1990〜2023年の中国における陸域炭素貯蔵と回復効果の高解像度評価
High-Resolution Assessment of Terrestrial Carbon Storage and Restoration Benefits in China, 1990–2023 (原題)
Zhen Wu, Xianjin Huang, Michael E. Meadows
🤖 gxceed AI 要約
日本語
本研究は、リモートセンシングと機械学習を統合し、中国の陸域生態系の炭素貯蔵量を高解像度で推定した。総炭素貯蔵量は113.48 Pg Cで、2014〜2023年に6.62 Pg C増加。主要な回復地域では追加の炭素貯蔵量が0.495 Pg Cと推定され、生態系回復の気候変動緩和効果を定量化した。
English
This study integrates remote sensing and machine learning to estimate terrestrial carbon storage in China at high resolution. Total carbon storage is 113.48 Pg C, increasing by 6.62 Pg C from 2014 to 2023. Additional carbon gains in major restoration regions are estimated at 0.495 Pg C, quantifying the climate mitigation benefits of ecosystem restoration.
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
中国の大規模な生態系回復の炭素吸収効果を定量的に示すもので、日本の気候変動適応策や森林管理政策の参考になる。また、機械学習を用いた炭素貯蔵量推定手法は、日本の温室効果ガスインベントリや土地利用変化の評価にも応用可能。
In the global GX context
This study provides robust evidence on the carbon sequestration potential of large-scale ecosystem restoration, relevant to global climate policy under the Paris Agreement. The machine learning approach for high-resolution carbon accounting can inform international standards for nature-based solutions and carbon crediting mechanisms.
👥 読者別の含意
🔬研究者:Provides a comprehensive dataset and methodology for terrestrial carbon accounting using ML, useful for improving carbon cycle models.
🏢実務担当者:Offers insights into the carbon benefits of restoration projects, which can inform corporate natural capital accounting and offset strategies.
🏛政策担当者:Demonstrates the measurable climate impact of restoration policies, supporting evidence-based decision-making for land-use and climate strategies.
📄 Abstract（原文）
Abstract Terrestrial ecosystems serve as critical carbon sinks, playing an important role in the global carbon cycle and climate regulation. However, previous estimates of carbon storage in China’s terrestrial ecosystems remain uncertain due to variability in data sources and methodological approaches. This study integrates multisource remote sensing data with extensive field-sampling data sets of carbon density, leveraging machine learning models to provide a comprehensive estimation of multiyear carbon storage and annual dynamics across multiple carbon pools. We estimated project-region carbon-stock changes using a scenario-based accounting framework. The total carbon storage, encompassing vegetation biomass and soil organic carbon, is estimated at 113.48 Pg C, with 20.4% in vegetation biomass and 79.6% in soil. Across land-use types, forests store the largest share, with 47.99 Pg C, followed by grasslands, croplands, barren lands, shrublands, and wetlands, which stored 32.32, 18.00, 14.65, 0.49, and 0.02 Pg C, respectively. National carbon stocks fluctuated over the study period but increased by 6.62 Pg C from 2014 to 2023. Scenario-based accounting indicates an additional baseline-adjusted project-region carbon-stock gain of 0.495 Pg C within major restoration regions during 2014–2023, equivalent to 0.050 Pg C yr–1. These results provide a high-resolution reconstruction of China’s terrestrial carbon stocks and clarify recent carbon-stock changes across ecosystems, regions, and major restoration areas.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1021/acs.est.6c00827first seen 2026-09-08 04:43:12
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