The Gross Domestic Product (GDP) in European Union was worth 19423.32 billion US dollars in 2024, according to official data from the World Bank. The GDP value of European Union represents 18.29 percent of the world economy. This dataset provides the latest reported value for - European Union GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product per capita in European Union was last recorded at 34859.60 US dollars in 2024. The GDP per Capita in European Union is equivalent to 276 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table presents Gross Domestic Product (GDP) and its main components according to the expenditure approach. Data is presented in US dollars. In the expenditure approach, the components of GDP are: final consumption expenditure of households and non-profit institutions serving households (NPISH) plus final consumption expenditure of General Government plus gross fixed capital formation (or investment) plus net trade (exports minus imports).

When using the filters, please note that final consumption expenditure is shown separately for the Households/NPISH and General Government sectors, not for the whole economy. All other components of GDP are shown for the whole economy, not for the sector breakdowns.

The table shows OECD countries and some other economies, as well as the OECD total, G20, G7, OECD Europe, United States - Mexico - Canada Agreement (USMCA), European Union and euro area.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

The Gross Domestic Product per capita in European Union was last recorded at 54290.99 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in European Union, when adjusted by Purchasing Power Parity is equivalent to 306 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita Ppp - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) In the Euro Area was worth 16406.13 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Euro Area represents 14.74 percent of the world economy. This dataset provides the latest reported value for - Euro Area GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table presents Gross Domestic Product (GDP) and its components according to the output approach. In the output approach, GDP is measured as the sum of gross value added (GVA) of all economic activities plus taxes less subsidies on products. This table includes breakdowns of GVA by type of economic activity according to Revision 4 of the International Standard Industrial Classification of All Economic Activities (ISIC).

The presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. Data is presented for each country in national currency as well as in euros for the European Union and the euro area. Data is also presented converted to US dollars using exchange rates. It is also possible to select current prices, chain linked volumes etc using the ‘Price base’ filter (the default view is current prices).

The table shows OECD countries and selected economies, as well as the OECD total, OECD Europe, European Union and euro area. These can be selected using the ‘Reference area’ filter.

These indicators were presented in the previous dissemination system in the SNA_TABLE1 dataset.
See ANA Changes for information on changes in methodology: ANA Changes
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

This table shows Gross Domestic Product (GDP) per capita (or per person), household final consumption expenditure per capita and actual individual consumption per capita. Final consumption expenditure is the expenditure of resident households on consumption goods or services, while individual consumption is the sum of household consumption plus the individual (not collective) consumption of the non-profit institutions serving households (NPISH) and General Government sectors. The indicators are in volume terms and are converted to US dollars using constant Purchasing Power Parities (PPPs).

When using the filters, please note that GDP is selected by default in the ‘Transaction’ filter but you can select the consumption measures using the ‘Transaction’ filter. The ‘Institutional sector’ filter shows that GDP and actual individual consumption relate to the total economy, while household final consumption expenditure relates to households.

The table shows OECD countries and selected economies, as well as the OECD total, OECD Europe, European Union and euro area . These can be selected using the ‘Reference area’ filter.

These indicators were presented in the previous dissemination system in the SNA_TABLE1 dataset.
See ANA Changes for information on changes in methodology: ANA Changes
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

Gross domestic product (GDP) is a measure for the economic activity. It refers to the value of the total output of goods and services produced by an economy, less intermediate consumption, plus net taxes on products and imports. GDP per capita is calculated as the ratio of GDP to the average population in a specific year. Basic figures are expressed in purchasing power standards (PPS), which represents a common currency that eliminates the differences in price levels between countries to allow meaningful volume comparisons of GDP. The values are also offered as an index calculated in relation to the European Union average set to equal 100. If the index of a country is higher than 100, this country's level of GDP per head is higher than the EU average and vice versa. Please note that this index is intended for cross-country comparisons rather than for temporal comparisons. Finally, the disparities indicator offered for EU aggregates is calculated as the coefficient of variation of the national figures. This time series offers a measure of the convergence of economic activity between the EU Member States. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

In this paper the authors conduct a meta-analysis to examine the link between R&D spending and economic growth in the EU and other regions. The results suggest that the growth-enhancing effect of R&D in the EU15 countries does not differ from that in other countries in general, but it is less significant than that for other industrialized countries. A closer inspection of the data reveals that the weak results for the EU15 stem from comparisons with the US – the US has been able to generate a stronger growth response from its R&D spending. Possible explanations for the US advantage include higher private sector investment in R&D and stronger public-private sector linkages than in the EU. Hence, to reduce the “innovation gap” vis-à-vis the US, it may not be enough for the EU to raise the share of R&D expenditures in GDP: continuous improvements in the European innovation system will also be needed, with focus on areas like private sector R&D and public-private sector linkages.

