IDEAS home Printed from https://ideas.repec.org/a/eee/ecolec/v135y2017icp259-268.html
   My bibliography  Save this article

The Importance of Learning for Achieving the UK's Targets for Offshore Wind

Author

Listed:
  • Lecca, Patrizio
  • McGregor, Peter G.
  • Swales, Kim J.
  • Tamba, Marie

Abstract

Using a purpose-built, multi-sectoral energy-economy-environmental model we evaluate the economic and environmental impact of a reduction in the levelized costs of offshore wind energy generation in the UK. Our modelling approach suggests that in order to significantly increase the offshore wind capacity in the UK the required fall in the generation cost should be larger than expected and certainly bigger than that implied by the most recent cost projections developed by the UK Department of Energy and Climate Change (DECC). Potential expansion of the offshore wind sector in the UK crucially depends on the price sensitivity of the energy supply sector and on agent's expectations. Only in our more optimistic scenario do we reach DECC's ambitious challenge of 22 GW offshore wind deployment in 2030 through a constant learning rate alone.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecolec:v:135:y:2017:i:c:p:259-268
    DOI: 10.1016/j.ecolecon.2017.01.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0921800915302822
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolecon.2017.01.021?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. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    2. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    3. 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.
    4. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    5. David I. Stern, 2012. "Interfuel Substitution: A Meta‐Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 26(2), pages 307-331, April.
    6. David J. Evans, 2005. "The elasticity of marginal utility of consumption: estimates for 20 OECD countries," Fiscal Studies, Institute for Fiscal Studies, vol. 26(2), pages 197-224, June.
    7. Tobin, James, 1969. "A General Equilibrium Approach to Monetary Theory," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 1(1), pages 15-29, February.
    8. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    9. Strachan, Neil & Kannan, Ramachandran, 2008. "Hybrid modelling of long-term carbon reduction scenarios for the UK," Energy Economics, Elsevier, vol. 30(6), pages 2947-2963, November.
    10. Lecca, Patrizio & McGregor, Peter G. & Swales, J. Kim & Turner, Karen, 2014. "The added value from a general equilibrium analysis of increased efficiency in household energy use," Ecological Economics, Elsevier, vol. 100(C), pages 51-62.
    11. 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.
    12. Hayashi, Fumio, 1982. "Tobin's Marginal q and Average q: A Neoclassical Interpretation," Econometrica, Econometric Society, vol. 50(1), pages 213-224, January.
    13. Allan, Grant & Hanley, Nick & McGregor, Peter & Swales, Kim & Turner, Karen, 2007. "The impact of increased efficiency in the industrial use of energy: A computable general equilibrium analysis for the United Kingdom," Energy Economics, Elsevier, vol. 29(4), pages 779-798, July.
    14. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    15. Kota, Sandhya & Bayne, Stephen B. & Nimmagadda, Sandeep, 2015. "Offshore wind energy: A comparative analysis of UK, USA and India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 685-694.
    16. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    17. Lecca, Patrizio & Swales, Kim & Turner, Karen, 2011. "An investigation of issues relating to where energy should enter the production function," Economic Modelling, Elsevier, vol. 28(6), pages 2832-2841.
    18. Barnes, Sebastian & Price, Simon & Sebastia Barriel, Maria, 2008. "The elasticity of substitution: evidence from a UK firm-level data set," Bank of England working papers 348, Bank of England.
    19. Blanchflower, David G & Oswald, Andrew J, 1994. "Estimating a Wage Curve for Britain: 1973-90," Economic Journal, Royal Economic Society, vol. 104(426), pages 1025-1043, September.
    20. Messner, Sabine & Schrattenholzer, Leo, 2000. "MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively," Energy, Elsevier, vol. 25(3), pages 267-282.
    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. Figus, Gioele & McGregor, Peter G. & Swales, J. Kim & Turner, Karen, 2020. "Do sticky energy prices impact the time paths of rebound effects associated with energy efficiency actions?," Energy Economics, Elsevier, vol. 86(C).
    2. Qu, Yang & Swales, J. Kim & Hooper, Tara & Austen, Melanie C. & Wang, Xinhao & Papathanasopoulou, Eleni & Huang, Junling & Yan, Xiaoyu, 2023. "Economic trade-offs in marine resource use between offshore wind farms and fisheries in Scottish waters," Energy Economics, Elsevier, vol. 125(C).
    3. 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).
    4. Qu, Yang & Hooper, Tara & Swales, J. Kim & Papathanasopoulou, Eleni & Austen, Melanie C. & Yan, Xiaoyu, 2021. "Energy-food nexus in the marine environment: A macroeconomic analysis on offshore wind energy and seafood production in Scotland," Energy Policy, Elsevier, vol. 149(C).
    5. Roberts, Simon H. & Foran, Barney D. & Axon, Colin J. & Warr, Benjamin S. & Goddard, Nigel H., 2018. "Consequences of selecting technology pathways on cumulative carbon dioxide emissions for the United Kingdom," Applied Energy, Elsevier, vol. 228(C), pages 409-425.
    6. Graziano, Marcello & Lecca, Patrizio & Musso, Marta, 2017. "Historic paths and future expectations: The macroeconomic impacts of the offshore wind technologies in the UK," Energy Policy, Elsevier, vol. 108(C), pages 715-730.
    7. Yamaguchi, Rintaro & Managi, Shunsuke, 2019. "Backward- and Forward-looking Shadow Prices in Inclusive Wealth Accounting: An Example of Renewable Energy Capital," Ecological Economics, Elsevier, vol. 156(C), pages 337-349.

