Integrating ESG factors into cost forecasting for sustainable project management: Empirical evidence from Kazakhstan

This paper examines how the Environmental, Social, and Governance (ESG) concept can be incorporated into project cost-forecasting models and how this incorporation affects predictive accuracy and risk management in the new market, specifically Kazakhstan. It examines the moderating effect of analytical sophistication and institutional contexts on the relationship between ESG integration and project cost performance. A quantitative research design was employed, and 720 project management and finance professionals in the construction, energy, mining, engineering, and infrastructure industries in Kazakhstan participated in data collection. The measurement reliability and validity were checked with the help of Cronbach's alpha, composite reliability (CR), average variance extracted (AVE), and the Kaiser-Meyer-Olkin (KMO) measure. Structural Equation Modeling (PLS-SEM) and the predictive metrics (R 2, f 2, Q 2) demonstrate that the explanatory power of structural relationships is moderate-to-strong and practically important. Findings show that ESG-incorporated forecasting has a substantial positive impact on the performance of project costs and risk reduction, especially with advanced analytical tools, including machine learning (ML). The ESG- cost performance relationship is partially mediated by cost Forecast Accuracy, whereas analytical sophistication enhances the predictive advantages of ESG integration. Regulatory harmonization and data maturity also contribute to the model's effectiveness. The research is among the first empirical applications to validate ESG- and AI-informed cost forecasting in an emerging-market setting, linking sustainability analytics and project management performance. The results have practical implications for managers, policymakers, and financial decision-makers in Kazakhstan and similar emerging markets, and they are replicable across countries to enable concurrent cross-country comparisons and longitudinal analyses of ESG-driven forecasting behaviors.