Modeling the impacts of policy measures on resident's PM2.5 reduction behavior: an agent-based simulation analysis.

With the rapid economic growth of China, the increasingly serious environmental problems of haze pollution have become a large concern. Urban resident's PM2.5 reduction behavior contributes significantly to Chinese haze pollution control. Resident-level policy measures are beneficial for encouraging residents to engage in PM2.5 reduction behaviors. The current research aims to explore the long-term intervention effects of three types of policies (i.e., command and control policies, economic incentive policies and education-guided policies) on resident's PM2.5 reduction intention and actual behavior. Based on the agent-based modeling and simulation approach, a resident's PM2.5 reduction behavioral simulation model is developed, and data adopted from a questionnaire survey are analyzed. The simulation results show that resident's PM2.5 reduction intention is motivated by the interactions among resident agents, and it eventually stabilizes at a higher level (from 4.11 to 4.48). Moreover, the effects of the three types of policy measures on PM2.5 reduction behavior vary depending on the specific scenarios. With respect to single-policy scenarios, these policies all enhance the actual resident's PM2.5 reduction behavior over the long term. The effects of command and control policies (M = 3.42) and education-guided policies (M = 3.44) are much better than those of the economic incentive policies (M = 3.15). Regarding policy combination scenarios, a combination of economic incentive policies and education-guided policies (M