RESUMEN
Under the tremendous challenges of frequent disasters, disaster resilience is imperative for risk reduction and sustainable management in poverty and disaster-prone areas. Ganzi Prefecture has a complicated topography and vulnerable ecosystems. Geological disasters have historically been the most serious risks in the region. To fully understand the potential risks and strengthen resilience, the study investigates the resilience level of 18 counties in Ganzi. Firstly, the paper develops a multidimensional index system based on the Baseline Resilience Indicators for Communities (BRIC) framework. And the entropy weighting method is used to calculate Ganzi's disaster resilience level from the aspects of "society-economy-infrastructure-environment." Then, the study uses exploratory spatial data (ESDA) to analyze the spatial-temporal evolution of disaster resilience. Finally, Geodetector is used to investigate the main driving factors of disaster resilience and their interactions. The results indicated that Ganzi's disaster resilience had maintained an upward trend from 2011 to 2019, with significant spatial divergence, which shows high resilience in the southeast and low resilience in the northwest. The economic indicator is the driving factor in the spatial difference of disaster resilience, and the interaction factor has a significantly stronger explanatory power for resilience. Therefore, the government should strengthen ecotourism development to help alleviate poverty in special industries and promote synergistic regional development.
Asunto(s)
Desastres , Ecosistema , Pobreza , ChinaRESUMEN
Aba's topography, weather, and climate make it prone to landslides, mudslides, and other natural disasters, which limit economic and social growth. Assessing and improving regional resilience is important to mitigate natural disasters and achieve sustainable development. In this paper, the entropy weight method is used to calculate the resilience of Aba under multi-hazard stress from 2010 to 2018 by combining the existing framework with the disaster resilience of the place (DROP) model. Then spatial-temporal characteristics are analyzed based on the coefficient of variation and exploratory spatial data analysis (ESDA). Finally, partial least squares (PLS) regression is used to identify the key influences on disaster resilience. The results show that (1) the disaster resilience in Aba increased from 2010 to 2018 but dropped in 2013 and 2017 due to large-scale disasters. (2) There are temporal and spatial differences in the level of development in each of the Aba counties. From 2010 to 2016, disaster resilience shows a significant positive spatial association and high-high (HH) aggregation in the east and low-low (LL) aggregation in the west. Then the spatial aggregation weakened after 2017. This paper proposes integrating regional development, strengthening the development level building, and emphasizing disaster management for Aba.