Ecological vulnerability assessment and spatial pattern optimization of resource-based cities: A case study of Huaibei City, China

Abstract

The continuous, intense exploitation of resources under rapid industrialization has made the ecological environment of resource-based cities increasingly vulnerability. Negative impacts of over-exploitation include soil erosion, declines in soil fertility, water pollution, and ground collapse—seriously threatening the survival and development of cities. We used Huaibei City, one of the representative coal resource-based cities, as a case study for measuring ecological vulnerability. We considered the interference effects of human mining activities, based on a typical Pressure-Sensitivity-Resilience (PSR) conceptual framework and constructed an ecological vulnerability index (EVI) system that couples natural and human factors. We then evaluated the EVI using the comprehensive index method. Finally, we combined a Bayesian network model with an entropy difference method, to optimize land use to minimize ecological vulnerability. We found that EVI ranged from 0.25 to 077, with highly vulnerable areas mainly concentrated in the southwest and north of the study area. The subset of {Soi = 1, MSA = 1} was selected as the optimal state subset of key variables for spatial pattern optimization, and primary and secondary optimization areas were mainly concentrated in Xiangshan and Duji Districts. This research will help protect the key ecological functional zones in the region, and provide a reference for policy-making in order to improve the comprehensive carrying capacity of resource-based cities.

Keywords:

• Ecological vulnerability
• pressure-sensitivity-resilience framework
• Bayesian network model
• spatial pattern optimization
• resource-based cities

Acknowledgments

The authors thank the anonymous reviewers for their helpful suggestions, and thank Ian Gilman at Yale University for his assistance with English language and grammatical editing.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, Zhai GF. The data are not publicly available due to their containing information that could compromise the privacy of research participants.