!["Our hope is that we can give [cities] something they can use to make good decisions."](https://www.thecooldown.com/wp-content/uploads/2025/09/city-energy-modelingimage-rec2bs9cOGiLTqOId-1.jpg?w=1920&h=800)
Researchers at Cornell recently developed software that can model a city's energy usage in just minutes using only a standard laptop. Even better, the software can run simulations of clean energy options, helping policymakers "prioritize the most cost-effective approaches to decarbonization," according to the Cornell Chronicle.
In a case study of Ithaca, New York, the model analyzed current energy use across more than 5,000 residential and commercial buildings. After establishing a baseline, the software simulated the impact of clean energy upgrades, including weatherization, electric heat pumps, and rooftop solar panels. The results provided city officials with reliable data-driven insights to guide decisions as Ithaca works toward its goal of carbon neutrality by 2030.
And sometimes these insights can be surprising.
The Ithaca case study, for example, showed that replacing gas-powered furnaces with electric heat pumps would actually increase the operational energy costs of buildings. But instead of writing off the clean energy swap entirely, the model suggested pairing the step with weatherization and rooftop solar panels to make the transition financially attractive. Similarly, while large commercial buildings would seem like the first place for a city to focus, the model actually showed that current federal and state financial incentives make multifamily residential buildings the most cost-effective targets.
"The models allow you to flag properties that are interesting to look into more closely," Cornell University associate professor and research lead Timur Dogan told the Cornell Chronicle. "You go from 5,000 buildings and we don't know what to do, to 'These 100 are the clear ones to go after first.'"
Researchers say this accessible tool is especially valuable for smaller cities with limited resources, helping to support county or state policy planning in reaching clean energy goals. But the researchers also hope it can eventually be used to serve big cities or an entire state.
"We're really excited that this can scale and is efficient enough even for a state to map out energy consumption and the potential for retrofits — what they mean from a carbon emissions perspective, a financial perspective, or just an efficiency perspective," Dogan said. "From a small city to a county or state, this can really shape policies and how you prioritize policies."
Carbon pollution from dirty energy usage is responsible for overheating our planet, driving extreme weather and putting entire ecosystems and biodiversity at risk. As the Cornell Chronicle points out, the building and construction industry is responsible for more than 37% of carbon pollution globally, according to the United Nations Environment Programme.
While urban building energy models are key tools for informing decarbonization policies, they have traditionally been expensive and computationally intensive, often requiring supercomputers that are out of reach for smaller municipalities. Cornell's new software overcomes those barriers, combining speed, affordability, and reliable performance on modest hardware, giving more cities the tools they need to make informed climate decisions.
Dogan told the Cornell Chronicle, "Our hope is that we can give [cities] something they can use to make good decisions."
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