An Empirical Analysis on the Nonlinear Relationship Between Economic Growth and Carbon Dioxide Emissions in China
Bibliographic record
Abstract
Based on the traditional theory of environmental Kuznets curve (EKC), this paper selects the panel data in 2000-2017 of 30 provincial administrative regions (provinces) in China as objects, and estimates the per capita carbon dioxide (CO2) emissions of each province.On this basis, an EKC econometric model with spatial effect was established, and used to empirically analyze the nonlinear relationship between economic growth and CO2 emissions.The main results are as follows: (1) The provinces differed greatly in per capita CO2 emissions; the per capita CO2 emissions of Inner Mongolia, Ningxia, Shanxi, Tianjin, and Liaoning were relatively high, while those of Hunan, Jiangxi, Guangxi, Sichuan and Hainan were relatively low.(2) In addition to obvious spatial correlation, the per capita CO2 emissions of the provinces have spatial heterogeneity: most provinces belong to cluster areas, but only a few fall in the areas of spatial outliers.(3) The EKC spatial econometric model shows that the economic growth has a significant inverted U relationship with CO2 emissions.In other words, with the growth in economy, the CO2 emissions firstly increase and then decrease.(4) CO2 emissions are clearly promoted by industrial structure, energy consumption structure and environmental regulation, but suppressed by the level of opening.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".