YOUR GUIDE TO PORTFOLIO-LEVEL ENERGY DATA

PORTFOLIO-LEVEL ENERGY DATA REQUIRED FOR YOUR GREENHOUSE GAS (GHG) INVENTORY

To unlock these benefits, teams first need to align on what “good” portfolio data looks like, both in structure and level of detail. Consistency and transparency at the facility level create a strong foundation for accurate aggregation and comparison across the portfolio. This initial step also reduces risk of overwork and overspend on data collection by aligning key stakeholders early.

#1: Key facility information

Consistent facility-level information enables more accurate benchmarking and performance comparison across portfolios. At a minimum, each property in your portfolio should have:

• • Address and building name.
  • Building floor area for the whole building area as applicable. Clarify whether the building floor area represents whole building, landlord-controlled areas, common areas, or vacant space, and make sure a common metric is used across the portfolio (e.g., gross floor area).
  • Primary property use or type (e.g., office, retail, multifamily, etc.).
  • Occupancy or vacancy rate. This information provides the context needed to interpret energy intensity and compare similar assets.
  • Which energy sources are used at each facility (i.e., is the facility all electric or does it use natural gas?). This information enables data validation and informs estimation where needed.

#2: Actual energy data by source

While estimated energy data can fill gaps and support high-level reporting, actual energy data helps firms make better financial and operational decisions with less risk. Real data helps teams identify underperforming assets and prioritize upgrades with the highest ROI that could be missed with estimated datasets, enabling firms to move beyond data management as a reporting exercise to informing better capital planning.

For actual energy data, collect monthly metered energy consumption data for each source (e.g. electricity, natural gas, district steam, etc.), in consistent units of measurement (e.g., kWh, therms, or MMBtu). At the portfolio level, a common issue is unclear coverage. For clarity, document what portion of the building the data represents.

#3: Missing or estimated data

Still, data gaps are common across large portfolios, and maintaining a complete dataset, even when some values are estimated, helps teams preserve reporting consistency, identify portfolio-wide trends, and make more informed investment decisions. In these cases, document:

• • Which energy source is being estimated (e.g., natural gas or steam).
  • Note the number of missing months of data that need to be gap-filled and estimated. Apply a consistent method to estimate missing data (e.g., Building Performance Database EUIs for building type and energy source).
  • Clearly document any estimated values in the final data set.

If you’re using a lot of estimated data right now, a data improvement plan can help. This plan outlines how your team will refine specific pieces of data, so the final metrics become less reliant on estimation over time, improving the quality of your portfolio-level dataset and making it more defensible and decision-ready.

#4: Renewable energy and RECs

Finally, include information on:

• • Renewable energy generated onsite (e.g., solar PV) and whether renewable energy certificates (RECs) were retained or sold. Note: If the RECs were sold, provide both the gross energy consumption and total onsite generation at the asset, not just the net load.
  • RECs purchased (e.g., volumes, vintages, and suppliers). This accurately reflects renewable contributions in both energy and emissions reporting.
  • Any green tariffs and specific electricity utility providers by the amount of energy, location, and contract details needed for market-based Scope 2 calculations.

PORTFOLIO-LEVEL ENERGY DATA QUALITY CHECKS FOR YOUR GHG INVENTORY

Once data is assembled, it’s time to evaluate accuracy and completeness. These checks help flag anomalies early before they affect your final calculations. This helps teams avoid costly reporting corrections, reduce assurance and compliance risk, and make more confident decisions.

#1: Review portfolio energy use intensity (EUI)

Calculate the annual EUI for each building. If needed, extrapolate partial year data (i.e., a building opened or closed during the year) to a full year for comparison purposes.

Compare the EUI against benchmarks for the building type and region. Significant deviations often indicate a data error, missing bills, incorrect floor area reporting, or misallocated metering. Review both total EUI and EUI by source (e.g., electricity, natural gas, district steam, etc.) to identify which utility type is driving anomalies.

#2: Evaluate vacancy impacts

Understanding how occupancy affects energy performance helps sustainability and asset management teams distinguish operational changes from true performance issues, improving insight into inefficient assets and assets at risk of non-compliance with building performance standards.

A building operating at 50% occupancy should not exhibit the same EUI as a fully occupied/leased property. Make note of these cases and how they impact site EUI in case there may be an underlying data issue. If EUI remains unchanged despite major occupancy shifts, review meter boundaries and building floor area allocations for potential discrepancies.

#3: Verify building floor area

Check that the energy data coverage aligns with the building floor area. Where full coverage is unavailable (e.g., landlord-controlled loads), document the percentage of the building floor area represented and estimate the uncovered data using defensible methods to achieve complete energy coverage.

#4: Analyze trends over time

Analyze YoY trends for each property and the overall portfolio to identify:

• • Sudden spikes or drops in consumption by energy source.
  • Unexpected changes in energy mix.
  • Sharp deviations that do not align with weather, operational changes, or occupancy trends.

While an annual quality check is a good start, we recommend reviewing data trends quarterly to catch issues earlier and reduce year-end reconciliation, and to review multi-year trends in addition to single year-over-year trends, which can sometimes be volatile based on changes in data collection and/or quality.