Harmonized Global to Regional Gridded Methane Inventories on rHEALPix DGGS
Notice bibliographique
Résumé
Overview This dataset provides a harmonized collection of global-to-regional methane emission inventories standardized within the rearranged Hierarchical Equal Area isoLatitude Pixelization (rHEALPix) Discrete Global Grid System (DGGS). It integrates 13 heterogeneous gridded inventories covering global, continental, national, and regional domains. National inventories include datasets for the United States, Canada, Mexico, China, India, Australia, and Switzerland, with an emphasis on energy-related emissions from coal mining and oil and gas activities. We also included the Copernicus Atmosphere Monitoring Service Regional (CAMS-REG) Emissions Inventory for Europea and a regional high-resolution inventory for New York State. All inventories were converted into a common equal-area DGGS framework, namely rHEALPix DGGS, ensuring consistent spatial structure, sectoral coding (IPCC 2006), and reporting units (Mg a⁻¹). The harmonization process preserves total emissions during grid conversion and provides a hierarchical, machine-readable, and mass-conserving representation of global methane sources. The resulting dataset enables direct comparison and aggregation across scales, sectors, and years, supporting applications in atmospheric inversions, hotspot detection, cross-inventory benchmarking, and mitigation planning. It also establishes a standardized, AI-ready foundation for spatial query, large-scale analytics, and automated model integration. All data files are distributed in interoperable tabular formats compatible with relational databases and cloud platforms, ensuring broad usability for researchers, policymakers, and data-driven applications. Geometries are not embedded in these tabular files but are instead provided separately as GeoParquet files, which define the polygon boundaries of rHEALPix cells at specific resolutions, and can be linked to the tabular files with the common dggID field by table joins. Detailed Dataset Description Each record corresponds to a unique combination of an rHEALPix cell identifier (dggsID) and a calendar year (Year), which ensures one observation per cell-year pair within each dataset. For global inventories, an additional field GID provides the ISO 3166-1 alpha-3 country code. Emission variables are presented in wide format, with each column corresponding to an IPCC 2006 category or subcategory. The CAMS-REG Emissions Inventory for Europea was converted at rHEALPix level 7, Switzerland Greenhouse Gas Inventory was converted at level 9, and the Gridded New York State Methane Emissions Inventory was converted at level 10. All other inventories were standardized to rHEALPix level 6. Global inventories EDGAR_DGGS_methane_emissions_ALL_SECTORS_1970-2022.csv Converted from the Emissions Database for Global Atmospheric Research (EDGAR v8.0) developed by the European Commission Joint Research Centre (JRC) (Crippa et al., 2023; 2024). Covers 1970–2022 at 0.1° resolution and includes all major IPCC sectors: energy, agriculture, industry, transport, and waste. GFEI_DGGS_methane_emissions_2016-2019-2020.csvBased on the Global Fuel Exploitation Inventory (GFEI) (Scarpelli et al., 2020; 2022; 2025). Provides global methane emissions from oil, gas, and coal exploitation at 0.1° resolution for the years 2016, 2019, and 2020. Continental inventories CAMS-REG_DGGS_methane_emissions_2005-2022.csv Converted from the Copernicus Atmosphere Monitoring Service Regional (CAMS-REG v8.1) Emissions Inventory for Europe (Kuenen et al., 2022). Provides continental-scale methane emissions at 0.05° × 0.1° resolution for 2005–2022. National inventories US_DGGS_methane_emissions_2012-2018.csvDerived from the U.S. Anthropogenic Methane Emissions dataset (Maasakkers et al., 2023), which downscales the U.S. EPA GHGI to 0.1° grids for 2012–2018, covering over 20 source categories across energy, agriculture, waste, and industrial sectors. US_OG_DGGS_methane_emissions_2021.csvConverted from the U.S. Oil and Gas Methane Emissions inventory (Omara et al., 2024), a measurement-informed, facility-level dataset for 2021 representing onshore oil and gas production emissions. Canada_DGGS_methane_emissions_2018.csvBased on the Canadian Anthropogenic Methane Inventory (Scarpelli et al., 2022a), spatially allocating Canada’s UNFCCC National Inventory Report (2018) to 0.1° grids and covering oil and gas, livestock, solid waste, coal, and wastewater. Mexico_DGGS_methane_emissions_2015.csvDerived from Mexico’s Anthropogenic Methane Inventory (Scarpelli et al., 2020a) at 0.1° resolution for 2015, encompassing livestock, fugitive fuel, solid waste, and wastewater sources. China_DGGS_methane_emissions_1990-2020.csvConverted from the CHN-CH₄ inventory (Guo et al., 2025), a 10 km × 10 km gridded dataset covering 1990–2020 and eight major sectors including rice, livestock, coal, oil and gas, combustion, and waste. Switzerland_DGGS_methane_emissions_2011.csvBased on the Switzerland Greenhouse Gas Inventory (Hiller et al., 2014), originally reported at 500 m resolution and covering anthropogenic methane sources such as livestock, waste, and energy combustion. It includes natural and semi-natural methane fluxes such as lakes, wetlands, wild animals, and forest soils. Because these sources are not part of the anthropogenic reporting structure defined by the IPCC, they were retained under their original text labels. India_Coal_DGGS_methane_emissions_2018.csvFrom the India Coal Mine Methane Inventory (Sadavarte et al., 2022), providing mine-level emissions estimated using Tier-2 IPCC methods for 2018. Australia_Coal_DGGS_methane_emissions_2018.csvAlso from Sadavarte et al. (2022), providing state-level coal mine methane emissions disaggregated to individual mines for 2018. CMS_Canada_DGGS_methane_emissions_2013.csvFrom the NASA Carbon Monitoring System (CMS) dataset (Sheng et al., 2017) for 2013, providing oil and gas methane emissions over Canada at 0.1° resolution. CMS_Mexico_DGGS_methane_emissions_2010.csvAlso from Sheng et al. (2017), representing oil and gas system emissions over Mexico for 2010 at 0.1° resolution. China_SACMS_DGGS_methane_emissions_2011.csvBased on the China Coal Mine Methane Inventory (Sheng et al., 2019) compiled from the State Administration of Coal Mine Safety (SACMS), representing 2011 coal mine emissions across China. Regional inventories NYS_DGGS_methane_emissions_2020.csvConverted from the Gridded New York State Methane Emissions Inventory (Loman et al., 2025), providing 100 m resolution emissions for 2020 across all anthropogenic sources within New York State. Grid geometries global_countries_dggs_geom_res6.parquetRepresents the global rHEALPix DGGS level 6 grid covering all land areas and offshore oil and gas facility locations (~160 km2 cell size). Each record includes the DGGS cell geometry and identifiers: the ISO 3166-1 alpha-3 country code (GID) and the DGGS zone identifier (zoneID). europe_dggs_geom_res7.parquet Provides rHEALPix DGGS level 7 geometries for Switzerland (~17.8 km2 cell size). Each record includes the cell geometry and DGGS cell identifier (zoneID), supporting the CAMS-REG inventory. newyorkstate_dggs_geom_res10.parquetContains the rHEALPix DGGS level 10 geometries for the state of New York (~0.02 km2 cell size). Each feature includes the geometry and corresponding DGGS cell identifier (zoneID), supporting the Gridded New York State Methane Emissions Inventory. switzerland_dggs_geom_res9.parquetProvides rHEALPix DGGS level 9 geometries for Switzerland (~0.22 km2 cell size). Each record includes the cell geometry and DGGS cell identifier (zoneID), supporting the gridded Switzerland Greenhouse Gas Inventory.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».