An AI-Assisted Environmental Data Governance Architecture for MRV in Sustainable Infrastructure

Balogun, H. 2026. An AI-Assisted Environmental Data Governance Architecture for MRV in Sustainable Infrastructure. International Conference on Electrical, Computer and Energy Technologies (ICECET 2026) . Rome, Italy 06 - 09 Jul 2026 IEEE .

TitleAn AI-Assisted Environmental Data Governance Architecture for MRV in Sustainable Infrastructure
AuthorsBalogun, H.
TypeConference paper
Abstract

Achieving global net-zero ambitions requires
infrastructure sectors to move beyond aspirational sustainability narratives toward verifiable, data-driven environmental accountability. The construction industry, responsible for a significant share of global greenhouse gas emissions, struggles with fragmented environmental data, partial lifecycle assessments,
and weak mechanisms for measurement, reporting, and
verification (MRV). These challenges undermine policy credibility and the integrity of environmental claims.

This research proposes designing an AI-assisted
environmental data governance framework to enable traceable, auditable, and ethically governed environmental performance assessment across the full lifecycle of infrastructure projects. The study positions Offsite Construction (OSC) as a demonstrative
case, given its higher levels of process standardisation and digital maturity compared to traditional construction. While OSC offers structural advantages for data capture and traceability, existing
datasets remain siloed, inconsistently governed, and weakly linked end-of-life performance.
The research maps current OSC environmental data practices in the United Kingdom, identifies governance gaps that limit lifecycle integration and MRV compliance, designs a standardised data governance architecture that links design, construction
operations and an AI-assisted MRV prototype, using selected OSC case studies.

The research contributes theoretically by foregrounding data governance as foundational infrastructure for environmental accountability, methodologically by repositioning AI as a governance-support tool rather than a black-box predictor, and empirically by advancing understanding of OSC’s role in sustainable infrastructure delivery. Beyond construction, the framework offers a transferable model for strengthening
environmental accountability across other infrastructure sectors, supporting credible climate policy, green finance, and net-zero delivery

KeywordsArtificial Intelligence, Data Governance; Offsite Construction; Net-Zero Accountability; Green Claims Directive; MRV Framework
Year2026
ConferenceInternational Conference on Electrical, Computer and Energy Technologies (ICECET 2026)
PublisherIEEE
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open (open metadata and files)

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