Leveraging Circular Economy Metrics for Data-Driven Forecasting of Solid Waste Production in Europe

Chun-Chih Chen () and Yu-Shing Chang
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Chun-Chih Chen: Department of International Business, National Taichung University of Science and Technology, Taichung 404, Taiwan
Yu-Shing Chang: Department of Business Administration, National Taichung University of Science and Technology, Taichung 404, Taiwan

Sustainability, 2024, vol. 16, issue 3, 1-15

Abstract: This study integrates circular economy (CE) metrics with machine learning techniques, specifically XGBoost and Shapley additive explanations (SHAP), to forecast municipal solid waste (MSW) in the EU, analyzing data from 2010 to 2020. It examines key economic and consumption indicators, including GDP per capita and energy consumption, along with CE metrics such as resource productivity, the municipal waste recycling rate, and the circular material use rate. The model demonstrates high predictive accuracy, with an R 2 of 99% for in-sample data and 75% for out-of-sample data. The results indicate a significant correlation between a higher GDP per capita and an increased gross municipal waste per capita (GMWp). Conversely, lower energy consumption is associated with reduced GMWp. Notably, the circular material use rate emerges as a crucial factor for sustainability, with increased use significantly decreasing the GMWp. In contrast, a higher resource productivity correlates with an increased GMWp, suggesting complex implications for waste generation. The recycling rate, while impactful, shows a more modest effect compared to the other factors. The culminating insights from this study emphasize the need for sustainable, integrated waste management and support the adoption of circular economy-aligned policies. They underscore the efficacy of merging CE metrics with advanced predictive models to bolster regional sustainability efforts.

Keywords: circular economy; municipal waste generation; machine learning; European Union (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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