A novel framework of ecological risk management for urban development in ecologically fragile regions: A case study of Turpan City, China

Assessing and managing ecological risks in ecologically fragile areas remain challenging at present. To get to know the ecological risk situation in Turpan City, China, this study constructed an ecological risk evaluation system to obtain the ecological risk level (ERL) and ecological risk index (ERI) based on the multi-objective linear programming-patch generation land use simulation (MOP-PLUS) model, analyzed the changes in land use and ecological risk in Turpan City from 2000 to 2020, and predicted the land use and ecological risk in 2030 under four different scenarios (business as usual (BAU), rapid economic development (RED), ecological protection priority (EPP), and eco-economic equilibrium, (EEB)). The results showed that the conversion of land use from 2000 to 2030 was mainly between unused land and the other land use types. The ERL of unused land was the highest among all the land use types. The ecological risk increased sharply from 2000 to 2010 and then decreased from 2010 to 2020. According to the value of ERI, we divided the ecological risk into seven levels by natural breakpoint method; the higher the level, the higher the ecological risk. For the four scenarios in 2030, under the EPP scenario, the area at VII level was zero, while the area at VII level reached the largest under the RED scenario. Comparing with 2020, the areas at I and II levels increased under the BAU, EPP, and EEB scenarios, while decreased under the RED scenario. The spatial distributions of ecological risk of BAU and EEB scenarios were similar, but the areas at I and II levels were larger and the areas at V and VI levels were smaller under the EEB scenario than under the BAU scenario. Therefore, the EEB scenario was the optimal development route for Turpan City. In addition, the results of spatial autocorrelation showed that the large area of unused land was the main reason affecting the spatial pattern of ecological risk under different scenarios. According to Geodetector, the dominant driving factors of ecological risk were gross domestic product rating (GDPR), soil type, population, temperature, and distance from riverbed (DFRD). The interaction between driving factor pairs amplified their influence on ecological risk. This research would help explore the low ecological risk development path for urban construction in the future.