Clean energy consumption and economic growth in China: a time-varying analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Assessing the causal relationships between clean energy consumption and economic growth in China, a central actor in the world’s climate future, have received considerable attention among scholars. However, due to the lack of methodological rigour in the causality analysis, available literature failed to provide solid inferences on the links between the variables. Therefore, this study aims to re-examine the variables’ dynamic linkages with a more well-established approach from 1965 to 2020. We use a time-varying framework that relaxes the assumption of parameter stability, a remarkable feature that distinguishes our paper from the previous studies. Utilizing the conventional Granger causality test, we fail to detect causation between the variables. However, the evidence of substantial time variation in the causal relationships implies that the standard framework’s inference is unreliable. The findings of our time-varying analysis indicate different forms of causality flows in various subperiods. This can be a dependable reason for China to follow its enhanced carbon neutrality target safely. The results of our study also emphasize the significance of considering time-varying causality tests to avoid the risk of misleading inferences.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it