Multi-Criteria Decision Support in Sustainable Forest Management

Sustainable forest management requires the reconciliation of ecological integrity, economic viability and social well-being across diverse landscapes and stakeholder groups. Multi-criteria decision support integrates quantitative models, spatial simulation and participatory processes to evaluate trade-offs among timber production, biodiversity conservation, carbon sequestration and cultural values. By combining optimisation algorithms, scenario analysis and stakeholder deliberation, these systems enable transparent exploration of alternative management plans and identification of Pareto-efficient solutions. Advances in data collection, remote sensing and computational power have driven the evolution of decision support systems from stand-level simulators to landscape-scale platforms capable of embedding uncertainty, climate change projections and socio-economic indicators. Crucially, participatory approaches enhance legitimacy and social learning by involving forest managers, landowners, local communities and policymakers in defining objectives, weighting criteria and interpreting outputs. This synthesis underscores the global significance of multi-criteria frameworks for adaptive forest governance and highlights practical applications ranging from regional planning in boreal and Mediterranean contexts to digital twin constructs that mirror real-time forest dynamics.

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Multi-Criteria Decision Support in Sustainable Forest Management publication trend

The graph below shows the total number of articles in multi-criteria decision support in sustainable forest management across all publications each year (not limited to Nature Index journals).

Technical terms

Decision Support System (DSS): A software platform that combines data inputs, models and visualisation tools to assist decision-makers in evaluating management scenarios and trade-offs.

Multi-Criteria Decision Analysis (MCDA): A methodological framework for ranking or selecting alternatives based on multiple, often conflicting criteria by assigning weights and aggregating performance scores.

Pareto Frontier: The set of management solutions in which no criterion can be improved without causing a decline in at least one other, illustrating optimal trade-off combinations.

Ecosystem Services: The benefits that people derive from forest ecosystems, including provisioning (e.g., timber), regulating (e.g., carbon storage), cultural (e.g., recreation) and supporting (e.g., nutrient cycling) services.

Digital Twin: A virtual representation of a forest or its components that integrates real-time data and simulation models to mirror physical processes and support adaptive management.

References

1. Sustainability language found in forest plans and its mathematical modeling potential. Discover Sustainability (2024).
2. Embracing sustainability in public-owned forest resources management: Lessons learned and perspectives. Frontiers in Sustainability (2023).
3. Multicriteria Decision Analysis and Participatory Decision Support Systems in Forest Management. Forests (2017).
4. Combining Decision Support Approaches for Optimizing the Selection of Bundles of Ecosystem Services. Forests (2018).
5. A Proposal for a Forest Digital Twin Framework and Its Perspectives. Forests (2022).
6. An approach to assess actors’ preferences and social learning to enhance participatory forest management planning. Trees Forests and People (2020).