This study aims to investigate the relationship between environmental sustainability, social development, and governance within Kazakhstan’s agro-industrial complex. The paper applies econometric modeling and statistical analysis to assess these relationships and provide strategic recommendations for sustainable development. A dataset from 2013 to 2023, sourced from the Bureau of National Statistics of the Republic of Kazakhstan, was utilized to assess the influence of transit routes and agriculture on ESG performance. Principal component analysis (PCA) and regression modeling identified three key components – environmental (84.3%), social (98.4%), and governance (88.33%) – as significant contributors to ESG variability. The results demonstrate that transit flows positively affect environmental and governance indicators (β = 0.266, p = 0.050), while agro-industrial activity has mixed effects: improved social sustainability but increased environmental pressure. The combined impact of transit corridors and the agro-industrial complex provides a more comprehensive explanation of ESG variability (R² = 0.998), reinforcing the need for integrated policy approaches. The findings highlight the strategic importance of aligning transit infrastructure and agro-industrial development with ESG frameworks. This paper contributes to the discourse on sustainable development by offering practical insights for policymakers on optimizing logistics and agricultural strategies to promote ESG adoption, particularly in agriculture-dependent economies.

Understanding the impact of economic factors on bank lending is crucial in Kazakhstan’s modern economy, characterized by volatile inflation and fluctuations in real wages. This paper aims to investigate the link between bank lending and economic factors such as inflation, real wages, and consumer expenditure in a regional context. Data from the Bureau of National Statistics and the National Bank, covering the period from 2012 to 2022, were used to uncover how economic factors influence bank lending. For the analysis, various economic indicators were integrated through normalization and averaging. Analysis reveals significant regional disparities in real wages and consumer expenditures, which impact the demand for bank credit. The results of the correlation matrix showed that both real wages (P-value < 0.001) and inflation (P-value < 0.001) significantly impact bank lending, with an R² value of 0.998, indicating that the model explains 99.8% of the variation in bank lending. The regression analysis highlights that regions with higher real wages, such as Astana, Almaty, and Atyrau, provide the most favorable conditions for banking sector growth, demonstrated by a strong relationship between wages and bank lending. In contrast, regions with lower wage levels, such as Turkestan and Zhambyl, show a significantly weaker connection (around 0.65), reflecting their lower attractiveness for banking investment and emphasizing the need for policies to address social inequality. The Durbin-Watson test confirmed no autocorrelation in residuals (DW = 1.89), although heteroscedasticity was detected, suggesting the need for further model adjustments. The study emphasizes the importance of developing economic policies that can balance regional development and improve financial stability.