Study on the Contribution of Shale Gas Exploitation and Utilization to Carbon Emission Reduction Based on LEAP Modeling
Bibliographic record
Abstract
The development and utilization of shale gas is the main path to solve the current high carbon dioxide emissions, and this paper proposes to use the LEAP model to explore the role of shale gas development and utilization on carbon emission reduction in all aspects.Under the principle and de inition of LEAP model framework, shale gas development and utilization scenarios and parameters are determined to facilitate the research and analysis work, and in order to realize the intelligent monitoring of carbon emission reduction work, the neural network two-layer carbon emission reduction prediction model is constructed.With the support of research data and LEAP model, the relationship between shale gas development and utilization and carbon emission reduction is studied and analyzed, and the carbon pulse analysis and prediction model validation model of LEAR simulation results are also supplemented.Although all three scenarios have different contributions to carbon emission reduction, the green scenario is the most obvious means of carbon dioxide emission reduction, with a total of 52.87 from 2010 to 2050, and the prediction model in this paper is able to satisfy the current demand for carbon dioxide emission reduction work, and provide a guiding reference for urban carbon emission reduction.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 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".