作为与政府多元化参与的信息因素相关的环境制度安排,企业,媒体和公众,环境信息披露试点政策,可以以及如何通过多重协同治理来影响碳排放效率?本研究使用2007年在中国实施的环境信息披露试点政策作为准自然实验。考察了2004-2021年284个地级市和2004-2021年A股上市公司,构建了一个涉及政府、public,企业,和媒体。通过数学推导和赋值分析,它探讨了在多方面的协作治理下,环境信息如何影响碳排放效率,评估政策实施前后利益相关者的战略选择和演变路径,使用双重机器学习等方法进行实证检验。这项研究强调了几个关键发现:首先,环境信息公开试点政策的实施显著提高了试点城市的碳全要素生产率,正如通过双机器学习(DML)政策效果评估所揭示的那样。第二,使用双重去偏LASSO(DDL)回归对潜在估计偏差进行调整表明,环境信息披露通过涉及政府的治理机制影响碳生产率,public,媒体,和企业。第三,因果路径分析提出了治理有效性的时序逻辑,从政府环境关注到企业环境责任。最后,将DML与调节作用模型相结合,揭示了环境立法建设的调节作用,为实现双碳目标提供新的见解,丰富信息对碳排放效率影响的经验证据。
As an environmental institutional arrangement related to the information factor of the diversified participation of the government, enterprises, the media and the public, the environmental information
disclosure pilot policy, can and how to affect the carbon emission efficiency through multiple collaborative governance? This study uses the Environmental Information
Disclosure Pilot Policy implemented in
China in 2007 as a quasi-natural experiment. It examines 284 prefecture-level cities from 2004 to 2021 and A-share listed companies from 2004 to 2021, constructing an evolutionary game dynamic model involving government, public, enterprises, and media. Through mathematical derivation and assignment analysis, it explores how environmental information impacts carbon emission efficiency under multifaceted collaborative governance, assessing the strategic choices and evolutionary paths of stakeholders before and after policy implementation, using methods like double machine learning for empirical testing. The study highlights several key findings: First, the implementation of the Environmental Information
Disclosure Pilot Policy significantly enhanced carbon total factor productivity in pilot cities, as revealed through Double Machine Learning (DML) policy effect evaluation. Second, adjustments for potential estimation biases using Doubly Debiased LASSO (DDL) regression indicated that environmental information
disclosure impacts carbon productivity via a governance mechanism involving government, public, media, and enterprises. Third, a causal pathway analysis suggested a sequential logic in governance effectiveness, starting from governmental environmental focus to corporate environmental responsibility. Lastly, integrating DML with a moderation effect model revealed a regulatory role for environmental legislation construction, offering new insights for achieving dual carbon goals and enriching empirical evidence on information\'s impact on carbon emission efficiency.