High-Temperature Risk Assessment and Adaptive Strategy in Dalian Based on Refined Population Prediction Method

Ziding Wang, Zekun Du, Fei Guo (), Jing Dong and Hongchi Zhang
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Ziding Wang: Architecture and Fine Art School, Dalian University of Technology, 2 Linggong Road, Dalian 116023, China
Zekun Du: Architecture and Fine Art School, Dalian University of Technology, 2 Linggong Road, Dalian 116023, China
Fei Guo: Architecture and Fine Art School, Dalian University of Technology, 2 Linggong Road, Dalian 116023, China
Jing Dong: Architecture and Fine Art School, Dalian University of Technology, 2 Linggong Road, Dalian 116023, China
Hongchi Zhang: Architecture and Fine Art School, Dalian University of Technology, 2 Linggong Road, Dalian 116023, China

Sustainability, 2025, vol. 17, issue 17, 1-26

Abstract: Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development.

Keywords: refined population forecasting; population projection models; high-temperature risk assessment; adaptive strategies (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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