AI for Carbon Tracking

Carbon tracking in buildings covers two distinct categories: embodied carbon (the emissions from manufacturing, transporting, and installing building materials) and operational carbon (the emissions from energy consumed during the building's use). AI helps quantify both and, more importantly, identifies where design decisions can reduce them.

Embodied carbon analysis requires connecting material specifications to Environmental Product Declaration (EPD) data. AI processes the project specifications and quantities to calculate the embodied carbon of the specified materials, then identifies alternatives that would reduce the total. Switching from a standard concrete mix to one with supplementary cementite materials, for example, can reduce embodied carbon by 30-40% with minimal cost impact — but only if the team knows to ask.

For operational carbon, AI connects building energy modeling with the local electrical grid's carbon intensity and projected future grid mix. A building that appears carbon-neutral based on current grid emissions may not remain so as the grid evolves, or vice versa. AI provides a lifecycle view that helps owners and designers make decisions about envelope performance, mechanical systems, and renewable energy based on projected carbon impact over the building's life, not just current conditions.