A Multicriteria Model for Assessing the Environmental Impact of Infrastructure Projects in Central Asian Countries

Abstract

This article examines the assessment of environmental risks and impacts associated with large -scale infrastructure projects in Central Asian countries (Kazakhstan, Kyrgyzstan, Uzbekistan, Turkmenistan, and Tajikistan) using advanced multicriteria decision-making methods, including the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and expert evaluation techniques. The study analyzes the relative importance of key environmental and socio -ecological criteria- such as water pollution, biodiversity loss, air contamination, and social impacts-in order to identify the most vulnerable factors affecting regional environmental sustainability. Based on the derived weighting system, alternative project implementation scenarios were ranked to determine the most environmentally sustainable and risk-minimized development options. The results demonstrate that minimizing impacts on water resources and biodiversity significantly enhances ecological resilience and reduces long-term environmental risks. The analysis also identifies key limitations, including incomplete regional datasets and the inherent subjectivity of expert judgments. Future research directions include the automation of environmental assessment processes through Big Data analytics and climate scenario modeling. The findings confirm the relev ance and effectiveness of multicriteria environmental risk assessment models for the evaluation and management of large-scale infrastructure projects in Central Asia. The proposed scenarios and rankings not only identify critical vulnerabilities but also provide actionable recommendations for risk mitigation, thereby contributing to the achievement of Sustainable Development Goals (SDGs), natural resource conservation, and improvements in quality of life. Further methodological development, database expansion, and the implementation of automated decision-support systems are expected to enhance the precision and efficiency of environmental management, supporting balanced regional development under climate change and global ecological pressures. The results have practical significance for governmental agencies, private sector actors, investors, and project developers, facilitating more evidence-based decision-making and sustainable investment strategies.