IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v130y2020ics1364032120302288.html
   My bibliography  Save this article

A review on learning effects in prospective technology assessment

Author

Listed:
  • Thomassen, Gwenny
  • Van Passel, Steven
  • Dewulf, Jo

Abstract

Global environmental problems have urged the need for developing sustainable technologies. However, new technologies that enter the market have often higher economic costs and potentially higher environmental impacts than conventional technologies. This can be explained by learning effects: a production process that is performed for the first time runs less smooth than a production process that has been in operation for years. To obtain a fair estimation of the potential of a new technology, learning effects need to be included. A review on the current literature on learning effects was conducted in order to provide guidelines on how to include learning effects in prospective technology assessment. Based on the results of this review, five recommendations have been formulated and an integration of learning effects in the structure of prospective technology assessment has been proposed. These five recommendations include the combined use of learning effects on the component level and on the end product level; the combined use of learning effects on the technical, economic and environmental level; the combined use of extrapolated values and expert estimates; the combined use of learning-by-doing and learning-by-searching effects and; a tier-based method, including quality criteria, to calculate the learning effect. These five complementary strategies could lead to a clearer perspective on the environmental impact and cost structure of the new technology and a fairer comparison base with conventional technologies, potentially resulting in a faster adoption and a shorter time-to-market for sustainable technologies.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:rensus:v:130:y:2020:i:c:s1364032120302288
    DOI: 10.1016/j.rser.2020.109937
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120302288
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2020.109937?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. 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.
    2. Esmaieli, M. & Ahmadian, M., 2018. "The effect of research and development incentive on wind power investment, a system dynamics approach," Renewable Energy, Elsevier, vol. 126(C), pages 765-773.
    3. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 115512, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Shih, Yi-Hsuan & Tseng, Chao-Heng, 2014. "Cost-benefit analysis of sustainable energy development using life-cycle co-benefits assessment and the system dynamics approach," Applied Energy, Elsevier, vol. 119(C), pages 57-66.
    5. 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.
    6. Ruffini, Eleonora & Wei, Max, 2018. "Future costs of fuel cell electric vehicles in California using a learning rate approach," Energy, Elsevier, vol. 150(C), pages 329-341.
    7. Matteson, Schuyler & Williams, Eric, 2015. "Residual learning rates in lead-acid batteries: Effects on emerging technologies," Energy Policy, Elsevier, vol. 85(C), pages 71-79.
    8. 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.
    9. Bergesen, Joseph D. & Suh, Sangwon, 2016. "A framework for technological learning in the supply chain: A case study on CdTe photovoltaics," Applied Energy, Elsevier, vol. 169(C), pages 721-728.
    10. Jong-Hyun Kim & Yong-Gil Lee, 2018. "Learning Curve, Change in Industrial Environment, and Dynamics of Production Activities in Unconventional Energy Resources," Sustainability, MDPI, Open Access Journal, vol. 10(9), pages 1-11, September.
    11. Nicodemus, Julia Haltiwanger, 2018. "Technological learning and the future of solar H2: A component learning comparison of solar thermochemical cycles and electrolysis with solar PV," Energy Policy, Elsevier, vol. 120(C), pages 100-109.
    12. 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.
    13. Yuan, Rong & Behrens, Paul & Tukker, Arnold & Rodrigues, João F.D., 2018. "Carbon overhead: The impact of the expansion in low-carbon electricity in China 2015–2040," Energy Policy, Elsevier, vol. 119(C), pages 97-104.
    14. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    15. Amela Ajanovic, 2015. "The future of electric vehicles: prospects and impediments," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 4(6), pages 521-536, November.
    16. 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.
    17. Liu, Xi & Du, Huibin & Brown, Marilyn A. & Zuo, Jian & Zhang, Ning & Rong, Qian & Mao, Guozhu, 2018. "Low-carbon technology diffusion in the decarbonization of the power sector: Policy implications," Energy Policy, Elsevier, vol. 116(C), pages 344-356.
