Adapting to Extreme Heat: Social, Atmospheric, and Infrastructure Impacts of Air-Conditioning in Megacities—The Case of New York City

Abstract

Extreme heat events are becoming more frequent and intense. In cities, this problem is often intensified by the presence of the urban heat island (UHI). This presents major challenges to reduce adverse health effects of hot weather, particularly in vulnerable populations. Here we explore the impacts of increasing residential air conditioning (AC) adoption as an adaptive measure to extreme heat, with New York City (NYC) as a case study. This study uses AC adoption data from New York City Housing and Vacancy Surveys to study impacts to indoor heat exposure, energy demand, and UHI. The Weather Research and Forecasting (WRF) model, coupled with a multi-layer building environment parameterization and building energy model (BEP-BEM) is used to perform this analysis. The BEP-BEM schemes are modified to account for partial AC use and used to analyze current and full AC adoption scenarios. A city-scale case study is performed over the summer months of June – August 2018, which includes three different extreme heat events. Simulation results show good agreement with surface weather stations. Results show that increasing AC systems to 100% usage across NYC results in a peak energy demand increase of 20%, while the UHI slightly increases on average by 0.42 °C. Results highlight potential tradeoffs in extreme heat adaptation strategies for cities, which may be necessary in the context of increasing extreme heat events.

Inequality in the availability of residential air conditioning across 115 US metropolitan areas

“…While limited availability of replication data precludes additional head-to-head comparisons, our patterns of predicted probability of AC ownership are broadly consistent with prevalence documented in Fraser et al ( 29 ), Guirguis et al ( 28 ), Gamarro et al ( 47 ), and Ahn et al ( 31 ). For example, predicted probabilities for census tracts in Maricopa County (Phoenix metro area) are higher than those for Los Angeles County (LA metro area) mirroring the pattern described in Fraser et al ( 29 ), and percentile rankings of tract prevalence in San Diego county suggest lower rates of AC along the coast, as in Guirguis et al ( 28 ).…”

“…Separately, Gronlund and Berrocal ( 32 ) establish that AC prevalence varies within cities [see also Ito et al ( 46 )]. This literature heretofore has tended to focus on the effect of local and regional climatic, societal, and built-environment attributes on AC access ( 29 , 28 , 47 , 32 , 31 ) and its implications ( 48 , 30 ) in a limited number of discrete locations. Compared to our findings, which include the additional effect of demographic correlates, application of Gronlund and Berrocal’s ( 32 ) structural-characteristics-only model to Detroit assessor data suggests a cross-tract median raw probability of any AC of 0.75, which is 10 percentage points lower than our estimate ( Supplementary Fig.S6 ).…”

Reflecting on Impacts of COVID19 on Sustainable Buildings and Cities

“…This question has been started to be addressed by a few research teams including those from the City College of New York. A recent article suggested the role of socio-economics in the use of HVAC systems for New York City [11]. Figure 3 illustrates a synthesis of this study where the percentage of residents with HVAC ownership is geospatially presented, along with the economic burden that using HVAC systems during the summer season may represent to the residents.…”

“…Fig.3Percentage of households with HVAC in New York City and projected associated cost of using HVAC systems as a function of income (source: Gamarro et al[11])…”