Potential of large language model-powered nudges for promoting daily water and energy conservation
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
The increasing amount of pressure related to water and energy shortages has
increased the urgency of cultivating individual conservation behaviors. While
the concept of nudging, i.e., providing usage-based feedback, has shown promise
in encouraging conservation behaviors, its efficacy is often constrained by the
lack of targeted and actionable content. This study investigates the impact of
the use of large language models (LLMs) to provide tailored conservation
suggestions for conservation intentions and their rationale. Through a survey
experiment with 1,515 university participants, we compare three virtual nudging
scenarios: no nudging, traditional nudging with usage statistics, and
LLM-powered nudging with usage statistics and personalized conservation
suggestions. The results of statistical analyses and causal forest modeling
reveal that nudging led to an increase in conservation intentions among
86.9%-98.0% of the participants. LLM-powered nudging achieved a maximum
increase of 18.0% in conservation intentions, surpassing traditional nudging by
88.6%. Furthermore, structural equation modeling results reveal that exposure
to LLM-powered nudges enhances self-efficacy and outcome expectations while
diminishing dependence on social norms, thereby increasing intrinsic motivation
to conserve. These findings highlight the transformative potential of LLMs in
promoting individual water and energy conservation, representing a new frontier
in the design of sustainable behavioral interventions and resource management.
