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Sylvia Sleep

Publications and source records attributed to Sylvia Sleep.

2 recordsLinked to original sources

Energy systems models are diagnostic tools, not projection machines

Energy systems models (ESMs) are a leading tool to guide the energy transition. They have been influential in supporting national decarbonisation strategies and regional system planning, but these complex models can be opaque. Their results are often presented predictively or prescriptively, with little exploration of uncertainty and without clear discussion of limitations. Consequently, projections of ESMs have often been given more authority than their evidence can bear, undermining their contributions to energy transition policy. We argue that ESMs should instead be applied as explanatory, diagnostic tools. Model studies should explore uncertainty to find robust insights and define limitations, interrogate model behaviour to find testable real-world explanations, and communicate these explanations plainly and responsibly. This approach shifts the evidentiary burden from plausible projections to real-world insights that can be broadly understood and debated, and considered alongside other forms of evidence. Used this way, ESMs can support robust, justifiable, and pluralistic decision-making.

physics.soc-ph

Controversy and consensus: common ground and best practices for life cycle assessment of emerging technologies

Public and private interest in life cycle assessment (LCA) has grown as environmental disclosure norms tighten, driving demand for decision-relevant assessment early in technological development cycles. Early-stage LCA has the potential to guide design choices, steer innovation, and mitigate lock-in of adverse environmental impacts. However, many aspects of early-stage LCA practice remain unsettled. We convened experts in a series of Faraday Discussion-style workshops to address recurring debates across six key topics for emerging technologies: appropriate use of LCA, uncertainty, comparison with incumbents, standardization, scale-up, and stakeholder engagement. For each issue, we present a declarative resolution, summarize key arguments for and against it, identify points of consensus, and provide recommendations. Across topics, the research network converged on practical priorities including framing studies to the decision context; setting minimum reporting expectations for data and study quality; and explicitly stating limits of transferability for scenario-based uncertainty assessment or analytically scaled-up projections. Disagreements persisted on when to formalize standards and how extensively uncertainty can/should be treated for low-maturity technologies. Supplementing the workshop findings with examples and context from relevant literature, we synthesize outcomes into a set of shared challenges and research priorities to strengthen transparent, evidence-based, and context-informed approaches for early-stage LCA.

cs.CY