Industrial transfer is reshaping the geographic layout of industries and facilitating the transfer and spread of environmental pollution. This study employs the pollution transfer estimation method to discuss the environmental effect of industrial transfer. By compiling statistics on industries of a certain scale according to time-series data, the researchers compute the pollution load generated by industrial transfer and the difference in pollution emissions for each region and industry. Through the constructed evaluation model, the empirical scope is Jiangsu, which is the most developed industry in China. The results reveal that there is an apparent spatial hierarchy among the transferred industries in Jiangsu. Most industries transfer from the southern Jiangsu region toward the central Jiangsu and northern Jiangsu regions. Environmental pollution is redistributed among prefecture-level cities because of intercity industrial transfer; the spatial characteristics of pollution exhibit a notable hierarchical pattern. Furthermore, the transferred pollution load differs considerably between industries. The textile industry and chemical raw material and chemical product industry are mainly transferred toward the Central Jiangsu and Northern Jiangsu regions, whereas the papermaking and paper product manufacturing industry is primarily redistributed to the Southern Jiangsu region. The empirical results can serve as a reference for analyzing the environmental pollution effects of regional industrial transfer.
High-quality economic development and the realization of ecological civilization have become the main goals of China’s economic development. This study constructed a global reference Malmquist–Luenberger productivity index model of directional distance function from the perspective of mixed disposability and divided water resources green efficiency into pure technical efficiency change (PEC) index, scale efficiency change (SEC) index, pure technology change (PTC) index and scale technology change (STC) index. The results show the following: (1) The value of China’s water resources green efficiency increased by 1.1% from 2000 to 2016. The central region improved the most (1.4%), followed by the western (1%) and eastern (0.9%) regions. The water resources green efficiency improved in all provinces except Guangxi and Yunnan. (2) The water resources green efficiency is significantly affected by national policies, and there may not be a significant positive correlation with economic development. At present, the water resources green efficiency in most provinces still needs to be improved. (3) From 2000 to 2016, China’s water resources green efficiency decomposition index showed an upward trend except for SEC, and PTC was the main driving force for improving China’s water resources green efficiency. (4) The variation of PEC among provinces showed an inverted “N” trend, while the differences of SEC and STC showed an ascending trend, and PTC showed an inverted “U” trend. The proportions of provinces in which PEC, SEC, and STC indices improved were 40%, 46.67%, and 60%, respectively.