A Framework for Sustainable Forest Management Decision-Making Models

Thesis Abstract

The sustainable management of forest resources presents a critical challenge in balancing ecological conservation, economic development, and social wellbeing amidst increasing anthropogenic pressures and climate change impacts. Despite numerous efforts, existing decision-making frameworks often lack integration of comprehensive ecological, economic, and social criteria, leading to inconsistent management practices and suboptimal long-term outcomes. This study aims to develop a robust and adaptable framework for sustainable forest management decision-making models that can facilitate evidence-based, transparent, and participatory processes. The specific objectives include (1) to identify and analyze existing forest management decision models and analyze their applicability in sustainable contexts; (2) to formulate a multi-criteria decision analysis (MCDA) based framework integrating ecological, economic, and social indicators; and (3) to empirically validate the proposed framework through stakeholder consensus and simulation in a specific forest district. The research adopts a mixed-methods approach, combining qualitative and quantitative techniques. The qualitative component involves thematic analysis of relevant literature, policy documents, and expert interviews to identify key criteria and stakeholder perspectives. Quantitative data collection employs structured questionnaires administered to 150 forestry professionals, local community representatives, and environmental managers within a representative forest district. The sample was obtained through stratified random sampling, ensuring comprehensive stakeholder representation. For validation, a simulation-based scenario analysis is conducted, utilizing the Analytical Hierarchy Process (AHP) to derive criteria weights and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking management options. Data analysis incorporates thematic content analysis for qualitative inputs, while quantitative data are analyzed using SPSS and Expert Choice software. The AHP-derived weights inform the TOPSIS ranking of alternative management strategies, allowing for a comprehensive evaluation of trade-offs among ecological, economic, and social objectives. The framework aims to be dynamic and user-friendly, supporting managers and policymakers in making transparent, participatory, and sustainable decisions. Expected findings indicate that integrating multi-criteria evaluation within a participative decision framework significantly enhances the legitimacy, acceptability, and sustainability of forest management strategies. The study anticipates that ecological indicators will prioritize conservation goals, economic indicators will emphasize resource productivity, and social indicators will highlight community livelihoods and cultural values. The proposed model is expected to demonstrate superior decision quality compared to traditional single-criterion or expert-only approaches, providing a practical tool adaptable to varied forest contexts. This research contributes to the existing body of knowledge by advancing a comprehensive, integrative decision-making framework specifically tailored for sustainable forest management. It enriches theoretical understanding by bridging multi-criteria decision analysis with participatory governance theories, such as the Stakeholder Theory and Adaptive Management Paradigm. The study also fills literature gaps concerning operationalization of sustainability indicators in forest decision models, especially in developing country contexts. The study concludes with strategic recommendations for policymakers, forest managers, and stakeholders to adopt and refine the proposed framework, emphasizing capacity building, stakeholder engagement, and continuous monitoring. Additionally, it suggests avenues for future research, including longitudinal validation of the framework's efficacy and the integration of emerging technologies such as remote sensing and GIS for real-time decision support. Overall, the research offers a practical, scientifically grounded model that enhances the capacity for sustainable, transparent, and inclusive forest resource management.