This dataset contains data on the electricity usage of each country.

It is used in my Today, the Underdogs… Tomorrow, the Challengers notebook analyzes underdogs like Africa and shows why they should have updated technology and data science because of their great potential.

This data is collected from the International Energy Agency.

Which countries have less access to electricity? Which have crazy high usage? Try to plot it out.

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2022, figures for 2023 are revised provisional figures and figures for 2024 are provisional.

Changes as of July 2025: Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the outdated data from June 2025. The most recent figures are 5 percent higher for 2024 solar electricity production. These figures are in these two tables (in Dutch): - StatLine - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - StatLine - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update is scheduled in November 2025. From that moment all figures will be fully consistent again. We apologize for the inconvenience.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

Load, wind and solar, prices in hourly resolution. This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020. See previous versions for historical data from a broader range of sources. All data processing is conducted in Python/pandas and has been documented in the Jupyter notebooks linked below.

Global Total Support on End-Use Electricity for Producers Share by Country (Million US Dollars, Constant = 2020), 2023 Discover more data with ReportLinker!

With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access.

This dataset contains long time series for production, consumption, and line flows, amounting to 20 years of data with a time resolution of one hour, for several thousands of loads and several hundreds of generators of various types representing the ultra-high-voltage transmission grid of continental Europe. The synthetic time series have been statistically validated agains real-world data.

Data generation algorithm

The algorithm is described in a Nature Scientific Data paper. It relies on the PanTaGruEl model of the European transmission network -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated data gathered from the ENTSO-E transparency platform, such as power consumption aggregated at the national level.

Network

The network information is encoded in the file europe_network.json. It is given in PowerModels format, which it itself derived from MatPower and compatible with PandaPower. The network features 7822 power lines and 553 transformers connecting 4097 buses, to which are attached 815 generators of various types.

Time series

The time series forming the core of this dataset are given in CSV format. Each CSV file is a table with 8736 rows, one for each hourly time step of a 364-day year. All years are truncated to exactly 52 weeks of 7 days, and start on a Monday (the load profiles are typically different during weekdays and weekends). The number of columns depends on the type of table: there are 4097 columns in load files, 815 for generators, and 8375 for lines (including transformers). Each column is described by a header corresponding to the element identifier in the network file. All values are given in per-unit, both in the model file and in the tables, i.e. they are multiples of a base unit taken to be 100 MW.

There are 20 tables of each type, labeled with a reference year (2016 to 2020) and an index (1 to 4), zipped into archive files arranged by year. This amount to a total of 20 years of synthetic data. When using loads, generators, and lines profiles together, it is important to use the same label: for instance, the files loads_2020_1.csv, gens_2020_1.csv, and lines_2020_1.csv represent a same year of the dataset, whereas gens_2020_2.csv is unrelated (it actually shares some features, such as nuclear profiles, but it is based on a dispatch with distinct loads).

Usage

The time series can be used without a reference to the network file, simply using all or a selection of columns of the CSV files, depending on the needs. We show below how to select series from a particular country, or how to aggregate hourly time steps into days or weeks. These examples use Python and the data analyis library pandas, but other frameworks can be used as well (Matlab, Julia). Since all the yearly time series are periodic, it is always possible to define a coherent time window modulo the length of the series.

Selecting a particular country

This example illustrates how to select generation data for Switzerland in Python. This can be done without parsing the network file, but using instead gens_by_country.csv, which contains a list of all generators for any country in the network. We start by importing the pandas library, and read the column of the file corresponding to Switzerland (country code CH):

import pandas as pd
CH_gens = pd.read_csv('gens_by_country.csv', usecols=['CH'], dtype=str)

The object created in this way is Dataframe with some null values (not all countries have the same number of generators). It can be turned into a list with:

CH_gens_list = CH_gens.dropna().squeeze().to_list()

Finally, we can import all the time series of Swiss generators from a given data table with

pd.read_csv('gens_2016_1.csv', usecols=CH_gens_list)

The same procedure can be applied to loads using the list contained in the file loads_by_country.csv.

Averaging over time

This second example shows how to change the time resolution of the series. Suppose that we are interested in all the loads from a given table, which are given by default with a one-hour resolution:

hourly_loads = pd.read_csv('loads_2018_3.csv')

To get a daily average of the loads, we can use:

daily_loads = hourly_loads.groupby([t // 24 for t in range(24 * 364)]).mean()

This results in series of length 364. To average further over entire weeks and get series of length 52, we use:

weekly_loads = hourly_loads.groupby([t // (24 * 7) for t in range(24 * 364)]).mean()

Source code

The code used to generate the dataset is freely available at https://github.com/GeeeHesso/PowerData. It consists in two packages and several documentation notebooks. The first package, written in Python, provides functions to handle the data and to generate synthetic series based on historical data. The second package, written in Julia, is used to perform the optimal power flow. The documentation in the form of Jupyter notebooks contains numerous examples on how to use both packages. The entire workflow used to create this dataset is also provided, starting from raw ENTSO-E data files and ending with the synthetic dataset given in the repository.

