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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・グリーン相乗的変革 (AI 翻訳)

Hao Wang

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

🤖 gxceed AI 要約

日本語

本研究は、中国の上場製造企業2,334社(2015-2024年)のデータを用いて、AIアフォーダンス(AIA)がデジタル・グリーン相乗的変革(DGST)を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、内部統制の質、市場競争力が媒介効果を持つ。AIAの閾値効果も確認された。

English

This study uses data from 2,334 Chinese A-share listed manufacturing firms (2015-2024) and a double machine learning model to show that AI affordance (AIA) significantly promotes digital-green synergistic transformation (DGST). Technological innovation diversification, internal control quality, and market competitive position mediate this effect. A threshold effect for AIA is also identified.

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

中国製造業に焦点を当てた研究だが、日本企業でもAIを活用したグリーン変革（GX）の推進が進んでおり、AIアフォーダンスとデジタル・グリーン相乗効果の実証結果は、日本の製造業のGX戦略にも示唆を与える。

In the global GX context

While centered on China, this paper provides empirical evidence on how AI capabilities can drive synergistic digital and green transformation, relevant to global manufacturing firms. The findings on mediation and threshold effects offer insights for corporate sustainability strategies and policy design.

👥 読者別の含意

🔬研究者:Provides a quantitative method (DML) to assess AI's impact on green transformation, and identifies mediating and moderating factors.

🏢実務担当者:Highlights that AI affordance can boost both digital and green performance, but only above a threshold, guiding investment decisions.

🏛政策担当者:Suggests that policies supporting AI adoption should consider firm-level factors like industry chain integration and analyst coverage for effective green transformation.

📄 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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