Delta Attribution: A New Method for Understanding Changes in Portfolio Emissions
Bibliographic record
Abstract
As organizations gain more experience in measuring the financed emissions of their investments and loans, they often find that the year-over-year changes in emissions are not what they were expecting. Financed emissions depend on factors beyond just the actions of the portfolio managers or the companies that they finance. An attribution model is used to attribute the changes in emission to the different factors causing the change. It is therefore a powerful tool for understanding, explaining, and managing portfolio emissions. Attribution models have two main components: the mathematical method for calculating the attribution and the choice of explaining factors. This article compares several different methods and alternative choices of factors. The proposed model uses a method and factors that are widely used in other applications in portfolio management and sustainable finance—however, their application to this problem is new. Consequently, the attribution results are familiar and intuitive to financial professionals. The new model is then applied to a multiyear global equity portfolio example.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".