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Journal of Advanced Transportation
Volume 2019, Article ID 5978753, 13 pages
Research Article

Optimal Location of Biogas Plants in Supply Chains under Carbon Effects: Insight from a Case Study on Animal Manure in North Dakota

1Department of Management, Bill Munday School of Business, St. Edward’s University, Austin, TX 78704, USA
2Department of Transportation, Logistics, and Finance, College of Business, North Dakota State University, Fargo, ND 58108, USA

Correspondence should be addressed to Yong Shin Park; ude.sdrawdets@1krapy

Received 12 November 2018; Revised 4 February 2019; Accepted 17 March 2019; Published 7 April 2019

Guest Editor: Belen M. Batista

Copyright © 2019 Yong Shin Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Faced with increasing concerns over the negative environmental impact due to human and industrial activities, biomass industry practitioners and policy makers have great interest in green supply chains to reduce carbon emissions from supply chain activities. There are many studies which model the biomass supply chain and its environmental impact. However, animal waste sourced biogas supply chain has not received much attention in the literature. Biogas from animal manure not only provides energy efficiency, but also minimizes carbon emissions compared to existing biomass products. Therefore, this study proposes a mixed integer linear program that minimizes total supply costs and carbon emissions from an animal waste sourced biogas supply chain while it also incorporates carbon price in the model to see the impact of a carbon policy on tactical and strategic supply chain decisions. To validate the model proposed, a case study of North Dakota is adopted where there is a high potential for a biogas plant to be developed. The results of our optimization experiment indicate that supply chain performance in terms of both costs and emissions is very sensitive to a carbon pricing mechanism.