Abstract
Climate-Smart Forestry (CSF) aims to integrate mitigation, adaptation, and socio-economic functions into sustainable forest management. This study applies a composite Index of Climate-Smart Forestry to long-term experimental plots in Bavaria, Germany, encompassing beech, spruce, and mixed stands subjected to different thinning intensities. The aim is to evaluate how tree species composition and thinning intensity affect forest smartness and to identify key indicators driving the three CSF pillars. Using a hierarchical framework, we selected and weighted nine structural and functional indicators to calculate the composite index and its subcomponents. While Generalized Additive Models were used to analyze the temporal dynamics of smartness values and their components over time, Linear Mixed Models assessed the influence of each indicator on mitigation, adaptation, and socio-economic criteria. Results show that mixed and broad-leaved forests generally perform better than coniferous monocultures, particularly in terms of adaptation. More intensive thinning enhances CSF performance by improving structural diversity and reducing vulnerability. Among the indicators, forest damage, growing stock, and slenderness coefficient emerged as the most influential across pillars. Roundwood production was also identified as a key variable, especially for the socio-economic dimension. The findings highlight the value of long-term monitoring plots in capturing dynamic forest responses and support the integration of indicator-based approaches into adaptive management strategies. Despite progress, challenges remain in quantifying socio-economic aspects and harmonizing data across scales. The ongoing digital implementation of the CSF method aims to address these gaps, enabling forest managers to apply the index in different contexts and better align forest practices with climate and sustainability goals.