The environmental policy of the Norwegian Government Pension Fund‐Global and investors' reaction over time
Bibliographic record
Abstract
Abstract Taking the greatest socially responsible sovereign wealth fund in the world, namely, the Norwegian Government Pension Fund‐Global (GPF‐G), as a proxy for the world market portfolio, and collecting investment data from 2006 to 2021, our research studies how the attitude of environmentally concerned investors has changed, on different markets, over time, particularly before and after the 2015 Paris Climate Agreement. We investigate, with an event study methodology, what happens to stock prices when companies are excluded from the GPF‐G portfolio for serious environmental violations, unacceptable level of greenhouse gas emissions, or for their involvement in thermal coal processing. In line with previous studies, our results show that Paris Agreement has acted as a catalyst for environmentally conscious behavior by international investors, and this is particularly true in Anglo‐Saxon countries. Especially US, Japanese, Chilean, Indian, and Canadian investors care about the environmental issues that have led to the exclusion from the fund investment portfolio. To the contrary, investors in China, Germany, Australia, and UK seem to have an opposite reaction, as the prices of the excluded stocks increase.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".