Data-Driven Environmental Governance: Policy Optimization Based on Mathematical Models and Intelligent Technologies

The externalities of environmental pollution have a profound impact on economic growth and social welfare. Traditional environmental governance policies often find it difficult to balance the relationship between development and protection. This paper constructs a theoretical model of environmental pollution, in which the use of corporate tax revenue for environmental protection is considered, and modern technologies such as artificial intelligence (AI) are combined to explore smarter environmental governance methods. It is found that under Laissez-faire, the growth rate is highest, but the pollution is also the largest, and the welfare is lower than under government subsidy. When the externality of pollution is small, government subsidies combined with technology to optimize resource allocation can bring higher social welfare. However, when the externality is high, the welfare is highest under the public provision. This study provides policymakers with a data-driven governance idea to help them choose more effective environmental protection policies under different conditions and achieve a win–win situation of development and protection.