Bridging wildfire risk science and forest management: a modular integrated framework for loss estimation and economic-environmental trade-off analysis

Wildfire is a growing and costly disturbance for forests, infrastructure, and the ecosystem services that depend on them, and its economic and ecological consequences are accelerating under climate change. Hazard science, economic loss estimation, and multi-objective forest management planning have each advanced substantially, but they remain only partially connected: hazard maps rarely translate into monetary loss estimates, and management trade-off studies that consider wildfire risk typically rely on qualitative or locally calibrated risk indicators rather than empirically validated losses as the risk-weighting criterion. Here we present an integrated modular framework that couples these elements within a single architecture. Module 1 generates spatially explicit Average Annual Loss (AAL) estimates for each quantifiable asset category, grounded in empirically derived burn probabilities from validated machine-learning hazard maps. Module 2 uses the Module 1 risk outputs as quantitative inputs to evaluate contrasting forest management strategies, identifying Pareto-optimal trade-offs between economic and environmental objectives under wildfire risk. Empirical implementation across Europe, reported through three companion peer-reviewed studies, shows that incorporating wildfire risk reduces the jointly attainable timber and carbon outcomes by approximately 10-20% compared with a no-wildfire-risk case, and that halving wildfire risk in the Mediterranean biome avoids an estimated €145.5 million in foregone timber and carbon value over a representative 50-year rotation. The framework is deliberately flexible with respect to specific asset categories and management contexts, making it potentially transferable to other fire-prone regions with the necessary data. We discuss alignment with international and European policy instruments and outline priority directions for further application and validation. Current limitations include reliance on historical fire data, simplified treatment of burn probability, and the absence of formal uncertainty propagation into the final monetary estimates.