    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. 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).
    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. Lehmann, Paul, 2013. "Supplementing an emissions tax by a feed-in tariff for renewable electricity to address learning spillovers," Energy Policy, Elsevier, vol. 61(C), pages 635-641.
    5. 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.
    6. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    7. Alabi, Oluwafisayo & Turner, Karen & Figus, Gioele & Katris, Antonios & Calvillo, Christian, 2020. "Can spending to upgrade electricity networks to support electric vehicles (EVs) roll-outs unlock value in the wider economy?," Energy Policy, Elsevier, vol. 138(C).
    8. 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.
    9. 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.
    10. Rout, Ullash K. & Fahl, Ulrich & Remme, Uwe & Blesl, Markus & Voß, Alfred, 2009. "Endogenous implementation of technology gap in energy optimization models--a systematic analysis within TIMES G5 model," Energy Policy, Elsevier, vol. 37(7), pages 2814-2830, July.
    11. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    12. 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.
    13. 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.
    14. Lecca, Patrizio & McGregor, Peter G. & Swales, J. Kim & Turner, Karen, 2014. "The added value from a general equilibrium analysis of increased efficiency in household energy use," Ecological Economics, Elsevier, vol. 100(C), pages 51-62.
    15. Elofsson, Katarina, 2014. "International knowledge diffusion and its impact on the cost-effective clean-up of the Baltic Sea," Working Paper Series 2014:06, Swedish University of Agricultural Sciences, Department Economics.
    16. Paul Lehmann & Patrik Söderholm, 2018. "Can Technology-Specific Deployment Policies Be Cost-Effective? The Case of Renewable Energy Support Schemes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(2), pages 475-505, October.
    17. Wei Jin, 2012. "Can Technological Innovation Help China Take on Its Climate Responsibility? A Computable General Equilibrium Analysis," CAMA Working Papers 2012-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    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. Garau, Giorgio & Lecca, Patrizio & Mandras, Giovanni, 2013. "The impact of population ageing on energy use: Evidence from Italy," Economic Modelling, Elsevier, vol. 35(C), pages 970-980.
    20. Desroches, Louis-Benoit & Garbesi, Karina & Kantner, Colleen & Van Buskirk, Robert & Yang, Hung-Chia, 2013. "Incorporating experience curves in appliance standards analysis," Energy Policy, Elsevier, vol. 52(C), pages 402-416.

    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:ecolec:v:135:y:2017:i:c:p:259-268. 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/ecolecon .

    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.