Carbon Trading Price and the Quantity and Quality of Green Technological Innovation: A Sustainability Perspective
炭素取引価格とグリーン技術革新の量と質：持続可能性の視点から (AI 翻訳)
Chenqian Pan, Chaolin He
🤖 gxceed AI 要約
日本語
本研究は、中国の炭素排出権取引パイロット地域に上場する企業のデータを用いて、炭素価格が企業のグリーン技術革新（特許の量と質）に与える影響を分析。固定効果モデルによる回帰分析の結果、炭素価格の上昇はグリーン特許の量と質を有意に向上させることが示された。また、資金制約が部分的に媒介し、環境規制とメディアの注目がその効果を増幅するメカニズムが明らかになった。異質性分析では、非ハイテク企業、中国北部・東部地域、国有企業で効果が顕著であった。
English
Using data from Chinese listed companies in carbon pilot regions (2013-2024), this study examines how carbon trading prices affect the quantity and quality of corporate green technological innovation. A two-way fixed effects model shows that a one-unit increase in carbon price significantly boosts green patent quantity by 0.018 and quality by 0.361. Financing constraints partially mediate this effect, while environmental regulation and media attention amplify it. The positive impact is stronger in non-high-tech enterprises, northern/eastern regions, and state-owned enterprises.
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
中国の炭素市場を対象とするが、炭素価格と企業イノベーションの因果関係を実証した点は、日本の炭素価格政策（GXリーグ等）の設計にも示唆を与える。日本の企業にとっても、炭素価格が技術開発投資に与える影響を理解する上で参考になる。
In the global GX context
This paper provides empirical evidence from China's carbon market, demonstrating a clear causal link between carbon prices and corporate green innovation. The findings on financing constraints and media attention as amplifying mechanisms offer insights for policymakers globally who are designing carbon pricing mechanisms to drive low-carbon transition. It also highlights regional and ownership heterogeneity, which can inform targeted policy design.
👥 読者別の含意
🔬研究者:Provides robust empirical evidence on the carbon price-green innovation nexus, including mediation and heterogeneity analyses, useful for future research on carbon pricing impacts.
🏢実務担当者:Corporate sustainability teams can understand how carbon pricing mechanisms may incentivize green innovation, especially in the Chinese context, and anticipate potential effects under similar policies.
🏛政策担当者:Offers evidence on the effectiveness of carbon pricing in promoting green innovation, with insights on mediating factors like financing constraints, relevant for designing carbon market policies.
📄 Abstract（原文）
Sustainable development has become an important global goal for environmental protection and economic growth. Promoting environmental sustainability and green development has become an inevitable trend for global economic transformation. The carbon emission trading market (carbon market) is a crucial market-based mechanism for pricing greenhouse gas emissions, where carbon trading prices signal the costs of emission reduction and drive firms to engage in green technology innovation for a low-carbon transition. Using a sample of A-share listed companies in China’s eight carbon pilot regions from 2013 to 2024, this study employs a two-way fixed effects model to examine how carbon prices affect both the quantity and quality of corporate green technological innovation. Baseline regressions show that a one-unit increase in carbon prices significantly boosts green patent quantity (GreNum) by 0.018 and quality (GreQua) by 0.361, both at the 1% significance level. Mechanism analysis reveals that financing constraints act as a partial mediator, while environmental regulation and media attention further amplify the positive impact of carbon prices on corporate green technological innovation. Heterogeneity analysis further indicates that this positive effect is more pronounced in non-high-tech enterprises, China’s northern and eastern regions, and state-owned enterprises. This study provides empirical evidence for optimizing carbon market allocation, supporting corporate low-carbon transition, and promoting global environmental sustainability and green development.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18073285first seen 2026-05-05 22:47:31
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。