Investigating the impact of artificial intelligence development on water pollution in China

Analysing the impacts of AI development on water pollution is of great significance for improving water environment management measures and promoting global sustainable development. In this study, macro and micro panel data of China from 2008 to 2019 were used to measure the development level of AI at the industry and provincial levels in China, and a panel data regression model was constructed to analyse the effects of AI development on water pollution and regional heterogeneity. The measurement results show that the development level of AI is increasing. Among the six industries, the intelligence level of the manufacturing industry has developed the fastest, by approximately 48.74 times compared with the baseline. At the provincial level, Guangdong Province made the greatest progress in intelligence level, increasing by approximately 23.6 times. The regression results show that AI technology is helpful in reducing the level of water pollution, and this conclusion remains valid after the regression of instrumental variables and robustness testing. In terms of regional heterogeneity, AI technology can significantly reduce the degree of water pollution in eastern and western China. However, the development of AI technology in the central region has no significant impact on water pollution, and a risk of increasing the degree of water pollution exists. The reasons may be that the eastern region has high levels of economic vitality and technological innovation and a strong ability to transform AI achievements, which can significantly improve the water environment. Industrial support policies in the western region are strong, and the application of AI can quickly improve production methods and significantly reduce water pollution levels. In comparison, the levels of economic development, technological innovation, and policy support in the central region need to be strengthened, so the environmental effect of AI is not significant. (c) 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.