Land use and land cover dynamics : Implications for thermal stress and energy demands

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Wen Zhou
  • Patrick Laux
  • Diarra Dieng
  • Muhammad Usman

Detail(s)

Original languageEnglish
Article number113274
Journal / PublicationRenewable and Sustainable Energy Reviews
Volume179
Online published12 Apr 2023
Publication statusPublished - Jun 2023

Abstract

This study examined the interaction between land use and land cover (LULC) dynamics, trend and thermal stress distribution using the universal thermal comfort index (UTCI) and different LULC classifications under two Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (i.e., SSP 370 and 585) climate and land use scenarios for the historical (1959–2014) and future period (2045–2100). The moderate to strong cold stress in the annual and winter climatology in the midlatitudes was replaced by no thermal stress in the summer, while the summertime ranged from moderate to strong heat stress. A negative correlation was observed between thermal stress and southern hemispheric primary forests. Perennial croplands had the most dynamic changes in intensity during the historical period. Primary and secondary forests had an active influence on global thermal stress. Areas in the tropics recording moderate heat stress coincided with secondary nonforest, pastureland, and annual cropland expansions. The conversion of forest to range land and croplands and the subsequent negative forest trends increased the severity of thermal stress. The future projection showed intense thermal stress; however, the SSP-585 signals were more potent. As a result, cooling demands will rise, and heating demands will decline, yet, improved thermal comfort necessitates a higher cooling capacity, especially in the summer. Thermal stress may make it difficult for many cooling systems to meet people's energy demands. These could be a driving factor in shaping better land use policies, improving energy demand preparedness, and elucidating the potentially severe impacts of thermal stress.

© 2023 Published by Elsevier Ltd.

Research Area(s)

  • Land use land cover, Thermal stress, Machine learning, Energy demand, Trend, CMIP6