Promoting or Hindering: The Impact of ESG Rating Differences on Energy Enterprises’ Green Transformation—A Causal Test from Double Machine-Learning Algorithms

There is a lack of comprehensive evaluation on the impact of ESG rating differences on the green transformation of energy enterprises in the transition era. This study leverages data from companies listed on the Shanghai Stock Exchange in China, applying double machine-learning algorithms to precisely estimate the causal relationship between variations in ESG ratings and the green transition efficiency of energy companies. The research shows that the difference in ESG ratings of third-party rating agencies significantly promotes the efficiency of green transformation of energy enterprises. This paper also studies the influencing factors of this effect: First, ESG rating differences significantly promote the improvement of green transition efficiency of energy enterprises; Second, the positive effect is more pronounced in energy companies with more balanced board structures. Finally, energy companies with high capital market attention can also contribute to this positive impact. Through the mechanism test, this paper finds that enterprise green innovation is an important mechanism for ESG rating divergence to positively promote the efficiency of energy enterprises’ green transformation. Furthermore, this paper analyzes the impact of ESG rating on enterprises from the perspective of market cognition and short-term behavior, which provides a new perspective for analyzing the practice of enterprises pursuing long-term transformation. The study also calls for a more sober reflection on the trend toward ESG in society.