Funding

This work was supported by the Cyber-Defence Campus of armasuisse and by an internal research grant of the Engineering and Architecture domain of HES-SO.

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe has further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors, such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels, determine the amount of electricity the average person requires in each country.

Global Total Support on End-Use Electricity for Consumers Share by Country (Million US Dollars, Constant = 2020), 2023 Discover more data with ReportLinker!

Expanded result datasets from the conference proceedings article "Where do Germany's electricity imports come from?", presented at the 21st International Conference on the European Energy Market Lisbon, 27-29 May 2025, Portugal (free preprint available here). The work was also discussed in a presentation at the 2nd NFDI4Energy Conference 2025 in Karlsruhe, Germany. Each file contains electricity imports of the Germany-Luxembourg bidding zone for the given year either per origin country or per generation technology, based on different measures. Additionally, complete hourly time series for each year and each measure are available. It should be emphasized that, as will be discussed in the article, there is no uniquely correct measure for the amount or origin/type of electricity imports, but rather different methods based on different interpretations, yielding potentially very different results. Abbreviations used for the different measures: CFT: Commercial flows total Netted CFT: Netted commercial flows total Pooled Netted CFT: Pooled netted commercial flows total Pooled Net CFT: Pooled net commercial imports total Pooled Net Phys.: Pooled net physical imports DC Flow Tracing: Direct coupling flow tracing AC Flow Tracing: Aggregated coupling flow tracing All results are given in TWh for yearly values, and MWh for hourly values. Measures are defined in the supplementary material. Additional information on the flow tracing method can be found in Schäfer et al. (2020) (free preprint, explanatory video). Total values can slightly differ despite conceptually being equal due to numerical inaccuracies in the flow-tracing calculation.

Energy and electricity substation locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.

For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.

The SECURES-Met dataset provides variables visible in the table.

SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the spatial aggregation level

• NUTS0 (country-wide),
• NUTS2 (province-wide),
• NUTS3 (Austria only),
• and EEZ (Exclusive Economic Zones, offshore only).

The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.

The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

This dashboard is part of SDGs Today. Please see sdgstoday.orgTracking SDG 7: The Energy Progress Report estimated that in 2019, 759 million people around the world lacked access to electricity. Due to current policies and the impact of COVID-19, it is predicted that by 2030, 660 million people will still not have access to electricity, with a majority residing in Sub-Saharan Africa. SDG 7 aims to “ensure access to affordable, reliable, sustainable and modern energy for all.” As one measure of progress, the UN agreed on SDG Indicator 7.1.1: “Proportion of population with access to electricity”.Access to electricity for 2020 through 2023 was computed for 244 countries and 46,624 administrative level 2 subnational units using Visible and Infrared Imaging Suite (VIIRS) nighttime lights and GHS-POP gridded population data with a combination of zonal statistics and table calculations. This work was completed in support of the Group on Earth Observations (GEO) Human Planet Initiative (HPI). CIESIN plans to update the SDG Indicator 7.1.1: Access to Electricity data set annually to help countries track their progress towards SDG 7 and to facilitate international comparisons.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2023)
• railways (OpenStreetMap, 2023)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

# OSMOSE WP1 DATASET
This dataset has been compiled within the OSMOSE project, from the European Union’s Horizon 2020 research and innovation program, to support adequacy studies performed in Work-Package 1.
Original data originate from two main sources : Plan4Res EU project and ENTSOE Pan European Common Database.

