Assessing environmental interference in northern China using a spatial distance model: From the perspective of geographic detection

Abstract

The rapid development of society and the expansion of human activities have resulted in interference with the natural environment. Assessing the environmental interference (EI) caused by human activities is highly important for socio-economic sustainable development. In this study, the spatial distance model (SDM) and resource endowment index (REI)-human activity index (HAI) ratio model were developed to calculate the environmental interference index (EII) in northern China (NC). The current spatial distribution and patterns of EII in NC were analyzed based on geographic information system (GIS) technology. In addition, the factors that influence the level of EI were examined through a geographical detector method. The results showed that the EII value in the eastern region was significantly higher than that in the western region and that differences in EI were spatial heterogeneity. The spatial distribution of EI was analyzed at the provincial, municipal and county scales, respectively. It was found that its distribution was closely related to urban development. The spatial distribution of EI displayed longitudinal zonality. East of 104.987°E, there were many large cities, such as Beijing, Tianjin, Qingdao and Zhengzhou, with high population densities and developed economies. Thus, these areas had high EI values. To the west of 104.987°E, such as in the Qinghai, Gansu, Xinjiang and Inner Mongolia regions, the EI values were generally low, with low environmental quality and fewer human activities. The level of EI in the Huang-Huai-Hai Plain region was higher than that in other areas, displaying obvious spatial dependence. Moreover, the distribution of EI exhibited high-high and low-low aggregation patterns, which accounted for 24.06% and 27.35% of the total study area, respectively. Specifically, in NC, the EI caused by human activities displayed obvious regional characteristics. In addition, the factors that influence EI were determined through a geographical detector model. The land use intensity was the direct factor related to changes in and the levels of EI, and the cover and growth of vegetation were the most important factors associated with mitigating human interference. The assessment results can provide a reference for the formulation of environmental governance and related policies.