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.

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, Morocco would face a GDP loss of ** percent by 2050 and a shrinkage of ** 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 Morocco'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, 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.

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.

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 6 and 14 percent of the region's GDP.

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 statistic shows the total GDP of the countries who formed the G7 and the E7 in 2015, alongside a project for the year 2050. The G7 includes; the United States, Japan, Germany, The United Kingdom, France, Italy, and Canada. The E7 includes; China, India, Brazil, Russia, Indonesia, Mexico, and Turkey. The projected GDP total of the E7 countries for 2050 was ***** trillion U.S. dollars.

Roughly 12 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 4.7 and 3.2 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 three percent of upper income countries' GDP at risk by 2050, in a no-adaptation scenario.

Under current climate policies, Ethiopia 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, in a scenario of limiting temperatures to *** degrees Celsius, the damage to Ethiopia's economy would stand at a GDP reduction of **** percent by 2050 and ** percent by 2100. The estimates did not consider potential adaptation measures to alleviate the economic loss.

EIA Projection: Real GDP data was reported at 32,006.383 USD bn in 2050. This records an increase from the previous number of 31,460.225 USD bn for 2049. EIA Projection: Real GDP data is updated yearly, averaging 23,236.614 USD bn from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 32,006.383 USD bn in 2050 and a record low of 16,397.199 USD bn in 2015. EIA Projection: Real GDP data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.A019: NIPA 2018: GDP by Expenditure: Constant Price: Annual: Projection: Energy Information Administration.

United States EIA Projection: Real GDP: Investment data was reported at 5,343.151 USD bn in 2050. This records an increase from the previous number of 5,229.798 USD bn for 2049. United States EIA Projection: Real GDP: Investment data is updated yearly, averaging 3,579.812 USD bn from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 5,343.151 USD bn in 2050 and a record low of 2,210.425 USD bn in 2016. United States EIA Projection: Real GDP: Investment data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.A019: NIPA 2018: GDP by Expenditure: Constant Price: Annual: Projection: Energy Information Administration.

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.

United States EIA Projection: Real GDP: Government Spending data was reported at 3,577.507 USD bn in 2050. This records an increase from the previous number of 3,549.675 USD bn for 2049. United States EIA Projection: Real GDP: Government Spending data is updated yearly, averaging 3,207.616 USD bn from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 3,577.507 USD bn in 2050 and a record low of 2,883.700 USD bn in 2015. United States EIA Projection: Real GDP: Government Spending data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.A019: NIPA 2018: GDP by Expenditure: Constant Price: Annual: Projection: Energy Information Administration.

Under current climate policies, Mozambique 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, in a scenario of limiting temperatures to *** degrees Celsius, the damage to Mozambique 's economy would stand at a GDP reduction of ** percent by 2050 and ** percent by 2100.

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)

Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.

