Four ways to make the most of a limited study budget to get high quality data you need

Waste characterization studies (WCS), sometimes called waste audits, help local governments and service providers understand what is actually in the waste streams headed for landfill, recycling, or composting. Waste managers need the WCS data not only for compliance but also to identify the most impactful opportunities to reduce waste and increase diversion.

However, WCS studies can require a significant investment. Much of that cost comes from data collection—material characterization professionals must travel to facilities, collect and hand sort samples, accurately identify the specific material of each item, and carefully record what they find. As the amount or complexity of data increases, so does the time required in the field.

With the right strategy, costs can be contained without sacrificing the quality or the value of the data you receive. Waste characterization has been part of Cascadia’s work for more than 30 years, including developing California’s standard methodology and contributing to the ASTM methodology. Drawing on that experience, we’ve identified several study design choices that can help focus a limited budget on data collection that provides the greatest value.

1. Focus on the questions that matter most

The scope of a research question has a direct effect on the amount of data needed to answer it. More questions—or more granular variations of those questions—require more data collection and can significantly increase the effort and cost of a study.

This makes it useful to distinguish between “must have” and “nice to have” information early in study design. A study focused on a few priority questions may provide more useful results than one that spreads the same resources across many different areas of inquiry.

For example, do you need to know what individual households are generating or could sampling of the aggregated waste at a facility provide the insight you need?

As one example, a Solid Waste Authority with six member jurisdictions might face two options: obtaining representative waste characterization data for the Authority’s overall waste generation, or obtaining jurisdiction-specific data for each member jurisdiction. The latter requires six times as many samples as the former, and a proportionally higher budget. Is robust data by jurisdiction a nice to have or a must have? If the Authority is comprised of jurisdictions with similar populations and demographics, differentiated data might not be necessary. If the jurisdictions are diverse, with a mix of urban, exurban, industrial, and rural, individual studies may be essential.

2. Match the material list to the study’s needs

A longer material list provides more detail, but that detail comes with tradeoffs. It takes longer to sort items into more precise categories in the field, increasing data collection costs. A long list can also decrease data quality: as a sample is divided into more categories, there are fewer items in each category, reducing the confidence level of composition estimates.

Using the shortest material list that meets the study’s needs can therefore reduce field time while helping preserve data quality.

Existing reporting can help inform a better list as well. For example, starting with the material categories included in your reporting requirements or in datasets you might want to compare against (such as reporting from service providers). Where less detail is needed, those categories can even be rolled up into a shorter list for your WCS.

3. Reconsider longstanding assumptions about sample size

Waste characterization studies commonly use garbage samples weighing at least 200 pounds each, consistent with an ASTM-recommended threshold that dates to 1970. Waste streams have changed considerably over the decades, creating an opportunity to revisit whether that sample size is necessary for every study.

Cascadia’s data analysis has found that reducing the sample weight from 200 to 150 pounds can preserve statistical data quality while reducing fieldwork costs by nearly 25 percent through faster sampling and sorting. For studies where lighter samples are appropriate, that efficiency can translate into meaningful savings.

4. Use existing data where it makes sense

New data collection isn’t always necessary across every part of the waste stream.

Cascadia’s analysis of residential composition data includes hundreds of samples from communities across 20 states. While communities differ, the data show considerable consistency in residential waste composition. Factors such as available curbside services and climate help explain some of the variation that does occur.

That consistency can create opportunities to conduct waste characterization less frequently or to use an existing residential dataset from a community with similar services and climate as a proxy rather than collecting entirely new (or as much new) residential data.

Commercial waste is much more location-specific because its composition reflects the mix of industries and businesses within a community. When resources for new data collection are limited, it may, therefore, make sense to prioritize local commercial waste characterization while using appropriate existing data and modeling to estimate residential composition.

Making each data point count

There is no single right approach to a waste characterization study. The appropriate design depends on the community, the questions being asked, and how the results will ultimately be used.

But a larger or more detailed study isn’t necessarily a more valuable one. Focusing the research questions, selecting a purposeful material list, considering appropriate sample weights, and using existing data where possible can all help concentrate limited resources on obtaining the information that matters most.

For communities facing competing priorities and limited budgets, thoughtful study design can help provide the data and data quality needed to make informed decisions—without collecting more than the study requires.

Ready to get started? Talk to us!

Cascadia has helped small communities, rural jurisdictions, Tribes, and service providers design and implement waste characterization studies that balance data needs and wishes with available resources. Our team loves talking trash and data, so please reach out for support.

Check out our contact form or email proposals@cascadiaconsulting.com to start a conversation!