Uncertainty Propagation in Bridge Construction Carbon Emission Quantification

Given the rapid expansion of infrastructure such as large-scale bridges in developing countries, their environmental impacts, particularly during the construction phase from the perspective of carbon emissions, have attracted have become a subject of increasing scholarly and policy interest. This study presents a probabilistic life cycle assessment for a large-scale bridge in Pakistan to illustrate uncertainty propagation in carbon emission quantification during construction, beginning with the explicit identification of uncertainties in emission factors. Monte Carlo simulation is employed to estimate the embodied carbon footprint associated with material production, transportation, and on-site construction activities, resulting in an average of $$72.5{ } \times { }10^{6} { }\;{\text{kg CO}}_{2} - {\text{eq }}\left( {{ sigma }{ } = { }11.6{ } \times { }10^{6} {\text{ kg CO}}_{2} - {\text{eq}}} \right).$$ 72.5 × 10 6 kg CO 2 - eq sigma = 11.6 × 10 6 kg CO 2 - eq . The uncertainty propagation indicates substantial variability $$\left( {{\text{CV }} = { }16{\text{\% }}} \right)$$ CV = 16 \% with $$\pm 20{-}30\%$$ ± 20 - 30 % confidence intervals, driven by probabilistic characteristics of material, transportation, and energy emission factors. From the expected value perspective, material extraction accounts for 55% of total emissions, transportation for 43%, and construction activities for 3%. At the component-level, the superstructure ( $$23.38{ } \times { }10^{6} { }\;{\text{kg CO}}_{{2}} - {\text{eq}}$$ 23.38 × 10 6 kg CO 2 - eq ) and deck ( $$23.27 \times 10^{6} \;{\text{ kg CO}}_{{2}} - {\text{eq}}$$ 23.27 × 10 6 kg CO 2 - eq ) represent the primary sources. To reduce life-cycle emissions and associated uncertainties, technical measures such as low-clinker binders, optimized logistics, and BIM-integrated stochastic carbon modules are recommended, complemented by policy interventions including performance-based carbon caps, geospatial emission factors, and blockchain-based supplier disclosures. This framework advances traditional LCA methodologies, enabling more informed low-carbon decision-making in resource-constrained contexts.