Energy Consumption and Mix Dataset by Our World in Data

Dataset Description

This dataset is a comprehensive collection of key metrics related to energy consumption and energy mix, maintained by Our World in Data. It includes global, regional, and country-level data on primary energy consumption, energy mix, electricity mix, fossil fuel production, and related energy metrics.

Key Metrics

The dataset contains several important metrics related to global energy:

• Energy Consumption (primary energy, per capita consumption, growth rates)
• Energy Mix (share of renewables, fossil fuels, etc.)
• Electricity Mix (sources of electricity generation such as coal, hydro, wind, solar)
• Fossil Fuel Production
• Primary Energy Consumption
• Per Capita and Per GDP Energy Indicators
• Yearly data by country and global aggregates

Possible Analyses

The "Energy Consumption and Mix" dataset offers a wide range of opportunities for analysis. Here are some examples of what can be done with this dataset:

1. Global Energy Consumption Trends

• Objective: Examine how global primary energy consumption has evolved over time.
• Approach:
  • Plot global energy consumption year by year, broken down by energy source (e.g., coal, oil, natural gas, renewables).
  • Analyze growth rates in total energy consumption.
  • Investigate how the share of renewables has changed in the global energy mix.
• Potential Insights: This analysis can provide insight into which energy sources are becoming more dominant globally and how the energy landscape has shifted.

2. Energy Mix by Country

• Objective: Compare the energy mix of different countries.
• Approach:
  • For each country, visualize the breakdown of energy sources (e.g., renewables, fossil fuels) over time.
  • Analyze which countries have transitioned towards cleaner energy sources.
  • Compare the energy mix of developed vs. developing countries.
• Potential Insights: This could show which countries are leading in the transition to renewable energy and which are still reliant on fossil fuels.

3. Electricity Generation Sources by Region

• Objective: Explore regional differences in electricity generation.
• Approach:
  • Compare regions based on the percentage of electricity generated from different sources (e.g., coal, hydro, wind, solar).
  • Analyze trends in renewable electricity generation by region.
• Potential Insights: This analysis can identify regions that have made the most progress in transitioning to cleaner electricity sources.

4. Energy Consumption Per Capita

• Objective: Investigate energy consumption on a per capita basis.
• Approach:
  • Calculate the per capita energy consumption for different countries and regions.
  • Compare energy consumption per capita over time.
  • Identify which countries have the highest and lowest per capita energy consumption.
• Potential Insights: This can help uncover disparities in energy access and usage between different countries or regions.

5. Energy Consumption and Economic Growth

• Objective: Analyze the relationship between energy consumption and GDP.
• Approach:
  • Plot energy consumption per capita vs. GDP per capita for different countries and regions.
  • Look for correlations between energy consumption growth and economic growth.
  • Explore how energy consumption patterns differ between high-income and low-income countries.
• Potential Insights: This analysis can highlight the role of energy in driving economic development and the efficiency of energy usage across income levels.

6. Carbon Emissions from Energy Sources

• Objective: Assess the impact of different energy sources on carbon emissions.
• Approach:
  • Calculate emissions based on the share of fossil fuels in the energy mix.
  • Analyze trends in carbon emissions as countries transition to cleaner energy sources.
  • Compare countries or regions with high fossil fuel dependency to those with higher renewable energy shares.
• Potential Insights: This analysis could highlight the environmental impact of different energy sources and track progress towards emissions reductions.

Citation

Hannah Ritchie, Pablo Rosado and Max Roser (2023) - “Energy” Published online at OurWorldinData.org. Retrieved from: https://ourworldindata.org/energy [Online Resource]

