Harnessing oil and gas superprofits for climate action
Bibliographic record
Abstract
Climate change disproportionately harms low-income countries, whilst international climate finance to support them remains inadequate. Negotiations about the New Collective Quantified Goal (NCQG) centre around how to cover increasing needs of developing countries. Windfall profits of the fossil fuel industry, which benefits from this dominant source of greenhouse gas emissions, could contribute to mobilizing more finance, both for the NCQG and wider needs of domestic and international climate finance. We find that the energy crisis of 2022 led to oil and gas industry ‘superprofits’ in the same year – defined as being above the stated expectations at the beginning of the year – amounting to about half a trillion dollars (US$490 bn above the $753 bn projected by the companies). Over $200 bn of this accrued to companies directly controlled by governments, two-thirds of which do not have a historical commitment to contribute to international climate finance. The remaining $280 bn of superprofits went to privately controlled companies, of which over 95% are headquartered in countries currently contributing to international climate finance. We argue that there is a clear case to include fossil fuel profits on the agenda of UNFCCC climate finance negotiations and to pursue an international agreement on minimum fossil fuel production taxes. Given that most privately controlled superprofits occurred in G20 countries and the group's ability to reach agreement on corporation taxes recently, the G20 could be a natural forum to pursue such policy action. Oil and gas superprofits in 2022 amount to almost the entire international global climate finance flows to developing countries from 2020 to 2024, hence, the magnitude and disposition of superprofits belies claims that adequate finance in general is unavailable.Governments directly control 42% of these superprofits; the majority of these were in non-OECD countries, with the Norwegian Equinor accounting for 75% of the $62 bn superprofits controlled by OECD countries.Taxing the remaining 58% of privately controlled superprofits is mainly a matter of policy action in the US, the UK, France and Canada and should be on the agenda of the G20 as a follow-up to its agreement on corporate taxation.In line with Article 2.1c and the need to increase funding for loss & damage, negotiations on the NCQG and contributions to the Loss & Damage Fund, should consider superprofits from high emitting industries, such as oil and gas. Oil and gas superprofits in 2022 amount to almost the entire international global climate finance flows to developing countries from 2020 to 2024, hence, the magnitude and disposition of superprofits belies claims that adequate finance in general is unavailable. Governments directly control 42% of these superprofits; the majority of these were in non-OECD countries, with the Norwegian Equinor accounting for 75% of the $62 bn superprofits controlled by OECD countries. Taxing the remaining 58% of privately controlled superprofits is mainly a matter of policy action in the US, the UK, France and Canada and should be on the agenda of the G20 as a follow-up to its agreement on corporate taxation. In line with Article 2.1c and the need to increase funding for loss & damage, negotiations on the NCQG and contributions to the Loss & Damage Fund, should consider superprofits from high emitting industries, such as oil and gas.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".