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Data

Consumption-based energy use per person

About this data

Source
Kulionis (2021) – processed by Our World in Data
Last updated
January 1, 2021
Date range
1995–2020
Unit
kilowatt-hours
Unit conversion factor
1000000000

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Sources and processing

This data is based on the following sources

Kulionis – Consumption-based energy

Consumption-based (trade-adjusted) energy use can be calculated from data published in the EXIOBASE v3.8.2 database using a multi-regional input-output (MRIO) model.

This MRIO model uses data on the quantity of goods traded, and the country-specific energy intensity of producing those goods to estimate the amount of energy embodied in exported goods, and in imported goods.

Consumption-based (trade-adjusted) energy use is then calculated as domestic energy use minus energy embodied in exported goods, plus imported goods.

This trade-adjusted energy use data was calculated by Viktoras Kulionis using this approach.

The EXIOBASE v3.8.2 database is available online: https://zenodo.org/record/5589597#.YYf3EdnMK_0.

We have calculated per capita metrics by combining this data with population data from the UN World Population Prospects: https://population.un.org/wpp/

We have calculated energy intensity metrics by combining this data with GDP data (measured in 2017 international dollars, which corrects for inflation and cross-country price differences) from the World Bank: https://data.worldbank.org/indicator

Carbon intensity of energy is calculated by combining this data with CO2. emissions data from the Global Carbon Project. Reference: Global Carbon Project. (2021). Supplemental data of Global Carbon Project 2021 (1.0) [Data set]. Global Carbon Project. https://doi.org/10.18160/gcp-2021.)

The EXIOBASE database contains a data error for Australia for 2018 and 2019, which provides erroneous results. We have therefore removed these two data points for Australia only.

Retrieved on
January 1, 2021
Retrieved from
https://zenodo.org/record/5589597#.YYf3EdnMK_0
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Kulionis, V. (2021). Consumption-based energy, calculated based on the EXIOBASE v3.8.2 database.

Consumption-based (trade-adjusted) energy use can be calculated from data published in the EXIOBASE v3.8.2 database using a multi-regional input-output (MRIO) model.

This MRIO model uses data on the quantity of goods traded, and the country-specific energy intensity of producing those goods to estimate the amount of energy embodied in exported goods, and in imported goods.

Consumption-based (trade-adjusted) energy use is then calculated as domestic energy use minus energy embodied in exported goods, plus imported goods.

This trade-adjusted energy use data was calculated by Viktoras Kulionis using this approach.

The EXIOBASE v3.8.2 database is available online: https://zenodo.org/record/5589597#.YYf3EdnMK_0.

We have calculated per capita metrics by combining this data with population data from the UN World Population Prospects: https://population.un.org/wpp/

We have calculated energy intensity metrics by combining this data with GDP data (measured in 2017 international dollars, which corrects for inflation and cross-country price differences) from the World Bank: https://data.worldbank.org/indicator

Carbon intensity of energy is calculated by combining this data with CO2. emissions data from the Global Carbon Project. Reference: Global Carbon Project. (2021). Supplemental data of Global Carbon Project 2021 (1.0) [Data set]. Global Carbon Project. https://doi.org/10.18160/gcp-2021.)

The EXIOBASE database contains a data error for Australia for 2018 and 2019, which provides erroneous results. We have therefore removed these two data points for Australia only.

Retrieved on
January 1, 2021
Retrieved from
https://zenodo.org/record/5589597#.YYf3EdnMK_0
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Kulionis, V. (2021). Consumption-based energy, calculated based on the EXIOBASE v3.8.2 database.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

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  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Consumption-based energy use per person”. Our World in Data (2026). Data adapted from Kulionis. Retrieved from https://archive.ourworldindata.org/20260512-095313/grapher/consumption-based-energy-per-capita.html [online resource] (archived on May 12, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Kulionis (2021) – processed by Our World in Data

Full citation

Kulionis (2021) – processed by Our World in Data. “Consumption-based energy use per person” [dataset]. Kulionis, “Consumption-based energy” [original data]. Retrieved May 16, 2026 from https://archive.ourworldindata.org/20260512-095313/grapher/consumption-based-energy-per-capita.html (archived on May 12, 2026).

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Data URL (CSV format)
https://ourworldindata.org/grapher/consumption-based-energy-per-capita.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/consumption-based-energy-per-capita.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/consumption-based-energy-per-capita.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/consumption-based-energy-per-capita.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/consumption-based-energy-per-capita.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/consumption-based-energy-per-capita.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/consumption-based-energy-per-capita.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/consumption-based-energy-per-capita.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear