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This study presents a novel framework to address the challenges of extensive data requirements and uncertainties in road pavement life cycle assessment (LCA). We introduce a streamlined framework that employs a structured data underspecification approach. In this study, we categorized the required data into four specificity levels (M1 to M4) to manage data uncertainty in a systematic way. Using 10 case studies with various data specificity levels, our method proves effective in adapting to different degrees of data scarcity. The proposed framework supports a confident decision-making process, in which the comparison indicator shows discerned results at least 90 % of Monte Carlo runs by reducing the data collection burden by 85.7 %. For example, in the Boston pavement scenarios, one can achieve a 90 % reliability level by selectively specifying the maintenance and repair (M&R) activities to an M3-level specificity, while maintaining all other data at a lower level of detail, i.e., M2. [Display omitted] [ABSTRACT FROM AUTHOR]

This contribution details a high-resolution approach to estimate the net greenhouse gas (GHG) impact of changing pavement albedo in urban areas by accounting for both changes in air temperature and building energy demand (BED) caused by the albedo change. The approach uses machine-learning-based meta-models that allow stakeholders to estimate the impact of pavement albedo modification for specific, detailed neighborhoods in a rapid, computationally efficient manner. This method is applied to a case study involving all buildings and the adjacent pavements in Boston, MA. Results from the case study indicate that increasing pavement albedo reduces average temperature and usually reduces carbon emissions from BED for densely-built and medium-density neighborhoods while results from low-density neighborhoods were mixed. Model results suggest that increasing pavement albedo would lead to BED GHG benefits in 88% of Boston neighborhoods. Increasing the albedo of the 1100 miles of roads in those communities would yield nearly 91,720 metric tons of reduced carbon emissions over the next fifty years. [ABSTRACT FROM AUTHOR]