Harnessing oil and gas superprofits for climate action
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».