A multi-method framework integrating ANP-ANN and PROMETHEE-GAIA for Circular Economy performance assessment: A case study of China

Abstract

This study evaluates the performance of Circular Economy (CE) initiatives across Chinese provinces using a hybrid multi-criteria decision analysis framework that integrates Analytic Network Process (ANP), Artificial Neural Networks (ANN), and Preference Ranking Organization Method for Enrichment of Evaluations-Geometrical Analysis for Interactive Assistance (PROMETHEE-GAIA), alongside Geographic Information Systems (GIS) for spatial analysis. Key dimensions—environmental, economic, regulatory, and social—are analyzed, revealing significant provincial variations. Specifically, provinces such as Guangdong and Fujian lead in CE implementation due to strong policy enforcement, substantial infrastructure investments, and high public awareness, while inland regions, including Henan and Hunan, fall behind due to weaker regulatory frameworks and lower investment levels. Analysis shows that environmental performance and economic impact are the most critical factors driving success. These results offer novel insights for policymakers and businesses, emphasizing the necessity for targeted interventions, such as enhanced regulatory frameworks and increased infrastructure funding, to promote balanced CE development across regions. Furthermore, the spatial analysis using GIS provides a critical tool for identifying the specific areas where policy and investment efforts should be concentrated, thus driving more equitable and efficient CE implementation nationwide.