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Concordia University is leading an innovative research study called “Open Waste”, a multi-year study that deploys internet of things sensors in community-level waste management infrastructure to accelerate to zero waste targets and gain unprecedented insights into waste behaviours. We are seeking local and international partners to guide and support our research efforts.

Why this is important

  •  45% of emission reduction targets are from material production and disposal processes; moving to a circular economy (CE) is an essential strategy to offset climate change.
  •  Zero waste goals are being set worldwide but only 19% of waste is recovered globally.
  •  Contamination, confusion on effective policies and lack of reliable data remain major challenges.
  • The potential for AI to accelerate ZW and CE goals will depend on generating large volumes of quality, geo-located waste data.

Research Goals

We will track waste flows and compositions at a campus scale with computer vision, fill-level, and weight sensors. We’ll invite the community to reach a zero waste goal through infrastructure, marketing, and policy interventions. Our research will:

  1.  Uncover how well communities understand waste sorting rules
  2.  Develop AI-driven software for waste sorting feedback
  3.  Develop recommender systems for a local reuse center
  4.  Develop a visualization and analytics platform that optimizes waste bin planning and education campaigns
  5.  Measure the ecological and financial cost-benefits of digitalization of community-level waste infrastructure
  6. Ensure data sovereignty and equitable, open waste data practices

The results of this study will lead to policy data and recommendations around Zero Waste, Circular Economy, and Digitalisation. Furthermore, the technology processes and software models will directly benefit domestic and international communities via the world’s first OpenWaste model. Future implementations in other contexts will expand the relevance of results and the volume of data collected.

YEARS FUNDED

2023

AMOUNT ALLOCATED

$13,944

PROJECT LEADER

Faisal Shennib
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