The Dual Effects of the Carbon Pricing Mechanism: A Balanced Path between Economic Costs and Environmental Benefits
Bibliographic record
Abstract
Global climate governance increasingly relies on carbon pricing mechanisms to achieve emission reduction targets.As of 2024, 75 carbon pricing policies have been implemented globally, covering 24% of greenhouse gas emissions.As the largest carbon emitter, China launched its national carbon market in 2021 but still faces the challenge of balancing economic growth and low-carbon transformation.This study systematically assesses the economic and environmental effects of carbon pricing mechanisms (carbon taxes, carbon trading systems, and hybrid models) by comparing international practices (such as the EU carbon market and the Canadian carbon tax) with China's policy framework.Hybrid mechanisms (such as the connection between China's pilot markets and the national carbon market) can enhance policy flexibility and reduce the compliance costs of emission control enterprises.At the same time, it encourages technological innovation, such as hydrogen energy steelmaking projects.Finally, long-term decarbonization relies on technological transformation and policy coordination, such as the combination of the EU carbon market and renewable energy subsidies, to promote the average annual stable growth of wind power installed capacity.The research emphasizes the necessity of differentiated policy design (such as free quota allocation and carbon tax rebates).The above conclusion provides a feasible path for optimizing China's carbon pricing framework and coordinating the "dual carbon" goals, highlighting the importance of cross-departmental policy coordination and risk adjustment carbon pricing mechanisms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".