Estimation and Prediction of Carbon Emission from Typical Coalfield Fire in Xinjiang, China: Based on Field Detection and Historical Data
Bibliographic record
Abstract
Coalfield fires release large amounts of greenhouse gases like CO2 and methane. Quick and effective carbon emission estimates are vital for assessing fire control and enhancing carbon reduction. In this work, field detection was applied to estimate the coal loss of typical coalfield fires in Xinjiang, and a simplified model was used to estimate the carbon emission based on coal loss. This method was verified by comparing with literature data. Using this model, the carbon emissions of Jiangjunmiao, Sigonghe, and Changcaodong coalfield fire zones were estimated to be 1,245,187.5 t, 293,730 t, and 511,122 t, respectively. Besides the field test data, the historical data from government was also collected, in order to calculate the increase rate of carbon emission. In Xinjiang, substantial progress has been made in coal fire management. Our estimation indicates that from 1958 to 2019, Xinjiang’s efforts to control coalfield fires resulted in a reduction of 8.93 Gt of carbon emissions, roughly sevenfold the total carbon emissions of China in 2023. From 2019 to 2023, the annual rates of change in carbon emissions for coal fire areas in Xinjiang that were under active control and those that were left unregulated were −4% and + 13.3%, respectively. In order to achieve the carbon reduction targets in the coalfield fire zone, the three surveyed Xinjiang coalfields need to achieve an annual reduction rate of 58%-73% to achieve the 2030 plan, 20%-29% to achieve the 2050 plan, and 15%-22% to achieve the 2060 plan.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".