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Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price

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  • Chenhao Fang

    (East China University of Science and Technology)

  • Tieju Ma

    (East China University of Science and Technology
    International Institute for Applied Systems Analysis)

Abstract

The adoption of new technologies with high efficiency and low emissions is of great importance in achieving sustainable development. Most studies of technology adoption have been criticized for idealistically assuming only one global decision agent. In this paper, an optimization model of technology adoption with heterogeneous agents is proposed. These agents have different market shares, and each one attempts to identify the optimal technology adoption for a portion of the entire system. The carbon emission trading mechanism is implemented to reduce carbon emissions. Agents’ acceptance of uncertain carbon prices is characterized by calculating their willingness to pay, and a bargaining process is introduced to reasonably allocate the profit. Computational tests are conducted with different market shares and different discounting factors. Numerical results show that implementing the carbon emission trading mechanism is an effective way to promote technology adoption and carbon emission reduction, although it does not certainly lead to less carbon emissions than implementing only a carbon cap. A small gap between agents’ market shares and an increase in the seller’s discounting factor will lead to more adoption in the entire system. A seller’s market may lead to less carbon emissions than implementing only a carbon cap, while a buyer’s market may lead to more carbon emissions. Moreover, it is suggested that governments can propose incentive policies to support small companies to develop and maintain carbon prices at a reasonable level to benefit sellers to promote technology adoption.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:300:y:2021:i:2:d:10.1007_s10479-019-03297-w
    DOI: 10.1007/s10479-019-03297-w
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    References listed on IDEAS

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    1. Rubinstein, Ariel, 1982. "Perfect Equilibrium in a Bargaining Model," Econometrica, Econometric Society, vol. 50(1), pages 97-109, January.
    2. Zhimin Huang & Yi-Ming Wei & Ke Wang & Hua Liao, 2017. "Energy economics and climate policy modeling," Annals of Operations Research, Springer, vol. 255(1), pages 1-7, August.
    3. 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.
    4. 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.
    5. Ding, Huiping & Zhao, Qilan & An, Zhirong & Tang, Ou, 2016. "Collaborative mechanism of a sustainable supply chain with environmental constraints and carbon caps," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 191-207.
    6. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    7. R. H. Coase, 2013. "The Problem of Social Cost," Journal of Law and Economics, University of Chicago Press, vol. 56(4), pages 837-877.
    8. Mélanie Heugues, 2014. "International environmental cooperation: a new eye on the greenhouse gas emissions’ control," Annals of Operations Research, Springer, vol. 220(1), pages 239-262, September.
    9. Cong, Rong-Gang & Wei, Yi-Ming, 2010. "Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options," Energy, Elsevier, vol. 35(9), pages 3921-3931.
    10. 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.
    11. Mohamed Amine Boutabba & Sandrine Lardic, 2017. "EU Emissions Trading Scheme, competitiveness and carbon leakage: new evidence from cement and steel industries," Annals of Operations Research, Springer, vol. 255(1), pages 47-61, August.
    12. Ma-Lin Song & Wei Zhang & Xiao-Ming Qiu, 2015. "Emissions trading system and supporting policies under an emissions reduction framework," Annals of Operations Research, Springer, vol. 228(1), pages 125-134, May.
    13. 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.
    14. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    15. Ciwei Dong & Bin Shen & Pui-Sze Chow & Liu Yang & Chi To Ng, 2016. "Sustainability investment under cap-and-trade regulation," Annals of Operations Research, Springer, vol. 240(2), pages 509-531, May.
    16. Eva Camacho-Cuena & Till Requate & Israel Waichman, 2012. "Investment Incentives Under Emission Trading: An Experimental Study," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 53(2), pages 229-249, October.
    17. Zhimin Huang & Yiming Wei & Ke Wang & Hua Liao (ed.), 2017. "Energy economics and climate policy modeling," CEEP-BIT Books, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology, number b15, december.
    18. 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.
    19. E. Allevi & A. Gnudi & I. V. Konnov & G. Oggioni, 2018. "Evaluating the effects of environmental regulations on a closed-loop supply chain network: a variational inequality approach," Annals of Operations Research, Springer, vol. 261(1), pages 1-43, February.
    20. Liu, Xianbing & Fan, Yongbin & Li, Chen, 2016. "Carbon pricing for low carbon technology diffusion: A survey analysis of China's cement industry," Energy, Elsevier, vol. 106(C), pages 73-86.
    21. S. Yu & H.-P. Weikard & X. Zhu & E. C. Ierland, 2017. "International carbon trade with constrained allowance choices: Results from the STACO model," Annals of Operations Research, Springer, vol. 255(1), pages 95-116, August.
    22. S. Du & F. Ma & Z. Fu & L. Zhu & J. Zhang, 2015. "Game-theoretic analysis for an emission-dependent supply chain in a ‘cap-and-trade’ system," Annals of Operations Research, Springer, vol. 228(1), pages 135-149, May.
    23. Mohamed Amine Boutabba & Sandrine Lardic, 2017. "EU Emissions Trading Scheme, competitiveness and carbon leakage: new evidence from cement and steel industries," Post-Print hal-02877954, HAL.
    24. Tang, Ling & Wu, Jiaqian & Yu, Lean & Bao, Qin, 2015. "Carbon emissions trading scheme exploration in China: A multi-agent-based model," Energy Policy, Elsevier, vol. 81(C), pages 152-169.
    25. Yue-Jun Zhang & Jun-Fang Hao, 2017. "Carbon emission quota allocation among China’s industrial sectors based on the equity and efficiency principles," Annals of Operations Research, Springer, vol. 255(1), pages 117-140, August.
    26. Lin, Boqiang & Li, Jianglong, 2015. "Analyzing cost of grid-connection of renewable energy development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1373-1382.
    27. Sabzevar, Nikoo & Enns, S.T. & Bergerson, Joule & Kettunen, Janne, 2017. "Modeling competitive firms' performance under price-sensitive demand and cap-and-trade emissions constraints," International Journal of Production Economics, Elsevier, vol. 184(C), pages 193-209.
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    Cited by:

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    3. Wen-Hsien Tsai & Shang-Yu Lai & Chu-Lun Hsieh, 2023. "Exploring the impact of different carbon emission cost models on corporate profitability," Annals of Operations Research, Springer, vol. 322(1), pages 41-74, March.

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