Straw Utilization in China—Status and Recommendations
Bibliographic record
Abstract
As the world’s largest grain producer, China’s straw yield was 700 million tonnes in 2014. With a national utilization rate of 80% in 2015, there is still a large amount of straw burned in open-field, resulting in air pollution and a reduction in the quantity available as a source of bioenergy. This paper conducts a literature review of success stories and major challenges in comprehensive straw utilization in and out of China. It is noted that nationwide long-term feasible and sustainable straw utilization at a high rate is a highly complex operation, involving most societal sectors, many people and facilities often at different regions. Scenarios were analyzed to estimate the energy potential and air emission reductions China would accomplish in 2020 by converting an additional 5 or 10% of straw-yield to biofuel. Currently, the approach to control straw burning in China is primarily administrative, relying heavily on prohibition and penalties, inconsistent across policy areas and geography, and lacking in long-term planning. Consequently, the effectiveness of the current approach is limited. The main cause of burning is a lack of infrastructure, effective preventive measures, and viable alternatives. Recommendations aimed at promoting a circular bio-economy around using crop straw as resources were provided, including improving straw utilization rates and reducing open-field burning.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".