Abstract: The relevance of the topic is explained by the growing interest in the development of artificial intelligence technologies and related products in the territory of the European Union member states, especially in the context of the developing industry. This article considers the step-by-step legal regulation in the field of artificial intelligence in the European Union, as well as the gradual transition from the stage of "soft" legal regulation to the creation of binding legal norms. The important fact of classification of artificial intelligence models on the level of risk into four categories is considered. The risk levels of artificial intelligence models can be divided into the group of unacceptable risk, high risk, moderate risk and minimal risk. The aim of this article is to examine the European approach to the regulation of artificial intelligence in order to further assess whether the objectives set by the European Union are in line with its capabilities. In order to achieve this goal, we have studied regulatory acts, international acts, scientific articles and books.

The dataset contains information about the contents of 100 Terms of Service (ToS) of online platforms. The documents were analyzed and evaluated from the point of view of the European Union consumer law. The main results have been presented in the table titled "Terms of Service Analysis and Evaluation_RESULTS." This table is accompanied by the instruction followed by the annotators, titled "Variables Definitions," allowing for the interpretation of the assigned values. In addition, we provide the raw data (analyzed ToS, in the folder "Clear ToS") and the annotated documents (in the folder "Annotated ToS," further subdivided).

SAMPLE: The sample contains 100 contracts of digital platforms operating in sixteen market sectors: Cloud storage, Communication, Dating, Finance, Food, Gaming, Health, Music, Shopping, Social, Sports, Transportation, Travel, Video, Work, and Various. The selected companies' main headquarters span four legal surroundings: the US, the EU, Poland specifically, and Other jurisdictions. The chosen platforms are both privately held and publicly listed and offer both fee-based and free services. Although the sample cannot be treated as representative of all online platforms, it nevertheless accounts for the most popular consumer services in the analyzed sectors and contains a diverse and heterogeneous set.

CONTENT: Each ToS has been assigned the following information: 1. Metadata: 1.1. the name of the service; 1.2. the URL; 1.3. the effective date; 1.4. the language of ToS; 1.5. the sector; 1.6. the number of words in ToS; 1.7–1.8. the jurisdiction of the main headquarters; 1.9. if the company is public or private; 1.10. if the service is paid or free. 2. Evaluative Variables: remedy clauses (2.1– 2.5); dispute resolution clauses (2.6–2.10); unilateral alteration clauses (2.11–2.15); rights to police the behavior of users (2.16–2.17); regulatory requirements (2.18–2.20); and various (2.21–2.25). 3. Count Variables: the number of clauses seen as unclear (3.1) and the number of other documents referred to by the ToS (3.2). 4. Pull-out Text Variables: rights and obligations of the parties (4.1) and descriptions of the service (4.2)

ACKNOWLEDGEMENT: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021, project no. 2020/37/K/HS5/02769, titled “Private Law of Data: Concepts, Practices, Principles & Politics.”

The Gross Domestic Product (GDP) In the Euro Area expanded 0.10 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Euro Area GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Recommended citation

Article citation will be added once the article is available.

Content

• Use of the dataset and full description
• Abstract
• Support
• Files included in the dataset
• Notes
• Data format description (columns)
• Data sources
• Changelog
• References

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.

The article will be referenced here as soon as it is published.

Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below.

Support

If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

Abstract

This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.

Files included in the dataset

The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows:

The "Source" flag indicates which input scenarios were used.

• PMRCP: RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.
• PMSSP: Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.

the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed.

The "Downscaling" flag indicates the downscaling technique used.

• IE: Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.
• IC: Regional emission intensity growth rates for all countries.
• CS: Constant emission shares as a reference case independent of the socio-economic scenario.

All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.

Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.

Notes

If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data.

