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Record W6949302174 · doi:10.5281/zenodo.13752654

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

2024· dataset· en· W6949302174 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasUnited Nations Framework Convention on Climate ChangeClimate changeConventionKyoto ProtocolAgricultureData setProduct (mathematics)Earth system science

Abstract

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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

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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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.075
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.014
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0750.088

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 head0.030
GPT teacher head0.259
Teacher spread0.229 · 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 designSimulation or modeling
Domainnot available
GenreDataset

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".

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Citations4
Published2024
Admission routes1
Has abstractyes

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