MétaCan
Menu
Retour à la cohorte
Enregistrement W4393746186 · doi:10.5281/zenodo.10366131

Global gridded anthropogenic emissions of air pollutants and methane for the period 1990-2050

2025· dataset· en· W4393746186 sur OpenAlexaboutno aff
Zbigniew Klimont, C. Heyes, Lena Höglund-Isaksson, Florian Lindl, Younha Kim, Peter Rafaj, Pallav Purohit, Katrin Kaltenegger, Adriana Gómez-Sanabria, Wilfried Winiwarter, Laura Warnecke, W. Schoepp, Gregor Kiesewetter, Robert Sander, Thanh Binh Nguyen

Notice bibliographique

RevueIIASA PURE (International Institute of Applied Systems Analysis) · 2025
Typedataset
Langueen
DomaineEnvironmental Science
ThématiqueAtmospheric and Environmental Gas Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEnvironmental scienceAir pollutantsPeriod (music)MethanePollutantMethane emissionsClimatologyAtmospheric sciencesAir pollutionMeteorologyGeographyEcologyGeologyBiology

Résumé

récupéré en direct d'OpenAlex

This version (VERSION 2.1) includes an update of the LRTAP MTFR scenario (Version 2). The baseline (LRTAP_Baseline) did not change from Version 2. The key updates for MTFR in VERSION 2.1 include: Global update of mitigation potential for municipal waste management, affecting emissions of CH4 and to a small extent also PM2.5. Updates to pace of feasible introduction of better technology in off-road sector; a more pessimistic outlook in less developed regions, and Update of maximum applicability rates (technical limitations) for residential heating sector. This has mostly implications (less reduction feasible) in the mid-term, i.e. until 2040, with less impact towards the end of the modelling horizon. For reference, a complete description of the VERSION 2 set is provided below: The global anthropogenic emissions of air pollutants including sulphur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), particulate matter (distinguishing PM2.5, PM10, BC, OC, OM), non-methane volatile organic compounds (VOC), carbon monoxide (CO), and methane (CH4) were developed at CIAM/IIASA [1]. These emission datasets have been produced with the GAINS model [2] (Amann et al., 2011) within work under UNECE Convention on Long-Range Transboundary Air Pollution (LRTAP) and cover the 1990-2050 period. The respective emission datasets consist of 5-yearly global annual and monthly emissions and include two to three scenarios, depending on the version of the dataset: a baseline that is referred to as current legislation case (LRTAP Baseline), and a scenarios exploring technical mitigation potential for all pollutant species (LRTAP MTFR), and a scenario that combines climate policy, behavioural changes, and strong technical mitigation of all pollutants (LRTAP LOW) VERSION 2 includes two scenarios (Baseline and MTFR), including a major update of the underlying data (including future drivers), included policies, and some revision of spatial proxies compared to Version 1. Brief explanation of assumptions used in these scenarios are provided below: The current legislation scenario (LRTAP Baseline) assumes implementation and effective enforcement of all committed energy and environmental policies affecting emissions of air pollutants and greenhouse gases. Following development of Version 1, CIAM has undertaken further review resulting in updates of historical data (up to 2020) and projections driving emissions of all species in the GAINS model. Most of the updates for historical data are for Europe and Central Asia while projections are updated globally. CIAM team has held consultation meetings with all EU27 and West Balkan countries as well as Switzerland, Norway, United Kingdom, and Republic of Moldova discussing information, data, assumptions in GAINS with national experts and comparing (and updating where found necessary) GAINS model results to reported data and emissions to the Centre on Emission Inventories and Projections (CEIP) [3]. For the EU27, the energy and agriculture projections are consistent with the objectives of the European Green Deal and Fit for 55 package making EU carbon neutral by 2050; these are consistent with the projections used in the EU fourth Clean Air Outlook [4] study. Note that compared to the CAO3 (used in Version 1), the energy projections for the EU include results of the discussion (and respective modelling work) on energy market measures to speed up the clean energy transition and end Europe's dependence on gas, oil, and coal imports from Russia (REPowerEU). For West Balkan revision of historical data and projections were done within the EU4Green projected funded by the EU; the new projections assume a more ambitious transformation of these economies allowing to reduce CO2 emissions faster. A dedicated consultation process with Republic of Moldova (funded by the UNECE) resulted in important updates and revisions of the data and outlook, although the backbone of the projection remained the same as in Version 1; the 9EAST project funded by the EU. The 9EAST project projections are also used for Georgia and Ukraine, as in Version 1. Consultation meetings and exchange with Canada, Norway, Switzerland, and UK within work on the revision of the Air Convention Gothenburg Protocol, resulted in updated historical data as well as revised projections achieving much better consistency with the data countries report to CEIP [3]. Global updates of NMVOC and CH4 emissions from fossil fuel extraction and distribution (oil, gas, coal) were developed reviewing global datasets on activity data and emission factors. For the rest of the world, the projections in GAINS were updated by downscaling more recent IEA and FAO outlooks (FAO, 