Optimizing forest road networks for economic environmental and hazard impacts using a resilient Markov Monte Carlo approach

Optimizing forest road networks is essential for long-term forest management, as it reconciles economic efficiency with environmental performance. This study uses Markov chain Monte Carlo (MCMC) simulation to find the ideal forest road network densities in the hilly region of Metsovo, Greece, from both theoretical and economic viewpoints. The proposed system evaluates key cost factors, including road construction, maintenance, and skidding, while also accounting for the ecological value of forest resources and services lost due to road development. Geographic Information Systems (GIS) are employed for spatial analysis, and the Discounted Cash Flow (DCF) Analysis method is applied to determine the most cost-efficient road network density. This approach integrates financial and ecological considerations to provide a comprehensive framework for optimizing forest road planning. The results show that appropriate densities can dramatically lower lifecycle costs and environmental impacts while still allowing for timber harvesting and ecosystem protection. The sensitivity analysis demonstrates how variations in construction and skidding costs affect road density decisions, underlining the significance of adaptive management. This framework gives forest managers and policymakers a practical tool for designing road networks that balance economic, ecological, and social goals, ensuring long-term forest sustainability and resilience.