IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v138y2021ics1364032120307747.html
   My bibliography  Save this article

Impacts of innovation on renewable energy technology cost reductions

Author

Listed:
  • Elia, A.
  • Kamidelivand, M.
  • Rogan, F.
  • Ó Gallachóir, B.

Abstract

Energy technology cost reductions are the result of many innovation trends in the energy system. The energy technology innovation system is increasingly well understood at an aggregate level and using qualitative concepts. However, the quantification of the multiple drivers of energy technology cost reduction trends remains poorly understood. This paper addresses this knowledge gap by presenting a systematic review of current practices. Despite their simplifications, one-factor learning curves (i.e. using a single driver) remain the most popular method for quantitative modelling of energy technology innovation. The role of multiple drivers on cost reductions has been cited in previous studies. This review enriches our understanding of these multiple drivers by examining their impact along different stages of technology development. The review quantifies the variation in these drivers and shows that the development of multi-factor learning curve models and bottom-up cost models are still in their infancy. With a focus on onshore wind and solar PV technologies, the review finds that most of the published multi-factor learning curve analyses are focused on addressing the impact of drivers related to i) manufacturing process improvements (i.e. learning by-doing) and ii) technology feature improvements (i.e. learning by-researching). This means that the other learning drivers such as market dynamics and learning by-interacting across different stakeholders and geographical areas are still poorly quantified, despite their impact on cost reduction being recognised in the innovation literature. There is a danger that misinformed policies are currently being developed in the absence of a good understanding of these multiple drivers.

