A Machine Learning Framework for Reducing CO2 Emissions in UK Green Buildings: A Review

Abstract

It is very important for the building industry to reduce its negative effects on the environment because it is a major source of carbon emissions. The UK wants to reach net-zero carbon emissions by 2050, which means that buildings need to have fewer carbon footprints. This makes it more important than ever to come up with new ways to do this. This research looks at a machine learning (ML) system that can anticipate the carbon effects of different design and material choices. The goal is to reduce both embodied and operational CO₂ emissions in green buildings. The methodology uses real-world building data from the UK to show which materials and practices can help reduce total lifespan emissions most effectively. By forecasting how much energy buildings will use and improving how they work, the use of machine learning models makes buildings more energy efficient. This lowers energy demand and operational carbon footprints by a large amount. Furthermore, the article investigates how machine learning (ML) applications might influence the selection of sustainable building materials, assuring long-term environmental sustainability. Specifically, the implementation of smart energy systems and predictive maintenance is essential for the mitigation of CO₂ emissions resulting from the operation of existing buildings. This work contributes to the reduction of emissions by proposing data-driven methods to optimize building performance, thereby providing essential tools for architects, engineers, and policymakers. This paper provides a roadmap for the UK to achieve its net-zero emissions objective by 2050 by optimizing energy consumption, reducing CO₂ emissions, and supporting the transition to sustainable building practices, in accordance with global actions to address climate change.