Doing well by doing good with the performance of United Nations Global Compact Climate Change Champions
Bibliographic record
Abstract
Abstract Are Climate Change Champions favorable to investors? This is the first study of portfolio performance of a fourth generation SRI screening strategy based on United Nations Global Compact firms who are Climate Change Champions. The operational changes made by UNGC firms are real and disproves the notion that UNGC firms are merely green-washing. We find that after firms join UNGC, there is a positive effect on long term portfolio performance. UNGC firms have lower volatility and so less risk than their competitors. We find an apparent mispricing of lower risk in market returns as standard asset pricing models may not be pricing investors’ aversion to climate change risk and preference for firms actively combating climate change. This lends support to Fama and Frenchs’ theory that says that these “tastes” are valid factors to provide a more complete asset pricing model. Our study encourages investment in UNGC-CCC firms as we find there is no underperformance penalty against a conventional portfolio because the lower return reflects lower risk. Thus, our evidence suggests that “doing good for society is also good for business.”
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".