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Analysis of Technological Portfolios for CO2 Stabilizations and Effects of Technological Changes

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  • Fuminori Sano, Keigo Akimoto, Takashi Homma and Toshimasa Tomoda

Abstract

In this study, cost-effective technological options to stabilize CO2 concentrations at 550, 500, and 450 ppmv are evaluated using a world energy systems model of linear programming with a high regional resolution. This model treats technological change endogenously for wind power, photovoltaics, and fuel-cell vehicles, which are technologies of mass production and are considered to follow the Òlearning by doingÓ process. Technological changes induced by climate policies are evaluated by maintaining the technological changes at the levels of the base case wherein there is no climate policy. The results achieved through model analyses ixnclude 1) cost-effective technological portfolios, including carbon capture and storage, marginal CO2 reduction costs, and increases in energy system cost for three levels of stabilization and 2) the effect of the induced technological change on the above mentioned factors. A sensitivity analysis is conducted with respect to the learning rate.

Suggested Citation

  • Fuminori Sano, Keigo Akimoto, Takashi Homma and Toshimasa Tomoda, 2006. "Analysis of Technological Portfolios for CO2 Stabilizations and Effects of Technological Changes," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 141-162.
    • Handle: RePEc:aen:journl:2006se-a06
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    Cited by:

    1. Xu, Zhongming & Fang, Chenhao & Ma, Tieju, 2020. "Analysis of China’s olefin industry using a system optimization model considering technological learning and energy consumption reduction," Energy, Elsevier, vol. 191(C).
    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. Zakerinia, Saleh, 2018. "Understanding the Role of Transportation in Meeting California’s Greenhouse Gas Emissions Reduction Target: A Focus on Technology Forcing Policies, Interactions with the Electric Sector and Mitigation," Institute of Transportation Studies, Working Paper Series qt0r69m651, Institute of Transportation Studies, UC Davis.
    4. Giraudet, Louis-Gaëtan & Guivarch, Céline & Quirion, Philippe, 2012. "Exploring the potential for energy conservation in French households through hybrid modeling," Energy Economics, Elsevier, vol. 34(2), pages 426-445.
    5. Rout, Ullash K. & Akimoto, Keigo & Sano, Fuminori & Tomoda, Toshimasa, 2010. "Introduction of subsidisation in nascent climate-friendly learning technologies and evaluation of its effectiveness," Energy Policy, Elsevier, vol. 38(1), pages 520-532, January.
    6. Coppens, Léo & Dietz, Simon & Venmans, Frank, 2024. "Optimal climate policy under exogenous and endogenous technical change: making sense of the different approaches," LSE Research Online Documents on Economics 124548, London School of Economics and Political Science, LSE Library.
    7. Graham Palmer, 2018. "A Biophysical Perspective of IPCC Integrated Energy Modelling," Energies, MDPI, vol. 11(4), pages 1-17, April.
    8. 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.
    9. Bibas, Ruben & Méjean, Aurélie & Hamdi-Cherif, Meriem, 2015. "Energy efficiency policies and the timing of action: An assessment of climate mitigation costs," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 137-152.
    10. Kuik, Onno & Brander, Luke & Tol, Richard S.J., 2009. "Marginal abatement costs of greenhouse gas emissions: A meta-analysis," Energy Policy, Elsevier, vol. 37(4), pages 1395-1403, April.
    11. 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.
    12. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
    13. Bashmakov, Igor, 2007. "Three laws of energy transitions," Energy Policy, Elsevier, vol. 35(7), pages 3583-3594, July.
    14. Nakata, Toshihiko & Sato, Takemi & Wang, Hao & Kusunoki, Tomoya & Furubayashi, Takaaki, 2011. "Modeling technological learning and its application for clean coal technologies in Japan," Applied Energy, Elsevier, vol. 88(1), pages 330-336, January.

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    JEL classification:

    • F0 - International Economics - - General

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