IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v35y2007i4p2412-2425.html
   My bibliography  Save this article

Carbon pricing and the diffusion of renewable power generation in Eastern Europe: A linear programming approach

Author

Listed:
  • Pettersson, Fredrik

Abstract

No abstract is available for this item.

Suggested Citation

  • Pettersson, Fredrik, 2007. "Carbon pricing and the diffusion of renewable power generation in Eastern Europe: A linear programming approach," Energy Policy, Elsevier, vol. 35(4), pages 2412-2425, April.
    • Handle: RePEc:eee:enepol:v:35:y:2007:i:4:p:2412-2425
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301-4215(06)00331-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Colpier, Ulrika Claeson & Cornland, Deborah, 2002. "The economics of the combined cycle gas turbine--an experience curve analysis," Energy Policy, Elsevier, vol. 30(4), pages 309-316, March.
    2. Duic, Neven & Juretic, Franjo & Zeljko, Mladen & Bogdan, Zeljko, 2005. "Croatia energy planning and Kyoto Protocol," Energy Policy, Elsevier, vol. 33(8), pages 1003-1010, May.
    3. Horn, Manfred, 1999. "Energy demand until 2010 in Ukraine," Energy Policy, Elsevier, vol. 27(12), pages 713-726, November.
    4. Patrik Söderholm & Ger Klaassen, 2007. "Wind Power in Europe: A Simultaneous Innovation–Diffusion Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 36(2), pages 163-190, February.
    5. Jaffe, Adam B. & Newell, Richard G. & Stavins, Robert N., 2005. "A tale of two market failures: Technology and environmental policy," Ecological Economics, Elsevier, vol. 54(2-3), pages 164-174, August.
    6. del Rio Gonzalez, Pablo & Hernandez, Felix & Gual, Miguel, 2005. "The implications of the Kyoto project mechanisms for the deployment of renewable electricity in Europe," Energy Policy, Elsevier, vol. 33(15), pages 2010-2022, October.
    7. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    8. Jonathan Kohler, Michael Grubb, David Popp and Ottmar Edenhofer, 2006. "The Transition to Endogenous Technical Change in Climate-Economy Models: A Technical Overview to the Innovation Modeling Comparison Project," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 17-56.
    9. Martinot, E., 1999. "Renewable energy in Russia: markets, development and technology transfer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 3(1), pages 49-75, March.
    10. Bartle, Alison, 2002. "Hydropower potential and development activities," Energy Policy, Elsevier, vol. 30(14), pages 1231-1239, November.
    11. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    12. Samuel Fankhauser & Lucia Lavric, 2003. "The investment climate for climate investment: Joint Implementation in transition countries," Climate Policy, Taylor & Francis Journals, vol. 3(4), pages 417-434, December.
    13. Karasalihovic, Daria & Maurovic, Lidia & Sunjerga, Snjezana, 2003. "Natural gas in croatia's energy-future," Applied Energy, Elsevier, vol. 75(1-2), pages 9-22, May.
    14. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    15. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    16. 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.
    17. Syri, Sanna & Amann, Markus & Capros, Pantelis & Mantzos, Leonidas & Cofala, Janusz & Klimont, Zbigniew, 2001. "Low-CO2 energy pathways and regional air pollution in Europe," Energy Policy, Elsevier, vol. 29(11), pages 871-884, September.
    18. Illum, Klaus & Meyer, Niels I, 2004. "Joint implementation: methodology and policy considerations," Energy Policy, Elsevier, vol. 32(8), pages 1013-1023, June.
    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. Zhou, Y. & Li, Y.P. & Huang, G.H., 2015. "Planning sustainable electric-power system with carbon emission abatement through CDM under uncertainty," Applied Energy, Elsevier, vol. 140(C), pages 350-364.
    2. Zhengping Liu & Wang Zhang & Hongxian Liu & Guohe Huang & Jiliang Zhen & Xin Qi, 2019. "Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    3. Wu, T. & Thomassin, P.J., 2018. "The Impact of Carbon Tax on Food Prices and Consumption in Canada," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275913, International Association of Agricultural Economists.
    4. Janos Szlavik & Maria Csete, 2012. "Climate and Energy Policy in Hungary," Energies, MDPI, vol. 5(2), pages 1-24, February.

    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. 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.
    2. 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.
    3. 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.
    4. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    5. 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).
    6. 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.
    7. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    8. Tian Tang & David Popp, 2014. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," NBER Working Papers 19921, National Bureau of Economic Research, Inc.
    9. 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.
    10. Tian Tang & David Popp, 2014. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," CESifo Working Paper Series 4705, CESifo.
    11. 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.
    12. Popp, David & Newell, Richard G. & Jaffe, Adam B., 2010. "Energy, the Environment, and Technological Change," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 873-937, Elsevier.
    13. 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.
    14. Li, Sheng & Zhang, Xiaosong & Gao, Lin & Jin, Hongguang, 2012. "Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: Methodology and case studies," Applied Energy, Elsevier, vol. 93(C), pages 348-356.
    15. 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).
    16. 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.
    17. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    18. 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.
    19. 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).
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:enepol:v:35:y:2007:i:4:p:2412-2425. 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/locate/enpol .

    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.