Data format description (columns)

"source"

For PMRCP files source values are

• RCPSSP
• PMRCP
• PMRCPMISC

For PMSSP files source values are

• SSPIAM
• PMSSP
• PMSSPMISC

For possible values of

"scenario"

For PMRCP files the scenarios have the format

For PMSSP files the scenarios have the format

Model codes in scenario names

• AIMCGE: AIM-CGE
• IMAGE: IMAGE
• GCAM4: GCAM
• MESGB: MESSAGE-GLOBIOM
• REMMP: REMIND-MAGPIE
• WITGB: WITCH-GLOBIOM

"country"

ISO 3166 three-letter country codes or custom codes for groups:

Additional "country" codes for country groups.

• EARTH: Aggregated emissions for all countries
• ANNEXI: Annex I Parties to the UNFCCC
• NONANNEXI: Non-Annex I Parties to the UNFCCC
• AOSIS: Alliance of Small Island States
• BASIC: BASIC countries (Brazil, South Africa, India and China)
• EU28: European Union (still including the UK)
• LDC: Least Developed Countries
• UMBRELLA: Umbrella Group

"category"

Category descriptions.

• IPCM0EL: Emissions: National Total excluding LULUCF
• ECO: Economical data
• DEMOGR: Demographical data

"entity"

Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).

Gases / gas baskets and underlying global warming potentials

• CH4: Methane (CH₄)
• CO2: Carbon Dioxide (CO₂)
• N2O: Nitrous Oxide (N₂O)
• FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF₆, NF₃
• FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF₆, NF₃
• KYOTOGHG: Kyoto greenhouse gases (SAR)
• KYOTOGHGAR4: Kyoto greenhouse gases (AR4)

"unit"

The following units are used:

• Million2011GKD: Million 2011 international dollars
• ThousandPers: Thousand persons
• kt: kilotonnes
• Mt: Megatonnes
• Gg: Gigagrams
• MtCO2eq: Megatonnes of CO₂ equivalents using the GWPs defined by "entity"
• GgCO2eq: Gigagrams of CO₂ equivalents using the GWPs defined by "entity"

Remaining columns

Years from 1850-2100.

Data Sources

The following data sources were used during the generation of this dataset:

Scenario data

• RCP scenarios website/data
• SSP basic elements website/data
• SSP IAM scenarios website/data
• SSP CMIP6 scenarios website/data

Historical data

• CDIAC data
• CEDS CMIP6 data paper/data
• EDGAR version 4.3.2: data, paper
• IMO GHG report report
• PRIMAP-hist v2.1 paper, <a

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

These data include three nationally representative internet panel surveys conducted in Greece (n=3071), Germany (n=2074) and the UK (n=2106) between 24 and 31 May 2019. There is only one wave. The questionnaire is the same in all three countries. It covered a variety of issues including attitudes towards the European Union, vote in a potential European Union referendum, European identity, political trust, political responsibility, European solidarity and freedom of movement. It also asked questions that tap into psychological processes, including emotions about the European Union/immigration/economy/how things are going, social dominance orientation and system justification. The surveys also include various demographic measures.The outbreak of the financial crisis has signalled a new period in the process of European integration. It has -more than ever before- brought to the forefront issues of transnational economic redistribution and has increased political contestation in and about the European Union (EU). Contrary to the pre-crisis era when Euroscepticism was mostly an expression of public protest limited to parties in the margins of their political systems, it has now developed into a widespread phenomenon with far-reaching implications for democracy in the EU and its members. However, we know surprisingly little about the nature of politicisation of European integration and the ways in which the structure of political conflict has changed as a result of the crisis. Seeking to build on the literature examining democratic contestation and the politicisation of European integration, and to contribute towards an improved understanding of the nature of Euroscepticism in times of crisis, this project offers an original contribution to the study of Euroscepticism by integrating three research objectives, which aim at (1) mapping and identifying the different dimensions of Euroscepticism and understanding whether these have changed as a result of the crisis; (2) exploring the underlying causes of Euroscepticism and explaining variation in levels of Euroscepticism at the country, party and individual levels; and (3) assessing the ways in which Euroscepticism feeds back into national politics by testing its consequences on domestic political behaviour. In answering these questions, this project relies on a novel interdisciplinary longitudinal and comparative research design and applies an original multi-method approach through the complementary use of quantitative and experimental methods. The project will examine the dimensions and causes of Euroscepticism through an analysis of cross-sectional and time series data in all EU member states. The longitudinal design enables us to compare Euroscepticism in the periods prior to and during the crisis. The project will study the consequences of Euroscepticism on domestic political behaviour by focusing on three countries, namely Britain, Germany and Greece, which are non-Eurozone members, creditor and debtor countries, respectively. Building on my work on Euroscepticism, this study makes a significant theoretical, empirical and methodological contribution to our understanding of the politics of opposition in Europe and the literature on the EU's democratic deficit. The project's findings will provide evidence-based knowledge about elite and citizen attitudes towards European integration, allowing for effective policy responses to the rise of Eurosceptic sentiment across Europe. The three-year length of the project (2016-2018) will enable me to become a research leader at the forefront of political analysis, linking the project's findings to the debate over the possible 2017 referendum on British EU membership, the possibility of Greek exit and the upcoming 2019 European Parliament (EP) elections. Through its insights on the changing nature of Euroscepticism, the project has the potential to stir debate regarding institutional reform, accountability and representation in the EU. An advisory board consisting of academics and policy-makers will be consulted through the duration of the project. In order to ensure the co-production of knowledge with relevant policy communities, I have established official partnership with two think tanks, the Brussels-based Foundation for European Progressive Studies and the London-based Policy Network. Research outputs will include dissemination to practitioners and the wider public through three policy-related workshops, executive summaries, policy publications, media articles and a dedicated project website; and academic dissemination through conference participation, journal articles and a monograph. Three public opinion surveys were conducted in Greece, Germany and the UK with adults aged over 18. These were online surveys conducted by market research companies which were Kiescompass in Greece, and YouGov in the UK and Germany. The survey questions were the same in each country. The sample included 3091 individuals from Greece, 2074 individuals from Germany, and 2106 individuals from the UK.

