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カーボンニュートラル移行をどう進めるか：企業の環境パフォーマンスにおけるデータ駆動型意思決定の役割

Navigating Carbon Neutrality Transitions: The Role of Data-Driven Decision Making in Corporate Environmental Performance (原題)

Shirin Saiyed, Made Ratih Nurmalasari

Advanced Business Journal📚 査読済 / ジャーナル2026-10-23#AI×ESG経営インパクト: 調達リスク対象セクター: cross_sector
DOI: 10.65875/y2ekws07
原典: https://doi.org/10.65875/y2ekws07

🤖 gxceed AI 要約

日本語

本論文は、データ駆動型意思決定（DDDM）がカーボンニュートラル移行期の企業環境パフォーマンスをどう高めるかを統合的にレビューする。影響の可視化、分析的診断、業務最適化、部門間調整、説明責任に基づく学習という5つのメカニズムを特定する一方、データ品質の低さ、システム分断、Scope 3推計の不確実性、リバウンド効果、クレジットによる実排出削減の代替といった弱化条件も示す。データ基盤・分析能力を意思決定ルーティン、実施介入、検証済み環境成果へ結びつける概念枠組みを提案し、DDDMを報告ツールではなく戦略的組織能力として扱うべきだと結論づける。

English

This integrative review examines how data-driven decision-making (DDDM) can strengthen corporate environmental performance during carbon-neutrality transitions. It identifies five value-creating mechanisms—impact visibility, analytical diagnosis, operational optimization, cross-functional coordination, and accountability-based learning—alongside weakening conditions such as poor data quality, fragmented systems, uncertain Scope 3 estimates, rebound effects, and reliance on carbon credits over gross reductions. The authors propose a conceptual framework linking data infrastructure and analytics to decision routines, implemented interventions, and verified outcomes, arguing DDDM is a strategic organizational capability rather than a reporting tool.

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

SSBJ基準・有価証券報告書での気候開示が本格化する日本企業にとって、データ基盤を開示対応ではなく経営意思決定と実排出削減に接続する視点は実務的示唆が大きい。Scope 3推計の不確実性やクレジット依存への警告は、国内のGX推進・移行金融の議論とも整合する。

In the global GX context

As ISSB/SSBJ adoption and CSRD raise the bar for verifiable climate data, this paper reframes disclosure infrastructure as a decision capability—directly relevant to global debates on greenwashing, Scope 3 uncertainty, and the credibility of net-zero transition plans. Its warning against substituting carbon credits for gross emissions reductions speaks to tightening transition-finance and assurance expectations.

👥 読者別の含意

🔬研究者:DDDMと環境パフォーマンスを結ぶ境界条件（データ品質・統治・Scope 3不確実性）を整理した枠組みは、実証研究の仮説設計に有用。

🏢実務担当者:炭素データ基盤を報告義務対応ではなく意思決定と削減行動に結びつける際のチェックリストとして活用できる。

🏛政策担当者:開示データの質と検証可能性を高める規制設計、クレジット依存を抑える政策インセンティブの検討に示唆を与える。

📄 Abstract（原文）

Corporate carbon-neutrality commitments have proliferated, but the credibility of corporate transitions depends on whether climate ambition is translated into measurable operational change. Firms must make connected decisions about energy, production, procurement, logistics, product design, capital allocation, and value-chain relationships while balancing environmental objectives with financial viability. This article examines how data-driven decision-making (DDDM) can strengthen corporate environmental performance during carbon-neutrality transitions. It develops an integrative review of recent research on digital transformation, environmental management, carbon accounting, sustainability disclosure, and net-zero governance, supplemented by authoritative standards and climate assessments published since 2021. The synthesis identifies five mechanisms through which DDDM may create environmental value: impact visibility, analytical diagnosis, operational optimization, cross-functional coordination, and accountability-based learning. It also identifies conditions under which these mechanisms are weakened, including poor data quality, fragmented systems, weak managerial incentives, uncertain Scope 3 estimates, rebound effects, and the substitution of carbon credits for gross emissions reductions. The article proposes a conceptual framework linking data infrastructure and analytical capabilities to decision routines, implemented interventions, and verified environmental outcomes. Governance quality, regulatory pressure, supplier influence, and organizational learning are theorized as important boundary conditions. The article concludes that DDDM should be treated as a strategic organizational capability rather than a reporting tool or technology purchase. Its contribution should be assessed not by the volume of data collected, but by whether the data change decisions and support additional, measurable, and verifiable environmental improvements without undermining long-term economic resilience.

🔗 Provenance — このレコードを発見したソース

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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。