Developing a Spatially-Explicit Agent-Based Life Cycle Analysis Framework for Improving the Environmental Sustainability of Bioenergy Systems

Start Date: 
Sep 1, 2011
End Date: 
Aug 31, 2014

Life cycle analysis (LCA) is a useful tool to quantify the environmental impacts of a product or process in an effort to improve its environmental profile.  LCA is easiest to implement on established industrial systems with available data; however, the systems that may benefit most from an LCA are emerging, uncertain, and difficult to quantify with traditional LCA methods.  The emerging bioenergy industry in the U.S. is undergoing rapid development, assisted by federal and state initiatives to reduce dependence on foreign oil and diversify energy portfolios.  There is no indication that the promoted initiatives are optimized for environmental performance, and data is needed to fill research gaps to determine the aggregate environmental impact of different bioenergy development pathways.

This project has two major goals:

•    First, we will use agent-based modeling (ABM) to improve the standard LCA modeling framework to overcome the issues involved with analyzing emerging technologies with dynamic and evolving supply chains.  The achievement of this goal will advance the LCA methodology and provide a new tool for environmental sustainability analysis.

•    Second, we will apply this improved LCA modeling framework to the U.S. bioenergy system (biofuels and biomass electricity) to predict the future supply chain dynamics under a variety of policy scenarios and evaluate the associated life cycle environmental impacts.  The completion of this work will help understand how environmental, economic, social, and technological components of the U.S. bioenergy system interact with each other as well as the sustainability implications. 

Overall, the intellectual merit of this project will improve the state of the LCA method to analyze dynamic, emerging systems.  In addition to this methodological advance, results of this project may directly impact decisions pertaining to bioenergy development.  Through this project, we will be able to understand how the entire bioenergy supply chain will response to different policy interventions, provide decision support information for the development and deployment of biofuels and biomass electricity at national and state scales, guide the biofuel and biomass electricity industries to advance technology development, and educate the next generation engineers for understanding complex issues in biofuel and biomass electricity systems and obtaining multidisciplinary skills.

Intellectual Merit.  Although being widely used in environmental system analysis for sustainability, LCA has long been criticized for a number of its inherent limitations, especially when studying emerging technologies such as the bioenergy system.  The integration of LCA with ABM and GIS proposed in this project will be able to address some of these limitations.  The results of this work will provide an analytical tool to characterize the U.S. switchgrass bioenergy system and understand environmental consequences of a variety of policy scenarios.  In addition, the spatially explicit AB-LCA modeling framework developed in this project will be generally applicable beyond the bioenergy case study and will help researchers and decision-makers study other systems.  In the long run, the integrated modeling framework will have the potential to be connected with other industrial ecology methods such as Material Flow Analysis (MFA), Substance Flow Analysis (SFA), Industrial Symbiosis (IS), etc.

Broader Impact.  The potential societal impacts of the project are particularly significant.  Domestically, state and national policymakers are increasingly considering how to address bioenergy appropriately.  Decisions are being made with a distinct lack of data, perspectives, and alternatives incorporating international issues (e.g., global energy and food prices, GHG emissions) and domestic concerns (e.g., water scarcity, subsidies).  Globally, there are significant opportunities and challenges associated with existing and potential future bioenergy pathways which need to be understood and appropriately managed.  The results of this project can thus play a key role in the public and policy debate.

National Science Foundation
Research Areas: 
agent-based modeling (ABM)
bioenergy supply chain
geographical information systems (GIS)