COFOG).

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IMF Copyright and Usage
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Annual country-level estimates of net emissions/removals for 215 countries are presented by six sectors, 14 sub-sectors and four gases, for the years 1970-2021. To facilitate retrieval and use, two aggregated indicators, Total GHG emissions including land-use, land-use change and forestry (LULUCF) and Total GHG emissions excluding land-use, land-use change and forestry are presented in addition to the sector-level information.

Annual country-level projections of emissions, assuming business-as-usual, are presented for three categories – CO2 emission, GHG emissions excluding LULUCF and GHG emissions including LULUCF – for the years 2022- 2030. 

National mitigation targets, or the implied targets, as estimated from the Nationally Determined Contributions are presented for the year 2030. Both the conditional, where applicable, and unconditional targets have been presented, along with an average of the two targets.  

Corresponding aggregates are presented for the above indicators by region and sub-region according to the M49 and the World Economic Outlook Classifications.

Estimates of emissions are in million metric tons of CO2 equivalent.

Sources: United Nations Framework Convention on Climate Change (UNFCCC). 2022. Greenhouse Gas Inventory Data - Detailed data by Party - Annex I. https://di.unfccc.int/detailed_data_by_party. Copyright 2022 United Nations Framework Convention on Climate Change; Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J.G.J., Grassi, G., Rossi, S., Vignati, E., GHG emissions of all world countries - 2021 Report, EUR 30831 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-41547-3, doi:10.2760/173513, JRC126363;  Food and Agriculture Organization of the United Nations (FAO). FAOSTAT Climate Change, Emissions, Emission Totals. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/GT. IMF staff calculations.

Category: Greenhouse Gas (GHG) Emissions

Data series: 
Metadata:

Under the United Nations Framework Convention on Climate Change (UNFCCC), countries that are Parties to the Convention submit national greenhouse gas (GHG) inventories to the Climate Change secretariat. The reporting and review requirements for GHG inventories are different for Annex I and non-Annex I Parties. 
For Annex-I Parties, National greenhouse gas inventories have been sourced from UNFCCC Greenhouse Gas Data Interface for the years from 1990 onwards. Emission estimates have been sourced from EDGAR for the years prior to 1990.
For Non-Annex-I countries, data on the GHG Data Interface is sparse, with inventories available for different time periods for different parties. Therefore, for the Non-Annex-I Countries, data has been sourced from two databases. 
For LULUCF, data has been taken from FAOSTAT. For sectors other LULUCF, data has been taken from EDGAR.

Methodology:

Inventories:
The annual greenhouse gas inventories have been sourced from UNFCCC (for Annex-I Parties), and FAO and EDGAR (for non-Annex-I Parties). Inventories have been extrapolated for the year 2021, where not available, using the country-level growth rates derived from the EDGAR dataset. 

Projections of Emissions:
IMF staff estimate the expected level of emissions in 2030 for a given country under a BAU scenario.  The 2030 BAU level estimates are developed using a spreadsheet-based model developed by IMF and World Bank staff known as the Carbon Pricing Assessment Tool (CPAT). Using projections of GDP, taxation regimes, global energy prices, along with assumptions on income and price elasticities and rates of technological change, the CPAT estimates energy-related GHG emissions, holding non-energy emissions fixed at 2019 levels (agriculture, waste, land use, land use change and forestry; industrial process emissions are assumed to scale with energy GHGs).

National Mitigation Targets derived from Nationally Determined Contributions:
The economy-wide 2030 NDC implied target level of emissions reflect IMF calculations based on information obtained from country’s Nationally Determined Contributions (NDCs) submitted under the UNFCCC. 
NDC reports submitted by countries under the UNFCCC embody country emission targets for 2030 and 2050. The targets outlined in the NDCs are not reported according to a uniform or consistent reporting structure / methodology.  Some countries report their targets as a percentage reduction from a baseline period, others present their targets as an absolute level of emissions and others report their targets as a percentage reduction from an assumed “business as usual” (BAU) level of emissions.  
To compare the targets from one country to another the IMF has developed a methodology to estimate the targets and present them in a consistent and standardized manner, wherein, the economy-wide 2030 emission targets in country NDCs reports are converted into an implied target level of emissions. 
Multiple targets are shown to aid analysis: conditional and unconditional NDCs are treated separately, as are targets with and without land use, land-use change and forestry (LULUCF). For countries where the NDC target is a range, an average is assumed. For countries where the estimated NDC target level is above our estimated BAU we assume that these targets are nonbinding and we do not assume that the country raises emissions above BAU, hence we set the NDC targets as equal to the BAU. 
Implied NDC estimates are periodically updated as countries revise or clarify their targets to the UNFCCC.

