一帯一路地域における農業貿易主導の農地拡大・森林破壊に起因する炭素蓄積損失データセット（2020年）
Dataset of carbon stock loss from cropland expansion-induced deforestation driven by agricultural trade in the Belt and Road Initiative regions in 2020 (原題)
Binbin Ma, Chen Zeng, Zhen Wang
🤖 gxceed AI 要約
日本語
全球森林損失・作物分布・土壌炭素などの空間データとEXIOBASE多地域産業連関表を組み合わせ、2020年の一帯一路地域で農業生産・消費・貿易が引き起こした農地拡大・森林破壊による炭素蓄積損失を推計したデータセット。EE-MRIOモデルで最終消費需要や貿易に埋め込まれた炭素損失を生産側・消費側・貿易側から追跡する。
English
This dataset estimates carbon stock losses from cropland expansion-induced deforestation across Belt and Road regions in 2020, combining multi-source spatial data (forest loss, crop distribution, soil/vegetation carbon) with the EXIOBASE multi-regional input-output model. An environmentally extended MRIO tracks carbon losses embodied in final consumption and trade, reported from production, consumption, and trade perspectives.
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
農産物サプライチェーンの土地変化・森林破壊リスクはScope 3やTNFD・生物多様性開示と直結する。日本の食品・商社・小売の調達リスク評価や、輸入農産物に埋め込まれた炭素損失の把握に示唆を与える。
In the global GX context
Land-use change and deforestation embodied in agricultural trade are increasingly material to Scope 3 accounting, TNFD, and EU deforestation regulation (EUDR). The EE-MRIO approach offers a transferable method for attributing embodied carbon loss to consuming economies and final demand.
👥 読者別の含意
🔬研究者:EE-MRIOと空間データを統合し、貿易に埋め込まれた土地変化炭素損失を推計する手法の実例として参照できる。
🏢実務担当者:輸入農産物サプライチェーンにおける森林破壊・土地変化リスクの定量把握に活用できる。
🏛政策担当者:貿易相手国・消費国側の責任配分や森林破壊規制の設計に資する推計枠組みを提供する。
📄 Abstract（原文）
This data set is constructed based on multi-source spatial data and global multi regional input-output data, mainly including the calculation results of carbon storage loss caused by agricultural production, consumption and trade driven farmland expansion and deforestation in the "the Belt and Road" region in 2020. The dataset is constructed using a multi region input-output (EE-MRIO) model that combines spatial analysis with environmental extension. Firstly, integrate the global forest loss and deforestation driving factors data provided by Global Forest Watch, MapSPAM 2020 crop spatial distribution data, as well as potential natural vegetation carbon storage, soil organic carbon, and net primary productivity (NPP) data, identify forest losses caused by different crop expansions, and estimate the corresponding vegetation carbon storage and soil carbon storage losses. During the spatial analysis process, the relevant raster data is uniformly processed to a resolution of 10 km × 10 km to ensure spatial consistency. Secondly, based on the 2020 EXIOBASE global multi regional input-output table, the carbon stock losses corresponding to different crops were matched with the agricultural production sector, and an environmental satellite account was constructed to track the carbon stock losses caused by final consumption demand through the EE-MRIO model. Finally, combined with the economic and trade links between countries and regions, the implied carbon stock loss of trade within the "the Belt and Road" region and between the region and other economies is calculated to form the research results data related to the production side, consumption side and trade .
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.57760/sciencedb.cjae.000czfirst seen 2026-10-30 04:52:51 · last seen 2026-11-05 05:06:14
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