High-Resolution Multiobjective Optimization of Sustainable Supply Chains for a Large-Scale Lignocellulosic Biofuel Industry

This chapter introduces an integrative framework coupling GIS tools, biophysical model, enterprise budgeting tools, GHG emission models, and optimization mechanism in determining a sustainable switchgrass biofuel supply chain network. The integrative framework is applied to a case study of replacing 30% of gasoline used in transportation in Tennessee. Using high-resolution spatial data in a multiobjective mixed-integer programming model, we find that land use choice makes substantial impacts on the deployment of the supply chains under different objectives. When considering private cost alone, hay and pasture land concentrated in the east and central Tennessee will be the major source for switchgrass production. If targeting GHG emission minimization solely, more than 500 thousand hectares of the state’s cropland is converted to switchgrass for biofuel production. Moreover, the trade-off between cost and GHG emissions in the supply chains shows that the marginal rate of substitution between total cost and GHG emissions on the frontier curve increases at an accelerating rate. Our findings illustrate the importance of land resource management on the sustainability of a dedicated energy crop supply chain.