Skip to main content

Advertisement

Log in

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

  • S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Allevi, E., Gnudi, A., Konnov, I. V., & Oggioni, G. (2018). Evaluating the effects of environmental regulations on a closed-loop supply chain network: A variational inequality approach. Annals of Operations Research, 261(1–2), 1–43.

    Article  Google Scholar 

  • Arrow, K. J. (1962). The economic implications of learning by doing. The Review of Economic Studies, 29(3), 155–173.

    Article  Google Scholar 

  • Arthur, W. B. (1989). Competing technologies, increasing returns, and lock-in by historical events. The Economic Journal, 99(394), 116–131.

    Article  Google Scholar 

  • Babonneau, F., & Haurie, A. (2018). Energy technology environment model with smart grid and robust nodal electricity prices. Annals of Operations Research, 274(1–2), 101–117.

    Google Scholar 

  • Boutabba, M. A., & Lardic, S. (2017). EU Emissions Trading Scheme, competitiveness and carbon leakage: New evidence from cement and steel industries. Annals of Operations Research, 255(1–2), 47–61.

    Article  Google Scholar 

  • Camacho-Cuena, E., Requate, T., & Waichman, I. (2012). Investment incentives under emission trading: An experimental study. Environmental & Resource Economics, 53(2), 229–249.

    Article  Google Scholar 

  • Chen, H., & Ma, T. (2014). Technology adoption with limited foresight and uncertain technological learning. European Journal of Operational Research, 239(1), 266–275.

    Article  Google Scholar 

  • Chen, H., & Ma, T. (2017). Optimizing systematic technology adoption with heterogeneous agents. European Journal of Operational Research, 257(1), 287–296.

    Article  Google Scholar 

  • Coase, R. H. (1960). The problem of social cost. Journal of Law and Economics, 3(4), 1–44.

    Article  Google Scholar 

  • Cong, R., & Wei, Y. (2010). Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options. Energy, 35(9), 3921–3931.

    Article  Google Scholar 

  • Ding, H., Zhao, Q., An, Z., & Tang, O. (2016). Collaborative mechanism of a sustainable supply chain with environmental constraints and carbon caps. International Journal of Production Economics, 181, 191–207.

    Article  Google Scholar 

  • Dong, C., Shen, B., Chow, P.-S., Yang, L., & Ng, C. T. (2016). Sustainability investment under cap-and-trade regulation. Annals of Operations Research, 240(2), 509–531.

    Article  Google Scholar 

  • Du, S., Ma, F., Fu, Z., Zhu, L., & Zhang, J. (2015). Game-theoretic analysis for an emission-dependent supply chain in a ‘cap-and-trade’ system. Annals of Operations Research, 228(1), 135–149.

    Article  Google Scholar 

  • Du, S., Qian, J., Liu, T., & Hu, L. (2018). Emission allowance allocation mechanism design: A low-carbon operations perspective. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2922-z.

    Article  Google Scholar 

  • Fan, Y., Mo, J., & Zhu, L. (2016). Carbon trading in China: Policy design and social-economic impact. Beijing: Science Press.

    Google Scholar 

  • Fang, C., & Ma, T. (2018). Technology adoption optimization with heterogeneous agents and carbon emission trading mechanism. In V. N. Huynh, M. Inuiguchi, D. Tran, & T. Denoeux (Eds.), Integrated uncertainty in knowledge modelling and decision making. IUKM 2018. Lecture notes in computer science (Vol. 10758). Cham: Springer.

    Google Scholar 

  • García-Cascales, M. S., Lamata, M. T., & Sánchez-Lozano, J. M. (2012). Evaluation of photovoltaic cells in a multi-criteria decision making process. Annals of Operations Research, 199(1), 373–391.

    Article  Google Scholar 

  • Grubler, A., & Gritsevskii, A. (1997). A model of endogenous technological change through uncertain returns and learning (R&D and investments). Working paper. IIASA, Laxenburg, Austria. http://pure.iiasa.ac.at/id/eprint/12525/1/A%20Model%20of%20Endogenous%20Technological%20Change.pdf.

  • Guo, J., Zhu, L., & Fan, Y. (2016). Emission path planning based on dynamic abatement cost curve. European Journal of Operational Research, 255(3), 996–1013.

    Article  Google Scholar 

  • Heller, W. P., & Starrett, D. A. (1976). On the nature of externalities. In Theory & measurement of economic externalities (pp. 9–27).

