University of Verona, Human Sciences Department, Verona, IT, Italy.
University of Klagenfurt, Department of Psychology, Austria.
J Environ Manage. 2021 Sep 15;294:112938. doi: 10.1016/j.jenvman.2021.112938. Epub 2021 Jun 30.
The present study uses agent-based modeling (ABM) to examine the effectiveness of a nudge policy for improving recycling behavior. In our simulation, agents' recycling behavior is computed by components of the Theory of Planned Behaviour (i.e., attitudes, perceived behavioral control, social norms) and influenced by other agents as well as their surrounding (i.e., amount of waste in the area). The simulation, based on real data from a Taiwan community district, confirms realistic recycling trends and demonstrates the usefulness and reliability of ABM as a method to examine the effectiveness of waste management policies. An additional step in our simulation was to manipulate the amount of waste in the community to test the effect of a nudge policy based on social norms. Results showed that the policy increases recycling activity, but predominantly in low waste scenarios. This suggests that nudges, in the form of norm-based policies, can be an effective solution to enhancing people's recycling behavior under specific circumstances.
本研究采用基于主体的建模（ABM）来检验推动政策对改善回收行为的有效性。在我们的模拟中，代理的回收行为是通过计划行为理论的组成部分（即态度、感知行为控制、社会规范）来计算的，并受到其他代理及其周围环境（即该区域的废物量）的影响。该模拟基于来自台湾社区的真实数据，证实了现实的回收趋势，并证明了 ABM 作为检验废物管理政策有效性的方法的有用性和可靠性。我们的模拟中的一个额外步骤是操纵社区中的废物量，以测试基于社会规范的推动政策的效果。结果表明，该政策增加了回收活动，但主要是在低废物情况下。这表明，在特定情况下，以规范为基础的政策推动可以成为增强人们回收行为的有效解决方案。
