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Enregistrement W4412211546

Technical Background to the ABC-iCAP report: Socio-Economic Analysis of Air Pollution and Climate Forcer Emission Reductions in the Arctic Council Member States with Special Focus on Black Carbon and Methane Emissions

2024· report· en· W4412211546 sur OpenAlexaboutno aff
Pontus Roldin, Jenny von Bahr, Annacarin Karlsson, Ågot K. Watne, Petri Räisänen, Lena Höglund-Isaksson, Shilpa Rao, Ulaş İm, Jørgen Brandt, Helena Elding Larsson, Anastasia Isaenko, Pam Pearson, Stefan Åström, Katarina Yaramenka, Per Strömberg, Risto Makkonen, Zbigniew Klimont, Russel Shearer, Simon Wilson

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueClimate Change Policy and Economics
Établissements canadiensnon disponible
Organismes subventionnairesEuropean CommissionStyrelsen för Internationellt Utvecklingssamarbete
Mots-clésThe arcticArcticMember statesAir pollutionMethaneEnvironmental sciencePollutionEnvironmental protectionPolitical scienceBusinessOceanographyChemistryInternational tradeEcology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This report has been prepared as one of the deliverables under the project Arctic Black Carbon impacting on Climate and Air Pollution (ABC-iCAP), funded by the European Union’s Partnership Instrument under the EU Foreign Policy Instrument (FPI). We have assessed the health, climate and socio-economic benefits of mitigating air pollution and climate forcers in the Arctic Council member states. We have compared the socio-economic benefits with the investment costs in technical feasible reductions of air pollutants and phasedown of fossil fuel energy according to three different air pollutants and climate forcers emission scenarios for the Arctic Council member states. A less ambitious baseline, current legislation (CLE), scenario was compared with scenarios with larger investments in technologies and policies to reduce emissions of air pollutants and climate forcers. The reference year was set to 2020. We analyzed the differences between the scenarios in the years 2030 and 2050. The socio-economic costs of climate forcers emissions were assessed using the concept of social cost of climate forcers. Air pollution - mortality related costs were calculated using the concept of monetary valuation of a statistical life.Our analysis demonstrates that investments in maximum technically feasible emission reductions (MFR) of air pollutants and short-lived climate forcers are efficient when it comes to reducing the air pollution related mortality and morbidity but insufficient as a climate mitigation option. Efficient climate mitigation requires a rapid phasedown of the production and use of fossil fuels. With phasedown of fossil fuels, as represented by the analyzed maximum technical feasible emission reduction and sustainable development (MFR&SD) transition scenario, the global surface temperature impact from climate forcers emissions by 2050 could be reduced by 49-58 %, 10-30 years after the emissions have occurred. Although we only considered the social costs for four climate damage sectors (agriculture, heat-stress related mortality, building energy expenditures and sea level rise), the calculated climate damage costs are immense. We estimated that the Arctic Council member states’ anthropogenic climate forcers emissions, in one single year as of 2020, result in a social cost of 1.4 trillion € (0.3-3.2 trillion €, 5 to 95 percentile range), integrated over the coming decades to centuries. This corresponds to ~5 % of the Arctic Council member states GDP in 2020. Of these climate warming damage costs, ~85 % can be attributed to carbon dioxide, ~9 % to methane, ~4 % to nitrous oxide and ~2 % to black carbon emissions. The emission sectors with the largest climate impacts, as well as climate change mitigation potentials, are power and heating plants, transports, industries, and residential combustion. Depending on which PM2.5 mortality exposure response function that is used, the estimated socio-economic costs of air pollution related mortality amount to between 680-1270 billion € in 2020. With investments in maximum technical feasible reductions of air pollutants and short-lived climate forcers without substantial reductions in the use of fossil fuels, as represented by the analyzed MFR scenario, the air pollution - mortality and morbidity related costs can be reduced with 42-68% in 2050, while with the MFR&SD scenario the costs can be reduced with 47-75%. Thus, active climate change mitigation policies are beneficial also from air pollution related health and social cost perspectives.The full socio-economic analysis shows that both the MFR and the MFR&SD scenario is highly socio-economically beneficial. In 2050 the net benefit of the MFR and MFR&SD scenarios, as compared with the CLE scenario, amounts to 510 (260-770) billion € and 830 (110-1850) billion €, respectively. The socio-economic benefits of the reduced air pollution and prevented climate change are in the same order of magnitude. The total benefit of climate forcers emission reductions in year 2050 are estimated to be 630 (150-1400) billion € while the improved air quality saves an additional 600 (350-840) billion €. The largest costs in the MFR&SD scenario are related to investments in renewable energy, with an estimated cost of 380 billion €.To reach the MFR&SD scenario, approximately in line with the 2 °C global warming target set by the Paris Agreement, the Arctic Council member states need to take the lead in rapidly phasing down the use of fossil fuels, as this is a prerequisite for effective climate mitigation. We suggest that more sectors and climate forcers could be considered for inclusion in the EU emission trading system and that USA and Canada should consider introducing similar national emission trading systems as soon as possible.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,053
Score d'incertitude au seuil0,106

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,005
Études des sciences et des technologies0,0010,001
Communication savante0,0040,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0090,003

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.

Tête enseignante Opus0,126
Tête enseignante GPT0,317
Écart entre enseignants0,191 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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