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Prediction of photovoltaic and solar water heater diffusion and evaluation of promotion policies on the basis of consumers’ choices

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  • Yamaguchi, Yohei
  • Akai, Kenju
  • Shen, Junyi
  • Fujimura, Naoki
  • Shimoda, Yoshiyuki
  • Saijo, Tatsuyoshi

Abstract

This paper proposes an integrated analytical framework consisting of the following three steps: (1) investigation of consumers’ preferences, (2) prediction of technology diffusion by taking into account consumers’ preferences, and (3) estimation of CO2 emission reduction caused by the diffusion of the examined technology. By using this framework, this paper evaluates the policy measures implemented for disseminating photovoltaics and solar water heaters in terms of the contribution to reducing CO2 emissions from the residential sector. We investigated consumer preferences for these technologies as well as the effects of attributes such as installation cost, energy price, energy efficiency, and perception on consumers’ choices. Considering these effects, we developed a model that estimates the diffusion of these technologies into the residential sector of Japan through 2025 and the resulting CO2 emission reduction. We found that the policy measures for the diffusion of photovoltaics that reduce initial cost (e.g., subsidy programs) are more cost effective for reducing CO2 emission than those reducing users’ operating expenditure (e.g., feed-in tariff programs). For solar water heater to be able to reduce the CO2 emissions considerably, the public perception must be improved.

Suggested Citation

  • Yamaguchi, Yohei & Akai, Kenju & Shen, Junyi & Fujimura, Naoki & Shimoda, Yoshiyuki & Saijo, Tatsuyoshi, 2013. "Prediction of photovoltaic and solar water heater diffusion and evaluation of promotion policies on the basis of consumers’ choices," Applied Energy, Elsevier, vol. 102(C), pages 1148-1159.
  • Handle: RePEc:eee:appene:v:102:y:2013:i:c:p:1148-1159
    DOI: 10.1016/j.apenergy.2012.06.037
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    References listed on IDEAS

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    Cited by:

