advancing life cycle assessment for innovative technologies to support public decision-making: consequential approaches, input-output integration, and uncertainty analysis

Emerging technologies have the potential to shape future economies and societies. However, their environmental impacts are usually assessed only after large-scale deployment, making it difficult to reverse negative effects. Early-stage assessment of medium- and long-term environmental effects is essential to support policymakers investing in technologies that can tackle climate change. Traditional attributional, process-based life cycle assessment faces several challenges when applied to emerging technologies and future-oriented analyses. Comprehensive methods combining different approaches, including uncertainty and sensitivity analyses, are still lacking. This thesis contributes to the methodological development and practical application of advanced LCA approaches to support policymakers in decarbonisation strategies, addressing the integration of consequential and prospective modelling, input-output (IO) databases, and uncertainty analysis. First, a literature review identifies the strengths and limitations of advanced LCA methodological approaches. The conceptual steps of an existing framework are then formalised through the development of a new prospective life cycle input–output tool (p-LIO), improving reproducibility, transparency, and accessibility for non-LCA specialists, applied conceptually to two case studies to investigate its limitations. Second, a computational tool is developed in Python using the Brightway library to estimate data and model uncertainty in the input-output database used in p-LIO, tested using the TO-SYN-FUEL technology and showing comparable results to commonly used LCA databases. Third, the thesis assesses the environmental impact of agrivoltaic systems in Emilia-Romagna, Italy, in a real-world regional decision-making context. Overall, the thesis highlights the need to standardise LCA frameworks, including uncertainty evaluation, to provide reliable results. Close collaboration between researchers and policymakers is crucial to translate academic methods into practical applications. Challenges remain in model validation, data quality, and aligning methodological rigour with policy demands for effective decarbonisation strategies.