Methodology

Data sources

The World Bank’s Global Electrification Database compiles nationally representative household survey data (e.g. LSMS, MTF, DHS) as well as census data from 1990 to 2019. It also incorporates data from the Socio-Economic Database for Latin America and the Caribbean, the Middle East and North Africa Poverty Database, and the Europe and Central Asia Poverty Database, all of which are based on similar surveys. At the time of this analysis, the Global Electrification Database contained 1,282 surveys from 139 countries, excluding surveys from high-income countries as classified by the United Nations. In general, about 28 percent of the countries publish or update their electricity data each year in time for global data collection since 2010.

Estimating missing values

Surveys are typically published every two to three years, but they can be irregular and infrequent in many regions. To estimate values, a multilevel, nonparametric modeling approach developed by the World Health Organization to estimate clean fuel usage was adapted to predict electricity access and used to fill in the missing data points for the time period between 1990 and 2019. Where data are available, access estimates are weighted by population. Multilevel nonparametric modeling takes into account the hierarchical structure of data (country and regional levels), using the regional classification of the United Nations.

The model is applied for all countries with at least one data point. In order to use as much real data as possible, results based on real survey data are reported in their original form for all years available. The statistical model is used to fill in data only for years where they are missing and to conduct global and regional analyses. In the absence of survey data for a given year, information from regional trends was borrowed. The difference between real data points and estimated values is clearly identified in the database.

Countries considered "developed" by the United Nations and classified as high-income are assumed to have electrification rates of 100 percent from the first year the country joined the category.

In the present report, to avoid having electrification trends from 1990 to 2010 overshadow efforts since 2010, the model was run twice:

  • With survey data + assumptions from 1990 to 2019 for model estimates from 1990 to 2019
  • With survey data + assumptions from 2010 to 2019 for model estimates from 2010 to 2019
Calculating the annual change in access rate

The annual change in access rate is calculated as the difference between the access rate in year 2 and the rate in year 1, divided by the number of years in order to annualize the value:

(Access Rate Year 2 – Access Rate Year 1) / (Year 2 – Year 1)

This approach takes population growth into account by working with the final national access rates.

Data sources

The WHO Household Energy Database keeps regularly updated nationally representative household survey data (WHO 2018). It relies on a number of sources (national statistical agencies, national surveys and censuses) and serves in this report as the basis for all modeling efforts (Bonjour and others 2014; Stoner and others 2019). The database has 1,440 surveys taken in 170 countries (including high-income countries) between 1960 and 2020; 21 percent of the surveys cover the years 2014 to 2019 and 88 new surveys cover 2017 to 2019. Modeled estimates (https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution) for low- and middle-income countries are only provided if there is underlying survey data on cooking fuels, so there are no estimates for Bulgaria, Cuba, Lebanon, and Libya. Population data are from the United Nations Population Division.

Model

As household surveys are conducted irregularly and reported heterogeneously, the WHO Global Household Energy Model (developed in collaboration with the University of Exeter, UK) is employed to estimate trends in household use of six fuel types: unprocessed biomass (e.g., wood), charcoal, coal, kerosene, gaseous fuels (e.g., LPG), electricity.

Trends in the proportion of the population using each fuel type are estimated using a Bayesian hierarchical model, with urban and rural disaggregation, drawing on country survey data. Smooth functions of time were the only covariate. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. Estimates produced by the model automatically respect the constraint that the total fuel use equals 100 percent. GHEM is implemented using the R programming language and the NIMBLE software package for Bayesian modeling with Markov chain Monte Carlo (MCMC). Summaries can be taken to provide both point estimates (e.g., means) and measures of uncertainty (e.g., 95 percent credible and 95 percent prediction intervals). The GHEM is applied to the WHO household-energy database to produce a comprehensive set of estimates, together with associated measures of uncertainty, of the use of four specific polluting fuels and two specific clean fuels for cooking, by country, for each year from 1990 to 2019. Further details on the modeling methodology and validation can be found in Stoner and others (2019), and more detailed analysis of individual fuel use can be found in Stoner and others (2021). Only surveys with less than 15 percent of the population reporting “missing” and “no cooking” and “other fuels” were included in the analysis. Surveys were also discarded if the sum of all mutually exclusive categories reported was not within 98-102%. Fuel use values were uniformly scaled (divided) by the sum of all mutually exclusive categories excluding “missing”, “no cooking” and “other fuels”. Countries classified by the World Bank as high income (60 countries) in the 2019 fiscal year were assumed to have transitioned to clean household energy. They are therefore reported as 100 percent access to clean fuel and technologies; no fuel-specific estimates were reported for high-income countries. In addition, no estimates were reported for low- and middle-income countries without data suitable for modelling (Bulgaria, Cuba, Lebanon, and Libya). Modeled specific-fuel estimates were reported for 132 low- and middle-income countries plus 2 countries with no World Bank income classification (Niue and Cook Islands), and estimates of overall clean fuel use were reported for 190 countries.

