Economic, environmental and social optimization of forest-based biomass supply chains for bioenergy and biofuels
Bibliographic record
Abstract
Utilization of forest-based biomass for bioenergy and biofuels production could generate additional revenue streams, reduce greenhouse gas (GHG) emissions and generate development opportunities for forest-dependent communities. Barriers such as the capital intensity of conversion technologies, complexity of biomass procurement logistics, and the need to establish sustainable supply chains must be overcome. Mathematical modeling has supported the optimal design of biomass supply chains for bioenergy or biofuels production separately, mostly from an economic perspective. Some studies incorporated environmental and/or social criteria in the optimal supply chain design. However, no study modeled forest-based biomass supply chains for the simultaneous bioenergy and biofuels production, considering economic, environmental and social benefits. The development of such model is the objective of this thesis. First, an optimization model is developed that determines the optimal network design and the optimal yearly flows of raw materials and products that maximize the net present value (NPV) of the supply chain. The model considers the flow of energy among co-located conversion technologies and is applied to a case study in Canada. Second, a life cycle environmental analysis is developed to analyze the environmental impacts of the supply chain alternatives in the case study. Third, the optimization model is reformulated as bi-objective with an environmental objective that maximizes the GHG emission savings associated with the supply chain. These savings are estimated by comparing the emissions of the forest-based biomass supply chain system, versus those of the baseline system where unused biomass is disposed with current methods and energy demands are satisfied with currently available sources. Finally, a multi-objective optimization model is generated that integrates a social objective. The social objective is quantified by a social benefit indicator that assigns different levels of impact of job creation based on the type and location of the jobs. The bi-objective and multi-objective optimization models are applied to the case study and solved using a Pareto-generating solution method. Results indicate a trade-off between the NPV of the supply chain and the other two objectives, and a positive correlation between the generation of high impact jobs in the region, and the overall GHG emission savings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".