Spatio-temporal patterns of Chinese urban recovery and system resilience under the pandemic new normal

Abstract

Enhancing urban resilience is an important measure to improve preparedness to public health challenges; therefore, understanding the patterns and determinants of urban recovery is of great significance for sustainable urban development under the pandemic new normal. We first propose an analytical framework of urban recovery capacity, and then apply the geographical detector model and geographic weighted regression model to investigate the dynamic characteristics of urban resilience and urban recovery capacity under the impact of COVID-19 in China. The results show that the overall pattern of vitality recovery follows the U-curve; however, the impact of COVID-19 on each region is significantly different, with the highest degree of recovery in the Northwest and East, and the lowest in the Central and West. The geographical detector model reveals that urban resilience indicators can predominantly explain the variations of urban recovery across cities. The geographically weighted regression model shows that environmental resilience, infrastructure resilience, and social resilience are positively correlated with urban recovery capacity, while economic resilience cannot improve urban recovery capacity in the short term. We suggest promoting urban system diversity and redundancy across different dimensions to enhance urban resilience, but caution that linearly promoting systemic redundancy might harm the long-term sustainability of resource allocations.