Uncertainty intervals

Many of the point estimates we provide here are accompanied by 95 percent uncertainty intervals, which imply a 95 percent chance that the true value lies within the given range. Small annual changes in the point estimate may be statistical noise arising from either the modeling process or survey variability and may therefore not reflect a real variation in the numbers relying on different fuels between years. The uncertainty intervals should therefore be taken into account when assessing changes in the access rate, or in the use of specific fuels, between years.

Global and regional aggregations

Population data from the United Nations Population Division (2018 revision) were used to derive the population-weighted regional and global aggregates. Low- and middle-income countries without data were excluded from all aggregate calculations; high-income countries were excluded from aggregate calculation for specific fuels.

Annualized growth rates and future projections

The annualized increase in the access rate is calculated as the difference between the access rate in year 2 and that in year 1, divided by the number of years to annualize the value:

(Access Rate Year 2–Access Rate Year 1) / (Year 2–Year 1)

This approach takes population growth into account by working with the final national access rate. Projected access rates, access deficits, and fuel use can be estimated using the GHEM, where uncertainty increases the further into the future estimates are calculated, reflecting how country trends may shift based on how unsettled they were during the data period.

Projections are hypothetical scenarios in which no new policies or interventions (positive or otherwise) take place, and as such are useful as baseline scenarios for comparing the effect of interventions.

Data sources

The data are derived from the International Energy Agency (IEA) World energy balances, with additional information at https://www.iea.org/reports/sdg7-data-and-projections; and from the United Nations Energy Statistics Database (http://data.un.org/Explorer.aspx?d=EDATA); both providing a breakdown of national energy flows by products over time.

Methodology

The main indicator (SDG 7.2.1 indicator) used in this report to track the renewables energy target is the share of renewable energy in total final energy consumption (TFEC) and is expressed as a percentage (%RENTFEC).

 

Main indicator: Share of renewable energy in total final energy consumption (in percentage)

This share is calculated as the ratio of final energy consumption from renewables after allocation of electricity and heat (AFECREN) to TFEC, calculated from the flows in the energy balances.

Where:

  • Final energy consumption after allocation of electricity and heat (AFEC) (in TJ)

The final energy consumption after allocation of electricity and heat represents the final energy consumption from renewables after allocation of electricity and heat. Final consumption of electricity and heat is allocated to renewables based on the share of the gross generation coming from renewable sources. In practice, this occurs by calculating the % share of electricity and heat produced by each renewable source, multiplying the final energy consumption of electricity and heat by those shares, and then allocating the resulting quantities to each renewable energy source’s final consumption. For instance, if total final consumption table reports 150 TJ for biogas energy, while total final consumption of electricity is 400 TJ and heat 100 TJ, and the share of biogas in total electricity output is 10 percent and 5 percent in heat, the total reported number for biogas consumption will be 195 TJ (150 TJ+400TJ*10%+100TJ*5%). As a result of this method, it is implicitly assumed that energy losses between energy supplied and energy consumed are proportional to their shares in production across all technologies.

  • Total final energy consumption (TFEC) (in TJ)

Total final energy consumption represents the sum of the energy from all sources used in the industry, transport, buildings, and other sectors of final consumption. It is derived from the national energy balance and excludes quantities used for non-energy purposes.

  • Renewable energy

Renewable energy includes hydro, solid biofuels, liquid biofuels, biogases, wind, solar, geothermal, tide/wave/oceans and renewable municipal waste.

 

Solid biofuels - It includes the following categories in International Energy Agency (IEA) statistics: primary solid biofuels and charcoal. It includes the following categories in UNSD statistics: fuelwood, charcoal, animal waste, black liquor, other vegetal material and residues, and renewable municipal waste (taken as 50% of total municipal waste as a proxy).

Note: Consumption of solid biofuels at national level is often estimated due to the difficulty of properly measuring it.

Traditional use of biomass - It refers to the energy use of local solid biomass resources by low-income households that do not have access to modern cooking and heating fuels or technologies. Solid biomass, such as wood, charcoal, agricultural residues and animal dung, is converted into energy through basic techniques, such as a three-stone fire, for heating and cooking in the residential sector, which is often inefficient and associated with negative impacts on human health and the environment. For the purposes of this report, the traditional use of biofuels is assumed to occur in the residential sector in non-Organisation for Economic Co-operation and Development (OECD) countries excluding Eurasia.

