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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. 

Abstract Our urban systems and their underlying sub-systems are designed to deliver only a narrow set of human-centered services, with little or no accounting or understanding of how actions undercut the resilience of social-ecological-technological systems (SETS). Embracing a SETS resilience perspective creates opportunities for novel approaches to adaptation and transformation in complex environments. We: i) frame urban systems through a perspective shift from control to entanglement, ii) position SETS thinking as novel sensemaking to create repertoires of responses commensurate with environmental complexity (i.e., requisite complexity), and iii) describe modes of SETS sensemaking for urban system structures and functions as basic tenets to build requisite complexity. SETS sensemaking is an undertaking to reflexively bring sustained adaptation, anticipatory futures, loose-fit design, and co-governance into organizational decision-making and to help reimagine institutional structures and processes as entangled SETS. 

Pervasive and accelerating climatic, technological, social, economic, and institutional change dictate that the challenges of the future will likely be vastly different and more complex than they are today. As our infrastructure systems (and their surrounding environment) become increasingly complex and beyond the cognitive understanding of any group of individuals or institutions, artificial intelligence (AI) may offer critical cognitive insights to ensure that systems adapt, services continue to be provided, and needs continue to be met. This paper conceptually links AI to various tasks and leadership capabilities in order to critically examine potential roles that AI can play in the management and implementation of infrastructure systems under growing complexity and uncertainty. Ultimately, various AI techniques appear to be increasingly well-suited to make sense of and operate under both stable (predictable) and chaotic (unpredictable) conditions. The ability to dynamically and continuously shift between stable and chaotic conditions is critical for effectively navigating our complex world. Thus, moving forward, a key adaptation for engineers will be to place increasing emphasis on creating the structural, financial, and knowledge conditions for enabling this type of flexibility in our integrated human-AI-infrastructure systems. Ultimately, as AI systems continue to evolve and become further embedded in our infrastructure systems, we may be implicitly or explicitly releasing control to algorithms. The potential benefits of this arrangement may outweigh the drawbacks. However, it is important to have open and candid discussions about the potential implications of this shift and whether or not those implications are desirable. 

Abstract Infrastructure are at the center of three trends: accelerating human activities, increasing uncertainty in social, technological, and climatological factors, and increasing complexity of the systems themselves and environments in which they operate. Resilience theory can help infrastructure managers navigate increasing complexity. Engineering framings of resilience will need to evolve beyond robustness to consider adaptation and transformation, and the ability to handle surprise. Agility and flexibility in both physical assets and governance will need to be emphasized, and sensemaking capabilities will need to be reoriented. Transforming infrastructure is necessary to ensuring that core systems keep pace with a changing world.