Trade-Offs between Economic and Environmental Optimization of the Forest Biomass Generation Supply Chain in Inner Mongolia, China
Bibliographic record
Abstract
The utilization of forest residue to produce forest biomass energy can mitigate CO2 emissions and generate additional revenue for related eco-enterprises and farmers. In China, however, the benefit of this utilization is still in question because of high costs and CO2 emissions in the entire supply chain. In this paper, a multi-objective linear programming model (MLP) is employed to analyze the trade-offs between the economic and environmental benefits of all nodes within the forest biomass power generation supply chain. The MLP model is tested in the Mao Wu Su biomass Thermoelectric Company. The optimization results show that (1) the total cost and CO2 emissions are decreased by US$98.4 thousand and 60.6 thousand kg, respectively; 3750 thousand kg of waste-wood products is reduced and 3750 thousand kg of sandy shrub stubble residue is increased; (2) 64% of chipped sandy shrub residue is transported directly from the forestland to the power plant, 36% of non-chipped sandy shrub residue is transported from the forestland to the power plant via the chipping plant; (3) transportation and chipping play a significant role in the supply chain; and (4) the results of a sensitivity analysis show that the farmer’s average transportation distance should be 84.13 km and unit chipping cost should be $0.01022 thousand for the optimization supply cost and CO2 emissions. Finally, we suggest the following: (1) develop long-term cooperation with farmers; (2) buy chain-saws for regularly used farmers; (3) build several chipping plants in areas that are rich in sandy shrub.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".