Driving Digital Shift: Carbon Emissions Trading as a Catalyst for Corporate Transformation in China–A Dual Machine Learning and DID Approach
デジタルシフトの推進：炭素排出権取引が企業変革の触媒に–二重機械学習とDIDアプローチ (AI 翻訳)
Haizhou Wang, School of Business, University of Chinese Academy of Social Sciences, Beijing, 102488, China
🤖 gxceed AI 要約
日本語
本論文は、中国A株上場企業のパネルデータを用い、炭素排出権取引政策が企業のデジタルトランスフォーメーションを促進するメカニズムを、二重機械学習と差分の差分法（DID）で実証。負債調達コストの削減とグリーンイノベーションの向上という2つの経路を特定し、「双炭」戦略とデジタル経済の協調推進にエビデンスを提供する。
English
This paper empirically examines how China's carbon emissions trading pilot promotes corporate digital transformation, using dual machine learning and difference-in-differences on panel data of A-share listed firms from 2010-2021. It identifies two pathways: reducing debt financing costs and enhancing green innovation output, providing evidence for synergizing dual-carbon strategy with digital economy.
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
本稿は、中国の炭素取引政策が企業のデジタル変革を促進する実証結果を示し、日本のGX政策（カーボンプライシングやデジタル化推進）に示唆を与える。特に、SSBJ対応や有報での気候関連開示が進む中、政策が企業行動に与える波及効果を理解する上で有用。
In the global GX context
This paper provides causal evidence that carbon pricing can drive digital transformation, a key enabler of decarbonization. It is globally relevant for policymakers designing integrated climate and digital strategies, and for researchers examining policy spillovers beyond emissions reduction.
👥 読者別の含意
🔬研究者:The dual ML+DID approach offers a robust causal inference method for studying carbon policy impacts on corporate digitalization.
🏢実務担当者:Insights on how carbon trading can reduce financing costs and spur green innovation, informing corporate strategy for digital and low-carbon transitions.
🏛政策担当者:Demonstrates a synergy between carbon pricing and digital economy, supporting integrated policy design for net-zero goals.
📄 Abstract（原文）
In response to the insufficient research on the collaborative mechanism between environmental regulation and enterprise digital transformation, this article proposes three hypotheses based on Porter's theory of competitive advantage: direct driving of carbon trading policies, alleviation of financing constraints, and green technology innovation; Then, using panel data from Chinese A-share listed companies from 2010 to 2021, and combining dual machine learning and double difference method, a quasi-experimental study was conducted to systematically identify the net effect of policies. The empirical verification results show that the carbon trading pilot policy significantly promotes the digital transformation of enterprises, and the core coefficient remains stable under multiple algorithms and passes the 1% significance test. This indicates that the policy can empower digital transformation through two paths: reducing debt financing costs and enhancing green innovation output, thereby providing empirical evidence for the coordinated promotion of the "dual carbon" strategy and the development of the digital economy.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.65102/is20261097first seen 2026-06-16 05:36:47
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