気候安定化のためのAI：予測・最適化・自律的緩和システム

AI for Climate Stabilization: Forecasting, Optimization, and Autonomous Mitigation Systems (原題)

Hunter Hughes

Crossrefプレプリント2026-01-01#AI×ESG経営インパクト: コスト削減対象セクター: cross_sector

DOI: 10.2139/ssrn.7319241

原典: https://doi.org/10.2139/ssrn.7319241

🤖 gxceed AI 要約

日本語

AIが地球システムモデルを置き換え、ニューラルオペレータや基盤気候モデルで1万倍高速な予測を実現する動向を整理。深層強化学習とモデル予測制御による系統平衡や産業最適化で再エネ抑制を最大87%緩和し、難脱炭素部門で15〜30%の省エネを達成。生成材料モデルによるMOF探索でDAC再生エネルギーを1200→650kWh/tCO₂に低減、衛星IoT・MRVで検証可能な炭素会計を提示。2030年に4.8〜6.2Gt、2040年に12.8Gtの削減可能性を示す。

English

This report frames AI as the operating system for climate stabilization, replacing slow Earth System Models with neural operators and foundation models delivering 10,000× faster forecasts. Deep RL and MPC enable grid balancing, curtailment mitigation (up to 87%), and 15–30% industrial energy savings. Generative materials models cut DAC regeneration energy from 1,200 to 650 kWh/tCO₂, while satellite-IoT MRV provides verifiable carbon accounting. Mature climate AI could abate 4.8–6.2 Gt CO₂e/yr by 2030, scaling to 12.8 Gt by 2040.

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

AI×気候緩和の定量可能性は、SSBJ開示や企業のScope削減目標・移行計画の実現手段として日本企業の脱炭素投資判断に直結する。MRVや炭素会計の検証可能性は有報・統合報告書の信頼性向上にも寄与しうる。

In the global GX context

Speaks directly to ISSB/CSRD disclosure credibility: verifiable MRV and AI-enabled abatement quantification strengthen transition-plan reporting and climate-risk modeling under TCFD. The enablement-ratio framing also informs the global debate on AI's own footprint versus mitigation benefit.

👥 読者別の含意

🔬研究者:AI×気候緩和の定量評価手法とMRV検証可能性の研究フロンティアを把握できる。

🏢実務担当者:系統最適化・産業省エネ・DAC・MRVへのAI活用が自社のScope削減・移行計画にどう効くかの示唆を得られる。

🏛政策担当者:AI活用の削減ポテンシャルとSRMガードレールを、気候政策・開示制度設計にどう組み込むかの論点を提供する。

📄 Abstract（原文）

Artificial intelligence is becoming the core operating system for planetary climate stabilization, replacing the latency‑bound, parameterization‑heavy workflows of classical Earth System Models with real‑time, physics‑informed emulation and cyber‑physical control. Modern neural operators and foundation climate models—such as Fourier Neural Operators, GraphCast, ClimaX, and NeuralGCM—deliver 10,000× faster global forecasts while resolving sub‑grid turbulence and convective cloud microphysics with physically constrained stability. Across infrastructure systems, deep reinforcement learning and model predictive control enable dynamic grid balancing, synthetic inertia injection, virtual power plant coordination, and industrial thermodynamic optimization, achieving up to 87% renewable curtailment mitigation and 15–30% energy reductions in hard‑to‑abate sectors. In carbon removal, generative materials models (Crystal Diffusion VAEs) discover high‑performance MOFs that cut DAC regeneration energy from 1,200 to 650 kWh/tCO₂, while multimodal satellite‑IoT MRV systems provide cryptographically verifiable carbon accounting with sub‑50 m leak localization and 95% credible interval bounds across oceanic and geological sinks. The report further establishes multi‑objective AI guardrails for Solar Radiation Management, reducing hydrological distortion by 65% and constraining termination shock risk through autonomous radiative forcing caps. Collectively, mature climate AI systems can abate 4.8–6.2 Gt CO₂e annually by 2030, scaling to 12.8 Gt CO₂e by 2040, with an Enablement Ratio exceeding 45:1, demonstrating that optimized AI‑driven mitigation overwhelmingly outweighs its computational footprint.

🔗 Provenance — このレコードを発見したソース

openaire https://doi.org/10.2139/ssrn.7319241first seen 2026-11-05 04:47:23

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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。