System-Level Approach for Identifying Main Uncertainty Sources in Pavement Construction Life-Cycle Assessment for Quantifying Environmental Impacts

Abstract

Poor data quality in pavement construction life-cycle inventory (LCI) causes uncertainty in quantifying the associated environmental impact through life-cycle assessment (LCA). To reduce such LCA uncertainty while enhancing the reliability, several studies have been conducted on a screening procedure based on a quality assessment of the LCI input data to identify main sources of the resulting uncertainty. However, they often create additional uncertainty in the screening process and thus result in erroneous outcomes in identifying main uncertainty sources. This paper proposes a new system-level approach that enables the identification of main uncertainty sources through input data quality assessment upon reducing additional uncertainty. Based on the proposed preset criteria and by leveraging environmental emission quantities associated with each process, the authors first propose to achieve a consistent weighting process and then derive the system-level aggregated data quality indicator (ADQI). By utilizing the ADQI, system-level LCA uncertainty information is obtained through a modified beta distribution. The proposed method was evaluated through case studies on real-world pavement construction projects of the Illinois Tollway, and the main uncertainty sources, named key processes, were identified through sensitivity analyses. In the case studies, the plant operation, cement production, and binder production were identified as key processes in the given pavement construction project, contributing more than half of the total uncertainty resulting from poor data quality. Based on these findings, the proposed work is expected to help practitioners improve the reliability of pavement construction LCA through uncertainty-informed decision making to better reflect real project characteristics in the identified key processes.