Incorporating an agent-based decision tool to better understand occupant pathways to GHG reductions in NYC buildings

A number of cities globally have developed ambitious goals to reduce greenhouse gas emissions (GHGs), and New York City has publicly committed to reducing emissions 80% by 2050 (80 × 50). While physical infrastructure is important, cities can gain important insights through information about human behavior, as people are the end users of buildings, transportation, and other physical assets. In this paper, we present a simplistic, pilot agent-based model (ABM) for New York City with projections about the city's potential for reaching the 80 × 50 goal in the building sector. Importantly, the ABM models occupant choices about technology adoption to predict the prevalence of green buildings in coming years. We find that even though traditional building types are slow to transition, CO₂ production still decreases substantially over the forecast interval. Traditional buildings begin to slow their dominance in the model pathways by approximately 10 years into the forecast. Although the ABM presented here relies on simplistic assumptions about human agents and brings a high level of uncertainty, it presents a useful pilot tool to begin to understand system-level impacts from micro-level actions of households and individuals, and provides vast potential for future use of ABMs for this task.