Constantine E. Kontokosta

TL;DR: In this article, the authors developed a predictive model of energy use at the building, district, and city scales using training data from energy disclosure policies and predictors from widely available property and zoning information.

TL;DR: Hurricane Sandy had a significant and immediate impact on neighborhoods classified as least resilient based on the calculated REDI scores, while the most resilient neighborhoods were shown to better withstand disruption to normal activity patterns and more quickly recover to pre-event functional capacity.

TL;DR: A building energy performance grading methodology using machine learning and city-specific energy use and building data that accounts for variations in the expected and actual performance of individual buildings, out-performing existing state-of-the-art methods.

TL;DR: This methodology has the potential to support collection truck route optimization based on expected building-level waste generation rates, and to facilitate new equitable solid waste management policies to shift behavior and divert waste from landfills based on benchmarking and peer performance comparisons.

TL;DR: In this paper, the authors examined the effects of ownership type, tenant demand, and real estate market location on building energy retrofit decisions in the commercial office sector and found that ownership type and local market do, in fact, influence the retrofit decision.