1. Modeling Household Energy Consumption and Energy-efficient Technology Adoption Jia Li, Ph.D. U.S. Environmental Protection Agency USAEE Annual Conference October 11 th, 2011

2. Motivation Technological change and energy efficiency are important factors of energy and climate change policy To avoid dangerous effects of climate change and stabilize carbon concentration at 450ppm by 2030, over half of the global CO 2 emission reductions will come from greater energy efficiency in world economy (IEA 2008) Diffusion and adoption of energy-efficient technology play a crucial role

3. The Energy Paradox Rates of diffusion and adoption of apparently cost- effective energy efficiency investments have been slow (e.g., Jaffe & Stavins 1994, Howarth & Sanstad 1995, Hasset & Metcalf 1993 &1996) Incomplete information Bounded rationality Principal/agent problem Low energy prices Transaction costs

4. The Role of Public Policy The role and effectiveness of policy instruments in promoting energy efficiency and GHG reductions are much debated e.g., Jaffe et al. 2003, Newell et al. 1999, Metcalf 1995, Howarth et al. 2000 In general, economists favor market-based mechanisms (e.g., energy taxes/emissions taxes) over regulatory approach e.g., Jaffe et al. 2003, Parry et al. 2010

5. Research Questions Factors influence consumer adoption of energy-efficient technology e.g., energy prices, initial capital costs, income, household characteristics, and energy and environmental policy The effectiveness of alternative policy instruments in encouraging short-run and long-run household energy efficiency behavior e.g., carbon policy, technology standards, financial incentives, information provision

6. Main Contributions A unified modeling framework of household technology choice and energy consumption (a discrete/continuous model) First application of second-order translog flexible functional form in joint discrete/continuous modeling Robust empirical analysis of household energy use behavior using a unique household-level dataset Insights on the effectiveness of alternative policy instruments in household energy efficiency and technology adoption

7. Model Setup where Z =a composite market good, E j =energy use j, j = 1,…,J, θ =household/housing characteristics.

8. Model Setup (2) Household energy production where i =technology choice index, x l(i),j =fuel input associated with technology i, φ ij =average energy-efficiency coefficient of technology i

9. Household Decisions Short-run: technology fixed, derived demand for fuel Long-run: technology choice

10. Short-run fuel demand Budget share equations Numeraire: Fuels: where μ l = disturbance in fuel use decisions

11. Long-run Technology Choice Assumption: ε ij are identically and independently distributed (i.i.d.) and follow extreme value (EV) type I distributions Discrete choice probability (logit):

12. Data 2,408 households in the 2003 California Statewide Residential Appliance Saturation Study (RASS) Utility energy tariffs Technology capital cost and energy performance Policy programs (e.g., standards, Energy Star, and financial incentives)

13. Estimated Equations A system of simultaneous equations that explicitly models four energy uses (space heating, water heating, clothes washing, and clothes drying) short-run demand for electricity and natural gas long-run choices of clothes washers, water heaters, space heating systems, and clothes dryers

14. Estimation Strategy A two-step limited information maximum likelihood (LIML) approach Recursive structure between the discrete and continuous equations First, the system of short-run demand equations is estimated using iterated feasible generalized nonlinear least squares method (equivalent to ML) Second, the long-run technology choice equations are estimated using ML and parametric commonality constraint is imposed from first step

15. Key Findings Conformity between the short-run and long-run models sustains in three out of four energy uses Estimation of short-run demand system yields satisfactory statistical properties Estimates of short-run income and price elasticities are in reasonable ranges

16. Short-run Demand – Price Elasticities ModelMean Standard Deviation MinimumMaximum Electricity Model 1a 0.25330.14800.15382.8310 Model 1b -0.13130.0609-0.5339-0.0474 Model 1c -0.06410.0831-0.7499-0.0359 Model 1d -0.13430.1410-1.0913-0.0677 Natural Gas Model 1a -0.20600.4769-0.33112.8901 Model 1b -0.14730.1992-0.26111.0758 Model 1c -0.12620.1107-0.34320.6230 Model 1d -0.11880.1439-0.17750.8373

17. Short-run Demand – Income Elasticities ModelMean Standard Deviation MinimumMaximum Electricity Model 1a 0.08160.0843-0.26232.1148 Model 1b 0.34360.08040.25600.8703 Model 1c 0.50320.06580.42671.0038 Model 1d 0.41820.17860.31891.6974 Natural Gas Model 1a -0.06980.0358-0.09330.1417 Model 1b -0.71270.2457-0.95370.8085 Model 1c -0.30960.1201-0.40290.4024 Model 1d -0.42010.1639-0.59630.5113

18. Long-run Technology Choices (1) The study confirms two important market failures The principal/agent problem Information imperfection Overall, differences in capital costs and expected operating costs do not significantly influence choices

19. Long-run Technology Choices (2) Energy Star is the most significant factor influencing adoption of energy-efficient clothes washers, followed by energy efficiency standards Household characteristics (e.g., home ownership and education) also strongly influence technology choices

20. Policy Implications Information provision appears to be highly effective in influencing household technology choice decisions Surprisingly, financial incentives (e.g., rebates or tax credits) are far less effective Energy price increases have limited impacts on short-run and long-run energy efficiency behavior Market-based policy instruments (e.g., carbon cap-and-trade programs) have limited impacts Energy policy needs to distinguish different segments of the markets