Institute of Chengdu-Chongqing Economic Zone Development, Chongqing Technology and Business University, Chongqing, 400067, China.
School of Business Administration (MBA), Zhejiang Gongshang University, Hangzhou, 310018, China.
J Environ Manage. 2024 Aug;366:121817. doi: 10.1016/j.jenvman.2024.121817. Epub 2024 Jul 16.
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.
作为一种与政府、企业、媒体和公众多元化参与的信息因素相关的环境制度安排，环境信息披露试点政策可以通过多种协同治理方式影响碳排放效率，以及如何影响碳排放效率？本研究利用中国 2007 年实施的环境信息披露试点政策，考察了 2004 年至 2021 年的 284 个地级市和 2004 年至 2021 年的 A 股上市公司，构建了一个涉及政府、公众、企业和媒体的多方协同治理的演化博弈动态模型。通过数学推导和赋值分析，探讨了在多方协同治理下环境信息如何影响碳排放效率，评估了政策实施前后利益相关者的战略选择和演化路径，采用双机器学习等方法进行实证检验。研究结果表明：首先，环境信息披露试点政策的实施显著提高了试点城市的碳全要素生产率，这是通过双机器学习（DML）政策效果评估得出的。其次，通过双重偏差最小二乘（DDL）回归进行潜在估计偏差调整，表明环境信息披露通过涉及政府、公众、媒体和企业的治理机制影响碳生产率。第三，因果路径分析表明，治理效果存在一个从政府环境关注到企业环境责任的顺序逻辑。最后，将 DML 与调节效应模型相结合，揭示了环境立法建设的调节作用，为实现双碳目标提供了新的思路，丰富了信息对碳排放效率影响的实证证据。
