Integrating coevolutionary strategies and risk preferences: a novel supervision insight for pollutant abatement

Abstract

The importance of pollutant abatement has been steadily growing in recent times, prompting an increased focus on developing effective regulatory mechanisms. This paper introduces a novel approach by combining theories of evolutionary games and opinion dynamics to formulate a coevolution model of game and preference. Recognizing the challenges posed by limited supervision ability and enterprises’ heterogeneous risk preferences, we propose a smart supervision mechanism. This mechanism incorporates the concepts of whitelist capability and observation period to establish intelligent supervision. Simulation results demonstrate the regulator’s ability to accurately discern enterprises’ preferences based on decision-making differences. The smart supervision mechanism proves to be more effective in achieving pollutant abatement goals compared to random supervision. Furthermore, our findings indicate that with higher supervision ability, increasing whitelist capability enhances cooperation rates. Conversely, lower supervision ability necessitates a shorter observation period and increased whitelist capability to achieve optimal pollutant abatement results. The study highlights that enterprises with a high cooperation rate experience more significant benefits, while risk-seeking enterprises benefit less due to heightened regulator attention at the same cooperation rate.