Under current climate policies, Sudan would face a GDP loss of 32 percent by 2050 and a shrinkage of over 80 percent by 2100 due to climate change. According to the source's estimates, this would be the most significant loss among all assessed countries in Africa. Even in a scenario of limiting temperatures to 1.5 degrees Celsius, the damage to Sudan's economy would stand at a GDP reduction of 22 percent by 2050 and 51 percent by 2100. Eight out of 10 countries estimated to record the largest GDP reduction because of climate change globally were located in Africa. The estimates did not consider potential adaptation measures to alleviate the economic loss.

The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

We have extended the time series of global GDP based on Version 5 at https://zenodo.org/record/5880037#.Yyx4lsi5fRQ, which makes the following changes:

a) includes annual global GDP from 2000 - 2020, the unit is PPP 2005 international dollars.

b) updates the GDP projections for the period 2025 - 2100 at five-year intervals under five SSPs, and the unit is PPP 2005 international dollars, which allows for comparsion against the historical values mention above.

This dataset consists of a total of 101 tif images with spatial resolutions of 1 km (in 7 zip files) and 0.25-degree, respectively. The gridded GDP are distributed over land, with Antarctica, oceans, and some non-illuminated or depopulated areas marked as zero. The spatial extents are 90S - 90N and 180E - 180W in standard WGS84 coordinate system.

For more details, please refer to the article: Global gridded GDP data set consistent with the shared socioeconomic pathways that is consistent with Version 5 (GDP unit is PPP 2005 U.S. dollars).

V1 dataset:Under the global framework of Shared Socioeconomic Pathways (SSPs), based on localized population and economic parameters, a Population Development Environment (PDE) model is adopted to construct population grid data for SSPs from 2020 to 2100; Using the Cobb Douglas model, construct economic data for SSPs from 2020 to 2100.The v1 dataset includes:Population grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5°GDP grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5 °Grid data on the output value of three industries in the Chinese region, with a spatial resolution of 0.1 °V2 dataset:Based on the data from the 7th National Population Census of China, starting from 2020, the parameters such as fertility rate, mortality rate, migration rate, and education level in the Population Development Environment (PDE) model were updated. Under the Shared Socioeconomic Pathways (SSP1-5), a new version (v2) of the total population and age and gender specific population projection dataset for China and its provinces from 2020 to 2100 was created. Based on the data from the 7th National Population Census and the 4th Economic Census of China, with 2020 as the starting year, the parameters of total factor productivity, capital stock, labor input, and capital elasticity coefficient in the Cobb Douglas model were updated. Under the shared SSP1-5, a new version (v2) of China and its provincial GDP projectiondataset from 2020 to 2100 was created.The v2 (2024 version) dataset includes:Total Population Data of China and Provinces (2020-2100)Population data by age and gender in China (2020-2100)China and Provincial GDP Data (2020-2100)

We developed and presented a set of comparable spatially explicit global gridded gross domestic product (GDP) for both historical period (2005 as representative) and for future projections from 2030 to 2100 at a ten-year interval for all five SSPs. The DMSP-OLS nighttime light (NTL) images and the LandScan Global Population database were used to generate LitPop map, which reduces the limitations of saturation problem of using NTL images alone or the assumption of even GDP per capita within an administrative boundary of gridded data set in GDP disaggregation. We used the LitPop maps to disaggregate national GDP and over 800 provincial gross regional product (GRP, in 2005 PPP USD) across the globe in 2005 and to downscaled to a spatial resolution of 30 arc-seconds (~1 km at equator). National and supranational GDP growth rate projections in 2030-2100 under five SSPs were then downscaled to 1-km grids based on the LitPop approach, which used NPP-VIIRS product as fixed NTL image in 2015 and the population projections of 0.125 arc-degreee (Jones and O'Neill, 2016), which are downscaled to 1-km based on LandScan population distribution pattern in 2015. We then upscaled this gridded GDP dataset to 0.25 arc-degree and provided here.

There are 41 tif files (2005 and 2030 - 2100 at a ten-year interval for five SSPs) for each spatial resolution. The gridded GDP are distributed over land with value of zero filled in the Antarctica, oceans and some desert or wilderness areas (non-illuminated and depopulated zones). The spatial extents are 60S - 90N and 180E - 180W in standard WGS84 coordinate system.

