This statistic displays the projected Muslim population of Europe from 2010 to 2050, compared with that of non-Muslims. For the 2050 projections, three different scenarios are presented, one for zero migration to Europe, one for medium migration and the last for a high level of immigration. In the scenario where zero-migration occurs the total non-Muslim population of Europe would actually decrease from ****** million people to ****** million people. In the high migration scenario, Muslims are predicted to number ***** million people, in which the total non-Muslim population of Europe is ****** million.

In 2024, Germany was the leading EU country in terms of population, with around 85 million inhabitants. In 2050, approximately 89.2 million people will live in Germany, according to the forecast. See the total EU population figures for more information. The global population The global population is rapidly increasing. Between 1990 and 2015, it increased by around 2 billion people. Furthermore, it is estimated that the global population will have increased by another 1 billion by 2030. Asia is the continent with the largest population, followed by Africa and Europe. In Asia,the two most populous nations worldwide are located, China and India. In 2014, the combined population in China and India alone amounted to more than 2.6 billion people. for comparison, the total population in the whole continent of Europe is at around 741 million people. As of 2014, about 60 percent of the global population was living in Asia, with only approximately 10 percent in Europe and even less in the United States. Europe is the continent with the second-highest life expectancy at birth in the world, only barely surpassed by Northern America. In 2013, the life expectancy at birth in Europe was around 78 years. Stable economies and developing and emerging markets in European countries provide for good living conditions. Seven of the top twenty countries in the world with the largest gross domestic product in 2015 are located in Europe.

The dataset includes the soil loss by water erosion projections by 2050. The data include 3 raster files for the corresponding three different greenhouse gas concentration scenarios (RCP2.6, RCP4.5, RCP8.5) , the erosivity projections and the C-factor 2050.

Boosting the efficiency of battery cells and electric vehicles could strongly impact the demand of raw materials in Europe by 2050. For instance, nickel's demand in the region might almost half if measures are taken in these two industries.

The EU Green Deal aims for climate neutrality by 2050, focusing on sustainability, emissions reduction, and boosting biodiversity.

Europe is aiming to implement a number of sustainability targets to restore and protect its biodiversity under the European Green Deal and post-2020 Global Biodiversity Framework. Yet, the land system consequences of meeting such targets are unclear, as multiple pathways may be able to deliver on these targets while minimizing trade-offs with different ecosystem services. This data presents the results of a land use simulation study consisting of three European-scale land use maps for 2050 at a 1km2 resolution. Each map is the outcome of a different scenario reflecting an alternative, normative view on meeting the same sustainability targets for the region, guided by the Nature Futures Framework developed by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. The Nature for Society scenario showcases a future where sustainability targets are met while favoring landscapes providing strong climate regulation, the Nature for Nature scenario favors greater species conservation, while the Nature as Culture scenario ensures the preservation of agricultural heritage features. All files are in GeoTIFF format. Methodological details can be found at the related publication. In addition to the NFF scenarios (based on SSP1) we also provide a result for 2050 for SSP1 and SSP3 without the specific NFF settings that assume green deal policy implementation.

(1) Output of the Renewable Energy Model (REM) as described in Insights into weather-driven extremes in Europe’s resources for renewable energy (Ho and Fiedler, 2024), last modification on 30.10.2023 from Linh Ho, named year_PV_wind_generation_v2.nc, with 23 years from 1995 to 2017. REM includes one simulation of photovoltaic (PV) power production and one simulation of wind power production across European domain, with a horizontal resolution of 48 km, hourly output for the period 1995--2017.