Electricity prices in Europe are expected to remain volatile through 2025, with Italy projected to have some of the highest rates among major European economies. This trend reflects the ongoing challenges in the energy sector, including the transition to renewable sources and the impact of geopolitical events on supply chains. Despite efforts to stabilize the market, prices still have not returned to pre-pandemic levels, such as in countries like Italy, where prices are forecast to reach ****** euros per megawatt hour in September 2025. Natural gas futures shaping electricity costs The electricity market's future trajectory is closely tied to natural gas prices, a key component in power generation. Dutch TTF gas futures, a benchmark for European natural gas prices, are projected to be ***** euros per megawatt hour in July 2025. The reduced output from the Groningen gas field and increased reliance on imports further complicate the pricing landscape, potentially contributing to higher electricity costs in countries like Italy. Regional disparities and global market influences While European electricity prices remain high, significant regional differences persist. For instance, natural gas prices in the United States are expected to be roughly one-third of those in Europe by March 2025, at **** U.S. dollars per million British thermal units. This stark contrast highlights the impact of domestic production capabilities on global natural gas prices. Europe's greater reliance on imports, particularly in the aftermath of geopolitical tensions and the shift away from Russian gas, continues to keep prices elevated compared to more self-sufficient markets. As a result, countries like Italy may face sustained pressure on electricity prices due to their position within the broader European energy market. As of August 2025, electricity prices in Italy have decreased to ****** euros per megawatt hour, reflecting ongoing volatility in the market.

he cost of capital (CoC) critically influences the levelized cost of renewable energy and, by extension, the global low-carbon transition. However, reliable and consistent CoC data remain scarce, limiting an appropriate reflection of CoC differences in energy system and integrated assessment models. We present a global dataset of CoC for renewable energy projects, covering 68 countries from 2010 to 2022 and focusing on three key technologies: utility-scale solar photovoltaics, onshore wind, and offshore wind. We systematically compile and standardize data from academic literature and international organizations, ensuring methodological comparability. Our dataset includes 1,429 data points, of which 366 provide nominal, after-tax weighted average cost of capital values. We conduct technical validation through cross-technology comparisons, temporal consistency checks, and source triangulation. By addressing a key data gap, this dataset aims to support evidence-based energy policy analysis and advance the understanding of how financing conditions impact renewable energy costs globally.

There are large inequalities in energy consumption between countries. The average US citizen still consumes more than ten times the energy of the average Indian, 4-5 times that of a Brazilian, and three times more than China. The gulf between these and very low-income nations is even greater- a number of low-income nations consume less than 100 kilowatt-hour equivalents per person.

Secondly, global average per capita energy consumption has been consistently increasing; between 1970 and 2014, average consumption increased by approximately 45%.

This growth in per capita energy consumption does, however, vary significantly between countries and regions. Most of the growth in per capita energy consumption over the last few decades has been driven by increased consumption in transitioning middle-income (and to a lesser extent, low income countries). In the chart we see a significant increase in consumption in transitioning BRICS economies (China, India and Brazil in particular); China’s per capita use has grown by nearly 250 percent since 2000; India by more than 50 percent; and Brazil by 38 percent.

Whilst global energy growth is growing from developing economies, the trend for many high-income nations is a notable decline. As we see in exemplar trends from the UK and US, the growth we are currently seeing in transitioning economies ended for many high-income nations by over the 1970s and 80s. Both the US and UK peaked in terms of per capita energy consumption in the 1970s, plateauing for several decades until the early 2000s. Since then, we see a reduction in consumption; since 2000, UK usage has decreased by 20 to 25%.

Source

Hannah Ritchie (2019) - "Access to Energy". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/energy-access'

Context

India is the world's third-largest producer and third-largest consumer of electricity. The national electric grid in India has an installed capacity of 370.106 GW as of 31 March 2020. Renewable power plants, which also include large hydroelectric plants, constitute 35.86% of India's total installed capacity. During the 2018-19 fiscal year, the gross electricity generated by utilities in India was 1,372 TWh and the total electricity generation (utilities and non-utilities) in the country was 1,547 TWh. The gross electricity consumption in 2018-19 was 1,181 kWh per capita. In 2015-16, electric energy consumption in agriculture was recorded as being the highest (17.89%) worldwide. The per capita electricity consumption is low compared to most other countries despite India having a low electricity tariff.

In light of the recent COVID-19 situation, when everyone has been under lockdown for the months of April & May the impacts of the lockdown on economic activities have been faced by every sector in a positive or a negative way. With the electricity consumption being so crucial to the country, we came up with a plan to study the impact on energy consumption state and region wise.

