Banking on Climate Chaos: Fossil Fuel Finance Report 2023
Bibliographic record
Abstract
This report analyzes fossil fuel financing and policies from the world's 60 largest commercial and investment banks. We reveal that fossil fuel financing from the world's 60 largest banks has reached nearly USD 5.5 trillion in the seven years since the adoption of the Paris Agreement, with $673 billion in 2022 alone. It also reveals that the Russian invasion of Ukraine in February 2022 gave fossil fuel companies a chance to rake in record profits totaling USD 4 trillion.In the nearly two years since the International Energy Agency announced that developing new oil and gas fields would restrict the chances of limiting global warming below 1.5°C, most banks have failed to adopt stringent exclusion policies for companies expanding fossil fuels. All Canadian and U.S. banks are still at square one when it comes to oil and gas expansion policies. Under their current policies, they can continue to support companies developing new oil and/or gas projects and also provide project and dedicated finance to most new extraction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".