控制水污染程度、提高水环境质量有利于南水北调中线工程高质量发展。在核算南水北调中线工程20个受水城市灰水足迹的基础上，计算各受水城市灰水足迹荷载系数，分析其时空分布特征，并利用对数平均迪氏指数(Logarithmic Mean Divisia Index，LMDI)模型对其驱动因素进行分解。结果表明:(1)2009～2020年南水北调中线工程受水区灰水足迹降幅达到24。54％，水环境质量得到一定改善;从灰水足迹的构成来看，农业占比最高，其次是生活，工业占比最低，且农业源、工业源污染的有效控制是受水区灰水足迹总量整体下降的主要原因。(2)在中线工程通水后，受水区灰水足迹荷载系数有所降低，但是水资源总量仍然远远不能满足水污染物的稀释需求，水环境压力巨大;各受水城市灰水足迹荷载系数存在显著不同，地区内部尤其是河南省内部的差异是受水区灰水足迹荷载系数不平衡的主要来源。(3)资本深化效应、资源禀赋效应及经济活度效应对灰水足迹荷载系数主要表现为正向驱动效应，而资本产出效应、经济环境效应则表现为负向驱动效应。各受水城市要继续走绿色发展道路，促进南水北调中线工程水资源可持续利用。
Spatial-temporal Variation and Driving Factors of Grey Water Footprint Loading Coefficient in Water-Receiving Area of Central Route of the South-to-North Water Diversion Project
Controlling water pollution and improving water environment quality is conducive to the high-quality development of the South-to-North Water Diversion Project's Central Route.On the basis of calculating the grey water footprint of 20 water-receiving cities of the Project's Central Route,this paper calculated the grey water footprint load coefficient of each water-receiving city,analyzed the spatial-temporal distribution characteristics.The Logarithmic Mean Divisia Index model was used to decompose the driving factors.The results showed that:(1)From 2009 to 2020,the grey water footprint in water-receiving area decreased by 24.54％,and the water environment quality was improved;From the perspective of the composition of grey water footprint,agriculture accounted for the highest proportion,followed by living,and industry for the lowest.Moreover,the effective control of pollution from agricultural and industrial sources was the main reason for the overall decline of the total grey water footprint.(2)After the water supply of the Central Route,the grey water footprint load coefficient of the water-receiving area decreased to certain extent.However,the total amount of water resources still failed to meet the dilution demand of water pollutants,and hence,the water environment pressure was large.The load coefficient of grey water footprint in water-receiving cities was significantly different.The difference within the regions,especially within Henan Province,was the main source of the unbalance of grey water footprint load coefficient.(3)The capital deepening effect,resource endowment effect and economic activity effect mainly showed positive effects on the grey water footprint load coefficient,while the capital output and economic environment effects showed negative driving effects.All water-receiving cities should continue to take the road of green development and promote the sustainable utilization of water resources in the South-to-North Water Diversion Project's Central Route.
South-to-North Water Diversion Project's Central Routegrey water footprint loading coefficientLMDI modeldriving factors
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