Empirical Analysis on How Financial Development Influences Low-Carbon Economic Gain Efficiency Considering the Panel Data of Yangtze River Economic Corridor
Bibliographic record
Abstract
This paper establishes an evaluation metric system (EMS) for low-carbon economic gain efficiency (LCEGE) containing the carbon sink element, and measures the LCEGEs in the 11 provincial administrative regions in the Yangtze River Economic Corridor (YREC) of 2000-2017 with the directional distance function (DDF) model. Furthermore, the Tobit model was selected for the empirical analysis on the influence of financial development on LCEGE. The results show that: the provinces in YREC had certain disparities in LCEGE. Most provinces achieved desirable LCEGEs, but a few provinces failed to do so. The LCEGE in the lower part changed little in the target period, while that in the middle part and upper part varied in two phases. Besides, there are obvious differences in the mean LCEGE between the lower part, middle part, and upper part. In the target period, the three parts of the YREC can be ranked as lower part, middle part, and upper part by LCEGE. The results of Tobit model reveal that the LCEGE in the YREC can be greatly promoted by financial scale, and clearly suppressed by financial structure. Among the control variables, foreign direct investment significantly promotes LCEGE; technical innovation, and energy structure significantly suppresses LCEGE; industrial structure and environmental regulation have an insignificant influence on LCEGE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".