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

Food and Agriculture Organization of the United Nations (2017). Food and Agriculture Organization Statistics: Prices - Deflators | Country: Sudan | Item: Gross Fixed Capital Formation Deflator | Element: Value US$, 2005 prices -, 1970-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 067-001-063. Dataset: Provides three implicit price deflator series by country: Gross Domestic Product (GDP) deflator, Gross Fixed Capital Formation (GFCF) deflator, and Agriculture, Forestry, Fishery Value-Added (VA_AFF) deflator. A deflator is a figure expressing the change in prices over a period of time for a product or a basket of products by comparing a reference period to a base period. The deflators presented here are obtained by dividing the series in current prices by those in constant 2005 prices (base year). Movements in an implicit price deflator reflect both changes in price and changes in the composition of the aggregate for which the deflator is calculated. All series are derived from the United Nations Statistics Division (UNSD) National Accounts Estimates of Main Aggregates database. The time-series and cross-sectional data provided here are from the FAOSTAT database of the Food and Agriculture Organization of the United Nations. Statistics include measures related to the food supply; forestry; agricultural production, prices, and investment; and trade and use of resources, such as fertilizers, land, and pesticides. As available, data are provided for approximately 245 countries and 35 regional areas from 1961 through the present. The data are typically supplied by governments to FAO Statistics through national publications and FAO questionnaires. Official data have sometimes been supplemented with data from unofficial sources and from other national or international agencies or organizations. In particular, for the European Union member countries, with the exception of Spain, data obtained from EUROSTAT have been used. Category: Agriculture and Food, International Relations and Trade Source: Food and Agriculture Organization of the United Nations Established in 1945 as a specialized agency of the United Nations, the Food and Agricultural Organization’s mandate is to raise levels of nutrition, improve agricultural productivity, better the lives of rural populations, and contribute to the growth of the world economy. Staff experts in seven FAO departments serve as a knowledge network to collect, analyze, and disseminate data, sharing policy expertise with member countries and implementing projects and programs throughout the world aimed at achieving rural development and hunger alleviation goals. The Statistics Division of the Food and Agricultural Organization collates and disseminates food and agricultural statistics globally. http://www.fao.org/ Subject: Prices, Agricultural Production, Agricultural Commodities, Agricultural Products

The Gross Domestic Product per capita In the Euro Area was last recorded at 56326.23 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, In the Euro Area, when adjusted by Purchasing Power Parity is equivalent to 317 percent of the world's average. This dataset provides the latest reported value for - Euro Area GDP per capita PPP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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