Challenges to sustainable large-scale shale gas development in China
Bibliographic record
Abstract
China's shale gas production has grown annually by 21% since 2017 with long-term national energy strategy calling for continued expansion. This large-scale shale gas development is challenged by constraints on water supply. It requires over 6,000 new wells to be drilled within the Yangtze River Basin in South China-one of China's most populated regions with sensitive ecological and geological conditions, posing significant environmental threats to the hydrosphere, atmosphere, and biosphere. Hydraulic fracturing-induced seismicity also adds to the existing earthquake risk for the Sichuan/Chongqing region. These potential negative impacts challenge both China's and the United Nations' sustainable development goals. We explore China's current shale gas operations in the Yangtze River Basin and their interaction with the environment from these multiple perspectives. We then suggest future improvements to practice that will promote sustainable development to jointly satisfy China's burgeoning energy needs. We conclude that China's shale gas industry would benefit from an innovation ecosystem that involves companies and research institutions, and that there is an urgent need to implement environmental regulations for shale gas extraction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".