  • Heugues, M. (2014). International environmental cooperation: A new eye on the greenhouse gas emissions’ control. Annals of Operations Research, 220(1), 239–262.

    Article  Google Scholar 

  • Huang, Z., Wei, Y.-M., Wang, K., & Liao, H. (2017). Energy economics and climate policy modeling. Annals of Operations Research, 255(1–2), 1–7.

    Article  Google Scholar 

  • Huenteler, J., Niebuhr, C., & Schmidt, T. S. (2016). The effect of local and global learning on the cost of renewable energy in developing countries. Journal of Cleaner Production, 128, 6–21.

    Article  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC). (2007). Fourth assessment report: Climate change 2007. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC). (2017). Chair’s vision paper. Addis Ababa, Ethiopia: AR6 Scoping Meeting.

  • Lei, X., Shiyun, T., Yanfei, D., & Yuan, Y. (2018). Sustainable operation-oriented investment risk evaluation and optimization for renewable energy project: A case study of wind power in China. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2878-z.

    Article  Google Scholar 

  • Lema, A., & Lema, R. (2013). Technology transfer in the clean development mechanism: Insights from wind power. Global Environmental Change, 23(1), 301–313.

    Article  Google Scholar 

  • Lin, B., & Li, J. (2015). Analyzing cost of grid-connection of renewable energy development in China. Renewable and Sustainable Energy Reviews, 50, 1373–1382.

    Article  Google Scholar 

  • Liu, X., Fan, Y., & Li, C. (2016). Carbon pricing for low carbon technology diffusion: A survey analysis of China’s cement industry. Energy, 106, 73–86.

    Article  Google Scholar 

  • Ma, T. (2010). Coping with uncertainties in technological learning. Management Science, 56(1), 192–201.

    Article  Google Scholar 

  • 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, 195(1), 296–306.

    Article  Google Scholar 

  • Phillips, J., Das, K., & Newell, P. (2013). Governance and technology transfer in the Clean Development Mechanism in India. Global Environmental Change, 23(6), 1594–1604.

    Article  Google Scholar 

  • Pigou, A. C. (1920). The economics of welfare. London: Macmillan.

    Google Scholar 

  • Rubinstein, A. (1982). Perfect equilibrium in a bargaining model. Econometrica, 50(1), 97–109.

    Article  Google Scholar 

  • Sabzevar, N., Enns, S. T., Bergerson, J., & Kettunen, J. (2017). Modeling competitive firms’ performance under price-sensitive demand and cap-and-trade emissions constraints. International Journal of Production Economics, 184, 193–209.

    Article  Google Scholar 

  • Song, M.-L., Zhang, W., & Qiu, X.-M. (2015). Emissions trading system and supporting policies under an emissions reduction framework. Annals of Operations Research, 228(1), 125–134.

    Article  Google Scholar 

  • Stańczak, J., & Bartoszczuk, P. (2010). CO2 emission trading model with trading prices. Climatic Change, 103(1–2), 291–301.

    Article  Google Scholar 

  • Tang, L., Wu, J., Yu, L., & Bao, Q. (2015). Carbon emissions trading scheme exploration in China: A multi-agent-based model. Energy Policy, 81, 152–169.

    Article  Google Scholar 

  • World Bank. (2000). Entering the 21st century: World development report 1999/2000. Washington, DC: World Bank.

    Google Scholar 

  • Yu, S., Weikard, H.-P., Zhu, X., & van Ierland, E. C. (2017). International carbon trade with constrained allowance choices: Results from the STACO model. Annals of Operations Research, 255(1–2), 95–116.

    Article  Google Scholar 

  • Zhang, H., Cao, L., & Zhang, B. (2017). Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China. Resources, Conservation and Recycling, 121, 23–32.

    Article  Google Scholar 

  • Zhang, Y.-J., & Hao, J.-F. (2017). Carbon emission quota allocation among China’s industrial sectors based on the equity and efficiency principles. Annals of Operations Research, 255(1–2), 117–140.

    Article  Google Scholar 

  • Zhao, J., Hobbs, B. F., & Pang, J. (2009). Long-run equilibrium modeling of emissions allowance allocation systems in electric power markets. Operations Research, 58(3), 529–548.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 71571069; the Ministry of Education of China under Grant 222201718006; and the China Scholarship Council under Grant 201806740014.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tieju Ma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, C., Ma, T. Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price. Ann Oper Res 300, 577–600 (2021). https://doi.org/10.1007/s10479-019-03297-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-019-03297-w

Keywords

Navigation