Modern biomass – For the purposes of this report, despite the lack of an agreed statistical definition for "modern biomass", it is considered as all liquid and gaseous biomass consumed directly for energy (and not to generate other forms of commercial energy such as electricity and district heat), as well as solid biomass consumed directly in modern applications, such as in industrial processes, boilers and other applications. In this report, all solid biomass consumed directly in OECD countries is assumed to be modern while for non-OECD countries excluding Eurasia, only the part that is not used in households is assumed to be modern.

  • Modern renewable energy

For the purposes of this report, it refers to the total renewable energy consumption minus the traditional use of biomass.

Data sources

The energy data are derived from the International Energy Agency (IEA) World Energy Balances (https://www.iea.org/data-and-statistics); and from the United Nations Energy Statistics Database (http://data.un.org/Explorer.aspx?d=EDATA); whereas the GDP data come from the World Bank’s World Development Indicators (WDI).

Methodology

The main indicator (SDG 7.3.1 indicator) used in this report to track the efficiency target is the energy intensity of the economy, computed as ratio between total energy supply (in MJ) and gross domestic product (GDP), measured at purchasing power parity at constant 2017 US dollars.

Energy intensity is an imperfect proxy for energy efficiency. It can be affected by a number of factors, such as climate, structure of the economy, nature of economic activities, etc. - which are not necessarily linked to pure efficiency improvements.

 

Main indicator: energy intensity of the economy, computed as energy supplied to the economy per unit value of economic output (in megajoules [MJ] per USD 2017 at purchasing power parity [2017 PPP$])

Where:

  • Total energy supply (TES) (in MJ)

The total energy supply represents the amount of energy that is available in the national territory during the reference period. It is defined by the International Recommendations for Energy Statistics (IRES, https://unstats.un.org/unsd/energystats/methodology/ires/ ) as:

Total energy supply =

Primary energy production

+ Import of primary and secondary energy

- Export of primary and secondary energy

- International (aviation and marine) bunkers

- Stock changes.

  • Gross domestic product (GDP) (in 2017 PPP$)

GDP is the sum of gross value added in all the sectors of an economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. GDP is measured at purchasing power parity at constant 2017 US dollars.

  • Compound annual growth rate (CAGR) (in percentage)

The compound annual growth rate (CAGR) represents the average annual growth rate during a period of time. Negative values represent improvements in energy intensity (less energy is used to produce one unit of economic output), while positive values indicate worsening (more energy is used to produce one unit of economic output).

Where:

EIt1: energy intensity in year t1

EIt2: energy intensity in year t2

Data sources

International financial flows to developing countries in support of clean and renewable energy considers three categories of investment flows. Total Official Development Assistance (ODA) and Other Official Flows (OOF) to developing countries quantify the public financial effort that donors provide to developing countries for renewable energies. The additional flows (from the IRENA database) capture the flows to non-ODA Recipients in developing regions and flows from countries and other public institutions not currently reporting to the Development Assistance Committee (DAC).

Methodology

SDG 7.a.1 is an indicator jointly produced by OECD and IRENA. The flows covered by the OECD are defined as all official loans, grants and equity investments received by countries on the DAC List of ODA Recipients from foreign governments and multilateral agencies, for the purpose of clean energy research and development and renewable energy production, including in hybrid systems. These figures are extracted from the OECD/DAC Creditor Reporting System (CRS).

The flows covered by IRENA are defined as all additional loans, grants and equity investments received by developing countries (defined as countries in developing regions, as listed in the UN M49 composition of regions) from all foreign governments, multilateral agencies and additional development finance institutions for the purpose of clean energy research and development and renewable energy production, including in hybrid systems. These additional flows cover the same technologies and other activities (research and development, technical assistance, etc.) as listed above and exclude all flows extracted from the OECD/DAC CRS to avoid duplication of data.

Data sources

The data are derived from the International Energy Agency (IEA) World energy balances, with additional information at https://www.iea.org/reports/sdg7-data-and-projections; and from the United Nations Energy Statistics Database (http://data.un.org/Explorer.aspx?d=EDATA); both providing a breakdown of national energy flows by products over time.

Methodology

Indicator 7.b.1 measures the installed renewable energy generating capacity in developing countries (in watts per capita) by dividing the maximum installed capacity of power plants that generate electricity from renewable energy sources at the year-end by the total population of a country at the mid-year. Data from IRENA are used to calculate the indicator.

IRENA’s electricity capacity database contains information about the electricity generating capacity installed at year-end, measured in MW. The data set covers all countries and areas from the year 2000 onwards. The data set also records whether the capacity is on-grid or off-grid and is split into 36 different renewable energy types that can be aggregated into the six main sources of renewable energy. For the population part of this indicator, IRENA uses population data from the United Nations World Population Prospects.

More detail on the methodology can be found in the SDG indicators metadata repository: https://unstats.un.org/sdgs/metadata/files/Metadata-07-0b-01.pdf