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 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

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.

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.

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.

Indicator data for the Blue Pacific 2050 Thematic Area 4: Resources And Economic Development.

  "Our ambition is for all Pacific peoples to benefit from a sustainable and resilient model of economic development. This includes enabling public policy and a vibrant private sector and others, that brings improved socio-economic wellbeing by ensuring access to employment, entrepreneurship, trade, and investment in the region."

Find more Pacific data on PDH.stat.

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.

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 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.

Regional Economy and Clusters Report

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.

This dataset presents essential statistics related to global population dynamics. It includes information such as the year, economy, economy label, absolute population values in thousands, and urban population percentages. The dataset covers the period from 1950 to 2050, providing insights into population trends and urbanization patterns across various economies. The columns in data set is

Year Economy
Economy Label
Absolute value in thousands
Absolute value in thousands Missing value
Urban population as percentage of total population
Urban population as percentage of total population Missing value

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.

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.

By 2050, Morocco could be among the world’s most dynamic emerging nations, Africa’s first climate-resilient economy, a regional leader in digital innovation, and a geopolitical bridge across three continents. This vision is not a distant dream; it is a trajectory already being shaped today. As global paradigms shift from climate upheaval to the AI revolution, from energy transitions to the fragmentation of traditional alliances, Morocco finds itself uniquely positioned to capitalise on its geographic, cultural, and strategic endowments.The stakes are high. What Morocco chooses to invest in over the next two decades will define its place in a reconfigured world order. This paper presents a forward-looking framework for policymakers, investors, civil society, and international partners seeking to contribute to and benefit from Morocco’s long-term transformation.Far more than a speculative blueprint, Morocco 2050 offers an integrated roadmap for sustainable growth, institutional resilience, and inclusive social progress. It invites its readers to envision Morocco not only as a gateway to Africa but as a model for balancing tradition and modernity, sovereignty and openness, continuity and innovation.Anchored in evidence and foresight, this report sets out a comprehensive strategy across sectors from renewable energy and digital economy to education, healthcare, logistics, and cultural industries. It explores the enablers of national renewal: good governance, adaptive policies, global partnerships, and the mobilisation of Morocco’s greatest asset, its people, both at home and across its diaspora.Morocco 2050 is a call to action. It is designed to guide decision-makers and attract visionary investors who understand that building the future is no longer optional; it is imperative. The time to shape Morocco’s success story is now.

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.

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: 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.