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.

Reinforced concrete (RC), renowned for its amalgamation of strength and durability, stands as a cornerstone in modern engineering, extensively employed in various structures such as buildings, bridges, and pipe culverts. However, prevalent issues of concrete spalling and exposed steel bars within RC structures pose significant challenges. An automated identification methodology is proposed to detect concrete spalling and exposed steel bars, leveraging machine vision technology and deep learning algorithms. Initially, a classifier is utilized to discern concrete spalling areas within the image domain at the image level. Subsequently, a semantic segmentation algorithm is applied to precisely delineate the contours of both concrete spalling areas and exposed steel bars at the pixel level. The efficacy and feasibility of the proposed method are validated through training and testing on both a publicly available dataset and actual RC structure images. The results illustrate that the average detection precision, Intersection over Union (IOU), recall, and F1-score for concrete spalling areas are 0.924, 0.872, 0.937, and 0.925, respectively, while for exposed steel areas, the corresponding values are 0.905, 0.820, 0.899, and 0.855. This method demonstrates promising prospects for wide-ranging applications in defect detection within RC structures.