Potential national income loss from climate risks can be computed using simple damage functions that estimate damages based on the temperature outcomes inferred from the emissions trajectories projected by the transition scenarios. Potential national income benefit from avoided climate damages can be computed by contrasting the damages estimates based on the temperature outcomes from the transition scenarios with the policy, or mitigation, costs from climate action needed to meet a particular temperature outcome.Sources: Network for Greening the Financial System (2023), Scenarios Portal; and International Institute for Applied Systems Analysis (2023), NGFS Phase 4 Scenario Explorer; IMF Staff Calculations.Category: Transition to a Low-Carbon EconomyMetadataThe framework of the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) allows to simulate, in a forward-looking fashion, the dynamics within and between the energy, land-use, economy, and climate systems. Consistent with that framework, the NGFS explores a set of seven climate scenarios, which are characterized by their overall level of physical and transition risk. The scenarios in the current Phase IV (NGFS climate scenarios data set) are Low Demand, Net Zero 2050, Below 2°C, Delayed Transition, Nationally Determined Contributions (NDCs), Current Policies, and Fragmented World. Each NGFS scenario explores a different set of assumptions for how climate policy, emissions, and temperatures evolve. To reflect the uncertainty inherent to modeling climate-related macroeconomic and financial risks, the NGFS scenarios use different models, over and above the range of scenarios. These integrated assessment models (IAMs) are, by their acronyms: GCAM, MESSAGEix-GLOBIOM, and REMIND-MAgPIE. GDP losses and benefits are derived based on the National Institute Global Econometric Model (NiGEM). NiGEM consists of individual country models for the major economies, which are linked together through trade in goods and services and integrated capital markets. Country level data (or country aggregates, whenever country level disaggregation is not present) for GDP, population, primary energy consumption by fuel type, useful energy and carbon taxes from the IAM output is used as an input into the NiGEM scenarios. Climate scenarios within NiGEM can be broadly categorized into physical and transition events. While the effects of physical and transition shocks alongside policy decisions are contemporaneous, the scenarios in NiGEM can be run in a “stacked” manner, where each scenario uses the information provided by the previous scenario as its starting point. This allows for decomposition of shocks and their effects. Results are available for three scenarios: Net Zero 2050, Delayed Transition, and Current Policies. For details please see the NGFS climate scenarios presentation, the Climate scenarios technical documentation, and the User guide for data access.MethodologyThe NGFS climate scenarios database contains information on mitigation policy costs, business confidence losses, chronic climate damages, and acute climate damages. Mitigation policy costs reflect transition risk in a narrow sense and is measured against the Current Policies scenario (for which it is zero). Business confidence losses result from unanticipated policy changes, and only in the Delayed Transition scenario. GDP losses from chronic risks arise from an increase in global mean temperature. Estimates of the macroeconomic impact of acute risks are based on physical risk modelling covering different hazards. Acute risks are modeled independent of the input IAM. Results are available at the original sources for four hazards: droughts impacting on crop yields, tropical cyclones directly damaging assets, heatwaves affecting productivity and demand, and riverine floods directly damaging assets too. Apart from floods acute risks are the result of randomized stochastic output, yielding 60th to 99th percentile GDP impacts. In accordance with the presentation of the scenario results by the NGFS, the 90th percentile has been chosen as the representative confidence bound. That way, the results are focusing on tail risk. While the choice of the percentile will lead to marked differences for the GDP losses indicator, its influence on the GDP benefits indicator is muted due to comparing like-with-like. Further, the sum of the impacts from the four hazards is taken as the acute physical risk measure; see what follows for the methodology in deriving the net benefits. Net benefits can be calculated by comparing the impact of stronger climate action to the reference scenario, the Current Policies scenario: Net Benefit = 100 * (GDP[Policy scenario] / GDP[Current Policies] – 1). GDP in either scenario can be inferred from the hypothetical baseline with no transition nor physical risk and the percentage losses due to mitigation policy (MP), business confidence (BC), chronic climate (CC), and acute climate (AC): GDP = Baseline * (1 + (MP + BC + CC + AC) / 100). Plugging this into the above equation one finds after some algebra: Net Benefit = (MP[Policy scenario] – MP[Current Policies] + BC[Policy scenario] – BC[Current Policies] + CC[Policy scenario] – CC[Current Policies] + AC[Policy scenario] – AC[Current Policies]) / (1 + (MP + BC + CC + AC)[Current Policies] / 100). Obviously, MP[Current Policies] = BC[Current Policies] = BC[Net Zero 2050] = 0. In order to achieve consistency in aggregation of the four components to the total benefit, the denominator is kept fixed, while for the individual contributions only one component at a time, MP, BC, CC, or AC, is used in the numerator. Results are presented for the 49 countries, five geographic regions covering the remainder of countries, and a global and European total. The coverage of the five remainder regions refers to the country classification of emerging market and developing economies in the IMF’s World Economic Outlook.Data series: Potential National Income Loss From Climate RisksPotential National Income Benefit From Avoided Climate Damages

Taking 2005 as the base year, the future population scenario was predicted by adopting the Logistic model of population. It not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted by using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation by nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data adopted the non-agricultural population. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of GDP per capita),the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.