Dataset replaced by: https://data.europa.eu/euodp/data/dataset/RLjA7dQOIiPWRJP4KWTIrQ

The product has been discontinued since: 27 May 2016.

Annual estimates of balanced UK regional gross domestic product (GDP). Current price estimates and chained volume measures for UK countries, ITL1, ITL2 and ITL3 regions.

The product has been discontinued since: 02 Feb 2018.

This study employs the Economic Complexity Index (ECI) and the Economic Fitness Index (EFI) as two different measures of economic complexity. The ECI is based on the methodology developed by Hausmann et al. (2014b), while the EFI was introduced by Tacchella et al. (2012) and later improved by Servedio et al. (2018) and Operti et al. (2018).
We used the International Trade Database at the Product Level (BACI) to determine global product complexities. The BACI dataset includes detailed export data for over 200 countries and territories. To ensure accuracy and eliminate fluctuations in export data, Hidalgo (2021) recommends excluding countries with exports of less than 1 billion US dollars, countries with a population of less than 1 million, and products with a global export value of less than 500 million US dollars from the dataset. To avoid endogeneity issues, we followed Boschma et al. (2013) and excluded Turkey from the sample. After cleaning the data in accordance with Hidalgo (2021) and Boschma et al. (2013), we calculated global product complexities for 124 to 141 countries from 2007 to 2020. Since foreign trade statistics of provinces obtained from TURKSTAT are classified according to Standard International Trade Classification (SITC revision 3) at 4-digit codes, we converted BACI dataset based on Harmonized System (HS) classification using Eurostat Metadata Server (RAMON) conversion tables.

Variables: countrycode: NUTS-3 codes of provinces country: Provinces id: identification numbers (given by the authors) gdpper: Real GDP per capita. dist: Distance of provinces from Istanbul in kilometers
eci: Economic Complexity Index (Hidalgo et al. 2014) fit: Economic Fitness Index (Tacchella et al. 2012, Servedio et a. 2018, Operti et al. 2018). ind_elec_cons: Ratio of industrial electricity consumption to total electricity consumption. education: Percentage of high school, faculty, master, and doctorate graduates in the population aged 15 and over. reer: Real Effective Exchange Rate. openness: Share of province-level exports and imports on total provincial GDP. ethnicity: is calculated as the average of the votes received by Kurdish political parties in the provinces. secim: An alternative for ethnicity. Calculated as the the ratio of the number of municipalities won by Kurdish political party mayoral candidates to the total number of municipalities in each province. East: A dummy variable for the provinces located to the east of the capital city, Ankara, are considered as eastern provinces and are labeled as 1, while the provinces located to the west are labeled as 0 and considered as western provinces. onuc: A dummy variable for 13 provinces that are in the scope of cross-border trade regulation are labeled as 1.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

CORINE Land Cover (CLC) database

Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/

Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

natura/*

Natura 2000 natural protection areas

Source: https://www.eea.europa.eu/data-and-maps/data/natura-10

Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

gebco/GEBCO_2014_2D.nc

GEBCO bathymetric dataset

Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/

Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

Population and GDP data for Swiss Cantons

Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html

Terms of Use:

https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html

https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html

nama_10r_3popgdp.tsv.gz

Population by NUTS3 region

Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en

Terms of Use:

https://ec.europa.eu/eurostat/about/policies/copyright

GDP_per_capita_PPP_1990_2015_v2.nc

Gross Domestic Product per capita (PPP) from years 1999 to 2015

Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer

Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015"

Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4

ppp_2019_1km_Aggregated.tif

The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777

License: Creative Commons Attribution 4.0 International Licens

data/bundle/era5-HDD-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics.

data/bundle/era5-runoff-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics.

shipdensity_global.zip

Global Shipping Traffic Density

Creative Commons Attribution 4.0

https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density

seawater_temperature.nc

Global Ocean Physics Reanalysis

Seawater temperature at 5m depth

Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services

License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence

Average annual population to calculate regional GDP data (thousand persons) by NUTS 3 region

This dataset comprises spatial and temporal economic data compiled from the Annual Regional Database of the European Commission (ARDECO) and education attainment from Eurostat, covering the period from 1980 to 2021(2024). The dataset consists of three files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification.

