Economic Impacts of Air Pollution on Health in the Arctic Council Countries
Notice bibliographique
Résumé
Introduction Ambient air pollution is a key factor for mortality and morbidity in the Arctic countries. It ranks among the 10 leading risk factors for premature death in Arctic Council Member and Observer countries. There are large economic implications of these health impacts including losses in labor productivity. Arctic Council countries (USA, Canada, Russia and Nordic countries) have affirmed their support to collectively bring black carbon emissions down by 25-33% by 2025 from 2013 levels. In this study, we investigate the health and economic implications of improved air quality actions in the Arctic. Methods We use the ECLIPSEv6b Current Air Quality Legislation (CLE) and the Maximum Feasible Reduction (MFR) Sustainable Development Scenario (SDS) scenario to examine a range of development of PM2.5, and ozone related air quality concentrations for 2020, 2030 and 2050 for the Nordic countries. We estimate the mortality and morbidity impacts of these scenarios using the Economic Valuation (EVA) model and use national estimates to verify these numbers. We further calculate the economic costs related to health effects of air pollution using the EVA model and estimates of number of premature deaths or years of life lost due to the exposure in each population. We also calculate the direct costs of illnesses (health care costs like hospitalizations and medications), direct non-health care costs (such as social services and childcare), and indirect costs (such as productivity losses). Results For Arctic Council Member countries, adhering to current legislation to reduce PM2.5 and ozone would avoid an estimated 66,000 premature deaths in 2030 compared to 2015. In the more ambitious Maximum Feasible Reduction scenario, an estimated 97,000 premature deaths would be avoided in 2030. We observe that hospital admissions due to cardiovascular and respiratory diseases (CHA and RHA) are 193183 in 2020 in the Arctic countries. The CLE scenario does not lead to a huge change in these numbers, but the MFR and SDS scenarios results in a huge decrease in these cases (33% decrease in CHA and RHA in 2030). The reductions stabilize over time and in 2050, reductions are the same as 2030. We also measure the morbidity in terms of work loss days and restricted activity days. We find that nearly 200 million workdays are lost or restricted due to air pollution in 2020 and the MF-SDS scenario yields significant reductions to nearly 132 million days due to enhanced policies on air pollution. The costs of illness and productivity days decline significantly across the scenarios. ConclusionsStrengthening air pollution legislations to the technically feasible level and phasing out fossil fuel use leads to a decrease in mortality by 35-50 % in 2050 and a decline in morbidity by 30-40% in the Arctic Council countries. The monetary related benefits in these countries are estimated at 250-750 billion euro in 2050. These benefits likely exceed the costs associated with these actions. Actions on reducing air pollution and fossil fuels are valuable input in supporting the currently proposed European Green Deal, revision of EU air quality legislation and the setting of a zero-pollution objectives for air quality.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».