An exploratory study of biomass harvesting among logging firms in Virginia and North Carolina

Abstract

Logging firms in the United States will need to harvest and transport woody biomass if national wood-based renewable energy production goals are to be achieved. Expansion of associated biomass markets could provide important revenue opportunities for logging firms and potentially alter the sector. To study biomass harvesting behavior among logging firms, a logistic regression model was developed using a backward likelihood ratio procedure and 114 Virginia and North Carolina logging firms that operate in proximity to established woody biomass markets. The sample was identified from a larger pool of firm owners who participated in a Virginia Sustainable Harvesting and Resource Professional (SHARP) Logger Program survey. Hypothesized predictors included logging operation and owner variables. Confirmatory factor analysis was used to verify the underlying structure of summated latent constructs. The model correctly classified biomass harvesting 78% of the time, exhibited acceptable fit, and contained normally distributed residual error. Significant predictors included firm mechanization, haul distance, and owner perspectives regarding job security satisfaction and attitudes about biomass harvesting. Analysis of variance and χ 2 were used to test for differences between significant predictors when factored across a three-point biomass market-action variable-no market, market/no harvest, and market/harvest. The follow-on market-action evaluations included firms from the SHARP Logger Program survey that share county-level centers of operation for which at least one of the firms noted proximity to a woody biomass market. Implications for logging sector research are discussed, along with practical considerations for regions where wood-based renewable energy initiatives are on the rise. © 2011 by the Society of American Foresters.