Carbon emission accounting method for enterprises considering green electricity and green certificate consumption
Bibliographic record
Abstract
Under the guidance of global carbon neutrality goals, the accuracy of corporate carbon emission accounting has become an increasingly key issue. Currently, the carbon emission reduction contribution of renewable energy power is uniformly included in the calculation of the national power grid’s average emission factor. This makes it difficult for companies to achieve effective carbon emission reductions through the purchase of green power or green power certificates. At the same time, a dynamic correction mechanism for electricity carbon emission factors has not yet been established in the accounting of indirect emissions caused by corporate electricity consumption, resulting in the risk of double accounting for the environmental value of green electricity. In view of the above problems, this study proposes a deduction mechanism based on green power consumption and a method for reducing green certificates. It constructs an enterprise carbon emission accounting index system that integrates green power and green certificate consumption, further establishing a comprehensive calculation model of enterprise carbon emissions. Through the case analysis of typical manufacturing enterprises, objects with annual electricity consumption of 10 million kilowatt hours and green electricity consumption accounting for 30% were selected for verification. After applying this model for calculation, the company’s carbon emissions decreased by about 20% compared with the traditional method, proving that the model can scientifically reflect the actual impact of green electricity and green certificates on the company’s carbon emissions. The novelty of this study is primarily demonstrated through three key contributions: First, it develops a practical approach for green electricity deduction and green certificate offset, addressing limitations in existing accounting frameworks; Second, an indicator system for carbon emission accounting that incorporates both green electricity and green certificate usage has been established, enhancing the precision and relevance of the accounting process; Third, the model’s reliability and practical utility have been confirmed through real-world enterprise data, offering a solid empirical foundation for corporate carbon emission accounting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".