Details of the estimation of projections and the translation of NDC targets are given in IMF Staff Climate Note 2021/005.

Disclaimer:

The IMF Staff estimates (e.g. Business as Usual Estimates, Emissions Targets) are considered experimental. The sources and methods used to compile these estimates are still in development. Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.
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Regional annual Air Emission Accounts are presented by industry and household for four gases - carbon dioxide, methane, nitrous oxide, and F-gases. The F-gases constitute of hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride and nitrogen trifluoride.


Emissions are presented in Million metric tons of CO₂ equivalent (MTCO2e)


Sources: Organisation for Economic Co-operation and Development (2022), Air Emission Accounts, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=AEA. Organisation for Economic Co-operation and Development (2022), Air Emission Accounts – OECD Estimates, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=OECD-AEA. United Nations Framework Convention on Climate Change (UNFCCC). 2022. Greenhouse Gas Inventory Data - Detailed data by Party - Annex I. https://di.unfccc.int/detailed_data_by_party. Copyright 2022 United Nations Framework Convention on Climate Change; EDGARv7.0, Crippa, M., Guizzardi, D., Banja, M., Solazzo, E., Muntean, M., Schaaf, E., Pagani, F., Monforti-Ferrario, F., Olivier, J., Quadrelli, R., Risquez Martin, A., Taghavi-Moharamli, P., Grassi, G., Rossi, S., Jacome Felix Oom, D., Branco, A., San-Miguel-Ayanz, J. and Vignati, E., CO2 emissions of all world countries - 2022 Report, EUR 31182 EN, Publications Office of the European Union, Luxembourg, 2022, doi:10.2760/730164, JRC130363. IMF staff calculations.

Category: Greenhouse Gas (GHG) Emissions

Data series: 
Metadata:

SEEA Air Emissions Accounts from official country sources have been accessed via the OECD Air Emissions Accounts database. 
In addition to the OECD database, the estimation procedure uses the emission inventories sourced from UNFCCC, EDGAR and CAIT.

Methodology:

The OECD Air Emission Accounts database presents estimates that align with the classifications, concepts and methods consistent with the System of Environmental-Economic Accounting Central Framework (SEEA-CF). In addition to the OECD database, the estimation procedure uses the emission inventories sourced from UNFCCC, EDGAR and CAIT. 

The annual greenhouse gas inventories have been sourced from UNFCCC (for Annex-I Parties), and EDGAR and CAIT (for non-Annex-I Parties). 

In cases where data is not available for the recent years, estimates of greenhouse gas emissions by industry and for households are trended forward using the growth rates derived from the EDGAR dataset.

Correspondence tables and industry output shares are used to concord the UNFCCC, EDGAR and CAIT estimates to their corresponding industrial and household activities.


Disclaimer:

Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.

", "keyword": [ "Greenhouse gas emissions", "Climate Change", "greenhouse gas intensities", "Economic activity" ], "issued": "2021-03-18T19:57:46.000Z", "modified": "2025-09-09T14:33:17.882Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "climatedata_Admin", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "https://www.imf.org/external/terms.htm", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/datasets/c8579761f19740dfbe4418b205654ddf_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_1_1_annual/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/kml?layers=0" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/filegdb?layers=0" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/featureCollection?layers=0" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/excel?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/geoPackage?layers=0" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/c8579761f19740dfbe4418b205654ddf/sqlite?layers=0" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=543872e1d86c49e3a3bdf38f2b758f92&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/543872e1d86c49e3a3bdf38f2b758f92_0", "title": "Quarterly Greenhouse Gas (GHG) Air Emissions Accounts", "description": "
Regional estimates are presented by industry and household for four gases - carbon dioxide, methane, nitrous oxide, and F-gases. The F-gases constitute of hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride and nitrogen trifluoride.

Emissions are presented in Million metric tons of CO₂ equivalent (MTCO2e).

