Rebate Program Participation and Daily Energy Use Profiles: Evidence from Residential Smart Meter Data

• In Event: How Policy Design and Social Context Shape Energy Behavior and Outcomes

Abstract

Rebates are a widely used policy tool for encouraging the adoption of energy-efficient appliances by lowering upfront costs. Although such programs can increase adoption, their policy value depends on whether they reduce energy use in practice rather than simply expand appliance ownership. Prior research suggests that efficiency gains do not always translate into lower consumption, because lower effective operating costs or improved convenience may increase demand for the energy services those appliances provide. Yet there is still limited evidence on how rebate participation changes household electricity-use patterns after adoption.

This study examines the effects of rebate participation in the City of Tallahassee’s residential energy-efficiency rebate program, focusing on kitchen and laundry-related appliances, including freezers, clothes washers, dishwashers, and refrigerators. Using 30-minute smart meter data for all households served by the municipal utility, we construct hourly measures of electricity consumption and estimate staggered difference-in-differences models comparing rebate participants with matched non-participants. This design allows us to evaluate not only whether electricity use changes after adoption, but also whether those changes are distributed unevenly across the day and across household subgroups.

We find that adoption of kitchen and laundry-related appliances is associated with higher household electricity consumption on average, with the increase driven primarily by less vulnerable households. The hourly results suggest that post-adoption effects are not uniform across the day. These patterns suggest that the adoption of energy-efficient appliances changes the timing of household electricity use over the course of the day. Subgroup analyses further indicate heterogeneous post-rebate responses across demographic and housing characteristics. The clearest differences appear for households with children under age three and for households in larger dwellings. In the under-3 comparison, the aggregate post-treatment effect is positive, and the hourly models indicate that subgroup differences are concentrated in the early morning and late afternoon.  In contrast, the poverty-based comparison shows weaker and less robust evidence of differential effects.

These findings have three policy implications. First, appliance efficiency improvements do not necessarily reduce total electricity use, particularly among households in larger dwellings and among less vulnerable subgroups. Second, heterogeneous responses across subgroups suggest that increased post-adoption consumption should not automatically be interpreted as wasteful rebound; in some contexts, it may reflect changes in household routines or increased demand for appliance-related services. Third, evaluating rebate programs requires attention not only to adoption outcomes, but also to post-adoption consumption patterns and subgroup heterogeneity. Hourly models are especially valuable in this regard because they reveal when electricity use changes over the day and capture temporal heterogeneity in post-adoption demand that aggregate measures would otherwise obscure.

Author

• Xue Gao, Florida State University
  Presenting Author