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Zusammenfassung:
The transition from a fossil-based economy to a bioeconomy (BE) is crucial for sustainable production and consumption. Yet, growing demand for bio-based resources challenges sustainability, underscoring the need for indicator-based monitoring. Forests play a central role by providing carbon sequestration, timber, biodiversity, habitats, and other ecosystem services but are vulnerable to overuse and conflicting management goals. This study proposes a framework for indicator-based BE monitoring in the German forest sector, combining empirical data with forest growth models (FGMs) to reconcile resource use with ecosystem protection and support policy development. The framework emphasizes ecological aspects and synergies among societal demands to optimize trade-offs between competing needs. Developed through literature review and expert consultations, the framework defines selection criteria ensuring concise, evidence-based indicators: they must (i) provide quantitative feedback on target achievement, (ii) draw on historical datasets, and (iii) be represented in FGMs for future projections. FGMs simulate interactions between management and ecological factors driving tree growth, mortality, disturbances, regeneration, and stand development. They track forest development via parameters assessing biomass, ecosystem state, and resilience. We identified 11 FGMs suitable for BE monitoring in Germany and propose five indicator groups: biomass carbon stocks, biodiversity, soil, water, and biomass extraction. Carbon and biomass indicators are well-integrated into FGMs, while biodiversity indicators remain only partially represented. Soil indicators are hampered by database gaps and process simplifications. Water indicators focus on drought stress quantification and require high temporal resolution process representation and meteorological input for accurate soil-plant-atmosphere interactions. These challenges highlight the need for further FGM development to improve and standardize indicator representation for BE monitoring.
