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mike.blackhurst@austin.utexas

Consistent RESIDENTIAL Efficiency Improvements ACROSS END-USES: Theoretical and Empirical Insights Mike Blackhurst Assistant Professor The University Of Texas At Austin Civil, Architectural, & Environmental Engineering. mike.blackhurst@austin.utexas.edu.

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mike.blackhurst@austin.utexas

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  1. Consistent RESIDENTIAL Efficiency Improvements ACROSS END-USES: Theoretical and Empirical InsightsMike BlackhurstAssistant ProfessorThe University Of Texas At AustinCivil, Architectural, & Environmental Engineering mike.blackhurst@austin.utexas.edu

  2. Multiple Perspectives on Technical Efficiency What happens if you double the efficiency of your air conditioner? The technologist says, “You use half the energy.” The economist says, “You turn down the thermostat.” The social scientist says, “Who made the decision?”

  3. The “Rebound Effect” • aka “Jevon’s paradox” or “the energy efficiency paradox” • Efficiency decreases resources needed for service • Efficiency also decreases the cost of service, which… • Induces income and substitution effects and… • Likely other behavioral responses and drivers

  4. Rebound Terminology

  5. Magnitude of Rebound Debated Net Energy Elasticity (% Change in Energy / % Change in Efficiency) Technically feasible energy savings

  6. Single-Service Rebound Model • Start with technical definition of efficiency: • Direct rebound usually estimated as own-price elasticity of demand • Indirectrebound (re-spending) is estimated by modeling by income and substitution effects in response to a discrete efficiency change

  7. Challenge to Single Service Model Modified from Blackhurst and Ghosh (under review)

  8. Two Service Model 0 0 0 0

  9. Two Service Model

  10. Two Service Model: Re-Arranged technical response (1st and 2nd order) direct rebound for C (1st order) indirect rebound from Cto T ind.of e correlation (1st order) indirect rebound from j to ifrom ecorrelation (2nd order) indirect rebound from i to j from e correlation (2nd order)

  11. Application of Two-Service Model Would homeowners in more efficient homes drive more? • Include electricity (C) and transportation (T) services • Used constant elasticity of substitution (CES) production function • Can provide draft manuscript for more details

  12. Empirical Assumptions

  13. Rebound Across Resid and Trans Sectors: Driven by Changes in Electricity Efficiency Cross-sector, From trans to resid with c.c. hei(ej) hej (Ei)Ei/E Direct Rebound [hei(Ei)+1] Ei/E Cross-sector, From residto trans with c.c. hei(ej) [hej(Ej)+1]Ej/E Cross-sector (indirect), independent of c.c. hei(Ej) Ej/E Technically feasible elasticity -1(Ei/E + hei(ej)Ej/E) (-1) Energy Elasticity, Short-run response Long-run response Results shown for median income range ($40-$45k)

  14. Rebound Across Resid and Trans Sectors: Driven by Changes in Electricity Efficiency Cross-sector, From trans to resid with c.c. hei(ej) hej (Ei)Ei/E Direct Rebound [hei(Ei)+1] Ei/E Cross-sector, From residto trans with c.c. hei(ej) [hej(Ej)+1]Ej/E Cross-sector (indirect), independent of c.c. hei(Ej) Ej/E Technically feasible elasticity -1(Ei/E + hei(ej)Ej/E) Energy Elasticity, Short-run response Long-run response Results shown for median income range ($40-$45k)

  15. Rebound Across Resid and Trans Sectors: Driven by Changes in Vehicle Efficiency Cross-sector, From residto trans with c.c. Cross-sector, From transto resid with c.c. Direct Rebound, hei(Ei) Cross-sector (indirect), independent of c.c. Technically feasible elasticity Energy Elasticity, Short-run response Long-run response Results shown for median income range ($40-$45k)

  16. Other Behavioral Drivers

  17. Other Behavioral Drivers Do homeowners correlate or compensate drivers of energy technology choice and use? • Limited qualitative insights • Correlation and compensation observed across a variety of “green” behaviors [Thøgersen & Ölander 2003] • Self-reported behavior changes with PV adoption [Keirstead 2007; McAndrews; Schweizer-Reis et al. 2000 ] • Implications for rebound?

  18. Empirical Research • Estimate the impact of marginal technical change within and across end uses on electricity use and rebound • If choose technology A versus • If choose both technology A and technology B

  19. Pecan Street Research Institute • High resolution consumption data • Static data

  20. Representative Sample Data Sample includes one year of monthly electricity consumption for 79 homes

  21. Model Specification • Where • Yitλrepresents monthly electricity consumption • βj are the predictor coefficient fixed effects • βiare the coefficient estimates for random effects • Sijλrepresents a series of household structural factors • Dijλ represents a series of household demographic factors • Bijλrepresents household behaviors and cognitive factors • Xijinteraction terms for different technology choice combinations • Ri represents the household identification codes

  22. Results with No Interaction Terms

  23. Rebound from Marginal Efficiency Gains: Demonstrative Empirical Results

  24. Rebound with Marginal Efficiency Gains Multi-pane windows installed, AC efficiency increased Multi-pane windows installed at indicated AC efficiency

  25. Rebound with Marginal Efficiency Gains

  26. Preliminary PV Results • Order of technical change matters + Statistically significant increase in electricity consumption - Statistically significant decrease in electricity consumption

  27. Implications • Literature is mixed as to whether consumers correlate or compensate valuations across energy technology choice/use • Empirical work suggests consumers MAY leverage efficiency gains for services ACROSS end uses; our results are also mixed • Rebound is relative to the current efficient technical state of the home and order of technical change • These findings suggest the dominant single-service rebound paradigm is misleading

