Decarbonizing energy industry through multi-criteria techno-economic method for preservation of renewable forest carbon sinks

The forest bioenergy sector is increasingly affected by decarbonization policies and carbon sink conservation requirements, creating new challenges for strategic wood procurement planning. This study developed a dynamic multi-objective linear programming-based decision support system (DMOLP-DSS) framework integrating carbon sink impacts, emission pricing, and techno-economic optimization for wood procurement. The methodology was evaluated using real-world municipal-level data on procurement, revenues, emissions, logistics, and environmental constraints. Expanding the procurement radius from 120 km to 140 km increased revenues from €106.9 million to €109.1 million while reducing emission costs from €7.8 million to €3.1 million. The optimized procurement strategy reduced spruce-related emissions by 11,528 tCO2e/year, corresponding to a decarbonization value of €1.17 million. The total decarbonization potential for all tree species at the municipal level was estimated at €7.37 million from 68,182 MWh/year of reduced unsustainable wood harvesting. The results demonstrate that spatially flexible biomass procurement can improve carbon sink preservation without requiring modifications to existing logistics systems. The proposed DSS provides a practical tool for integrating carbon pricing and multi-objective optimization into sustainable forest-based energy supply chains. Although broader cross-regional application requires improved harmonization of forest resource datasets, the framework provides scalable support for regional decarbonization policy implementation. The framework also supports Industry 5.0 sustainability objectives.