The dataset is exhaustive in its demonstration of energy consumption state wise.

Content

Data is in the form of a time series for a period of 17 months beginning from 2nd Jan 2019 till 23rd May 2020. Rows are indexed with dates and columns represent states. Rows and columns put together, each datapoint reflects the power consumed in Mega Units (MU) by the given state (column) at the given date (row).

Acknowledgements

Power System Operation Corporation Limited (POSOCO) is a wholly-owned Government of India enterprise under the Ministry of Power. It was earlier a wholly-owned subsidiary of Power Grid Corporation of India Limited. It was formed in March 2009 to handle the power management functions of PGCIL.

The dataset has been scraped from the weekly energy reports of POSOCO.

Inspiration

Extensive research on power usage in the country is what inspired us to compile the dataset. We are making it public along with our research of the same. This is our first step towards independent data-based research. We are open to suggestions, compliments and criticism alike.

Do leave an upvote if you found the dataset helpful. It keeps me motivated to keep working hard :)

This dataset provides values for ELECTRICITY PRICE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).

It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.

Five separate datasets are included:

Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).

Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.

Folder (3) provides the corresponding shapefiles.

Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.

Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).

Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).

Countries/territories included in the dataset:

Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Central African Republic
Chad
Congo Republic
Democratic Republic of the Congo
Djibouti
Egypt
Equatorial Guinea
Eritrea
Eswatini
Ethiopia
Gabon
The Gambia
Ghana
Guinea
Guiné-Bissau
Côte d'Ivoire
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Senegal
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Togo
Tunisia
Uganda
Tanzania
Zambia
Zimbabwe

References

Sterl, S., Hussain, B., Miketa, A. et al. An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Sci Data 9, 664 (2022). https://doi.org/10.1038/s41597-022-01786-5

See also

Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10650822

India is the third largest producer of electricity in the world. The national electric grid in India has an installed capacity of 399.467 GW as of 31 March 2022. Renewable power plants, which also include large hydroelectric plants, constitute 39.2 % of total installed capacity. During the fiscal year (FY) 2019-20, the gross electricity generated by utilities in India was 1,383.5 TWh and the total electricity generation (utilities and non utilities) in the country was 1,598 TWh.The gross electricity consumption in FY2019 was 1,208 kWh per capita.[7] In FY2015, electric energy consumption in agriculture was recorded as being the highest (17.89%) worldwide. The per capita electricity consumption is low compared to most other countries despite India having a low electricity tariff.

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*****************************RES means renewable energy***********************************

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Description

Uncover this dataset showcasing sustainable energy indicators and other useful factors across all countries from 2000 to 2020. Dive into vital aspects such as electricity access, renewable energy, carbon emissions, energy intensity, Financial flows, and economic growth. Compare nations, track progress towards Sustainable Development Goal 7, and gain profound insights into global energy consumption patterns over time.

Key Features:

• Entity: The name of the country or region for which the data is reported.
• Year: The year for which the data is reported, ranging from 2000 to 2020.
• Access to electricity (% of population): The percentage of population with access to electricity.
• Access to clean fuels for cooking (% of population): The percentage of the population with primary reliance on clean fuels.
• Renewable-electricity-generating-capacity-per-capita: Installed Renewable energy capacity per person
• Financial flows to developing countries (US $): Aid and assistance from developed countries for clean energy projects.
• Renewable energy share in total final energy consumption (%): Percentage of renewable energy in final energy consumption.
• Electricity from fossil fuels (TWh): Electricity generated from fossil fuels (coal, oil, gas) in terawatt-hours.
• Electricity from nuclear (TWh): Electricity generated from nuclear power in terawatt-hours.
• Electricity from renewables (TWh): Electricity generated from renewable sources (hydro, solar, wind, etc.) in terawatt-hours.
• Low-carbon electricity (% electricity): Percentage of electricity from low-carbon sources (nuclear and renewables).
• Primary energy consumption per capita (kWh/person): Energy consumption per person in kilowatt-hours.
• Energy intensity level of primary energy (MJ/$2011 PPP GDP): Energy use per unit of GDP at purchasing power parity.
• Value_co2_emissions (metric tons per capita): Carbon dioxide emissions per person in metric tons.
• Renewables (% equivalent primary energy): Equivalent primary energy that is derived from renewable sources.
• GDP growth (annual %): Annual GDP growth rate based on constant local currency.
• GDP per capita: Gross domestic product per person.
• Density (P/Km2): Population density in persons per square kilometer.
• Land Area (Km2): Total land area in square kilometers.
• Latitude: Latitude of the country's centroid in decimal degrees.
• Longitude: Longitude of the country's centroid in decimal degrees.

