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Towards a low-carbon economy: Coping with technological bifurcations with a carbon tax

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  • Chi, Chunjie
  • Ma, Tieju
  • Zhu, Bing

Abstract

Technological learning is understood as an endogenous mechanism for the diffusion of advanced clean energy technologies. Technological learning is quite uncertain. Previous research showed that an optimization model with uncertain technological learning could generate technological bifurcations: various local optimal solutions of technology development strategies with very similar total costs but different environmental impacts. With a simplified energy system optimization model, this paper explores technological bifurcations and the effect of a carbon tax on the development and diffusion of new energy technologies. With a three-stage analysis, the main findings of this paper are (1) that technological learning, instead of its uncertainty, is an essential mechanism for technological bifurcations, and (2) a carbon tax can reduce carbon emission but not necessarily technological bifurcations. An implication from these findings is that with a carbon tax, there still could be potential for other policy interventions to reduce carbon emissions without much additional cost.

Suggested Citation

  • Chi, Chunjie & Ma, Tieju & Zhu, Bing, 2012. "Towards a low-carbon economy: Coping with technological bifurcations with a carbon tax," Energy Economics, Elsevier, vol. 34(6), pages 2081-2088.
    • Handle: RePEc:eee:eneeco:v:34:y:2012:i:6:p:2081-2088
    DOI: 10.1016/j.eneco.2012.02.011
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    Cited by:

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    2. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    3. Li, Jun & Hamdi-Cherif, Meriem & Cassen, Christophe, 2017. "Aligning domestic policies with international coordination in a post-Paris global climate regime: A case for China," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 258-274.
    4. Ma, Tieju & Chen, Huayi, 2015. "Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration," European Journal of Operational Research, Elsevier, vol. 243(3), pages 995-1003.
    5. Zeng, Yongchao & Shi, Yingying & Shahbaz, Muhammad & Liu, Qin, 2024. "Scenario-based policy representative exploration: A novel approach to analyzing policy portfolios and its application to low-carbon energy diffusion," Energy, Elsevier, vol. 296(C).
    6. Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.

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    More about this item

    Keywords

    Technological learning; Uncertainty; Optimization model; Carbon tax; Technological bifurcation;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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