Environmental, Social, and Governance (ESG) integration has emerged as a critical determinant of corporate financial sustainability, particularly in resource-intensive industries such as mining. This study develops a quantitative analytical framework to evaluate the influence of ESG performance on the financial outcomes of mining enterprises. Grounded in stakeholder theory and signaling theory, the research conceptualizes ESG as both a risk-mitigation mechanism and a value-creation driver. Using a structured synthesis of prior empirical literature, the study constructs a multidimensional ESG–financial performance model incorporating environmental compliance, social responsibility, and governance quality as explanatory variables, while profitability, firm value, and return on assets represent dependent constructs.

The study highlights that ESG integration enhances investor confidence, reduces information asymmetry, and improves long-term financial stability. Empirical insights from global and sector-specific studies indicate a statistically significant relationship between ESG disclosures and firm performance metrics, particularly in capital-intensive sectors like mining. The proposed framework provides a structured approach for quantifying ESG effects using panel data regression techniques and composite ESG scoring models. The findings suggest that ESG adoption is not merely a compliance requirement but a strategic financial lever for mining enterprises operating in volatile global markets.

This study investigates the impact of Generative Artificial Intelligence (Generative AI) on strategic decision-making and organizational performance in U.S. businesses using machine learning techniques. An empirical analysis is conducted using organizational data to evaluate Linear Regression, Decision Tree, Random Forest, and Gradient Boosting models for predicting performance outcomes based on AI adoption and strategic factors.Results indicate that Generative AI adoption significantly enhances organizational performance through improvements in strategic decision effectiveness, innovation, productivity, and operational efficiency. Among the evaluated models, Gradient Boosting Regression demonstrates the highest performance (R² = 0.94, lowest error rates), followed by Random Forest (R² = 0.91), while Linear Regression and Decision Tree show comparatively lower accuracy. Feature importance analysis identifies AI Adoption Level, Strategic Decision Effectiveness, and AI Investment as the most influential predictors of organizational performance. Overall, the findings confirm that ensemble machine learning models provide robust predictive capability for assessing the strategic value of Generative AI in organizations.