Suggested Citation

  • Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  • Handle: RePEc:eee:rensus:v:138:y:2021:i:c:s1364032120307747
    DOI: 10.1016/j.rser.2020.110488
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120307747
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2020.110488?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. Hendry, Chris & Harborne, Paul, 2011. "Changing the view of wind power development: More than "bricolage"," Research Policy, Elsevier, vol. 40(5), pages 778-789, June.
    2. Kim, Dong Wook & Chang, Hyun Joon, 2012. "Experience curve analysis on South Korean nuclear technology and comparative analysis with South Korean renewable technologies," Energy Policy, Elsevier, vol. 40(C), pages 361-373.
    3. Hayashi, Daisuke & Huenteler, Joern & Lewis, Joanna I., 2018. "Gone with the wind: A learning curve analysis of China's wind power industry," Energy Policy, Elsevier, vol. 120(C), pages 38-51.
    4. Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
    5. Yao, Xilong & Liu, Yang & Qu, Shiyou, 2015. "When will wind energy achieve grid parity in China? – Connecting technological learning and climate finance," Applied Energy, Elsevier, vol. 160(C), pages 697-704.
    6. Ulsrud, Kirsten & Rohracher, Harald & Muchunku, Charles, 2018. "Spatial transfer of innovations: South-South learning on village-scale solar power supply between India and Kenya," Energy Policy, Elsevier, vol. 114(C), pages 89-97.
    7. Pan, Haoran & Kohler, Jonathan, 2007. "Technological change in energy systems: Learning curves, logistic curves and input-output coefficients," Ecological Economics, Elsevier, vol. 63(4), pages 749-758, September.
    8. Cuppen, Eefje & Pesch, Udo & Remmerswaal, Sanne & Taanman, Mattijs, 2019. "Normative diversity, conflict and transition: Shale gas in the Netherlands," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 165-175.
    9. Rubin, Edward S. & Yeh, Sonia & Antes, Matt & Berkenpas, Michael & Davison, John, 2007. "Use of experience curves to estimate the future cost of power plants with CO2 capture," Institute of Transportation Studies, Working Paper Series qt46x6h0n0, Institute of Transportation Studies, UC Davis.
    10. Rai, Varun & Victor, David G. & Thurber, Mark C., 2010. "Carbon capture and storage at scale: Lessons from the growth of analogous energy technologies," Energy Policy, Elsevier, vol. 38(8), pages 4089-4098, August.
    11. Bradshaw, Amanda & de Martino Jannuzzi, Gilberto, 2019. "Governing energy transitions and regional economic development: Evidence from three Brazilian states," Energy Policy, Elsevier, vol. 126(C), pages 1-11.
    12. 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.
    13. Lam, Long T. & Branstetter, Lee & Azevedo, Inês M.L., 2017. "China's wind industry: Leading in deployment, lagging in innovation," Energy Policy, Elsevier, vol. 106(C), pages 588-599.
    14. Jabbour, Liza & Tao, Zhigang & Vanino, Enrico & Zhang, Yan, 2019. "The good, the bad and the ugly: Chinese imports, European Union anti-dumping measures and firm performance," Journal of International Economics, Elsevier, vol. 117(C), pages 1-20.
    15. Kavlak, Goksin & McNerney, James & Trancik, Jessika E., 2018. "Evaluating the causes of cost reduction in photovoltaic modules," Energy Policy, Elsevier, vol. 123(C), pages 700-710.
    16. Salim, Ali & Razavi, Mohammad Reza & Afshari-Mofrad, Masoud, 2017. "Foreign direct investment and technology spillover in Iran: The role of technological capabilities of subsidiaries," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 207-214.
    17. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    18. Yeh, Sonia & Rubin, Edward S., 2007. "A centurial history of technological change and learning curves for pulverized coal-fired utility boilers," Energy, Elsevier, vol. 32(10), pages 1996-2005.
    19. Wei, Max & Smith, Sarah J. & Sohn, Michael D., 2017. "Experience curve development and cost reduction disaggregation for fuel cell markets in Japan and the US," Applied Energy, Elsevier, vol. 191(C), pages 346-357.
    20. Wilson, Charlie, 2012. "Up-scaling, formative phases, and learning in the historical diffusion of energy technologies," Energy Policy, Elsevier, vol. 50(C), pages 81-94.
    21. Jordaan, Sarah M. & Romo-Rabago, Elizabeth & McLeary, Romaine & Reidy, Luke & Nazari, Jamal & Herremans, Irene M., 2017. "The role of energy technology innovation in reducing greenhouse gas emissions: A case study of Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1397-1409.
    22. Gregory F. Nemet, 2012. "Subsidies for New Technologies and Knowledge Spillovers from Learning by Doing," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 31(3), pages 601-622, June.
    23. Coulomb, L. & Neuhoff, K., 2006. "Learning curves and changing product attributes: the case of wind turbines," Cambridge Working Papers in Economics 0618, Faculty of Economics, University of Cambridge.
    24. Vazquez, Miguel & Hallack, Michelle, 2018. "The role of regulatory learning in energy transition: The case of solar PV in Brazil," Energy Policy, Elsevier, vol. 114(C), pages 465-481.
    25. Fouquet, Roger, 2010. "The slow search for solutions: Lessons from historical energy transitions by sector and service," Energy Policy, Elsevier, vol. 38(11), pages 6586-6596, November.
