This advanced data-driven model gathers, processes, and analyses information about your building to calculate its carbon footprint with precision. It doesn’t stop there: CalCO2 also fuels a recommendations engine which is vetted by experts, ensuring that nothing is missed in helping you take practical steps to achieve net-zero goals.

While CalCO2 is already transforming the way carbon data is measured and applied, we envision it evolving into a next-generation AI model—capable of learning, predicting, and guiding sustainable decisions like never before.

The BCF model requires data across key carbon areas:

• Embodied Carbon: Material types and amounts used in construction and renovations.
• Operational Carbon: Energy consumption for heating, cooling, lighting, and appliances.
• Transport Carbon: Emissions from commuting.

We integrate multiple sources, including site inspections, energy bills, material specifications, and user inputs, to ensure a comprehensive analysis.

CalCO2 processes the gathered data to produce outputs based on rules and machine learning architecture developed in-house.

• Results are broken down into clear, actionable categories so you can see exactly where emissions occur.
• The model adapts to building type, size, and use for tailored precision.

After our experts give their recommendations, CalCO2 analyses and enhances those it deems necessary, highlighting any missed potentials. The results? An analysis into your buildings’ and portfolios’ footprint and targeted solutions that match your desired outcomes – a balance of financial, sustainable, and ethical decision making.

• Continuously refine recommendations based on real-world results and new data.
• Predict future carbon performance using scenario modelling.
• Benchmark against similar buildings to highlight opportunities for improvement.