| Summary: | © 2020 Elsevier B.V. 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.
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