IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v182y2022ics004016252200364x.html
   My bibliography  Save this article

Roles of diffusion patterns, technological progress, and environmental benefits in determining optimal renewable subsidies in the US

Author

Listed:
  • Tibebu, Tiruwork B.
  • Hittinger, Eric
  • Miao, Qing
  • Williams, Eric

Abstract

In this paper we develop an integrated model to identify optimal subsidy schedules for clean energy technologies that maximize social benefits less subsidy costs. The model uses historical cost, adoption, and emissions data and accounts for both environmental and technological progress benefits of the subsidy. An alternative analytical model is also presented to analyze key technological features affecting subsidy design. We focus on three important factors in determining the social benefits of subsidizing the use of clean energy technology: the price (or cost) sensitivity of adoption, induced cost reductions through learning, and environmental benefits. We quantify how distinct profiles of these three factors result in qualitatively different optimal subsidy plans for utility wind and residential solar power in 13 electricity grid regions in the US. Results show that optimal subsidy schedules for utility wind depend on the region, starting at $20–60/MWh, and are roughly constant over time. In contrast, optimal residential solar subsidies either decline over time (starting from $8–70/MWh) or are not desirable (subsidy of zero). The results imply that the optimal subsidy for utility wind is justified mainly through the direct environmental benefits, unlike residential solar PV in which the subsidy is primarily justified by indirect technological progress benefits.

