The first step is always to diagnose the cause of the discrepancy. Energy models are based on specific assumptions. The most practical initial action is to compare those assumptions (e.g., hours of operation, internal loads, local weather year used) with the actual, measured conditions during the performance period. This data-driven review often reveals the root cause, such as higher occupancy or different schedules than modeled, before moving to more invasive measures like retro-commissioning.

LEED O+M v4.1’s Enhanced Refrigerant Management credit requires the disclosure of all refrigerants and a plan for phasing out those that are not CFC-free or have a GWP > 2,500. While the refrigerant has a low ODP, its GWP of 2,500 is at the threshold. The building team must report this new refrigerant and likely update the phase-out plan to address it in the future, making it a compliance action item, not an automatic penalty.

LEED O+M emphasizes continuous improvement and proper implementation of policies. The most professional and effective response is corrective and systemic: retraining the vendor, revising the operational plan, and ensuring documentation reflects the change. This fixes the immediate problem and strengthens the management system. Simply switching chemicals or ignoring the policy failure does not address the root cause of the operational breakdown.

LEED O+M certifications are awarded for a specific performance period. Meeting a code or ordinance at the time of certification is sufficient. However, for recertification, the project must meet the current, more stringent requirements. This scenario highlights the dynamic nature of O+M—performance must improve over time to keep pace with evolving standards and to achieve recertification.

A systematic, data-driven approach starts with analyzing submeter data. A predictable, seasonal spike points to a weather-dependent end-use. Irrigation is the most likely cause, as summer months typically require more watering. Checking the dedicated irrigation submeter (if installed per LEED best practices) is the logical first step to confirm or rule out this hypothesis before investigating less predictable causes like leaks or tenant behavior.

The credit rewards actions that reduce vehicle dependence. While infrastructure (like bike racks) is necessary, the most impactful strategy is one that actively educates and incentivizes occupants to change behavior. A comprehensive mobility guide provides the practical information needed to use alternatives (transit schedules, carpool links, financial incentives), thereby directly supporting the credit’s intent to increase actual usage, not just provide amenities.

LEED O+M requires reporting actual, continuous performance data. The correct approach is transparency: report the data for the entire period, document the major change in the narrative, and explain its impact. This provides an honest account of the year’s performance. The new, more efficient equipment will then establish a new operational baseline for future performance periods and recertification efforts.

The audit pinpoints a major opportunity. The most direct and impactful strategy is to implement source separation for the largest identified waste stream (organics) and secure a diversion pathway (composting). This addresses the root of the problem and will dramatically reduce the landfill diversion rate. Education alone is insufficient without providing the infrastructure and service, while volume reduction (pulping) doesn’t achieve the diversion required for higher credit scores.

A fundamental principle of green cleaning is ‘prevention first.’ Effective entryway mat systems (a combination of grates, scrapers, and absorbent mats) prevent dirt, dust, pesticides, and other pollutants from entering the building in the first place. This protects indoor air quality more effectively than trying to remove contaminants after they have spread throughout the building via foot traffic, and it reduces the need for harsh cleaning chemicals.

The first step is always to diagnose the cause of the discrepancy. Energy models are based on specific assumptions. The most practical initial action is to compare those assumptions (e.g., hours of operation, internal loads, local weather year used) with the actual, measured conditions during the performance period. This data-driven review often reveals the root cause, such as higher occupancy or different schedules than modeled, before moving to more invasive measures like retro-commissioning.0