A Statistical Analysis of Key Factors Influencing the Location of Biomass-using Facilities

Bioenergy and biofuels are emerging industries in the U.S. economy that will require statistical and economical analyses of woody biomass resources, supply chains, and other key factors that influence the siting of industrial facilities. This thesis develops models using logistic regression to improve the understanding of the key factors that influence the locations of existing wood-using bioenergy and biofuels plants, and other wood-using plants. The scope of the study is 13 Southeastern states.¹ Logistic regression models are developed at the state and regional levels. The resolution of the study is the ZIP Code tabulation area (ZCTA). There are 9,416 ZCTAs in the 13–state study region.

Because a small number of woody biomass-using bioenergy and biofuels plants exist relative to the large number of traditional woody biomass-using facilities (e.g., wood composites, sawmills, and secondary mills), two sample groups are developed. The first group combines all wood-using mills with wood-using bioenergy and biofuels plants, and compares ZCTAs with these types of mills with ZCTAs that do not contain any such facilities. This follows a more modern planning view of total woody biomass management. The second group combines only one type of mill, pulp and paper mills, with wood-using bioenergy and biofuels plants, and compares ZCTAs of these mill types with ZCTAs that do not contain such facilities.

For both groups in the entire study region, logging residues harvesting costs (negative influence) and the availability of thinnings within an 80-mile haul distance (positive influence) are statistically significant factors (p-value < 0.0001) in the logistic models. Population is statistically significant and has a negative influence on site location for six of the thirteen states in the region (p-values ranged from < 0.0001 to 0.0197) for the first group. Twenty-five optimal locations in the Southeastern states (ZCTAs) are predicted from the logistic regression models. A de-clustering algorithm is developed as part of this study to avoid locating potential bioenergy and biofuels sites in close proximity to competing mills within same ZCTA. ______________________

¹ Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia.