Reducing Nitrous Oxide Emissions to Mitigate Climate Change and Protect the Ozone Layer
Bibliographic record
Abstract
Reducing nitrous oxide (N2O) emissions offers the combined benefits of mitigating climate change and protecting the ozone layer. This study estimates historical and future N2O emissions and explores the mitigation potential for China's chemical industry. The results show that (1) from 1990 to 2012, industrial N2O emissions in China grew by some 37-fold from 5.07 to 174 Gg (N2O), with total accumulated emissions of 1.26 Tg, and (2) from 2012 to 2020, the projected emissions are expected to continue growing rapidly from 174 to 561 Gg under current policies and assuming no additional mitigation measures. The total accumulated mitigation potential for this forecast period is about 1.54 Tg, the equivalent of reducing all the 2011 greenhouse gases from Australia or halocarbon ozone-depleting substances from China. Adipic acid production, the major industrial emission source, contributes nearly 80% of the industrial N2O emissions, and represents about 96.2% of the industrial mitigation potential. However, the mitigation will not happen without implementing effective policies and regulatory programs.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".