Ecological Indicators (Dec 2021)
Evaluation of ecological environmental quality and factor explanatory power analysis in western Chongqing, China
Abstract
With accelerating economic growth, expanding demand, and increasing development intensity, the quality of the eco-environment has been decreasing. The western Chongqing area plays an important role in the Yangtze River Economic Belt, China. There are many high-intensity development activities in this area, which lead to an increasingly prominent contradiction between humans and the environment. Thus, this paper first evaluates the eco-environment of western Chongqing by combining the analytic hierarchy process (AHP), coefficient of variation, matter element model, and geographic information system (GIS) using remote sensing image data and other reference data of this area. The powerful spatial analysis function of GIS and the comprehensive evaluation of multiple factors with multiple models have been fully used to improve the reliability of the evaluation results. Furthermore, the geographic detector model has been applied to detect the influence of various indicators on eco-environmental quality. The results show the eco-environmental quality of the counties in western Chongqing and the explanatory power of each indicator. Jiangjin District and Yongchuan District have the best eco-environmental qualities, while Tongnan District and Bishan have low qualities. Overall, the eco-environmental quality of this area is better in rural areas than in cities, better in the south than in the north, and better in places with high elevation than in places with low elevation. The main indicators influencing the eco-environmental quality in the study area are as follows: emissions of PM10(0.9472), population density (0.8802), proportion of professional teachers in primary and secondary schools (0.8773), per capita disposable income of all residents (0.8349), and natural population growth rate (0.8180). Eco-environmental quality is obviously affected by multiple indicators, and the influence of any two indicators is greater than that of a single indicator. Among the 171 interaction results, 58 (34%) are nonlinear enhancements and 113 (66%) are bifactor enhancements. The results indicate that a comprehensive multifactor measure based on the intensity of the interaction between indicators is an effective method for evaluating eco-environmental quality and may provide a useful reference to planners involved in eco-environmental protection and economic construction.
