AI・ESG開示・グリーンサプライチェーン協働の三次元離散モデルにおける組織的応答性
Organizational Responsiveness in a Three-Dimensional Discrete Model of AI, ESG Disclosure, and Green Supply Chain Collaboration (原題)
Fang Sun
🤖 gxceed AI 要約
日本語
AI投資成熟度・ESG開示品質・グリーンサプライチェーン協働を統合した三次元離散非線形モデルを構築。構造的補完性と組織的応答性を分離し、応答速度が動学安定性を変えることを示す。ベースラインでは低・高補完性の安定均衡と不安定中間状態が共存し、応答性閾値でNeimark-Sacker分岐やカオスが生じる。AI-ESG-グリーンSCの補完性が強くても、調整速度が速ければ常に有益とは限らないと結論。
English
This study builds a bounded three-dimensional discrete nonlinear model linking AI-investment maturity, ESG disclosure quality, and green supply chain collaboration. It separates structural complementarity from organizational responsiveness, showing that the same long-run configuration can shift dynamic stability as adjustment speed changes. Baseline calibration yields stable low- and high-complementarity regimes separated by an unstable intermediate state, with Neimark-Sacker bifurcations, quasiperiodic motion, and chaos emerging at distinct responsiveness thresholds. Stronger AI-ESG-green-supply-chain complementarity does not guarantee that faster adjustment is always beneficial.
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基準・有報でのESG開示とAI投資・サプライチェーン脱炭素を同時に進める日本企業にとって、開示品質と組織応答速度のバランス設計を理論的に示唆する。統合報告書や投資家対応のガバナンス設計に示唆を与える。
In the global GX context
Amid ISSB/CSRD-driven disclosure mandates and AI adoption in sustainability reporting, this paper offers a theoretical lens on how pacing organizational responses to AI-ESG-supply-chain complementarity affects stability, relevant to transition governance and disclosure-infrastructure design.
👥 読者別の含意
🔬研究者:AI・ESG・サプライチェーンを統合した非線形動学モデルと分岐分析の枠組みを、持続可能性ガバナンス研究に応用できる。
🏢実務担当者:ESG開示品質とAI投資、グリーン調達の連動を進める際、変革速度と減衰設計が安定性を左右する点を経営判断に活かせる。
🏛政策担当者:開示義務やAI・脱炭素政策の設計において、企業の応答速度が制度効果の安定性に与える影響を考慮すべき。
📄 Abstract(原文)
Artificial intelligence (AI) investment, environmental, social, and governance (ESG) disclosure, and green supply chain collaboration increasingly operate as an interconnected corporate sustainability system, yet their relationships are commonly examined through static or approximately linear models. This study develops a bounded three-dimensional discrete nonlinear framework in which firms periodically adjust AI-investment maturity, ESG disclosure quality, and green supply chain collaboration in response to cross-domain benefits and self-limiting organizational costs. The model contributes by separating structural complementarity from organizational responsiveness, allowing the same long-run configuration to remain feasible while its dynamic stability changes with adjustment speed. A logit adaptive rule keeps all states within the open unit interval, while Hill-type functions capture activation thresholds and saturation. Under the baseline calibration, the system exhibits three interior fixed points: stable low- and high-complementarity regimes separated by an unstable intermediate state. Schur-Jury analysis establishes local stability conditions, and Neimark--Sacker analysis shows that the low and high regimes lose stability at distinct responsiveness thresholds, with subcritical and supercritical bifurcations, respectively. Along the high-regime branch, increasing responsiveness generates quasiperiodic motion, intermittent chaotic subwindows, and re-entrant stable period-3 behavior. A positive leading Lyapunov exponent confirms deterministic chaos, while multi-start diagnostics show that quasiperiodic and chaotic dynamics can coexist with a stable period-3 attractor under different initial conditions over part of the parameter range. The findings show that stronger AI-ESG-green-supply-chain complementarity does not imply that faster organizational adjustment is always beneficial. Effective governance must therefore consider not only the strength of cross-domain reinforcement but also the pacing and damping of organizational responses.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.62762/jnda.2026.451847first seen 2026-09-14 04:47:50
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