Abstract
In this paper, we measure the energy efficiency implicit in residential energy consumption using a panel dataset comprised of 40,246 observations from US households observed over 1997–2009. We fit a stochastic frontier model of the minimum input of energy needed to meet the level of energy services demanded by the household. This benchmarking exercise produces a transient and a persistent efficiency index for each household and each time period. We estimate that the US residential sector could save approximately 10% of its total energy consumption if it reduced persistent inefficiencies and 17% if it were possible to eliminate transient inefficiencies. These figures are in line with recent economy-wide assessments for the USA. Our results suggest that savings in energy use and associated emissions of greenhouse gases may benefit from both policy measures that attain short-run behavioral changes (e.g., nudges, social norms, display of real-time information about usage, and real-time pricing) as well measures aimed at the long run, such as energy-efficiency regulations, incentives on the purchase of high-efficiency equipment, and incentives towards a change of habits in the use of the equipment.
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Notes
See, for example, former President Obama’s strategy at https://www.whitehouse.gov/the-press-office/2015/08/03/fact-sheet-president-obama-announce-historic-carbon-pollution-standards (last accessed 31 August 2015).
The Act also requires the General Services Administration (GSA, a federal agency) to develop policies and practices to implement the measures for the realty services provided by GSA to other government agencies.
See http://www.nyc.gov/html/gbee/html/plan/ll84_scores.shtml (last accessed 27 August 2015).
See https://www.gov.uk/guidance/standard-assessment-procedure and http://www.seai.ie/Your_Building/BER/BER_Assessors/Technical/DEAP/ (last accessed 27 August 2015).
Filippini and Hunt (2012), for example, note that the ranking of countries in terms of energy intensity of GDP changes dramatically once a proper estimate of energy efficiency is obtained that takes into account the composition of economic activities within the country’s economy and other factors.
Earlier empirical work has used stochastic frontiers for other sectors and the whole economy. For instance, Buck and Young (2007) use a stochastic frontier model to estimate the level of energy efficiency of a sample of Canadian commercial buildings, whereas Filippini and Hunt (2011) focus on the economy-wide level of energy efficiency of OECD countries. Zhou et al. (2012) estimate a stochastic frontier model using an energy distance function for 21 OECD countries using 2001 data. Boyd (2008) estimates an energy input distance function using data on the energy consumption of 37 firms from 1992 to 1997. Zhou and Ang (2008) use DEA, a nonparametric approach, to examine the energy efficiency performance of 21 OECD countries over 5 years (1997–2001). Wei et al. (2009) use DEA and panel data to estimate the level of energy efficiency of Chinese provinces.
In an energy demand model specification, the “Underlying Energy Demand Trend” can be represented by a time trend variable or by time dummies. These variables should capture the effect of technical progress (improved energy efficiency) and non-technical progress exogenous factors that influence all households in the same way. To note, these two factors are conflicting. The technical progress is expected to reduce energy demand trough increased energy efficiency in energy-using equipment and appliances. On the other hand, the non–technical progress exogenous factors, for instance a general change in the behavior of the people, sometimes tend to work in the opposite direction.
To note, approximately 60% of the energy consumption is for space heating, water heating, and air conditioning whereas washing, cooking, and lighting account for 40%.
In the microeconomics literature, there are three approaches to estimate the level of energy efficiency (the input requirement function, the sub-vector input distance function, and the energy demand frontier function). For a presentation of these approaches, see Filippini and Hunt (2015b).
In theory, it would be more appropriate to estimate a system of input demand functions. In practice, however, we do not have detailed information on the stock of appliances, and heating and cooling systems, or the breakdown of energy use by type of equipment, in our dataset.
In an input demand frontier function, the level of efficiency is measured as deviations in input from this frontier and not from an isoquant. See Filippini and Hunt (2015b).
See Kumbhakar and Lovell (2000, p. 148) for a discussion on the interpretation of the efficiency in an input demand function.
For a discussion on this issue, see Schmidt and Lovell (1979).
This indicator provides the level of inefficiency in the use of energy and varies from 0% to infinity. From this indicator, it is also possible to compute an indicator of the level of efficiency in the use of energy, the energy efficiency, which varies from 0 to 100%.
Imposing a distribution is a strong assumption for EF. However, this assumption is crucial for the identification of the efficiency for each household. The half-normal distribution is standard in the production frontier literature. Alternative distributions are the truncated normal or the gamma distribution. See Kumbhakar and Lovell (2000, p. 148) for a discussion.
These were all national surveys.
This includes single-family homes and duplexes/townhomes with at most two dwelling units. As of 2009, these structures accounted for 71.3% of the total dwelling units in the USA (see http://www.census.gov/compendia/statab/2012/tables/12s0989.pdf, last accessed 27August 2015).
All these measures are based on the conditional mean of the efficiency term. See also Greene (2008).
We used the software NLOGIT.
Lambda (λ) is the ratio of the standard deviation of the inefficiency term to the standard deviation of the stochastic term and gives information on the relative contribution of u it and v it on the decomposed error term ε it . In this case, it shows that the one-sided error component is relatively large.
Given the use of a log-log functional form, most of the coefficients are elasticities.
The REM does not prevent households from using less energy by adopting new technologies over time. This possibility is captured by the UEDT in the form of year dummies. Most of these year dummies have negative coefficients.
As discussed in detail in Greene (2008, pp. 184-185), as efficiency estimation in stochastic frontier models is based on a conditional distribution of uit|εit, conventional likelihood-based inference does not hold and it is not possible to construct a conventional confidence interval like it is usually done for other econometric models. For this reason, confidence intervals are not reported.
For example, an improvement of 5 percentage points would bring a household from, say, 0.75 to 0.80. When the energy efficiency improvement being considered would bring a household over 100% efficiency, we set it to 100%. We ignore any rebound effect associated with energy efficiency improvements. Our calculation assumes that the energy efficiency improvements are attained only in the use of electricity.
The eGRID documentation recommends using non-baseload emissions rates for calculations related to energy efficiency improvements. See http://www.epa.gov/cleanenergy/energy-resources/egrid/ (last accessed 27 August 2015).
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Alberini, A., Filippini, M. Transient and persistent energy efficiency in the US residential sector: evidence from household-level data. Energy Efficiency 11, 589–601 (2018). https://doi.org/10.1007/s12053-017-9599-z
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DOI: https://doi.org/10.1007/s12053-017-9599-z