地理空間炭素計測インテリジェンスネットワーク
Geospatial Carbon Measurement Intelligence Network (原題)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
衛星放射輝度・現地観測・大気インバージョン・因果推論・不確実性対応AIを統合し、陸域・大気・産業の炭素ストックとフラックスを検出・推定・保証する相互運用型の計測基盤を提案する。観測可能な信号と炭素会計上の主張を区別し、較正・空間支持・参照品質・反事実ベースラインが計測の信頼性を左右すると論じる。森林・農地・湿地・都市・産業施設・公的インベントリへの応用と、来歴管理・信頼度報告・独立検証可能な保証ゲートを備えたアーキテクチャを示す。
English
Proposes an interoperable geospatial carbon measurement network integrating satellite radiance, field data, atmospheric inversion, causal inference and uncertainty-aware AI across land, atmosphere and industry. It separates observable signals from accounting claims, showing calibration, spatial support, reference quality and counterfactual baselines determine credibility. Includes provenance manifests, confidence reporting and independently testable assurance gates for forests, agriculture, wetlands, cities and industrial facilities.
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
SSBJ・有報でのScope3や土地利用排出開示が進む日本では、MRVの測定妥当性と保証可能性を高める設計論として、サプライチェーン上流の炭素会計や自然資本開示の実務に直結する。
In the global GX context
Aligns with ISSB/CSRD and voluntary carbon market integrity debates by framing measurement validity, reproducibility and assurance as preconditions for credible disclosure—directly relevant to MRV and additionality scrutiny in global carbon accounting.
👥 読者別の含意
🔬研究者:炭素計測の妥当性・再現性・地理的転用を統合的に扱う研究設計の枠組みとして参照できる。
🏢実務担当者:サプライチェーンや土地利用排出のMRV設計、データ来歴・信頼度報告の実装指針として活用できる。
🏛政策担当者:国家インベントリや炭素市場の保証制度設計に、測定妥当性と独立検証ゲートの考え方を取り込む際の参考になる。
📄 Abstract（原文）
Abstract: The Geospatial Carbon Measurement Intelligence Network is formulated as an interoperable scientific and computational system for detecting, reconciling, estimating and assuring carbon stocks and fluxes across land, atmosphere and industrial landscapes. It integrates satellite radiance, field observations, geospatial boundaries, atmospheric inversion, causal evaluation and uncertainty-aware artificial intelligence within a traceable analytical lifecycle. The monograph separates observable signals from carbon accounting claims, showing how calibration, spatial support, reference quality and counterfactual baselines determine the credibility of carbon measurement. Five chapters progress from foundational measurement design through statistical identification, predictive modelling, scalable data engineering and cross-sector governance. Ten research themes organise the source-based synthesis, while original schematic figures, analytical tables and domain-specific mathematical formulations connect physical measurement to decision systems. The proposed architecture incorporates quality-controlled acquisition, modular inference services, provenance manifests, confidence reporting and independently testable assurance gates. Applications encompass forests, agricultural land, wetlands, urban areas, industrial facilities and public inventories in diverse institutional environments. The central contribution is an integrated research design and implementation framework that treats measurement validity, reproducibility, geographic transfer and social accountability as interdependent conditions for trustworthy carbon intelligence. Keywords geospatial carbon intelligence, remote sensing, satellite observations, carbon measurement reporting verification, atmospheric inversion, carbon stocks, carbon fluxes, land use, biomass, soil carbon, greenhouse gas, earth-system digital twin, causal inference, uncertainty quantification, data provenance, sensor fusion, spatial machine learning, physics-informed AI, accounting boundaries, additionality, integrity assurance, climate governance
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.62311/nesx/rb-63sep-978-81-17475-37-9first seen 2026-10-17 04:47:00
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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。