This study develops a novel dynamic selection approach for Circular Economy indicators, integrating expert and participatory inputs to address the Water-Energy-Food-Ecosystems nexus. Twenty indicators were ranked by diverse stakeholders, and their interrelationships analysed through Interpretive Structural Modeling, identifying six hierarchical importance levels. Cross-Impact matrix multiplication applied to classification (MICMAC) analysis classified indicators into four categories based on driving and dependence power, revealing seven key driving indicators linked to natural environment regeneration, resource retention, and eliminating negative externalities. The proposed methodology facilitates prioritisation and implementation of indicators across different systems, bridging gaps between research, policy, and practice.
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