Globally, buildings account for up to 42 % of annual emissions. Of this, two-thirds are generated by heating, cooling, and electricity used to power buildings. The remaining third comes from embodied carbon, which is the emissions from extracting and producing construction materials. To top it off, roughly one-third of all water consumption and waste generation occurs during building construction, operation, and maintenance. 

The built environment presents a significant opportunity for carbon savings in sustainable housing and commercial buildings. The integration of artificial intelligence (AI) provides a tool to enhance traditional engineering methods, static engineering modelling, and rule-based building management. In fact, AI modelling can reach up to 20% higher accuracy in carbon predictions. It can also reduce operating emissions by 15% and improve energy efficiency by 25%. 

This article outlines three pathways to integrate AI into both new construction and existing buildings to achieve carbon reductions.  

Reducing operational carbon in buildings often dominates conversations in building sustainability. However, building design and the selection of sustainable materials should be the first steps. They help create high-efficiency, net-zero buildings, including passive house design. 

Instead of relying on traditional, static modelling, developers can use AI-based generative design software to test different building design options, allowing them to compare simulations that meet requirements and design out carbon from the very beginning.

Through building information modelling, architects can input structural requirements, regional climate considerations, and local zoning laws, enabling the AI tool to identify equivalent alternative building materials before the design is finalized. These approaches follow core design principles that prioritize efficiency, material selection, and early-stage carbon reduction, which can be further optimized by procuring low-carbon building materials using Environmental Product Declarations (EPDs). By leveraging EPDs, developers can easily identify products with lower embodied carbon and environmental impacts, ultimately supporting long-term energy conservation based on local weather conditions and building use.

Energy usage in older buildings is inefficient. The largest energy losses in buildings occur through the building envelope, poor ventilation, and window design. Increasing energy efficiency with upgraded materials and electrified systems will lead to reduced greenhouse gas emissions. 

In new builds, the operational emissions are primarily from Heating, Ventilation, and Air Conditioning (HVAC) systems. Traditional building management systems operate on static schedules. They maintain a consistent temperature regardless of the environment and the dynamic activities occurring inside and outside the building.

To reduce their carbon footprint, owners of new and existing buildings can adopt smart building technologies. Integrating deep reinforcement learning (DRL) agents with localized Internet of Things (IoT) digital twins can make a building’s HVAC system more predictive. It can adjust heating and cooling based on indoor temperature and air quality, weather forecasts, occupancy schedules, and real-time energy costs. 

Adjusting for these factors, proactive measures can be taken to optimize and reduce energy consumption, such as pre-cooling or pre-heating if the weather is forecast to change quickly. Increasing energy efficiency reduces operating costs. Further, data relayed back to the municipal grid can inform renewable energy load requirements to effectively manage usage fluctuations.