    18. 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.
    19. Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
    20. Shanjun Li & Junji Xiao & Yimin Liu, 2015. "The Price Evolution in China's Automobile Market," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 24(4), pages 786-810, October.
    21. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    22. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    23. 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.
    24. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.
    25. Gazheli, Ardjan & van den Bergh, Jeroen, 2018. "Real options analysis of investment in solar vs. wind energy: Diversification strategies under uncertain prices and costs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2693-2704.
    26. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 115511, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    27. Few, Sheridan & Schmidt, Oliver & Offer, Gregory J. & Brandon, Nigel & Nelson, Jenny & Gambhir, Ajay, 2018. "Prospective improvements in cost and cycle life of off-grid lithium-ion battery packs: An analysis informed by expert elicitations," Energy Policy, Elsevier, vol. 114(C), pages 578-590.
    28. 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.
    29. Adriano Vinca & Marianna Rottoli & Giacomo Marangoni & Massimo Tavoni, 2017. "The Role of Carbon Capture and Storage Electricity in Attaining 1.5 and 2°C," Working Papers 2017.54, Fondazione Eni Enrico Mattei.
    30. Stefan Reichelstein & Anshuman Sahoo, 2018. "Relating Product Prices to Long‐Run Marginal Cost: Evidence from Solar Photovoltaic Modules," Contemporary Accounting Research, John Wiley & Sons, vol. 35(3), pages 1464-1498, September.
    31. Millinger, M. & Ponitka, J. & Arendt, O. & Thrän, D., 2017. "Competitiveness of advanced and conventional biofuels: Results from least-cost modelling of biofuel competition in Germany," Energy Policy, Elsevier, vol. 107(C), pages 394-402.
    32. Köberle, Alexandre C. & Gernaat, David E.H.J. & van Vuuren, Detlef P., 2015. "Assessing current and future techno-economic potential of concentrated solar power and photovoltaic electricity generation," Energy, Elsevier, vol. 89(C), pages 739-756.
    33. Talavera, D.L. & Pérez-Higueras, P. & Ruíz-Arias, J.A. & Fernández, E.F., 2015. "Levelised cost of electricity in high concentrated photovoltaic grid connected systems: Spatial analysis of Spain," Applied Energy, Elsevier, vol. 151(C), pages 49-59.
    34. 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.
    35. Andrew Sudmant & Joel Millward-Hopkins & Sarah Colenbrander & Andy Gouldson, 2016. "Low carbon cities: is ambitious action affordable?," Climatic Change, Springer, vol. 138(3), pages 681-688, October.
    36. Lim, Jin Han & Dally, Bassam B. & Chinnici, Alfonso & Nathan, Graham J., 2017. "Techno-economic evaluation of modular hybrid concentrating solar power systems," Energy, Elsevier, vol. 129(C), pages 158-170.
    37. Pillai, Unni, 2015. "Drivers of cost reduction in solar photovoltaics," Energy Economics, Elsevier, vol. 50(C), pages 286-293.
    38. Gert Berckmans & Maarten Messagie & Jelle Smekens & Noshin Omar & Lieselot Vanhaverbeke & Joeri Van Mierlo, 2017. "Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030," Energies, MDPI, Open Access Journal, vol. 10(9), pages 1-20, September.
    39. 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.
    40. Murphy, Helen T. & O’Connell, Deborah A. & Raison, R. John & Warden, Andrew C. & Booth, Trevor H. & Herr, Alexander & Braid, Andrew L. & Crawford, Debbie F. & Hayward, Jennifer A. & Jovanovic, Tom & M, 2015. "Biomass production for sustainable aviation fuels: A regional case study in Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 738-750.
    41. 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.
    42. MacGillivray, Andrew & Jeffrey, Henry & Winskel, Mark & Bryden, Ian, 2014. "Innovation and cost reduction for marine renewable energy: A learning investment sensitivity analysis," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 108-124.
