Water shortages and poor water quality have become an urgent problem that is constraining the sustainable development of China. Grey water has been found to bring greater stress on the water supply than freshwater consumption, and the grey water footprint (GWF) has received significant attention as a comprehensive indicator to assess wastewater pollution. In this study, we analysed the grey water footprint in the Yangtze River Basin from 2003 to 2017 and established a Logarithmic mean divisia index (LMDI) model to decompose the grey water footprint efficiency into six key factors. Our findings are as follows: (1) The average grey water footprint (AGWF) in the central regions was 40% higher than eastern region and 172% higher than western region; (2) Economic effects and capital deepening effects are the main factors affecting positive changes in grey water footprint efficiency; (3) Based on an analysis of the driving factors of greywater footprint efficiency in each province, we conducted a territorial classification according to the primary driving factors in each province. Our results reflect the spatial distribution characteristics of the influencing factors on the grey water footprint effect in the Yangtze River Basin and will enable the government to formulate relevant policies for each subregion.

The inefficient application of water resources has become an urgent problem restricting the world’s sustainable development. Virtual Water Trade opens a new perspective on improving water resource utilization efficiency. Based on a multi-regional input–output model and the logarithmic mean Divisia index, the virtual water flows between 2000–2014 in 43 countries and regions have been evaluated, and the driving forces of changes in virtual water flows for the European Union were revealed. During the study period, the total amount of virtual water flow continued to increase. The United Kingdom is a net virtual water importer that depends on the European Union significantly. There was a large amount of virtual water flow from the European Union to the United States during 2000–2012. However, China gradually seized the share of virtual water from European Union exports after 2012. Economic effects and virtual water intensity effects are the most significant drivers of virtual water flows. The difference is that the economic effect positively drives virtual water flows, while the virtual water intensity effect negatively influences. The results reveal the nature of the United Kingdom in the virtual water trade and can provide post-Brexit recommendations.