Nurbakhyt Nurmukhametov

This study aims to study Kazakhstan’s regions and identify places with the best potential for developing smart cities based on cluster analysis. To analyze the differentiation by the level of development, 17 regions of Kazakhstan are grouped according to 2020 data from the statistical bulletin of the National Bureau of Statistics of the Republic of Kazakhstan. The formation of groups of regions with different values of indicators was carried out based on agglomerative clustering using the single linkage, complete linkage, and Ward’s clustering methods. In agglomerative clustering, the algorithm groups regions based on observations into clusters, and indicators determine each area’s innovative development level. The instrument to build clustering is the “RStudio” software package. As a result, regions with their essential characteristics were identified, and an assessment of their prospects was obtained with the most significant potential for developing and managing “smart cities” – Atyrau region, Almaty city, and Astana city. The remaining clusters include regions where favorable conditions for the development of innovations have not yet been formed, which require more resources and efforts to build “smart cities.” Therefore, they should not be the first to implement this concept. They need a more balanced, integrated approach, ideally supported by experience in implementing the idea in more promising regions. In a sense, clustering also allowed for identifying potential (or even existing) innovation clusters in regions of Kazakhstan. The study results can be used in developing government programs to form smart cities and further study the potential of smart cities.

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.

Acknowledgments
This study is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant “Strategy of structural and technological modernization of the basic sectors of the economy of the Republic of Kazakhstan based on ESG: criteria, mechanisms and forecast scenarios” BR24993089).