AndreaBozzo/ceres-open-data-index huggingface dataset:
Environmental Taxes

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: * Environmental Taxes * Taxes on Energy (including fuel for transport) * Taxes on Pollution * Taxes on Resources * Taxes on Transport (excluding fuel for transport) 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.
Bilateral Trade in Environmental Goods

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: * Environmental goods exports * Environmental goods exports as share of total exports * Environmental goods imports * Environmental goods imports as share of total imports * Environmental goods trade balance * Total trade in environmental goods 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]
DIGNAD

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.
Atmospheric CO₂ Concentrations

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: * Monthly Atmospheric Carbon Dioxide Concentrations * Monthly Atmospheric Carbon Dioxide Concentrations, Year on Year Percentage Change 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.
Annual Greenhouse Gas (GHG) Air Emissions Accounts

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: * Annual greenhouse gas (GHG) air emissions accounts 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.
Environmental Protection Expenditures

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: * Expenditure on environment protection * Expenditure on biodiversity & landscape protection * Expenditure on environmental protection n.e.c. * Expenditure on environmental protection R&D * Expenditure on pollution abatement Expenditure on waste management * Expenditure on waste water management 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).
Climate-driven INFORM Risk

It is produced based on the INFORM Risk with IMF staff calculations. Climate-driven Hazard & Exposure reflects the probability of physical exposure associated with specific climate-driven hazards. Vulnerability represents economic, political and social characteristics of the community that can be destabilized in case of a hazard event. Lack of coping capacity relates to the ability of a country to cope with disasters in terms of formal, organized activities and the effort of the country’s government as well as the existing infrastructure which contribute 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: * Climate-driven INFORM Risk Indicator * Climate-driven Hazard & Exposure * Lack of coping capacity * Vulnerability Methodology: Climate-driven INFORM Risk Indicator is produced based on the INFORM Risk with IMF staff adjustments to focus on climate-driven risks only. INFORM Risk has three dimensions: hazard & exposure, vulnerability and lack of coping capacity. The hazard & exposure dimension has been adjusted by IMF staff to include climate related components only (i.e., flood, tropical cyclone, and drought). This results in a Climate-driven INFORM Risk Indicator. Vulnerability and lack of coping capacity dimensions are based on the INFORM Risk with no adjustments.
CO₂ Emissions, Emissions Intensities, and Emissions Multipliers

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: * CO2 emissions * CO2 emissions intensities * CO2 emissions multipliers 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]
Bilateral Trade in Low Carbon Technology Products

Category: Mitigation,Transition to a Low-Carbon Economy Data series: * Imports of low carbon technology products * Imports of low carbon technology products as share of total imports * Exports of low carbon technology products * Exports of low carbon technology products as share of total exports * Total trade in low carbon technology products * Trade balance in low carbon technology products 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]
Forest and Carbon

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: * Forest area * Land area * Carbon stocks in forests * Share of forest area * Index of forest extent * Index of carbon stocks in forests 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. Share 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. Index 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. Index 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.
Renewable Energy

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: * Electricity Generation * Electricity Installed Capacity 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.
Change in Mean Sea Levels

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: * Sea level: Jason.1 * Sea level: Jason.2 * Sea level: Jason.3 * Sea level: TOPEX.Poseidon * Sea level: Trend 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.