Automated Scope 2 Carbon Reporting for UK SMEs: Integrating Real-Time Grid Intensity with Half-Hourly Smart Meter Data Under SECR and MHHS
Authors/Creators
Description
Scope 2 carbon emissions, those attributable to purchased electricity, are the single largest reported emission category for most UK commercial organisations, yet their accurate quantification at half-hourly resolution remains rare. The Streamlined Energy and Carbon Reporting (SECR) framework requires annual Scope 2 reporting but mandates only the use of annual average grid carbon intensity factors, a methodology that systematically misrepresents the actual carbon consequences of consumption, which vary by up to a factor of ten across the day and season as renewable generation fluctuates. This paper presents a methodology for automated Scope 2 carbon quantification by integrating half-hourly smart meter consumption data with grid carbon intensity factors at matching temporal resolution. Applied to 1,112 commercial buildings over a 24-month period, the methodology demonstrates that annual SECR Scope 2 estimates based on a single annual average intensity factor diverge by up to 23.4% from half-hourly matched estimates. This discrepancy is systematic: buildings with evening-heavy consumption profiles (entertainment: +20.2%, food service: +23.4%) are underestimating their Scope 2 emissions, while daytime-heavy buildings (education: -14.4%, healthcare: -7.1%) are overestimating. Furthermore, half-hourly intensity matching enables identification of low-carbon consumption windows that could reduce reported Scope 2 emissions by 8-15% through demand shifting, without any reduction in energy use. For a representative 10,000 sq ft office building consuming 450 MWh annually, this equates to 8.5 tCO2e avoided through demand shifting alone. The implications for SECR compliance automation, corporate net-zero strategy, and energy procurement policy are discussed, with three specific SECR reform recommendations.
Files
Files
(26.7 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:a2cad2e91e624b5870c8287e6bff060c
|
26.7 kB | Download |