Emissions inventories for climate modelling of net-zero aviation CO2 pathways
ネットゼロ航空CO2経路の気候モデリングのための排出インベントリ (AI 翻訳)
Dray, Lynnette, Dessens, Olivier, Rap, Alexandru, YOSHIOKA, MASARU, Zhang, Weiyu, Schafer, Andreas
🤖 gxceed AI 要約
日本語
このデータセットは、航空システムモデルAIM2015を用いて生成された全球航空排出インベントリを提供する。2015年、2050年ベースライン、および4つのネットゼロシナリオ（SAF、水素、DAC、需要）について、燃焼技術と飛行機雲回避のサブケースを含む。9変数のグリッド化された排出量を高解像度で提供し、気候モデリングに利用可能。
English
This dataset provides global aviation emissions inventories generated using the AIM2015 model for 2015, a 2050 baseline without mitigation, and four net-zero CO2 scenarios (SAF, Hydrogen, DAC, Demand) with sub-cases for combustor technology and contrail avoidance. Gridded emissions for nine variables at 1-degree x 1-degree x 2000ft x month x 6-hour resolution, suitable for climate modeling of aviation decarbonization pathways.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本データセットは、航空脱炭素化が重要な日本のグリーン成長戦略や航空業界の取り組みに直接関連する。ネットゼロ経路の排出インベントリを提供し、日本における航空CO2削減の政策評価や技術選択のシミュレーションに貢献する。
In the global GX context
This dataset is crucial for global climate modeling of aviation's net-zero transition, providing detailed inventories for different technology pathways. It supports international efforts under ICAO and aligns with national climate pledges, offering a resource for modeling emissions reductions from sustainable aviation fuels, hydrogen, and direct air capture.
👥 読者別の含意
🔬研究者:Use this dataset for climate modeling and scenario analysis of aviation emissions and their climate impact.
🏢実務担当者:Aviation companies can use these inventories to understand potential emissions outcomes under different net-zero strategies.
🏛政策担当者:Informs policy design for aviation decarbonization, including technology incentives and contrail management.
📄 Abstract（原文）
This dataset contains global aviation emissions inventories generated using the aviation systems model AIM2015 for the NERC MAGICA project ( NE/Z503836/1 ). Emissions inventories are generated for 2015, for a 2050 baseline with no aviation CO2 emissions mitigation policy, and for four different scenarios which reach net zero CO2 emissions in 2050 in different ways. Each net zero scenario additionally has four sub-cases based on assumptions about combustor technology in the next generation of aircraft engines, and whether contrail avoidance is widely adopted. Inventories are provided in eighteen separate files, comprising: · A baseline inventory for 2015; · A no-policy scenario for 2050, in which no policies aimed at reducing aviation CO2 are applied; · For each of the net zero scenarios (SAF, Hydrogen, DAC, Demand) in 2050: o A scenario with no lean burn engines and no contrail avoidance; o A scenario with lean burn engines and no contrail avoidance; o A scenario with no lean burn engines and contrail avoidance; and o A scenario with both lean burn engines and contrail avoidance. Within each file, gridded emissions are provided for nine variables of interest to climate modelling: · Kerosene burnt (kg, total including SAF), and kerosene SAF burnt (kg) which also allow direct estimation of the location of CO 2 , H 2 O and SOx from kerosene combustion via emissions factors; · Hydrogen burnt (kg), which also allows direct estimation of hydrogen aircraft combustion H 2 O via emissions factors; · NOx produced (kg, from all fuels); · nvPM produced (g, from all fuels); · vPM (sulphate component, g, from all fuels); · vPM (unburnt hydrocarbons and engine oil component, g, from all fuels); · 3D Distance flown by kerosene aircraft (km); and · 3D Distance flown by hydrogen aircraft (km). In each case, variables are gridded by latitude, longitude, altitude, month and daily time bin, at 1 degree x 1 degree x 2,000 ft x month x 6-hour UTC bin resolution. Files are provided in netCDF format, but csv files containing the same data (at 1 degree by 1 degree by 2000ft by month and 6-hour UTC time bin) are also available from the authors on request.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20395303first seen 2026-05-27 04:13:40
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。