Energy-saving behavior of urban residents in China: A multi-agent simulation

Abstract

Abstract With recent improvements in residents’ quality of life and the implementation of the two-child policy, guiding the energy-saving behavior of urban residents has become a focus for achieving the national goals of the sustainable development strategy in China. Considering the subjective initiatives of individuals in a realistic environment is the key to studying energy-saving behavior and guiding policy making. This study builds a simulation model of the energy-saving behavior of urban residents using agent-based modeling and simulation (ABMS) methods, which are based on complex adaptive theory. By means of artificial neural networks and the Netlogo simulation platform, the subsequent effect of behavioral outcomes due to the short- and long-term influence of energy-saving behavior and intentions is analyzed in different policy situation. The results show that energy-saving intentions and behavior are poorly matched in the absence of an external policy framework. In the optimal policy situation, residents’ energy-saving intentions and behavior have improved significantly. Policies can significantly encourage energy-saving intentions to become behavior. Different kinds of situational factors have different effects on intentions and the four types of energy-saving behavior. Finally, relevant policy implications are proposed based on analysis of the simulation results.