2018; IEA, 2023). The maximum technically feasible reduction (LRTAP MTFR) scenario uses the same activity data (energy scenario, agriculture scenario) as the Baseline case, described above. Compared to Version 1, the results differ mostly driven by the changes in the Baseline activity but also due to updates to the model structure (for CH4). The general idea of the MTFR scenario (as described in the documentation to Version 1) is to explore the potential for further emission mitigation applying technical measures which are characterized with lowest emission factors (as defined in the GAINS model databases), attainable with reduction technologies for which experience exists (Amann et al., 2013; Gomez Sanabria et al., 2022; Höglund-Isaksson et al., 2020). These include highly efficient end of pipe technologies in industry (filters, scrubbers, primary measures), transport sector, residential combustion (clean burning stoves, pellet stoves and boilers), measures in agriculture including: new low emission houses (including cleaning of ventilation air where applicable), covered storage of manures, immediate or efficient application of manures on land, urea use with inhibitors. For the solvent use sector and fossil fuel production and distribution, control of leaks, improved maintenance, incineration as well as substitution or low solvent products are applied. VERSION 2 does not include an update of the LRTAP LOW scenario. At the same time this scenario from Version 1 is not compatible with the Baseline of the VERSION 2 and so shall not be used in conjunction with it. Format of the datasets: The datasets include gridded sectoral emissions provided as netcdf files with monthly resolution for the period 1990-2050 for the Baseline scenario and the set of 2030, 2040, 2050 for the MTFR scenario. Emissions include international shipping but not international aviation. Open burning of biomass includes only emissions from open burning of agricultural residues but not forest, peat or savannah fires. The sectors for which gridded data are provided (might vary by pollutant, i.e. some layers will be missing) include: Energy sector Residential combustion (cooking and heating) Transportation Industry (combustion and processes) Solvent use Waste management Agriculture (livestock and fertilizer application) Open burning of agricultural residues International shipping Changes in spatial distribution of emissions in VERSION 2 (compared to Version 1): VERSION 2 includes improved spatial patterns mostly specific to methane, including information on gas compressor stations, gas pipelines, ports and rice cultivation. Other, non-pollutant specific improvements include: Spatial resolution changed to 0.1° × 0.1° Spatiotemporal pattern for agricultural waste burning improved for years 2005 - 2020 Distribution of emissions incorporating population uses more recent population data and urban/rural classification Road traffic allocation harmonised globally Spatial distribution for domestic heating takes into account different fuel types for the majority of regions in the EMEP domain. Acknowledgments: The development of the emission scenarios and respective spatially explicit allocation of emissions has been supported by: UNECE Convention on Long-range Transboundary Air Pollution funding towards the EMEP Centre for Integrated Assessment Modelling (CIAM) hosted at IIASA FORCeS (Constrained aerosol forcing for improved climate projections) project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821205 (https://forces-project.eu/) EYE-CLIMA: Improving emission estimates of climate forcers project funded by the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101081395 (https://eyeclima.eu/) [1] Centre for Integrated Assessment Modelling (CIAM) hosted by the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria (https://iiasa.ac.at/policy/applications/centre-for-integrated-assessment-modelling-ciam) [2] https://gains.iiasa.ac.at/models/gains_models4.html [3] www.ceip.at [4] https://environment.ec.europa.eu/topics/air/clean-air-outlook_en References: Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Höglund-Isaksson, L., Klimont, Z., Nguyen, B., Posch, M., Rafaj, P., Sandler, R., Schöpp, W., Wagner, F., Winiwarter, W., 2011. Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications. Environ. Model. Softw. 26, 1489–1501. https://doi.org/10.1016/j.envsoft.2011.07.012 Amann, M., Klimont, Z., Wagner, F., 2013. Regional and Global Emissions of Air Pollutants: Recent Trends and Future Scenarios. Annu. Rev. Environ. Resour. 38, 31–55. https://doi.org/10.1146/annurev-environ-052912-173303 FAO, 2018. The future of food and agriculture. Alternative pathways to 2050. Food and Agriculture Organization of the United Nations (FAO), Rome; https://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en/ Gómez-Sanabria, A., Kiesewetter, G., Klimont, Z. et al. Potential for future reductions of global GHG and air pollutants from circular waste management systems.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,104
Score d'incertitude au seuil0,207

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,004
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,003

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 Opus0,008
Tête enseignante GPT0,254
Écart entre enseignants0,246 · 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
GenreJeu de données

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
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueIIASA PURE (International Institute of Applied Systems Analysis)Même sujetAtmospheric and Environmental Gas DynamicsTravaux en français237 207