Quantitative Estimation of Biomass Energy and Evaluation of Biomass Utilization - A Case Study of Jilin Province, China
Bibliographic record
Abstract
Jilin Province, as a large agricultural province, has abundant reserve of biomass resources. At the same time Jilin Province is currently suffering from energy shortage. Besides, consumption of conventional fossil fuels has resulted in the exacerbation of global warming and air pollution. Biomass energy as a renewable and substitutive energy, can mitigate the energy crisis and global warming, and improve environmental quality once it is fully utilized. This paper estimated the supply potential of biomass energy and integrated LCA and environmental cost analysis to make evaluation on biomass utilization taking biomass power generation system as example. Acquirable and utilizable amount of biomass energy in Jilin Province is equivalent to 21.26 tce, which can be accounted for 25.6% of total energy consumption in Jilin Province in 2011. Among all biomass energy, 59.1% comes from straw and agricultural residues, followed by 33.8% from livestock manure. According to the LCA results, total environmental impact of biomass power generation system is 0.721, much smaller than 25.321 of thermal power generation system. General cost of biomass power generation is higher, however its environmental cost is much lower than thermal power generation system (396 yuan/104kWh < 1819 yuan/104kWh). The results showed that biomass utilization has better environmental advantages and has the potential for the mitigation of energy crisis in Jilin Province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".