AI-Boosted ESG: Transforming Enterprise ESG Performance Through Artificial Intelligence

This dataset is a collection of data and code used in the article AI-Boosted ESG: Transforming Enterprise ESG Performance Through Artificial Intelligence. The hypotheses of this paper include: 1. AI can promote ESG performance; 2.AI can improve ESG performance by improving green technology innovation, labor employment quality and analyst attention, as well as reducing management expense rate; 3. The enhancement effect of AI on ESG performance is more obvious in large-scale enterprises, manufacturing enterprises and enterprises in the eastern region. This dataset includes the three relevant tests above, as well as the relevant procedure codes for several robustness tests, including changing the AI word frequency statistics, using the multi-time-point difference-in-differences model, changing the model type to Tobit model, lagging one stage, and shortening the sample period. The data provided is collated to a certain extent. If you need specific original data or some other related material, you can contact corresponding author Jiayi Yu to ask for it at [e-mail].

本数据集汇聚了用于《AI-Boosted ESG：通过人工智能提升企业ESG绩效》一文的各类数据和代码。该论文提出了以下假设：1. 人工智能可促进ESG绩效的提升；2. 通过提升绿色技术创新、劳动力就业质量、分析师关注度以及降低管理费用比率，人工智能能够改善ESG绩效；3. 人工智能对ESG绩效的提升效应在大型企业、制造业企业以及东部地区的企业中更为显著。该数据集包含上述三项相关检验，以及针对包括调整人工智能词汇频率统计、采用多时点双重差分模型、将模型类型更改为Tobit模型、滞后一阶以及缩短样本期限等稳健性检验的相关程序代码。所提供的数据在一定程度上已进行整理。如需特定原始数据或相关材料，可联系对应作者余佳怡，邮箱为[e-mail]。