Building resilient cities has become an emerging risk management strategy, thus it is necessary to make a scientific evaluation on urban resilience. In this study, both the Driving Force-Pressure-State-Impact-Response (DPSIR) framework and the BP neural network are innovatively adopted to construct a comprehensive urban resilience evaluation system. Prefecture-level cities in Hubei Province are examined for empirical analysis. The results show that: (1) The urban resilience in Hubei Province exhibits an intermittent growth pattern, progressing in a west-to-east direction. This growth is characterized by three years of advancement followed by a one-year period of stagnation. (2) There is a spatial negative correlation. Owing to uneven development within Hubei Province, it can be seen that Wuhan, the provincial capital, holds a dominant position. (3) Resource and environmental pressure has become the main obstacle to the construction of resilient cities in Wuhan. The primary limiting factors for other cities are the degree of socioeconomic growth and the capacity of the government to handle affairs. This study, based on the process-oriented nature of resilience, constructs an indicator system for urban resilience evaluation under the DPSIR framework, fully reflecting the characteristics of urban resilience. It not only enriches the theory and methodology of urban resilience evaluation but also offers valuable references for governments to formulate effective strategies for sustainable urban development.
Keywords: BP neural network; Driving force-pressure-state-impact-response (DPSIR); Obstacle diagnosis model; Sustainable development; Urban resilience.
© 2024. The Author(s).