IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/0632.html

Developing the PAGE2002 Model with Endogenous Technical Change

Author

Listed:
  • Alberth, S.
  • Hope, C.

Abstract

Presented research demonstrates the inclusion of endogenous technical change into the PAGE2002 integrated assessment model of climate change. The ‘experience curve’ or learning-by-doing concept, made popular by the Boston Consulting Group during the 1960’s provides a mechanism with which to describe cost reduction through experiential learning. The implementation of learning requires both a restructuring of the way costs are modelled as well as the inclusion of an explicit learning function with initial abatement costs and learning coefficients calibrated to historical renewable energy data. The discounted values for total abatement costs are calculated for both the standard PAGE2002 model without an explicit learning function and the modified PAGE2002 model. The results were found to be of a similar magnitude, partially due to the myopic effects of discounting, though the result was found to be highly sensitive to the learning rate used, which in our case was a conservative estimate.

Suggested Citation

  • Alberth, S. & Hope, C., 2006. "Developing the PAGE2002 Model with Endogenous Technical Change," Cambridge Working Papers in Economics 0632, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:0632
    Note: IO
    as

    Download full text from publisher

    File URL: https://www.jbs.cam.ac.uk/wp-content/uploads/2023/12/eprg-wp0613.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Popp, David, 2004. "ENTICE: endogenous technological change in the DICE model of global warming," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 742-768, July.
    2. Karsten Neuhoff, 2005. "Large-Scale Deployment of Renewables for Electricity Generation," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 21(1), pages 88-110, Spring.
    3. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    4. 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.
    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. Alestra, C. & Cette, G. & Chouard, V. & Lecat, R., 2022. "Growth impact of climate change and response policies: The advanced climate change long-term (ACCL) model1," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 96-112.
    2. Alberth, Stephan & Hope, Chris, 2007. "Climate modelling with endogenous technical change: Stochastic learning and optimal greenhouse gas abatement in the PAGE2002 model," Energy Policy, Elsevier, vol. 35(3), pages 1795-1807, March.

    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. 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.
    2. 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.
    3. 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.
    4. Tian Tang & David Popp, 2016. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(1), pages 195-222, January.
    5. 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.
    6. Arnaud de La Tour & Matthieu Glachant & Yann Ménière, 2013. "What cost for photovoltaic modules in 2020? Lessons from experience curve models," Working Papers hal-00805668, HAL.
    7. Gregory F. Nemet, 2006. "How well does Learning-by-doing Explain Cost Reductions in a Carbon-free Energy Technology?," Working Papers 2006.143, Fondazione Eni Enrico Mattei.
    8. 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.
    9. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    10. Adao, Bernardino & Narajabad, Borghan & Temzelides, Ted, 2012. "Renewable Technology Adoption and the Macroeconomy," Working Papers 14-007, Rice University, Department of Economics.
    11. Simon Dietz & Nicholas Stern, 2014. "Endogenous growth, convexity of damages and climate risk: how Nordhaus� framework supports deep cuts in carbon emissions," GRI Working Papers 159, Grantham Research Institute on Climate Change and the Environment.
    12. Lecocq, Franck & Shalizi, Zmarak, 2007. "How might climate change affect economic growth in developing countries ? a review of the growth literature with a climate lens," Policy Research Working Paper Series 4315, The World Bank.
    13. Lehmann, Paul, 2009. "Climate policies with pollution externalities and learning spillovers," UFZ Discussion Papers 10/2009, Helmholtz Centre for Environmental Research (UFZ), Division of Social Sciences (ÖKUS).
    14. del Río, Pablo & Bleda, Mercedes, 2012. "Comparing the innovation effects of support schemes for renewable electricity technologies: A function of innovation approach," Energy Policy, Elsevier, vol. 50(C), pages 272-282.
    15. 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.
    16. Armon Rezai & Frederick Van Der Ploeg, 2017. "Abandoning Fossil Fuel: How Fast and How Much," Manchester School, University of Manchester, vol. 85(S2), pages 16-44, December.
    17. 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.
    18. 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.
    19. Stokes, Leah C., 2013. "The politics of renewable energy policies: The case of feed-in tariffs in Ontario, Canada," Energy Policy, Elsevier, vol. 56(C), pages 490-500.
    20. Guo, Jian-Xin & Zhu, Lei & Fan, Ying, 2016. "Emission path planning based on dynamic abatement cost curve," European Journal of Operational Research, Elsevier, vol. 255(3), pages 996-1013.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cam:camdae:0632. 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: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    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.