The goal of this research is to create a framework for a predictive and dynamic Life Cycle Assessment (LCA) tool. LCA quantifies the environmental impacts of a product or process from resource extraction through disposal. The proposed work will result in transformational advances in LCA, including the ability to forecast the stochastic nature of developing systems and incorporating spatial and temporal considerations currently lacking in LCA. A developing switchgrass-to-energy system in the southeastern United States will be used as a case study to show how a more sophisticated mathematical approach can ultimately lead to a more robust and effective Life Cycle Assessment tool. Switchgrass is a native perennial grass that has been proposed for cellulosic ethanol production. While the results of this analysis will be specific to the developing bioenergy industry, the methods will be applicable to any developing system with large geographic and temporal uncertainties. Potential uses include predicting the changing urban metabolism of growing cities, identifying transformations in material flow over space and time, and forecasting industrial networks for developing products.
This research will address criticisms of the LCA method by advancing its mathematical basis. LCA generally uses fairly simple linear or matrix-based calculations to characterize the environmental effects of established products. It has not been effective estimating potential impacts of theoretical or developing systems. Historically, LCAs have been “snapshots in time” without systematic consideration of geography or development over time. LCA also relies on average values or point estimates that do not adequately reflect overall system variability, resulting in potential bias. This research will create a more robust and useful LCA by transferring the latest knowledge from decision theory and incorporating stochastic modeling to forecast changing land use patterns and their effect on the environmental impacts of bioenergy.
Three hypotheses will be tested to demonstrate the effectiveness of dynamic LCA techniques on the case study:
- Geographic and temporal considerations will have a significant effect on switchgrass-to-energy LCA results. Changes in soil carbon sequestration are largely dependent on previous land use and agricultural management over time. Water quality impacts are determined by proximity to surface waters, soil type, and local precipitation. These impacts cannot be analyzed using standard LCA methods.
- Cultivation of switchgrass will improve the overall environmental profile of the southeastern agriculture system. Conversion from row crop agriculture to a low input, perennial crop should improve water quality, increase soil organic carbon, and reduce farm energy inputs; however, there may be some adverse impacts resulting from inactive land conversion to switchgrass. I hypothesize that at a system level, any adverse impacts will be offset by the greater environmental benefits of row crop agriculture conversion. Aggregate effects on the southeastern agricultural region will be obtained by comparing the environmental impacts of current land uses with the predicted switchgrass adoption scenarios.
- The environmental profile for switchgrass-to-energy will improve as switchgrass prices increase. Conversion of row crop agriculture to switchgrass will increase with higher switchgrass prices. As more row crop agriculture relative to the amount of fallow or conservation land is converted to switchgrass, the environmental profile will improve.
- A Game Theory Analysis of Market Incentives for US Switchgrass Ethanol
- A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment
- Framework for Analyzing Transformative Technologies in Life Cycle Assessment
- Policy incentives for switchgrass production using valuation of non-market ecosystem services
- Potential for Integrating Diffusion of Innovation Principles into Life Cycle Assessment of Emerging Technologies
- Regional Differences in Water Quality Impacts from the Bioenergy Mandate: A Scenario-Based Approach to Quantifying the Impacts from RFS2
- Satisfying the Rural Residential Demand in Liberia with Decentralized Renewable Energy Schemes