The “energy-efficiency gap” is a topic that has received much attention in the academic literature. While the role of market and behavioural failures have been discussed at length, much less focus has been on quantifying the magnitude of model error, misspecification and uncertainty that contributes to the gap potentially not being as large as some would suggest (Fowlie et al. (2015); Alcott and Greenstone, 2017.
A better understanding of the variation in returns to energy efficiency, the extent to which this varies over time and for different household types is a key factor in understanding both why households may be reluctant to invest, and in providing more accurate policy evaluations. Recent research has confirmed the importance of time-scale when examining the effect of building energy codes – Kotchen (2017) found noticeable differences in the energy savings associated with building code changes depending on whether the policy was evaluated three or eleven years after implementation. However, evaluations of energy efficiency investments tend to consider only the short-term effects, usually a window of 1-2 years on either side of the installation.
If energy savings vary over time, this could affect the accuracy of measurement and the attractiveness of the investment. Further, variations in energy prices both before and after the installation may affect both expectations and realisations of the investment’s net present value (NPV).
This research contributes by providing an extensive analysis of the magnitude of uncertainty and heterogeneity that exists in the energy savings associated with installing different energy efficiency measures. We exploit an extremely large database of home energy efficiency upgrades and metered energy consumption, covering over four million households and a period of eight years. By combining statistical matching and a range of panel econometric estimators we control for unobserved heterogeneity and selection into various government schemes which funded the upgrades. The database covers the universe of households entering into energy efficiency schemes administered by energy suppliers in the UK, thus reducing the potential for site-selection bias which can affect the generalisability of results from programme evaluations (Allcott, 2015).
The data allows us to examine the variation in performance depending on when measures were installed; how they perform over time; how this varies by dwelling and socioeconomic characteristics; how the NPV of different investments varies under a range of energy price scenarios; and the distributional effects of the principal measures installed through UK energy efficiency policies from 2005-2012.
Results indicate significant cross-sectional and temporal variation in energy savings; that the persistence of savings varies by the type of measure installed and the socioeconomic characteristics of the household. We find that for the typical UK dwelling, engineering models on which much policy evaluations are based, can overstate actual savings by more than 70 percent in some cases. The measures are generally still NPV positive under a range of price scenarios, but the returns are much lower than expected. This is important for both evaluation of policies to fund energy efficiency and for improving our understanding of the range of uncertainties that might exist in the returns to energy efficiency investments. As this uncertainty is then compounded by uncertainty in energy prices, this provides a further insight into why the energy-efficiency gap might be smaller than previous estimates. This research also raises concerns over distributional factors given how the costs of policies are subsequently levied on households.
Tags: Energy efficiencyMcCoy-Why-the-EE-gap-is-smaller-than-we-think.pptx 1.67 MBMcCoy-Further-bridging-the-energy-efficiency-gap-heterogeneity-model-erro-amd-time-consistency.pdf 404.97 KB