The study focussed on the responses of private forest owners (PFOs) following natural disturbances and identified the key factors influencing their decision making regarding the salvage of damaged forests. Based on a survey of 1,515 PFOs, we used logistic regression to analyse the impact of socio-demographic, ownership, management and market-related factors on the decision to carry out salvage logging. The results showed that previous experience in forest management and cooperation with forestry service providers were the most significant factors, while age, education level and market prices had no statistically significant influence. Using the K-means clustering method, we identified three different PFO groups: outsourcing-oriented (32%), self-reliant (42%) and less active managers (26%), which differ in their responsiveness and willingness to act. In the second part of the study, we developed a structured multi-criteria decision model combining the best-worst method (BWM) and the fuzzy TOPSIS method to assess the barriers faced by PFOs and the effectiveness of the proposed solutions. The barriers and solutions were identified through a literature review, the PFO survey and interviews with stakeholders. The barriers were ranked by the different stakeholders using the BWM method, while the PFOs used fuzzy TOPSIS to assess the extent to which each solution addressed the most important barriers. The highest ranked solutions included fostering collaboration between PFOs and business cooperation with forestbased value chain actors, improving advisory services and information sharing, and utilising digital tools. Strategic planning in advance, with designated intervention areas and coordinated actions by forest coordination teams, also proved to be essential. The study offers an innovative and transferable methodological approach to decision support that involves multiple stakeholders and can be applied to other natural resource management contexts.
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