Coal Consumption Environmental Kuznets Curve (EKC) in China and Australia: Evidence From ARDL Model
Bibliographic record
Abstract
In this study, coal consumption (CS) for EKC is analyzed for two countries which are China and Australia by ARDL model (Autoregressive Distributed Lag Model). China and Australia are among the countries which are heavily dependent on coal for energy demands. China is the current leader in the world for coal consumption. In this study, we aim to analyze the effect of economic growth on CS for China and Australia. The importance of the study is that it is the first study for time series studies in the literature of single country studies that analyze CS EKC. Analysis of CS EKC is important since the world is still heavily depended on coal for energy demands. CS EKC is verified for Australia between GDP (gross domestic product per capita), CS and square of GDP (GP) for the period between 1980 and 2016. CS EKC is verified for China between GDP, CS, GP and energy consumption (ENEC) for the period between 1980 and 2014.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".