| Title | A policy framework for city eligibility analysis: TISM and fuzzy MICMAC-weighted approach to select a city for smart city transformation in India |
|---|---|
| ID_Doc | 39516 |
| Authors | Kumar, H; Singh, MK; Gupta, MP |
| Title | A policy framework for city eligibility analysis: TISM and fuzzy MICMAC-weighted approach to select a city for smart city transformation in India |
| Year | 2019 |
| Published | |
| Abstract | The increasing rate of urban population and deteriorating conditions of physical, institutional, social, and economic infrastructure in cities are demanding smarter ways to improve public utilities and services in India. Smart city development promotes an established, interconnected, and sustainable urban system. The Indian government has launched "100 Smart Cities Mission" for planned urbanization in the country. The "100 cities" have been selected from a two-round city challenge competition. However, some controversial viewpoints have made questionable remarks over the selection process. For the effective planning of smart cities, an exhaustive analysis is essential to find the existing critical infrastructure, key resources, and development trends. The purpose of this study is to aid city planners and decision-makers to determine city eligibility in a multidimensional way and to develop evaluation criteria for city selection process to meet the goal of smart city mission. This article proposes a weighted criteria model to assess the city selection eligibility. The factors are identified from the literature studies. Total interpretive structure modeling is used to analyze the complex interrelationships among the factors and to develop a selection hierarchy. The fuzzy MICMAC process is used to classify the factors based on driving power and dependence. The stabilized driving power is applied to calculate the corresponding weights for each factor. In study findings, the most driving, linkage and dependent factors are identified for analyzing city selection eligibility. The policy-makers, government officials, and decisions-makers would get benefitted from the study outcomes to select top "N" number of cities for Smart Cities Mission. |
Similar Articles
| ID | Score | Article |
|---|---|---|
37665
|
Nagpal, R; Mehrotra, D; Sehgal, R; Srivastava, G; Lin, JCW Overcoming Smart City Barriers Using Multi-Modal Interpretive Structural Modeling(2023)Journal Of Signal Processing Systems For Signal Image And Video Technology, 95.0, 2-3 | |
| 39540
|
Mishra, AP; Sen, A; Kumar, A Exploring Potentials and Challenges in Making Smart Cities in India: A Case Study of Allahabad City, Uttar Pradesh(2017) | |
| 37010
|
Hanine, M; Boutkhoum, O; El Barakaz, F; Lachgar, M; Assad, N; Rustam, F; Ashraf, I An Intuitionistic Fuzzy Approach for Smart City Development Evaluation for Developing Countries: Moroccan Context(2021)Mathematics, 9.0, 21 | |
| 37403
|
Gupta, K; Hall, RP The Indian Perspective of Smart Cities(2017) | |
| 36264
|
Yadav, G; Mangla, SK; Luthra, S; Rai, DP Developing a sustainable smart city framework for developing economies: An Indian context(2019) | |
| 75200
|
Ozkaya, G; Erdin, C Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS technique(2020)Heliyon, 6, 10 | |
| 45720
|
Jothimani, P; Chenniappan, P; Chidambaranathan, V Factors impinge on the development of a smart city: a field study(2022)Environmental Science And Pollution Research, 29, 57 | |
| 39427
|
Praharaj, S; Han, H Building a typology of the 100 smart cities in India(2019)Smart And Sustainable Built Environment, 8, 5 | |
| 40073
|
Antwi-Afari, P; Owusu-Manu, DG; Ng, ST; Asumadu, G Modeling the smartness or smart development levels of developing countries' cities(2021)Journal Of Urban Management, 10, 4 | |
| 45114
|
Sharma, K; Tayal, S Indian smart city ranking model using taxicab distance-based approach(2022)Energy Systems-Optimization Modeling Simulation And Economic Aspects, 13, 3 |