Spatial Heterogeneity of the Impact Factors on Gray Water Footprint Intensity in China

The gray water footprint intensity represents the amount of freshwater resources that need dilution of pollutants per unit of economic output, which indicates the relationship among water pollution, water resources and economy. In this paper, the gray water footprint of 31 provinces (autonomous regions) in China was estimated based on different water bodies. The spatial pattern and spatial agglomeration characteristics of gray water footprint in China from 2000 to 2014 were explored from the perspective of spatial autocorrelation. By extending the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact of the total population, urbanization rate, per capita output value, the proportion of the tertiary industry, environmental pollution control intensity and R&D investment intensity on the degree of gray water footprint intensity are explored, with ridge regression analysis to solve the problem of collinearity affecting factors. Meanwhile, the Geographically Weighted Regression (GWR) model is used to detect the spatial heterogeneity and spatio-temporal variation characteristics of the factors influencing gray water footprint intensity among regions. The study found that from 2000 to 2014, the gray water footprint of 31 provinces and cities in China was unstable; the domestic gray water footprint accounted for the largest proportion; the agricultural gray water footprint was mainly derived from nitrogen fertilizer, and the industrial and domestic gray water footprint was mainly derived from ammonia nitrogen. Water pollution varies from east to west. The total intensity of gray water footprint shows a downward trend, which is related to economic development and improvement of technological level. There is a positive correlation between the urbanization rate and the intensity of the gray water footprint. The total population, the per capita output value, the proportion of the tertiary industry, the intensity of environmental pollution control, the intensity of R&D input and the intensity of the gray water footprint are negatively correlated, and the influencing factors boast obvious spatial heterogeneity. The purpose is to reveal the key factors influencing gray water footprint intensity to ensure the sustainable development of economy, resources and environment through the formulation of regional differences in regulation and control policies.