## Content
The dataset contains 35 weather years (1982-2016) of data for 33 EU countries.
General data granularity is country level.
Some RES data are provided at the granularity of the 99-clusters of the e-Highway2050 project.
- non-thermosensitive load profile (1 profile per country). Profiles sum to 1. (Source: Plan4Res and OSMOSE)
- electric heating profiles (1 profile per country, 1 file per weather year). Profiles sum to 1 on average but not individually. (Source: Plan4Res and OSMOSE)
- electric vehicles profiles (1 profile per country, 1 file per weather year). Profiles sum to slightly more than 1, depending on the weather scenario used. The difference to 1 corresponds to the thermo-sensitive effect due to the heating and the air-conditioning necessary for the well-functioning of the motor and the comfort of the passengers in this weather scenario. Its varies from country to country. (Source : OSMOSE, JRC for daily profiles, CS3 for weather and annual vehicle usage profiles)
- onshore wind power-factor profiles (1 profile per country, 1 file per weather year and 1 profile per cluster, 1 file per weather year). (Source: PECD)
- offshore wind power-factor profiles (1 profile per country per weather year and 1 profile per cluster per weather year). (Source: PECD)
- solar pv power-factor profiles (1 profile per country, 1 file per weather year and 1 profile per cluster, 1 file per weather year). (Source: PECD)
- hydro data (installed capacities (Run-of-River - ror, reservoir, Pump Storage Plants - PSP), volumes of the reservoirs and PSP, annual energies (ror and reservoir), per country and per cluster. Source: (OSMOSE based on MAF2018 and MAF2019)
- hydro energies in GWh (ror - daily, reservoir - weekly, per country, 1 file per weather year). (Source: OSMOSE based on PECD)
Full details of data processing performed by OSMOSE WP1 can be found in appendix B of OSMOSE deliverable D1.3 (available at https://www.osmose-h2020.eu/resource-center).

## AntaresSimulator studies
The dataset also contains 2 study skeletons (country and cluster granulariry) and R scripts allowing to build studies to be run with AntaresSimulator (https://antares-simulator.org).
To run these studies, unzip the archives "OSMOSE_DATASET" (which actually contains the dataset) and "ANTARES_R" (which contains R scripts) and follow the instructions in "ANTARES_R/README.md".

## Forecast data
The dataset also comprises 10 weather years (1982-1991) of AntaresSimulator imput time-series corresponding to day-ahead forecast data for demand, solar and wind for 2030 and 2050.
These forecast data have been computed based on the installed capacities defined in OSMOSE CGA scenario.
Details of the computation process can be found in the report included in this distribution.

## OSeMOSYS dataset
This file contains the OSeMOSYS parameter values (costs, demand, potentials, emission limits...) used in the study "Comparing the relative impacts of investment constraints and temporal detail on the outcomes of capacity expansion models applied to power systems".

## Licences
Data is published under the terms of the "Creative Commons Attribution 4.0 International" licence (https://creativecommons.org/licenses/by/4.0)
R code is published under the terms of the "MIT" licence (https://opensource.org/licenses/MIT)

## Acknowledgements
The OSMOSE project(https://www.osmose-h2020.eu) received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 773406.
Plan4Res EU project (https://zenodo.org/record/3802550)
C3S (https://cds.climate.copernicus.eu)
ENTSOE Pan European Common Database (https://zenodo.org/record/3702418 and https://zenodo.org/record/3985078) and MAF (https://www.entsoe.eu/outlooks/midterm/previous-maf-versions)
e-Highway2050 project (https://cordis.europa.eu/project/id/308908/reporting)

150 views (2 recent) (i) This dataset covers the indicator for monitoring progress towards renewable energy targets of the Europe 2020 strategy implemented by Directive 2009/28/EC on the promotion of the use of energy from renewable sources. This is indicator is a Sustainable Development Goal (SDG). It has been chosen for the assessment of the progress towards the objectives and targets of the EU Sustainable Development Strategy. The data collection covers the full spectrum of the Member States of the European Union. The share of energy from renewable sources is calculated for four indicators: Transport (RES-T) Heating and Cooling (RES-H&C) Electricity (RES-E) Overall RES share (RES) (iii) all Member States of the European Union. The EU aggregate is also shown. Additionally and sometimes at a later stage, EFTA-countries (Iceland and Norway), some of the EU candidate countries (Montenegro, North Macedonia, Albania, Serbia and Turkey) and potential candidate countries (Bosnia & Herzegovina and Kosovo (UNSCR 1244/99)) are also displayed, depending on their transmissiosn.

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.

This dataset contains the usage data of a single electric car collected in as part of the EVE study (Enquête des Vehicles Electrique) run by the Observatoire du Transition Energétique Grenoble (OTE-UGA). This dataset includes the following variables for a single Renault ZOE 2014 Q90: - Speed, distance covered, and other drivetrain data variables; - State of charge, State of health and other battery characteristics; as well as - external temperature variables. The Renault ZOE 2014 Q90 has a battery capacity of 22 KWh and a maximum speed of 135 KM/h. More information about on the specifications can be found here If you find this dataset useful or have any questions, please feel free to comment on the discussion dedicated to this dataset on the OTE forum . The electric car is used for personal use exclusively including occasional transit to work but mostly for personal errands and trips. The dataset was collected using a CanZE app and a generic car lighter dongle. The dataset spans three years from October 2020 to October 2023. A simple Python notebook that visualises the datasets can be found here. More complex use-cases for the datasets can be found in the following links: - Comparison of the carbon footprint of driving across countries: link - Feedback indicators of electric car charging behaviours: link There is also more information on the collection process and other potential uses in the data paper here. Please don't hesitate to contact the authors if you have any further questions about the dataset.

