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<p>The Smart Cities Mission (SCM), launched by the Government of India in 2015, targets citizen-centric and sustainable urban development across 100 cities. Assessing the relative progress of these cities is essential for evidence-based policy but is methodologically non-trivial because the underlying criteria are multi-dimensional, heterogeneous (quantitative and qualitative), and often conflicting. Single-indicator rankings cannot capture the multi-attribute nature of smartness; traditional composite indices typically assign weights in an ad hoc manner and do not transparently handle trade-offs between beneficial and non-beneficial criteria. This study develops a transparent, reproducible ranking framework for Indian smart cities by integrating two complementary Multi-Criteria Decision Making (MCDM) techniques—the Analytic Hierarchy Process (AHP) for criterion weighting and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for alternative ranking. Ten representative smart cities are evaluated against eight criteria (area, population, Gross Domestic Product, traffic management, education and skill development, waste management, healthcare index, and air pollution index). AHP pairwise comparisons derive criterion weights; the Consistency Ratio is verified (CR = 0.067 &lt; 0.10). The weights captured through the AHP is used in weighted-normalized matrix of TOPSIS, this makes sure the proper integration of two methods. Closeness coefficients basically rank the cities into positive and negative ones. By this it could be analyzed that Mumbai, Delhi aur Bengaluru ranked as top 3 cities in both the methods, while Dehradun and Noida rank lowest. Such ranks agreement shows the confirmation of the integrated methods. It has also been derived that Pollution (non-beneficial) &amp; waste management (beneficial) are the most influential criteria. This study helps the city policy makers to identify the city specific requirements, their lacking and target infrastructure and capital requirements.</p>

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Smart cities ranking based on MCDM approaches for sustainable development

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Abhishek Goyal,
  • Kalpna Sagar,
  • Shivani,
  • Pawan Kumar Pal

摘要

The Smart Cities Mission (SCM), launched by the Government of India in 2015, targets citizen-centric and sustainable urban development across 100 cities. Assessing the relative progress of these cities is essential for evidence-based policy but is methodologically non-trivial because the underlying criteria are multi-dimensional, heterogeneous (quantitative and qualitative), and often conflicting. Single-indicator rankings cannot capture the multi-attribute nature of smartness; traditional composite indices typically assign weights in an ad hoc manner and do not transparently handle trade-offs between beneficial and non-beneficial criteria. This study develops a transparent, reproducible ranking framework for Indian smart cities by integrating two complementary Multi-Criteria Decision Making (MCDM) techniques—the Analytic Hierarchy Process (AHP) for criterion weighting and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for alternative ranking. Ten representative smart cities are evaluated against eight criteria (area, population, Gross Domestic Product, traffic management, education and skill development, waste management, healthcare index, and air pollution index). AHP pairwise comparisons derive criterion weights; the Consistency Ratio is verified (CR = 0.067 < 0.10). The weights captured through the AHP is used in weighted-normalized matrix of TOPSIS, this makes sure the proper integration of two methods. Closeness coefficients basically rank the cities into positive and negative ones. By this it could be analyzed that Mumbai, Delhi aur Bengaluru ranked as top 3 cities in both the methods, while Dehradun and Noida rank lowest. Such ranks agreement shows the confirmation of the integrated methods. It has also been derived that Pollution (non-beneficial) & waste management (beneficial) are the most influential criteria. This study helps the city policy makers to identify the city specific requirements, their lacking and target infrastructure and capital requirements.