Methodology

Data sources

The World Bank’s Global Electrification Database (GED) were used for electrification, and compile 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 percentage of 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 the importance of 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 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’. 

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 are derived from the IEA Energy Balances (additional information can be found at www.iea.org/sdg) and the United Nations Energy Statistics Database (http://data.un.org/Explorer.aspx?d=EDATA), which provide a breakdown of national energy flows by products over a time series.

Methodology

IEA World Energy Balances, 2018 and United Nations Statistics Division (UNSD) data, 2018, serve as the underlying data used to calculate the indicator.

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

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

This share is calculated as the ratio of final energy consumption from 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 rather a series of calculations reflecting the fact that measurement occurs at the final energy level. The reason for these calculations come from the fact that, at this level in the energy balance, electricity and heat are indistinguishable as to whether they come from renewable or non-renewable sources. Assumptions need to be made in order to fully account for the renewable component of such energy. It was decided to allocate the final consumption of electricity and heat to renewables based on the share of the gross production 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. 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 terajoules [TJ])

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

  • Renewable energy (in TJ)

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: This is a convention, and consumption through solid biofuels is estimated rather than measured properly.

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.

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, only the part that is not used in households is assumed to be modern.

  • Modern renewable energy (in TJ)

It refers to the total renewable energy consumption minus the traditional use of biomass.

Data sources

The energy data are derived from the IEA Energy Balances (additional information can be found at www.iea.org/sdg) and the United Nations Energy Statistics Database (http://data.un.org/Explorer.aspx?d=EDATA), whereas the GDP PPP data come from the World Bank’s World Development Indicators (WDI).

Methodology
  • Main indicator: Energy intensity of total primary energy supply (in megajoules [MJ] per USD 2011 at purchasing power parity [2011 PPP$])

The main indicator (SDG 7.3.1 indicator) used in this report for efficiency is the energy intensity of total primary energy supply (TPES) computed as the ratio between global total primary energy supply (in MJ) and gross domestic product (GDP) 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 efficiency, 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.

  • Gross domestic product (GDP) (in 2011 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 2011 US dollars.

  • Total primary energy supply (TPES) (in MJ)

Total primary energy supply equals to Total Energy Supply as defined by the International Recommendations for Energy Statistics (IRES), as made up of production plus net imports minus international marine and aviation bunkers plus-stock changes.

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

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).