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A Systematic Review of Methodological Advances in Urban Heatwave Risk Assessment: Integrating Multi-Source Data and Hybrid Weighting Methods
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As climate change intensifies, urban populations face growing threats from frequent and severe heatwaves, underscoring the urgent need for advanced risk assessment frameworks to inform adaptation strategies. This systematic review synthesizes methodological innovations in urban heatwave risk assessment (2007–2024), analyzing 259 studies through bibliometric analysis (CiteSpace 6.4.R1) and multi-criteria evaluation. We propose the hazard–exposure–vulnerability–adaptability (HEVA) framework, an extension of Crichton’s risk triangle that integrates dynamic adaptability metrics and supports high-resolution spatial analysis for urban heatwave risk assessment. Our systematic review reveals three key methodological gaps: (1) Inconsistent indicator selection across studies; (2) limited analysis of microclimatic variations; (3) sparse integration of IoT- or satellite-based monitoring. The study offers practical solutions for enhancing assessment accuracy, including refined weighting methodologies and high-resolution spatial analysis techniques. We conclude by proposing a research agenda that prioritizes interdisciplinary approaches—bridging urban planning, climate science, and public health—while advocating for policy tools that address spatial inequities in heat risk exposure. These insights advance the development of more precise, actionable assessment systems to support climate-resilient urban development.
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“Conventional heatwave analysis has commonly relied on statistical methods, physical modeling, and conventional machine learning approaches [12, 13].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [13]
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GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
Alihodžić, A. (2026) · arXiv · doi:10.48550/arxiv.2605.16435
“Conventional heatwave analysis has commonly relied on statistical methods, physical modeling, and conventional machine learning approaches [12, 13].”
Metadata
- Authors
- Xu, C., Wei, R., & Tong, H.
- Affiliations
- Shandong Jianzhu University (CN)
- Corresponding author
- Tong, Hui
- ORCID
- Xu, Chang
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2025-04-21
- Volume
- 17
- Issue
- 8
- Pages
- 3747
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4409637725
- Cited by
- 18
- References
- 3
- Citations by year
- 2026: 14 · 2025: 4
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- China Ministry of Housing and Urban-Rural Development Scientific and Technological Project Plan (2022-K-184, X20006Z, 21YJC840038)
- Shandong Jianzhu University Doctoral Fund Project, Research on the Correlation between Urban Morphological Factors and Microclimate (2022-K-184, X20006Z, 21YJC840038)
- China Ministry of Education Humanities and Social Sciences Research Youth Fund Project (2022-K-184, X20006Z, 21YJC840038)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Social Sciences › Sociology and Political Science › Disaster Management and Resilience
- Keywords
- urban heatwaves, multi-source data fusion, hybrid weighting method, microclimate, IoT monitoring, satellite monitoring, spatial inequity, climate change adaptation, urban resilience, vulnerability indicators
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Systematic Review of Methodological Advances in Urban Heatwave Risk Assessment: Integrating Multi-Source Data and Hybrid Weighting Methods. https://app.cochat.ai/papers/10.3390/su17083747
- The paper
- Xu, C., Wei, R., & Tong, H. (2025). A Systematic Review of Methodological Advances in Urban Heatwave Risk Assessment: Integrating Multi-Source Data and Hybrid Weighting Methods. Sustainability. https://doi.org/10.3390/su17083747