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Application of the economic theory of self-control to model energy conservation behavioral change in households

Author

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  • Lundgren, Berndt

    (Department of Real Estate and Construction Management, Royal Institute of Technology)

  • Schultzberg, Mårten

    (Department of Real Estate and Construction Management, Royal Institute of Technology)

Abstract

Smart meters and in-house displays hold a promise of energy conservation for those who invest in such technology. Research has shown that households only have a limited interest in such technology and information is thus often neglected, with rather limited energy savings. Surprisingly few empirical investigations have a theoretical foundation that may explain what is going on from a behavioral perspective. In this study the economic theory of self-control is used to model energy-efficient behavior in middle-income households in Sweden. Our results show that different levels of energy-efficient behavior do not really have any impact on the actual consumption levels of electricity. Instead, different beliefs exist of being energy-efficient, but the households do not act accordingly. Our results suggest that the payment time period should be changed to stimulate the monitoring of bills and to introduce a gaming strategy to change incentives for energy conservation.

Suggested Citation

  • Lundgren, Berndt & Schultzberg, Mårten, 2019. "Application of the economic theory of self-control to model energy conservation behavioral change in households," Working Paper Series 19/1, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    • Handle: RePEc:hhs:kthrec:2019_001
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    References listed on IDEAS

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    1. Thaler, Richard H & Shefrin, H M, 1981. "An Economic Theory of Self-Control," Journal of Political Economy, University of Chicago Press, vol. 89(2), pages 392-406, April.
    2. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    3. Sexton, Richard J & Johnson, Nancy Brown & Konakayama, Akira, 1987. "Consumer Response to Continuous-Display Electricity-Use Monitors in a Time-of-Use Pricing Experiment," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(1), pages 55-62, June.
    4. Henryson, Jessica & Hakansson, Teresa & Pyrko, Jurek, 2000. "Energy efficiency in buildings through information - Swedish perspective," Energy Policy, Elsevier, vol. 28(3), pages 169-180, March.
    5. Bernard, Jean-Thomas & Bolduc, Denis & Yameogo, Nadège-Désirée, 2011. "A pseudo-panel data model of household electricity demand," Resource and Energy Economics, Elsevier, vol. 33(1), pages 315-325, January.
    6. Telles Esteves, Gheisa Roberta & Cyrino Oliveira, Fernando Luiz & Antunes, Carlos Henggeler & Souza, Reinaldo Castro, 2016. "An overview of electricity prepayment experiences and the Brazilian new regulatory framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 704-722.
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    Cited by:

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    2. Chou, Jui-Sheng & Truong, Dinh-Nhat & Kuo, Ching-Chiun, 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, Elsevier, vol. 224(C).
    3. Jun Bai & Shixiang Li & Nan Wang & Jianru Shi & Xianmin Li, 2020. "Spatial Spillover Effect of New Energy Development on Economic Growth in Developing Areas of China—An Empirical Test Based on the Spatial Dubin Model," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    4. Marlene Ofelia Sanchez-Escobar & Julieta Noguez & Jose Martin Molina-Espinosa & Rafael Lozano-Espinosa & Genoveva Vargas-Solar, 2021. "The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review," Energies, MDPI, vol. 14(20), pages 1-28, October.
    5. Marlene Ofelia Sanchez-Escobar & Julieta Noguez & Jose Martin Molina-Espinosa & David Escobar-Castillejos & Sergio Ruiz-Loza, 2023. "Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings," Energies, MDPI, vol. 16(19), pages 1-39, September.

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    More about this item

    Keywords

    Energy use; Energy efficient; In-house displays; mediation model; smart meters; two-level time series;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • R22 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other Demand

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