← 論文一覧に戻る

低炭素技術における競争優位の解読:環境技術投資管理の役割

Decoding competitive advantage in low-carbon technology: the role of environmental technology investions management (原題)

Shujaat Abbas, Zahoor Ahmed

International Journal of Innovation Studies📚 査読済 / ジャーナル2026-09-01#政策Origin: Global対象セクター: cross_sector
DOI: 10.1016/j.ijis.2026.100227
原典: https://doi.org/10.1016/j.ijis.2026.100227
📄 PDF

🤖 gxceed AI 要約

日本語

本研究は、1995年から2023年までの37のOECD諸国のパネルデータを用いて、環境管理慣行が低炭素技術の競争優位に与える影響を分析。操作変数分位回帰により、環境税、環境発明、低炭素技術輸入、公共R&Dの効果が競争力レベルに応じて異なることを発見。環境税の増加はポーター仮説を支持し、競争優位に有意な正の効果を持つ。

English

This study analyzes the impact of environmental management practices on competitive advantage in low-carbon technologies using panel data from 37 OECD countries (1995-2023). Employing instrumental variable quantile regression, it finds heterogeneous effects of environmental taxation, inventions, low-carbon imports, and public R&D. Increased environmental taxation positively affects competitiveness, supporting the Porter hypothesis, while other factors vary by innovation ecosystem maturity.

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

日本のGX政策(GX推進法、成長志向型カーボンプライシング)において、環境税や公共R&Dの効果を実証的に示す本結果は、政策設計の参考となる。特に、イノベーション生態系の成熟度に応じた効果の違いは、日本の産業構造に応じた政策調整に示唆を与える。

In the global GX context

This cross-country empirical evidence on environmental taxation and innovation supports global policy debates on carbon pricing and green industrial policy. It provides insights for ISSB-aligned transition planning and climate disclosure by highlighting the role of policy in shaping corporate competitiveness in low-carbon markets.

👥 読者別の含意

🔬研究者:Provides empirical evidence on heterogeneous effects of environmental policies on low-carbon competitiveness, useful for policy evaluation research.

🏢実務担当者:Highlights the importance of green innovation and R&D strategies for maintaining competitiveness in low-carbon markets.

🏛政策担当者:Offers evidence that environmental taxation can enhance national competitiveness, supporting carbon pricing and green R&D policies.

📄 Abstract(原文)

Achieving sustainable development goals relies mainly on the transition from fossil fuels to green alternatives. Rising environmental concerns have created a new market for low-carbon technologies and products. As a result, achieving a competitive advantage in low-carbon technologies has become increasingly important for sustainable economic development. This study explores the impact of environmental management practices on competitive advantage in low-carbon technologies across 37 OECD countries, using panel data from 1995 to 2023. Specifically, the study examines the role of environmental invention, environmental taxation, low-carbon technology imports, public R&D in industrial production, and public R&D in energy technologies. To achieve this objective, the study employs advanced instrumental variable quantile regression analysis to capture heterogeneous effects across different levels of competitiveness. The findings reveal heterogeneous effects of environmental inventions, environmental taxation, low-carbon technology imports, public R&D in industrial production, and public R&D in energy technologies on competitive advantage in low-carbon technologies. An increase in environmental taxation has a significant positive effect on competitive advantage, which supports the Porter hypothesis, whereas environmental inventions, low-carbon technology imports, and public R&D spending have differential impacts based on the maturity of prevailing innovation ecosystems. The findings provide practical implications for policymakers and industry stakeholders by highlighting the importance of green innovation incentives, low-carbon technology imports, and adaptive public R&D funding. These policies can strengthen national competitiveness in low-carbon technologies and facilitate the transition toward a sustainable low-carbon economy.

🔗 Provenance — このレコードを発見したソース

🔔 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。