Investors’ Moral and Financial Concerns—Ethical and Financial Divestment in the Fossil Fuel Industry
Bibliographic record
Abstract
It is discussed intensively whether divestment decease sales in the fossil fuel industry or whether investors divest from the fossil fuel industry because of stranded assets. Furthermore, it is unclear what the consequences of these activities are for the fossil fuel industry. Therefore, the study explores the direction of causality between cash flow factors, such as production factors and sources of financing and sales of the fossil fuel industry using lagged regression models and applying the Granger causality test. Our sample consists of fossil fuel companies from the Carbon Underground 200 list. Because R-squared values for both lagged financial factors and lagged sales were similar, we suggest a “bi-directional causality” between the financial flow factors and sales. We conclude that divestment (because of ethical concerns) can cause lower sales and that lower sales can cause divestment because of fear of the risk of stranded assets. Because a third factor usually causes bi-directional causations, we conclude that the need for the fossil fuel industry to reduce greenhouse gas emissions is the third factor that influences both the ethical and financial motivation of divestment. Consequently, the study contributes to theoretical approaches to divestment.
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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 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".