Monday, November 13, 2023
5:15 PM - 6:45 PM
Encouraging Energy-Efficient Behavior in Households via Lifestyle-Based Norm Messaging Intervention
Session ID
Poster Presentations
Behavior - based Programs
Hoonyong Lee

Residential energy consumption has been a major contributing factor to the climate crisis as energy production is one of the largest sources of greenhouse gas emissions. Communicating with householders about energy-efficient behavior can lead to energy conservation, but it is a challenging and costly process requiring significant time and resources. This study developed a cost-effective approach that groups householders with neighbors having similar energy usage patterns and encourages them to conform to the norm of energy-saving efforts in their neighbor group. This approach provides normative messaging intervention, through a monthly electricity bill, that compares householders’ monthly energy usage with the average of their neighbor group members with the same lifestyle, which helps them better recognize their group and thus increase the intervention effects. This approach was tested through a real-world experiment with over 6,000 householders, evenly assigned into a control group (no intervention) and neighbor-based (intervention with only energy usage comparison), and lifestyle-based (intervention with additional lifestyle information) treatment groups. This study clustered the householders by using two clustering algorithms: (1) an adaptive K-means clustering, grouping householders with similar patterns of energy usage; and (2) a hierarchical clustering, consolidating small clusters, where 90% of householders were clustered into four clusters. Then, statistical analysis was conducted by using the difference-in-difference, which showed that the normative messaging intervention was effective in energy conservation, and the lifestyle-based intervention was more effective, resulting in an average of 1-2% monthly energy usage reduction as compared to the control group. The intervention effect shows statistical significance at 5 to 10 percent levels for each clustering group, which demonstrates that effective messaging interventions can be customized for each clustering group based on their energy usage patterns. This normative messaging intervention can be implemented on a large scale without additional investment, helping to reduce overall residential energy consumption.