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Waste sorting is getting gradually important as we produce growing amounts of waste every year. However, waste sorting has proven to be challenging due to waste complexity and lack of motivation and knowledge among citizens. In particular, waste sorting in public spaces has been found to be difficult. We present a study on waste sorting in public spaces with an automatic waste sorting bin called Waste Wizard. Waste Wizard uses machine learning to classify and sort waste. We deployed it in three public contexts: a zoo, a retail store, and a music festival. Our findings show how practices, knowledge and attitudes influence the complicated process of public waste sorting. Users also displayed playfulness in their interaction and furthermore generated ideas and wishes for public waste sorting. We discuss our findings regarding current work within sustainable HCI and implications for public waste sorting.
Jacobsen et al. (Sun,) studied this question.
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