Beyond national climate action: the impact of region, city, and business commitments on global greenhouse gas emissions
Notice bibliographique
Résumé
This article quantifies the net aggregate impact in 2030 of commitments by individual non-state and subnational actors (e.g. regions, cities and businesses, collectively referred to as ‘NSAs’) to reduce greenhouse gas (GHG) emissions. The analysis was conducted for NSAs operating within ten major emitting economies that together accounted for roughly two-thirds of global GHG emissions in 2016. Our assessment includes 79 regions (e.g. subnational states and provinces), approximately 6,000 cities, and nearly 1,600 companies with a net emissions coverage of 8.1 GtCO2e/year, or a quarter of the ten economies’ total GHG emissions in 2016. The analysis reflects a proposed methodology to aggregate commitments from different subnational (i.e. regional and city government) and non-state (i.e. business) actors, accounting for overlaps. If individual commitments by NSAs in the ten high-emitting economies studied are fully implemented and do not change the pace of action elsewhere, projected GHG emissions in 2030 for the ten economies would be 1.2–2.0 GtCO2e/year or 3.8%–5.5% lower compared to scenario projections for current national policies (31.6–36.8 GtCO2e/year). On a country level, we find that the full implementation of these individual commitments alone could result in the European Union and Japan overachieving their nationally determined contributions (NDCs), while India could further overachieve its unconditional NDC target. In the United States, where the national government has rolled back climate policies, NSAs could become a potential driving force for climate action. Key policy insightsFull implementation of reported and quantifiable individual commitments by regions, cities and businesses (NSAs) in ten major economies could reduce emissions by 3.8%–5.5% in 2030 below current national policies scenario projections.National governments’ mitigation targets could be more ambitious if they would take NSA commitments into account. With full implementation of such action, the European Union and Japan would overachieve their NDC targets. For the United States such action could help meeting its original 2025 NDC target in spite of rollbacks in national climate policies.The full universe of NSA climate action expands far beyond the subset of commitments analysed in this study; NSAs could become a strong driving force for enhanced action towards the Paris climate goals. Full implementation of reported and quantifiable individual commitments by regions, cities and businesses (NSAs) in ten major economies could reduce emissions by 3.8%–5.5% in 2030 below current national policies scenario projections. National governments’ mitigation targets could be more ambitious if they would take NSA commitments into account. With full implementation of such action, the European Union and Japan would overachieve their NDC targets. For the United States such action could help meeting its original 2025 NDC target in spite of rollbacks in national climate policies. The full universe of NSA climate action expands far beyond the subset of commitments analysed in this study; NSAs could become a strong driving force for enhanced action towards the Paris climate goals.
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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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».