Rahmattullah, Mohammad Arief
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Optimizing Waqf for Sustainable Land Management: An ISM-Based Analysis of the Indonesian Green Waqf Initiative Handayani, Ika Fitri; Fadlin, Afina Taraika; Nafisah, Najmah; Rahmattullah, Mohammad Arief; Satria, Yusuf Yuda; Medias, Fahmi
JESI (Jurnal Ekonomi Syariah Indonesia) Vol. 16 No. 1 (2026)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/jesi.2026.16(1).82-98

Abstract

Purpose: This study examines the hierarchical factors influencing Green Waqf-based land development in Indonesia. It aims to identify primary barriers, outline necessary institutional changes, and formulate a strategic model for ecosystem conservation and socio-economic welfare. Methodology: Using a mixed-methods approach based on Interpretive Structural Modeling (ISM), primary data were collected via interviews and questionnaires from an expert panel of academics, nazhir, and farmers. Results: ISM analysis reveals that the most critical constraints are the lack of regulatory support, limited fintech optimization, and low nazhir professionalism. Overcoming these requires structured education, specialized training, and robust inter-agency coordination. Furthermore, establishing specific legal provisions and socialization systems are foundational objectives for systemic success. Conclusion: While Green Waqf holds immense potential for climate resilience and land rehabilitation, its current implementation Achieving sustainable impact requires legal formalization, capacity building, and cross-sectoral collaboration. Implications: Policymakers must establish specific legal frameworks and guidelines for biological waqf assets (e.g., trees). Waqf institutions should adopt fintech solutions for transparency, and local communities must be empowered as active partners in agroforestry and environmental restoration. Originality: Moving beyond normative literature, this study pioneers a structured hierarchical governance model for Green Waqf, mathematically mapping previously ill-defined causal relationships among regulatory, technological, and social variables.