Based on the shift-share analysis model, the study identifies the transfer paths of water environmental pollution in river basins. Combined with synergetics theory, the three-stage SE-DEA-Malmquist index model and coupling coordination degree model are employed to measure and evaluate the collaborative governance efficiency of water environmental pollution transfer across the seven major river basins in China from 2006 to 2018. The results indicate that: from 2006 to 2018, the upper and middle reaches of the river basins were mostly pollution discharge transfer-out areas, while the lower reaches were mainly transfer-in areas. Compared with the upper reaches, the water environmental quality in the middle and lower reaches improved more significantly. Specifically, the lower reaches achieved effective improvement in water environmental quality due to the optimization of production structure, but the total amount of water pollution discharge still showed an expanding trend. In the process of undertaking industrial capacity transfer from the lower reaches, the local pollution discharge level in the upper and middle reaches increased to a certain extent. Under the influence of the water environmental pollution transfer mechanism, the collaborative governance efficiency of river basins exhibited temporal and spatial differentiation characteristics. The coupling coordination degree of total factor productivity (TFP) in transfer-out areas was significantly higher than that in transfer-in areas, with the difference fluctuating around 0.085. The collaborative governance efficiency in the upper and middle reaches was relatively similar-both the coupling coordination degree of TFP and technical efficiency in transfer-out areas were significantly greater than those in transfer-in areas, while the gap in the coupling coordination degree of technological progress between transfer-out and transfer-in areas was relatively small. This study suggests constructing a differentiated environmental regulation system, optimizing the river basin collaborative governance mechanism, and strengthening the dynamic supervision of the water environmental pollution transfer process.

This study used the panel data of 30 provinces in China (except the Xizang Autonomous Region, Hong Kong, Macao and Taiwan regions of China) from 2010 to 2020, measured the development level of the digital economy by constructing an evaluation indicator system, and explored the water resources pressure and its spatial and temporal variations from the perspective of water quantity and quality based on the water footprint methodology. The impact of the digital economy on water resources pressure was further analyzed through a benchmark regression model and a spatial econometric model. The empirical results indicate that: The water stress index, the water stress based on the blue water footprint and the water quality stress based on the grey water footprint showed an overall trend of gradual increase. Also, the stress index in the eastern and central regions was generally higher than that in the western region. The development of the digital economy significantly reduces the regional water stress index, water quantity pressure and water quality pressure, and there is regional heterogeneity in the impact on the overall regional water stress, which is significant in the eastern and central regions, but not in the western region. The results of the spatial Durbin model test indicated that, the development of the digital economy not only reduced the water stress of the province but also had a negative spillover effect on the water stress of the surrounding provinces.