Sources:  Organisation for Economic Co-operation and Development (2022), Air Emission Accounts, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=AEA; Organisation for Economic Co-operation and Development (2022), Air Emission Accounts – OECD Estimates, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=OECD-AEA; Organisation for Economic Co-operation and Development (2022), Quarterly National Accounts, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=QNA%20; United Nations Framework Convention on Climate Change (UNFCCC). 2022. Greenhouse Gas Inventory Data - Detailed data by Party - Annex I. https://di.unfccc.int/detailed_data_by_party. Copyright 2022 United Nations Framework Convention on Climate Change; Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J., Grassi, G., Rossi, S. and Vignati, E., GHG emissions of all world countries, EUR 30831 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-41547-3, doi:10.2760/074804, JRC126363; IEA (2022) Monthly electricity data, www.iea.org/statistics, All rights reserved; as modified by IMF;  IEA (2022) Monthly oil statistics, www.iea.org/statistics, All rights reserved; as modified by IMF;  IEA (2022) Monthly gas statistics, www.iea.org/statistics, All rights reserved; as modified by IMF;  Country Authorities; IMF staff calculations.

Category: Greenhouse Gas (GHG) Emissions

Data series: 
Metadata:

Quarterly greenhouse gas air emissions from production and household consumption are adjusted for seasonality. SEEA Air Emissions Accounts from official country sources have been accessed via the OECD Air Emissions Accounts database.

Methodology:

The OECD Air Emission Accounts database presents estimates that align with the classifications, concepts and methods consistent with the System of Environmental-Economic Accounting Central Framework (SEEA-CF). In addition to the OECD database, the estimation procedure uses the emission inventories sourced from UNFCCC, EDGAR and CAIT. Correspondence tables and industry output shares are used to concord the UNFCCC, EDGAR and CAIT estimates to their corresponding industrial and household activities. 
Annual estimates of greenhouse gas emissions by industry and for households are trended forward using the latest emission data available. They are temporally disaggregated using the best temporal aggregation method in conjunction with seasonally adjusted sub-annual indicators of economic activity highly correlated with the annual estimates, under a prior assumption on linkages with the annual estimates.
Quarterly estimates for the most recent period (for which annual estimates do not exist) are extrapolated using the timelier sub-annual indicators.


Disclaimer:

The estimates are considered experimental. The sources and methods used to compile these estimates are still in development. Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.


", "keyword": [ "Greenhouse gas emissions", "Climate Change", "greenhouse gas intensities", "Economic activity" ], "issued": "2021-03-30T18:15:50.000Z", "modified": "2025-09-08T21:31:02.374Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "climatedata_Admin", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "https://www.imf.org/external/terms.htm", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/datasets/543872e1d86c49e3a3bdf38f2b758f92_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_1_1_quarterly/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/kml?layers=0" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/filegdb?layers=0" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/featureCollection?layers=0" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/excel?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/geoPackage?layers=0" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/543872e1d86c49e3a3bdf38f2b758f92/sqlite?layers=0" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=9c3764c0efcc4c71934ab3988f219e0e&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/9c3764c0efcc4c71934ab3988f219e0e_0", "title": "Atmospheric CO₂ Concentrations", "description": "
Levels of CO₂ concentrations are presented at the World Level. 

Data are reported as a dry air mole fraction defined as the number of molecules of carbon dioxide divided by the number of all molecules in air, including CO₂ itself, after water vapor has been removed. The mole fraction is expressed as parts per million (ppm).

Source: Dr. Pieter Tans, National Oceanic and Atmospheric Administration (NOAA). Global Monitoring Laboratory, Trends in Atmospheric Carbon Dioxide Data (https://gml.noaa.gov/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography, Carbon Dioxide Measurements (https://scrippsco2.ucsd.edu/).

Category:  Climate and Weather

Data series: 
Metadata:

Data is available since 1958 and are acquired from the National Oceanic and Atmospheric Association Global Monitoring Laboratory.  The monthly CO₂ concentrations measured at Mauna Loa Observatory, Hawaii, are used to represent world estimates. The carbon dioxide data on Mauna Loa constitute the longest record of direct measurements of CO2 in the atmosphere.

