IDEAS home Printed from https://ideas.repec.org/a/spr/masfgc/v30y2025i4d10.1007_s11027-025-10215-y.html
   My bibliography  Save this article

Optimizing subsidy trajectory for carbon-mitigation technologies

Author

Listed:
  • Lei Zhu

    (Beihang University)

  • Junqi Liu

    (Beihang University)

  • Qin Li

    (Beihang University)

  • Yuan Xu

    (The Chinese University of Hong Kong)

Abstract

Governments worldwide have implemented various subsidies for carbon-mitigation technologies, both on supply and demand sides, to accelerate their commercialization. This approach has become increasingly crucial as nations strengthen their climate commitments in response to the growing climate crisis. However, these subsidies can place significant financial burdens on public funds and consumers. Our study assesses how China could have optimized its development and subsidy strategies for major carbon-mitigation technologies in electricity generation. We develop an analytical framework for optimal subsidies that aims to support technological development while either minimizing subsidy costs or shortening the time to commercialization. Using a multiperiod dynamic programming model, we explore the relationships between technology adoption and subsidies, considering adopters' willingness to adopt. Our findings reveal the existence of an optimal subsidy trajectory for commercializing emerging technologies, which can be phased out based on learning rates and benchmark prices. Empirically, we demonstrate that China's feed-in tariff trajectories for wind and solar PV technologies could be improved, potentially reducing subsidy budgets by 18.76% and 20.07%, respectively, while still meeting the same deployment targets. Given the constraints on subsidy budgets and carbon neutrality timelines, our research offers insights to enhance the efficiency and effectiveness of technological development and deployment at both national and global levels.

