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Digital as a Rhetorical Resource Under Institutional Complexity: A Longitudinal Comparative Discourse Analysis of Carbon Reporting in Vietnamese Listed Firms

制度複雑性の下での修辞的資源としてのデジタル:ベトナム上場企業におけるカーボン報告の縦断的比較言説分析 (AI 翻訳)

L. Nguyen, D. Phan

Journal of Risk and Financial Management📚 査読済 / ジャーナル2026-06-22#炭素会計対象セクター: cross_sector
DOI: 10.3390/jrfm19060450
原典: https://doi.org/10.3390/jrfm19060450

🤖 gxceed AI 要約

日本語

ベトナム上場企業7社の70の年次・サステナビリティ報告書(2015~2024年)を縦断的比較言説分析し、デジタル化がカーボン報告において修辞的に利用される様子を調査。運営上可視的な排出を持つ企業は「時間的緩衝」、サプライチェーンを介した排出の企業は「境界変位」としてデジタル化を利用し、実質的な脱炭素化と乖離することを示した。

English

This longitudinal comparative discourse analysis of 70 annual and sustainability reports (2015-2024) from seven Vietnamese listed firms reveals that digitalization functions as an ambivalent rhetorical resource in carbon reporting. Firms with operationally visible emissions use digitalization for temporal buffering, while those with supply-chain-mediated emissions employ boundary displacement, framing accountability as contingent on fragmented supplier data. The study concludes that digital reporting sophistication should not be conflated with substantive decarbonization.

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

ベトナムは日本のSSBJや有報と直接関係ないが、新興国におけるカーボン報告の実態を提供。日本のサプライチェーン開示(Scope3)にも示唆がある。

In the global GX context

This paper empirically specifies institutional decoupling theory in the context of carbon reporting, showing how emissions visibility and organizational control shape distinct pathways of discursive decoupling. It offers valuable insights for global disclosure scholarship, particularly for understanding how digitalization rhetoric can obscure substantive decarbonization efforts.

👥 読者別の含意

🔬研究者:This paper provides a nuanced theoretical framework for understanding how digitalization discourse can be used to decouple reporting from actual emission reductions.

🏢実務担当者:Supply-chain managers should be aware that digital reporting sophistication may mask incomplete emission data and require cross-validation.

🏛政策担当者:Regulators should design oversight that cross-validates digital infrastructure with concrete emission reduction measures to prevent 'digital institutional camouflage'.

📄 Abstract(原文)

This study examines how digitalization discourse is mobilized in public carbon reporting under institutional complexity and how it varies across different carbon-accountability structures in an emerging-market context within the Global South. A longitudinal comparative discourse analysis was conducted on 70 annual and sustainability reports (2015–2024) from seven Vietnamese listed firms, contrasting firms with internal carbon accountability against those with supply-chain-mediated accountability. The 2015–2024 timeframe was deliberately selected to capture a critical decade of regulatory evolution, marked by the aftermath of the Paris Agreement and the escalating enforcement of net-zero and environmental, social, and governance (ESG) disclosure mandates. Findings reveal that digitalization functions as an ambivalent rhetorical resource rather than a uniformly substantive sustainability enabler. Firms with operationally visible emissions utilize digitalization for “temporal buffering,” deferring immediate physical abatement by framing technology as a future transition pathway. Conversely, firms with supply-chain-mediated emissions employ “boundary displacement,” framing accountability as contingent on fragmented supplier data. These patterned responses constitute “digital institutional camouflage”. We conclude that digital reporting sophistication should not be conflated with substantive decarbonization; effective oversight requires cross-validating digital infrastructures with concrete emission-reduction measures. Ultimately, this study empirically specifies institutional decoupling theory by demonstrating how emissions visibility and organizational control shape distinct pathways of discursive decoupling.

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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。