Carbon emission reduction in construction industry: Qualitative insights on procurement, policies and artificial intelligence

Purpose: The construction industry is a major contributor to global carbon emissions. This study investigates the role of procurement and contracting methods in carbon emission reduction (CER) in the construction industry. It also examines artificial intelligence’s (AI’s) potential to drive low-carbon practices, aiming to identify transformative policies and practices. Design/methodology/approach: This study employed a qualitative methodology, engaging in semi-structured interviews with nine industry professionals alongside an innovative engagement with Generative Pre-trained Transformer (GPT) technology to gather insights into procurement and project delivery methods (PDM) role in CER. The study involved identifying patterns, organizing themes, and analyzing data to extract meaningful insights on effective policies and strategies for CER in the construction industry. Findings: The results underscore the importance of early contractor involvement and integrated PDM for CER in construction. Results emphasize the pivotal role of project owners in directing projects toward sustainability, highlighting the need for client demand. The research identifies cost constraints, limited material availability, and human resource capacity as key barriers in the US. The study proposes innovative materials, financial incentives, education, and regulatory standards as effective interventions. It also explores the future use of AI in enhancing CER, suggesting new avenues for technological integration. Originality/value: The study provides empirical insights into the role of procurement and PDM in CER within the US construction industry by using qualitative approach and use of a GPT. It underscores the interplay between contracting methods, stakeholder engagement, and AI’s emerging role, for enhancing policies and practices to decarbonize the US construction industry.