Recycling Contamination

Using personalized, curbside feedback to cut contamination in
residential recycling

Reducing recycling contamination through cart tags that turn everyday sorting mistakes into teachable moments.

Developed in San José, California, the Contamination and Recycling Tagging (CART) program uses personalized feedback to reduce contamination in residential recycling. City ambassadors check the contents of residents’ recycling carts and leave “Oops” tags or “Good Job” tags based on what they find, turning a costly waste problem into a teachable moment at the curb.

A randomized controlled trial of San José’s implementation found the tags cut contamination by 19% within one to two weeks of a single tagging. Once all households are tagged, the program is estimated to save the city $3.5 million per year.

The Challenge:

  • In 2022, 57% of San José’s single-family recycling waste stream was contaminated, costing the city an estimated $20.4 million over two years.
  • That contamination represented 18% of San José’s total waste hauling costs.

The Solution:

  • City ambassadors check residents’ recycling carts and leave “Oops” or “Good Job” tags based on what they find, delivering personalized feedback right at the curb.
  • Ambassadors also engage in conversations with residents when approached on their tagging routes.

The Learnings:

  • A randomized controlled trial found cart tags reduced contamination 19% one to two weeks after a single tagging, an estimated $3.5 million in annual savings once all households are tagged.
  • For every $100,000 spent in the program’s first 16 months, the city received an estimated $160,000 in savings.
  • “Oops” tags drove the results: they increase the salience of recycling, serve as accessible reference guides, and prompt learning and correction.
  • Personalized conversations did not appear to change resident recycling behavior.

What you can do with this evidence:

  • Reduce waste hauling costs by cutting contamination at the source.
  • Use clear, personalized, data-driven feedback to change resident behavior at scale.

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