Will Low-Carbon Economy Promote Employment or Not: An Empirical Study Based on 1998-2010 Provincial Panel Data in China
Bibliographic record
Abstract
Sampling on China’s provincial panel data from 1998 to 2010, this paper constructs China’s Cumulative Malmquist Carbon Dioxide Emission Performance Index (CMCPI) as a proxy variable to measure its development of low-carbon economy. System GMM estimation method is applied to explore the relationship between CMCPI and the employment in China, including the total employment and the employment structure in energy-intensive and low-power industries. The main three conclusions are as follows: (i) China’s CO 2 Emission Performance is highest in Eastern China and lowest in Central China; (ii) the provincial differences of MCPI is mainly due to the provincial technology changes rather than efficiency changes; (iv) higher carbon dioxide emission performance significantly promotes the employment of low-power industries and the total employment, but it seems to impede the employment of energy-intensive industries in Eastern China. It is found that higher CMCPI would increase the total employment and improve the employment structure in Eastern China, while that doesn’t happen in Central and Western China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".