In a recent lecture at the University of Victoria’s Department of Civil Engineering, Dr. Bianca Howard presented research from Columbia University’s Building Energy Research Laboratory focused on developing more realistic and inclusive pathways for building decarbonization. Her talk explored how building physics, optimization, machine learning, and artificial intelligence can be combined to evaluate and control energy consumption in buildings, while also accounting for the social and economic realities that shape decision-making. 
Dr. Howard began by addressing a central challenge in building decarbonization: the need to balance competing goals such as cost, emissions reduction, affordability, equity, and workforce impacts. Using New York City as a case study, she showed how conventional techno-economic approaches often prioritize capital cost and return on investment while overlooking the people and institutions connected to buildings. Her research demonstrated that these traditional models frequently fail to capture outcomes that matter to policymakers, residents, and communities.
To address this gap, Dr. Howard presented a social-physical urban building energy model that integrates technical performance with social objectives. Drawing on NYC Open Data, U.S. Census data, and Department of Energy reference buildings, her team modeled a residential block in Harlem and evaluated retrofit strategies across multiple dimensions. In addition to cost and greenhouse gas emissions, the analysis explicitly incorporated energy burden and job creation as optimization objectives.
The results showed that when social metrics are included, the set of “optimal” solutions changes significantly. Strategies that minimize emissions at the lowest cost often rely heavily on electrification measures that do little to reduce, and can even increase, household energy bills. In contrast, solutions that reduced energy burden emphasized passive measures such as wall and roof insulation, improved windows, and more targeted electrification. When job creation was prioritized, retrofit portfolios shifted again, favoring envelope upgrades and window replacements that generate more labor hours per unit of emissions reduction.
Dr. Howard concluded by emphasizing that co-benefits such as affordability, health, and employment are already widely acknowledged in practice, but they remain difficult to operationalize in modeling and planning tools. Her work suggests that making these co-benefits more tangible and quantifiable can help decision-makers navigate tradeoffs more transparently. Future research will expand the model citywide, incorporate public health outcomes, explore policy incentives, and work directly with stakeholders and city officials to apply these methods in real planning contexts.
