Impact of pollution-related punitive measures on the adoption of cleaner production technology: Simulation based on an evolutionary game model

Abstract

China has recently implemented strict punitive measures on the polluting behavior of enterprises. These measures have had immediate benefits, but their long-term effects remain unknown. This study determined the potential impact of punitive measures on the clean transition behavior of enterprises under different policy scenarios in order to explore a framework for optimizing punitive measures. We built a network-based evolutionary game model to simulate the group behavior of enterprises regarding their adoption of cleaner production technology (CPT). The model is multi-dimensional and considers punitive measures by introducing three key attributes: intensity, coverage, and accuracy. The results indicate that enhancing the intensity and coverage of punishment can promote the diffusion of CPT in enterprises. However, it will reduce the average revenue of such enterprises, with adverse effects on economic development. By contrast, improving the accuracy of punitive measures will promote the diffusion of CPT without negative effects on revenue. Moreover, improving accuracy alongside the other two attributes conjointly will help to enlarge the comprehensive benefits. Therefore, we argue that the short-term performance and long-term benefits of clean transition should be considered when designing punitive measures, while the optimization of attribute parameters is crucial.