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Towards a low-carbon digital future: AI affordance and digital-green synergistic transformation of manufacturing firms

低炭素デジタル未来に向けて：製造企業におけるAIアフォーダンスとデジタル・グリーン相乗変革 (AI 翻訳)

Hao Wang

Mendeley Dataデータセット2026-05-08#AI×ESGOrigin: CN
DOI: 10.17632/r66hhzrgbk.1
原典: https://doi.org/10.17632/r66hhzrgbk.1

🤖 gxceed AI 要約

日本語

本論文は、中国A株上場製造企業2,334社の13,298件のデータを用いて、AIアフォーダンスがデジタル・グリーン相乗変革を有意に促進することを二重機械学習モデルで実証。技術革新の多様化、内部統制品質、市場競争力が媒介効果を持ち、産業チェーン統合度やアナリストカバレッジが低い企業で効果が高い。さらに、AIアフォーダンスの閾値効果も確認。

English

Using data from 2,334 Chinese listed manufacturing firms (2015-2024), this study employs a double machine learning model to confirm that AI affordance significantly promotes digital-green synergistic transformation. It finds mediation through technological innovation diversification, internal control quality, and market competitive position, with stronger effects in firms with lower industrial chain integration, analyst coverage, and policy intensity. A threshold effect of AI affordance is also revealed.

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

中国製造業を対象とした研究だが、AI活用によるグリーン変革の実証は日本企業にも示唆を与える。特に、デジタルとグリーンの相乗効果を定量化した点は、日本のGX推進におけるAI投資の優先順位付けに有用となりうる。

In the global GX context

This paper provides empirical evidence on how AI affordance drives synergistic digital-green transformation in manufacturing, a key area for global decarbonization. The findings on threshold effects and mediating factors offer actionable insights for firms and policymakers seeking to leverage AI for climate goals, complementing studies in developed economies.

👥 読者別の含意

🔬研究者:Provides empirical evidence linking AI affordance to digital-green transformation, with novel use of double machine learning and threshold analysis.

🏢実務担当者:Highlights conditions under which AI investment yields greatest carbon reduction and digital synergy, guiding strategic resource allocation.

🏛政策担当者:Supports policies that promote AI adoption alongside green transformation, with evidence on the importance of firm-level capabilities and market positioning.

📄 Abstract（原文）

This dataset comprises an unbalanced panel of 13,298 firm-year observations across 2,334 Chinese A-share listed manufacturing firms spanning the period from 2015 to 2024. Data were comprehensively sourced from the CNRDS, CSMAR, Wind, and DIB databases, alongside corporate annual reports. Utilizing this dataset, a double machine learning (DML) model is employed to confirm that AI affordance (AIA) significantly promotes digital-green synergistic transformation (DGST). Furthermore, technological innovation diversification (TID), internal control quality (ICQ), and market competitive position (MCP) mediate this impact. Additionally, the positive impact is more pronounced in firms with lower levels of industrial chain integration (ICI), analyst coverage (AC), and policy intensity (PI). Further analysis reveals that the impact of AIA on DGST exhibits a single threshold effect, becoming positive and significant if and only if AIA surpasses a certain threshold.

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