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.

Explore real GDP growth projections dataset, including insights into the impact of COVID-19 on economic trends. This dataset covers countries such as Spain, Australia, France, Italy, Brazil, and more.

growth rate, Real, COVID-19, GDP

Spain, Australia, France, Italy, Brazil, Argentina, United Kingdom, United States, Canada, Russia, Turkiye, World, China, Mexico, Korea, India, Saudi Arabia, South Africa, Germany, Indonesia, JapanFollow data.kapsarc.org for timely data to advance energy economics research..Source: OECD Economic Outlook database.- India projections are based on fiscal years, starting in April. The European Union is a full member of the G20, but the G20 aggregate only includes countries that are also members in their own right. Spain is a permanent invitee to the G20. World and G20 aggregates use moving nominal GDP weights at purchasing power parities. Difference in percentage points, based on rounded figures.

China Gross Domestic Product (GDP): Single Hit Scenario data was reported at 106,000.000 RMB bn in 2021. This records an increase from the previous number of 97,800.000 RMB bn for 2020. China Gross Domestic Product (GDP): Single Hit Scenario data is updated yearly, averaging 24,450.000 RMB bn from Dec 1992 (Median) to 2021, with 30 observations. The data reached an all-time high of 106,000.000 RMB bn in 2021 and a record low of 2,720.000 RMB bn in 1992. China Gross Domestic Product (GDP): Single Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.EO: GDP by Expenditure: Forecast: Non OECD Member: Annual. GDP-Gross domestic product, value, market prices Expenditure approach System of national Accounts 2008:https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf European system of accounts ESA2010:https://ec.europa.eu/eurostat/documents/3859598/5925693/KS-02-13-269-EN.PDF/44cd9d01-bc64-40e5-bd40-d17df0c69334 Understanding NATIONAL ACCOUNTS:https://www.oecd.org/sdd/UNA-2014.pdf

China Ref. Year = 2015: GDP: Volume: Imports of Goods and Services: Double Hit Scenario data was reported at 15,100.000 RMB bn in 2021. This records an increase from the previous number of 14,800.000 RMB bn for 2020. China Ref. Year = 2015: GDP: Volume: Imports of Goods and Services: Double Hit Scenario data is updated yearly, averaging 5,955.000 RMB bn from Dec 1992 (Median) to 2021, with 30 observations. The data reached an all-time high of 15,200.000 RMB bn in 2019 and a record low of 478.000 RMB bn in 1992. China Ref. Year = 2015: GDP: Volume: Imports of Goods and Services: Double Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.EO: GDP by Expenditure: Volume: Forecast: Non OECD Member: Annual. MGSV - Imports of goods and services, volume (national accounts basis) System of national Accounts 2008:https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf European system of accounts ESA2010:https://ec.europa.eu/eurostat/documents/3859598/5925693/KS-02-13-269-EN.PDF/44cd9d01-bc64-40e5-bd40-d17df0c69334 Understanding NATIONAL ACCOUNTS:https://www.oecd.org/sdd/UNA-2014.pdf

The data deposited as part of this project includes data from research on UK smart city case studies in the form of reports and interview transcripts, as well as notes of what data was used in which project publication outputs. Data from a small qualitative survey of UK smart city activity in the form of policy reports is also included. The collection includes data from field research in Tianjin, conducted by researchers at Cardiff University in the form of field notes and notes from interviews.Europe and China both face the challenges of climate change and associated environmental degradation, and of finding ways in which to promote economic transition away from carbon-intensive economic and consumption patterns, and towards a green economy. The city is where these challenges are centred, and where solutions have to be found: cities are both producers of environmental externalities, and the locations where the negative effects of climate change will be felt most acutely. A promising approach focuses on treating new and existing cities as 'experimental areas' where transitions to a green economy can be trialled. Eco-cities and smart cities have been proposed as potential solutions to the need for a green economy: they are seen as 'socio-technical experiments' which are potential drivers for local, national and international environmental socio-economic change and transition. Both China and several European countries are actively engaged in planning and building experimental cities focused on the green economy. Many of these projects combine elements of eco-city planning (focusing on the visible 'hardware' of environmental sustainability: planning, architecture, renewable energy and smart grid technologies, etc.), with 'smart city' planning (focusing is on 'software': information systems, social capital, knowledge transfer, etc.). We propose analysis of what we call the 'smart eco-city', defined as an experimental city which functions as a potential niche where both environmental and economic reforms can be tested and introduced in areas which are both spatially proximate (the surrounding region) and in an international context (through networks of knowledge, technology and policy transfer and learning). The aim of this project is to provide the first systematic comparative analysis of green economy-focused eco-city projects in China and Europe. This will inform the identification of opportunities and pathways for shaping national and collaborative international urban and economic policy responses, engaging the state, the business sector and communities in delivering 'smart eco-city' projects that can promote the growth of the green economy. The research addresses key issues: a.) how experimental cities have fared in terms of promoting successful transitions to a green economy in Europe and China since 2000; b.) how to evaluate success in smart eco-city initiatives; c.) what are the main obstacles to successful projects d.) what generalizable lessons can be drawn from successful smart eco-cities, in socio-economic and policy terms; e.) how knowledge can be effectively shared across the context of European and Chinese urban-economic policymaking for smart eco-cities. In order to address these crucial issues our team will carry out international, interdisciplinary multi-method research which will include a total of eight in-depth smart eco-city case studies in China, the UK, Germany, the Netherlands, and France. This will involve documentary research as well as interviews with European and Chinese policymakers, business people, financiers, local communities and other stakeholders. The project will also involve research aimed at building the first qualitative-quantitative database of smart eco-city projects: this will form the backbone of our policy toolkit and will be a state-of-the-art contribution to current knowledge on smart- and eco-city planning and policy. The interviews took place in both the UK and China with policymakers and corporate executives working on selected smart city case study projects. The survey of a policy document looks at documents related to smart urban projects in the UK in 2012-16, whereby all reports from city councils in the UK were sourced and used for analysis. Webometric analysis created webometric data collected through AntConc software, and supported through analysis of policy reports.

