Bayesian logistic regression models for siting biomass-using facilities
Huang, X  2010.  M.S. Thesis. The University of Tennessee. Knoxville. 149p.

Abstract:
The 20th century was marked by rapid growth and increased prosperity in the world. Key sources of oil for western markets are located in complex geopolitical environments that increase economic and social risk. The amalgamation of economic, environmental, social and national security concerns for petroleum-based economies have created a renewed emphasis on alternative sources of energy which include biomass. The stability of sustainable biomass markets hinge on improved methods to predict, visualize business risk, and cost the supply chain. This thesis develops Bayesian logistic regression models to quantify significant factors that influence the siting of biomass-using facilities and predict potential locations in the Southeastern United States for three types of biomass-using facilities. Group I combines all biomass-using mills, biorefineries using agricultural residues and wood-using bioenergy/biofuels plants. Group II included pulp and paper mills, and biorefineries that use agricultural and woody residue. Group III included food processing mills and biorefineries that use agricultural and woody residues. The resolution of this research is the 5-digit ZIP Code Tabulation Area (ZCTA), and there are 9,416 ZCTAs in the 13-state Southeastern study region. Maximum likelihood estimates indicated that “population density” was a common, significant variable (p-value < 0.0001) with a negative influence for all three groups. Bayesian estimation assuming a Gaussian prior distribution provides the highest correct classification rate of 86.40% for Group I. Bayesian methods assuming the non-informative uniform prior has the highest correct classification rate of 95.97% for Group II. Bayesian methods assuming a Gaussian prior gives the highest correct classification rate of 92.67% for Group III. Given the comparative low sensitivity for Group II and Group III, a hybrid model that integrates classification trees and local Bayesian logistic regression is developed as part of this research to further improve the predictive power. Twenty-five optimal locations (5-digit ZCTAs), based upon the best fitted Bayesian logistic regression model and the hybrid model, are predicted and plotted for 13 Southeastern states for the biomass-using facility groupings.