(331b) Methods for Quantitative Consideration of Ecosystem Services in Supply Chain Design and Optimization

Daniel J. Garcia^a, Fengqi You^b*

1. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60626
2. Department of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853

*Email: Fengqi.you@cornell.edu

Submitted for consideration in session 23C00 Design, Analysis, and Optimization of Sustainable Energy Systems and Supply Chains

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 ecosystems that will be degraded or destroyed is not considered in supply chain design and operations. When they are, they are treated relatively qualitatively or only a few ES are considered at a time.^2-4 In addition to replacing ecosystems entirely, current industrial facilities or enterprises that have already displaced natural ecosystems could further diminish ES provided to their local communities. For example, air quality near a significant polluter of total suspended particulates in the air is worse than air quality in areas not near such a polluter.⁵ This may seem obvious, but the underlying concept that enterprises could diminish local ES – in this case clean air provisioning – is important to consider and quantify in supply chain operations, regardless of the enterprise under study or the set of affected ES. With better understanding of how an enterprise or supply chain produces or diminishes economic and ecological value, more sustainable supply chains and operations can be achieved. However, there are many different ecosystems, ES, and methods for valuing ES, making quantitative integration of ES values into decision-making frameworks difficult. On the other hand, many of the individual ES valuation methods are now quite sophisticated, and the ES research community has recently emphasized quantitative application of these methods as soon as possible.⁶

In this work, we integrate ES values into a supply chain design and operations framework utilizing three different evaluation strategies for ES. To the best of our knowledge, this work is the first to demonstrate quantitative application of several ES valuation approaches in a single decision-making modeling framework. We consider ES valuation by the hedonic method, the avoided cost method, and the aggregated benefit transfer method. In the hedonic valuation method, changes in nearby housing values after the construction of a nearby industrial enterprise or reduction of existing enterprises’ impacts on local ES, subject to stringent statistical tests, are considered to be proxies for the decrease or increase, respectively, in local ES.^7,8 The avoided cost method considers negative economic costs that would have been avoided if the ecosystem had been undisturbed.⁹ Finally, the aggregated benefit transfer approach identifies the overall value of all ES in a particular ecosystem, and applies that value to other, similar ecosystems.¹⁰ There are drawbacks to each method, but they represent a range of valuation techniques often used in ecological economics to influence policy and make land use decisions.¹¹

We formulate a multiobjective mixed integer linear program (MILP) that considers ES degeneration or regeneration from the three aforementioned methods. Two objectives of overall cost and Green GDP (nominal GDP plus sum of all ES values in a region) are considered.¹² We apply the model to a case study on bioenergy production in the US state of Illinois, where the state would like to boost renewable energy production while simultaneously improving the environment its people live in. 160 PJ of bioenergy (approximate production of corn ethanol in Illinois in 2016) can be produced as bioethanol or biogas. For bioethanol production, we assume existing forests and/or prairie are repurposed as cropland for input feedstocks, losing the existing ES. We model these ES impacts with the aggregated benefit transfer method. Bioenergy may also be produced by biogas from swine manure, which in turn regenerates local ES lost from on-site storage and spilling of the manure, modeled with the hedonic method and avoided cost method. Spatial data for distribution of ecosystems in Illinois were analyzed in ArcGIS 10.5 and integrated directly into the MILP model.¹³ Data from the USDA and US Census Bureau were also integrated directly into the model. Traditional supply chain and process construction costs are considered in the overall cost objective, and ES values lost or regenerated in addition to the value of ethanol and electricity produced are considered in the Green GDP objective.

Green GDP ranges from -$125M/y to $132M/y with corresponding costs ranging from $107M/y to $260M/y. Solutions with low Green GDP do not often convert manure to biogas and only produce ethanol only from corn. As Green GDP increases, more manure is converted to biogas, and a transition to switchgrass over corn emerges to produce ethanol. We thus conclude that considering ES values significantly influences supply chain design and operations decision-making. With our more holistic approach, more cost-effective and ecologically-friendly solutions are identified.

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