The estimation results of the global financial sector contagion on the financial sectors across US, Europe and China based on the Eq (12).

This dataset consists of transcripts and notes of interviews conducted in China between April 2017 and December 2018. The interviews were on the theme of informal finance in China and its recent transformation in the light of technological and regulatory changes. The interviewees included executives in financial and technological companies, officials, judges and lawyers.China's rapid economic growth in recent decades has been attributed to its reliance on informal contracting and trust-based relationships. This claim is a reflection of the absence in China of some of the more formal legal and regulatory institutions of the market economies of the global north. Although the claim that China lacks formal legal mechanisms of market governance may have been somewhat overstated, it is the case that informal finance, particularly in the form of trade credit, family lending and communal investing, has played a major role in supporting China's growth. The prevalence of informal finance constitutes a significance source of flexibility for China's economy given the limitations of the formal sector, which remains dominated by state-owned banks lending largely to state-owned enterprises. Informal finance is also evolving quickly and is converging with the use of internet technologies to deliver finance ('fintech') through such mechanisms as crowdfunding. However, there are downsides to the reliance of the Chinese economy on informal finance and significant risks arise from its convergence with fintech. The large shadow banking sector, by virtue of its positioning outside most of the regulations applying to mainstream banks, adds to systemic risks. The formal and informal sector coexist in an uneasy relationship: they may substitute for each other, or provide complementary modes of finance, but they can also operate to reinforce and magnify systemic risks, as in the case of the crisis in Wenzhou after 2011. Similarly, the rise of fintech is a double edged sword. On the one hand, cloud computing and big data may be facilitating new forms of social credit and collective investment schemes which have the potential to meet the needs of the growing social credit sector. Crowdfunding may provide a new and flexible form of financing for start-ups and innovative ventures. However, these new forms of finance also have the potential to undercut or render otiose regulations designed to maintain market transparency, and to intensify the risks facing investors. Against this background, the project explores the phenomenon of informal finance in China, identifies the risks and potential associated with it, and assesses how regulation can best respond to the risks while not sacrificing the innovations and flexibility associated with it, particularly in the context of 'fintech'. These are qualitative datasets are derived from interview-based fieldwork. An anonymisation log has been provided in each case.

China-EU import and export trade structure in equipment manufacturing industry (unit: Billion US dollars).

The United States recorded a trade deficit of 60.18 USD Billion in June of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

China Ref. Year = 2015: GDP: Volume: Exports of Goods and Services: Double Hit Scenario data was reported at 17,700.000 RMB bn in 2021. This records an increase from the previous number of 17,000.000 RMB bn for 2020. China Ref. Year = 2015: GDP: Volume: Exports of Goods and Services: Double Hit Scenario data is updated yearly, averaging 8,215.000 RMB bn from Dec 1992 (Median) to 2021, with 30 observations. The data reached an all-time high of 17,800.000 RMB bn in 2019 and a record low of 663.000 RMB bn in 1992. China Ref. Year = 2015: GDP: Volume: Exports of Goods and Services: Double Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.EO: GDP by Expenditure: Volume: Forecast: Non OECD Member: Annual. XGSV - Exports of goods and services, volume (national accounts basis) Exports of goods and services:https://stats.oecd.org/glossary/detail.asp?ID=918 System of national Accounts 2008:https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf European system of accounts ESA2010:https://ec.europa.eu/eurostat/documents/3859598/5925693/KS-02-13-269-EN.PDF/44cd9d01-bc64-40e5-bd40-d17df0c69334 Understanding NATIONAL ACCOUNTS:https://www.oecd.org/sdd/UNA-2014.pdf

CN: Ref. Year = 2015: GDP: Volume: Exports of Goods and Services: Single Hit Scenario data was reported at 18,200.000 RMB bn in 2021. This records an increase from the previous number of 17,100.000 RMB bn for 2020. CN: Ref. Year = 2015: GDP: Volume: Exports of Goods and Services: Single Hit Scenario data is updated yearly, averaging 8,215.000 RMB bn from Dec 1992 (Median) to 2021, with 30 observations. The data reached an all-time high of 18,200.000 RMB bn in 2021 and a record low of 663.000 RMB bn in 1992. CN: Ref. Year = 2015: GDP: Volume: Exports of Goods and Services: Single Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.EO: GDP by Expenditure: Volume: Forecast: Non OECD Member: Annual. XGSV - Exports of goods and services, volume (national accounts basis) Exports of goods and services:https://stats.oecd.org/glossary/detail.asp?ID=918 System of national Accounts 2008:https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf European system of accounts ESA2010:https://ec.europa.eu/eurostat/documents/3859598/5925693/KS-02-13-269-EN.PDF/44cd9d01-bc64-40e5-bd40-d17df0c69334 Understanding NATIONAL ACCOUNTS:https://www.oecd.org/sdd/UNA-2014.pdf