The output has a European domain with the same size as in the reanalysis dataset COSMO-REA6. This is a rotated grid with the coordinates of the rotated North Pole −162.0, 39.25, and of the lower left corner −23.375, −28.375. See Bollmeyer et al. (2014, http://doi.org/10.1002/qj.2486). Data downloaded from https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/

(2) Weather pattern classification daily for Europe from 1995 to April 2020, named EGWL_LegacyGWL.txt, from James (2007, http://doi.org/10.1007/s00704-006-0239-3)

(3) The installation data of PV and wind power in Europe for one scenario in 2050 from the CLIMIX model, processed to have the same horizontal resolution as in REM, named installed_capacity_PV_wind_power_from_CLIMIX_final.nc. Original data were provided at 0.11 degree resolution, acquired from personal communication with the author from Jerez et al. (2015, http://doi.org/10.1016/j.rser.2014.09.041)

(4) Python scripts of REM, including: - model_PV_wind_complete_v2.py: the main script to produce REM output - model_PV_wind_potential_v2.py: produce potential (capacity factor) of PV and wind power for model evaluations, e.g., against CDS and Renewables Ninja data, as descript in Ho and Fiedler (2024) - model_PV_wind_complete_v1_ONLYyear2000.py: a separate Python script to produce REM output only for the year 2000. Note that the data for 2000 from COSMO-REA6 were read in a different approach (using cfgrib) probably due to the time stamp changes at the beginning of the milenium, also explains the larger size of the final output - utils_LH_archive_Oct2022.py: contains necessary Python functions to run the other scripts

(5) Jupyter notebook files to reproduce the figures in Ho and Fiedler (2024), named Paper1_Fig*_**.ipynb

(6) Time series of European-aggregated PV and wind power production hourly during the period 1995--2017, processed data from the dataset (1) to facilitate the reproduction of the figures, including two installations scale-2019 and scenario-2050: - Timeseries_all_hourly_1995_2017_GW_scale2019.csv - Timeseries_all_hourly_1995_2017_GW_scen2050.csv

To achieve the decarbonization goal, electricity consumption in the European Union is forecast to increase in the coming years. According to the Fit for 55 (FF55) scenario, the total electricity demand of the EU will reach almost *** petawatt-hours by 2030 and *** petawatt-hours by 2050. EU's estimated electricity demand inspired by the REPowerEU and Radical Action scenario is larger, surpassing **** petawatt-hours by 2050 in the second case.

EUROPOP2023 are the latest Eurostat long-term population projections produced at national level for 30 countries: all 27 European Union (EU) Member States and three European Free Trade Association (EFTA) countries (Iceland, Norway, and Switzerland), covering the time horizon from 2022 to 2100. Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and structure based on a set of assumptions regarding fertility, mortality, and net migration. They are presented for a long time period that covers more than a half-century (50 years).

The datasets consist of the baseline population projections and five sensitivity tests, which are described as follows:

• no migration – it is assumed that net migration is zero for each year within the 2023-2100 time horizon;
• lower migration – it is assumed that the net migration is lower due to a 33% reduction in non-EU immigration flows for each year within the 2023-2100 time horizon;
• higher migration – it is assumed that the net migration is higher due to a 33% increase in non-EU immigration flows for each year within the 2023-2100 time horizon;
• lower fertility – it is assumed that the fertility rates are lower 20% than the baseline assumptions for each year within the 2023-2100 time horizon;
• lower mortality – it is assumed that the mortality rates are lower resulting in an increase of approximately two years in life expectancy at birth by 2070 compared to the baseline assumptions.

In each sensitivity test, the assumptions for the year 2022 were maintained as in the baseline projections. This is because, for that year, there is a combination of observed data (i.e. beneficiaries on temporary protections at the end of December 2022), information from the national authorities, and forecasting.

Data are available by single-year time interval, as detailed below:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, age-specific mortality rates and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Additionally, the demographic balances and indicators are available for the baseline projections and each of the five sensitive variants, including also:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators including proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

STP2024 are the short-term population projections covering the time horizon from 2023 to 2050, and produced at national level for 30 countries: all 27 European Union (EU) Member States and three European Free Trade Association (EFTA) countries (Iceland, Norway, and Switzerland). Similar to long-term projections, these are 'what-if scenario' that aim to show the hypothetically developments of the population size and structure based on a set of assumptions regarding fertility, mortality and net migration. The latest demographic data published on Eurostat website, as of 06 September 2024, were used as input in building the assumptions, thereby including the published post-2021 census revisions and data related to the temporary protection granted to persons displaced from Ukraine due to Russia's invasion.

