The Global Gridded Geographically Based Economic Data (G-Econ), Version 4 contains derived one degree grid cells of Gross Domestic Product (GDP) data in Grid and ASCII formats for both Market Exchange Rate (MER) and Purchasing Power Parity (PPP) for the years 1990, 1995, 2000 and 2005. MER is the exchange rate between local and U.S. dollar currencies for a given time period established by the market. PPP is the exchange rate between a country's currency and U.S. dollars adjusted to reflect the actual cost in U.S. dollars of purchasing a standardized market basket of goods in that country using the country's currency. The original data from the G-Econ Project at Yale University is also available in tabular format and includes latitude and longitude geographic coordinates of the grid cells, area of grid cells, as well as country names, distance to coast, elevation, vegetation, population, precipitation and temperature.

Regulatory Indicators for Sustainable Energy (RISE) is a tool for policymakers to compare national policy frameworks for sustainable energy and identify opportunities to attract investment. RISE assesses countries’ policy support for each of the pillars of sustainable energy – access to electricity, access to clean cooking (for 54 access-deficit countries), energy efficiency, and renewable energy. With over 30 indicators covering 140 countries and representing over 98 percent of the world population, RISE provides a reference point to help policymakers benchmark their sector policy and regulatory framework against those of regional and global peers, and a powerful tool to help develop policies and regulations that advance sustainable energy goals. Indicators in each pillar are scored between 0 and 100 and are weighted equally to reach a score for the pillar.

Electricity Consumption on the Grid: by State: Rio de Janeiro data was reported at 3,141,167.187 MWh in Dec 2024. This records a decrease from the previous number of 3,160,462.159 MWh for Nov 2024. Electricity Consumption on the Grid: by State: Rio de Janeiro data is updated monthly, averaging 3,073,664.674 MWh from Jan 2004 (Median) to Dec 2024, with 252 observations. The data reached an all-time high of 3,959,162.460 MWh in Jan 2015 and a record low of 2,366,320.314 MWh in Jun 2004. Electricity Consumption on the Grid: by State: Rio de Janeiro data remains active status in CEIC and is reported by Energy Research Company. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBC029: Electricity Consumption on the Grid: by State.

Electricity Consumption on the Grid: Industrial: by State: Rio Grande do Norte data was reported at 106,415.000 MWh in Feb 2025. This records a decrease from the previous number of 106,781.000 MWh for Jan 2025. Electricity Consumption on the Grid: Industrial: by State: Rio Grande do Norte data is updated monthly, averaging 103,391.500 MWh from Jan 2004 (Median) to Feb 2025, with 254 observations. The data reached an all-time high of 116,622.000 MWh in Aug 2024 and a record low of 72,295.003 MWh in Feb 2004. Electricity Consumption on the Grid: Industrial: by State: Rio Grande do Norte data remains active status in CEIC and is reported by Energy Research Company. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBC029: Electricity Consumption on the Grid: by State.

For data privacy reasons, houses within a residential environment are summed up to a "virtual" micro-geographic segment (so-called micro-cell), which on average comprises eight, but at least five households. Houses in which at least five households live become a distinct micro-cell, while houses with less than five households are combined with similar houses on the same street. Combined houses are as close as possible in spatial terms. Structural indicators are aggregated on the micro-cell level and subsequently computed household level averages are computed (microm 2016, p.8). If such data exist, the calculated data is made consistent with official data sources (microm 2014, p. 2). Additionally, due to the cooperation with SOEP, it is possible to validate the small-scale regional data of microm (microm 2016, p. 8). The dataset is based on the variable group microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined.

This dataset is part of a larger research project that the Chalmers University of Technology conducts in east Africa with respect to solar PV powered mini-grids, rural electrification, and its impact on sustainable development goals. The aim of the data collection was thus to gather relevant social, economic, electrification, geographic, demographic and energy use data in order to investigate the impact of solar mini-grids on the economic development, income generation and biomass fuel consumption of rural off-grid households in two remote towns in Ethiopia. Since, the data analysis approach to be used was initially determined to be Propensity Score Method (PSM), the data collection was made in two separate groups: Intervention (mini-grid electrified) groups, and control (non-electrified) groups.

For data privacy reasons, houses within a residential environment are summed up to a "virtual" micro-geographic segment (so-called micro-cell), which on average comprises eight, but at least five households. Houses in which at least five households live become a distinct micro-cell, while houses with less than five households are combined with similar houses on the same street. Combined houses are as close as possible in spatial terms. Structural indicators are aggregated on the micro cell level and subsequently computed household level averages are computed (microm 2016, p.8). If such data exist, the calculated data is made consistent with official data sources (microm 2014, p. 2). Additionally, due to the cooperation with SOEP, it is possible to validate the small scale regional data of microm (microm 2016, p. 8). The dataset is based on the variable group microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (including those purely used for business), and number of residential houses (excluding those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined.

