Forest-based biomass supply chain planning considering uncertainties and governmental policies

Environmental concerns have increased interest in advancing the bioeconomy, promoting the utilization of renewable resources, such as biomass, to produce value-added bioproducts. Forest-rich countries such as Canada have a significant advantage due to the abundance of biomass residues generated from logging activities and primary forest product industries. However, utilizing forest-based biomass for bioproduction faces challenges, with high supply chain costs and economic feasibility among the main barriers. Therefore, planning and design of forest-based biomass supply chains to optimize economic benefits presents a viable approach to address these challenges. Additionally, governments have introduced programs and policies to support the bioeconomy, and studies show that the success of bioproduction projects often depend on such support. Evaluating the role and impact of these policies is therefore essential, as overlooking them does not reflect real-world conditions. Moreover, policy-related and supply chain parameters are often subject to uncertainty, which should be incorporated into long-term planning, as overlooking them can significantly alter the outcomes. In this research, unlike previous studies, governmental policies and their associated uncertainties are incorporated into the design and planning of a forest-based biomass supply chain for production of value-added bioproducts. Single-objective and bi-objective optimization models are developed to maximize the Net Present Value (NPV) and minimize greenhouse gas emissions. The models are implemented with and without policies, including feedstock subsidies, capital investment incentives, enhanced first-year depreciation allowances, and carbon pricing, and applied to the Williams Lake Timber Supply Area in British Columbia, Canada. Results show that considering all policies simultaneously increases the number of facility establishments and biomass utilization, yielding approximately 44 million NPV over 20 years for 16 facilities, compared to 29 million for 10 facilities without policies. Despite this increase, financial returns remain limited. Bi-objective results indicate that significant emission reduction can be obtained with modest decreases in NPV. For instance, a 47% reduction in emissions leads to only a 4. 1% decrease in NPV. Incorporating uncertainty through possibilistic chance-constrained programming results in more conservative designs with fewer facilities, lower profits, and lower emissions compared to those from deterministic model (eight versus 16 facilities and a 43% decrease in NPV).