For each file, the following columns are included: Identifier:

NUTS Code: The unique identifier for the NUTS (2016) region

Year: The year of the data point

Variables:

 3. - 8. Hours Worked by NACE sector in 1000 hours (empHour_*)   9. - 15. Employment by NACE sector in 1000 jobs (emp_*)   16. Total employment in 1000 jobs (empl)   17. GDP at constant prices ref. 2015 in mio EUR (gdp)   18. - 23. GVA by NACE sector at constant prices ref. 2015 in mio EUR (gva_*)   24. Total Labour Force in 1000 jobs (labour)   25. Total Population (Regional Accounts) in persons (pop)   26. - 31. Compensation of Employees by NACE sector at constant prices ref. 2015 in mio EUR (wage_*)   32. Share of low education workers in per cent (loweduc) [not available for NUTS3]   33. Share of high education workers in per cent (terteduc) [not available for NUTS3]

The temporal dimension is yearly, ranging from 1980 to 2021(2024). The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.

This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 45-47 belonging to section 3.3 Sectoral Income and Employment. The full text of the deliverable can be accessed via: https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.

Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. London Boroughs),
  • NUTS2 (e.g. London),
  • NUTS1 (e.g. England), and
  • NUTS0 (e.g. United Kingdom) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

Community designs (CD) per billion GDP by NUTS 3 regions

The European exposure data for BN-FLEMO models contains three datasets that can be used with BN-FLEMO models for the estimation of flood loss. The dataset contains: (1) European asset map with unit area values of residential and commercial buildings in EURO per square meter based on reconstruction cost and NUTS-3 regions or national GDP per capita. The values are mapped on CORINE land cover classes for urban areas (111 and 112). (2) Residential building areas in Europe with building area sizes in square meter for each NUTS-3 region. The building area sizes were calculated based on the building geometries extracted from the OSM database. (3) Flood experience in Europe with geometries of historic flood events (1985- 2015) with start date of the events. This dataset can be used to calculate the number of past flood events in an area.

Gross domestic product (GDP) at current market prices by NUTS 3 region

This dataset accompanies the manuscript submitted to Sustainability (Manuscript ID: sustainability-3761958). It contains data used to analyse the Environmental Kuznets Curve (EKC) for greenhouse gas emissions in Poland, using spatial panel econometric methods at the NUTS-2 and NUTS-3 levels.

The data sources include:

• Statistics Poland (GUS) – for regional indicators such as GDP, population, energy consumption.

• EDGAR (Emissions Database for Global Atmospheric Research) – for greenhouse gas emissions in CO₂-equivalent terms.

• Eurostat GeoJSON files – for regional boundaries of Poland at NUTS-2 and NUTS-3 level.

Key transformations:

• Greenhouse gas emissions (CO₂eq), originally available at NUTS-2, were downscaled to NUTS-3 using a proportional allocation method based on regional population and industrial emissions intensity (from highly polluting facilities).

• Additional variables include population, population density, current and deflated GDP (base year 2000, deflator = 100%), and energy consumption.

The dataset is provided in Excel format (Dataset_NUTS2.xlsx, Dataset_NUTS3.xlsx) and GeoJSON format for spatial layers.

We publish our out-of-sample estimates on historical GDP per capita levels between the years 1300 and 2000 together with the collected source data on countries and regions in a comprehensive dataset comprising 5,690 observations (1,313 source data observations, and 4,377 out-of-sample estimates). All references to the source data are provided in the manuscript.Locations refer to NUTS-2 regions in Europe (2021 edition), metro- and micropolitan statistical areas for the United States, metropolitan areas for Canada, and regions of similar size for other countries, e.g. oblasts in Russia. For countries, we use ISO 3166-1 alpha-3 country codes.The column GDPpc is denoted in 2011 USD PPP, matching the unit provided in the Maddison project. The column flag describes whether the value in column GDPpc is taken from source data (see manuscript for references) or an out-of-sample estimate. If it is an out-of-sample estimate, the columns GDPpc_lower and GDPpc_upper provide 90 percent confidence intervals obtained by bootstrapping.The code to generate all estimates will be published soon to ensure reproducibility of results.

Community design (CD) applications per billion GDP by NUTS 3 region (2003-2014)

Annual estimates of balanced UK regional gross value added (GVA(B)). Current price estimates, chained volume measures and implied deflators for UK countries, ITL1, ITL2 and ITL3 regions, with a detailed industry breakdown.

European Union trade mark (EUTM) applications per billion GDP by NUTS 3 region (2000-2014)