Cornell University researchers have developed an innovative urban building energy model that is significantly assisting the city of Ithaca, New York, in its objective to achieve carbon neutrality by 2030. This advanced tool enables the rapid simulation of a city's building energy consumption and a thorough cost-benefit analysis of various decarbonization strategies.
The model, created by Associate Professor Timur Dogan and his team at Cornell's Environmental Systems Lab, can process a city's building energy data on a standard laptop in minutes. It then meticulously evaluates strategies such as improving building insulation (weatherization), transitioning to electric heat pumps for heating and cooling, and installing rooftop solar panels. For Ithaca, the model analyzed over 5,000 residential and commercial buildings, incorporating data from geospatial maps, tax records, and utility records.
The insights generated from this analysis have been vital in shaping Ithaca's climate strategy. The findings suggest that while replacing gas furnaces with heat pumps alone might increase operational costs for some, this transition becomes economically feasible when combined with weatherization improvements and solar energy integration. This aligns with broader research indicating that combining energy efficiency measures with renewable energy generation maximizes benefits, as weatherization reduces overall energy demand, making systems like heat pumps more effective and potentially allowing for smaller, less costly installations.
Notably, the model identified that retrofitting multifamily residential buildings offers the most cost-effective pathway, especially when considering available financial incentives, compared to larger commercial structures. Research published in the Journal of Building Performance Simulation highlights the model's scalability and accessibility, positioning it as a valuable resource for municipalities with limited resources for such detailed analysis. The building and construction sector globally accounts for over 37% of carbon dioxide emissions, underscoring the critical importance of tools like this for urban decarbonization efforts.
The model's capacity to quickly process data and pinpoint priority retrofits, such as those for multifamily buildings, can substantially reduce the capital required to identify properties suitable for electrification and create attractive investment opportunities. This advanced modeling capability is particularly significant as cities worldwide confront the urgent need to reduce their carbon footprint. The United Nations Environment Program acknowledges the substantial contribution of the building sector to global emissions, emphasizing the necessity of data-driven decisions in decarbonization. The model's accessibility, not requiring advanced computing power, makes it a powerful resource for smaller cities and municipalities that often lack specialized expertise and resources for climate action planning. The research, co-authored by Chengxuan Li and others from Dogan's lab, demonstrates a significant advancement in making complex energy modeling accessible and actionable for urban planning.