Factor analysis and policy simulation of domestic waste classification behavior based on a multiagent study-Taking Shanghai's garbage classification as an example

摘要

Sorting garbage and recycling renewable resources is a new and effective means to address the "garbage siege." To examine residents' classification behaviors and scientifically assess effects of policies, this study comprehensively analyzes factors influencing residents' classification behavior at the government, resident and societal dimensions and construct multiagent decision-making functions and an interaction model by Anylogic platform. The empirical results show publicity and education have the most significant impacts on classification behavior followed by classification infrastructure and subjective norms. The simulation results show publicity and education, charging policies and government subsidies significantly transform unclassified residents into classified residents and all three approaches double rates of classification behavior. From perspectives of multisubject interaction and multicenter governance, this study analyzes motivations behind classification behavior from a micro level and quantifies effects of policies from a macro level, providing a common research method and paradigm that can be used in behavior research and policy formulation.