Spatio-temporal pattern evolution and dynamic simulation of urban ecological resilience in Guangdong Province, China

Abstract

Currently, in-depth analyses concerning the dynamic simulation of urban resilience and forecasting future development trends are lacking. To address urban vulnerability and promote regional balance and sustainable development, this study assessed the urban ecological resilience of Guangdong Province from 2000 to 2020 using the entropy weight TOPSIS method. Furthermore, we examined the spatial and temporal variations and evolution of urban ecological resilience through measures such as kernel density estimation, Theil index, and the center of gravity standard deviation ellipse. We employ obstacle degree and back-propagation (BP) neural network models to identify the primary barriers and conduct dynamic simulations. Our findings revealed that, from an evolutionary resilience perspective, urban ecological resilience is an inherent characteristic of urban ecosystems. It consistently possesses the dynamic ability to defend against disturbances, respond promptly when interference occurs, and continually learn and innovate, regardless of the urban ecology's state of disturbance. Urban ecological resilience in Guangdong Province has steadily improved with minimal fluctuations, establishing a trend characterized by low concentration and high convergence. Regarding barrier factors, the disposal rate of domestic waste, number of college students per 10,000 people, number of R&D personnel per 10,000 labor force, and per capita park green space area are the primary constraints on urban ecological resilience in Guangdong Province. Dynamic simulations from 2022 to 2030 suggest that urban resilience will experience gradual development with a decreasing overall resilience level. Areas with lower and median resilience values will predominate, while the number of cities with higher resilience levels will see a reduction. Future development trends indicate notable temporal and spatial variations. In the east and west directions, the urban resilience level forms a "U" shape, while in the north and south directions, it is higher in the south and lower in the north.

Keywords: Back propagation neural network model; Dynamic evolution; Guangdong province; Obstacle factors; Urban ecological resilience.

Fig. 1

Study area overview.

Fig. 2

Kernel density distribution of urban ecological resilience in Guangdong Province.

Fig. 3

Theil index contribution.

Fig. 4

Theil index.

Fig. 5

Spatial distribution of urban ecological resilience of Guangdong Province in 2000, 2005, 2010, 2015 and 2020.

Fig. 6

Standard deviation ellipse and gravity center trajectory of urban ecological resilience in Guangdong Province.

Fig. 7

The obstacle factors of urban ecological resilience of Guangdong in 2000, 2010, and 2020.

Fig. 8

Evolution trend of the urban resilience level of Guandong province in 2022–2030.

Fig. 9

Urban ecological resilience of Guangdong Province in 2022, 2026, and 2030.