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A dynamic programming approach for modeling low-carbon fuel technology adoption considering learning-by-doing effect

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  • Chen, Yuche
  • Zhang, Yunteng
  • Fan, Yueyue
  • Hu, Kejia
  • Zhao, Jianyou

Abstract

Promoting the adoption of low-carbon technologies in the transportation fuel portfolio is an effective strategy to mitigate greenhouse gas emissions from the transportation sector worldwide. However, as one of the most promising low-carbon fuels, cellulosic biofuel has not fully entered commercial production. Governments could provide guidance in developing cellulosic biofuel technologies, but no systematic approach has been proposed yet. We establish a dynamic programming framework for investigating time-dependent and adaptive decision-making processes to develop advanced fuel technologies. The learning-by-doing effect inherited in the technology development process is included in the framework. The proposed framework is applied in a case study to explore the most economical pathway for California to develop a solid cellulosic biofuel industry under its Low Carbon Fuel Standard. Our results show that cellulosic biofuel technology is playing a critical role in guaranteeing California’s 10% greenhouse gas emission reduction by 2020. Three to four billion gallons of cumulative production are needed to ensure that cellulosic biofuel is cost-competitive with petroleum-based fuels or conventional biofuels. Zero emission vehicle promoting policies will discourage the development of cellulosic biofuel. The proposed framework, with small adjustments, can also be applied to study new technology development in other energy sectors.

Suggested Citation

  • Chen, Yuche & Zhang, Yunteng & Fan, Yueyue & Hu, Kejia & Zhao, Jianyou, 2017. "A dynamic programming approach for modeling low-carbon fuel technology adoption considering learning-by-doing effect," Applied Energy, Elsevier, vol. 185(P1), pages 825-835.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p1:p:825-835
    DOI: 10.1016/j.apenergy.2016.10.094
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    Cited by:

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    6. Chen, Yuche & Gonder, Jeffrey & Young, Stanley & Wood, Eric, 2019. "Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 134-145.
    7. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    8. Oluleye, Gbemi & Gandiglio, Marta & Santarelli, Massimo & Hawkes, Adam, 2021. "Pathways to commercialisation of biogas fuelled solid oxide fuel cells in European wastewater treatment plants," Applied Energy, Elsevier, vol. 282(PA).
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    10. Aui, Alvina & Wang, Yu, 2023. "Cellulosic ethanol production: Assessment of the impacts of learning and plant capacity," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

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