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地理空間量子AI気候金融デジタルツイン：炭素インテグリティ、インフラ強靭性、システムリスク、包摂的開発

:Geospatial Quantum–AI Climate–Finance Digital Twins: Carbon Integrity, Infrastructure Resilience, Systemic Risk and Inclusive Development (原題)

Murali Krishna Pasupuleti

International Journal of Academic and Industrial Research Innovations(IJAIRI)📚 査読済 / ジャーナル2026-08-30#気候金融
DOI: 10.62311/nesx/rp5ag-30082026
原典: https://doi.org/10.62311/nesx/rp5ag-30082026

🤖 gxceed AI 要約

日本語

本論文は、気候金融の意思決定を支援する地理空間量子AI気候金融デジタルツイン（GQAI-CFDT）フレームワークを提案する。地理空間観測、炭素MRV、インフラ状態、金融ネットワーク、社会経済指標を統合し、状態空間モデルやグラフ理論、ベイズ更新、多目的最適化を用いる。数値デモは概念実証であり、実証的な因果主張はない。

English

This paper proposes a Geospatial Quantum-AI Climate-Finance Digital Twin (GQAI-CFDT) framework integrating geospatial observations, carbon MRV, infrastructure state, financial networks, and socioeconomic indicators. It uses coupled state-space models, graph and topology representations, Bayesian updating, and multi-objective optimization. A numerical demonstration illustrates mechanics only, without empirical causal claims.

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

日本ではSSBJ開示や統合報告書が進む中、地理空間情報と気候リスクを統合したデジタルツインは、企業のTCFD開示や地域インフラ強靭化に示唆を与える。ただし実証データがなく、現段階では概念枠組みとしての参考価値に留まる。

In the global GX context

Globally, this framework aligns with ISSB and CSRD trends toward integrated climate-risk assessment. It offers a blueprint for linking carbon integrity, infrastructure resilience, and systemic risk in digital twins, but lacks empirical validation, limiting immediate applicability.

👥 読者別の含意

🔬研究者:研究者は、気候金融とAIを統合するデジタルツインの概念設計を参考にできる。

🏢実務担当者:実務者は、気候リスクと炭素クレジットを統合した評価の枠組みとして検討可能。

🏛政策担当者:政策担当者は、地理空間情報を活用した気候リスク管理の方向性を確認できる。

📄 Abstract（原文）

Abstract: Climate finance increasingly depends on the credibility of carbon claims, the resilience of physical infrastructure, the containment of networked financial risk, and the equitable distribution of transition benefits. Yet these domains are commonly modelled in separate analytical systems, creating blind spots when climate hazards, carbon-market quality, infrastructure disruption and financial contagion interact across geography. This paper develops a Geospatial Quantum-AI Climate-Finance Digital Twin (GQAI-CFDT) framework that integrates geospatial observations, carbon measurement-reporting-verification signals, infrastructure state variables, market and credit networks, socioeconomic indicators, topological descriptors and probabilistic machine intelligence in a unified decision architecture. The methodology combines a coupled state-space model, graph and persistent-topology representations, Bayesian risk updating, constrained multi-objective optimisation and digital-twin feedback. A normalized 0-10 numerical demonstration is used only to illustrate the mechanics of the proposed framework; no empirical causal claims are made. The illustrative baseline produces an integrated capability score of 7.60 and a risk-adjusted score of approximately 7.02 after uncertainty, market-fragility and inequity penalties. Scenario analysis shows how stronger carbon verification and infrastructure resilience can improve the decision score, while correlated climate-financial shocks can materially reduce it. The paper contributes a cross-sector architecture that links carbon integrity to spatial evidence, infrastructure resilience to hazard propagation, systemic risk to network topology, and inclusive development to distributional constraints. It thereby offers a research blueprint for future empirical digital twins capable of supporting auditable climate investment, resilience planning, transition-risk management and equity-aware policy design. Keywords: geospatial intelligence; quantum-AI; climate finance; digital twins; carbon integrity; measurement reporting and verification; infrastructure resilience; systemic risk; topological data analysis; climate transition; inclusive development; spatial equity; network contagion; probabilistic forecasting; resilient investment

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