In 2024, India’s real gross domestic product (GDP) growth rate was around **** percent, the highest in South Asia. In contrast, Nepal reported the lowest real GDP growth rate in the region at approximately **** percent that year, but it was forecasted to increase to **** percent in 2026.Economy in South Asia In general, South Asia encompasses Sri Lanka, Pakistan, Afghanistan, Bangladesh, Nepal, India and Bhutan. In 2020, India had a GDP of over *** trillion U.S. dollars, while Bangladesh and Sri Lanka followed. The Maldives and Bhutan were among the countries with the lowest GDP in the Asia-Pacific region. In South Asia, the main economic activities include the services sector as well as the industrial and manufacturing sectors.Society in South AsiaFrom the South Asian countries, Bangladesh had the highest share of people living below the poverty line. The Maldives and Sri Lanka exhibited the highest and second-highest GDP per capita among the South Asian countries in 2021.

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.

In 2021, the BRICS countries with the highest estimated GDP per capita were Russia and China, with between 12,000 and 13,000 U.S. dollars per person. Brazil and South Africa's GDP per capita are thought to be closer to the 7,000 mark, while India's GDP per capita is just over 2,000 U.S. dollars. This a significant contrast to figures for overall GDP, where China has the largest economy by a significant margin, while India's is the second largest. The reason for this disparity is due to population size. For example, both China's population and overall GDP are roughly 10 times larger than those of Russia, which results in them having a comparable GDP per capita. Additionally, India's population is 23 times larger than South Africa's, but it's GDP is just seven times larger; this results in South Africa having a higher GDP per capita than India, despite it being the smallest of the BRICS economies.

In financial year 2024, the gross domestic product from agriculture sector of the southern Indian state of Tamil Nadu amounted to over *** trillion Indian rupees. This was a significant increase from about ************ rupees in the previous year.

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.

Since the beginning of the 21st century, the BRICS countries have been considered the five foremost developing economies in the world. Originally, the term BRIC was used by economists when talking about the emerging economies of Brazil, Russia, India, and China, however these countries have held annual summits since 2009, and the group has expanded to include South Africa since 2010. China has the largest GDP of the BRICS country, at 16.86 trillion U.S. dollars in 2021, while the others are all below three trillion. Combined, the BRICS bloc has a GDP over 25.85 trillion U.S. dollars in 2022, which is slightly more than the United States. BRICS economic development China has consistently been the largest economy of this bloc, and its rapid growth has seen it become the second largest economy in the world, behind the U.S.. China's growth has also been much faster than the other BRICS countries; for example, when compared with the second largest BRICS economy, its GDP was less than double the size of Brazil's in 2000, but is almost six times larger than India's in 2021. Since 2000, the country with the second largest GDP has fluctuated between Brazil, Russia, and India, due to a variety of factors, although India has held this position since 2015 (when the other two experienced recession), and it's growth rate is on track to surpass China's in the coming decade. South Africa has consistently had the smallest economy of the BRICS bloc, and it has just the third largest economy in Africa; its inclusion in this group is due to the fact that it is the most advanced and stable major economy in Africa, and it holds strategic importance due to the financial potential of the continent in the coming decades. Future developments It is predicted that China's GDP will overtake that of the U.S. by the end of the 2020s, to become the largest economy in the world, while some also estimate that India will also overtake the U.S. around the middle of the century. Additionally, the BRICS group is more than just an economic or trading bloc, and its New Development Bank was established in 2014 to invest in sustainable infrastructure and renewable energy across the globe. While relations between its members were often strained or of less significance in the 20th century, their current initiatives have given them a much greater international influence. The traditional great powers represented in the Group of Seven (G7) have seen their international power wane in recent decades, while BRICS countries have seen theirs grow, especially on a regional level. Today, the original BRIC countries combine with the Group of Seven (G7), to make up 11 of the world's 12 largest economies, but it is predicted that they will move further up on this list in the coming decades.

As of 2024, Mumbai had a gross domestic product of *** billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around *** billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

The average for 2025 based on 11 countries was 3.67 percent. The highest value was in India: 6.2 percent and the lowest value was in Thailand: 1.8 percent. The indicator is available from 1980 to 2030. Below is a chart for all countries where data are available.

In the financial year of 2024, the gross domestic product of the southern state of Tamil Nadu in India amounted to about ** trillion Indian rupees. This was an increase from about seven trillion Indian rupees in the fiscal year 2012.

