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Modeling Switchgrass Production in South Carolina Based on Farmers’ Decisions: A Stochastic and Spatial AnalysisEPA Grant Number: FP917172
Title: Modeling Switchgrass Production in South Carolina Based on Farmers’ Decisions: A Stochastic and Spatial Analysis
Investigators: Sharp, Benjamin Elias
Institution: Clemson University
EPA Project Officer: Zambrana, Jose
Project Period: August 18, 2010 through August 17, 2013
Project Amount: $111,000
RFA: STAR Graduate Fellowships (2010) RFA Text | Recipients Lists
Research Category: Fellowship - Science & Technology for Sustainability: Environmental Behavior & Decision Making , Academic Fellowships
Some bioenergy development has resulted in economic and environmental backlash, such as with corn and sugar cane; yet in other cases, it remains a promising alternative energy solution. Examining future bioenergy systems before processes become established offers opportunities to better understand potential outcomes and reveal to stakeholders more desirable paths of development in terms of environmental and economic costs. This research will expand on traditional Life Cycle Assessment (LCA) for determining environmental impact. Making use of innovative LCA techniques will generate information that will translate to clear and meaningful information for emerging bioenergy systems.
Bioenergy production has had mixed success in terms of environmental impact, net energy generating, and economic costs. By performing careful analyses prior to alternative energy industries becoming established, we can estimate probable development scenarios. Resulting data will reveal opportunities and help avoid negative outcomes. This project analyzes switchgrass production for bioenergy in South Carolina and will help to determine its potential environmental impact and economic viability.
South Carolina lacks a major energy source. It does have, however, a favorable agricultural climate, suggesting that bioenergy could be an option for the region. For this reason, it is important to explore the likelihood of South Carolina growers to begin producing switchgrass (Panicum virgatum) as an energy crop. Aggregate estimations will be based on a model that takes into account data on farmers’ willingness to adopt new crops, the expected profitability, and the spatial compatibility of growing switchgrass. This stochastic model will be geared toward fitting results into an overall LCA of the switchgrass-for-energy industry.Expected Results:
By incorporating projected switchgrass production data into existing standards of life-cycle measures, it is possible to communicate relevant information about likely outcomes. The results will shed light on the environmental impact and the economic viability of growing switchgrass for bioenergy. Furthermore, these probabilistic scenarios of production can be adjusted according to different system perturbations such as incentives or technological breakthroughs. Subsequent results will inform farmers, processors, policy makers, energy providers, and energy consumers. With this type of shared knowledge, switchgrass-to-energy may become a sustainable bioenergy success for South Carolina.
Potential to Further Environmental/Human Health Protection
Overcoming path dependency in energy production and consumption is a pressing challenge. Developing processes for understanding how alternatives are adopted and identifying the drawbacks are important for identifying how shifts to sustainable, renewable energy might be realized. Using the switchgrass-bioenergy industry in South Carolina as an emerging system, this research will offer a set of techniques to reveal important insights for the development of similar large-scale energy solutions.
bioenergy, biofuel, sustainable energy, spatial modeling, geographic information system, land use, switchgrass,