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Analysing the impact of ENERGY STAR rebate policies in the US

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Abstract

In this paper, we estimate the impact of rebate policies in various US states on the sales share of ENERGY STAR household appliances. We use annual ENERGY STAR sales data for clothes washers, dishwashers, refrigerators and air conditioners from 2001 to 2006 and the variation in the coverage of rebates across US states and over time to identify the impact of rebate policies. We use, at first, a difference-in-differences approach to estimate this impact. Then, we take into account the possibility of rebate policies being endogenous and use instrumental variables approaches in fixed effects panel data regression models. Results suggest that rebate policies increase the sales share of ENERGY STAR household appliances by 3.3 to 6.6 percentage points, and this represents an impact of 9 to 18 % on the mean level of the sales share of ENERGY STAR household appliances in the US between 2001 and 2006. We conclude that rebates on ENERGY STAR appliances increase the uptake of energy-efficient appliances.

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Notes

  1. For a more comprehensive description of the ENERGY STAR programme, please refer to Brown et al. (2002) and McWhinney et al. (2005).

  2. http://www.energystar.gov/index.cfm?c=about.ab_history, website accessed 17 April, 2012

  3. The “rebound effect” is a phenomenon, described by Khazzoom (1980), whereby electricity consumption increases due to an increase in energy efficiency. It is caused by the fact that an increase in the level of energy efficiency leads to a decrease in the price of energy services and, via a substitution effect, an increase in demand for these energy services. This, subsequently, causes an increase in the demand for electricity. This rebound effect is referred to as the direct rebound effect. Indirect rebound effect leads to the energy saving being off-set by more energy usage in another activity. The literature on the rebound effect provides no consensus on the degree of rebound observed. While there are numerous studies, the interested reader may refer to volume 28 of Energy Policy in June 2000 for a more detailed discussion of the rebound effect. For more recent discussions of the rebound effect please refer to, for example, Van den Bergh (2011), Turner (2013), Gillingham et al. (2015), Borenstein (2015) and Saunders (2015).

  4. 1 PJ (Petajoule) = 1015 J. 740 PJ is equivalent to 205.5 × 106 MWh. 1 MWh = 3.6 × 109 J.

  5. 1 EJ (Exajoule) = 1018 J. 4.8 EJ is equivalent to 1.3 × 109 MWh. 1 Tg (Teragram) = 1012 g

  6. The ENERGY STAR programme has been jointly administered by the EPA and the US Department of Energy (DOE) since 1996.

  7. http://www.energystar.gov/index.cfm?c=about.ab_milestones, website accessed 21 August, 2012

  8. See Eto (1996), Nadel and Geller (1996) and Nadel (2000) for a history of utility DSM programmes in the USA.

  9. The panel nature of our data will also mitigate any omitted variable bias arising from omitted variables that exhibit low within-variation.

  10. The information on revenue and sales of individual utility companies are reported annually to the EIA in form 861.

  11. ENERGY STAR website, accessed 24 July, 2008

  12. “[...] America’s largest and most influential grassroots environmental organization”, http://www.sierraclub.org/, website accessed 25 April 2012.

  13. http://www.census.gov/compendia/statab/cats/elections/gubernatorial_and_state_legislatures.html, website accessed 30 September, 2011.

  14. The F statistic for this test is 197.65 which easily exceeds the critical value of 2.32 for the F 10,1071-distribution at the 1 % level of significance.

  15. Wooldridge (2002, p. 939) provides a description of this method.

  16. We have also performed a regression by including the “Fraction of Democratic House members” as an explanatory variable in Eq. 1 and the estimated coefficient is not statistically significant even at the 10 % level of significance. This also indicates that the variable is appropriate as an instrument since it does not influence our dependent variable directly.

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Correspondence to Souvik Datta.

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We would like to thank participants at the ETH Zürich Lunch Seminar Series, the Annual Conference of European Association of Environmental and Resource Economists, the Annual Congress of the Swiss Society of Economics and Statistics and the Empirical Methods in Energy Economics Workshop for helpful comments. All omissions and remaining errors are ours alone.

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Appendix

Appendix

Table 5 Coverage of rebate policies in the USA by state and year for clothes washers (CW), dishwashers (DW), refrigerators (RF) and air conditioners (AC)
Table 6 Fixed effects models of ENERGY STAR share
Table 7 First stage of IV/2SLS estimation
Table 8 Fixed effects models of ENERGY STAR share
Table 9 First stage of IV/2SLS estimation
Table 10 Fixed effects models of ENERGY STAR share
Table 11 First stage of IV/2SLS estimation

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Datta, S., Filippini, M. Analysing the impact of ENERGY STAR rebate policies in the US. Energy Efficiency 9, 677–698 (2016). https://doi.org/10.1007/s12053-015-9386-7

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