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Dynamic analysis of policy drivers for bioenergy commodity markets

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  • Jeffers, Robert F.
  • Jacobson, Jacob J.
  • Searcy, Erin M.

Abstract

Biomass is increasingly being considered as a feedstock to provide a clean and renewable source of energy in the form of both liquid fuels and electric power. In the United States, the biofuels and biopower industries are regulated by different policies and have different drivers, which impact the maximum price the industries are willing to pay for biomass. This article describes a dynamic computer simulation model that analyzes future behavior of bioenergy feedstock markets given policy and technical options. The model simulates the long-term dynamics of these markets by treating advanced biomass feedstocks as a commodity and projecting the total demand of each industry, as well as the market price over time. The model is used for an analysis of the United States bioenergy feedstock market that projects supply, demand, and market price given three independent buyers: domestic biopower, domestic biofuels, and foreign exports. With base-case assumptions, the biofuels industry is able to dominate the market and meet the federal Renewable Fuel Standard (RFS) targets for advanced biofuels. Further analyses suggest that United States bioenergy studies should include estimates of export demand in their projections, and that GHG-limiting policy would partially shield both industries from export dominance.

Suggested Citation

  • Jeffers, Robert F. & Jacobson, Jacob J. & Searcy, Erin M., 2013. "Dynamic analysis of policy drivers for bioenergy commodity markets," Energy Policy, Elsevier, vol. 52(C), pages 249-263.
    • Handle: RePEc:eee:enepol:v:52:y:2013:i:c:p:249-263
    DOI: 10.1016/j.enpol.2012.08.072
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    References listed on IDEAS

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    Cited by:

    1. Naoum Tsolakis & Jagjit Singh Srai, 2018. "Mapping supply dynamics in renewable feedstock enabled industries: A systems theory perspective on ‘green’ pharmaceuticals," Operations Management Research, Springer, vol. 11(3), pages 83-104, December.
    2. Azadeh, Ali & Vafa Arani, Hamed, 2016. "Biodiesel supply chain optimization via a hybrid system dynamics-mathematical programming approach," Renewable Energy, Elsevier, vol. 93(C), pages 383-403.
    3. Sung, Bongsuk, 2015. "Public policy supports and export performance of bioenergy technologies: A dynamic panel approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 477-495.
    4. Horschig, Thomas & Adams, P.W.R. & Gawel, Erik & Thrän, Daniela, 2018. "How to decarbonize the natural gas sector: A dynamic simulation approach for the market development estimation of renewable gas in Germany," Applied Energy, Elsevier, vol. 213(C), pages 555-572.
    5. Kung, Chih-Chun & Zhang, Ning & Choi, Yongrok & Xiong, Kai & Yu, Jiangli, 2019. "Effectiveness of crop residuals in ethanol and pyrolysis-based electricity production: A stochastic analysis under uncertain climate impacts," Energy Policy, Elsevier, vol. 125(C), pages 267-276.
    6. Rahdar, Mohammad & Wang, Lizhi & Hu, Guiping, 2014. "Potential competition for biomass between biopower and biofuel under RPS and RFS2," Applied Energy, Elsevier, vol. 119(C), pages 10-20.
    7. Beagle, E. & Belmont, E., 2016. "Technoeconomic assessment of beetle kill biomass co-firing in existing coal fired power plants in the Western United States," Energy Policy, Elsevier, vol. 97(C), pages 429-438.

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