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Evaluating ESG Initiatives to Eco‐Efficiency Toward Net Zero Emissions: Evidence From Global Value Chain of Taiwanese Semiconductor Manufacturing Companies

ネットゼロ排出に向けたESGイニシアチブの環境効率評価：台湾半導体製造企業のグローバルバリューチェーンからの証拠 (AI 翻訳)

Wen‐Chi Yang, Wen‐Min Lu, Irene Wei Kiong Ting, Alagu Perumal Ramasamy

Business Strategy and the Environment📚 査読済 / ジャーナル2026-07-31#AI×ESG経営インパクト: 調達リスク対象セクター: semiconductor
DOI: 10.1002/bse.71372
原典: https://doi.org/10.1002/bse.71372
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🤖 gxceed AI 要約

日本語

本研究は、台湾の半導体製造企業（TSMC）のバリューチェーンにおける37社のデータを用い、逆DEAと確率制約DEAを適用してESGイニシアチブが環境効率に与える影響を分析。環境次元は環境効率に負の影響を与える一方、社会・ガバナンス次元は正の相関を示すが統計的に有意ではない。地域による環境効率の差異が顕著で、地域別戦略の必要性を示唆。

English

This study analyzes 37 firms in TSMC's value chain using inverse DEA and chance-constrained DEA to examine ESG initiatives' impact on eco-efficiency. Environmental dimension negatively affects eco-efficiency, while social and governance dimensions show positive but insignificant correlation. Significant regional differences highlight the need for tailored strategies in the semiconductor industry.

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

日本企業にとって、サプライチェーン全体のGHG排出削減目標の配分方法はSSBJ開示やScope 3対応に直結する。特に半導体業界は日本も強みを持つ分野であり、台湾の事例は日本企業のネットゼロ戦略に示唆を与える。

In the global GX context

This paper contributes to global disclosure scholarship by demonstrating a quantitative method for allocating GHG reduction targets across a value chain, relevant to ISSB and CSRD requirements. The regional differences in eco-efficiency underscore the need for context-specific transition strategies.

👥 読者別の含意

🔬研究者:Provides a novel DEA-based approach for ESG and eco-efficiency analysis in supply chains.

🏢実務担当者:Offers a method for setting GHG reduction targets across suppliers, useful for Scope 3 management.

🏛政策担当者:Highlights regional disparities in eco-efficiency, informing targeted industrial policies.

📄 Abstract（原文）

ABSTRACT This study examines the impacts of environmental, social, and governance (ESG) initiatives and eco‐efficiency and the achievement of net‐zero emissions within the Taiwanese semiconductor manufacturing company (TSMC). This study sources its data from 37 firms within TSMC's value chain for years 2020–2023 from the Refinitiv and Taiwan Economic Journal databases and employs an inverse data envelopment analysis (DEA) approach to allocate greenhouse gas (GHG) emission targets and develop a chance‐constrained DEA model to address the high uncertainty in the industry. Results indicate significant differences in eco‐efficiency across firms in different regions, with the environmental dimension exhibiting a statistically significant negative effect on eco‐efficiency performance. While the social and governance dimensions are positively correlated with eco‐efficiency, this relationship is not statistically significant. This study introduces innovative methodological approaches, including the inverse DEA method for optimal GHG reduction and the chance‐constrained DEA model, to handle the high level of uncertainty in the industry. The findings provide valuable insights for policymakers, operational managers, and industries prioritizing ESG initiatives, and the significant regional differences in eco‐efficiency highlight the need for tailored, region‐specific strategies to improve eco‐efficiency across the semiconductor industry. By offering novel contributions through its methodology and empirical findings, this study enhances the present understanding of how ESG initiatives influence eco‐efficiency and supports the development of effective policies for achieving net‐zero emissions in high‐tech industries.

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