    43. Lin, Boqiang & Li, Jianglong, 2015. "Analyzing cost of grid-connection of renewable energy development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1373-1382.
    44. Leibowicz, Benjamin D., 2015. "Growth and competition in renewable energy industries: Insights from an integrated assessment model with strategic firms," Energy Economics, Elsevier, vol. 52(PA), pages 13-25.
    45. Béla Nagy & J Doyne Farmer & Quan M Bui & Jessika E Trancik, 2013. "Statistical Basis for Predicting Technological Progress," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    46. Ye, Liang-Cheng & Rodrigues, João F.D. & Lin, Hai Xiang, 2017. "Analysis of feed-in tariff policies for solar photovoltaic in China 2011–2016," Applied Energy, Elsevier, vol. 203(C), pages 496-505.
    47. Jong-Hyun Kim & Yong-Gil Lee, 2017. "Analyzing the Learning Path of US Shale Players by Using the Learning Curve Method," Sustainability, MDPI, Open Access Journal, vol. 9(12), pages 1-8, December.
    48. 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.
    49. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    50. Chen, Yuche & Zhang, Yunteng & Fan, Yueyue & Hu, Kejia & Zhao, Jianyou, 2017. "A dynamic programming approach for modeling low-carbon fuel technology adoption considering learning-by-doing effect," Applied Energy, Elsevier, vol. 185(P1), pages 825-835.
    51. Maria Rosario Garzón Sampedro & Carlos Sanchez Gonzalez, 2016. "Spanish photovoltaic learning curve," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 11(2), pages 177-183.
    52. Wei, Max & Smith, Sarah Josephine & Sohn, Michael D., 2017. "Non-constant learning rates in retrospective experience curve analyses and their correlation to deployment programs," Energy Policy, Elsevier, vol. 107(C), pages 356-369.
    53. Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, July.
    54. 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.
    55. Lavidas, George, 2019. "Energy and socio-economic benefits from the development of wave energy in Greece," Renewable Energy, Elsevier, vol. 132(C), pages 1290-1300.
    56. Beck, Marisa & Rivers, Nicholas & Wigle, Randall, 2018. "How do learning externalities influence the evaluation of Ontario's renewables support policies?," Energy Policy, Elsevier, vol. 117(C), pages 86-99.
    57. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
    58. Jridi, Omar & Jridi, Maher & Barguaoui, Saoussen Aguir & Nouri, Fethi Zouheir, 2016. "Energy paradox and political intervention: A stochastic model for the case of electrical equipments," Energy Policy, Elsevier, vol. 93(C), pages 59-69.
    59. Yan Xu & Jiahai Yuan & Jianxiu Wang, 2017. "Learning of Power Technologies in China: Staged Dynamic Two-Factor Modeling and Empirical Evidence," Sustainability, MDPI, Open Access Journal, vol. 9(5), pages 1-14, May.
    60. Heuberger, Clara F. & Rubin, Edward S. & Staffell, Iain & Shah, Nilay & Mac Dowell, Niall, 2017. "Power capacity expansion planning considering endogenous technology cost learning," Applied Energy, Elsevier, vol. 204(C), pages 831-845.
    61. Xiping Wang & Shaoyuan Qie, 2018. "Study on the investment timing of carbon capture and storage under different business modes," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 8(4), pages 639-649, August.
    62. Ajanovic, Amela & Haas, Reinhard, 2018. "Economic prospects and policy framework for hydrogen as fuel in the transport sector," Energy Policy, Elsevier, vol. 123(C), pages 280-288.
    63. Lecca, Patrizio & McGregor, Peter G. & Swales, Kim J. & Tamba, Marie, 2017. "The Importance of Learning for Achieving the UK's Targets for Offshore Wind," Ecological Economics, Elsevier, vol. 135(C), pages 259-268.
