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 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!

Analysis of ‘Power consumption in India(2019-2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/twinkle0705/state-wise-power-consumption-in-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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 :)

--- Original source retains full ownership of the source dataset ---

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

SECURES-Energy

Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided.

The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation.

Further information:

• Project website SECURES: https://www.secures.at/
• All project-related publications: https://www.secures.at/publications
• Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf

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

1. Hourly profiles ERA5-Land 1981-2010
2. Hourly profiles RCP 4.5/RCP 8.5 2011-2100

Production profiles:

Demand profiles:

Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO.

Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2].

SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the spatial aggregation level NUTS0 (country-wide).

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 shapefiles of the different NUTS levels.

Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883.

The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

[1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories.

[2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report –

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.

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

Expanded result datasets from the forthcoming conference proceedings article "Where do Germany's electricity imports come from?", to be 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 Poooled 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.

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 have 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.

Here, we focus on the production of electricity from renewable sources. As such, we focus on a statistic distinct from SDG 7.2.1 “Renewable energy share in the total final energy consumption”. Data for this Pacific regional indicator are relevant for SDG 7.b.1 “Installed renewable energy-generating capacity in developing countries (in watts per capita)”. Call Number: [EL] Physical Description: 5 p.

This dataset, related to the article 'Power System Modelling in the Baltic Countries: Data Accessibility and Consistency Aspects' (2023), compares electricity generation data for 2020 and 2022 from various sources in Latvia, providing both input and output values and associated metadata.

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.

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

As renewables have become a compelling investment proposition, investment into new renewable power has grown from less than USD 50 billion per year in 2004, to about USD 300 billion per year in the recent years, exceeding investments into new fossil fuel power by a factor of three in 2018. Yet, renewable investments remain below their potential. Scaled up renewable energy investment, on the foundation of sound enabling policy frameworks, is critical to accelerate the global energy transformation and reap its many benefits, while achieving climate and development targets. By addressing key risks and barriers, public finance, including climate finance, plays an important role in bridging the financing gap and attracting further investment from the private sector to renewables. Institutional investors, such as pension funds, insurance companies, endowments and sovereign wealth funds, have the potential to scale up major investments. IRENA’s Sustainable Energy Marketplace showcases several financial instruments and funds available to source investment for individual projects.

Residential electricity prices data for Saudi Arabia, UAE, Bahrain, Oman and Kuwait collected from multiple sources. Saudi Arabia electricity tariffs: KAPSARC dataOman: Authority for Electricity Regulations - Link 2019 Annual Report Bahrain: Electricity & Water Authority - Link - ​​​​​​​​​​​​​​​​​​​​​Electricity Consumption Tariff for the years 2016-2019UAE electricity prices: Dubai: Dubai Electricity & Water Authority - Link Sharjah: Sharjah Electricity & Water Authority - Link Access Abu Dhabi prices dataset Link, Source: Abu Dhabi Distribution Company - Link Water & Electricity Tariffs 2017Other emirates in UAE: Federal Electricity & Water Authority - Link Global average price - link World average price is 0.14 U.S. Dollar per kWh for household users and 0.13 U.S. Dollar per kWh for business users.Note: Global average price for world countries include all items in the electricity bill such as the distribution and energy cost, various environmental and fuel cost charges and taxes.All prices are converted to (US cent/KWh). Citation: Alghamdi, Abeer. 2020. “GCC Residential Electricity Tariffs.” [dataset]. https://datasource.kapsarc.org/explore/dataset/gcc-electricity/information/?disjunctive.country_city&disjunctive.category&disjunctive.slabs.

This is the accompanying dataset for the publication "Assess Space-Based Solar Power in European-Scale Power System Decarbonization".

The dataset contains the following input data files:

powerplants.csv: power plant-level technical and geographic information used to build generator inputs for the PyPSA-Eur model, including fuel type, capacity, location, efficiency and commissioning year

electricity_demand.csv: hourly electricity demand per country for a full year, used by the PyPSA-Eur model, in MW

europe-2020-era5.nc: hourly weather-based capacity factors for renewable energy technologies per country and technology, derived from ERA5 reanalysis data, used by the PyPSA-Eur model, in per unit (p.u.)

sbsp_rd1(rd2)_profile_2020.nc: hourly normalized generation profiles for RD1 or RD2 SBSP configurations in 2020, representing power output as a fraction of maximum generation, used by the PyPSA-Eur model, in per unit (p.u.)

sbsp_rd1(rd2)_profile_2050.nc: hourly normalized generation profiles for RD1 or RD2 SBSP configurations in 2050, representing power output as a fraction of maximum generation, used by the PyPSA-Eur model, in per unit (p.u.)

costs.csv: cost assumptions, used by the PyPSA-Eur model, units defined in units column

resources/: geospatial and technology-specific input data used by the PyPSA-Eur model, including regional boundaries, renewable generation profiles, spatial constraints, and power plant reference data

