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Enregistrement W6949302174 · doi:10.5281/zenodo.13752654

The PRIMAP-hist national historical emissions time series (1750-2023) v2.6

2024· dataset· en· W6949302174 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGreenhouse gasUnited Nations Framework Convention on Climate ChangeClimate changeConventionKyoto ProtocolAgricultureData setProduct (mathematics)Earth system science

Résumé

récupéré en direct d'OpenAlex

Recommended citation Gütschow, J.; Busch, D.; Pflüger, M. (2024): The PRIMAP-hist national historical emissions time series v2.6 (1750-2023). zenodo. doi:10.5281/zenodo.13752654. Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016 Content Use of the dataset and full description Abstract Support Sources Files included in the dataset Notes Data format description (columns) References Changelog Abstract The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2023, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC. For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "common reporting format" (CRF). For developing countries, non-AnnexI in terms of the UNFCCC, this is the data available through the UNFCCC DI portal (di.unfccc.int) with additional country submissions read from pdf and where available xls(x) or csv files. For a list of these submissions please see below. For South Korea the 2023 official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. Gaps in the country reported data are filled using third party data such as CDIAC, EI (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR v8.0 (all sectors for CO2, CH4, N2O, except energy CO2), and EDGAR v7.0 (IPPU, f-gases). Lower priority data are harmonized to higher priority data in the gap-filling process. For the third party priority time series gaps in the third party data are filled from country reported data sources. Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions. The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.6 includes data for 2023. For energy CO2 growth rates from the EI Statistical Review of World Energy are used to extend the country reported (CR) or CDIAC (TP) data to 2023. For CO2 from cement production Andrew cement data are used. For other gases and sectors we have to rely on numerical methods to estimate emissions for 2023. Version 2.6 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized. The PRIMAP-hist v2.6 dataset is an updated version of Gütschow, J.; Pflüger, M.; Busch, D. (2024): The PRIMAP-hist national historical emissions time series v2.5.1 (1750-2022). zenodo. doi:10.5281/zenodo.10705513. The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset. Detailed per country information is available from the detailed changelog which is available on the primap.org website and on zenodo. Use of the dataset and full description Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset. Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016 Please notify us (mail@johannes-guetschow.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset. When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below. Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation. We have also included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care. Support If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag "v2.6" in any issue you create regarding this dataset. If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@climate-resource.com. Climate Resource makes this data available CC BY 4.0 licence. Free support is limited to simple questions and non-commercial users. We also provide additional data, and data support services to clients wanting more frequent updates, additional metadata or to integrate these datasets into their workflows. Get in touch at contact@climate-resource.com if you are interested. Sources Global CO2 emissions from cement production v240517 data, paper: Andrew(2024), Andrew (2019) EI Statistical Review of World Energy website: Energy Institute (2024) CDIAC data: Hefner and Marland (2023), data: Hefner (2024), paper: Gilfillan and Marland (2021) CEDS: data: Hoesly et al. (2020), paper: Hoesly et al. (2018) EDGAR version 8.0: data/website: European Commission, JRC (2023), report: European Commission. Joint Research Centre. (2023) EDGAR version 7.0: data, website, Reports: JRC (2022), reports: EuropeanCommission Joint Research Centre (2022), European Commission Joint Research Centre (2021), EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001) FAOSTAT database data: Food and Agriculture Organization of the United Nations (2024) RCP historical data data, paper: Meinshausen et al. (2011) UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal website, data: UNFCCC (2024e), Pflüger and Gütschow (2024) UNFCCC Bnnial Update Reports, National Communications, and National Inventory Reports for developing countries website-BURs, website-NCs, data: UNFCCC (2024d), UNFCCC (2024b). Notes: Not all BUR and NC submissions are included as reading the data is time consuming and not all submission contain sufficient data to be used in PRIMAP-hist. Not all submissions included in PRIMAP-hist are available in the github repository as we do not (yet) have code that we can publish for all submissions. For a list of added submision see section 'Data source updates (v2.6)' in the changelog UNFCCC First Biannial Transparency Reports website, [data] UNFCCC (2024a) Notes: So far (Sep. 2024) only Australia has submitted in the new tabular format (CRT). Canada has submitted CRF tables as an appendix and for Uzbekistan we have read data from a submitted PDF. UNFCCC Common Reporting Format (CRF) website, paper, data (24-01-08): UNFCCC (2024c) (processed as described in Jeffery et al. (2018)) Official country repositories (non-UNFCCC) Belarus: Greenhouse gas statistics (1990-2022) website: National Statistical Committee of theRepublic of Belarus (2024) EU, Iceland, Norway, Switzerland: National emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanism, April 2024 website: European Environment Agency(2024) Japan: National GHG Inventory 2024 website: National Institute for Environmental Studies,Japan (2024) New Zealand: New Zealand's Greenhouse Gas Inventory 1990–2022 website: Ministry for theEnvironment (2024) South Korea: 2023 Inventory website, data: Republic of Korea (2023) Taiwan / Republic of China: 2023 Inventory website, data: Republic of China - EnvironmentalProtection Administration (2023) Türkiye: Greenhouse Gas Emissions Statistics, 1990-2022 website: Turkish Statistical Institute(2024) United Kingdom: Final UK greenhouse gas emissions national statistics: 1990 to 2022 website: Department for Energy Security and Net Zero (2024) United States of America: Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2022 website: US EPA (2024) For the pre-1990 LULUCF time-series we use the following additional data sources: Houghton land use CO2 website: Houghton (2008) HYDE land cover data website: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011) SAGE Global Potential Vegetation Dataset website: Ramankutty and Foley (1999) FAO Country Boundarie

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)0,001
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,075
Score d'incertitude au seuil0,250

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,006
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,014
Études des sciences et des technologies0,0010,000
Communication savante0,0030,002
Science ouverte0,0020,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0750,088

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 Opus0,030
Tête enseignante GPT0,259
Écart entre enseignants0,229 · 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'étudeSimulation ou modélisation
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

Citations4
Publié2024
Routes d'admission1
Résumé présentoui

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