MétaCan
Menu
← Back to cohort
Record 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 on 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

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersEuropean CommissionStyrelsen för Internationellt Utvecklingssamarbete
KeywordsThe arcticArcticMember statesAir pollutionMethaneEnvironmental sciencePollutionEnvironmental protectionPolitical scienceBusinessOceanographyChemistryInternational tradeEcology
DOInot available

Abstract

fetched live from 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.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.003

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 head0.126
GPT teacher head0.317
Teacher spread0.191 · 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 designNot applicable
Domainnot available
GenreOther

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

Explore more

Same topicClimate Change Policy and Economics→French-language works237,207→