(658d) Considering Ecosystem Services in US Bioenergy Supply Chains
In this work, we consider ES values for the first time into a bioenergy supply chain design and optimization model. There is a clear need to consider ecosystem service values into the design of sustainable bioenergy systems2 and energy systems in general.3 However, few biofuels value chain design studies consider ecosystems and the value of the ES they provide, though some works consider ES at the process design level.4 Some important initial studies address qualitative impacts of bioenergy supply chains on ES. For example, Guo et al. (2016) employ estimated ES impact factors in a large-scale, spatial-temporal bioenergy value chain model.5 The vast number and different relative impacts of ES is part of the challenge in considering ES values in the design of biofuels value chains. To address this challenge, we turn to the field of ecological economics which characterizes, classifies, and places a monetary value on ES.6 The Economics of Ecosystems and Biodiversity (TEEB) study contains a database of hundreds of ES values from hundreds of different studies of varied ecosystems in many regions around the globe, the largest database of its kind.7 There is enough data (over 1,310 data points) in the TEEB database to make reasonable, spatially-explicit estimates on the monetary value of ecosystems at a regional or global scale.8 We propose to add these ES values into a US bioenergy supply chain design and optimization model. By considering both natural and nominal economics, we can arrive at a more sustainable â both economically and environmentally â biofuel value chain.
We formulate a mixed integer nonlinear program (MINLP) that considers ES values explicitly within an economic objective function. The modelâs goal is to identify the most economically favorable biomass sourcing and biofuel/bioenergy production strategy that meets renewable energy targets for the US, considering traditional supply chain capital and operating costs as well as loss of ES values due to feedstock production. We identify 2,033 US counties that are fit for agriculture as potential candidates for either biomass sourcing, biofuel production, or both via geo-spatial USDA data.9 The 50 counties with the most population are selected as possible destinations of the finished fuel, with no county able to accept more fuel than it can use. Several different biomass feedstocks and conversion technologies, including biochemical and thermochemical techniques, are considered in the model. To manage model complexity, we introduce a branch-and-refine algorithm with piecewise linear approximations to model the nonconvex capital cost terms, shortening computation times.10 We introduce overall supply chain cost (to be minimized) and Green GDP11 (to be maximized) as economic objectives, demonstrating how the supply chain design shifts when considering ES values economically.
1. Assessment ME. Ecosystems and Human Well-being: Biodiversity 26. Synthesis. World Resources Institute, Washington, DC. 2005.
2. Garcia DJ, You F. The water-energy-food nexus and process systems engineering: A new focus. Computers & Chemical Engineering. 2016;91:49-67.
3. Gong J, You F. Sustainable design and synthesis of energy systems. Current Opinion in Chemical Engineering. 2015;10:77-86.
4. Gopalakrishnan V, Bakshi BR, Ziv G. Assessing the capacity of local ecosystems to meet industrial demand for ecosystem services. AIChE Journal. 2016;62(9):3319-3333.
5. Guo M, Richter GM, Holland RA, Eigenbrod F, Taylor G, Shah N. Implementing land-use and ecosystem service effects into an integrated bioenergy value chain optimisation framework. Computers & Chemical Engineering. 2016;91:392-406.
6. De Groot R, Brander L, van der Ploeg S, et al. Global estimates of the value of ecosystems and their services in monetary units. Ecosystem services. 2012;1(1):50-61.
7. Van der Ploeg S, De Groot R. The TEEB Valuation Databaseâa searchable database of 1310 estimates of monetary values of ecosystem services. Foundation for Sustainable Development, Wageningen, The Netherlands. 2010.
8. Costanza R, de Groot R, Sutton P, et al. Changes in the global value of ecosystem services. Global Environmental Change. 5// 2014;26:152-158.
9. Boryan CG, Yang Z, Willis P, Di L. Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers. 2016.
10. Garcia DJ, You F. Multiobjective optimization of product and process networks: General modeling framework, efficient global optimization algorithm, and case studies on bioconversion. AIChE Journal. 2015;61(2):530-554.
11. Boyd J. Nonmarket benefits of nature: What should be counted in green GDP? Ecological economics. 2007;61(4):716-723.