Modeling household energy consumption and energy efficient technology adoption
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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. Motivation. Technological change and energy efficiency are important factors of energy and climate change policy

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Modeling Household Energy Consumption and Energy-efficient Technology Adoption

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Modeling Household Energy Consumption and Energy-efficient Technology Adoption

Jia Li, Ph.D.

U.S. Environmental Protection Agency

USAEE Annual Conference

October 11th, 2011


  • 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 CO2 emission reductions will come from greater energy efficiency in world economy (IEA 2008)

  • Diffusion and adoption of energy-efficient technology play a crucial role

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

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

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

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

Model Setup


Z=a composite market good,

Ej=energy use j, j = 1,…,J,

θ =household/housing characteristics.

Model Setup (2)

  • Household energy production


    i=technology choice index,

    xl(i),j=fuel input associated with technology i,

    φij=average energy-efficiency coefficient of technology i

Household Decisions

  • Short-run: technology fixed, ‘derived’ demand for fuel

  • Long-run: technology choice

Short-run fuel demand

Budget share equations



where μl = disturbance in fuel use decisions

Long-run Technology Choice

Assumption: εij are identically and independently distributed (i.i.d.) and follow extreme value (EV) type I distributions

Discrete choice probability (logit):


  • 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)

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

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

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

Short-run Demand – Price Elasticities

Short-run Demand – Income Elasticities

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

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

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

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