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<p>The adoption of circular economy (CE) practices in hospitals is critical for reducing waste and enhancing sustainability, yet implementation remains fragmented due to complex systemic barriers. This study identifies seven key factors influencing CE adoption—leadership commitment (F1), regulatory support (F2), financial resources (F3), staff awareness (F4), waste infrastructure (F5), technological capability (F6), and monitoring systems (F7)—and analyzes their interdependencies using an integrated Fuzzy ISM-MICMAC and unsupervised machine learning approach. In this hybrid methodology, Fuzzy ISM-MICMAC is applied first to establish the hierarchical relationships and classify the driving and dependent powers of the factors, and then the outputs from this structural analysis inform the K-means clustering, which groups hospitals based on the combined influence of these factors. Fuzzy ISM reveals a hierarchical structure, with leadership (F1) as the dominant driver, while MICMAC classifies monitoring (F7) and human resources (F4) as dependent outcomes. K-means clustering of hospitals in Banten Province, Indonesia, groups them into three clusters: high adopters (aligned with leadership and policy), low adopters (struggling with resources), and uneven adopters (showing mixed performance). Spatial analysis further highlights regional disparities, emphasizing the need for context-specific strategies. The hybrid methodology bridges structural modeling and data-driven insights, offering policymakers a framework to prioritize interventions—starting with leadership and governance before operational measures. However, the study’s focus on one province limits generalizability, warranting future research in diverse settings.</p>

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Hospital Circularity Reimagined: Exploring Interdependent Drivers of Sustainable Transitions

  • 期刊论文

  • 发表日期：

    2026年3月19日

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  • Asep Marfu,
  • Agung Purwanto,
  • Dwi Atmanto,
  • Henita Rahmayanti,
  • Arita Marini,
  • Samlibry Adhitia,
  • Catur Wahyu Prasetyo,
  • Sandhi Prasetiawan,
  • Daiman Daiman,
  • Septantri Shinta Wulandari,
  • Anna Sofia Atichasari,
  • Heri Sapari Kahpi

摘要

The adoption of circular economy (CE) practices in hospitals is critical for reducing waste and enhancing sustainability, yet implementation remains fragmented due to complex systemic barriers. This study identifies seven key factors influencing CE adoption—leadership commitment (F1), regulatory support (F2), financial resources (F3), staff awareness (F4), waste infrastructure (F5), technological capability (F6), and monitoring systems (F7)—and analyzes their interdependencies using an integrated Fuzzy ISM-MICMAC and unsupervised machine learning approach. In this hybrid methodology, Fuzzy ISM-MICMAC is applied first to establish the hierarchical relationships and classify the driving and dependent powers of the factors, and then the outputs from this structural analysis inform the K-means clustering, which groups hospitals based on the combined influence of these factors. Fuzzy ISM reveals a hierarchical structure, with leadership (F1) as the dominant driver, while MICMAC classifies monitoring (F7) and human resources (F4) as dependent outcomes. K-means clustering of hospitals in Banten Province, Indonesia, groups them into three clusters: high adopters (aligned with leadership and policy), low adopters (struggling with resources), and uneven adopters (showing mixed performance). Spatial analysis further highlights regional disparities, emphasizing the need for context-specific strategies. The hybrid methodology bridges structural modeling and data-driven insights, offering policymakers a framework to prioritize interventions—starting with leadership and governance before operational measures. However, the study’s focus on one province limits generalizability, warranting future research in diverse settings.