Suggested Citation

  • Lei Zhu & Junqi Liu & Qin Li & Yuan Xu, 2025. "Optimizing subsidy trajectory for carbon-mitigation technologies," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 30(4), pages 1-30, April.
    • Handle: RePEc:spr:masfgc:v:30:y:2025:i:4:d:10.1007_s11027-025-10215-y
    DOI: 10.1007/s11027-025-10215-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11027-025-10215-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11027-025-10215-y?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. Jinyue Yan & Ying Yang & Pietro Elia Campana & Jijiang He, 2019. "City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China," Nature Energy, Nature, vol. 4(8), pages 709-717, August.
    2. 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.
    3. van Cranenburgh, Sander & Chorus, Caspar G., 2017. "Willingness to Pay-inference in the absence of rejected propositions," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 35-42.
    4. Grau, Thilo, 2014. "Responsive feed-in tariff adjustment to dynamic technology development," Energy Economics, Elsevier, vol. 44(C), pages 36-46.
    5. Alberini, Anna & Boyle, Kevin & Welsh, Michael, 2003. "Analysis of contingent valuation data with multiple bids and response options allowing respondents to express uncertainty," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 40-62, January.
    6. Dost, Florian & Wilken, Robert, 2012. "Measuring willingness to pay as a range, revisited: When should we care?," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 148-166.
    7. Hilary S. Boudet, 2019. "Public perceptions of and responses to new energy technologies," Nature Energy, Nature, vol. 4(6), pages 446-455, June.
    8. Helm, Carsten & Mier, Mathias, 2021. "Steering the energy transition in a world of intermittent electricity supply: Optimal subsidies and taxes for renewables and storage," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    9. Hunt Allcott & Michael Greenstone, 2012. "Is There an Energy Efficiency Gap?," Journal of Economic Perspectives, American Economic Association, vol. 26(1), pages 3-28, Winter.
    10. Liu, Da & Liu, Yumeng & Sun, Kun, 2021. "Policy impact of cancellation of wind and photovoltaic subsidy on power generation companies in China," Renewable Energy, Elsevier, vol. 177(C), pages 134-147.
    11. Lee, Hyounkyu & Park, Taeil & Kim, Byungil & Kim, Kyeongseok & Kim, Hyoungkwan, 2013. "A real option-based model for promoting sustainable energy projects under the clean development mechanism," Energy Policy, Elsevier, vol. 54(C), pages 360-368.
    12. Tobias S. Schmidt & Robin Born & Malte Schneider, 2012. "Assessing the costs of photovoltaic and wind power in six developing countries," Nature Climate Change, Nature, vol. 2(7), pages 548-553, July.
    13. O. Schmidt & A. Hawkes & A. Gambhir & I. Staffell, 2017. "The future cost of electrical energy storage based on experience rates," Nature Energy, Nature, vol. 2(8), pages 1-8, August.
    14. Florian Egli & Bjarne Steffen & Tobias S. Schmidt, 2018. "A dynamic analysis of financing conditions for renewable energy technologies," Nature Energy, Nature, vol. 3(12), pages 1084-1092, December.
    15. M. Pahle & O. Tietjen & S. Osorio & F. Egli & B. Steffen & T. S. Schmidt & O. Edenhofer, 2022. "Safeguarding the energy transition against political backlash to carbon markets," Nature Energy, Nature, vol. 7(3), pages 290-296, March.
    16. Jin, Jianjun & Wan, Xinyu & Lin, Yongsheng & Kuang, Foyuan & Ning, Jing, 2019. "Public willingness to pay for the research and development of solar energy in Beijing, China," Energy Policy, Elsevier, vol. 134(C).
    17. Wang, Hua & Whittington, Dale, 2005. "Measuring individuals' valuation distributions using a stochastic payment card approach," Ecological Economics, Elsevier, vol. 55(2), pages 143-154, November.
    18. Bigerna, Simona & Wen, Xingang & Hagspiel, Verena & Kort, Peter M., 2019. "Green electricity investments: Environmental target and the optimal subsidy," European Journal of Operational Research, Elsevier, vol. 279(2), pages 635-644.
    19. Amador, Francisco Javier & González, Rosa Marina & Ramos-Real, Francisco Javier, 2013. "Supplier choice and WTP for electricity attributes in an emerging market: The role of perceived past experience, environmental concern and energy saving behavior," Energy Economics, Elsevier, vol. 40(C), pages 953-966.
    20. Zhang, M.M. & Zhou, D.Q. & Zhou, P. & Chen, H.T., 2017. "Optimal design of subsidy to stimulate renewable energy investments: The case of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 873-883.
    21. 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.
    22. Alexa Spence & Christina Demski & Catherine Butler & Karen Parkhill & Nick Pidgeon, 2015. "Public perceptions of demand-side management and a smarter energy future," Nature Climate Change, Nature, vol. 5(6), pages 550-554, June.
    23. Zhao, Yibing & Wang, Can & Sun, Yuwei & Liu, Xianbing, 2018. "Factors influencing companies' willingness to pay for carbon emissions: Emission trading schemes in China," Energy Economics, Elsevier, vol. 75(C), pages 357-367.
    24. Hua Wang & Jie He, 2011. "Estimating individual valuation distributions with multiple bounded discrete choice data," Applied Economics, Taylor & Francis Journals, vol. 43(21), pages 2641-2656.
    25. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    26. Sendstad, Lars H. & Hagspiel, Verena & Mikkelsen, Wilhelm Jebsen & Ravndal, Ruben & Tveitstøl, Martin, 2022. "The impact of subsidy retraction on European renewable energy investments," Energy Policy, Elsevier, vol. 160(C).
    27. Ulf J. J. Hahnel & Gilles Chatelain & Beatrice Conte & Valentino Piana & Tobias Brosch, 2020. "Mental accounting mechanisms in energy decision-making and behaviour," Nature Energy, Nature, vol. 5(12), pages 952-958, December.
    28. Botzen, W.J.W. & van den Bergh, J.C.J.M., 2012. "Risk attitudes to low-probability climate change risks: WTP for flood insurance," Journal of Economic Behavior & Organization, Elsevier, vol. 82(1), pages 151-166.
    29. Welsh, Michael P. & Poe, Gregory L., 1998. "Elicitation Effects in Contingent Valuation: Comparisons to a Multiple Bounded Discrete Choice Approach," Journal of Environmental Economics and Management, Elsevier, vol. 36(2), pages 170-185, September.
    30. Doherty, Ronan & O'Malley, Mark, 2011. "The efficiency of Ireland's Renewable Energy Feed-In Tariff (REFIT) for wind generation," Energy Policy, Elsevier, vol. 39(9), pages 4911-4919, September.
    31. Tu, Qiang & Betz, Regina & Mo, Jianlei & Fan, Ying, 2019. "The profitability of onshore wind and solar PV power projects in China - A comparative study," Energy Policy, Elsevier, vol. 132(C), pages 404-417.
    32. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.
    33. Kotchen, Matthew J. & Boyle, Kevin J. & Leiserowitz, Anthony A., 2013. "Willingness-to-pay and policy-instrument choice for climate-change policy in the United States," Energy Policy, Elsevier, vol. 55(C), pages 617-625.
    34. Trotter, Ian Michael & da Cunha, Dênis Antônio & Féres, José Gustavo, 2015. "The relationships between CDM project characteristics and CER market prices," Ecological Economics, Elsevier, vol. 119(C), pages 158-167.
    35. 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.
    36. Che, Xiao-Jing & Zhou, P. & Chai, Kah-Hin, 2022. "Regional policy effect on photovoltaic (PV) technology innovation: Findings from 260 cities in China," Energy Policy, Elsevier, vol. 162(C).
    37. Alberini, Anna & Bigano, Andrea & Ščasný, Milan & Zvěřinová, Iva, 2018. "Preferences for Energy Efficiency vs. Renewables: What Is the Willingness to Pay to Reduce CO2 Emissions?," Ecological Economics, Elsevier, vol. 144(C), pages 171-185.
    38. Koto, Prosper Senyo & Yiridoe, Emmanuel K., 2019. "Expected willingness to pay for wind energy in Atlantic Canada," Energy Policy, Elsevier, vol. 129(C), pages 80-88.
    Full references (including those not matched with items on IDEAS)

