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Technology adoption with limited foresight and uncertain technological learning

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  • Chen, Huayi
  • Ma, Tieju

Abstract

Most previous optimization models on technology adoption assume perfect foresight over the long term. In reality, decision-makers do not have perfect foresight, and the endogenous driving force of technology adoption is uncertain. With a stylized optimization model, this paper explores the adoption of a new technology, its associated cost dynamics, and technological bifurcations with limited foresight and uncertain technological learning. The study shows that when modeling with limited foresight and technological learning, (1) the longer the length of the decision period, the earlier the adoption of a new technology, and the value of a foresight can be amplified with a high learning rate. However, when the decision period is beyond a certain length, further extending its length has little influence on adopting the new technology; (2) with limited foresight, decisions aiming at minimizing the total cost of each decision period will commonly result in a non-optimal solution from the perspective of the entire decision horizon; and (3) the range of technological bifurcation is much larger than that with perfect foresight, but uncertainty in technological learning tends to reduce the range by removing the early adoption paths of a new technology.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:1:p:266-275
    DOI: 10.1016/j.ejor.2014.03.031
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    References listed on IDEAS

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    1. 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.
    2. Messner, S. & Golodnikov, A. & Gritsevskii, A., 1996. "A stochastic version of the dynamic linear programming model MESSAGE III," Energy, Elsevier, vol. 21(9), pages 775-784.
    3. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    4. repec:aen:journl:2006se-a04 is not listed on IDEAS
    5. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Azar, Christian & Lindgren, Kristian & Andersson, Bjorn A., 2003. "Global energy scenarios meeting stringent CO2 constraints--cost-effective fuel choices in the transportation sector," Energy Policy, Elsevier, vol. 31(10), pages 961-976, August.
    8. Martinsen, Dag & Krey, Volker & Markewitz, Peter, 2007. "Implications of high energy prices for energy system and emissions--The response from an energy model for Germany," Energy Policy, Elsevier, vol. 35(9), pages 4504-4515, September.
    9. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    10. Ma, T. & Grubler, A. & Nakamori, Y., 2009. "Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 195(1), pages 296-306, May.
    11. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    12. 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.
    13. Leonardo Barreto, Socrates Kypreos, 2002. "Multi-regional technological learning in the energysystems MARKAL model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 189-213.
    14. Ma, Tieju & Nakamori, Yoshiteru, 2009. "Modeling technological change in energy systems – From optimization to agent-based modeling," Energy, Elsevier, vol. 34(7), pages 873-879.
    15. Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.
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    3. Jessica Thomsen & Noha Saad Hussein & Arnold Dolderer & Christoph Kost, 2021. "Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model," Energies, MDPI, vol. 14(2), pages 1-22, January.
    4. Chenhao Fang & Tieju Ma, 2021. "Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price," Annals of Operations Research, Springer, vol. 300(2), pages 577-600, May.
    5. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
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    7. Chen, Huayi & Zhou, P., 2019. "Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?," Omega, Elsevier, vol. 89(C), pages 257-270.
    8. Lambert, Jerry & Hanel, Andreas & Fendt, Sebastian & Spliethoff, Hartmut, 2023. "Evaluation of sector-coupled energy systems using different foresight horizons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

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