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Analyzing solar energy policies using a three-tier model: A case study of photovoltaics adoption in Arizona, United States

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  • Zhai, Pei

Abstract

This study reveals the interrelationships among a variety of policies supporting solar energy adoption in the U.S. and then calculates the amount of financial subsidies required to support mandatory policies such as Renewable Portfolio Standard (RPS). To illuminate interrelationships among these policies, this study proposes three tiers of descriptive model: the top tier includes mandatory policies such as the Renewable Portfolio Standard (RPS); the middle tier is composed of financial support mechanisms, such as tax credit and rebates; and the bottom tier comprises policies that provide funding sources, such as Public Benefit funds. Based on our proposed model, this study further builds a model which calculates the amount of financial subsidies required to support RPS targets of distributed photovoltaics (PV) adoption. The model is applied to the case study of residential PV adoption in the state of Arizona by 2025. The financial requirements are calculated considering of the uncertainty of federal tax credits (extension or termination after 2016) and compared with planned funds that support PV adoption. This study points out that if states would pursue a sustainable PV adoption targets, they should make more efforts on financial support programs.

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  • Zhai, Pei, 2013. "Analyzing solar energy policies using a three-tier model: A case study of photovoltaics adoption in Arizona, United States," Renewable Energy, Elsevier, vol. 57(C), pages 317-322.
    • Handle: RePEc:eee:renene:v:57:y:2013:i:c:p:317-322
    DOI: 10.1016/j.renene.2013.01.058
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    1. Lee, Jongsung & Chang, Byungik & Aktas, Can & Gorthala, Ravi, 2016. "Economic feasibility of campus-wide photovoltaic systems in New England," Renewable Energy, Elsevier, vol. 99(C), pages 452-464.
    2. Zhi, Qiang & Sun, Honghang & Li, Yanxi & Xu, Yurui & Su, Jun, 2014. "China’s solar photovoltaic policy: An analysis based on policy instruments," Applied Energy, Elsevier, vol. 129(C), pages 308-319.
    3. Zeng, Ziqiang & Nasri, Ehsan & Chini, Abdol & Ries, Robert & Xu, Jiuping, 2015. "A multiple objective decision making model for energy generation portfolio under fuzzy uncertainty: Case study of large scale investor-owned utilities in Florida," Renewable Energy, Elsevier, vol. 75(C), pages 224-242.
    4. Karakaya, Emrah & Sriwannawit, Pranpreya, 2015. "Barriers to the adoption of photovoltaic systems: The state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 60-66.
    5. Guanyi Yu & Weidong Chen & Junnan Wang & Yumeng Hu, 2022. "Research on Decision-Making for a Photovoltaic Power Generation Business Model under Integrated Energy Services," Energies, MDPI, vol. 15(15), pages 1-12, August.
    6. Elie, Luc & Granier, Caroline & Rigot, Sandra, 2021. "The different types of renewable energy finance: A Bibliometric analysis," Energy Economics, Elsevier, vol. 93(C).
    7. Aslani, Alireza & Wong, Kau-Fui V., 2014. "Analysis of renewable energy development to power generation in the United States," Renewable Energy, Elsevier, vol. 63(C), pages 153-161.
    8. Guanyi Yu & Qiang Lin & Xiaoqian Qi, 2021. "Government Incentive Contracts for Microgrid Users," Energies, MDPI, vol. 14(4), pages 1-16, February.

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