Parameter sensitivity and data uncertainty assessment of the cradle-to-gate environmental impact of state-of-the-art passive daytime radiative cooling materials

Uncertainty remains a significant challenge in life cycle assessment (LCA), despite the availability of comprehensive models and databases. Addressing this requires tailored uncertainty and sensitivity analysis methods, such as parameter variation, scenario analysis, and Monte Carlo simulations. This study assesses the uncertainty and sensitivity of the environmental impact of ten daytime radiative cooling (RC) materials, contributing to the development of a novel cementitious-based RC material within the MIRACLE project. The study investigates three key sources of input uncertainty: (1) parameter sensitivity, analyzing variations in production processes; (2) Monte Carlo analysis, assessing uncertainty within Ecoinvent datasets used for RC material modeling; and (3) pedigree matrix evaluation, incorporating an additional layer of uncertainty where data are incomplete. The sensitivity analysis reveals that sputtering rate and pumping power significantly impact the environmental footprint of RC materials. Doubling the pumping power doubles the environmental impact, while the lowest sputtering rate increases the impact by over 600%. Scenario analysis further shows that best- and worst-case outcomes vary by up to 1278%, underscoring the need for precise process data. Monte Carlo analysis demonstrates that increasing the number of records used for material modeling broadens the range of outcomes but with limited dispersion, indicating that each impact category is characterized by independent uncertainties. The pedigree matrix is a useful tool when uncertainty data are missing but has a relatively small influence on overall uncertainty. Process-related parameter choices contribute more significantly to uncertainty than inventory datasets. Accurate modeling of key production steps, particularly sputtering rate and pumping power, is essential for understanding environmental impact variability. These findings emphasize the importance of tailored uncertainty assessment methodologies in LCA studies, particularly for emerging materials like radiative cooling technologies. By improving uncertainty assessment approaches, this study enhances the reliability of environmental impact assessments in material innovation.