Urban ecological risk transmission model based on Bayesian network

Simulating this ecological risk transmission process helps to judge the key path and propose the accurate urban risk control measures. This paper develops an urban ecological risk transmission conceptual framework based on Bayesian Network to quantify the non-linear transmission paths via HA (human activity) -ES (ecosystem service) -ER (ecological risk). Ecosystem services are used as ecological risk measurement endpoints to quantify service provision and possible adverse effects from human activities. Direct and indirect impacts of urban human activities that threaten ecosystem services were considered. Nine cities in the Pearl River Delta (China) are selected as a case to simulate the ecological risk trans-mission process. Results show that: (1) Zhongshan and Guangzhou tied for the city most at ecological risk and Foshan jumped to the city with the highest ecological risk after considering the impact of pollution; (2) After analyzing the sensitive of human activities to ecological risks, the key risk trans-mission path of each city can be judged. There are three types of key paths: pollution dominant path (including Guangzhou, Foshan, Dongguan and Zhuhai), ES dominant path (including Shenzhen, Zhong-shan and Jiangmen) and dual dominant path (including Zhaoqing and Huizhou); (3) Buildup area, afforestation area and wetland protection & restoration investment are the top three key human ac-tivities that impact the urban ecological risk. Adjusting these key variables can be simulated to effectively reduce the urban ecological risk. The model can be used as a useful tool to guide policy and risk man-agement decisions, and to engage and negotiate with stakeholders with different views. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study developed a conceptual framework for urban ecological risk transmission, simulated the transmission process in nine cities, identified key risk transmission paths for each city, and found that adjusting key variables can effectively reduce urban ecological risk.