What Can Policy-Makers Do to Increase the Effectiveness of Building Renovation Subsidies?

Abstract

Heating is responsible for a substantial share of global energy consumption and still relies strongly on fossil fuels. In order to reduce energy consumption for heating, subsidies for building renovations are a common policy measure in Europe. Policy makers often combine them with information and advice measures. Policy mixes of this kind have been acknowledged widely in the literature, but their effectiveness needs further empirical examination. Based on a survey of the recipients of renovation subsidies and on four focus groups, we examine the (cost) effectiveness of subsidies, as follows: The effectiveness of renovation subsidies was measured by the extent to which receiving subsidies contributed either to the decision to renovate at all, or to the decision to enhance the quality or scope of the renovation. Fifty percent of the recipients surveyed reported that the subsidies contributed to a more energy-efficient renovation than was initially intended. The other fifty percent must be considered as free riders. Multivariate analyses further show that homeowners who used advice services and attributed outstandingly positive characteristics to the policy implementer were more likely to spend subsidies to improve energy efficiency. The findings demonstrate the importance of applying a combination of financial and persuasive policy measures. Additionally, they illustrate the importance of non-financial and non-technical factors, such as the communication competencies of the implementer, when designing policy measures.

Effect of Policy Implementation on Energy Retrofit Behavior and Energy Consumption in a Simulated Neighborhood

“…Although this number has been measured to 10% , it is likely response bias played a big part in reducing this number in the empirical research paper. Other research finds free-rider rates between 7-100%, and it is difficult to establish a "correct" number (Alberini et al 2014;Collins & Curtis 2018;Grösche et al 2009;Nauleau 2014;Rivers & Shiell 2016;Studer & Rieder 2019). Additionally, survey-based free-rider measurements Studer & Rieder 2019) are subject to response bias, artificially lowering the free-rider rate.…”

“…Other research finds free-rider rates between 7-100%, and it is difficult to establish a "correct" number (Alberini et al 2014;Collins & Curtis 2018;Grösche et al 2009;Nauleau 2014;Rivers & Shiell 2016;Studer & Rieder 2019). Additionally, survey-based free-rider measurements Studer & Rieder 2019) are subject to response bias, artificially lowering the free-rider rate. As simulated agents will not alter their reply to make the state continue the subsidy program, they will not have this response bias, thus reporting a higher free-rider rate than real surveys.…”