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Agriculture’s global environmental impacts are widely expected to continue expanding, driven by population and economic growth and dietary changes. This Review highlights climate change as an additional amplifier of agriculture’s environmental impacts, by reducing agricultural productivity, reducing the efficacy of agrochemicals, increasing soil erosion, accelerating the growth and expanding the range of crop diseases and pests, and increasing land clearing. We identify multiple pathways through which climate change intensifies agricultural greenhouse gas emissions, creating a potentially powerful climate change–reinforcing feedback loop. The challenges raised by climate change underscore the urgent need to transition to sustainable, climate-resilient agricultural systems. This requires investments that both accelerate adoption of proven solutions that provide multiple benefits, and that discover and scale new beneficial processes and food products. 

The world faces an increasing need to phase out harmful chemicals and design sustainable alternatives across various consumer products and industrial applications. Alternatives assessment is an emerging field with focus on identifying viable solutions to substitute harmful chemicals. However, current methods fail to consider trade-offs from human and ecosystem exposures, and from impacts associated with chemical supply chains and product life cycles. To close this gap, we propose a life cycle based alternatives assessment (LCAA) framework for consistently integrating quantitative exposure and life cycle impact performance in the substitution process. We start with a pre-screening based on function-related decision rules, followed by three progressive tiers from (1) rapid risk screening of various alternatives for the consumer use stage, to (2) an assessment of chemical supply chain impacts for selected alternatives with substantially different synthesis routes, and (3) an assessment of product life cycle impacts for alternatives with substantially different product life cycles. Each tier focuses on relevant impacts and uses streamlined assessment methods. While the initial risk screening will be sufficient for evaluating chemicals with similar supply chains, each additional tier helps further restricting the number of viable solutions, while avoiding unacceptable trade-offs. We test our LCAA framework in a proof-of-concept case study for identifying suitable alternatives to a harmful plasticizer in household flooring. Results show that the use stage dominates human health impacts across alternatives, supporting that a rapid risk screening is sufficient unless very different supply chains or a broader set of alternative materials or technologies are considered. Combined with currently used indicators for technical and economic performance, our LCAA framework is suitable for informing function-based substitution at the level of chemicals, materials and product applications to foster green and sustainable chemistry solutions. 

Purpose Product developers using life cycle toxicity characterization models to understand the potential impacts of chemical emissions face serious challenges related to large data demands and high input data uncertainty. This motivates greater focus on model sensitivity toward input parameter variability to guide research efforts in data refinement and design of experiments for existing and emerging chemicals alike. This study presents a sensitivity-based approach for estimating toxicity characterization factors given high input data uncertainty and using the results to prioritize data collection according to parameter influence on characterization factors (CFs). Proof of concept is illustrated with the UNEP-SETAC scientific consensus model USEtox. Methods Using Monte Carlo analysis, we demonstrate a sensitivity-based approach to prioritize data collection with an illustrative example of aquatic ecotoxicity CFs for the vitamin B derivative niacinamide, which is an antioxidant used in personal care products. We calculate CFs via 10,000 iterations assuming plus-or-minus one order of magnitude variability in fate and exposure-relevant data inputs, while uncertainty in effect factor data is modeled as a central t distribution. Spearman’s rank correlation indices are used for all variable inputs to identify parameters with the largest influence on CFs. Results and discussion For emissions to freshwater, the niacinamide CF is near log-normally distributed with a geometric mean of 0.02 and geometric standard deviation of 8.5 PAF m3 day/kg. Results of Spearman’s rank correlation show that degradation rates in air, water, and soil are the most influential parameters in calculating CFs, thus benefiting the most from future data refinement and experimental research. Kow, sediment degradation rate, and vapor pressure were the least influential parameters on CF results. These results may be very different for other, e.g., more lipophilic chemicals, where Kow is known to drive many fate and exposure aspects in multimedia modeling. Furthermore, non-linearity between input parameters and CF results prevents transferring sensitivity conclusions from one chemical to another. Conclusions A sensitivity-based approach for data refinement and research prioritization can provide guidance to database managers, life cycle assessment practitioners, and experimentalists to concentrate efforts on the few parameters that are most influential on toxicity characterization model results. Researchers can conserve resources and address parameter uncertainty by applying this approach when developing new or refining existing CFs for the inventory items that contribute most to toxicity impacts.