How many people today grab a takeaway coffee cup from the local cafe to drink on the go? We don’t know, but the number must be enormous.. Most every one of the above have a plastic top that will last 100s of years. Some cafes still use plastic cups that last a similar time. Is 10 minutes of coffee worth 100s of years of trash?
These items can be seen littering our gutters and on our streets all over the place. If they were all cardboard, they would still be littered, but they would, at least, be gone in a short time.
They do not need to be made of plastic.
On the way home from the gym last week, a distance of about 1 km (1/2 mile), I counted the items of plastic litter on the curb as I walked. In that short distance I counted 63 pieces of plastic litter. Plastic drink bottles, bottle tops, candy wrappers, plastic film, polystyrene fragments etc. That seemed to be a lot to me. I guess it is a generational thing. Our parents would have been horrified to see that amount, whereas it seems to go unnoticed by our youth of today. In another 20 years how many pieces will there be on this stretch, -- 200? What will today’s youth think of that new amount then when they are older? Will their children be so readily accepting of a higher amount of litter? AI targets plastic waste systems
Posted on April 30, 2026 by DrRossH in GeneralSource: AI targets plastic waste systems – Inside Waste
Managing municipal living plastic waste presents complex, system-wide challenges across collection, recycling and treatment, requiring the careful balancing of environmental, economic and resource outcomes. However, consistent assessment is often limited by gaps in data and measurement inaccuracies, which reduce the reliability and transferability of existing evaluation models. A recent study published in Engineering by Ziyang Wang, Shen Yang, Junqi Wang and Shi-Jie Cao proposes an artificial intelligence-enhanced framework to strengthen how cities assess and manage plastic waste.
The research outlines how effective waste management depends on the interaction between material flows, infrastructure and socio-economic conditions. Factors such as waste composition, population density, economic activity and disposal pathways, including recycling, landfilling and incineration, all influence lifecycle emissions and costs. These variables are critical when assessing the transition to a more circular plastics economy.
A key feature of the study is the use of machine learning to address data limitations and improve the accuracy of city-level waste estimates. Baseline material flows were established through field measurements and differential scanning calorimetry, then refined using an artificial neural network model to reduce bias. The framework incorporates multiple data sources to cross-check results, fill gaps and account for uncertainty, strengthening the credibility of environmental and economic assessments.
The study also evaluates a range of intervention scenarios to reduce emissions. Measures such as source reduction and the use of bio-based materials offer meaningful short- to medium-term gains. High-quality recycling pathways deliver the largest impact, with an optimal combined scenario projected to cut annual greenhouse gas emissions by more than 96 per cent by 2060 compared to baseline levels.
Economic and technical considerations are also examined. Mechanical recycling is identified as the most practical near-term option due to its lower cost and greater maturity compared to chemical recycling. Over time, a combined approach is expected to deliver significant emissions reductions alongside strong economic returns.
The authors conclude that long-term strategies should prioritise reducing plastic use and designing products for circularity, rather than relying solely on higher recycling rates. The framework offers a practical tool for policymakers and planners seeking to improve waste systems under data constraints and supports more informed decision-making in the shift towards zero-waste cities.
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