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Willingness to Pay for Home Energy Management Systems: A Survey in New York and Tokyo

Author

Listed:
  • Ayu Washizu

    (Faculty of Social Sciences, Waseda University, 1-6-1, Nishiwaseda Shinjyuku, Tokyo 169-8050, Japan)

  • Satoshi Nakano

    (The Japan Institute for Labour Policy and Training, 4-8-23, Kamishakujii, Nerimaku, Tokyo 177-8502, Japan)

  • Hideo Ishii

    (Advanced Collaborative Research Organization for Smart Society, Waseda University, 3-4-1, Okubo Shinjyuku, Tokyo 169-8555, Japan)

  • Yasuhiro Hayashi

    (Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 3-4-1, Okubo Shinjyuku, Tokyo 169-8555, Japan)

Abstract

This study evaluates the acceptability of home energy management systems (HEMS) in New York and Tokyo using a questionnaire survey. We investigated three basic functions of HEMS: money saving, automatic control, and environmental impact, and then quantified people’s propensity to accept each of these three functions by measuring their willingness to pay. Using the willingness to pay results, we estimated the demand probability under a given usage price for each of the three functions of home energy management systems and analyzed how socio-economic and demographic factors influence the demand probability. The demand probability related to a home energy management system function decreases as the usage price of the function increases. However, depending on people’s socio-economic characteristics, the rate of decrease in demand probability relative to the rate of increase in usage price varies. Among the three functions of home energy management systems, we found that the automatic control function showed the highest demand probability in New York and Tokyo, emphasizing the significance of an automatic control function. In New York, when the home energy management system has an automatic control function, its demand probability increases, which is further enhanced if people trust their utility company. In Tokyo, when a home energy management system has an environmental impact function, its demand probability increases at a given price. People in Tokyo have anxieties related to new technologies such as home energy management systems. Therefore, it is necessary to enhance their comprehension of a home energy management systems to address this anxiety.

Suggested Citation

  • Ayu Washizu & Satoshi Nakano & Hideo Ishii & Yasuhiro Hayashi, 2019. "Willingness to Pay for Home Energy Management Systems: A Survey in New York and Tokyo," Sustainability, MDPI, vol. 11(17), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4790-:d:263298
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    References listed on IDEAS

    as
    1. Bayram, Islam Safak & Ustun, Taha Selim, 2017. "A survey on behind the meter energy management systems in smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1208-1232.
    2. Nichols, Brice G. & Kockelman, Kara M., 2014. "Life-cycle energy implications of different residential settings: Recognizing buildings, travel, and public infrastructure," Energy Policy, Elsevier, vol. 68(C), pages 232-242.
    3. Rainer Winkelmann & Stefan Boes, 2006. "Analysis of Microdata," Springer Books, Springer, number 978-3-540-29607-2, December.
    4. F. Vidogbéna & A. Adégbidi & R. Tossou & F. Assogba-Komlan & T. Martin & M. Ngouajio & S. Simon & L. Parrot & S. T. Garnett & K. K. Zander, 2016. "Exploring factors that shape small-scale farmers’ opinions on the adoption of eco-friendly nets for vegetable production," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 18(6), pages 1749-1770, December.
    5. Yamamoto, Yoshihiro, 2015. "Opinion leadership and willingness to pay for residential photovoltaic systems," Energy Policy, Elsevier, vol. 83(C), pages 185-192.
    6. Li, Rongling & Dane, Gamze & Finck, Christian & Zeiler, Wim, 2017. "Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands," Applied Energy, Elsevier, vol. 203(C), pages 623-634.
    7. Balta-Ozkan, Nazmiye & Davidson, Rosemary & Bicket, Martha & Whitmarsh, Lorraine, 2013. "Social barriers to the adoption of smart homes," Energy Policy, Elsevier, vol. 63(C), pages 363-374.
    8. Schlomann, Barbara & Schleich, Joachim, 2015. "Adoption of low-cost energy efficiency measures in the tertiary sector—An empirical analysis based on energy survey data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1127-1133.
    9. Gangale, Flavia & Mengolini, Anna & Onyeji, Ijeoma, 2013. "Consumer engagement: An insight from smart grid projects in Europe," Energy Policy, Elsevier, vol. 60(C), pages 621-628.
    10. Stigka, Eleni K. & Paravantis, John A. & Mihalakakou, Giouli K., 2014. "Social acceptance of renewable energy sources: A review of contingent valuation applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 100-106.
    11. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    12. Oltra, Christian & Boso, Alex & Espluga, Josep & Prades, Ana, 2013. "A qualitative study of users' engagement with real-time feedback from in-house energy consumption displays," Energy Policy, Elsevier, vol. 61(C), pages 788-792.
    13. Lin, Boqiang & Tan, Ruipeng, 2017. "Estimation of the environmental values of electric vehicles in Chinese cities," Energy Policy, Elsevier, vol. 104(C), pages 221-229.
    14. Stenner, Karen & Frederiks, Elisha R. & Hobman, Elizabeth V. & Cook, Stephanie, 2017. "Willingness to participate in direct load control: The role of consumer distrust," Applied Energy, Elsevier, vol. 189(C), pages 76-88.
    15. Dütschke, Elisabeth & Paetz, Alexandra-Gwyn, 2013. "Dynamic electricity pricing—Which programs do consumers prefer?," Energy Policy, Elsevier, vol. 59(C), pages 226-234.
    16. van Dam, S.S. & Bakker, C.A. & Buiter, J.C., 2013. "Do home energy management systems make sense? Assessing their overall lifecycle impact," Energy Policy, Elsevier, vol. 63(C), pages 398-407.
    17. Koichiro Ito & Takanori Ida & Makoto Tanaka, 2018. "Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand," American Economic Journal: Economic Policy, American Economic Association, vol. 10(1), pages 240-267, February.
    18. Dyson, Mark E.H. & Borgeson, Samuel D. & Tabone, Michaelangelo D. & Callaway, Duncan S., 2014. "Using smart meter data to estimate demand response potential, with application to solar energy integration," Energy Policy, Elsevier, vol. 73(C), pages 607-619.
    19. Scott, Fiona L. & Jones, Christopher R. & Webb, Thomas L., 2014. "What do people living in deprived communities in the UK think about household energy efficiency interventions?," Energy Policy, Elsevier, vol. 66(C), pages 335-349.
    20. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
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    3. Chen, Chien-fei & Nelson, Hannah & Xu, Xiaojing & Bonilla, Gregory & Jones, Nicholas, 2021. "Beyond technology adoption: Examining home energy management systems, energy burdens and climate change perceptions during COVID-19 pandemic," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    4. Artur Felipe da Silva Veloso & José Valdemir Reis Júnior & Ricardo de Andrade Lira Rabelo & Jocines Dela-flora Silveira, 2021. "HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service," Future Internet, MDPI, vol. 13(11), pages 1-45, October.
    5. Wang, Weihua & Zhang, Yuting & Zhao, Junjie, 2023. "Technological or social? Influencing factors and mechanisms of the psychological digital divide in rural Chinese elderly," Technology in Society, Elsevier, vol. 74(C).
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    8. Christian Pfeiffer & Markus Puchegger & Claudia Maier & Ina V. Tomaschitz & Thomas P. Kremsner & Lukas Gnam, 2020. "A Case Study of Socially-Accepted Potentials for the Use of End User Flexibility by Home Energy Management Systems," Sustainability, MDPI, vol. 13(1), pages 1-19, December.

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