This statistic shows the projected top ten largest national economies in 2050. By 2050, China is forecasted to have a gross domestic product of over ** trillion U.S. dollars.

This statistic shows the top ten countries projected to have the greatest average annual growth in gross domestic product from 2016 to 2050. From 2016 to 2050, Vietnam is projected to have an average annual GDP growth rate of * percent.

The impact of climate change has been forecasted to affect the economies of South-East Asian Nations (ASEAN) the hardest. The maximum projected loss incurred by the ASEAN in the event of a 3.2°C temperature rise is 37.4 percent. This is more than double the forecast loss of the Advanced Asia economies and 10 percent higher than the next largest forecast loss of the Middle East & Africa.

Dataset

This dataset was created by Kodavati Eshwar

Contents

The statistic depicts U.S. health expenditure as a percentage of the GDP from 2007 to 2009, and a forecast for 2050. In 2009, U.S. health expenditure accounted for 18 percent of the GDP.

Under current climate policies, Kenya would face a GDP loss of ** percent by 2050 and a shrinkage of around ** percent by 2100 due to climate change. According to the source's estimates, in a scenario of limiting temperatures to *** degrees Celsius, the damage to Kenya's economy would stand at a GDP reduction of **** percent by 2050 and ** percent by 2100. The estimates did not consider potential adaptation measures that could alleviate the economic loss.

Under current climate policies, Sudan would face a GDP loss of ** percent by 2050 and a shrinkage of over ** 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 *** degrees Celsius, the damage to Sudan's economy would stand at a GDP reduction of ** percent by 2050 and ** 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.

This is a Microsoft Excel spreadsheet containing input data (from H-D) that were used to calibrate the IPAT model for both historical (1980–2010) and projected (2015–2050) data sets. Also shows results of the calibrated model, predicting Tj and Ij thru 2050 (historical calibration) and 2150 (projected calibration). (XLSX)

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

This is the dataset of Historical GDP growth data of India from 1961 to 2021. Use this dataset to do Data Visualisation and Data Analytics.

Task : Predict year 2030 and 2050 GDP and Per Capita of India, and comment your results on Discussion page.

Brazil Forecast: Infrastructure Investments to(GDP) Gross Domestic ProductRatio: Transformation data was reported at 1.805 % in 2050. This records a decrease from the previous number of 1.807 % for 2049. Brazil Forecast: Infrastructure Investments to(GDP) Gross Domestic ProductRatio: Transformation data is updated yearly, averaging 2.504 % from Dec 2021 (Median) to 2050, with 30 observations. The data reached an all-time high of 3.154 % in 2026 and a record low of 1.805 % in 2050. Brazil Forecast: Infrastructure Investments to(GDP) Gross Domestic ProductRatio: Transformation data remains active status in CEIC and is reported by Ministry of Development, Industry, Trade and Services. The data is categorized under Brazil Premium Database’s Investment – Table BR.OG003: Infrastructure Investments: Forecast.

Estimates of lost GDP due to five leading NCDs and due to all NCDs in the United States, 2015–2050 (in trillions of 2010 USD).

The United States gross domestic product (GDP) was forecast to reach over 30.1 trillion U.S. dollars in 2025. Furthermore, by 2035, it is expected to surpass 43.9 trillion U.S. dollars. GDP refers to the market value of all final goods and services produced within a country in a given period.

Brazil Forecast: GDP: Transformation Scenario: 2019 Prices data was reported at 10,380,448.468 BRL mn in 2050. This records an increase from the previous number of 10,295,513.580 BRL mn for 2049. Brazil Forecast: GDP: Transformation Scenario: 2019 Prices data is updated yearly, averaging 8,963,692.970 BRL mn from Dec 2021 (Median) to 2050, with 30 observations. The data reached an all-time high of 10,380,448.468 BRL mn in 2050 and a record low of 7,014,039.130 BRL mn in 2021. Brazil Forecast: GDP: Transformation Scenario: 2019 Prices data remains active status in CEIC and is reported by Ministry of Development, Industry, Trade and Services. The data is categorized under Brazil Premium Database’s Investment – Table BR.OG003: Infrastructure Investments: Forecast.

