Economic modeling of forest biomass supply for bioenergy production in southern United States

The demand for forest biomass for energy production is on the rise. With 214 million acres of forestland, the U.S. South has a huge potential for forest biomass supply. Consequently, there is a need to predict the forest biomass supply in response to potential bioenergy demand scenarios and assess the effects on conventional forest products markets and landowners. This study models woody biomass supply variations in face of mature woody bioenergy markets and their associated impact on forests in 13 southern states. It also proposes woody biomass harvesting guidelines that can aid in ensuring sustainability.;The study results show that the emergence of a new woody biomass based energy market could lead to price increases for merchantable timber, resulting in increased returns to forest landowners. The increased harvesting resulting from a woody biomass market will cause timber prices to rise; significantly so, for many scenarios. These increases are likely to have a negative effect on the existing forest products industry as they will be competing for higher priced timber with the wood for energy conversion plants. Consequently, merchantable timber will be diverted from existing forest product industries in order to generate renewable energy. The impacts of wood based energy markets tend to be higher on non sawtimber than sawtimber industries. At a higher level of wood demand for energy, however, sawtimber industry will be impacted. The price changes are expected to be higher in the case of softwoods relative to hardwoods. While woody biomass harvest is expected to increase with higher prices, the forest inventory and forestland acreage trend is hard to predict as increased plantations, afforestation of non-forestlands tend to counter the increased removals due to woody bioenergy markets. The woody biomass markets will likely be beneficial to timber harvesting firms, and businesses that support woody biomass for energy operations as well.;Results of this study show that with management and technological advancements, Southern forests could make a significant contribution towards woody bioenergy and timber supply for other forest product sectors. Across the various bioenergy scenarios, these new demands would affect the markets for all wood products and lead to price increases for timber products and higher returns to private landowners. The degree to which other wood consumers are impacted would depend on expansion in supply, which in turn depends on intensification of forest management and changes in land use (primarily from agricultural to forestry). The sustainability issues surrounding bioenergy are defined by the negative externalities associated with accelerated harvesting in the South. Research indicates that management systems and standards can be designed to protect these values, defining another interface with future policy. As expected, imposing sustainability constraints such as harvesting limits restricts biomass availability for energy production and lowers landowner revenues.;The elasticity values arrived through a dynamic partial adjustment model for Southern forestry sector support the inelastic nature of forestry. The econometric results showed that bioenergy, pulpwood, and sawtimber stumpage are substitutes, while capital investment and stumpage are complements. Consistent to economic theory own price elasticities were negative and final product elasticities were positive. Results also show that the elasticities of derived demand for stumpage are variable and increasing over time. The Le Chatelier principle is met for all the elasticities except for energy input for sawtimber and pulpwood industries, and labor for bioenergy industry. The results show that producers adjust to different factors of demand. Low capital adjustment parameters for the sawtimber and pulpwood industries equations indicate that there is a significant lag in capital cost adjustment period (15 years or more), while bioenergy industries respond relatively quickly to factor input fluctuations, taking just four years.;(Abstract shortened by UMI.)...