Potential Use cases

• Energy Consumption Prediction: Predict future energy usage, aid planning, and track SDG 7 progress.
• Carbon Emission Forecasting: Forecast CO2 emissions, support climate strategies.
• Energy Access Classification: Categorize regions for infrastructure development, understand sustainable energy's role.
• Sustainable Development Goal Tracking: Monitor progress towards Goal 7, evaluate policy impact.
• Energy Equity Analysis: Analyze access, density, and growth for equitable distribution.
• Energy Efficiency Optimization: Identify intensive areas for environmental impact reduction.
• Renewable Energy Potential Assessment: Identify regions for green investments based on capacity.
• Renewable Energy Investment Strategies: Guide investors towards sustainable opportunities.

This article investigates the dynamic impact of green energy consumption (GE), financial inclusion (FI), and military spending (MS) on environmental sustainability (ES) by utilizing a sample of 121 countries from 2003 to 2022. The dataset is divided into high-income, upper-middle income and low and lower-middle-income countries. We employed a two-step system GMM approach, which was further robust through panel Quantile and Driscoll-Kraay (D-K) regressions. The findings divulged that green energy resources benefit ES at global and all income levels because of having a significant negative impact of 5.9% on ecological footprints. At the same time, FI and MS significantly enhance ecological footprints by 7% and 6.9%, respectively, proving these factors detrimental to ES. Moreover, conflicts (CON), terrorism (TM), institutional quality (IQ), and socioeconomic conditions (SEC) also have a significantly positive association with global ecological footprints and most of the income level groups. Dissimilarly, financial inclusion and armed conflicts have a non-significant influence on ecological footprints in low-income and high-income countries, respectively. Furthermore, institutional quality enhances ES in upper-middle and low and lower-middle-income countries by negatively affecting ecological footprints. At the same time, terrorism significantly reduces ecological footprints in high-income countries. This research also provides the imperative policy inferences to accomplish various SDGs.

China Electricity Consumption: per Capita: Average data was reported at 6,257.000 kWh in 2022. This records an increase from the previous number of 6,032.000 kWh for 2021. China Electricity Consumption: per Capita: Average data is updated yearly, averaging 1,066.997 kWh from Dec 1978 (Median) to 2022, with 45 observations. The data reached an all-time high of 6,257.000 kWh in 2022 and a record low of 261.265 kWh in 1978. China Electricity Consumption: per Capita: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCB: Electricity Summary.

Japan JP: Residential Electricity Price: USD per kWh data was reported at 0.330 USD/kWh in 2022. This records an increase from the previous number of 0.260 USD/kWh for 2021. Japan JP: Residential Electricity Price: USD per kWh data is updated yearly, averaging 0.220 USD/kWh from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 0.330 USD/kWh in 2022 and a record low of 0.190 USD/kWh in 2002. Japan JP: Residential Electricity Price: USD per kWh data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.GGI: Environmental: Environmental Policy, Taxes and Transfers: OECD Member: Annual.

Pakistan Electricity Consumption: Total data was reported at 110,764.000 GWh in 2024. This records a decrease from the previous number of 114,300.000 GWh for 2023. Pakistan Electricity Consumption: Total data is updated yearly, averaging 71,541.500 GWh from Jun 1991 (Median) to 2024, with 34 observations. The data reached an all-time high of 116,816.000 GWh in 2021 and a record low of 31,534.000 GWh in 1991. Pakistan Electricity Consumption: Total data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Pakistan – Table PK.RB006: Electricity Generation and Consumption.