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Novel energy technologies, especially decentralized electricity generation systems, are increasingly being designed and implemented. However, potential environmental impacts are frequently recognized after installing new energy systems at full scale, at which point modification comes at a high cost. Life cycle assessment (LCA) can be used throughout the design-to-commercialization process to prevent this outcome, despite the challenges of emerging energy technology LCAs, like comparability, lack of data, scale-up difficulties, and uncertainties that are not typically faced while evaluating existing and established systems. The complexity and urgency of evaluating climate change impacts of novel energy technologies during the research and development stage reveal the need for guidance, presented in this study, with an emphasis on data collection, data processing, and uncertainty analysis. We outline best practices in choosing among several methods that have been employed in LCA studies to fill gaps in input data, including machine learning. Additionally, we discuss how design can be guided by LCA through assessment setting and delineation of scenarios or case studies, in order to prevent unnecessary effort and maximize the amount of useful, interpretable results. We also discuss the utility of complementary analyses, including global sensitivity analysis, neural network, Monte Carlo analysis that differentiates between uncertainty and variability parameters, and optimization. This guidance has the potential to make emerging electricity generation system implementation ultimately effective in reducing greenhouse gas emissions, through the methodological use of LCA in the design process. 

Frontline communities of California experience disproportionate social, economic, and environmental injustices, and climate change is exacerbating the root causes of inequity in those areas. Yet, climate adaptation and mitigation strategies often fail to meaningfully address the experience of frontline community stakeholders. Here, we present three challenges, three errors, and three solutions to better integrate frontline communities' needs in climate change research and to create more impactful policies. We base our perspective on our collective firsthand experiences and on scholarship to bridge local knowledge with hydroclimatic research and policymaking. Unawareness of local priorities (Challenge 1) is a consequence of Ignoring local knowledge (Error 1) that can be, in part, resolved with Information exchange and expansion of community-based participatory research (Solution 1). Unequal access to natural resources (Challenge 2) is often due to Top-down decision making (Error 2), but Buffer zones for environmental protection, green areas, air quality, and water security can help achieve environmental justice (Solution 2). Unequal access to public services (Challenge 3) is a historical issue that persists because of System abuse and tokenism (Error 3), and it may be partially resolved with Multi-benefit projects to create socioeconomic and environmental opportunities within frontline communities that include positive externalities for other stakeholders and public service improvements (Solution 3). The path forward in climate change policy decision-making must be grounded in collaboration with frontline community members and practitioners trained in working with vulnerable stakeholders. Addressing co-occurring inequities exacerbated by climate change requires transdisciplinary efforts to identify technical, policy, and engineering solutions. 

SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of meta-information to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community. Establishing such standards will require national and international consensus on what meta-information should accompany SARS-CoV-2 wastewater measurements. To establish a recommendation on minimum information to accompany reporting of SARS-CoV-2 occurrence in wastewater for the research community, the United States National Science Foundation (NSF) Research Coordination Network on Wastewater Surveillance for SARS-CoV-2 hosted a workshop in February 2021 with participants from academia, government agencies, private companies, wastewater utilities, public health laboratories, and research institutes. This report presents the primary two outcomes of the workshop: (i) a recommendation on the set of minimum meta-information that is needed to confidently interpret wastewater SARS-CoV-2 data, and (ii) insights from workshop discussions on how to improve standardization of data reporting.