{"help": "https://energydata.info/en/api/3/action/help_show?name=datastore_search", "success": true, "result": {"include_total": true, "limit": 100, "records_format": "objects", "resource_id": "299b2bcd-dda6-44b7-83cd-ffa40c653091", "total_estimation_threshold": null, "records": [{"_id":1,"Code":"1.1_ACCESS.ELECTRICITY.TOT","Indicator Name":"Access to electricity (% of total population)","Long definition":"Access to electricity (% of total population): Percentage of total population with access to electricity","Topic":"Access to energy","Unit of measure":"Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. A multilevel nonparametric modeling approach, which was developed by the World Health Organization (WHO) for the estimation of clean fuel usage  and has already widely used for access to cooking, was therefore adapted to electricity access and used to fill in the missing data points from 1990-2014. This approach leads to time series being comprised of survey data and estimates.  Multilevel nonparametric modeling takes into account the hierarchical structure of the data: survey points are correlated within countries, which are then clustered within regions. Time is the only explanatory variable. Regional grouping are based on UN sub-regions, with the Sub-Sahara Africa further being divided into Eastern Africa, Middle Africa, Southern Africa, and Western Africa. The model is applied for all countries with at least one data point. However, in order to make use of real data as much as possible, results based on real survey data are reported in their original form for all the years where they are available. The statistical model is used only to fill out data for years where they are missing. \n"},{"_id":2,"Code":"1.2_ACCESS.ELECTRICITY.RURAL","Indicator Name":"Access to electricity (% of rural population with access)","Long definition":"Access to electricity (% of rural population with access): Percentage of rural population with access to electricity","Topic":"Access to energy","Unit of measure":"Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. A multilevel nonparametric modeling approach, which was developed by the World Health Organization (WHO) for the estimation of clean fuel usage  and has already widely used for access to cooking, was therefore adapted to electricity access and used to fill in the missing data points from 1990-2014. This approach leads to time series being comprised of survey data and estimates.  Multilevel nonparametric modeling takes into account the hierarchical structure of the data: survey points are correlated within countries, which are then clustered within regions. Time is the only explanatory variable. Regional grouping are based on UN sub-regions, with the Sub-Sahara Africa further being divided into Eastern Africa, Middle Africa, Southern Africa, and Western Africa. The model is applied for all countries with at least one data point. However, in order to make use of real data as much as possible, results based on real survey data are reported in their original form for all the years where they are available. The statistical model is used only to fill out data for years where they are missing. \n"},{"_id":3,"Code":"1.3_ACCESS.ELECTRICITY.URBAN","Indicator Name":"Access to electricity (% of urban population with access)","Long definition":"Access to electricity (% of urban population with access): Percentage of urban population with access to electricity","Topic":"Access to energy","Unit of measure":"Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. A multilevel nonparametric modeling approach, which was developed by the World Health Organization (WHO) for the estimation of clean fuel usage  and has already widely used for access to cooking, was therefore adapted to electricity access and used to fill in the missing data points from 1990-2014. This approach leads to time series being comprised of survey data and estimates.  Multilevel nonparametric modeling takes into account the hierarchical structure of the data: survey points are correlated within countries, which are then clustered within regions. Time is the only explanatory variable. Regional grouping are based on UN sub-regions, with the Sub-Sahara Africa further being divided into Eastern Africa, Middle Africa, Southern Africa, and Western Africa. The model is applied for all countries with at least one data point. However, in order to make use of real data as much as possible, results based on real survey data are reported in their original form for all the years where they are available. The statistical model is used only to fill out data for years where they are missing. \n"},{"_id":4,"Code":"2.1_ACCESS.CFT","Indicator Name":"Access to Clean Fuels and Technologies for cooking (% of total population with access)","Long definition":"Access to Clean Fuels and Technologies for cooking (% of total population): Percentage of total population with access to clean fuels and technologies for cooking","Topic":"Access to energy","Unit of measure":"Data for access to Clean Fuels and Technologies for cooking are based on the The World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). To develop the historical evolution of Clean Fuels and Technologies Use rates, a multi-level non-parametrical mixed model, using both fixed and random effects, was used to derive polluting fuel use estimates for 150 countries (ref. Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980-2010. Environ Health Perspect (): .doi:10.1289/ehp.1205987.). For a country with no data, estimates are derived by using regional trends or assumed to be universal access if a country is classified as developed by the United Nations."},{"_id":5,"Code":"2.2_ACCESS.CFT.RURAL","Indicator Name":"Access to Clean Fuels and Technologies for cooking (% of rural population with access)","Long definition":"Access to Access to Clean Fuels and Technologies for cooking (% of rural population with access): Percentage of rural population with access to Clean Fuels and Technologies for cooking","Topic":"Access to energy","Unit of measure":"Data for access to Clean Fuels and Technologies for cooking are based on the The World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). To develop the historical evolution of Clean Fuels and Technologies Use rates, a multi-level non-parametrical mixed model, using both fixed and random effects, was used to derive polluting fuel use estimates for 150 countries (ref. Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980-2010. Environ Health Perspect (): .doi:10.1289/ehp.1205987.). For a country with no data, estimates are derived by using regional trends or assumed to be universal access if a country is classified as developed by the United Nations."},{"_id":6,"Code":"2.3_ACCESS.CFT.URBAN","Indicator Name":"Access to Clean Fuels and Technologies for cooking (% of urban population with access)","Long definition":"Access to Clean Fuels and Technologies for cooking (% of urban population with access): Percentage of urban population with access to Clean Fuels and Technologies for cooking","Topic":"Access to energy","Unit of measure":"Data for access to Clean Fuels and Technologies for cooking are based on the The World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). To develop the historical evolution of Clean Fuels and Technologies Use rates, a multi-level non-parametrical mixed model, using both fixed and random effects, was used to derive polluting fuel use estimates for 150 countries (ref. Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980-2010. Environ Health Perspect (): .doi:10.1289/ehp.1205987.). For a country with no data, estimates are derived by using regional trends or assumed to be universal access if a country is classified as developed by the United Nations."},{"_id":7,"Code":"6.1_PRIMARY.ENERGY.INTENSITY","Indicator Name":"Energy intensity level of primary energy (MJ/2011 USD PPP)","Long definition":"Energy intensity level of primary energy (MJ/2011 USD PPP): A ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. A lower ratio indicates that less energy is used to produce one unit of output.","Topic":"Energy efficiency","Unit of measure":"MJ/2011 USD PPP"},{"_id":8,"Code":"2.1_SHARE.TOTAL.RE.IN.TFEC","Indicator Name":"Renewable energy share of TFEC (%)","Long definition":"Renewable energy share of TFEC (%): Share of renewable energy in total final energy consumption","Topic":"Renewable Energy","Unit of measure":"%"},{"_id":9,"Code":"4.1_SHARE.RE.IN.ELECTRICITY","Indicator Name":"Renewable electricity share of total electricity output (%)","Long definition":"Renewable electricity share of total electricity output (%): Electricity generated by power plants using renewable resources as a share of total electricity output.","Topic":"Renewable Energy","Unit of measure":"%"},{"_id":10,"Code":"3.1_RE.CONSUMPTION","Indicator Name":"Renewable energy consumption (TJ)","Long definition":"Renewable energy consumption (TJ): This indicator includes energy consumption from all renewable resources: hydro, solid biofuels, wind, solar, liquid biofuels, biogas, geothermal, marine and waste","Topic":"Renewable Energy","Unit of measure":"TJ"},{"_id":11,"Code":"4.1.2_REN.ELECTRICITY.OUTPUT","Indicator Name":"Renewable electricity output (GWh)","Long definition":"Renewable electricity output (GWh): Electric output (GWh) of power plants using renewable resources, including wind, solar PV, solar thermal, hydro, marine, geothermal, solid biofuels, renewable municipal waste, liquid biofuels and biogas. Electricity production from hydro pumped storage is excluded.","Topic":"Renewable Energy","Unit of measure":"GWh"},{"_id":12,"Code":"4.1.1_TOTAL.ELECTRICITY.OUTPUT","Indicator Name":"Total electricity output (GWh)","Long definition":"Total electricity output (GWh): Total number of GWh generated by all power plants","Topic":"Renewable Energy","Unit of measure":"GWh"},{"_id":13,"Code":"1.1_TOTAL.FINAL.ENERGY.CONSUM","Indicator Name":"Total final energy consumption (TFEC) (TJ)","Long definition":"Total final energy consumption (TFEC): This indicator is derived form energy balances statistics and is equivalent to total final consumption excluding non-energy use","Topic":"Renewable Energy","Unit of measure":"TJ"}], "fields": [{"id": "_id", "type": "int"}, {"id": "Code", "type": "text"}, {"id": "Indicator Name", "type": "text"}, {"id": "Long definition", "type": "text"}, {"id": "Topic", "type": "text"}, {"id": "Unit of measure", "type": "text"}], "_links": {"start": "/api/3/action/datastore_search?resource_id=299b2bcd-dda6-44b7-83cd-ffa40c653091", "next": "/api/3/action/datastore_search?resource_id=299b2bcd-dda6-44b7-83cd-ffa40c653091&offset=100"}, "total": 13, "total_was_estimated": false}}