This dataset covers the indicator for monitoring progress towards renewable energy targets of the Europe 2020 strategy implemented by Directive 2009/28/EC on the promotion of the use of energy from renewable sources (RED I) and the Fit for 55 strategy uner the Green Deal implemented by Directive (EU) 2018/2001 on the promotion of the use of energy from renewable sources (RED II).

Data until 2020 are calculated on the basis of RED I, while data for 2021 follow RED II. Due to the change of legal basis, a break in series occurs between 2020 and 2021. Readers are encouraged to analyse the differences between both Directives (RED I and RED II), the energy sector and all national specificities before drawing any conclusions from the comparison of year 2021 with previous time series. The SHARES Manual provides details on the methodology used for the calculation of the share of renewables.

Hydro is normalised (averaged over a number of years to smooth out the effects of climatic variation) and excluding pumping. Wind is also normalised (and from 2021 onwards as per RED II separately for on-shore and off-shore). Solar includes solar photovoltaics and solar thermal power generation. All other renewables include electricity generation from gaseous and liquid biofuels, renewable municipal waste, geothermal, and tide, wave & ocean. Only electricity produced from compliant liquid biofuels can be accounted. For 2021 onwards (as per RED II), also solid and gaseous biofuels combusted in installations above a certain threshold need to comply with sustainability and greenhouse gas emissions saving criteria. Renewable energy sources used for heating and cooling include solar thermal, geothermal energy, ambient heat captured by heat pumps for heating (and from 2021 onwards, renewable cooling, as per RED II), solid, liquid and gaseous biofuels, and the renewable part of waste. Only heat produced from compliant liquid biofuels can be accounted for. From 2021 onwards (as per RED II), solid and gaseous biofuels combusted in installations above a certain threshold need to comply with sustainability and greenhouse gas emissions saving criteria. RED II modifies the multipliers for the use of renewable electricity in different means of transport.

The calculation is based on data collected in the framework of Regulation (EC) No 1099/2008 on energy statistics and complemented by specific supplementary data transmitted by national administrations to Eurostat.

In some countries the statistical systems are not yet fully developed to meet all requirements of RED I or RED II, in particular with respect to ambient heat captured from the environment by heat pumps, renewable cooling or sustainability of solid and gaseous biofuels.

This is a Sustainable Development Goal (SDG) indicator. It has been chosen for the assessment of the progress towards the objectives and targets of the EU Sustainable Development Strategy. The data collection covers the full spectrum of the Member States of the European Union. Time series starts in the year 2004.

The share of energy from renewable sources is calculated for four indicators:

• Transport (RES-T)
• Heating and Cooling (RES-H&C)
• Electricity (RES-E)
• Overall RES share (RES)

More details are available in the SHARES tool manual. In addition, more information (like detailed calculations used to obtain the results) are available in the Excel and zip file in the SHARES section.

In particular, for RES-E it is possible to obtain results higher than 100%. This is due to the definition of the calculation, where the numerator ‘gross final consumption of electricity from renewable sources’ is defined as the gross electricity production from renewable sources. The denominator ‘gross final consumption of electricity’ is, for the purpose of the calculations in the SHARES tool, defined as gross electricity production from all energy sources plus total imports of electricity minus total exports of electricity. Therefore, if a country produces more electricity from renewable sources than total electricity it consumes, the RES-E ratio would be higher than 100% (e.g. Norway).

Global Tax Expenditure on End-Use Electricity for Residential Share by Country (Million US Dollars, Constant = 2020), 2023 Discover more data with ReportLinker!

This dataset contains a range of electric mobility variables used in this paper. The dataset contains data on 32 European countries from 2000 - 2020. The analysis in the paper uses the 11 year period between 2009 and 2019, with some robustness tests with 2020 included.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions and power plant types around the world by country. This information was obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The location of the countries is placed by lat/lon coordinates that was provided by CARMA. The dataset provides the country, region type, plant count, and lat/lon for each individual country. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. % Fossil: The percentage of total electricity that is generated by the combustion of coal, oil, or natural gas. % Hydro: The percentage of total electricity that is generated by hydroelectric power facilities. % Nuclear: The percentage of total electricity that is generated by nuclear power facilities. % Other Renewable: The percentage of total electricity that is generated by the use of wind, solar, biomass, geothermal, captured heat, or hydrogen energy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information