(658d) Considering Ecosystem Services in US Bioenergy Supply Chains | AIChE

(658d) Considering Ecosystem Services in US Bioenergy Supply Chains


Garcia, D. - Presenter, Northwestern University
You, F., Cornell University
Ecosystems provide essential services – termed ecosystem services (ES) – to humanity that make our lives possible, such as nutrient cycling within a forest or flood management from mangroves.1 However, despite these critical services that ecosystems provide, many ecosystems around the world are either degraded or destroyed in the name of economic growth.1 Usually, the inherent value of the degraded or destroyed ecosystems is not considered. It is possible that the economic development that takes the place of existing ecosystems does not provide as much value as the original ecosystem, resulting in a net loss of value. However, if the original ecosystem’s value is not considered, then this net loss to society would instead be mistakenly interpreted as a net economic gain. Industries that require land, mainly agriculture, can quickly and efficiently destroy ecosystems. Perhaps the most well-known example in the US is the Dust Bowl, caused by rapid expansion of poor farming practices in the Great Plains, resulting in degraded soil that was easily picked up by wind. Biofuels rely heavily on agriculture to produce the industry’s feedstocks. Thus, biofuels could pose a significant threat to ecosystems around the globe if feedstock sourcing is not responsibly planned.

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 – both economically and environmentally – biofuel value chain.

We formulate a mixed integer nonlinear program (MINLP) that considers ES values explicitly within an economic objective function. The model’s 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.


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