Comparative Analysis of Carbon Tax and Carbon Emission Trading Policies in the Context of Carbon Emission Reduction: Based on the Perspective of the Power Industry
Bibliographic record
Abstract
In this paper, we focus on China's power sector to investigate ways to improve emissions reductions in the context of global warming and rising carbon emissions. This paper reviews the current state of adapted carbon emission reduction policies in China's power industry and assesses the potential effectiveness of two mechanisms, carbon taxation, and carbon trading, in achieving substantial emissions reductions. China's power sector is a major contributor to global carbon emissions, and the paper explores the balance between short-term carbon tax policies and long-term carbon trading strategies aimed at promoting an early peak in carbon emissions as well as carbon neutrality within the country. This research finds that the carbon tax can have positive impacts in the short term, whereas carbon trading exhibits higher efficiency in the long term. The paper addresses this by proposing a combination of the two mechanisms to achieve effective emissions reductions in the power industry, supporting China's goal of achieving carbon neutrality by the time of 2060. Finally, we explore the feasibility and benefits of implementing a carbon tax, highlighting its suitability for near-term implementation in the power industry. We conclude that a combination of carbon taxes and carbon trading can make a significant contribution to China's carbon peak and carbon neutrality targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".