Clustering and Analysis of Circular Economy Indicators by Economic Sector and Waste Type Using Machine Learning
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Abstract
This study aims to analyze and classify patterns of circular economy systems across different economic sectors. It integrates secondary data analysis with machine learning techniques. To date, no prior study has systematically linked circular economy (CE) indicators with both economic sectors and types of waste. The research utilizes secondary data from 877 research articles indexed in the Scopus database published between 2010 and 2024. Six key CE indicators are examined, including the material circularity indicator (MCI), waste hierarchy index (WHI), recycling rate (RR), reuse potential index (RPI), resource efficiency indicator (REI), and circular material use rate (CMUR). Principal Component Analysis (PCA) and K-Means Clustering are applied to identify distinct patterns within the dataset. The findings reveal that the agriculture and food industries are strongly represented in the "circular-dominant" cluster, while the construction and metallurgy sectors are primarily associated with the "linear-dominant" cluster, which exhibits limitations in circular performance. However, the analysis faced certain limitations, including data imbalance across sectors and inconsistencies in indicator definitions. These insights can support the design of sector-specific circular economy policies and contribute to the development of recommendation systems for selecting appropriate indicators based on waste types and industrial contexts. The results serve as a foundation for accelerating the practical transition toward a circular economy.
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