    26. Qiu, Yueming & Anadon, Laura D., 2012. "The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization," Energy Economics, Elsevier, vol. 34(3), pages 772-785.
    27. Strupeit, Lars & Neij, Lena, 2017. "Cost dynamics in the deployment of photovoltaics: Insights from the German market for building-sited systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 948-960.
    28. Nemet, Gregory F., 2012. "Inter-technology knowledge spillovers for energy technologies," Energy Economics, Elsevier, vol. 34(5), pages 1259-1270.
    29. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    30. Pillai, Unni, 2015. "Drivers of cost reduction in solar photovoltaics," Energy Economics, Elsevier, vol. 50(C), pages 286-293.
    31. Weyant, John P., 2011. "Accelerating the development and diffusion of new energy technologies: Beyond the "valley of death"," Energy Economics, Elsevier, vol. 33(4), pages 674-682, July.
    32. Ek, Kristina & Söderholm, Patrik, 2010. "Technology learning in the presence of public R&D: The case of European wind power," Ecological Economics, Elsevier, vol. 69(12), pages 2356-2362, October.
    33. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    34. Grubler, Arnulf, 2010. "The costs of the French nuclear scale-up: A case of negative learning by doing," Energy Policy, Elsevier, vol. 38(9), pages 5174-5188, September.
    35. Haase, Rachel & Bielicki, Jeffrey & Kuzma, Jennifer, 2013. "Innovation in emerging energy technologies: A case study analysis to inform the path forward for algal biofuels," Energy Policy, Elsevier, vol. 61(C), pages 1595-1607.
    36. Pettersson, Fredrik & Söderholm, Patrik, 2009. "The diffusion of renewable electricity in the presence of climate policy and technology learning: The case of Sweden," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 2031-2040, October.
    37. Jamasb, Tooraj & Pollitt, Michael G., 2015. "Why and how to subsidise energy R+D: Lessons from the collapse and recovery of electricity innovation in the UK," Energy Policy, Elsevier, vol. 83(C), pages 197-205.
    38. Liu, Hengwei & Liang, Dapeng, 2013. "A review of clean energy innovation and technology transfer in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 486-498.
    39. McNerney, James & Doyne Farmer, J. & Trancik, Jessika E., 2011. "Historical costs of coal-fired electricity and implications for the future," Energy Policy, Elsevier, vol. 39(6), pages 3042-3054, June.
    40. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    41. Neij, Lena & Heiskanen, Eva & Strupeit, Lars, 2017. "The deployment of new energy technologies and the need for local learning," Energy Policy, Elsevier, vol. 101(C), pages 274-283.
    42. Jacobsson, Staffan & Johnson, Anna, 2000. "The diffusion of renewable energy technology: an analytical framework and key issues for research," Energy Policy, Elsevier, vol. 28(9), pages 625-640, July.
    43. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
    44. Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
    45. Wiebe, Kirsten S. & Lutz, Christian, 2016. "Endogenous technological change and the policy mix in renewable power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 739-751.
    46. Berthélemy, Michel & Escobar Rangel, Lina, 2015. "Nuclear reactors' construction costs: The role of lead-time, standardization and technological progress," Energy Policy, Elsevier, vol. 82(C), pages 118-130.
    47. Berry, David, 2009. "Innovation and the price of wind energy in the US," Energy Policy, Elsevier, vol. 37(11), pages 4493-4499, November.
    48. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    49. Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.
    50. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    51. Kobos, Peter H. & Malczynski, Leonard A. & Walker, La Tonya N. & Borns, David J. & Klise, Geoffrey T., 2018. "Timing is everything: A technology transition framework for regulatory and market readiness levels," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 211-225.
    52. Payne, Adam & Duke, Richard & Williams, Robert H., 2001. "Accelerating residential PV expansion: supply analysis for competitive electricity markets," Energy Policy, Elsevier, vol. 29(10), pages 787-800, August.
    53. Nemet, Gregory F. & Zipperer, Vera & Kraus, Martina, 2018. "The valley of death, the technology pork barrel, and public support for large demonstration projects," Energy Policy, Elsevier, vol. 119(C), pages 154-167.
    54. Kumar Sahu, Bikash, 2015. "A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 621-634.
    55. Charlie Wilson & Arnulf Grubler, 2011. "Lessons from the history of technological change for clean energy scenarios and policies," Natural Resources Forum, Blackwell Publishing, vol. 35(3), pages 165-184, August.
    56. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.
    57. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    58. Jiao, Hao & Zhou, Jianghua & Gao, Taishan & Liu, Xielin, 2016. "The more interactions the better? The moderating effect of the interaction between local producers and users of knowledge on the relationship between R&D investment and regional innovation systems," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 13-20.
    59. Clarke, Leon & Weyant, John & Birky, Alicia, 2006. "On the sources of technological change: Assessing the evidence," Energy Economics, Elsevier, vol. 28(5-6), pages 579-595, November.
