Research on evolutionary game and intelligent monitoring in cross-regional water pollution control

Water pollution has become a critical environmental issue that transcends regional boundaries, posing significant challenges to policy formulation and monitoring efforts. This study tackles the complexities of developing cross-regional water pollution control policies and assessing governance efficacy. Leveraging evolutionary game analysis, we delve into the strategic decisions and interactions among upstream governments, enterprises, and downstream governments. Additionally, we harness the Support Vector Machine (SVM) model for intelligent water quality monitoring to validate the effectiveness of control measures. Our findings reveal that upstream governments should prioritize high-investment regulatory approaches to foster positive externalities for downstream industries and public health. Conversely, downstream governments should adopt flexible regulatory strategies to mitigate the burden on downstream industries. Environmental tax adjustments should be gradual to avoid abrupt shocks to enterprises. Furthermore, the central government’s subsidy policies play a pivotal role in fostering upstream–downstream cooperation, with lower subsidies encouraging upstream governments to expedite regulatory implementation and higher subsidies decelerating downstream governments’ regulatory process. This study validates the practicality of intelligent water quality monitoring technology through a case study, emphasizing the need for strengthened upstream–downstream cooperation to achieve effective governance. The novelty of this research lies in integrating game theory into cross-regional water pollution control strategies and blending intelligent monitoring technology to create a comprehensive analytical framework that offers robust decision support for governance initiatives.