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Technology Diffusion in Energy-Economy Models: The Case of Danish Vintage Models

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  • Henrik Klinge Jacobsen

Abstract

Technological progress is an important issue in long-term energy demand projections and in environmental analyses. Different assumptions on technological progress and diffusion of new technologies are among the reasons for diverging results obtained using bottom-up and top-down models for analysing the costs of greenhouse gas mitigation. This paper examines the effect on aggregate energy efficiency of using technological vintage models to describe technology diffusion. The focus is on short- to medium-term issues. Three different models of Danish energy supply and demand are used to illustrate the consequences of the vintage modelling approach. The fluctuating utilisation rates for power capacity in Denmark are found to have a significant impact on average fuel efficiencies. Diffusion of electric appliances is linked to economic activity and saturation levels for each appliance. In the sector of residential heat demand, fuel price increases are found to accelerate diffusion by increasing replacement rates for heating equipment.

Suggested Citation

  • Henrik Klinge Jacobsen, 2000. "Technology Diffusion in Energy-Economy Models: The Case of Danish Vintage Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 43-71.
  • Handle: RePEc:aen:journl:2000v21-01-a02
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    References listed on IDEAS

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    Cited by:

    1. Balachandra, P. & Kristle Nathan, Hippu Salk & Reddy, B. Sudhakara, 2010. "Commercialization of sustainable energy technologies," Renewable Energy, Elsevier, vol. 35(8), pages 1842-1851.
    2. Ruth, Matthias & Amato, Anthony, 2002. "Vintage structure dynamics and climate change policies: the case of US iron and steel," Energy Policy, Elsevier, vol. 30(7), pages 541-552, June.
    3. repec:gam:jsusta:v:8:y:2016:i:5:p:457:d:69688 is not listed on IDEAS
    4. Pandey, Rahul, 2002. "Energy policy modelling: agenda for developing countries," Energy Policy, Elsevier, vol. 30(2), pages 97-106, January.
    5. Szabo, Laszlo & Hidalgo, Ignacio & Ciscar, Juan Carlos & Soria, Antonio, 2006. "CO2 emission trading within the European Union and Annex B countries: the cement industry case," Energy Policy, Elsevier, vol. 34(1), pages 72-87, January.
    6. Hidalgo, Ignacio & Szabo, Laszlo & Carlos Ciscar, Juan & Soria, Antonio, 2005. "Technological prospects and CO2 emission trading analyses in the iron and steel industry: A global model," Energy, Elsevier, vol. 30(5), pages 583-610.
    7. Sue Wing, Ian, 2008. "The synthesis of bottom-up and top-down approaches to climate policy modeling: Electric power technology detail in a social accounting framework," Energy Economics, Elsevier, vol. 30(2), pages 547-573, March.
    8. Bonilla, David, 2007. "Fuel Price Changes and the Adoption of Cogeneration in the U.K. and Netherlands," The Electricity Journal, Elsevier, vol. 20(7), pages 59-71.
    9. Worrell, Ernst & Biermans, Gijs, 2005. "Move over! Stock turnover, retrofit and industrial energy efficiency," Energy Policy, Elsevier, vol. 33(7), pages 949-962, May.
    10. Zvingilaite, Erika & Klinge Jacobsen, Henrik, 2015. "Heat savings and heat generation technologies: Modelling of residential investment behaviour with local health costs," Energy Policy, Elsevier, vol. 77(C), pages 31-45.
    11. William X. Wei & Dezhi Chen & Daiping Hu, 2016. "Study on the Evolvement of Technology Development and Energy Efficiency—A Case Study of the Past 30 Years of Development in Shanghai," Sustainability, MDPI, Open Access Journal, vol. 8(5), pages 1-21, May.
    12. Bonilla, David & Akisawa, Atsushi & Kashiwagi, Takao, 2003. "Modelling the adoption of industrial cogeneration in Japan using manufacturing plant survey data," Energy Policy, Elsevier, vol. 31(9), pages 895-910, July.

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    • F0 - International Economics - - General

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