Our industry is engaged in an important dialogue to improve the efficiency and resilience of real assets through transparency and industry collaboration. This article is a contribution to this larger conversation and does not necessarily reflect GRESB’s position.
Accurate environmental reporting is often framed as a coverage challenge: collect more data, automate more meters, improve response rates. In practice, the issue can be more structural. Gaps in reporting may reflect not just limited access to tenant data, but an incomplete understanding of the infrastructure itself—where supplies exist, how meters are allocated, and which assets are actually connected.
This became evident within a diversified UK real estate portfolio comprising multi-let commercial, retail, industrial, and operational social infrastructure assets.
At the outset of the 2022–23 reporting cycle, verified coverage across the portfolio was limited. Only 13% of energy consumption was supported by confirmed meter-backed data, alongside 11% for water and 16% for waste. Landlord-controlled assets benefited from automated readings and routine monitoring, but much of the remaining portfolio relied on manual tenant submissions. Responses were inconsistent, and some were absent altogether. As a result, elements of the energy baseline relied on estimation, and reporting confidence was constrained.
However, as work progressed, it became clear that the challenge was not solely about increasing response rates or installing automation. The portfolio did not have a fully verified record of its supply infrastructure. In some cases, it was unclear which units had gas supplies, whether meters were correctly allocated, or whether certain connections existed at all.
A portfolio-wide meter mapping exercise was undertaken to establish clarity. Rather than focusing immediately on expanding data collection, the priority was to validate the underlying infrastructure: mapping electricity and gas meters to occupational units, identifying misallocations, and confirming where supplies did not exist.
This distinction proved important. Confirming the absence of a supply can be as significant as confirming its presence. Without that clarity, reporting frameworks risk embedding structural inaccuracies—assuming consumption where none occurs or overlooking active supply points.
Only after this validation exercise was automation rolled out in phases, beginning with the largest assets and expanding across the portfolio. By the 2024–25 reporting cycle, energy coverage had increased to 85%, with water reaching 79% and waste improving more gradually to 20%, reflecting different operational constraints.
The numerical improvement was significant. More importantly, the portfolio’s environmental baseline became more defensible. Estimated consumption was reduced, previously unknown supply points were identified, and the risk of structural misreporting diminished.
The experience suggests that environmental data gaps are not always behavioral or technological in origin. Sometimes they reflect incomplete infrastructure knowledge. In this context, meter mapping functioned less as a data-gathering exercise and more as a validation layer—strengthening the integrity of subsequent automation and reporting.
Portfolio structures, lease arrangements, and access constraints vary, and no single approach will suit all contexts. However, this case illustrates a broader point: before scaling automation or drawing performance conclusions, it may be worth asking a more fundamental question—do we fully understand the physical infrastructure that underpins the data?
Automation can improve consistency. Infrastructure clarity determines credibility.
This article was written by Kimran Johal, Senior Sustainability Consultant at EVORA. Learn more about EVORA here.
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