Methodology:

The details of measurement of CO₂ concentrations at the Mauna Loa Observatory are available at the following link: https://gml.noaa.gov/ccgg/about/co2_measurements.html

The year-on-year changes in the CO₂ concentrations are calculated by the IMF.


Disclaimer:

The Mauna Loa data are being obtained at an altitude of 3400 m in the northern subtropics, and may not be the same as the globally averaged CO₂ concentration at the surface.
Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.
", "keyword": [ "Global Warming", "C02 emissions", "Climate Indicator" ], "issued": "2021-02-27T17:15:15.000Z", "modified": "2025-09-23T14:32:55.062Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "climatedata_Admin", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "https://www.imf.org/external/terms.htm", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://climatedata.imf.org/datasets/9c3764c0efcc4c71934ab3988f219e0e_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_3_2_Climate_Indicators_Monthly_Atmospheric_Carbon_Dioxide_concentrations/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/kml?layers=0" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/filegdb?layers=0" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/featureCollection?layers=0" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/excel?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/geoPackage?layers=0" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://climatedata.imf.org/api/download/v1/items/9c3764c0efcc4c71934ab3988f219e0e/sqlite?layers=0" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=b1e6c0ea281f47b285addae0cbb28f4b&sublayer=0", "landingPage": "https://climatedata.imf.org/datasets/b1e6c0ea281f47b285addae0cbb28f4b_0", "title": "Land Cover Accounts", "description": "

Annual estimates of land cover and Climate Altering Land Cover Index are presented at country and regional levels for the years, 1992-2020. 

Estimates of land cover are presented in thousand hectares and the Climate Altering Land Cover Index is unitless.

Sources: Food and Agriculture Organization of the United Nations (FAO). 2020. FAOSTAT Land, Inputs and sustainability, Land, Land Cover. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/LC; IMF staff calculations.

Category: Climate and Weather

Data series: 

Metadata:

The FAOSTAT Land Cover domain contains statistics of land cover area, aggregated at national level and by land cover category following the international land cover classification of the United Nations System of Environmental-Economic Accounting Central Framework (UN SEEA 2012). The FAOSTAT land cover data are compiled by national aggregation of geospatial information which is distributed via publicly available Global Land Cover mapping products.

Methodology:

The Land Cover accounts are derived from publicly available Global Land Cover maps (GLC). The methodology adopted by FAO for the compilation of land cover datasets can be seen at https://fenixservices.fao.org/faostat/static/documents/LC/LC_e_2021.pdf

Land cover has important linkages to climate regulation and climate change and therefore can be used to construct climate change indicators. One simple way to present the influence land cover can have on the climate is by assigning each land cover class as either climate regulating, climate altering, or climate neutral. Classification of land cover according to the effect that they are likely to have on the climate is shown below:

1. Climate altering land cover: Artificial surfaces (including urban and associated areas); Herbaceous crops

2. Climate regulating land cover: Woody crops; Multiple or layered crops; Grasslands; Tree-covered areas; Mangroves; Shrub-covered areas; Shrubs and/or herbaceous vegetation, aquatic or regularly flooded; Permanent snow and glaciers; Inland water bodies; Coastal water bodies and intertidal areas

3. Climate neutral: Sparsely natural vegetated areas; Terrestrial barren land.

Using the above information, a climate altering land cover index (CALCI) was compiled.  The Climate Altering Land Cover Index (CALCI) reflects the changes in the share of climate altering land cover as compared to the base year, 2015. The year 2015 was selected as the base for the index since all countries reported land cover data for that year.  CALCI aggregates are calculated by region and sub-region according to the M49 and the World Economic Outlook Classifications.


Disclaimer:

Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.

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The IMF recently created the Resilience and Sustainability Trust (RST), which helps low-income and vulnerable middle-income countries build resilience to external shocks, such as climate change, and ensure sustainable growth. One of the requirements for the successful deployment of RST funds is the assessment of funding needs to deal with such long-term challenges, including investments in resilience to natural disasters.

Motivated by these challenges, the IMF’s Research Department has developed the DIGNAD (Debt-Investment-Growth and Natural Disasters) toolkit. The toolkit builds on the extension of the Debt, Investment and Growth model of Buffie et al. (2012) to natural disasters following Marto, Papageorgiou and Klyuev (2018) , allowing the user to run the model from an Excel interface. The model captures the challenges of closing infrastructure gaps in developing countries that frequently face natural disasters. In addition to permanent damages to public and private capital, natural disasters cause temporary losses of productivity, inefficiencies during the reconstruction process, and damages to the sovereign's creditworthiness.

