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Policy simulation for promoting residential PV considering anecdotal information exchanges based on social network modelling

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  • Wang, Ge
  • Zhang, Qi
  • Li, Yan
  • Li, Hailong

Abstract

Surveys and empirical researches have revealed that the households’ perceptions of benefits play a more important role than the benefits themselves in the decision process of adopting residential photovoltaic (PV). However, it has been overlooked in previous models about the green technology diffusion. This work developed an innovation diffusion model based on a social network, which was integrated with an anecdotal information exchange process. The contributions were to model the households’ evaluation, which changes with social influence, and analyze the impact of such dynamics on the adoption of residential PV. A case study was conducted for villages in Beijing. Different scenarios about policies have been considered concerning both the economic benefits and the information diffusion on social network. The results show that: (1) Providing insurance against the damage of PV to adopters for free can improve the adoption rate from 24% up to 62% (full insurance), and the new adopter acquisition cost is only 36% of that of providing additional subsidy; (2) The enhancement of communications (e.g. Bulletin Board System (BBS) and Social Networking Services (SNS)) creates an obstacle to the residential PV adoption when the majority of households have insufficient knowledge about the PV system; and (3) Information campaigns and information screening are both effective and necessary in mitigating the negative effect from the enhancement of communications at the initial stage of the residential PV market.

Suggested Citation

  • Wang, Ge & Zhang, Qi & Li, Yan & Li, Hailong, 2018. "Policy simulation for promoting residential PV considering anecdotal information exchanges based on social network modelling," Applied Energy, Elsevier, vol. 223(C), pages 1-10.
    • Handle: RePEc:eee:appene:v:223:y:2018:i:c:p:1-10
    DOI: 10.1016/j.apenergy.2018.04.028
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    7. Juana Castro & Stefan Drews & Filippos Exadaktylos & Joël Foramitti & Franziska Klein & Théo Konc & Ivan Savin & Jeroen van den Bergh, 2020. "A review of agent‐based modeling of climate‐energy policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    8. Zhang, Qi & Liu, Jiangfeng & Yang, Kexin & Liu, Boyu & Wang, Ge, 2022. "Market adoption simulation of electric vehicle based on social network model considering nudge policies," Energy, Elsevier, vol. 259(C).
    9. Sinha, Avik & Shah, Muhammad Ibrahim & Mehta, Atul & Sharma, Rajesh, 2022. "Impact of Energy Innovation on Greenhouse Gas Emissions: Moderation of Regional Integration and Social Inequality in Asian Economies," ADBI Working Papers 1304, Asian Development Bank Institute.
    10. Marcochi de Melo, Diego & Villavicencio Gastelu, Joel & Asano, Patrícia T.L. & Melo, Joel D., 2022. "Spatiotemporal estimation of photovoltaic system adopters using fuzzy logic," Renewable Energy, Elsevier, vol. 181(C), pages 1188-1196.
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