The dataset (proj_stp24) includes data by single-year time interval for two types of projections:

• Baseline projections:
  • Projected total population on 1 January, the working-age population (defined as persons aged from 15 to 74), and its share in the total population;
  • Assumptions on total fertility rates, life expectancy at birth by sex, and total net migration levels;
  • Total numbers of projected live births and deaths.

• No migration sensitivity test – it is assumed that the net migration is zero for each year within the 2024-2050 time horizon.
  • Projected total population on 1 January, the working-age population (defined as persons aged from 15to 74), and its share in the total population;
  • The 2023 net migration levels remain the same as in the baseline projections to reflect the nowcast data;
  • Total numbers of projected live births and deaths.

STP2025 are the latest short-term population projections covering the time horizon from 2024 to 2050, produced at national level for 30 countries: all 27 European Union (EU) Member States and three European Free Trade Association (EFTA) countries (Iceland, Norway, and Switzerland). Similar to long-term projections, these are 'what-if scenario' that aim to show the hypothetically developments of the population size and structure based on a set of assumptions regarding fertility, mortality, and net migration. The latest demographic data published on Eurostat website as of 15 May 2025, were used as input in building the assumptions, thereby including the published post-2021 census revisions and data related to temporary protection granted to persons displaced from Ukraine due to Russia's invasion.

The dataset (proj_stp25) includes data by single-year time interval for two types of projections:

• Baseline projections:
  • Projected total population on 1 January, working-age population (15-74 years) and its share in the total population;
  • Assumptions on total fertility rates, life expectancy at birth by sex, and total net migration levels;
  • Total numbers of projected live births and deaths.

• No migration sensitivity test – it is assumed that the net migration is zero in each year of the 2025-2050 time horizon.
  • Projected total population on 1 January, working-age population (15-74 years) and its share in the total population;
  • The 2024 net migration levels remain as in the baseline projections to reflect the nowcast data;
  • Total numbers of projected live births and deaths.

This dataset contains the underling raw data of IAM COMPACT "Study 2 - Energy security, resilience, flexibility, costs, and environmental indicators in renewable energy systems". This study aims to respond to the research question “Evaluating energy system flexibility, energy security, energy system resilience in baseline, renewable, and cost-optimal scenarios”. It focuses on exploring methods and tools for modelling and comparing future energy systems in form of scenarios and uses a set of parameters to assess flexibility, resilience, costs, prices, etc. in renewable energy scenarios. To cover all the required areas, a comprehensive literature review is carried out so as to define indicators for evaluating renewable energy systems. These indicators are also refined with feedback from policy-makers and stakeholders. Three scenarios were created for the EU: Baseline 2050, based on current commitments and projections 1.5 Tech 2050, assuming ambitious measures to meet with the 1.5ºC increase of the Paris agreement; and Smart Energy Europe 2050, designed with a 100% renewable energy system. Results of the study have been documented in D4.9 – European sub-national deep dives (DOI 10.5281/zenodo.13784964)

The EU Reference Scenario is one of the European Commission's key analysis tools in the areas of energy, transport and climate action. It is updated regularly as it projects the impact of current EU policies on energy and transport trends as well as changes in the expected amount of greenhouse gas emissions.

It provides projections for indicators, such as the share of renewable energy sources or levels of energy efficiency, on a five-year period up until 2050 for the EU as a whole and for each EU country.

The Reference Scenario is a projection of where our current set of policies coupled with market trends are likely to lead. The EU has set ambitious objectives for 2020, 2030 and 2050 on climate and energy, so the Reference Scenario allows policy makers to analyse the long-term economic, energy, climate and transport outlook based on the current policy framework.

The Reference Scenario is not designed as a forecast of what is likely to happen in the future. It rather provides a benchmark against which new policy proposals can be assessed.

With the active participation of national experts from all EU countries, the European Commission works in partnership with a modelling consortium led by the National Technical University of Athens to develop the Reference Scenario.

This statistic displays the age distribution of Europe in 2020, with forecasts for 2025 and 2050. In 2020, the largest age group in Europe were those aged between 35 and 39, at roughly ** million people. By 2025 however, the largest age group is forecasted to be those aged 40-44 (**** million) and by 2050 those aged 60-64 (**** million).