The toolkit enables users to evaluate debt sustainability risks following natural disasters amidst the need to rebuild public infrastructure through the lens of a rich general equilibrium structure. The model can also be used to analyze the effects of ex-ante policies, such as building adaptation infrastructure, increasing fiscal buffers, or improving public investment efficiency.
The model can be calibrated using country-specific macroeconomic indicators, and users can additionally calibrate the size and timing of natural disasters, and the various mechanisms through which they affect macroeconomic aggregates. The toolkit will therefore be of use to economists and policymakers looking to develop tailored analysis of the macro-fiscal impacts of natural disasters and investments in resilience.

DIGNAD has become a workhorse model in the IMF to study the effects of climate risk due to natural disasters and how investments in adaptation infrastructure can help mitigate these risks. Most recently, DIGNAD-based analyses were published in the Rwanda and Bangladesh staff reports accompanying the request for an arrangement under the RST facility.
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Imports of environmental goods comprise all environmental goods entering the national territory. A relatively high share of environmental goods imports indicates that an economy purchases a significant share of environmental goods from other economies. 
Exports of environmental goods comprise all environmental goods leaving the national territory. A relatively high share of environmental goods exports indicates that an economy produces and sells a significant share of environmental goods to other economies. 
An economy’s environmental goods trade balance is the difference between its exports and imports of environmental goods.

Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, environmental goods), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in environmental goods, while a value of less than one indicates a relative disadvantage.

Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. Accessed on 2023-06-28; International Monetary Fund (IMF) Direction of Trade Statistics (DOTS). https://data.imf.org/dot. Accessed on 2023-06-28. World Economic Outlook (WEO) Database. https://www.imf.org/en/Publications/WEO/weo-database/2022/April. Accessed on 2023-06-28; IMF staff calculations.

Category: Cross-Border Indicators

Data series: 
  • Comparative advantage in environmental goods
  • Environmental goods exports
  • Environmental goods exports as percent of GDP
  • Environmental goods exports as share of total exports
  • Environmental goods imports
  • Environmental goods imports as percent of GDP
  • Environmental goods imports as share of total imports
  • Environmental goods trade balance
  • Environmental goods trade balance as percent of GDP
  • Total trade in environmental goods
  • Total trade in environmental goods as percent of GDP
Metadata:

Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.

Methodology:

Environmental goods imports and exports are estimated by aggregating HS 6-digit commodities identified as environmental goods based on OECD and Eurostat, The Environmental Goods & Services Industry: Manual for Data Collection and Analysis, 1999, and IMF research. Total goods imports and exports are estimated by aggregating all commodities. 

Environmental goods trade balance is calculated as environmental goods exports less environmental goods imports. A positive trade balance means an economy has a surplus in environmental goods, while a negative trade balance means an economy has a deficit in environmental goods.
Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are environmental goods to the proportion of global exports that are environmental goods. 
Total trade in environmental goods is calculated as the sum of environmental goods exports and environmental goods imports. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.
National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.

Methodology Attachment 
Environmental Goods Harmonized System Codes
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Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. Accessed on 2023-06-28; IMF staff calculations.

Category: Cross-Border Indicators

Data series: 
Metadata:

Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. 

Methodology:

Bilateral trade flows of environmental goods are estimated by aggregating, by partner country, HS 6-digit commodities identified as environmental goods based on OECD and Eurostat, The Environmental Goods & Services Industry: Manual for Data Collection and Analysis, 1999, and IMF research. Total goods imports and exports by partner country are estimated by aggregating all commodities. 

Environmental goods trade balance by partner country is calculated as environmental goods exports to a given partner country less environmental goods imports from a given partner country. A positive trade balance means an economy has a surplus in environmental goods with a given partner country, while a negative trade balance means an economy has a deficit in environmental goods with a given partner country.

Total trade in environmental goods by partner country is calculated as the sum of environmental goods exports to a given partner country and environmental goods imports from a given partner country.

Methodology Attachment 
Environmental Goods Harmonized System Codes
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