| Popis: |
The year 2022 was one of the warmest years ever recorded, illustrating theaccelerating effects of climate change. There is widespread scientific consensus that action must be taken to reduce greenhouse gas emissions from individual behavior, so policymakers increasingly use behavioral interventions to encourage consumers’ adoption of sustainable behaviors. However, changing behavior is difficult and interventions that have the goal of increasing sustainable behavior are often not effective. Predominant "one-size-fits-all" approaches that provide the same interventions to all consumers fail to take into account that consumers have different motives for engaging in sustainable behaviors and often respond differently to interventions. The goal of this project is to develop a deep machine learning approach that policymakers can use to personalize behavioral interventions. We combine lab studies and a large field experiment to design an approach for targeting interventions that accounts for individual responsiveness to interventions, motives for and against sustainable behaviors, as well as interactions between sustainable behaviors. The insights from this project advance our theoretical understanding of heterogeneous treatment effects towards sustainability and provide practical guidance for policymakers to trigger behavioral change more effectively. |