This Excel file contains the data behind the graphs of the following JRC report:

Tsiropoulos I., Nijs W., Tarvydas D., Ruiz Castello P., Towards net-zero emissions in the EU energy system by 2050 – Insights from scenarios in line with the 2030 and 2050 ambitions of the European Green Deal, EUR 29981 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-13096-3, doi:10.2760/081488, JRC118592.

The report is downloadable from: https://ec.europa.eu/jrc/en/publication/towards-net-zero-emissions-eu-energy-system-2050

This statistic displays the projected Muslim population proportions in selected European countries in 2050, by scenario. In 2010 the proportion of Muslims in the population of Germany was *** percent, compared with *** percent in the UK and *** percent in France. Depending on the different migration scenarios estimated here, Germany's share of Muslims in the population could rise up to **** percent of it's population by 2050, higher than both the UK and France, with projected Muslim populations of **** and ** percent respectively.

A number of data layers are provided that accompany the publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" by Yusuf Yigini and Panos Panagos in "Science of The Total Environment, Volumes 557–558, 1 July 2016, Pages 838–850" (http://dx.doi.org/10.1016/j.scitotenv.2016.03.085) Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. Soil is the largest organic carbon pool of the terrestrial ecosystems on earth which interacts strongly with climate, and land cover change. In this study, a geo-statistical model is used to estimate the current and the future soil organic carbon (SOC) stocks in Europe. A geo-statistical approach is proposed to achieve spatiotemporal prediction of soil organic carbon stocks in Europe. The model consists of two sub-models (Figure 1). The base model predicts current soil organic carbon stocks at European scale using regression-kriging, and future model uses the regression coefficients and projects the estimation to the near future (2050). Figure 1. Prediction and Projection Workflow Model and Model Outputs It was hypothesized that soil organic carbon is driven largely by climate, land use and inherent soil properties. Moreover, it is anticipated that the complex relationship between soil organic carbon and its drivers is time independent and will remain in the future. From this point of view, the covariates which have been used to predict current soil organic carbon stocks in Europe can also help to predict future conditions by transferring the knowledge from today to the future. The first phase of the study predicts current soil organic carbon content (Figure 2) by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors (Table 1). An approach is demonstrated to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950 - 2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios. Figure 2. Soil organic carbon prediction map which represents the present conditions simulated by the base model (background map: ESRI, USGS, NOAA). Table 1. Present and Projected Soil organic Carbon Stocks (Pg) for EU26. (Cyprus and Croatia were excluded due to data unavailability) Available Data: Soil Organic Carbon Stocks (Current), tonnes.ha-1 Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs). (EU26), tonnes.ha-1 Metadata for "Soil Organic Carbon Stocks (Current)" Spatial Coverage: European Union, 26 Member States (no data for Cyprus and Croatia) Resolution: 1000m Format: Raster (GRID) Projection: ETRS89 Lambert Azimuthal Equal Area Input data: Climate Data (Current) from WorldClim Data Portal: Bio-climatic parameters, Annual Precipitation, Grid Size: 1000m Land Cover 2010, European Commission, Joint Research Centre, Sustainability Assessment Unit: Pan-European Land Use Modelling Platform (LUMP), Grid Size: 1000m Soil Data, Joint Research Centre European Soil Database, Ballabio et al. 2016: Clay, Silt, Sand, Soil Structure, Available Water Capacity, Grid Size: 1000m Output Data Layers: SOC_Stocks_EU26 : Current Predication of European Soil Organic Carbon Stocks (tonnes.ha-1) Metadata for "Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs). (EU26), tonnes.ha-1" Spatial Coverage: European Union, 26 Member States (no data for Cyprus and Croatia) Resolution: 1000m Format: Raster (GRID) Projection: ETRS89 Lambert Azimuthal Equal Area Input data: Climate Data (2050), WorldClim Data Portal: Bio-climatic parameters, Annual Precipitation, Grid Size: 1000m Land Cover 2050, European Commission, Joint Research Centre, Sustainability Assessment Unit: Pan-European Land Use Modelling Platform (LUMP), Grid Size: 1000m Soil Data, Joint Research Centre European Soil Database, Ballabio et al. 2016: Clay, Silt, Sand, Soil Structure, Available Water Capacity, Grid Size: 1000m Output Data Layers: Projected Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3), (tonnes.ha-1) - the names are self-explanatory after reading the paper (http://dx.doi.org/10.1016/j.scitotenv.2016.03.085) cc426: CCSM4, RCP 2.6 cc445: CCSM4, RCP 4.5 cc460: CCSM4, RCP 6 cc485: CCSM4, RCP 8.5 hd26: HadGEM2-AO, RCP 2.6 hd45: HadGEM2-AO, RCP 4.5 hd60: HadGEM2-AO, RCP 6 hd85: HadGEM2-AO, RCP 8.5 ip26: IPSL-CM5A-LR, RCP 2.6 ip45: IPSL-CM5A-LR, RCP 4.5 ip60: IPSL-CM5A-LR, RCP 6 ip85: IPSL-CM5A-LR, RCP 8.5 mg26: MRI-CGCM3, RCP 2.6 mg45: MRI-CGCM3, RCP 4.5 mg60: MRI-CGCM3, RCP 6 mg85: MRI-CGCM3, RCP 8.5

