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Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents

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  • Ma, T.
  • Grubler, A.
  • Nakamori, Y.

Abstract

This paper presents a stylized model of technology adoptions for sustainable development under the three potentially most important "stylized facts": increasing returns to adoption, uncertainty, and heterogeneous agents following diverse technology development and adoption strategies. The stylized model deals with three technologies and two heterogeneous agents: a risk-taking one and a risk-averse one. Interactions between the two agents include trade in resources and goods, and technological spillover (free riding and technology trade). With the two heterogeneous agents, we run optimizations to minimize their aggregated costs in order to find out what rational behaviors are under different assumptions if the two agents are somehow cooperative. By considering uncertain carbon taxes, the model also addresses environmental issues as potential driving forces for technology adoptions.

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  • 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.
    • Handle: RePEc:eee:ejores:v:195:y:2009:i:1:p:296-306
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    Cited by:

    1. Chen, Huayi & Ma, Tieju, 2021. "Technology adoption and carbon emissions with dynamic trading among heterogeneous agents," Energy Economics, Elsevier, vol. 99(C).
    2. Wen Li & Tong Zhou & Qiang Mei & Xiangyang Liu & Muhammad Imran, 2023. "Evolution of micro and small enterprises’ work safety behavior in high-risk industries," Small Business Economics, Springer, vol. 60(1), pages 85-104, January.
    3. Ozer, Muammer, 2011. "Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions," European Journal of Operational Research, Elsevier, vol. 211(2), pages 359-369, June.
    4. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    5. Fleiter, Tobias & Worrell, Ernst & Eichhammer, Wolfgang, 2011. "Barriers to energy efficiency in industrial bottom-up energy demand models--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3099-3111, August.
    6. 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.
    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. Tom Savage & Antonio del Rio Chanona & Gbemi Oluleye, 2023. "Robust Market Potential Assessment: Designing optimal policies for low-carbon technology adoption in an increasingly uncertain world," Papers 2304.10203, arXiv.org.
    9. Guo, Shu & Choi, Tsan-Ming & Chung, Sai-Ho, 2022. "Self-design fun: Should 3D printing be employed in mass customization operations?," European Journal of Operational Research, Elsevier, vol. 299(3), pages 883-897.
    10. 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.
    11. Zhang, Qiao & Zhang, Jianxiong & Zaccour, Georges & Tang, Wansheng, 2018. "Strategic technology licensing in a supply chain," European Journal of Operational Research, Elsevier, vol. 267(1), pages 162-175.
    12. 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.
    13. 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.
    14. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    15. C. Wilson & A. Grubler & N. Bauer & V. Krey & K. Riahi, 2013. "Future capacity growth of energy technologies: are scenarios consistent with historical evidence?," Climatic Change, Springer, vol. 118(2), pages 381-395, May.
    16. Grzegorz Drozdowski, 2021. "Economic Calculus Qua an Instrument to Support Sustainable Development under Increasing Risk," JRFM, MDPI, vol. 14(1), pages 1-12, January.

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