A COMBINED EWM AND MULTIVARIATE STATISTICAL APPROACH FOR EVALUATING GREEN FINANCE AND ECONOMIC SUSTAINABILITY
グリーンファイナンスと経済持続可能性を評価するためのEWMと多変量統計アプローチの組み合わせ (AI 翻訳)
HongYue Deng
🤖 gxceed AI 要約
日本語
中国30省のデータを用い、エントロピー重み法と多変量統計でグリーンファイナンスと経済持続可能性の関係を分析。高い結合係数0.976を示し、生態持続可能性は高エネルギー部門への資本摩擦に依存し、産業排出削減には資本化が寄与することを発見。政策立案者に精密な資源配分の枠組みを提供。
English
Using data from 30 Chinese provinces, this study combines entropy weight method and multivariate statistics to analyze the nexus between green finance and economic sustainability. It finds a coupling coefficient of 0.976, revealing that ecological sustainability relies on capital friction against high-energy sectors, while targeted capitalization accelerates industrial emission reductions. Offers a framework for precision resource allocation.
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、グリーントランスフォーメーション（GX）推進に向け、地域ごとの特性に応じた金融配分が課題。本手法は中国の省レベル分析だが、都道府県レベルでの応用可能性を示唆。SSBJや有報での地域別開示にも示唆。
In the global GX context
Global context: This paper provides a robust statistical method to evaluate green finance effectiveness, relevant to transition finance frameworks and climate disclosure (TCFD, ISSB). The high coupling coefficient underscores the need for differentiated policies across regions, aligning with global discussions on just transition and resource allocation.
👥 読者別の含意
🔬研究者:Methodological approach for spatial heterogeneity in green finance analysis; canonical correlation with entropy weight.
🏢実務担当者:Insights on how capital allocation can be optimized for regional sustainability targets.
🏛政策担当者:Framework for precision policy design based on regional economic and ecological disparities.
📄 Abstract（原文）
Unraveling the nexus between financial allocation and ecological performance demands a framework capable of dismantling complex spatial heterogeneities. This study constructs a multidimensional statistical pipeline analyzing cross-sectional data across thirty Chinese provinces. Following objective dimension reduction via the entropy weight method, spatial clustering strictly partitions the landscape into distinct developmental tiers. Canonical correlation analysis subsequently extracts orthogonal structural equations, yielding a systemic coupling coefficient of 0.976. The derived loadings explicitly quantify a dual-edged transition mechanism: ecological sustainability relies heavily on capital friction against high-energy sectors (loading: -0.869), running parallel to targeted capitalization that accelerates industrial emission reductions (loading: 0.784). Beyond immediate operational insights, this research provides a mathematically rigorous blueprint for macroeconomic restructuring. By mapping these exact directional constraints, the framework equips policymakers to abandon homogeneous interventions, facilitating precision green resource allocation across highly polarized regional economies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.61784/wjebr3100first seen 2026-05-15 21:28:01
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