Does Intelligent Manufacturing Enhance Enterprise Esg Performance? Empirical Evidence from China
スマートマニュファクチャリングは企業のESGパフォーマンスを向上させるか?中国からの実証的証拠 (AI 翻訳)
Chen Xu, Batkhuyag Ganbaatar
🤖 gxceed AI 要約
日本語
本研究は、2015年から2023年の中国A株上場企業を対象に、スマートマニュファクチャリングがESGパフォーマンスに与える影響を分析。固定効果回帰、PSM-DID、システムGMMを用い、スマート製造がESGを有意に向上させることを実証。メカニズムとして情報透明性、グリーン技術革新、協調ガバナンスの促進効果と、資金制約の抑制効果を特定した。
English
This study examines the impact of intelligent manufacturing on ESG performance for Chinese A-share listed companies (2015-2023) using two-way fixed-effects panel regression, PSM-DID, and System GMM. It finds that intelligent manufacturing significantly improves ESG ratings. Mechanism analysis reveals mediating effects through information transparency, green innovation, and synergistic governance, while financing constraints suppress the positive impact.
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
日本でもスマートマニュファクチャリング(Society 5.0)が推進されており、本稿はデジタル変革とESGの関係を示すエビデンスとして参考になる。SSBJや有報でのESG開示が進む中、製造業のDX投資がESG評価向上に寄与する可能性を示唆する。
In the global GX context
This paper provides empirical evidence from an emerging market (China) that digital transformation through intelligent manufacturing can enhance ESG performance. It adds to the global literature on the AI-ESG nexus and offers insights for ISSB/CSRD frameworks by identifying mechanisms such as green innovation and governance.
👥 読者別の含意
🔬研究者:Provides novel evidence on the causal mechanisms linking intelligent manufacturing to ESG performance in an emerging market context.
🏢実務担当者:Corporate managers can leverage intelligent manufacturing to improve ESG ratings, particularly through transparency and green innovation.
🏛政策担当者:Policymakers should consider supporting digital transformation and easing financing constraints to maximize ESG benefits from manufacturing automation.
📄 Abstract(原文)
This study investigates the impact of intelligent manufacturing on the Environmental, Social, and Governance (ESG) performance of Chinese A-share listed companies from 2015 to 2023. As China undergoes simultaneous industrial upgrading and ESG institutionalization, understanding this relationship is critical for sustainable corporate development. Adopting a mixed-methods approach, the research employs a two-way fixed-effects panel regression complemented by multiple case studies of industry leaders, including Haier and CATL. Intelligent manufacturing is measured at the firm level, while ESG performance is captured through comprehensive enterprise ratings. The empirical results demonstrate that intelligent manufacturing exerts a significantly positive and robust effect on ESG performance. These findings remain consistent across various robustness checks, including Propensity Score Matching with Difference-in-Differences (PSM-DID) and System GMM estimation to address potential endogeneity. Mechanism analysis reveals that the positive impact is primarily driven by three mediating channels: enhanced information transparency, strengthened green technological innovation capacity, and optimized synergistic governance. Conversely, financing constraints are found to exert a suppressing mediation effect, highlighting the role of financial frictions in shaping ESG outcomes. Heterogeneity analysis further indicates that the ESG-enhancing effects are more pronounced in high-tech enterprises and firms located in non-Western regions of China. By integrating econometric rigor with qualitative insights, this research provides a multidimensional framework for understanding how digital transformation serves as a catalyst for corporate sustainability. The findings offer practical implications for policymakers and corporate managers aiming to leverage intelligent transformation to meet evolving ESG standards in emerging markets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://ijssei.in/index.php/ijssei/article/download/446/205first seen 2026-07-18 08:24:37
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。