Testing a norm-based policy for waste management: An agent-based modeling simulation on nudging recycling behavior

Andrea Ceschi (Corresponding author), Riccardo Sartori, Stephan Dickert, Andrea Scalco, Elena M. Tur, Francesco Tommasi, Keren Delfini

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)
521 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number112938
Number of pages10
JournalJournal of Environmental Management
Volume294
DOIs
Publication statusPublished - 15 Sept 2021

Keywords

  • Agent based modelling
  • Norm-based policy
  • Nudges
  • Recycling behavior
  • Social norms
  • Theory of planned behavior
  • Policy
  • Reproducibility of Results
  • Waste Management
  • Humans
  • Systems Analysis
  • Taiwan
  • Recycling

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