The impacts of human driving factors on grey water footprint in China using a GWR model

Abstract

Water is a key natural resource on which human economic and social development depends. In China today, water source shortage and water pollution impose a major constraint on sustainable development in China. Consumption terminal and water resource utilization are closely related by their water footprint, which is a comprehensive index of the effect of human activities on water resources. Water footprint has become a common indicator for measuring water resources and environmental pressures in a region. However, the water footprint theory only takes into account water resource pressure from the amount of resource consumption and does not consider the harm caused by water pollution. Thus, this may underestimate the seriousness of the water resource problem using water footprint theory. Compared with traditional water footprint theory, the grey water footprint theory can be a more direct reflection of human impact on the water resource quality. Accurate analysis of the impact of human factors on the environment is an important part of the current research on sustainable development. The GWR measurement model is more accurate than the traditional ordinary least squares(OLS) model because of its spatial factors. According to the theory of absorbing pollutants proposed by Hoekstra and Chapgain, we estimated the grey water footprint of 31 provincial regions in China in 2012 and explored the features of spatial distribution of the Chinese grey water footprint using the method of spatial autocorrelation analysis. We quantitatively examined the impacts of China's population, affluence, and technology on the grey water footprint by constructing a STIRPAT model based on the GWR. The results show that China's grey water footprint has strongly positive spatial correlation and imbalance of spatial distribution at provincial scale and the order of degree of humanistic factors threatening water environment is urbanization, population, industry structure and affluence. Additionally,our results show that 1% change in urbanization, population, per capita GDP or share of agriculture results in 1.03%, 0.85%, 0.63% or 0.52% change in the grey water footprint,respectively. There are spatial differences in the impacts of human factors on the grey water footprint. The impact of population on the grey water footprint gradually increases from north to south, the impact of affluence gradually decreases from west to east, and the impacts of agriculture and urbanization on the grey water footprint gradually increase from south to north.The calculation results using existing sample data indicate that an inverted U- shaped environmental Kuznets curve will appear in certain conditions, and the curve relation may not exist if the present industrial structure and model of urbanization do not change. These results can provide a more scientific basis for water resource management policy.