The impacts of China's ETS on firm competitiveness: Evidence from the power and heat production sector
Bibliographic record
Abstract
Greenhouse Gas (GHG) emissions are one of the primary causes of climate change. In the coming decades, the economic costs of climate change are estimated to be 10 % of the global GDP. To mitigate threats of the catastrophic consequences of climate change, an Emission Trading Scheme (ETS) is proposed as a cost-efficient carbon pricing approach to reduce the GHG emissions from production activities. Since 2013, the Chinese government has launched seven pilot ETS projects. In the pilot projects, China established the national carbon trading market in 2021. The national ETS project covers more than 2000 power and heat production plants, which account for 40 % to 50 % of China's industrial emissions and 10 % of the worldwide GHG emissions. Despite the rapid expansion of China's ETS is, there is no consensus on the effectiveness or impacts of ETS. This study investigates the impacts of the pilot ETS on the competitiveness of the participating firms, measured by profitability, production investment, and environmental performance with a Difference-in-Differences (DID) method. Our study finds that the pilot ETS had a positive impact on profitability and GHG emissions reduction among the participating firms, and a negative impact on production investments. This study provides evidence that the pilot ETS has been effective in producing incentives for firms to reduce their emissions, which is not only beneficial to the environment, but also enhances firm's profitability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".