Extreme Weather Shocks and Crime: Empirical Evidence from China and Policy Recommendations

Rising global temperatures and increasing extreme weather events pose challenges to social stability and public security. This study examines the relationship between extreme weather and crime in China using fixed-effects quasi-Poisson and negative binomial regression models, along with a generalized additive model to explore nonlinear effects. The results show that extreme heat significantly increases crime, following an “S” shaped pattern. This intense heat heightens emotional instability and impulsivity, leading to a crime surge. While moderate heat reduces crime, extreme cold and heavy rainfall have no significant effects. These findings highlight the need for stratified policy interventions. Based on empirical evidence, this study proposes three key recommendations: (1) developing a weather warning and public security risk coordination system, (2) promoting community-based crime prevention through mutual assistance networks and infrastructure improvements, and (3) enhancing psychological interventions to mitigate mental health challenges linked to extreme weather. Integrating meteorological data, law enforcement, and interventions to help potential perpetrators can strengthen urban resilience and public safety against climate-induced crime risks.

Authors

Institutions

Publication Details

Journal
Climate
Published
2025-05-03
DOI
https://doi.org/10.3390/cli13050094
Citations
1
Primary Topic
Agricultural risk and resilience
Type
article
Field-Weighted Citation Impact
1.19

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Extreme Weather Shocks and Crime: Empirical Evidence from China and Policy Recommendations

Ping Jiang, Huaxing Lin
1 citations
Climate
Agricultural risk and resilience
1.19
article

Extreme Weather Shocks and Crime: Empirical Evidence from China and Policy Recommendations

Ping Jiang, Huaxing Lin
article en
1 citations

Abstract

Rising global temperatures and increasing extreme weather events pose challenges to social stability and public security. This study examines the relationship between extreme weather and crime in China using fixed-effects quasi-Poisson and negative binomial regression models, along with a generalized additive model to explore nonlinear effects. The results show that extreme heat significantly increases crime, following an “S” shaped pattern. This intense heat heightens emotional instability and impulsivity, leading to a crime surge. While moderate heat reduces crime, extreme cold and heavy rainfall have no significant effects. These findings highlight the need for stratified policy interventions. Based on empirical evidence, this study proposes three key recommendations: (1) developing a weather warning and public security risk coordination system, (2) promoting community-based crime prevention through mutual assistance networks and infrastructure improvements, and (3) enhancing psychological interventions to mitigate mental health challenges linked to extreme weather. Integrating meteorological data, law enforcement, and interventions to help potential perpetrators can strengthen urban resilience and public safety against climate-induced crime risks.

ClimateVol. 13(5)
Fudan University (CN)
National Natural Science Foundation of China, Fudan University
Openalex Percentile: Top 21%
Agricultural risk and resilience
1.19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Extreme Weather Shocks and Crime: Empirical Evidence from China and Policy Recommendations — Ping Jiang, Huaxing Lin · Climate (2025) | TGRS Research Map | TGRS