Big Ten Countries include Argentina, Brazil, China, India, Indonesia, Mexico, Poland, South Africa, South Korea, and Turkey. The annual data for the years 2002-2019 was used. Growth Rate (GR), the literature’s basic economic variable, is selected as the dependent variable. As for the independent variable, the “Global Terror Index (GTI)” was used to represent the terror indicator. Besides, due to their effect on the growth rate, the ratio of Foreign Direct Investment (FDI) to the Gross Domestic Product (GDP), and the ratio of External Balance (EB) to Gross Domestic Product (GDP) are included in the model as the control variables.

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

The size of the five original BRICS economies in 2023 - Brazil, Russia, China, India, South Africa - is comparable to the United States and the EU-27 put together. On a PPP (purchasing power parity) basis, China ranks as the world's largest economy. India takes up the economic parity of about **** the EU-27. The rise of these developing economies gave rise to questions on the role the United States plays in international trade and cross-border finance. FX reserve managers around the world expect to shift their holdings towards the Chinese yuan in the long term, as of 2023.

The aim of the article is to compare health system outcomes in the BRICS countries, assess the trends of their changes in 2000−2017, and verify whether they are in any way correlated with the economic context. The indicators considered were: nominal and per capita current health expenditure, government health expenditure, gross domestic product (GDP) per capita, GDP growth, unemployment, inflation, and composition of GDP. The study covered five countries of the BRICS group over a period of 18 years. We decided to characterize countries covered with a dataset of selected indicators describing population health status, namely: life expectancy at birth, level of immunization, infant mortality rate, maternal mortality ratio, and tuberculosis case detection rate. We constructed a unified synthetic measure depicting the performance of individual health systems in terms of their outcomes with a single numerical value. Descriptive statistical analysis of quantitative traits consisted of the arithmetic mean (xsr), standard deviation (SD), and, where needed, the median. The normality of the distribution of variables was tested with the Shapiro–Wilk test. Spearman's rho and Kendall tau rank coefficients were used for correlation analysis between measures. The correlation analyses have been supplemented with factor analysis. We found that the best results in terms of health care system performance were recorded in Russia, China, and Brazil. India and South Africa are noticeably worse. However, the entire group performs visibly worse than the developed countries. The health system outcomes appeared to correlate on a statistically significant scale with health expenditures per capita, governments involvement in health expenditures, GDP per capita, and industry share in GDP; however, these correlations are relatively weak, with the highest strength in the case of government's involvement in health expenditures and GDP per capita. Due to weak correlation with economic background, other factors may play a role in determining health system outcomes in BRICS countries. More research should be recommended to find them and determine to what extent and how exactly they affect health system outcomes.

Explore macroeconomic statistics and indicators, including GDP, Gross Fixed Capital Formation, National Income, and more. This dataset covers a wide range of countries such as Afghanistan, Albania, Algeria, Australia, Brazil, China, Germany, India, United States, and many more.

GDP, Gross Domestic Product, Capita, GFCF, Gross Fixed Capital Formation, Value, Added, Gross, Output, National, Income, Manufacturing, Agriculture, Population, National Accounts

Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Democratic Republic of the Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States of America, Uruguay, Uzbekistan, Vanuatu, Venezuela, Yemen, Zambia, Zimbabwe

Follow data.kapsarc.org for timely data to advance energy economics research.

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

Energy is a crucial development indicator of production, consumption, and nation-building. However, energy diversification highlighting renewables remains salient in economic development across developing economies. This study explores the economic impact of renewables (RE) and fossil fuel (NRE) utilization in 17 emerging nations. We use annual data with timeframe between 1980 and 2016 and propose a bootstrap panel causality approach with a Fourier function. This allows the examination of multiple structural breaks, cross-section dependence, and heterogeneity across countries. We validate four main hypotheses on the causal links attached to the energy consumption (EC)-growth nexus namely neutrality, conservation, growth, and feedback hypotheses. The findings reveal a causal relationship running from RE to GDP for Brazil, Egypt, Indonesia, Korea, Pakistan, and the Philippines, confirming the growth hypothesis. Besides, the results validate the conservation hypothesis with causality from GDP to RE for China, Colombia, Egypt, Greece, India, Korea, South Africa, and Turkey. We identify causality from NRE to GDP for Pakistan, Mexico, Malaysia, Korea, India, Greece, Egypt, and Brazil; and from GDP to NRE for Thailand, Peru, Malaysia, India, Greece, Egypt, and Colombia. We demonstrate that wealth creation can be achieved through energy diversification rather than relying solely on conventional energy sources.

