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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
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    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.
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