Evaluating the effectiveness of Life Cycle Assessment (LCA) as a tool for measuring environmental sustainability performances in complex systems

This doctoral research investigates the capacity of Life Cycle Assessment to serve as a scientifically robust, empirically grounded methodology for quantitatively evaluating environmental sustainability in complex systems. Despite its ISO 14040/14044 standardization and comprehensive cradle‑to‑grave perspective, LCA faces persistent challenges, particularly concerning data quality, methodological variability, and uncertainty management. These issues intensify within increasingly complex sustainability frameworks and growing reliance on complementary policy instruments (e.g., eco-labelling schemes), that aim to harmonize life cycle–based information to improve policymaking and market transparency. Considering these points, this doctoral study investigated the following research question: To what extent can LCA, as an objective and data‑driven methodology, effectively measure and support the assessment of environmental sustainability in complex systems? To answer this question, the research combines three components: (i) a critical review of LCA’s theoretical foundations and integration with regulatory frameworks; (ii) empirical application to two case studies of increasing complexity (a product-level LCA supporting eco-design strategies and a process-level LCA comparing alternative waste-treatment scenarios); and (iii) the development of a prototype LCA tool featuring a modular architecture and web-based interface. Both case studies employ the ecoinvent database (attributional cut‑off system model) and openLCA software and integrate sensitivity analysis and Monte Carlo simulation to characterize parameter uncertainty. The findings substantiate that LCA is an effective methodological framework for identifying environmental hotspots and supporting informed decision-making, contingent upon the use of robust datasets, explicit and transparent modelling assumptions, and the systematic implementation of uncertainty analyses. However, broader adoption of this methodology requires improved integration of external data sources, harmonized quality standards, and user-friendly digital solutions. Based on experience gained from empirical case studies, it was then developed a prototype of a modular, web-based LCA tool, by leveraging openLCA open-source features and a relational data backend. It was designed to enhance usability, data traceability, and visualization for non-expert users. The prototype illustrates the promise of digital architectures to lower operational barriers, while underscoring the need for further development to ensure flexibility and methodological rigor. Although constrained by two case studies, one database, and a preliminary prototype, the research delivers both theoretical and practical contributions. It clarifies how modelling approaches and uncertainty propagation influence result reliability, and introduces a modular, web-based LCA tool that enhances usability, data traceability, and scenario analysis. These contributions strengthen LCA’s role as a transparent, data-driven decision-support method and provide a pathway for its broader adoption in industrial and policy contexts, in support of the European Green Deal and the UN Sustainable Development Goals.