Several prominent research strategy organizations recommend applying life cycle assessment (LCA) early in the development of emerging technologies. For example, the US Environmental Protection Agency, the National Research Council, the Department of Energy, and the National Nanotechnology Initiative identify…
Several prominent research strategy organizations recommend applying life cycle assessment (LCA) early in the development of emerging technologies. For example, the US Environmental Protection Agency, the National Research Council, the Department of Energy, and the National Nanotechnology Initiative identify the potential for LCA to inform research and development (R&D) of photovoltaics and products containing engineered nanomaterials (ENMs). In this capacity, application of LCA to emerging technologies may contribute to the growing movement for responsible research and innovation (RRI). However, existing LCA practices are largely retrospective and ill-suited to support the objectives of RRI. For example, barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. This dissertation focuses on development of anticipatory LCA tools that incorporate elements of technology forecasting, provide robust explorations of uncertainty, and engage diverse innovation actors in overcoming retrospective approaches to environmental assessment and improvement of emerging technologies. Chapter one contextualizes current LCA practices within the growing literature articulating RRI and identifies the optimal place in the stage gate innovation model to apply LCA. Chapter one concludes with a call to develop anticipatory LCA – building on the theory of anticipatory governance – as a series of methodological improvements that seek to align LCA practices with the objectives of RRI.
Chapter two provides a framework for anticipatory LCA, identifies where research from multiple disciplines informs LCA practice, and builds off the recommendations presented in the preceding chapter. Chapter two focuses on crystalline and thin film photovoltaics (PV) to illustrate the novel framework, in part because PV is an environmentally motivated technology undergoing extensive R&D efforts and rapid increases in scale of deployment. The chapter concludes with a series of research recommendations that seek to direct PV research agenda towards pathways with the greatest potential for environmental improvement.
Similar to PV, engineered nanomaterials (ENMs) are an emerging technology with numerous potential applications, are the subject of active R&D efforts, and are characterized by high uncertainty regarding potential environmental implications. Chapter three introduces a Monte Carlo impact assessment tool based on the toxicity impact assessment model USEtox and demonstrates stochastic characterization factor (CF) development to prioritize risk research with the greatest potential to improve certainty in CFs. The case study explores a hypothetical decision in which personal care product developers are interested in replacing the conventional antioxidant niacinamide with the novel ENM C60, but face high data uncertainty, are unsure regarding potential ecotoxicity impacts associated with this substitution, and do not know what future risk-relevant experiments to invest in that most efficiently improve certainty in the comparison. Results suggest experiments that elucidate C60 partitioning to suspended solids should be prioritized over parameters with little influence on results. This dissertation demonstrates a novel anticipatory approach to exploration of uncertainty in environmental models that can create new, actionable knowledge with potential to guide future research and development decisions.