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26 November 2025

Resilience Assessment and Evolution Characteristics of Urban Earthquakes in the Sichuan–Yunnan Region Based on the DPSIR Model

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1
School of Emergency Management, Institute of Disaster Prevention, Sanhe 065201, China
2
School of Environment and Disaster Prevention, Institute of Disaster Prevention, Sanhe 065201, China
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Author to whom correspondence should be addressed.
This article belongs to the Topic Climate, Health and Cities: Building Aspects for a Resilient Future

Abstract

The Sichuan–Yunnan region, a primary seismic-prone zone on the Qinghai–Tibet Plateau, has experienced heightened seismic exposure due to rapid urbanisation. In order to address the issue of disaster risks and to promote sustainable urban development, this study establishes an integrated urban seismic resilience evaluation framework based on the DPSIR (Driving–Pressure–State–Impact–Response) model. The CRITIC–AHP combined weighting method was utilised to determine indicator weights, and data from 37 prefecture-level cities (2010, 2015, 2020) were analysed to reveal spatial–temporal evolution patterns and correlations. The results demonstrate a consistent improvement in regional seismic resilience, with the overall index increasing from 0.501 in 2010 to 0.526 in 2020. Sichuan exhibited a “decline-then-rise” trend (0.570 to 0.566 to 0.585), while Yunnan demonstrated continuous growth (0.517 to 0.557). The spatial pattern underwent an evolution from “west–low,central–eastern–high” to “south–high, north–low,” with over half of the cities attaining relatively high resilience by 2020. Chengdu and Kunming have been identified as dual high-resilience cores, diffusing resilience outward to neighbouring regions. In contrast, mountainous areas such as Garze and Aba have been found to exhibit low resilience levels, primarily due to high seismic stress and limited socioeconomic capacity. Subsystem analysis has revealed divergent resilience pathways across provinces, while spatial autocorrelation has demonstrated fluctuating global Moran’s I values and temporary local clustering. This research provides a scientific foundation for seismic disaster mitigation and offers a transferable analytical framework for enhancing urban resilience in earthquake-prone regions globally.

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