What Does Air Quality Information Disclosure Deliver and to Whom? Evidence from the Ambient Air Quality Standard (2012) Program in China

Air quality information disclosure has emerged as a popular policy tool to reduce emissions, yet its impact on both environmental and economic performance remains ambiguous. This study employs a comprehensive measure of environmental-economic efficiency, the Green Total Factor Productivity (GTFP), to investigate the effect of China's mandated air quality disclosure program from 2003 to 2016. Using a difference-in-differences approach, we find that cities subject to disclosure experienced an average decline of 8% in GTFP. To further explore the heterogeneity in the treatment effect, we apply causal forest, a state-of-the-art causal machine learning technique for estimating individual treatment effects. The analysis uncovers substantial variation in the impact of information disclosure across cities, suggesting that the negative average effect may be partially attributed to mistargeting. We identify financial constraints, industrial composition, and urban scale as key moderators of the disclosure program's effectiveness. Moreover, our findings indicate that disclosing negative information, such as severe pollution levels or low environmental rankings, has a more pronounced impact compared to neutral content. By identifying key moderators and differential impacts of disclosure content, this study provides a foundation for targeted policy design to enhance the effectiveness of environmental information regulations.