The statistic shows the gross domestic product (GDP) of the United States from 1987 to 2024, with projections up until 2030. The gross domestic product of the United States in 2024 amounted to around 29.18 trillion U.S. dollars. The United States and the economy The United States’ economy is by far the largest in the world; a status which can be determined by several key factors, one being gross domestic product: A look at the GDP of the main industrialized and emerging countries shows a significant difference between US GDP and the GDP of China, the runner-up in the ranking, as well as the followers Japan, Germany and France. Interestingly, it is assumed that China will have surpassed the States in terms of GDP by 2030, but for now, the United States is among the leading countries in almost all other relevant rankings and statistics, trade and employment for example. See the U.S. GDP growth rate here. Just like in other countries, the American economy suffered a severe setback when the economic crisis occurred in 2008. The American economy entered a recession caused by the collapsing real estate market and increasing unemployment. Despite this, the standard of living is considered quite high; life expectancy in the United States has been continually increasing slightly over the past decade, the unemployment rate in the United States has been steadily recovering and decreasing since the crisis, and the Big Mac Index, which represents the global prices for a Big Mac, a popular indicator for the purchasing power of an economy, shows that the United States’ purchasing power in particular is only slightly lower than that of the euro area.

Brazil Forecast: Infrastructure Investments to(GDP) Gross Domestic ProductRatio: Reference data was reported at 1.746 % in 2050. This records an increase from the previous number of 1.736 % for 2049. Brazil Forecast: Infrastructure Investments to(GDP) Gross Domestic ProductRatio: Reference data is updated yearly, averaging 2.228 % from Dec 2021 (Median) to 2050, with 30 observations. The data reached an all-time high of 2.932 % in 2024 and a record low of 1.736 % in 2049. Brazil Forecast: Infrastructure Investments to(GDP) Gross Domestic ProductRatio: Reference data remains active status in CEIC and is reported by Ministry of Development, Industry, Trade and Services. The data is categorized under Brazil Premium Database’s Investment – Table BR.OG003: Infrastructure Investments: Forecast.

Brazil Forecast: GDP: Reference Scenario: 2019 Prices data was reported at 15,163,158.126 BRL mn in 2050. This records an increase from the previous number of 14,861,959.297 BRL mn for 2049. Brazil Forecast: GDP: Reference Scenario: 2019 Prices data is updated yearly, averaging 10,908,436.138 BRL mn from Dec 2021 (Median) to 2050, with 30 observations. The data reached an all-time high of 15,163,158.126 BRL mn in 2050 and a record low of 7,098,443.900 BRL mn in 2021. Brazil Forecast: GDP: Reference Scenario: 2019 Prices data remains active status in CEIC and is reported by Ministry of Development, Industry, Trade and Services. The data is categorized under Brazil Premium Database’s Investment – Table BR.OG003: Infrastructure Investments: Forecast.

Brazil Forecast: Infrastructure Investments: Stock: Investment to(GDP) Gross Domestic ProductRatio of 3% data was reported at 7,810,073.892 BRL mn in 2050. This records an increase from the previous number of 7,597,733.008 BRL mn for 2049. Brazil Forecast: Infrastructure Investments: Stock: Investment to(GDP) Gross Domestic ProductRatio of 3% data is updated yearly, averaging 6,083,982.919 BRL mn from Dec 2033 (Median) to 2050, with 18 observations. The data reached an all-time high of 7,810,073.892 BRL mn in 2050 and a record low of 4,553,790.949 BRL mn in 2033. Brazil Forecast: Infrastructure Investments: Stock: Investment to(GDP) Gross Domestic ProductRatio of 3% data remains active status in CEIC and is reported by Ministry of Development, Industry, Trade and Services. The data is categorized under Brazil Premium Database’s Investment – Table BR.OG003: Infrastructure Investments: Forecast.

Estimates of foregone GDP due to the five leading NCDs and due to all NCDs excluding the treatment cost effect in the United States, 2015–2050 (trillions of 2010 USD).

The map identifies those countries that are most vulnerable to food insecurity. A country’s vulnerability is estimated according to: (1) population growth in 2000 to 2050 projected by the United Nations; (2) wealth expressed in GDP per capita in 2005; (3) land potential for rain-fed cereal production per capita of 2050 population; (4) total renewable water resources per capita of 2050 population; and (5) impact of climate change projected in 2050 on crop production potential. High income countries with 2005 GDP per capita exceeding US$ 7500 (in 1990 US$) are assumed not to be vulnerable to food insecurity. Source: Data compilation by authors from various sources (United Nations, World Bank, FAO, GAEZ 2009).