Enhancing Environmental Sustainability through AI-Driven Digital Twin Systems for Net-Zero Carbon Smart Construction

The construction sector significantly contributes to global greenhouse gas (GHG) emissions, accounting for up to 20% of total global emissions while playing a major role in climate change. Rapid urbanization alongside resource-intensive building practices exacerbates environmental challenges, which highlight the urgent need for net-zero carbon and sustainable solutions. The study aims to critically examine how Digital Twin (DT) systems and Artificial Intelligence (AI) can enhance environmental sustainability and support net-zero carbon goals in smart construction. Based on a comprehensive literature review and recent scholarly works on sustainability, AI integration in the built environment, and digital twin applications, findings show that AI-driven digital twin systems provide significant benefits ranging from predictive energy optimization, real-time carbon monitoring, improved decision-making regarding material selection, logistics, and waste reduction. Altogether, these systems facilitate resilience in smart cities through Internet of Things (IoT), Building Information Modelling (BIM), and machine learning integration to optimize resource efficiency. Meanwhile, challenges including data integration, cybersecurity, high costs of implementation, and ethical concerns are major barriers. Despite this, the study contributes to the academic domain by advancing digital transformation knowledge in sustainable construction and providing industry insights on practical ways to achieve zero-carbon goals. It highlights the need for future research to focus on standardization, policy frameworks, and the use of scalable adoption strategies.