  28. Implications • Consistent efficiency change across end uses can mitigate consumer responses; however… • Consumers can and do expend energy services; thus… • Models of rebound need to recognize service expansion

  29. Implications • The literature assumes PV exclusively replaces conventional grid energy sources; however… • Behavioral implications of PV are entirely unclear • Consumers will treat long-run operating cost of PV as zero • Results are mixed with respect to consumers responses to both efficiency change and installation of PV

  30. Related Ongoing/Future Work • Rebound across resources (water/electricity/natural gas/gasoline) • Comparing Empirical and Estimated Energy Consumption (RECS/BeOpt) • Does Weather Influence the Use of PV for Discretionary Electricity End Uses? • Estimating Total and End-Use Residential Water (Energy) Demands Using Energy (Water) Demands • Comparing the Observed and Estimated Performance of Residential Water Efficient Fixtures and Appliances

  31. Acknowledgements • This work was funded by • The University of Texas at Austin • Bill and Melinda Gates Foundation Fellowship • PhD students • Nour El-ImaneBouhou • Pamela Torres • Alison Wood • MS Students • BrukBerhanu • Neftali Torres • Post doc • Sarah Taylor-Lange

  32. Consistent RESIDENTIAL Efficiency Improvements ACROSS END-USES: Theoretical and Empirical InsightsMike BlackhurstAssistant ProfessorThe University Of Texas At AustinCivil, Architectural, & Environmental Engineering mike.blackhurst@austin.utexas.edu

  33. References • Blackhurst, MF, and NK Ghosh. “The Rebound Effect with Consistent Efficiency Improvements and Implications for Cross-Sector Rebound.” Ecological Economics (submitted for review). • Attari, S. Z., M. L. DeKay, C. I. Davidson, and W. B. de Bruin. 2010. “Public Perceptions of Energy Consumption and Savings.” Proceedings of the National Academy of Sciences 107 (37): 16054–16059. • Thøgersen, J., and F. Ölander. 2003. “Spillover of Environment-Friendly Consumer Behaviour.” Journal of Environmental Psychology 23 (3): 225–236. • Keirstead, J. 2007. “Behavioural Responses to Photovoltaic Systems in the UK Domestic Sector.” Energy Policy 35 (8): 4128–4141. • McAndrews, K. “To Conserve or Consume: Behavior Change in Residential Solar PV Owners.” The University of Texas at Austin, 2012. • Hausman, Jerry A. “Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables.” The Bell Journal of Economics 10, no. 1 (April 1, 1979): 33–54. doi:10.2307/3003318. • Sanstad, Alan H., Carl Blumstein, and Steven E. Stoft. “How High Are Option Values in Energy-Efficiency Investments?” Energy Policy 23, no. 9 (1995): 739–743. • Hartman, R. S. “Self-Selection Bias in the Evolution of Voluntary Energy Conservation Programs.” The Review of Economics and Statistics (1988): 448–458. • Michelsen, C., and R. Madlener. “Homeowners’ Preferences for Adopting Residential Heating Systems: A Discrete Choice Analysis for Germany.” FCN Working Papers (2011). • Cummings, Ronald G., and Laura O. Taylor. “Unbiased Value Estimates for Environmental Goods: A Cheap Talk Design for the Contingent Valuation Method.” The American Economic Review 89, no. 3 (June 1, 1999): 649–665. • Nair, Gireesh, Leif Gustavsson, and KrushnaMahapatra. “Factors Influencing Energy Efficiency Investments in Existing Swedish Residential Buildings.” Energy Policy 38, no. 6 (June 2010): 2956–2963. doi:10.1016/j.enpol.2010.01.033. • Bateman, Ian J., Georgina M. Mace, Carlo Fezzi, Giles Atkinson, and Kerry Turner. “Economic Analysis for Ecosystem Service Assessments.” Environmental and Resource Economics 48, no. 2 (2011): 177–218. • Dahl, C. A. “A Survey of Energy Demand Elasticities in Support of the Development of the NEMS” (1993). http://mpra.ub.uni-muenchen.de/13962/. • Brons, Martijn, Peter Nijkamp, Eric Pels, and Piet Rietveld. “A Meta-Analysis of the Price Elasticity of Gasoline Demand. A SUR Approach.” Energy Economics 30, no. 5 (September 2008): 2105–2122. doi:10.1016/j.eneco.2007.08.004. • Graham, Daniel J., and Stephen Glaister. “The Demand for Automobile Fuel: A Survey of Elasticities.” Journal of Transport Economics and Policy (2002): 1–25. • BLS (U.S. Bureau of Labor Statistics). “Consumer Expenditure Survey,” 2011. http://www.bls.gov/cex/.

  34. Single service rebound model • Using technical definition of efficiency: • Using CES production function

  35. Rebound with Marginal Efficiency Gains Multi-pane windows installed, AC efficiency increased Multi-pane windows installed at indicated AC efficiency

  36. Rebound Across Resid and Trans Sectors: Driven by Changes in Electricity Efficiency Cross-sector, From trans to resid with c.c. hei(ej) hej (Ei)Ei/E Direct Rebound [hei(Ei)+1] Ei/E Cross-sector, From residto trans with c.c. hei(ej) [hej(Ej)+1]Ej/E Cross-sector (indirect), independent of c.c. hei(Ej) Ej/E Technically feasible elasticity -1(Ei/E + hei(ej)Ej/E) (-1) Energy Elasticity, Short-run response Long-run response Results shown for median income range ($40-$45k)

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