Calculating inter-sectoral carbon flows of a mining sector via hypothetical extraction method
Bibliographic record
Abstract
Mining is among the oldest industries. It is the primary source of raw materials for most of the sectors. Little is known about the complex inter-sectoral carbon linkages of the mining industry. In this work, we estimate the inter- and intra-sectoral carbon linkage impacts of the mining sector across ten major economies by applying an input-output model, and the hypothetical extraction method and its modified version. The hypothetical extraction method removes an industrial block from an economic system, and afterwards, it makes a comparison between the before and after removal values. China with 195.47 Mt has the highest mining emissions, followed by USA, India, and Canada with 110.99 Mt, 108.79 Mt, and 76.92 Mt, respectively. The India’s mining sector with 26.33 t/104 $ is the most carbon-intensive, followed by Japan and Canada with 6.84 t/104 $ and 5.22 t/104 $, respectively. China’s carbon emissions with -11.56% and -11.28%, respectively, have been affected the most by the total extraction of mining sector and forward carbon linkages, while for the backward carbon linkage, Canada with -1.33% has been affected the most. Canada has the highest mixed and internal emissions of 0.42 Mt and 47.88 Mt, respectively. However, China has the highest net-backward and net-forward emissions of 16.91 Mt and 189.22 Mt, respectively. For all nations, the mining sector is a net exporter of emissions to other industries. Based on the numerical findings, in this work, we discuss the mitigation measures for both the direct and indirect mining emissions.
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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 | 0.001 | 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) | 0.001 | 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 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".