Waste audit data provides a detailed breakdown of what’s in each waste stream. For the Blue Box, it quantifies the weight of each material group, contamination rates, and cross-contamination. This information is particularly useful when setting payment terms and revenue sharing parameters for a new processing contract. The City and County of Peterborough hired AET Group Inc, through the 2020 REOI, to analyze past waste audit data from a number of programs, similar to theirs, in order to help clarify a few specifications in their new contract. They studied 2-stream Medium Urban and Rural Regional Municipal Groups, and now the results are in.
Revenue based on composition
Previously, material revenue was shared based simply on the ratio of tonnage scaled of fibre and container streams, and paid using the material composite indices as updated monthly in the CIF price sheet. Specific details of inbound materials from each municipality was uncharted territory. With the shift into a new contract, the composition and ratio of recyclables was an important consideration in a new marketed material revenue situation.
More granularity for income sharing was sought for two purposes. Each municipality aimed:
- to be paid based on the composition of actual materials inbound, not an overall basket of goods; and
- to obtain a truer value of the recyclable materials processed at the MRF.
The waste composition data analysis generated by AET, combined with local sample inbound audit data, assisted in determining a representative breakdown of material ratio for contract negotiations and allowed for a deeper understanding of inbound material composition from each distinctive municipality. This helped set the terms of the cooperative procurement arrangement, or simply put, who pays what and who earns what. In addition, the data identifies issues for focused corrective action with a goal to minimize costs and maximize revenues.
Waste audit data provides greater insight into operational activities. Comparing other municipal waste audit data with local samples allowed us to evaluate our programs with our peers and to identify specific areas of improvement with the contractor.
Comparing urban and rural MRF inputs
Data was gathered from 2016 to 2019 from eight municipalities, four Medium Urban (urban) and four Rural Regional (rural). Based on these data sets, the following was noted when comparing average weight, contamination, and stream ratio percentages.
It is projected that income generated from specific commodities, instead of basket of goods pricing, will decrease the net cost of the blue box programs for the City and County of Peterborough. It is also expected that, with more sampling completed over time, the costs and income will adequately represent the true overall worth of recyclables and provide a clearer image of program finances for the two municipalities.
Data driven decision-making leads to better relationships
When sharing infrastructure, it is important for all parties to play and pay their fair share, in order to maintain harmonious relationships. Without data, it can be difficult to substantiate claims. This initial research, which included valuable waste audit data, provided each municipality the chance to identify unique differences that will impact the bottom line with respect to revenue sharing (ratio of fibre to containers) as well as processing costs (contamination rates).
For more information about the Medium Urban and Rural Regional 2016-2019 Waste Audit, contact Ben Dunbar at AET or CIF staff.
Apply to participate in the CIF/SO Waste Composition Studies by June 26, 2020
Comprehensive waste audits typically cost municipalities upwards of $50,000! Funding to help cover these costs is available through the CIF’s 2020 REOI. Don’t miss out on the opportunity to complete waste audits in your community. Applications to participate in the CIF/SO Waste Composition Studies 2021 must be submitted by June 26, 2020. Apply in four easy steps:
What do you use waste audit data for?
For targeting problem material for promotion and education.
To get a clear picture of generation, diversion, capture, opportunity, and challenges.
For justifying long term waste management planning.
To find out how much recyclable material ends up in the garbage.
For program improvements.