Analysis of Influencing Factors on Regional Carbon Emission Intensity in China-Based on Empirical Research with Provincial Panel Data
Bibliographic record
Abstract
This thesis analyzes the influencing factors of carbon emission intensity based on the five types of panel data in 31 provinces (cities and autonomous regions) throughout China collected in China’s “12th Five-Year Plan”, and has conclusions as follows: the carbon intensity in various regions is mainly influenced by energy intensity, economic growth, proportion of the secondary industry, and fiscal expenditure. The carbon intensity basically maintains positive correlation with energy intensity and economic growth, while the correlation between carbon intensity and industrial structure, and that between carbon intensity and fiscal expenditure varies from each other. There are different industrial structures and corresponding changing laws in various provinces and cities. The favorable influences on carbon intensity by fiscal expenditure are not remarkable, and yet the relationship between proportion of fiscal expenditure and carbon intensity still shows positive correlation in most of the provinces. In the long run, the major way of realizing energy saving and emission reduction for various provinces still lies in adjustment and optimization of both industrial structure and energy consumption structure.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".