For data privacy reasons, houses within a residential environment are summed up to a "virtual" micro-geographic segment (so-called micro-cell), which on average comprises eight, but at least five households. Houses in which at least five households live become a distinct micro-cell, while houses with less than five households are combined with similar houses on the same street. Combined houses are as close as possible in spatial terms. Structural indicators are aggregated on the micro cell level and subsequently computed household level averages are computed (microm 2016, p.8). If such data exist, the calculated data is made consistent with official data sources (microm 2014, p. 2). Additionally, due to the cooperation with SOEP, it is possible to validate the small-scale regional data of microm (microm 2016, p. 8). The dataset is based on the variable group microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined.

Electricity Consumption on the Grid: Commercial: by State: Acre data was reported at 24,136.150 MWh in Dec 2024. This records a decrease from the previous number of 24,711.320 MWh for Nov 2024. Electricity Consumption on the Grid: Commercial: by State: Acre data is updated monthly, averaging 17,494.703 MWh from Jan 2004 (Median) to Dec 2024, with 252 observations. The data reached an all-time high of 26,683.489 MWh in Oct 2024 and a record low of 6,955.270 MWh in Jun 2004. Electricity Consumption on the Grid: Commercial: by State: Acre data remains active status in CEIC and is reported by Energy Research Company. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBC029: Electricity Consumption on the Grid: by State.

National Grid - Nilai saat ini, data historis, perkiraan, statistik, grafik dan kalender ekonomi - Jul 2025.Data for National Grid including historical, tables and charts were last updated by Trading Economics this last July in 2025.

The Global Earthquake Proportional Economic Loss Risk Deciles is a 2.5 minute grid of earthquake hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

A corpus of academic discourse texts belonging to the Economics domain (according to the Dewey Decimal classification, DDC33 - Economics), annotated at structural level conformant to the XCES standard. The terms contained in the texts are included in a bilingual terminological glossary (see Orossimo terminological resource of the same domain).

Electricity Consumption on the Grid: by State: Mato Grosso do Sul data was reported at 612,654.205 MWh in Dec 2024. This records an increase from the previous number of 589,674.583 MWh for Nov 2024. Electricity Consumption on the Grid: by State: Mato Grosso do Sul data is updated monthly, averaging 428,080.500 MWh from Jan 2004 (Median) to Dec 2024, with 252 observations. The data reached an all-time high of 630,203.871 MWh in Mar 2024 and a record low of 251,231.949 MWh in Jul 2004. Electricity Consumption on the Grid: by State: Mato Grosso do Sul data remains active status in CEIC and is reported by Energy Research Company. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBC029: Electricity Consumption on the Grid: by State.

Detailed information for usage is provided in the article.

• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467).
• Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312)
• Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries).
• Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).

This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

The Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 minute grid of cyclone hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset includes projected population and economic data for 1-km resolution grids in Guangxi under Shared Socioeconomic Pathways (SSP1, SSP2, SSP3, SSP4, SSP5) from 2020 to 2100. The data content and description corresponding to the file name are as follows: SSPs_POP_Gx: Population Data of Guangxi.Time Scale: 2020-2100Data format: Excel format, different SSPs results are saved in separate Excel files, with a storage format of 301753 (grids) × 81 (years) for each Excel file Grid resolution: 1km * 1km Unit: person SSPs_GDP_Gx: Economic Data of GuangxiTime Scale: 2020-2100Data format: Excel format, different SSPs results are saved in separate Excel files, with a storage format of 301753 (grids) × 81 (years) for each Excel file Grid resolution: 1km * 1km Unit: 100 Million RMB (2020 price)

Electricity Consumption on the Grid: Others: by State: Rio de Janeiro data was reported at 599,250.094 MWh in Dec 2024. This records an increase from the previous number of 557,146.186 MWh for Nov 2024. Electricity Consumption on the Grid: Others: by State: Rio de Janeiro data is updated monthly, averaging 575,168.789 MWh from Jan 2004 (Median) to Dec 2024, with 252 observations. The data reached an all-time high of 732,585.679 MWh in Dec 2020 and a record low of 378,815.734 MWh in Mar 2005. Electricity Consumption on the Grid: Others: by State: Rio de Janeiro data remains active status in CEIC and is reported by Energy Research Company. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBC029: Electricity Consumption on the Grid: by State.

Number of Electricity Consumers Served by the Grid: Commercial: by State: Roraima data was reported at 14,724.000 Unit in Dec 2024. This records an increase from the previous number of 14,697.000 Unit for Nov 2024. Number of Electricity Consumers Served by the Grid: Commercial: by State: Roraima data is updated monthly, averaging 11,312.500 Unit from Jan 2004 (Median) to Dec 2024, with 252 observations. The data reached an all-time high of 14,724.000 Unit in Dec 2024 and a record low of 5,869.000 Unit in Apr 2004. Number of Electricity Consumers Served by the Grid: Commercial: by State: Roraima data remains active status in CEIC and is reported by Energy Research Company. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBC029: Electricity Consumption on the Grid: by State.

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