A stochastic differential game based pollution management study of regional alliance

Abstract

The growing complexity of cross-regional pollution poses significant challenges to environmental governance, primarily due to fragmented policy implementation, conflicting interests and dynamic environmental systems. This study addresses these challenges by proposing a regional alliance mechanism that integrates stochastic differential game theory in order to optimise pollution control strategies in an uncertain environment. The primary objective is to develop a dynamic framework that captures the strategic interactions between regions and stochastic environmental factors, such as climate variability and technological advancements. A stochastic differential game model is constructed that incorporates dynamic equations for emission reduction technologies, pollution mitigation efforts and demand responses. Three governance scenarios are analysed: self-sufficiency, a unilateral alliance and a major alliance. Our main findings suggest that forming stable regional alliances significantly improves pollution control efficiency compared to isolated actions. Major alliances achieve the highest profitability due to resource integration and coordinated strategies. Sensitivity analysis reveals the pivotal role of technological effectiveness coefficients and cost parameters in shaping regional decisions. Notably, random factors introduce volatility, emphasising the need for adaptive governance mechanisms. Based on these insights, policy recommendations include fostering regional alliances through legal and financial incentives, prioritising green technology R&D, and establishing dynamic monitoring systems to balance economic and environmental outcomes. This research provides a novel methodological framework for managing transboundary pollution, offering actionable strategies for achieving sustainable development goals.