Development of Aggregated Sustainability Indicators for Quality of Life Evaluations in Urban Areas of the Republic of Kazakhstan

Zhiyenbayev M. Kunicina N. Mansurova M. Patlins A. Caiko J. Beliaev V. Grants R. Bisenieks M. Shyntore G.
November 2024 Multidisciplinary Digital Publishing Institute (MDPI)

Sustainability (Switzerland)
2024 #16 Issue 21

This study presents a novel methodology for evaluating the quality of life in urban areas of Kazakhstan through the development of tailored sustainability indicators. While urban livability and sustainability are global concerns, few studies have focused on developing comprehensive, region-specific metrics for assessing these factors in Kazakhstan, a country with diverse urban environments and newly established administrative divisions. This research fills a critical gap by creating aggregated indicators that address economic, social, environmental, and infrastructural dimensions in a uniquely Kazakhstan context. The study goes beyond the existing literature by integrating local dynamics and challenges, offering a nuanced understanding of urban sustainability that reflects the country’s regional characteristics. The findings provide policymakers and urban planners with actionable insights to improve quality of life and make informed decisions. Moreover, the methodology is designed for ongoing application, allowing for continuous monitoring and adaptation to evolving urban development strategies across Kazakhstan. This research advances the field by offering a replicable framework that can be adapted to other regions with similar complexities.

AHP , Kazakhstan , regional differences , sustainability assessment , urban development

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Artificial Intelligence and Big Data Department, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Faculty of Computer Science, Information Technology and Energy, Riga Technical University, Rīga, LV-1048, Latvia
Computer Engineering Department, International University of Information Technologies, Almaty, 050000, Kazakhstan

Artificial Intelligence and Big Data Department
Faculty of Computer Science
Computer Engineering Department

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