Suggested Citation

  • Tibebu, Tiruwork B. & Hittinger, Eric & Miao, Qing & Williams, Eric, 2022. "Roles of diffusion patterns, technological progress, and environmental benefits in determining optimal renewable subsidies in the US," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
  • Handle: RePEc:eee:tefoso:v:182:y:2022:i:c:s004016252200364x
    DOI: 10.1016/j.techfore.2022.121840
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016252200364X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2022.121840?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fischer, Carolyn & Newell, Richard G., 2005. "Environmental and Technology Policies for Climate Change and Renewable Energy," Discussion Papers 10789, Resources for the Future.
    2. Wand, Robert & Leuthold, Florian, 2011. "Feed-in tariffs for photovoltaics: Learning by doing in Germany?," Applied Energy, Elsevier, vol. 88(12), pages 4387-4399.
    3. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    4. Jonathan E. Hughes & Molly Podolefsky, 2015. "Getting Green with Solar Subsidies: Evidence from the California Solar Initiative," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(2), pages 235-275.
    5. Williams, Eric & Hittinger, Eric & Carvalho, Rexon & Williams, Ryan, 2017. "Wind power costs expected to decrease due to technological progress," Energy Policy, Elsevier, vol. 106(C), pages 427-435.
    6. Chowdhury, Sanjeeda & Sumita, Ushio & Islam, Ashraful & Bedja, Idriss, 2014. "Importance of policy for energy system transformation: Diffusion of PV technology in Japan and Germany," Energy Policy, Elsevier, vol. 68(C), pages 285-293.
    7. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    8. Sarzynski, Andrea & Larrieu, Jeremy & Shrimali, Gireesh, 2012. "The impact of state financial incentives on market deployment of solar technology," Energy Policy, Elsevier, vol. 46(C), pages 550-557.
    9. Williams, Eric & Carvalho, Rexon & Hittinger, Eric & Ronnenberg, Matthew, 2020. "Empirical development of parsimonious model for international diffusion of residential solar," Renewable Energy, Elsevier, vol. 150(C), pages 570-577.
    10. Riddhi Panse & Vinish Kathuria, 2015. "Modelling Diffusion Of Wind Power Across Countries," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-36.
    11. Matteson, Schuyler & Williams, Eric, 2015. "Learning dependent subsidies for lithium-ion electric vehicle batteries," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 322-331.
    12. Newbery, David, 2018. "Evaluating the case for supporting renewable electricity," Energy Policy, Elsevier, vol. 120(C), pages 684-696.
    13. Nicolini, Marcella & Tavoni, Massimo, 2017. "Are renewable energy subsidies effective? Evidence from Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 412-423.
    14. Hitaj, Claudia & Löschel, Andreas, 2019. "The impact of a feed-in tariff on wind power development in Germany," Resource and Energy Economics, Elsevier, vol. 57(C), pages 18-35.
    15. Newell, Richard G. & Pizer, William A. & Raimi, Daniel, 2019. "U.S. federal government subsidies for clean energy: Design choices and implications," Energy Economics, Elsevier, vol. 80(C), pages 831-841.
    16. Dalla Valle, Alessandra & Furlan, Claudia, 2011. "Forecasting accuracy of wind power technology diffusion models across countries," International Journal of Forecasting, Elsevier, vol. 27(2), pages 592-601, April.
    17. Dalla Valle, Alessandra & Furlan, Claudia, 2011. "Forecasting accuracy of wind power technology diffusion models across countries," International Journal of Forecasting, Elsevier, vol. 27(2), pages 592-601.
    18. Arthur van Benthem & Kenneth Gillingham & James Sweeney, 2008. "Learning-by-Doing and the Optimal Solar Policy in California," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 131-152.
    19. Tibebu, Tiruwork B. & Hittinger, Eric & Miao, Qing & Williams, Eric, 2021. "What is the optimal subsidy for residential solar?," Energy Policy, Elsevier, vol. 155(C).
    20. Steven E. Sexton & A. Justin Kirkpatrick & Robert Harris & Nicholas Z. Muller, 2018. "Heterogeneous Environmental and Grid Benefits from Rooftop Solar and the Costs of Inefficient Siting Decisions," NBER Working Papers 25241, National Bureau of Economic Research, Inc.
    21. Neij, Lena, 1997. "Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology," Energy Policy, Elsevier, vol. 25(13), pages 1099-1107, November.
    22. Macintosh, Andrew & Wilkinson, Deb, 2011. "Searching for public benefits in solar subsidies: A case study on the Australian government's residential photovoltaic rebate program," Energy Policy, Elsevier, vol. 39(6), pages 3199-3209, June.
    23. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    24. Bryan Bollinger & Kenneth Gillingham, 2012. "Peer Effects in the Diffusion of Solar Photovoltaic Panels," Marketing Science, INFORMS, vol. 31(6), pages 900-912, November.
    25. Dong, Changgui & Sigrin, Benjamin & Brinkman, Gregory, 2017. "Forecasting residential solar photovoltaic deployment in California," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 251-265.
    26. Klaassen, Ger & Miketa, Asami & Larsen, Katarina & Sundqvist, Thomas, 2005. "The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom," Ecological Economics, Elsevier, vol. 54(2-3), pages 227-240, August.
    27. Wiser, Ryan & Pickle, Steven & Goldman, Charles, 1998. "Renewable energy policy and electricity restructuring: a California case study," Energy Policy, Elsevier, vol. 26(6), pages 465-475, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tiruwork B. Tibebu & Eric Hittinger & Qing Miao & Eric Williams, 2024. "Adoption Model Choice Affects the Optimal Subsidy for Residential Solar," Energies, MDPI, vol. 17(3), pages 1-19, February.
    2. Juan Qian & Ruibing Ji, 2022. "Impact of Energy-Biased Technological Progress on Inclusive Green Growth," Sustainability, MDPI, vol. 14(23), pages 1-24, December.
    3. Danlu Xu & Zhoubin Liu & Jiahui Zhu & Qin Fang & Rui Shan, 2023. "Linking Cost Decline and Demand Surge in the Hydrogen Market: A Case Study in China," Energies, MDPI, vol. 16(12), pages 1-13, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tibebu, Tiruwork B. & Hittinger, Eric & Miao, Qing & Williams, Eric, 2021. "What is the optimal subsidy for residential solar?," Energy Policy, Elsevier, vol. 155(C).
    2. Tiruwork B. Tibebu & Eric Hittinger & Qing Miao & Eric Williams, 2024. "Adoption Model Choice Affects the Optimal Subsidy for Residential Solar," Energies, MDPI, vol. 17(3), pages 1-19, February.
    3. Grafström, Jonas & Poudineh, Rahmat, 2023. "No evidence of counteracting policy effects on European solar power invention and diffusion," Energy Policy, Elsevier, vol. 172(C).
    4. Kurdgelashvili, Lado & Shih, Cheng-Hao & Yang, Fan & Garg, Mehul, 2019. "An empirical analysis of county-level residential PV adoption in California," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 321-333.
    5. Strupeit, Lars, 2017. "An innovation system perspective on the drivers of soft cost reduction for photovoltaic deployment: The case of Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 273-286.
    6. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    7. Xu, Jiuping & Li, Li & Zheng, Bobo, 2016. "Wind energy generation technological paradigm diffusion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 436-449.
    8. Saed Alizamir & Francis de Véricourt & Peng Sun, 2016. "Efficient Feed-In-Tariff Policies for Renewable Energy Technologies," Operations Research, INFORMS, vol. 64(1), pages 52-66, February.
    9. Lu, Ze-Yu & Li, Wen-Hua & Xie, Bai-Chen & Shang, Li-Feng, 2015. "Study on China’s wind power development path—Based on the target for 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 197-208.
    10. Wichsinee Wibulpolprasert & Umnouy Ponsukcharoen & Siripha Junlakarn & Sopitsuda Tongsopit, 2021. "Preliminarily Screening Geographical Hotspots for New Rooftop PV Installation: A Case Study in Thailand," Energies, MDPI, vol. 14(11), pages 1-30, June.
    11. Crago, Christine Lasco & Chernyakhovskiy, Ilya, 2017. "Are policy incentives for solar power effective? Evidence from residential installations in the Northeast," Journal of Environmental Economics and Management, Elsevier, vol. 81(C), pages 132-151.
    12. Grafström, Jonas & Poudineh, Rahmat, 2023. "Invention and Diffusion in the Solar Power Sector," Ratio Working Papers 364, The Ratio Institute.
    13. Bessi, Alessandro & Guidolin, Mariangela & Manfredi, Piero, 2021. "The role of gas on future perspectives of renewable energy diffusion: Bridging technology or lock-in?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    14. Klein, Martin & Deissenroth, Marc, 2017. "When do households invest in solar photovoltaics? An application of prospect theory," Energy Policy, Elsevier, vol. 109(C), pages 270-278.
    15. Abajian, Alexander & Pretnar, Nick, 2021. "An Aggregate Perspective on the Geo-spatial Distribution of Residential Solar Panels," MPRA Paper 105481, University Library of Munich, Germany.
    16. Toka, Agorasti & Iakovou, Eleftherios & Vlachos, Dimitrios & Tsolakis, Naoum & Grigoriadou, Anastasia-Loukia, 2014. "Managing the diffusion of biomass in the residential energy sector: An illustrative real-world case study," Applied Energy, Elsevier, vol. 129(C), pages 56-69.
    17. Best, Rohan & Burke, Paul J. & Nishitateno, Shuhei, 2019. "Evaluating the effectiveness of Australia's Small-scale Renewable Energy Scheme for rooftop solar," Energy Economics, Elsevier, vol. 84(C).
    18. Lan, Haifeng & Gou, Zhonghua & Lu, Yi, 2021. "Machine learning approach to understand regional disparity of residential solar adoption in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    19. 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.
    20. Furlan, Claudia & Mortarino, Cinzia, 2018. "Forecasting the impact of renewable energies in competition with non-renewable sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1879-1886.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:182:y:2022:i:c:s004016252200364x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.