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Data

Share of primary energy consumption that comes from solar power

Using the substitution method
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Energy

What you should know about this indicator

  • Figures are based on gross generation and do not account for cross-border electricity supply.
  • Primary energy is measured using the "substitution method" (also called "input-equivalent" primary energy). This method is used for non-fossil sources of electricity (namely renewables and nuclear), and measures the amount of fossil fuels that would be required by thermal power stations to generate the same amount of non-fossil electricity. For example, if a country's nuclear power generated 100 TWh of electricity, and assuming that the efficiency of a standard thermal power plant is 38%, the input-equivalent primary energy for this country would be 100 TWh / 0.38 = 263 TWh = 0.95 EJ. This input-equivalent primary energy takes account of the inefficiencies in energy production from fossil fuels and provides a better approximation of each source's share of energy consumption. You can find more details in the Statistical Review of World Energy's methodology document.
Share of primary energy consumption that comes from solar power
Using the substitution method
Measured as a percentage of the total , using the .
Source
Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data
Last updated
June 27, 2025
Next expected update
June 2026
Date range
1965–2024
Unit
%

Related research and writing

Energy

Hannah Ritchie and Pablo Rosado

What’s the difference between direct and substituted primary energy?

Hannah Ritchie and Pablo Rosado

Explore charts that include this data

Sources and processing

This data is based on the following sources

Energy Institute – Statistical Review of World Energy

The Energy Institute Statistical Review of World Energy analyses data on world energy markets from the prior year.

Retrieved on
June 27, 2025
Retrieved from
https://www.energyinst.org/statistical-review/
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.
Energy Institute - Statistical Review of World Energy (2025).

The Energy Institute Statistical Review of World Energy analyses data on world energy markets from the prior year.

Retrieved on
June 27, 2025
Retrieved from
https://www.energyinst.org/statistical-review/
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.
Energy Institute - Statistical Review of World Energy (2025).

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

Reuse this work

  • 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: Share of primary energy consumption that comes from solar power”, part of the following publication: Hannah Ritchie, Pablo Rosado, and Max Roser (2023) - “Energy”. Data adapted from Energy Institute. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/solar-share-energy.html [online resource] (archived on March 4, 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:

Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data

Full citation

Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data. “Share of primary energy consumption that comes from solar power – Using the substitution method” [dataset]. Energy Institute, “Statistical Review of World Energy” [original data]. Retrieved March 31, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/solar-share-energy.html (archived on March 4, 2026).

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/solar-share-energy.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/solar-share-energy.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/solar-share-energy.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/solar-share-energy.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/solar-share-energy.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

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

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