For more details, please refer to the corresponding article: Global gridded GDP data set consistent with the shared socioeconomic pathways by Wang and Sun (2022).

Version history:This data are a new version of Geiger et al (2017, http:doi.org/10.5880/PIK.2017.003). Please use this updated version of this dataset which contains the following correction of errors in the original dataset: The linear interpolation in GDP per capita for Aruba (ABW) between observations in 2005 and SSP2 projections in 2010 was replaced by observed GDP per capita values for the years 2006-2009, as the SSP2 projection for Aruba turned out to be incorrect. As a result of this, the national GDP per capita and GDP timeseries for Aruba between 2006 and 2009 is different from the previous version. We here provide three different economic time series that amend or combine various existing time series for Gross Domestic Product (GDP), GDP per capita, and population to create consistent and continuous economic time series between 1850 and 2009 for up to 195 countries. All data, including the data description are included in a zip folder (2018-010_GDP_1850-2009_Data_v2.zip): (1) A continuous table of global income data (in 1990 Geary-Khamis $) based on the Maddison Project data base (MPD) for 160 individual countries and 3 groups of countries from 1850-2010: Maddison_Project_data_completed_1850-2010.csv. (2) A continuous table of global income data (in 2005 PPP $, PPP = purchasing power parity) for 195 countries based on a merged and harmonized dataset between MPD and Penn World Tables (PWT, version v8.1) from 1850-2009, and additionally extended using PWT v9.0 and World Development Indicators (WDI), that is consistent with future GDP per capita projections from the Shared Socioeconomic Pathways (SSPs): GDP-per-capita-national_PPP2005_SSP-harmonized_1850-2009_v2.csv. (3) A continuous table of global GDP data (in 2005 PPP $) for 195 countries from 1850-2009 based on the second income data set multiplied by country population data, again consistent with future SSP GDP projections: GDP-national_PPP2005_SSP-harmonized_1850-2009_v2.csv. These data are supplemented by a masking table indicating MPD original data and amended data based on current country definitions (Maddison_data_availability_masked_1850-2010.csv) and a file with PPP conversion factors used in this study (PPP_conversion_factors_PPP1990-PPP2005.csv). We use various interpolation and extrapolation methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set aims to provide valuable input, e.g., for climate impact research in order to consistently analyze economic impacts from pre-industrial times to the distant future. More information about data sources and data format description is given in the data description file (2018-010_Data-Description-GDP_1850-2009_v2.pdf).

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

The dataset includes Business as Usual (BAU) forecast of world global CO2 emissions per GDP (Cp$) for 2020-2100.

The CO2 emission forecast is from the publication “Dataset Global Warming Forecast using Acceleration Factors” [6]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase.

The GDP forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world GDP will change from 126.3 MM$/y in 2020 to 728.1 MM$/y in 2100, a 476% increase.

CO2 emissions per GDP (Cp$) are calculated by dividing the CO2 emissions per year by the GDP in the same year.

The world 0.000268 tCO2/$GDP Cp$ in 2020 will decrease by 64% in 2100 to 0.000096 tCO2/$GDP.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.5 billion inhabitants on the continent at the beginning of 2024, the number of inhabitants is expected to reach 3.8 billion by 2100. In total, the global population is expected to reach nearly 10.4 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2021. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Estimated GDPs by 1/12-degree grids during 1850—2100 by 10 year intervals. In the estimation, national GDP data (past data until 2010; future projection under SSPs after 2020) is downscaled considering spatial and economic interactions among cities, urban growth patterns compatible with SSPs, and other auxiliary geographic data (land cover, road network, etc.). For the estimation methods, see Murakami et al. (in prep)Murakami, D., Yoshida, Y., Yamagata, Y. (in prep) Gridded GDP projections compatible with the five SSPs (Shared Socioeconomic Pathways).

We here provide spatially-explicit economic time series for Gross Cell Product (GCP) with global coverage in 10-year increments between 1850 and 2100 with a spatial resolution of 5 arcmin. GCP is based on a statistcal downscaling procedure that among other predictors uses national Gross Domestic Product (GDP) time series and gridded population estimates as input. Historical estimates until 2000 are harmonized with future socio-economic projections from the Shared Socioeconomic Pathways (SSPs) according to SSP2 from 2010 onwards. We further provide a mapping file with identical spatial resolution to associate GCP values with specifc countries. Based on this mapping we provide nationally aggregated GDP estimates between 1850-2100 in a separate csv-file. Additionally, we provide a mapping file with identical spatial resolution providing national assets-GDP ratios, that can be used to transform GCP to asset values based on 2016 estimates from Credit Suisse's Global Wealth Databook 2016.

We here provide three different economic time series that amend or combine various existing time series for Gross Domestic Product (GDP), GDP per capita, and population to create consistent and continuous economic time series between 1850 and 2009 for up to 195 countries:

(1) A continuous table of global income data (in 1990 Geary-Khamis $) based on the Maddison Project data base (MPD) for 160 individual countries and 3 groups of countries from 1850-2010: Maddison_Project_data_completed_1850-2010.csv.(2) A continuous table of global income data (in 2005 PPP $, PPP = purchasing power parity) for 195 countries based on a merged and harmonized dataset between MPD and Penn World Tables (PWT, version v8.1) from 1850-2009, and additionally extended using PWT v9.0 and World Development Indicators (WDI), that is consistent with future GDP per capita projections from the Shared Socioeconomic Pathways (SSPs): GDP-per-capita-national_PPP2005_SSP-harmonized_1850-2009.csv.(3) A continuous table of global GDP data (in 2005 PPP $) for 195 countries from 1850-2009 based on the second income data set multiplied by country population data, again consistent with future SSP GDP projections: GDP-national_PPP2005_SSP-harmonized_1850-2009.csv.

These data are supplemented by a masking table indicating MPD original data and amended data based on current country definitions (Maddison_data_availability_masked_1850-2010.csv) and a file with PPP conversion factors used in this study (PPP_conversion_factors_PPP1990-PPP2005.csv).

We use various interpolation and extrapolation methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set aims to provide valuable input, e.g., for climate impact research in order to consistently analyze economic impacts from pre-industrial times to the distant future. More information about data sources and data format description is given in the data description file (Data-Description-GDP_1850-2009.pdf).

Version history: Please use the updated version of this dataset which contains correction of errors in the original dataset. For a detailed description of the changes please consult the CHANGELOG included in the data description document of the new version.

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Gross Domestic Product (GDP) in India was worth 3912.69 billion US dollars in 2024, according to official data from the World Bank. The GDP value of India represents 3.69 percent of the world economy. This dataset provides the latest reported value for - India GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The median age in India was 27 years old in 2020, meaning half the population was older than that, half younger. This figure was lowest in 1970, at 18.1 years, and was projected to increase to 47.8 years old by 2100. Aging in India India has the second largest population in the world, after China. Because of the significant population growth of the past years, the age distribution remains skewed in favor of the younger age bracket. This tells a story of rapid population growth, but also of a lower life expectancy. Economic effects of a young population Many young people means that the Indian economy must support a large number of students, who demand education from the economy but cannot yet work. Educating the future workforce will be important, because the economy is growing as well and is one of the largest in the world. Failing to do this could lead to high youth unemployment and political consequences. However, a productive and young workforce could provide huge economic returns for India.

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 median age of the population in Malaysia is expected to reach 29.5 years in 2020, meaning that half of the population will be younger than this age and half will be older. Malaysia had an estimated population of 32.52 million in 2019, in line with a steady rise expected to continue through at least 2027. The average age has increased since its low point of 15.6 years in 1965 and is projected to increase to around 48 years by 2100.

Implications of average age

Average age is a single index that summarizes the age distribution of a population. In recent years, around 69 percent of the population in Malaysia has been aged between 15-64 years. This suggests that millions of Malaysians are of working age, and most are working: Malaysia has a relatively low unemployment rate at 3.36 percent as of 2018. Malaysia is considered to have one of the strongest economies in the region. The Malaysian economy

Malaysia’s gross domestic product (GDP) is estimated to be around 373 billion U.S. dollars, with services and industry being the economic sectors with the greatest shares in GDP generation. The Malaysian economy is fueled by its natural resources, though its commerce and technology sectors have also expanded. Malaysia’s trade balance has been positive consistently for the last decade, i.e. the country has consistently reported trade surplus.