    64. Pehnt, Martin, 2006. "Dynamic life cycle assessment (LCA) of renewable energy technologies," Renewable Energy, Elsevier, vol. 31(1), pages 55-71.
    65. Victor, Nadejda & Nichols, Christopher & Zelek, Charles, 2018. "The U.S. power sector decarbonization: Investigating technology options with MARKAL nine-region model," Energy Economics, Elsevier, vol. 73(C), pages 410-425.
    66. 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.
    67. Anelí Bongers, 2017. "Learning and forgetting in the jet fighter aircraft industry," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    68. Rickard Arvidsson & Anne‐Marie Tillman & Björn A. Sandén & Matty Janssen & Anders Nordelöf & Duncan Kushnir & Sverker Molander, 2018. "Environmental Assessment of Emerging Technologies: Recommendations for Prospective LCA," Journal of Industrial Ecology, Yale University, vol. 22(6), pages 1286-1294, December.
    69. Arias-Gaviria, Jessica & van der Zwaan, Bob & Kober, Tom & Arango-Aramburo, Santiago, 2017. "The prospects for Small Hydropower in Colombia," Renewable Energy, Elsevier, vol. 107(C), pages 204-214.
    70. 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.
    71. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 107692, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    72. Zou, Hongyang & Du, Huibin & Brown, Marilyn A. & Mao, Guozhu, 2017. "Large-scale PV power generation in China: A grid parity and techno-economic analysis," Energy, Elsevier, vol. 134(C), pages 256-268.
    73. Anton Finenko & Kamal Soundararajan, 2016. "Flexible solar photovoltaic deployments for Singapore: an economic assessment," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 39(3/4), pages 157-180.
    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. 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).
    2. 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.
    3. 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).
    4. 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).
    5. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    6. Nemet, Gregory F. & Lu, Jiaqi & Rai, Varun & Rao, Rohan, 2020. "Knowledge spillovers between PV installers can reduce the cost of installing solar PV," Energy Policy, Elsevier, vol. 144(C).
    7. 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.
    8. 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.
    9. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).
    10. Grafström, Jonas & Poudineh, Rahmat, 2021. "A review of problems associated with learning curves for solar and wind power technologies," Ratio Working Papers 347, The Ratio Institute.
    11. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    12. 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.
    13. Tadeusz Skoczkowski & Sławomir Bielecki & Joanna Wojtyńska, 2019. "Long-Term Projection of Renewable Energy Technology Diffusion," Energies, MDPI, Open Access Journal, vol. 12(22), pages 1-24, November.
    14. 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).
    15. 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, Open Access Journal, vol. 11(8), pages 1-16, April.
    16. Hong, Soonpa & Yang, Taeyong & Chang, Hyun Joon & Hong, Sungjun, 2020. "The effect of switching renewable energy support systems on grid parity for photovoltaics: Analysis using a learning curve model," Energy Policy, Elsevier, vol. 138(C).
    17. Wei, Yi-Ming & Qiao, Lu & Lv, Xin, 2020. "The impact of mergers and acquisitions on technology learning in the petroleum industry," Energy Economics, Elsevier, vol. 88(C).
    18. Ding, Hao & Zhou, Dequn & Zhou, P., 2020. "Optimal policy supports for renewable energy technology development: A dynamic programming model," Energy Economics, Elsevier, vol. 92(C).
    19. Matthias Buyle & Amaryllis Audenaert & Pieter Billen & Katrien Boonen & Steven Van Passel, 2019. "The Future of Ex-Ante LCA? Lessons Learned and Practical Recommendations," Sustainability, MDPI, Open Access Journal, vol. 11(19), pages 1-24, October.
    20. Yu Sang Chang & Dosoung Choi & Hann Earl Kim, 2017. "Dynamic Trends of Carbon Intensities among 127 Countries," Sustainability, MDPI, Open Access Journal, vol. 9(12), pages 1-21, December.

    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:130:y:2020:i:c:s1364032120302288. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.