Methodology

Data sources

The World Bank’s Global Electrification Database (GED) was used for electrification, and compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of analysis, the GED contained 950 surveys from 144 countries, excluding high-income countries classified as developed by the United Nations, for 1990–2017.

Estimating missing values

To estimate missing values, a multilevel nonparametric modeling approach, which was developed by the World Health Organization for estimating clean fuel use, was adapted to electricity access and used to fill in the missing data points for 1990–2016. The model takes into account the hierarchical structure of data (country and regional levels). Regional groupings are based on UN breakdown, with Sub-Saharan Africa further divided into Eastern Africa, Central Africa, Southern Africa, and Western Africa.

The model is applied for all countries with at least one data point. The statistical model is used only to fill in data for years where they are missing. The difference between real data points and estimated values is clearly identified in the database.

Countries considered as “developed” by the UN, and classified as high income are assumed to have an electrification rate of 100% from the first year the country entered the category.

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

Information on the types of technologies and fuels used by households for cooking is regularly collected on nationally-representative household surveys or censuses. WHO regularly collects and compiles such household energy data in the WHO Household energy database. The data housed in this database is then the input data for a statistical model used to derive point estimates for global monitoring of household energy use and health impacts. For 2016 estimates, a version of the WHO Household energy database containing over 1100 surveys with data from 157 countries for the years 1974 to 2016 was used as data inputs in the statistical model. For more information, please see: http://www.who.int/airpollution/data/en/

Methodology

Modelling techniques: The households (%) that mainly use fuels such as wood, charcoal, crop waste, coal, dung and kerosene for cooking were considered ‘exposed’. Currently there is a very little to no nationally representative data capturing the type of solid fuel cookstove. However, recognizing how the fuel and technology impact the level of household air pollution, in future updates, WHO will estimate exposure and disease burden attributed to both the fuel and solid fuel stove in combination (pending data availability). The percentage of households mainly using electricity, natural gas, liquefied petroleum gas, biogas, biofuels (e.g. ethanol), or solar energy for cooking were assumed to be ‘unexposed’. 

The households (%) mainly using electricity, natural gas, liquefied petroleum gas, biogas, biofuels (e.g. ethanol), or solar energy for cooking were assumed to be ‘unexposed’. 

Together with the University of Exeter, the WHO has developed a global hierarchical household energy model (GHHEM) for producing estimates of overall polluting (and clean) fuel and technology usage. Set within a Bayesian hierarchical modelling framework, trends in the proportions of the population mainly using “polluting” or “clean” fuels and technologies are estimated for each country, based on survey information for that country, and using time as the only covariate.

The GHHEM is implemented using Markov chain Monte Carlo (MCMC), a type of Bayesian analysis. Summaries of these distributions can be taken to provide both point estimates (e.g. means) and measures of uncertainty (e.g. 95% credible and 95% prediction intervals). The GHHEM 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 polluting fuels and technologies from cooking, by country, for each year for which survey data was available (1990-2016).

Data analysis: Only survey data providing individual fuel breakdowns and with less than 15% of the population reporting “missing” and “no cooking” and “other fuels” were included in the analysis. Countries with no household fuel data but classified as high income according to the World Bank country classification (37 countries) were assumed to have fully transitioned to clean household energy and therefore are reported as >95% access to clean technologies and fuels. No estimates were reported for low and middle income countries without data (Lebanon, Libya and Turkey).

Regional and global aggregates: Population data from the United Nations Population Division were used to derive the population-weighted regional and global aggregates. Low-and middle income countries without data were excluded from the aggregate calculations.

Calculating the annual growth rate

The annual 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.

Data sources

The data is derived from the IEA Energy Balances (additional information can be found at www.iea.org/sdg), which provide a breakdown of national energy flows by products over a time series. The report focuses primarily on consumed energy rather than produced energy and it assumes equivalent energy losses between energy supplied and energy consumed across all technologies. Thus to derive the electricity consumption by fuel type, the SDG7 methodology calculated the % share of electricity produced by fuel type and multiples the final electricity consumed by that % to approximate the share of each technology in Electricity TFEC.

