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Spatiotemporal dynamics of global population and heat exposure (2020–2100)

Based on improved SSP-consistent population projections

Bibliographic Data

ID15544934
AuthorsMengya Li (0009-0007-1835-9782, East China Normal University, corresponding author), Bing-Bing Zhou (Ocean University of China), Minyi Gao (East China Normal University), Yimin Chen (0000-0003-1976-6041, Sun Yat-sen University, corresponding author), Ming Hao (0000-0003-1380-3569, Ministry of Natural Resources), Guohua Hu (0000-0002-7492-1393, East China Normal University), Xia Li (0000-0003-3050-8529, East China Normal University, corresponding author)
Year2022
Volume17
Issue9
Pages094007-094007
Publication date2022-08-05
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac8755
OpenAlexW4290099020
LanguageEN
Citations received15
References cited49

To address future environmental change and consequent social vulnerability, a better understanding of future population (FPOP) dynamics is critical. In this regard, notable progress has been made in producing FPOP projections that are consistent with the Shared Socioeconomic Pathways (SSPs) at low resolutions for the globe and high resolutions for specific regions. Building on existing endeavors, here we contribute a new set of 1 km SSP-consistent global population projections (FPOP in short for the dataset) under a machine learning framework. Our approach incorporates a recently available SSP-consistent global built-up land dataset under the Coupled Model Intercomparison Project 6, with the aim to address the misestimation of future built-up land dynamics underlying existing datasets of future global population projections. We show that the overall accuracy of our FPOP outperforms five existing datasets at multiple scales and especially in densely-populated areas (e.g. cities and towns). Followingly, FPOP-based assessments of future global population dynamics suggest a similar trend by population density and a spatial Matthew effect of regional population centralization. Furthermore, FPOP-based estimates of global heat exposure are around 300 billion person-days in 2020 under four SSP-Representative Concentration Pathway (RCPs), which by 2100 could increase to as low as 516 billion person-days under SSP5-RCP4.5 and as high as 1626 billion person-days under SSP3-RCP8.5—with Asia and Africa contributing 64%–68% and 21%–25%, respectively. While our results shed lights on proactive policy interventions for addressing future global heat hazard, FPOP will enable future-oriented assessments of a wide range of environmental hazards, e.g. hurricanes, droughts, and flooding

Climate change · Climate model · Environmental resource management · Geography · Population · Population growth · Projections of population growth · Representative Concentration Pathways · Vulnerability (computing · Climate Change and Health Impacts · Computer Science · Environmental Science · Impact of Light on Environment and Health · Urban Transport and Accessibility · Demography · Ecology

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Unique citing works15
Citations per year5
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 15

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