Demystifying the landscape of carbon quantification and reporting standards: a practical note for the financial sector
Bibliographic record
Abstract
Abstract In response to the global challenge of climate change, financial institutions are increasingly called upon to assess and disclose their carbon emissions. Various global carbon quantification and reporting standards were developed, such as the Greenhouse Gas (GHG) Protocol, Task Force on Climate-related Financial Disclosures (TCFD), Partnership for Carbon Accounting Financials (PCAF) and others. Unfortunately, the now diverse landscape of standards increases the complexity for institutions seeking to develop voluntary carbon quantification and reporting. This study addresses the complexity issue by developing a criteria-based tool that summarizes the various components and requirements of the carbon standards. We propose eight criteria that summarize the standards’ key elements, requirements and relevance to the financial industry. We analyze seven major carbon quantification and reporting standards, systematically evaluating them against our tool. By doing so, we provide financial institutions with valuable insights in selecting appropriate standards to inform their emissions quantification and reporting decisions.
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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 | 0.261 | 0.323 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.033 | 0.041 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".