Methodology

IEA World Energy Balances, 2017 and United Nations Statistics Division data serve as the underlying data used to calculate the indicator.

The indicator used in this report to track RE within an energy system is the share of RE in TFEC and is expressed as a percentage (%RENTFEC).

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

The denominator (TFEC) is calculated as the sum of total final consumption minus non-energy use for all energy sources, or equally, the sum of the energy consumed in the industry, transport, and other sectors. The numerator (AFECREN), on the other hand, is not a direct summation of the underlying raw data but a series of calculations reflecting the fact that monitoring occurs at the final energy level. At this level in the energy balance, electricity and heating are secondary energy obtained by different primary energy sources, of which some are renewable. Assumptions need to be made in order to fully account for the renewable component of such secondary sources. It was decided to allocate the final consumption of electricity and heating to renewables based on the share of renewables in gross production.

Modern biomass consumption (in TJ)

Final consumption of modern biomass. Modern biomass is defined as all solid biomass consumed by OECD countries or solid biomass through modern applications, such as district heating, CHP and other applications.

Modern RE consumption (in TJ)

This is total renewable energy consumption minus consumption through traditional uses of biomass/use of biomass. It covers all forms of RE directly measured, including wind, hydro, solar, geothermal, marine, biogas, liquid biofuel, RE waste, and modern biomass.

Renewable energy consumption (in TJ)

This indicator includes RE consumption of all technologies: hydro, biomass, wind, solar, liquid biofuels, biogas, geothermal, marine and renewable wastes

Total final energy consumption (TFEC) (in terajoules [TJ])

This indicator is derived from national energy balance statistics and is equivalent to a country’s total final consumption excluding non-energy uses of fuels.

Consumption through traditional uses of biomass/use of biomass (in TJ)

Final consumption of traditional uses of biomass. Biomass uses are considered traditional when biomass is consumed in the residential sector in non-Organisation for Economic Co-operation and Development (OECD) countries. It includes the following categories in International Energy Agency (IEA) statistics: primary solid biomass, charcoal and non-specified primary biomass and waste.

Note: This is a convention, and consumption through traditional uses of biomass/use of biomass is estimated rather than measured directly.

Data sources

The data is derived from the IEA Energy Balances (additional information can be found at www.iea.org/sdg) and the World Bank’s World Development Indicators (WDI), and supplemented by United Nations Statistical Division for countries not covered by IEA or WDI.

Methodology

Ratio between global total primary energy supply and gross domestic product measured at purchasing power parity at constant 2011 US dollars. Energy intensity alone is not an indicator for energy efficiency. In this case, it indicates how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of economic output, with this result impacted by improvements in energy intensity, but also changes in economic structure, such as the movement of economic activity away from energy-intensive industrial sectors to less intensive service sector activities.

A ratio between final energy consumption and gross domestic product, measured at purchasing power parity at constant 2011 US dollars, globally. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output.

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 2011 US dollars.

Sum of energy consumption by the different end-use sectors, excluding non-energy uses of fuels. TFEC is broken down into energy demand in the following sectors: industry, transport, residential, services, agriculture, and others. It excludes international marine and aviation bunkers, except at world level where it is included in the transport sector.

As defined by the International Energy Agency (IEA), total primary energy supply is production plus net imports minus international marine and aviation bunkers plus/minus stock changes.

where,

FEIt1: final energy intensity in year t1

FEIt2: final energy intensity in year t2

 

Compound annual growth rate (CAGR) of final energy intensity 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 numbers indicate increase in energy intensity (more energy is used to produce one unit of economic output).

where,

PEIt1: primary energy intensity in year t1

PEIt2: primary energy intensity in year t2

 

Compound annual growth rate (CAGR) of primary energy intensity 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 numbers indicate increase in energy intensity (more energy is used to produce one unit of economic output).

Ratio between total final energy consumption and value added, at purchasing power parity at constant 2011 US dollars, in the agriculture sector (including forestry and fishing).

Ratio between total final energy consumption and value added, measured at purchasing power parity at constant 2011 US dollars, in the industrial sector (including energy industry own use).

Ratio between total final energy consumption and value added, measured at purchasing power parity at constant 2011 US dollars, in the services sector (including commercial and public services).