LLM-generated personalized nudges for improving pro-environmental behavior: Field evidence from resource conservation
Encouraging pro-environmental behavior remains a major challenge for sustainable cities. Conventional feedback nudges can show individuals how their current behavior compares with environmental goals but often provide limited guidance on what to do differently in daily life. This study examines whether supplementing weekly feedback on participants' behavior with LLM-generated personalized action suggestions improves pro-environmental behavior, using daily electricity and hot-water conservation as a case study. We developed an LLM agent that generated weekly conservation messages from participant profiles, recent consumption records, and prior interaction history, combining a usage report ...
| Authors | Zonghan Li, Yi Liu, Chunyan Wang et al. |
|---|---|
| Primary category | cs.CY |
| Published | 4 April 2026 |
| Last updated at source | 28 July 2026 |
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