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《Acta Ecologica Sinica》 2016-01
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The spatial correlation of the provincial grey water footprint and its loading coefficient in China

SUN Caizhi;HAN Qin;ZHENG Defeng;College of Urban and Environment,Liaoning Normal University;  
Water is one of our most important resources,being closely linked to the development of society and human beings. With the rapid development of its economy,water scarcity and water pollution have become very serious problems in China. The total volume of water resources in China is rather large,but the per capita water resources are much lower than the global average. The discharge of waste and polluted water increased from 593 × 108 t in 1998 to 785 × 108 t in 2012.Traditionally,the volume of water resources and water pollution are studied separately,with little research comprehensively evaluating both. Thus,the concept of the grey water footprint was introduced in this context. Hoekstra and Chapagain first presented the concept of the grey water footprint in 2008,after which the idea was further developed by a few of the Water Footprint Network's grey water footprint groups. The grey water footprint is defined as the volume of freshwater needed to dilute the load of contaminants based on existing environmental water quality standards. The concept of the grey water footprint provides a metric for the comprehensive assessment of water resource scarcity due to pollution. Overall,studies that investigate the assessment of grey water footprints from a regional perspective,and analyze the spatial correlation patterns of grey water footprints are few. Applying the formula for calculating grey water footprints designed by Hoekatra,this study calculated the grey water footprint and grey water footprint load coefficient of 31 provinces,municipalities,and autonomous regions in China,from 1998 to 2012. Spatial autocorrelation methods were used to study the grey waterfootprint,load coefficient,partial correlation pattern,and tendency towards change. In this paper,analyses of the agricultural,industrial,and domestic sectors will be presented. The results show that: 1) the total grey water footprint of China is increased from 5078. 58 × 108m3 in 1998 to 4400. 85 × 108m3 in 2012; however,the grey water footprint fluctuated during those 15 years. The contribution of each source to the grey water footprint is as follows: agriculture produces the most,and industry the least; 2) the grey water footprint load coefficient of China also tended to fluctuate during this period. The average grey water footprint load coefficient of China over the 15 years surveyed was divided into five categories: the first,which have high grey water footprint load coefficients,include Tianjin and Ningxia; the second is an area with a higher-than-average grey water footprint load coefficient,and it includes Beijing,Hebei,Shanghai,Shandong,Shanxi,and Henan; the third is an area comprising 9 provinces of which each has a medium grey water footprint load coefficient,and it includes Jiangsu,Gansu,Shannxi Liaoning,Jilin,Heilongjiang,Inner Mongolia,Anhui,and Hubei;the fourth is an area of 9 provinces with a lower than average grey water footprint load coefficient,and includes Zhejiang,Hunan,Guangdong,Guangxi Hainan Chongqing,Sichuan,Guizhou,and Xinjiang; the last is an area comprising 5provinces of which each has low grey water footprint load coefficients,and includes Fujian,Jiangxi,Qinghai,Yunnan,and Xizang. 3) Through the analysis of the global spatial autocorrelation index of grey water footprint load coefficients in China from 1998 to 2012 shows that the grey water footprint load coefficient exists a spatial cluster feature,but the cluster phenomenon was attenuated year by year. By analyzing its local spatial autocorrelation index,it was found that the regions with an H-H correlation are mainly located in northern China,whereas the regions with L-L correlations are mainly located in southern of China. The spatial correlation pattern of grey water footprint load coefficients is closely related with regional water resources and grey water footprints.
【Fund】: 教育部新世纪优秀人才项目(2013-13-0844);; 国家社会科学基金(11BJY063)
【CateGory Index】: TV213.4
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