    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. Yao, Xing & Fan, Ying & Zhu, Lei & Zhang, Xian, 2020. "Optimization of dynamic incentive for the deployment of carbon dioxide removal technology: A nonlinear dynamic approach combined with real options," Energy Economics, Elsevier, vol. 86(C).
    2. Bigerna, Simona & Polinori, Paolo, 2014. "Italian households׳ willingness to pay for green electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 110-121.
    3. Wang, Hua & Fang, Ke & Shi, Yuyan, 2011. "Benefit-Cost Analysis with Local Residents’ Stated Preference Information: A Study of Non-Motorized Transport Investments in Pune, India," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 2(3), pages 1-37, August.
    4. Wang, Hua & Shi, Yuyan & Kim, Yoonhee & Kamata, Takuya, 2013. "Valuing water quality improvement in China: A case study of Lake Puzhehei in Yunnan Province," Ecological Economics, Elsevier, vol. 94(C), pages 56-65.
    5. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
    6. Marius Yapo & Jie He & Bruno Gagnon & Luc Savard & Roland Leduc, 2015. "La valeur économique pour l’amélioration de la qualité de l’eau: le cas de la rivière Magog et du lac Magog (Québec, Canada)," Cahiers de recherche 15-15, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    7. He, Jie & Huang, Anping & Xu, Luodan, 2015. "Spatial heterogeneity and transboundary pollution: A contingent valuation (CV) study on the Xijiang River drainage basin in south China," China Economic Review, Elsevier, vol. 36(C), pages 101-130.
    8. Tu, Qiang & Mo, Jianlei & Betz, Regina & Cui, Lianbiao & Fan, Ying & Liu, Yu, 2020. "Achieving grid parity of solar PV power in China- The role of Tradable Green Certificate," Energy Policy, Elsevier, vol. 144(C).
    9. Provencher, Bill & Lewis, David J. & Anderson, Kathryn, 2012. "Disentangling preferences and expectations in stated preference analysis with respondent uncertainty: The case of invasive species prevention," Journal of Environmental Economics and Management, Elsevier, vol. 64(2), pages 169-182.
    10. Wang, Hua & Xie, Jian & Li, Honglin, 2010. "Water pricing with household surveys: A study of acceptability and willingness to pay in Chongqing, China," China Economic Review, Elsevier, vol. 21(1), pages 136-149, March.
    11. Farrell, Niall, 2023. "Policy design for green hydrogen," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    12. Voltaire, Louinord & Pirrone, Claudio & Bailly, Denis, 2013. "Dealing with preference uncertainty in contingent willingness to pay for a nature protection program: A new approach," Ecological Economics, Elsevier, vol. 88(C), pages 76-85.
    13. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
    14. Hyacinth Ichoku & William Fonta & Abbi Kedir, 2009. "Measuring individuals’ valuation distributions using a stochastic payment card approach: application to solid waste management in Nigeria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 11(3), pages 509-521, June.
    15. Gunther Glenk & Rebecca Meier & Stefan Reichelstein, 2021. "Cost Dynamics of Clean Energy Technologies," Schmalenbach Journal of Business Research, Springer, vol. 73(2), pages 179-206, June.
    16. Hagspiel, Verena & Kort, Peter M. & Wen, Xingang, 2025. "Green technology investment: Announced vs. unannounced subsidy retraction," Journal of Economic Dynamics and Control, Elsevier, vol. 170(C).
    17. Ousmane Z. Traoré & Lota D. Tamini & Bernard Korai, 2023. "Willingness to pay for credence attributes associated with agri‐food products—Evidence from Canada," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 71(3-4), pages 303-327, September.
    18. Wang, Hua & Fang, Ke & Shi, Yuyan, 2010. "Economic valuation of development projects : a case study of a non-motorized transport project in India," Policy Research Working Paper Series 5422, The World Bank.
    19. Wang, Hua & He, Jie & Huang, Desheng, 2020. "Public distrust and valuation biases: Identification and calibration with contingent valuation studies of two air quality improvement programs in China," China Economic Review, Elsevier, vol. 61(C).
    20. Thunström, Linda & Nordström, Jonas & Shogren, Jason F., 2015. "Certainty and overconfidence in future preferences for food," Journal of Economic Psychology, Elsevier, vol. 51(C), pages 101-113.

    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:spr:masfgc:v:30:y:2025:i:4:d:10.1007_s11027-025-10215-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.