Both the ever-complex international and subnational supply chains could relocate health burdens and economic benefits across India, leading to the widening of regional inequality. Here, we simultaneously track the unequal distribution of fine particle matter (PM2.5) pollution, health costs, and value-added embodied in inter- and intranational exports for Indian states in 2015 by integrating a nested multiregional input-output (MRIO) table constructed based on EXIOBASE and an Indian regional MRIO table, Emissions Database for Global Atmospheric Research (EDGAR), the Community Multi-Scale Air Quality (CMAQ) model, and a concentration-response function. The results showed that the annual premature deaths associated with PM2.5 pollution embodied in inter- and intranational exports were 757,356 and 388,003 throughout India, accounting for 39% and 20% of the total premature deaths caused by PM2.5 pollution, respectively. Richer south and west coastal states received around half of the national Gross Domestic Product (GDP) induced by exports with a quarter of the health burden, while poorer central and east states bear approximately 60% of the health burden with less than a quarter of national GDP. Our findings highlight the role of exports in driving the regional inequality of health burdens and economic benefits. Therefore, tailored strategies (e.g., air pollution compensation, advanced technology transfer, and export structure optimization) could be formulated.

In 2021, Macao had the highest estimated gross domestic product (GDP) growth with **** percent, followed by the Maldives with an estimated GDP growth of **** percent. Many economies were forecasted to have seen a decline in GDP in 2021, possibly due to COVID-19, reaching up to *** percent in Myanmar. Nevertheless, almost economies were forecasted to recover in 2022 and 2023.

The economic state in Asia

In 2020, China led the Asia Pacific region in terms of GDP with approximately **** trillion U.S. dollars, followed by India, South Korea, and Australia. In comparison, the GDP value for emerging and developing Asia was at aproximately **** trillion international dollars in that year. In terms of GDP per capita, Singapore ranked the highest with approximately **** U.S. dollars, followed by Australia with a per capita GDP of around **** U.S. dollars.

Higher GDP growth for developing Asia Pacific countries

For 2022 and 2023, it was forecasted that Macao and the Maldives would have the highest GDP growth. Overall, Afghanistan had the highest predicted rise in GDP growth from 2021 to 2023. South Asia, Southeast Asia, and Southwest Asia were forecasted to be leading the region’s economic growth with comparably higher GDP growth rates. Developed countries including Australia, New Zealand and Japan were projected to have stagnant GDP growth.

The study ´Current questions on the economy and transformation´ has been conducted by forsa on behalf of the Press and Information Office of the Federal Government. In the survey period from 15 April to 17 April 2024, the German population was asked about their opinions on the economic transformation.
Topics: Current challenges of economic development in Germany compared to ten years ago; assessment of the appropriateness of the activities of the following actors with regard to overcoming the economic challenges: federal government, opposition in the Bundestag, state governments, companies and business associations, trade unions; preference for a future orientation of the German economy towards: climate protection and green technologies, established industries; importance of the following aspects with regard to the federal government´s actions: higher investment in infrastructure, greater expansion of renewable energies, promotion of climate-neutral industry, promotion of the establishment of future industries, improvement of working conditions, increasing the efficiency of public administration work, relieving companies of bureaucracy, no further debt, expansion of partnerships with Brazil, India and South Africa; attitude towards selected statements: ‘Made in Germany’ is recognised worldwide as a seal of quality, German economy should also become more independent of other countries in the long term despite higher costs in the short term, Germany needs more skilled workers from abroad.

Demography: sex; age (grouped); school leaving certificate; net household income (grouped); party preference in the next federal election; voting behaviour in the last federal election.

Additionally coded: respondent ID; size of locality; region; weight.

In 2023, services contributed around 58.42 percent to South Korea's gross domestic product (GDP), while 31.59 percent came from South Korea’s industry sector, and a little less than two percent of South Korea’s GDP was generated by the country’s agriculture sector. South Korea’s services sector employed over 70 percent of the South Koreans workforce in 2018 . South Korea’s workforce Much of the over 51 million inhabitants of South Korea are employed, and the unemployment rate is expected to remain under four percent through 2024. South Korea is experiencing the effects of an aging labor force, with a decrease in population share of people entering the work force, and a simultaneous increase of the number of those aged 65 years and above. Despite that, the country’s economy has remained a powerhouse, growing at around 2.5 percent from 2018 to 2019. The South Korean economy South Korea is known as an economic success story; it rose from one of the poorest countries before the 1960’s to a developed country with a high income level. Overall, South Korea’s total GDP was estimated to be approximately 1.7 trillion U.S. dollars in 2019, and is expected to continue to increase through 2024. South Korea is considered to be one of the core economies driving the next generation of economic growth, alongside the BRIC countries (Brazil, Russia, India, and China).