    1. Alderete Peralta, Ali & Balta-Ozkan, Nazmiye & Longhurst, Philip, 2022. "Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach," Applied Energy, Elsevier, vol. 305(C).
    2. Shahriyar Nasirov & Paula Gonzalez & Jose Opazo & Carlos Silva, 2023. "Development of Rooftop Solar under Netbilling in Chile: Analysis of Main Barriers from Project Developers’ Perspectives," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    3. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea," Applied Energy, Elsevier, vol. 197(C), pages 29-39.
    4. Jeong, Gicheol, 2013. "Assessment of government support for the household adoption of micro-generation systems in Korea," Energy Policy, Elsevier, vol. 62(C), pages 573-581.
    5. Baur, Lucia & Uriona M., Mauricio, 2018. "Diffusion of photovoltaic technology in Germany: A sustainable success or an illusion driven by guaranteed feed-in tariffs?," Energy, Elsevier, vol. 150(C), pages 289-298.
    6. de la Hoz, Jordi & Martín, Helena & Miret, Jaume & Castilla, Miguel & Guzman, Ramon, 2016. "Evaluating the 2014 retroactive regulatory framework applied to the grid connected PV systems in Spain," Applied Energy, Elsevier, vol. 170(C), pages 329-344.
    7. Kaur, S. & Pollitt, M. G., 2024. "Farmers preferences for incentives on solar pumps: Evidence from a choice experiment in Punjab," Cambridge Working Papers in Economics 2435, Faculty of Economics, University of Cambridge.
    8. Anita M. Bunea & Pietro Manfredi & Pompeo Della Posta & Mariangela Guidolin, 2019. "What do adoption patterns of solar panels observed so far tell about governments' incentive? insight from diffusion models," Papers 1909.10017, arXiv.org.
    9. Alipour, M. & Salim, H. & Stewart, Rodney A. & Sahin, Oz, 2020. "Predictors, taxonomy of predictors, and correlations of predictors with the decision behaviour of residential solar photovoltaics adoption: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    10. Hoz, Jordi de la & Martín, Helena & Montalà, Montserrat & Matas, José & Guzman, Ramon, 2018. "Assessing the 2014 retroactive regulatory framework applied to the concentrating solar power systems in Spain," Applied Energy, Elsevier, vol. 212(C), pages 1377-1399.
    11. Bunea, Anita M. & Della Posta, Pompeo & Guidolin, Mariangela & Manfredi, Piero, 2020. "What do adoption patterns of solar panels observed so far tell about governments’ incentive? Insights from diffusion models," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    12. Higgins, Andrew & Grozev, George & Ren, Zhengen & Garner, Stephen & Walden, Glenn & Taylor, Michelle, 2014. "Modelling future uptake of distributed energy resources under alternative tariff structures," Energy, Elsevier, vol. 74(C), pages 455-463.
    13. Xintao Li & Xue’er Xu & Diyi Liu & Mengqiao Han & Siqi Li, 2022. "Consumers’ Willingness to Pay for the Solar Photovoltaic System in the Post-Subsidy Era: A Comparative Analysis under an Urban-Rural Divide," Energies, MDPI, vol. 15(23), pages 1-22, November.
    14. Jinah Yang & Daiki Min & Jeenyoung Kim, 2020. "The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    15. Higgins, Andrew & McNamara, Cheryl & Foliente, Greg, 2014. "Modelling future uptake of solar photo-voltaics and water heaters under different government incentives," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 142-155.
    16. Sara Ghaboulian Zare & Reza Hafezi & Mohammad Alipour & Reza Parsaei Tabar & Rodney A. Stewart, 2021. "Residential Solar Water Heater Adoption Behaviour: A Review of Economic and Technical Predictors and Their Correlation with the Adoption Decision," Energies, MDPI, vol. 14(20), pages 1-26, October.
    17. Selvakkumaran, Sujeetha & Ahlgren, Erik O., 2019. "Determining the factors of household energy transitions: A multi-domain study," Technology in Society, Elsevier, vol. 57(C), pages 54-75.
    18. Qingbin Wang & Laurel Valchuis & Ethan Thompson & David Conner & Robert Parsons, 2019. "Consumer Support and Willingness to Pay for Electricity from Solar, Wind, and Cow Manure in the United States: Evidence from a Survey in Vermont," Energies, MDPI, vol. 12(23), pages 1-13, November.
    19. Chayjan, Melika Rezaei & Dehghanian, Farzad & Kakhki, Mohammad Daneshvar, 2024. "Modeling residential photovoltaic adoption: A system dynamics approach for solar energy expansion," Energy Policy, Elsevier, vol. 189(C).
    20. repec:grz:wpsses:2016-02 is not listed on IDEAS
    21. Anita M. Bunea & Mariangela Guidolin & Piero Manfredi & Pompeo Della Posta, 2022. "Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns," Forecasting, MDPI, vol. 4(2), pages 1-21, April.
    22. Balcombe, Paul & Rigby, Dan & Azapagic, Adisa, 2014. "Investigating the importance of motivations and barriers related to microgeneration uptake in the UK," Applied Energy, Elsevier, vol. 130(C), pages 403-418.
    23. Felipe Moraes do Nascimento & Julio Cezar Mairesse Siluk & Fernando de Souza Savian & Taís Bisognin Garlet & José Renes Pinheiro & Carlos Ramos, 2020. "Factors for Measuring Photovoltaic Adoption from the Perspective of Operators," Sustainability, MDPI, vol. 12(8), pages 1-29, April.
    24. van Blommestein, Kevin & Daim, Tugrul U. & Cho, Yonghee & Sklar, Paul, 2018. "Structuring financial incentives for residential solar electric systems," Renewable Energy, Elsevier, vol. 115(C), pages 28-40.
    25. Sanders, Kelly T. & Webber, Michael E., 2015. "Evaluating the energy and CO2 emissions impacts of shifts in residential water heating in the United States," Energy, Elsevier, vol. 81(C), pages 317-327.

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