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    Description

    The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The "median family income," "timberland annual growth-to-removal ratio," and "transportation delays" were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site locations, and locations not preferential for a mill location. The model predicted that higher probability locations for smaller biomass mills (feedstock capacity, the size of sawmills) were in southern Alabama, southern Georgia, southeast Mississippi, southern Virginia, western Louisiana, western Arkansas, and eastern Texas. The higher probability locations for large capacity mills (feedstock capacity, the size for pulp and paper mills) were in southeastern Alabama, southern Georgia, central North Carolina, and the Mississippi Delta regions.

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    Citation

    Young, Timothy M.; Han, Lee D.; Perdue, James H.; Hargrove, Stephanie R.; Guess, Frank M.; Huang, Xia; Chen, Chung-Hao. 2017. Impact of trucking network flow on preferred biorefinery locations in the southern United States. BioResources, 12(3): 4754-4775. 22 p.

    Keywords

    Biorefinery, Bayesian, Biomass, Transportation, Logistics, Traffic flow, Models, Site locations

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