    60. Grafström, Jonas & Lindman, Åsa, 2017. "Invention, innovation and diffusion in the European wind power sector," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 179-191.
    61. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    62. Yi Zhou & Alun Gu, 2019. "Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    63. Lovering, Jessica R. & Yip, Arthur & Nordhaus, Ted, 2016. "Historical construction costs of global nuclear power reactors," Energy Policy, Elsevier, vol. 91(C), pages 371-382.
    64. Loiter, Jeffrey M. & Norberg-Bohm, Vicki, 1999. "Technology policy and renewable energy: public roles in the development of new energy technologies," Energy Policy, Elsevier, vol. 27(2), pages 85-97, February.
    65. Bento, Nuno & Fontes, Margarida, 2016. "The capacity for adopting energy innovations in Portugal: Historical evidence and perspectives for the future," Technological Forecasting and Social Change, Elsevier, vol. 113(PB), pages 308-318.
    66. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    67. Trappey, Amy J.C. & Trappey, Charles V. & Liu, Penny H.Y. & Lin, Lee-Cheng & Ou, Jerry J.R., 2013. "A hierarchical cost learning model for developing wind energy infrastructures," International Journal of Production Economics, Elsevier, vol. 146(2), pages 386-391.
    68. Bento, Nuno & Fontes, Margarida, 2015. "The construction of a new technological innovation system in a follower country: Wind energy in Portugal," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 197-210.
    69. Bednyagin, Denis & Gnansounou, Edgard, 2012. "Estimating spillover benefits of large R&D projects: Application of real options modelling approach to the case of thermonuclear fusion R&D programme," Energy Policy, Elsevier, vol. 41(C), pages 269-279.
    70. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    71. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    72. Grubler, Arnulf, 2012. "Energy transitions research: Insights and cautionary tales," Energy Policy, Elsevier, vol. 50(C), pages 8-16.
    73. Jamasb, T. & Köhler, J., 2007. "Learning Curves For Energy Technology and Policy Analysis: A Critical Assessment," Cambridge Working Papers in Economics 0752, Faculty of Economics, University of Cambridge.
    74. Bolinger, Mark & Wiser, Ryan, 2012. "Understanding wind turbine price trends in the U.S. over the past decade," Energy Policy, Elsevier, vol. 42(C), pages 628-641.
    75. Yu, Yang & Li, Hong & Che, Yuyuan & Zheng, Qiongjie, 2017. "The price evolution of wind turbines in China: A study based on the modified multi-factor learning curve," Renewable Energy, Elsevier, vol. 103(C), pages 522-536.
    76. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    77. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    78. Tang, Tian, 2018. "Explaining technological change in the US wind industry: Energy policies, technological learning, and collaboration," Energy Policy, Elsevier, vol. 120(C), pages 197-212.
    79. Nguyen, Ly & Kinnucan, Henry W., 2019. "The US solar panel anti-dumping duties versus uniform tariff," Energy Policy, Elsevier, vol. 127(C), pages 523-532.
    80. de La Tour, Arnaud & Glachant, Matthieu & Ménière, Yann, 2013. "Predicting the costs of photovoltaic solar modules in 2020 using experience curve models," Energy, Elsevier, vol. 62(C), pages 341-348.
    81. Chen, Shih-Hsin & Lin, Wei-Ting, 2017. "The dynamic role of universities in developing an emerging sector: a case study of the biotechnology sector," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 283-297.
    82. Kamp, Linda M. & Smits, Ruud E. H. M. & Andriesse, Cornelis D., 2004. "Notions on learning applied to wind turbine development in the Netherlands and Denmark," Energy Policy, Elsevier, vol. 32(14), pages 1625-1637, September.
    83. Sagar, Ambuj D. & van der Zwaan, Bob, 2006. "Technological innovation in the energy sector: R&D, deployment, and learning-by-doing," Energy Policy, Elsevier, vol. 34(17), pages 2601-2608, November.
    84. Winskel, Mark & Markusson, Nils & Jeffrey, Henry & Candelise, Chiara & Dutton, Geoff & Howarth, Paul & Jablonski, Sophie & Kalyvas, Christos & Ward, David, 2014. "Learning pathways for energy supply technologies: Bridging between innovation studies and learning rates," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 96-114.
    85. Hu, Rui & Skea, Jim & Hannon, Matthew J., 2018. "Measuring the energy innovation process: An indicator framework and a case study of wind energy in China," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 227-244.
    86. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    87. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    88. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
    89. Musiolik, Jörg & Markard, Jochen, 2011. "Creating and shaping innovation systems: Formal networks in the innovation system for stationary fuel cells in Germany," Energy Policy, Elsevier, vol. 39(4), pages 1909-1922, April.
    90. 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.
    91. Dismukes, David E. & Upton, Gregory B., 2015. "Economies of scale, learning effects and offshore wind development costs," Renewable Energy, Elsevier, vol. 83(C), pages 61-66.
    92. Lindman, Åsa & Söderholm, Patrik, 2016. "Wind energy and green economy in Europe: Measuring policy-induced innovation using patent data," Applied Energy, Elsevier, vol. 179(C), pages 1351-1359.
