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.

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.

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.

Portugal PT: Population Projection: Mid Year: Growth data was reported at -0.630 % in 2050. This records a decrease from the previous number of -0.600 % for 2049. Portugal PT: Population Projection: Mid Year: Growth data is updated yearly, averaging -0.030 % from Jun 1991 (Median) to 2050, with 60 observations. The data reached an all-time high of 0.690 % in 1993 and a record low of -0.630 % in 2050. Portugal PT: Population Projection: Mid Year: Growth data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Portugal – Table PT.US Census Bureau: Demographic Projection.

Algeria DZ: Number of Deaths data was reported at 421,380.000 Person in 2050. This records an increase from the previous number of 411,025.000 Person for 2049. Algeria DZ: Number of Deaths data is updated yearly, averaging 181,615.500 Person from Jun 1987 (Median) to 2050, with 64 observations. The data reached an all-time high of 421,380.000 Person in 2050 and a record low of 130,990.000 Person in 1990. Algeria DZ: Number of Deaths data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s Algeria – Table DZ.US Census Bureau: Demographic Projection.

This statistic indicates that range of variation in GDP, based on climate change impacts in 2050, broken down by region. It is predicted that in 2050 climate change impacts in the Middle East will lead to a decrease between * and ** percent of the region's GDP.

Qatar QA: UCB Projection: Population: Mid Year: Growth data was reported at 0.130 % in 2050. This records an increase from the previous number of 0.110 % for 2049. Qatar QA: UCB Projection: Population: Mid Year: Growth data is updated yearly, averaging 1.950 % from Jun 1986 (Median) to 2050, with 65 observations. The data reached an all-time high of 17.820 % in 2004 and a record low of -0.320 % in 2035. Qatar QA: UCB Projection: Population: Mid Year: Growth data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Qatar – Table QA.US Census Bureau: Demographic Projection.

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.

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).

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.

Dominican Republic DO: Population Projection: Mid Year data was reported at 12,542,490.000 Person in 2050. This records an increase from the previous number of 12,508,980.000 Person for 2049. Dominican Republic DO: Population Projection: Mid Year data is updated yearly, averaging 8,231,374.000 Person from Jun 1950 (Median) to 2050, with 101 observations. The data reached an all-time high of 12,542,490.000 Person in 2050 and a record low of 2,352,968.000 Person in 1950. Dominican Republic DO: Population Projection: Mid Year data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Dominican Republic – Table DO.US Census Bureau: Demographic Projection.

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 map shows the annual average economic damage of floods from watercourses in future climate (with climate projection 2050). The economic risk is calculated as a weighted combination of the 3 economic damage maps with high, medium and low probability, expressed in €/m2/year.

Roughly ** percent of the annual GDP of lower income countries worldwide in 2050 could be at risk of loss due to exposure to climate hazards, in a slow transition scenario without adaptation measures. Extreme heat and water stress are forecast to have the biggest impact, at *** and *** percent, respectively. In contrast, in upper income countries, the same hazards would put less than one percent of the annual GDP at risk. Nevertheless, climate hazards would still put almost ***** percent of upper income countries' GDP at risk by 2050, in a no-adaptation scenario.

This map shows the economic damage of a flood from the sea with a small chance, medium chance and large chance in future climate (with climate projection 2050). The flood damage is calculated in function of the water depth, season (worst possible scenario), flow rate and ascent rate, expressed in €/m2.

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).

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.