The dataset contains the intermediate output data files:

elec_s_37_ec_lcopt_Co2L0.7-3H.nc: PyPSA-Eur network for the year 2020 generated before integrating SBSP, containing techno-economic model outputs including capacities, flows, and costs

elec_s_37_ec_lcopt_Co2L0.0-3H_maximum.nc: PyPSA-Eur network for the year 2050 assuming maximum projected technology costs for all generation technologies

elec_s_37_ec_lcopt_Co2L0.0-3H_minimum.nc: PyPSA-Eur network for the year 2050 assuming minimum projected technology costs for all generation technologies

elec_s_37_ec_lcopt_Co2L0.0-3H.nc: PyPSA-Eur network for the year 2050 assuming average projected technology costs for all generation technologies

The dataset contains the result data files from PyPSA-Eur model runs with integrated SBSP under various scenario settings. Each folder corresponds to a specific combination of scenario year (e.g. 2020, 2050) and SBSP capital cost (in EUR/MW). All results reflect optimized power system configurations with SBSP included.

The following files are provided as examples from the "2050_RD1_267869" scenario, which represents the year 2050 with RD1 (SBSP) assumed to have a capital cost of 267869 EUR/MW:

optimized_2050_rd1_267869_network.nc: Optimized PyPSA-Eur network for 2050 with integrated RD1

2050_middle_rd1_hourly_energy_supply.csv: Hourly energy supply by technology in the optimized network, in MW

2050_middle_rd1_optimization_output.txt: Full solver output from the optimization process

2050_middle_rd1_statistics_cleaned.csv: Aggregated statistics of key components in the network

active_rd1_SBSP_buses.txt: List of buses (nodes) where SBSP is actively installed

generators_2050_rd1_267869.csv: Detailed parameters of all generators in the optimized network

storage_units_2050_rd1_267869.csv: Detailed parameters of all storage units

stores_2050_rd1_267869.csv: Detailed parameters of all stores

sbsp_rd1_p_nom_opt_results.csv: Optimized SBSP installed capacity per node, in MW

Each folder follows the same structure, with file names indicating the year and SBSP capital cost. These results support analysis of SBSP deployment under varying techno-economic assumptions.

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

Global Total Support on Natural Gas for Electricity Generation Share by Country (Million US Dollars, Constant = 2020), 2023 Discover more data with ReportLinker!

This data-set contains all data resources, either directly downloadable via this platform or as links to external databases, to execute the generic modeling tool as described in D5.4

• 0 generic model
  file: d5.4-generic-model_february-2020.pdf
  Report with methodology and source code for the generic model
• 1.1 COPERNICUS
  https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=download.%20
  The European Urban Atlas provides reliable, inter-comparable, high-resolution land use and land cover data for 800 Functional Urban Area (FUA) for the 2012 reference year in EEA39 countries.
• 1.2a 1km grid shapefiles for NOx emissions in EU
  The shapefiles are: nox.dbf nox.prj nox.shp nox.shx
• 1.2b 1km grid shapefiles for PM10 emissions in EU
  The shapefiles are: pm10.dbf pm10.prj pm10.shp pm10.shx
• 2.1 Eurostat energy consumption data
  https://ec.europa.eu/eurostat/web/energy/data/database?p_p_id=NavTreeportletprod_WAR_NavTreeportletprod_INSTANCE_QAMy7Pe6HwI1&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_count=1
• 2.2 JODI gas consumption data
  https://www.jodidata.org/gas/database/data-downloads.aspx
  Complete data series for all products, flows and countries, from January 2009 to one month-old can be downloaded, for free, in a beta version of .csv format.
• 2.3 Meteo data UK
  https://www.metoffice.gov.uk/services/data/datapoint/datapoint-products
• 2.4 Meteo data NL
  https://www.knmi.nl/nederland-nu/klimatologie/daggegevens
• 2.5 hourly electricity load profiles
  https://data.open-power-system-data.org/time_series/
• 3.1 fleet data
  file: 200210_summary_fleet_2016.csv
  Summary of historical fleet for EU28+NO by age and vehicle type: Diesel, CNG, Petrol, Electric, PHEV, LPG
• 3.2 modal shares
  file: 200210_summary_shares.csv
  This data set holds the modal share of different modes, for EU cities at level of vehicle kilometers. Modes considered: bus car heavy duty truck >32t heavy duty truck 16-32t heavy duty truck 3_5-7_5t heavy duty truck 7_5-16t light duty truck metro/tram moped motorcycle passenger train slow mode van
• 3.3 emission factors
  file: 200212_summary_copert_ef.xlsx
  Emission factor for passenger cars (in g/km )from COPERT V, urban conditions. Data dimensions: 1. type: diesel, petrol, CNG, LPG, PHEV (in line with fleet data) 2. pollutant: NOx or PM 3. Peak/off-peak: (P/OP) 4. year of built: YOB
• 5.1 Crude death rates
  http://apps.who.int/healthinfo/statistics/mortality/causeofdeath_query/start.php
  the total number of deaths in a country per year per 1,000 people broken down by age cohort and sex
• 5.2 UN population data
  https://population.un.org/wpp/Download/Standard/Population/
  the number of inhabitants at country level broken down by age cohort and sex the time a person has lived broken down by age cohort and sex, and country