Recently, financial institutions and investors have placed an increasing emphasis on ESG (environmental, social, and governance) as a principal indicator for the evaluation of companies. However, the current ESG scoring systems lack uniformity and are often subjective. It is of great importance to be able to make accurate predictions regarding the ESG scores of corporations. A Stacked Generalization Model that employs Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as base learners, with Bayesian Ridge Regression (BRR) as the meta-model for integrating the predictions of these diverse models is proposed. The goal is to develop an ESG score prediction model for Chinese companies. The experimental data set encompasses Chinese A-share listed companies from 2012 to 2020. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) are employed for model evaluation and are compared with seven benchmark models. The results demonstrate that SGM-BRR reduces the RMSE by 18.4%, 17.3%, 13.7%, and 76.1%, the MAE by 15.4%, 18.4%, 15.8%, and 68.4%, and increases the R² by 2%, 1.4%, 2%, and 6% for ESG, E, S, and G scores, respectively. Furthermore, the model’s performance is validated across different industries, with SGM-BRR exhibiting the most optimal performance of RMSE, MAE, and R² in 27, 25, and 27 groups, respectively. Consequently, the model demonstrates broad applicability and stability performance in ESG score prediction.

As climate change increasingly threatens sustainable development goals, climate risk has emerged as a critical factor shaping corporate strategic decisions and capital allocation behavior. Using a sample of Chinese A-share listed firms over the period 2008 to 2024, this study constructs a firm-level climate risk index through textual analysis of annual report disclosures and examines its effect on corporate green investment. We find that climate risk is positively associated with corporate green investment. Mechanism analysis reveals that climate risk operates through dual pathways: it enhances firms’ green strategic orientation as the internal governance mechanism, and heightens public green attention as the external governance mechanism, both of which are positively related to increasing firms’ green investment. Heterogeneity analysis shows that the promoting effect is more pronounced for state-owned enterprises, heavy-polluting firms, firms with negative media coverage, and firms located in northern regions of China. Further analysis reveals that climate risk is more strongly associated with green transformation investment over end-of-pipe treatment investment, and firms that increase green transformation investment in response to climate risk achieve stronger firm growth. These findings provide new evidence on how climate risk shapes corporate environmental behavior, offering practical insights for promoting sustainable investment and advancing corporate contributions to sustainable development.