Hierarchically Weighted Evidence for Fuzzy–Probabilistic Carbon Footprint Uncertainty Analysis

Accurate carbon footprint accounting is essential for advancing carbon management and optimising energy systems. However, existing uncertainty analyses are often difficult to reproduce and interpret because the underlying evidence is scattered across multiple sources, thereby making it hard to attribute uncertainty to specific drivers. To address this gap, we propose a transparent uncertainty quantification framework that (i) distills multi-source evidence into auditable rules, (ii) propagates ambiguity through fuzzy aggregation, and (iii) maps interval outputs to probabilistic representations for scenario-based Monte Carlo simulation. In the case study, the proposed method yields more stable carbon footprint estimates than conventional approaches, with lower variability across repeated simulations, and it enables clearer uncertainty attribution by separating random variation from structural uncertainty. Overall, this paper provides a traceable and interpretable method for high-precision uncertainty quantification in carbon footprint assessment.