Title: Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives,
Abstract: Government agencies offer economic incentives to citizens for conservation
actions, such as rebates for installing efficient appliances and compensation
for modifications to homes. The intention of these conservation actions is
frequently to reduce the consumption of a utility. Measuring the conservation
impact of incentives is important for guiding policy, but doing so is
technically difficult. However, the methods for estimating the impact of public
outreach efforts have seen substantial developments in marketing to consumers
in recent years as marketers seek to substantiate the value of their services.
One such method uses Bayesian Stuctural Time Series (BSTS) to compare a market
exposed to an advertising campaign with control markets identified through a
matching procedure. This paper introduces an extension of the matching/BSTS
method for impact estimation to make it applicable for general conservation
program impact estimation when multi-household data is available. This is
accomplished by household matching/BSTS steps to obtain conservation estimates
and then aggregating the results using a meta-regression step to aggregate the
findings. A case study examining the impact of rebates for household turf
removal on water consumption in multiple Californian water districts is
conducted to illustrate the work flow of this method.