Introduction
Energy efficiency and ESG performance in commercial property are often discussed in terms of future targets – retrofit programmes, net zero pathways and sustainability reporting. Yet in many buildings, the most significant ESG risk is far more immediate: how the building is actually being operated. This case study, involving a multi-let office building in Cambridge, demonstrates how unmanaged plant operation can result in prolonged financial and environmental waste – and how data-led analysis can play a vital role in improving governance, transparency and ESG outcomes.
Unexpected Consequences
BCW Consultancy was appointed by tenants to review the reasonableness of service charges for a purpose-built office building extending to 45,615 sq ft. During the review of the 2025 service charge budget, one cost item immediately stood out: electricity expenditure of approximately £250,000 per annum. For a building of this size and use, the figure appeared unusually high. While the initial concern was commercial, the implications extended further – raising questions around energy management, landlord oversight and ESG performance. What began as a routine sense check revealed an operational failure with significant cost and carbon consequences. The first stage of the review focused on eliminating common causes of high energy costs. BCW confirmed that: electricity invoices were accurate; tariffs and standing charges were appropriate; and there were no supplier or billing errors.
With billing issues ruled out, attention turned to how the building was being operated. Excessive energy use is often assumed to be a tariff or market issue, when it can point to deeper governance and control failures. BCW has been working in partnership with Optimal Monitoring, deploying Emma AI across a number of commercial assets to independently assess energy performance.
Half-hourly electricity consumption data for the Cambridge building was requested from the landlord’s managing agent and analysed using Emma AI. Energy consumption was highly consistent throughout day and night, largely unchanged at weekends and lacking the typical “V-shaped” demand profile expected. The data strongly indicated that core plant was operating continuously, 24 hours a day. This suggested several years of avoidable carbon emissions caused not by occupier behaviour, but by ineffective management.
When presented with the analysis, the landlord’s managing agent initially stated that shutdown periods were already in place. However, the Emma AI visualisations clearly contradicted this position. The strength of the data – particularly the absence of any overnight or weekend reduction – prompted the managing agent to take immediate action. This highlights an important point: independent data analysis can cut through assumptions and provide an objective basis for challenge, without relying on anecdotal assurances.
A reset, but no reduction
Following the review, the managing agent confirmed that the BMS had been reset and shutdown periods reinstated. Tenants reasonably expected to see a reduction in energy consumption and associated carbon emissions. However, analysis of a further two weeks of half-hourly data showed no meaningful improvement. The issue had not been resolved and inefficient operation had likely been ongoing for a prolonged period.
Further investigation by BCW revealed that excessive consumption began around four years earlier, coinciding with the installation of a new BMS. The BMS consultant identified that a controller had failed to connect properly to the system, preventing effective shutdown control.
Despite the scale and duration of the waste, the problem went unnoticed by the landlord’s agent until the independent analysis. From an ESG perspective, this illustrates how governance failures, rather than technology limitations, often sit at the heart of poor environmental performance. With increasing scrutiny, energy waste presents financial and reputational risk. The implications are material: electricity costs of approximately £250,000 per annum, potentially spanning four years; service charge costs approaching £15 per sq ft; and eroding asset value and occupier confidence.
Service charge budgets are expected to reduce once energy consumption returns to normal and historic overpayments may be subject to legal action. Carbon emissions associated with unnecessary consumption will need to be reflected in ESG and sustainability reporting.
Conclusion
This case is not an anomaly. Poorly configured or unmanaged plant can operate inefficiently for years, particularly in multi-let buildings where responsibility is fragmented and tenants lack access to operational data.
For landlords and managing agents, the ESG lessons are clear: environmental performance can be undermined by basic operational failures; sustainability strategies lose credibility without data-led validation; and governance, not capital investment, is often the weakest link.
The key takeaway is, if energy use doesn’t look right, it usually isn’t. Independent, data-led analysis can quickly reveal whether a building is being operated efficiently, protecting occupiers from unnecessary cost, supporting landlords’ ESG obligations, and preventing years of avoidable carbon waste.
Have a read of the full case study here...
Betsy Wong is the founder and director of BCW Consultancy
Image: ©Adobe Stock
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EMMA AI is a unique Artificial Intelligence (AI) platform that measures energy consumption across your individual sites, pinpointing exactly where money is being wasted and taking proactive measures to reduce the waste – all without having to install a single piece of equipment!