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Record W4392580782 · doi:10.5194/egusphere-egu24-8920

Economic Impacts of Air Pollution on Health in the Arctic Council Countries

2024· preprint· en· W4392580782 on OpenAlexaboutno aff
Shilpa Rao, Jørgen Brandt, Zbigniew Klimont, Ulaş İm, Pontus Roldin, Simon Wilson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticAir pollutionPollutionThe arcticNatural resource economicsEnvironmental planningEnvironmental scienceBusinessEnvironmental protectionEconomicsOceanography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.056
GPT teacher head0.350
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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