Enhancing Sustainability in Post-Construction Management: Leveraging API-Driven AI and Digital Twins for Optimising M&E Systems

The built environment contributes approximately 40% of global energy consumption and 36% of carbon dioxide emissions, with mechanical and electrical (M&E) systems representing the largest operational energy consumers. While AI-enabled Digital Twin technologies demonstrate proven energy savings of 10-38% in academic literature, practical adoption in post-construction building management remains limited, particularly in public-sector contexts. This research investigates how API-driven AI and Digital Twin technologies can enhance sustainability in postconstruction M&E systems within the Further Education sector in South Wales. A convergent parallel mixed-methods design combined quantitative survey research (n=87 built environment professionals) with qualitative semi-structured interviews (n=6 FE sector stakeholders). Analysis was guided by a three-domain conceptual framework addressing Technological Capability, Human and Organisational Readiness, and Policy and Governance Alignment, interpreted through established technology adoption theories including Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Diffusion of Innovations (DOI). Findings reveal that only 6.9% of respondents reported full AI-Digital Twin integration, with 40.2% describing partial implementation. Predictive maintenance benefits received strongest endorsement (94.3% agreement), supported by qualitative accounts of verified energy savings including 18% reduction in gas consumption. Adoption is constrained by interconnected barriers: budget limitations (92% rating as moderate barrier or higher), legacy system incompatibility, cybersecurity concerns, and skills gaps. A moderate-strong positive correlation between technology familiarity and barrier perception, suggests increased understanding leads to more realistic assessment of implementation challenges. The research concludes that while perceived usefulness is well-established, perceived ease of use and facilitating conditions remain constrained by structural factors including short-term funding cycles and fragmented legacy infrastructure. Recommendations include phased digital retrofit strategies, open-protocol procurement, and sector-specific implementation guidance.

Name dc:type.qualificationname

msc

Level dc:type.qualificationlevel

masters

Grantor dc:publisher.institution

University of Wales Trinity Saint David

Year dc:date.issued

2026

Author dc:creator

• Davies, Paul

• TH Adeiladu adeiladau

Dc Identifier Grantnumber

UWTSD

OAI identifier oai:identifier

oai:repository.uwtsd.ac.uk:4316