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

Spatial-temporal evolution and driving factors of grey water footprint efficiency in the Yangtze River Economic Belt

At present, the deterioration of the water ecosystem has constituted a bottleneck for the further development of the Yangtze River Economic Belt (YREB). As a crucial indicator for evaluating the degree of water pollution, grey water footprint (GWF) is of great significance for rationally evaluating the water environment of the YREB. In this study, we calculated the GWF efficiency of the YREB based on the panel data of 9 provinces and 2 cities from 2005 to 2019. On this basis, spatiotemporal methods and Logarithmic Mean Divisia Index (LMDI) model were adopted to analyze the spatial-temporal evolution characteristics and driving factors of GWF efficiency in the YREB. This study drew the following conclusions: (1) the GWF efficiency in the YREB was on an uptrend, with the average annual growth rates of the upstream, midstream and downstream being 17.35 %, 18.31 % and 17.8 % respectively from 2005 to 2019. (2) The GWF efficiency in the YREB showed a weak trend of polarization and the gap between different regions continued to widen. Besides, it was characterized by stability and owned a positive spatial correlation in both geographic distance and economic distance. (3) The improvement of the technology level, water use efficiency, wastewater treatment capacity, economic development level and the reduction in the industrial pollution intensity contributed positively to boosting the GWF efficiency. Meanwhile, the effect of environmental regulation made a significant negative contribution to GWF efficiency. Therefore, in the process of building the YREB, while emphasizing the coordinated development of the economy, all regions should also carry out joint pollution control.

Understanding China's industrialization driven water pollution stress in 2002-2015-A multi-pollutant based net gray water footprint analysis

China produces a large amount of industrial effluent with multiple pollutants contained, along with a flourishing economy. This study aims to examine the dynamics between China's industrialization and accompanying environmental pressure based on the gray water footprint (GWF) concept. A newly proposed net GWF (NetGWF) and the decoupling index (DI) are applied to evaluate China's industrial activities during 2002-2015 in different modes considering typical, all, and individual pollutants. The NetGWF dynamics are further quantitatively decomposed into 17 effects of not only commonly assessed drivers but also industrial fixed capital formation, inventory variation, and import, using an advanced dynamic decomposition analysis approach. Results show NetGWF is an effective indicator measuring domestic water pollution stress from industrialization, with NetGWF-AllPlt (estimated using all pollutants) validated to be more reliable and sensitive than NetGWF-COD&NH3N (estimated using Chemical oxygen demand and Ammonia nitrogen). An overall decoupling between China's industrialization and wastewater pollution is identified with most of DIs less than 1.0 caused mainly by decreased (by around 40%) industrial NetGWFs for 2002-2015. Industrial fixed capital formation and export have caused main components of China's industrial GWF, with proportions of 37.3% and 30.8%, respectively, followed by urban household consumption (16.8%). Volatile phenol, Petroleum, and Ammonia nitrogen are recognized as three decisive contaminants to the industrial NetGWFs. Technological development is the dominant contributor (-50%) to decreasing China's industrial NetGWFs, while fixed capital formation (18%) and export (16%) are principal drivers increasing the NetGWFs. Based on these, we expect to provide informative findings for building a pollution-decoupled industrialization.

Spatiotemporal dynamics and influencing factors of the global material footprint

Environmental pressures have rapidly increased in various regions worldwide due to globalization. Thus, sustainable consumption and production are crucial for sustainable resource development. The material footprint (MF) of 180 countries was calculated from 1995 to 2015, and spatial autocorrelation analysis was conducted to investigate the spatiotemporal trend of the global MF. The results show that the global MF presented an upward trend from 1995 to 2015, increasing by 83%, and we find that the global per capita MF exhibits clustering, with an increasing trend during the study period. The findings indicate that resource consumption is similar in neighboring areas, especially in countries with a high MF surrounded by countries with a high MF (high-high clustering) and countries with low-low clustering. In addition, the number of countries with high clustering increased during the study period. We should take advantage of clustering, improve resource utilization, increase the technical carrying capacity, and develop energy-saving technologies. In African regions with low-low clustering, the economy of the surrounding areas should be stimulated to strengthen economic and technological clustering. In addition, advanced technology should be incorporated to improve the efficiency of using natural resources. This study can provide a reference for the spatial distribution of sustainable resource development.

Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China

In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative grey multiple convolution model (DGMC(1,N)) was applied. In order to improve the accuracy of the model, the accumulation of deformation derivatives is introduced, and the particle swarm optimization algorithm is used to solve the optimal order. The DGMC(1,N) model was compared with GM(1,2) and GM(1,1) models. The results show that the DGMC(1,N) model has the highest prediction accuracy. Finally, DGMC(1,N) model is used to predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality in the Zhanghe River (using chemical oxygen demand (COD) as the water quality indicator).