Eco-Environmental Risk Assessment and Its Precaution Partitions Based on a Knowledge Graph: A Case Study of Shenzhen City, China

Abstract

The eco-environment is under constant pressure caused by the rapid pace of urbanization and changes in land use. Shenzhen is a typical “small-land-area, high-density” megalopolis facing various dilemmas and challenges; we must understand the eco-environmental risk (ER) of rapidly urbanizing regions and promote high-quality regional development. Therefore, with the help of the Python and Neo4j platforms, this study applies the theoretical foundation of knowledge graphs (KGs) and deep learning to form the KG of an ER; with this, we sort and establish an evaluation system in two dimensions, namely social and ecological, and introduce the Monte Carlo simulation to quantify the ER in Shenzhen City and its uncertainty from 2000 to 2020 to propose sub-regional programs and targeted measures for the prevention and control of the ER. The results are as follows: The eco-environmental risk index (ERI) of the study area as a whole showed a slight increase from 2000 to 2020; at the same time, the low-risk regions were mainly located in the east and southeast, while the high-risk regions were mainly located in the west–central and northwestern parts. In addition, three sample points (points A, B, and C) were selected using the Monte Carlo method to simulate the transfer of uncertainty from the indicator weights to the assessment results. Finally, based on the quantitative results, an accurate zoning scheme for ER prevention and control was provided to the decision makers, and appropriate countermeasures were proposed.