Domain: Waste Management & Environmental Services
Question:
The organization reports waste generation and diversion performance over time to demonstrate progress toward public zero-waste or diversion goals. Reported results fluctuate noticeably month to month, largely due to changes in occupancy, event activity, and hauling frequency, even when there have been no meaningful operational changes. Describe how the organization should report and communicate its waste data so the story remains credible and the method is consistent. In your response, also include what normalization factors or context should accompany reported diversion rates, how to handle comparisons across periods with different operating conditions, what methodological details should be documented and kept consistent and how to present progress in a way that avoids overstating the improvements or exaggerating setbacks that are a result of scheduling and volume effects

Expected points in a correct answer:
States credible reporting is less about precision and more about consistency and transparency
States occupancy, events and hauling frequency change month-to-month results regardless of day-to-day practices staying the same
States the organizations goals should be to report data that separates what changed (service levels, occupancy, event load) and what didn't change (program operations, signage, procedures, training)
States the key credibility rule is to keep the method consistent across reporting periods so trends reflect real change, not shifting assumptions
States to explicitly present the data based on how it is collected, whether through service based estimates or through actual weights
States key credibility rule is to keep method consistent to be able to show trends and not just assumptions
States to clearly document if the method is changed, mark a break in the data stream including how it change comparability
States to provide absolute totals to show true system load
States to use a normalized metric to account for changes in building use and schedule
States to provide a rolling average along monthly data so large swings do not create red flags
States the need for the data to be presented in this way to show stakeholders progress regardless of fluctuations
States need to annotate real-world drivers in a log to explain swings in data
States that month-to-month diversion shifts are typically driven by service changes, event spikes, cleanouts, or extra pulls—not sudden behavior changes
Lists specific examples of events to annotate in the waste log, such as major events/seasonal surges, extra pulls/overflow service, container reconfiguration, renovations/cleanouts, and schedule changes affecting occupancy
States that when variability occurs, the organization should communicate it openly by explaining it as the result of documented drivers rather than being defensive
Provides explanation of benefits for presenting the data in this manner like control, transparency and honest progress towards zero waste goals
References a workbook to calculate total monthly waste and monthly operation log to help annotate any swings in data
States to create trends of data externally (i.e. rolling averages, normalization metrics)
States to use G27:G32 if item based data was used
States to input data in the Waste Log tab in cells B5:F7
States specific examples of service-based inputs used for estimation, such as container size, container count, pickup frequency, and conversion assumptions
States specific examples of normalized metrics such as per occupant, per operating day, or per event-day
States specific rolling average timeframes such as 3-month or 12-month
States that the waste log approach prevents reporting from looking inconsistent or unreliable
States to show rolling average and normalized metrics alongside monthly data when communicating variability to stakeholders
Mentions specific examples of method changes that would constitute a break in the data series, such as new containers, new hauler arrangements, or compactor added/removed