Study on Evaluation of Carbon Accounting Information Quality in Coal-fired Power Generation Enterprises
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, by selecting the 2016 annual report or social responsibility report of China's five major coal-fired power generation groups and using content analysis method to make an empirical analysis of the carbon accounting information quality, we found that the carbon accounting information disclosure quality of the five major power generation groups is at medium level. The indicators with higher scores include Pollution Emission, Low-Carbon Awareness and Green Funding. The indicators with lower scores include Carbon Emission Investment, Carbon Emission Transaction. Then, we put forward a series of countermeasures to solve the problems of carbon accounting, including improving carbon accounting research system and guidelines, raising awareness of environmental protection, strengthening social supervision, increasing government carbon emissions monitoring, and implementing a carbon accounting auditing system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it