ESG Rating Disagreement and Corporate Total Factor Productivity:Inference and Prediction

This paper explores the relationship between ESG rating disagreement and total factor productivity (TFP) based on data from Chinese domestic ESG rating agencies and financial data of A-share listed companies in China from 2015 to 2022. On one hand, the empirical results show that ESG rating disagreement reduces corporate TFP, a conclusion that is validated through multiple robustness tests. The mechanism analysis reveals an interaction effect between green innovation and ESG rating disagreement. Specifically, in firms without ESG rating disagreement, green innovation promotes the improvement of TFP; however, in firms with disagreement, although ESG rating disagreement may drive green innovation, this does not lead to an increase in TFP. Furthermore, ESG rating disagreement lower corporate TFP by increasing financing constraints. The heterogeneity analysis indicates that this effect is more pronounced in non-state-owned, asset-intensive, and low-pollution enterprises. On the other hand, XGBoost regression demonstrates that ESG rating disagreement play a significant role in predicting TFP, with SHAP values showing that the main effects are more evident in firms with larger ESG rating disagreement.

Color Image Enhancement Method Based on Weighted Image Guided Filtering

A novel color image enhancement method is proposed based on Retinex to enhance color images under non-uniform illumination or poor visibility conditions. Different from the conventional Retinex algorithms, the Weighted Guided Image Filter is used as a surround function instead of the Gaussian filter to estimate the background illumination, which can overcome the drawbacks of local blur and halo artifact that may appear by Gaussian filter. To avoid color distortion, the image is converted to the HSI color model, and only the intensity channel is enhanced. Then a linear color restoration algorithm is adopted to convert the enhanced intensity image back to the RGB color model, which ensures the hue is constant and undistorted. Experimental results show that the proposed method is effective to enhance both color and gray images with low exposure and non-uniform illumination, resulting in better visual quality than traditional method. At the same time, the objective evaluation indicators are also superior to the conventional methods. In addition, the efficiency of the proposed method is also improved thanks to the linear color restoration algorithm.