An integrated model for quantifying the impacts of pavement albedo and urban morphology on building energy demand
Name
BED_manuscript_revised_R1_noTC2-1.pdf
Description
Accepted version
Size
1.14 MB
Format
Adobe PDF
Checksum (MD5)
1d60a9a0ed7f5498d4b143525f040f7a
Author(s)
Xu, Xin
AzariJafari, Hessam
Gregory, Jeremy
Norford, Leslie
Kirchain, Randolph
Date Issued
2020
Journal
Energy and Buildings
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Architecture
MIT Materials Research Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.ENBUILD.2020.109759
