Divest from the Carbon Bubble? Reviewing the Implications and Limitations of Fossil Fuel Divestment for Institutional Investors
Bibliographic record
Abstract
Climate change policies that rapidly curtail fossil fuel consumption will lead to structural adjustments in the business operations of the energy industry. Due to an uncertain global climate and energy policy framework, it is difficult to determine the magnitude of fossil energy reserves that could remain unused. This ambiguity has the potential to create losses for investors holding securities associated with any aspects of the fossil fuel industry. Carbon bubble risk is understood as financial exposure to fossil fuel companies that would experience impairments from assets stranded by policy, economics or innovation. A grassroots divestment campaign is pressuring institutions sell their fossil fuel company holdings. By September 2014, investors had responded by pledging to divest US $50 billion of portfolios. Though divestment campaigns are primarily focused on a moral and political rationale, they also regularly frame divesting as a strategy for mitigating stranded asset risk. We review aspects of the divestment movement alongside the context of carbon bubble risk. Several common hypotheses on reducing stranded carbon asset exposure through divestment are critically examined. We find that institutional investors are limited in their ability to reduce exposure to carbon through divesting and that the financial sector is likely to absorb many ¡®fossil free¡¯ funds.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".