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Life cycle assessment (LCA) analysts are increasingly being asked to conduct life cycle-based systems level analysis at the earliest stages of technology development. While early assessments provide the greatest opportunity to influence design and ultimately environmental performance, it is the stage with the least available data, greatest uncertainty, and a paucity of analytic tools for addressing these challenges. While the fundamental approach to conducting an LCA of emerging technologies is akin to that of LCA of existing technologies, emerging technologies pose additional challenges. In this paper, we present a broad set of market and technology characteristics that typically influence an LCA of emerging technologies and identify questions that researchers must address to account for the most important aspects of the systems they are studying. The paper presents: (a) guidance to identify the specific technology characteristics and dynamic market context that are most relevant and unique to a particular study, (b) an overview of the challenges faced by early stage assessments that are unique because of these conditions, (c) questions that researchers should ask themselves for such a study to be conducted, and (d) illustrative examples from the transportation sector to demonstrate the factors to consider when conducting LCAs of emerging technologies. The paper is intended to be used as an organizing platform to synthesize existing methods, procedures and insights and guide researchers, analysts and technology developer to better recognize key study design elements and to manage expectations of study outcomes. K E Y W O R D Searly stage technology assessment, environmental impacts, industrial ecology, life cycle assessment (LCA), research and development (R&D), unintended consequences

Biobased product life cycle assessments (LCAs) have focused largely on energy (fossil fuel) usage and greenhouse gas emissions during the agriculture and production stages. This paper compiles a more comprehensive life cycle inventory (LCI) for use in future bioproduct LCAs that rely on corn or soybean crops as feedstocks. The inventory includes energy, C, N, P, major pesticides, and U.S. EPA criteria air pollutants that result from processes such as fertilizer production, energy production, and on-farm chemical and equipment use. Agroecosystem material flows were modeled using a combination of GREET (the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation model), a linear fractionation model that describes P biogeochemical cycling, and Monte Carlo Analysis. Results show that the dominant air emissions resulted from crop farming, fertilizers, and on-farm nitrogen flows (e.g., N20 and NO). Seed production and irrigation provided no more than 0.002% to any of the inventory emissions or energy flows and may be neglected in future LCAs of corn or soybeans as feedstocks from the U.S. Corn Belt. Lime contributes significantly (17% of total emissions) to air emissions and should not be neglected in bioproduct LCAs.

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Intensive agricultural systems are largely responsible for the increase in global reactive nitrogen compounds, which are associated with significant environmental impacts. The nitrogen cycle in agricultural systems is complex and highly variable, which complicates characterization in environmental assessments. Appropriately representing nitrogen inputs into an ecosystem is essential to better understand and predict environmental impacts, such as the extent of seasonally occurring hypoxic zones. Many impacts associated with reactive nitrogen are directly related to annual nitrogen loads, and are not adequately represented by average values that de-emphasize extreme years. To capture the inherent variability in agricultural systems, this paper employs Monte Carlo analysis (MCA) to model major nitrogen exports during crop production, focusing on corn-soybean rotations within the U.S. Corn Belt. This approach yields distributions of possible emission values and is the first step in incorporating variable nutrient fluxes into life cycle assessments (LCA) and environmental impact assessments. Monte Carlo simulations generate distributions of nitrate emissions showing that 80% of values range between 15 and 90 kg NO39-) N/ha (mean 38.5 kg NO3(-) N/ha; median 35.7 kg NO3(-) N/ha) for corn fields and 5-60 kg NO3(-) N/ha (mean 20.8 kg NO3(-) N/ha; median 16.4 kg NO3(-) N/ha) for soybean fields. Data were also generated for grain and residue nitrogen, N2O, NO(x), and NH3. Results indicate model distributions are in agreement with available measured emissions.