IDEAS home Printed from https://ideas.repec.org/r/cwl/cwldpp/1685.html

The Perils of the Learning Model For Modeling Endogenous Technological Change

Citations

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


Cited by:

  1. Stephen Murray & Cesar Aguado & Victor M. Castaño, 2022. "In-Company Technical Training in Developing Countries," Journal of Education and Training, Macrothink Institute, vol. 9(2), pages 82-99, August.
  2. 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.
  3. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
  4. 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.
  5. Narbel, Patrick André & Hansen, Jan Petter, 2014. "Estimating the cost of future global energy supply," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 91-97.
  6. Aguilera, Roberto F. & Ripple, Ronald D., 2012. "Technological progress and the availability of European oil and gas resources," Applied Energy, Elsevier, vol. 96(C), pages 387-392.
  7. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
  8. De Cian, Enrica & Buhl, Johannes & Carrara, Samuel & Michela Bevione, Michela & Monetti, Silvia & Berg, Holger, "undated". "Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach," MITP: Mitigation, Innovation and Transformation Pathways 249784, Fondazione Eni Enrico Mattei (FEEM).
  9. Yuichiro Kamada & Fuhito Kojima, 2013. "Voter Preferences, Polarization, and Electoral Policies," Discussion Papers 12-021, Stanford Institute for Economic Policy Research.
  10. Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.
  11. Bento, Nuno & Gianfrate, Gianfranco & Groppo, Sara Virginia, 2019. "Do crowdfunding returns reward risk? Evidences from clean-tech projects," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 107-116.
  12. 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.
  13. repec:aen:journl:ej36-1-06 is not listed on IDEAS
  14. Elizabeth Baldwin & Yongyang Cai & Karlygash Kuralbayeva, 2018. "To Build or Not to Build? Capital Stocks and Climate Policy," CESifo Working Paper Series 6884, CESifo.
  15. Verdolini, Elena & Anadon, Laura Diaz & Lu, Jiaqi & Nemet, Gregory F., 2015. "The effects of expert selection, elicitation design, and R&D assumptions on experts' estimates of the future costs of photovoltaics," Energy Policy, Elsevier, vol. 80(C), pages 233-243.
  16. 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.
  17. 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).
  18. Triulzi, Giorgio & Alstott, Jeff & Magee, Christopher L., 2020. "Estimating technology performance improvement rates by mining patent data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  19. 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.
  20. Aguilera, Roberto F., 2014. "Production costs of global conventional and unconventional petroleum," Energy Policy, Elsevier, vol. 64(C), pages 134-140.
  21. repec:sae:envval:v:25:y:2016:i:1:p:7-28 is not listed on IDEAS
  22. Eskeland, Gunnar S. & Rive, Nathan A. & Mideksa, Torben K., 2012. "Europe’s climate goals and the electricity sector," Energy Policy, Elsevier, vol. 41(C), pages 200-211.
  23. Schmid, Eva & Knopf, Brigitte, 2015. "Quantifying the long-term economic benefits of European electricity system integration," Energy Policy, Elsevier, vol. 87(C), pages 260-269.
  24. Funk, Jeffrey L. & Magee, Christopher L., 2015. "Rapid improvements with no commercial production: How do the improvements occur?," Research Policy, Elsevier, vol. 44(3), pages 777-788.
  25. Edenhofer, Ottmar & Hirth, Lion & Knopf, Brigitte & Pahle, Michael & Schlömer, Steffen & Schmid, Eva & Ueckerdt, Falko, 2013. "On the economics of renewable energy sources," Energy Economics, Elsevier, vol. 40(S1), pages 12-23.
  26. Shayegh, Soheil & Sanchez, Daniel L. & Caldeira, Ken, 2017. "Evaluating relative benefits of different types of R&D for clean energy technologies," Energy Policy, Elsevier, vol. 107(C), pages 532-538.
  27. Narbel, Patrick A. & Hansen, Jan Petter, 2014. "Estimating the cost of future global energy supply," Discussion Papers 2014/14, Norwegian School of Economics, Department of Business and Management Science.
  28. Christopher L Benson & Christopher L Magee, 2015. "Quantitative Determination of Technological Improvement from Patent Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-23, April.
  29. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2015. "Bending the learning curve," Energy Economics, Elsevier, vol. 52(S1), pages 86-99.
  30. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
  31. Enrica Cian & Valentina Bosetti & Massimo Tavoni, 2012. "Technology innovation and diffusion in “less than ideal” climate policies: An assessment with the WITCH model," Climatic Change, Springer, vol. 114(1), pages 121-143, September.
  32. Ritchie, Justin & Dowlatabadi, Hadi, 2017. "Evaluating the Learning-by-Doing Theory of Long-Run Oil, Gas, and Coal Economics," RFF Working Paper Series 17-14, Resources for the Future.
  33. Ding, H. & Zhou, D.Q. & Liu, G.Q. & Zhou, P., 2020. "Cost reduction or electricity penetration: Government R&D-induced PV development and future policy schemes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
  34. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
  35. Hirth, Lion, 2013. "The Optimal Share of Variable Renewables. How the Variability of Wind and Solar Power Affects their Welfare-optimal Deployment," Energy: Resources and Markets 162373, Fondazione Eni Enrico Mattei (FEEM).
  36. 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).
  37. Amavilah, Voxi Heinrich, 2014. "Human Knowledge and a Commonsensical Measure of Human Capital: A Proposal," MPRA Paper 57670, University Library of Munich, Germany.
  38. Benson, Christopher L. & Magee, Christopher L., 2014. "On improvement rates for renewable energy technologies: Solar PV, wind turbines, capacitors, and batteries," Renewable Energy, Elsevier, vol. 68(C), pages 745-751.
  39. Amavilah, Voxi Heinrich, 2011. "The Full Value of the Nobel Prize - Part 1: Mining “Data Without Theory”," MPRA Paper 33483, University Library of Munich, Germany.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.