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Portfolio Optimization of Nanomaterial Use in Clean Energy Technologies

Moore, Elizabeth A., Babbitt, Callie W., Gaustad, Gabrielle, Moore, Sean T.
Environmental science & technology 2018 v.52 no.7 pp. 4440-4448
case studies, clean energy, decision support systems, economic costs, electric vehicles, emissions, energy, environmental impact, fossil fuels, lithium batteries, models, nanomaterials, photovoltaic cells, renewable energy sources, risk, supply balance, uncertainty, variance
While engineered nanomaterials (ENMs) are increasingly incorporated in diverse applications, risks of ENM adoption remain difficult to predict and mitigate proactively. Current decision-making tools do not adequately account for ENM uncertainties including varying functional forms, unique environmental behavior, economic costs, unknown supply and demand, and upstream emissions. The complexity of the ENM system necessitates a novel approach: in this study, the adaptation of an investment portfolio optimization model is demonstrated for optimization of ENM use in renewable energy technologies. Where a traditional investment portfolio optimization model maximizes return on investment through optimal selection of stock, ENM portfolio optimization maximizes the performance of energy technology systems by optimizing selective use of ENMs. Cumulative impacts of multiple ENM material portfolios are evaluated in two case studies: organic photovoltaic cells (OPVs) for renewable energy and lithium-ion batteries (LIBs) for electric vehicles. Results indicate ENM adoption is dependent on overall performance and variance of the material, resource use, environmental impact, and economic trade-offs. From a sustainability perspective, improved clean energy applications can help extend product lifespans, reduce fossil energy consumption, and substitute ENMs for scarce incumbent materials.