    93. Lin, Boqiang & He, Jiaxin, 2016. "Learning curves for harnessing biomass power: What could explain the reduction of its cost during the expansion of China?," Renewable Energy, Elsevier, vol. 99(C), pages 280-288.
    94. Michel Berthélemy & Lina Escobar Rangel, 2015. "Nuclear reactors' construction costs: The role of lead-time, standardization and technological progress," Post-Print hal-01523016, HAL.
    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. Pavlović, Boban & Ivezić, Dejan & Živković, Marija, 2022. "Transition pathways of household heating in Serbia: Analysis based on an agent-based model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Semmler, Willi & Di Bartolomeo, Giovanni & Minooei Fard, Behnaz & Braga, Joao Paulo, 2022. "Limit pricing and entry game of renewable energy firms into the energy sector," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 179-190.
    3. Liang, Ting & Zhang, Yue-Jun & Qiang, Wei, 2022. "Does technological innovation benefit energy firms’ environmental performance? The moderating effect of government subsidies and media coverage," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    4. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
    5. Cao, Dongqin & Peng, Can & Yang, Guanglei & Zhang, Wei, 2022. "How does the pressure of political promotion affect renewable energy technological innovation? Evidence from 30 Chinese provinces," Energy, Elsevier, vol. 254(PA).
    6. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng & Vaz-Serra, Paulo, 2022. "Economic and environmental impacts of public investment in clean energy RD&D," Energy Policy, Elsevier, vol. 168(C).
    7. Su, Xiang & Tan, Junlan, 2023. "Regional energy transition path and the role of government support and resource endowment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    8. Kaitano Dube, 2021. "Sustainable Development Goals Localisation in the Hospitality Sector in Botswana and Zimbabwe," Sustainability, MDPI, vol. 13(15), pages 1-15, July.
    9. Hu, Xing & Guo, Yingying & Zheng, Yali & Liu, Lan-cui & Yu, Shiwei, 2022. "Which types of policies better promote the development of renewable energy? Evidence from China's provincial data," Renewable Energy, Elsevier, vol. 198(C), pages 1373-1382.
    10. Jing, Yifan & Zhu, Li & Yin, Baoquan & Li, Fangfang, 2023. "Evaluating the PV system expansion potential of existing integrated energy parks: A case study in North China," Applied Energy, Elsevier, vol. 330(PA).
    11. Lisa Thormann & Ulf Neuling & Martin Kaltschmitt, 2021. "Opportunities and Challenges of the European Green Deal for the Chemical Industry: An Approach Measuring Innovations in Bioeconomy," Resources, MDPI, vol. 10(9), pages 1-31, September.
    12. Shi, Yangyan & Feng, Yu & Zhang, Qi & Shuai, Jing & Niu, Jiangxin, 2023. "Does China's new energy vehicles supply chain stock market have risk spillovers? Evidence from raw material price effect on lithium batteries," Energy, Elsevier, vol. 262(PA).
    13. Sillman, J. & Hynynen, K. & Dyukov, I. & Ahonen, T. & Jalas, M, 2023. "Emission reduction targets and electrification of the Finnish energy system with low-carbon Power-to-X technologies: Potentials, barriers, and innovations – A Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    14. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

    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. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    2. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    3. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    4. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    6. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    7. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    8. Choi, Donghyun & Kim, Yeong Jae, 2023. "Local and global experience curves for lumpy and granular energy technologies," Energy Policy, Elsevier, vol. 174(C).
    9. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    10. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    11. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    12. Wiser, Ryan & Millstein, Dev, 2020. "Evaluating the economic return to public wind energy research and development in the United States," Applied Energy, Elsevier, vol. 261(C).
    13. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    14. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    15. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng & Vaz-Serra, Paulo, 2022. "Economic and environmental impacts of public investment in clean energy RD&D," Energy Policy, Elsevier, vol. 168(C).
    16. 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.
    17. He, Zhengxia & Cao, Changshuai & Kuai, Leyi & Zhou, Yanqing & Wang, Jianming, 2022. "Impact of policies on wind power innovation at different income levels: Regional differences in China based on dynamic panel estimation," Technology in Society, Elsevier, vol. 71(C).
    18. Hernandez-Negron, Christian G. & Baker, Erin & Goldstein, Anna P., 2023. "A hypothesis for experience curves of related technologies with an application to wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    19. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    20. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.

    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:rensus:v:138:y:2021:i:c:s1364032120307747. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.