Main output generated with the custom-region possibility-for-electricity-autarky workflow.

This output provides similar data to https://doi.org/10.5281/zenodo.3246302 (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:

The spatial extent has been expanded to include Iceland.

Two new spatial resolutions have been added: ehighways and ehighways_disaggregated.

ehighways defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. ehighways_disaggregated provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the ehighways resolution. The mapping from statistical units to ehighways regions is defined in ./ehighways/statistical_units_to_ehighways_regions.csv. ./ehighways/units.png shows a map of the resulting 98 ehighways regions. The region colours are used to help differentiate regions and have no other meaning.

This dataset is used as an input to the Sector-Coupled Euro-Calliope workflow.

[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)

This map shows the economic damage of a flood from the sea with a small chance, medium chance and large chance in future climate (with climate projection 2050). The flood damage is calculated in function of the water depth, season (worst possible scenario), flow rate and ascent rate, expressed in €/m2.

Projected changes in annual precipitation in percentages under A1B scenario, multi-model ensemble mean for the time periods 2021-2050 relative to 1961-1990 mean. Map presents changes using ensemble mean of several regional climate models (RCMs), run by different climate modelling communities in the frame of the EU FP6 Integrated Project ENSEMBLES (Contract number 505539). Data are presented as changes in relative terms (according to 1961-1990 period) in spatial resolution of approximately 25 km.

This map shows the annual average social impact of flooding from the sea in future climate (with climate projection 2050). The social risk is calculated as a weighted combination of the 3 social damage maps with high, medium and low probability, expressed in a relative score/m²/year.

Subset of output generated with the possibility-for-electricity-autarky workflow which provides the same data as given by https://doi.org/10.5281/zenodo.3246302, but at a spatial resolution that does not strictly match administrative units in Europe. Three files are not generated with the possibility-for-electricity-autarky workflow: nuts_to_regions.csv, transmission.csv, and units.png. The source of these files is as follows: nuts_to_regions.csv defines the mapping of administrative spatial units in Europe to 98 spatial regions. These 98 regions are based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. They cover 35 European countries, describing 19 at a national resolution and the rest on a subnational resolution. Subnational regions are aggregated from NUTS3-2006 administrative units. Those aggregated from NUTS3-2006 administrative units also have mappings from NUTS3 units in other NUTS years since region naming/borders can change by NUTS year (see here). units.png shows a map of the resulting 98 model regions. The region colours are used to help differentiate regions and have no other meaning. transmission.csv provides derived grid transfer capacities between the 98 model regions, according to the e-HIGHWAY 2050 project output as well as planned connections in the medium and long term, predominantly high voltage DC (HVDC), according to ENTSOE 2018 network development plan. [1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)