Smart City Gnosys
Smart city article details
| Title | Multi-Criteria Decision-Making Model To Achieve Sustainable Developmental Goals In Industry 4.0 For Smart City Infrastructure |
|---|---|
| ID_Doc | 38175 |
| Authors | Akila D.; Pal S.; Sarkar B.; Jayalaksshmi S.; Muthaiyah S.; Anbananthen K.S.M. |
| Year | 2024 |
| Published | HighTech and Innovation Journal, 5, 4 |
| DOI | http://dx.doi.org/10.28991/HIJ-2024-05-04-018 |
| Abstract | Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of pollution emissions are key driving forces for enhancing sustainability. Compared to other methodologies, Multi-Criteria Decision Making (MCDM) considers the interplay between conditions. This is why it is an excellent approach to assess the sustainability of any city. Our experimental findings highlight the impact of MCDM and sustainability towards achieving a city’s sustainable development goals. Compared to other methods, the SC-MCDM system is more successful rate of 89.7%, a more sustainable rate of 92.1%, a more precise ratio 93%), more accurate (95%), and a less mean absolute error, and mean squared error rate of 8.3% while trying to achieve sustainable city development goals. © 2024, Ital Publication. All rights reserved. |
| Author Keywords | Industry 4.0; Multi-Criteria Decision Making; Sustainable City; Sustainable Developmental Goal |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
24349  | 0.9 | Shao Q.-G.; Jiang C.-C.; Lo H.-W.; Liou J.J.H. | Establishing A Sustainable Development Assessment Framework For A Smart City Using A Hybrid Z-Fuzzy-Based Decision-Making Approach | Clean Technologies and Environmental Policy, 25, 9 (2023) |
| 25713 | 0.89 | Sánchez O.; Castañeda K.; Vidal-Méndez S.; Carrasco-Beltrán D.; Lozano-Ramírez N.E. | Exploring The Influence Of Linear Infrastructure Projects 4.0 Technologies To Promote Sustainable Development In Smart Cities | Results in Engineering, 23 (2024) |
| 38176 | 0.886 | Ye F.; Chen Y.; Li L.; Li Y.; Yin Y. | Multi-Criteria Decision-Making Models For Smart City Ranking: Evidence From The Pearl River Delta Region, China | Cities, 128 (2022) |
| 19646 | 0.878 | Abu-Rayash A.; Dincer I. | Development Of An Integrated Model For Environmentally And Economically Sustainable And Smart Cities | Sustainable Energy Technologies and Assessments, 73 (2025) |
| 53313 | 0.874 | Correia D.; Teixeira L.; Marques J.L. | Study And Analysis Of The Relationship Between Smart Cities And Industry 4.0: A Systematic Literature Review | International Journal of Technology Management and Sustainable Development, 21, 1 (2022) |
| 23951 | 0.874 | Lin S.-S.; Zheng X.-J. | Enhancing Smart City Assessment: An Advanced Mcdm Approach For Urban Performance Evaluation | Sustainable Cities and Society, 118 (2025) |
| 15737 | 0.874 | Yan M.; Yang F.; Li C.; Guo D. | Constructing A Standard System Of City Sustainable Development Assessment Technology Based On The Sdgs | Chinese Journal of Population Resources and Environment, 22, 3 (2024) |
| 36592 | 0.871 | Kutty A.A.; Kucukvar M.; Onat N.C.; Ayvaz B.; Abdella G.M. | Measuring Sustainability, Resilience And Livability Performance Of European Smart Cities: A Novel Fuzzy Expert-Based Multi-Criteria Decision Support Model | Cities, 137 (2023) |
| 53844 | 0.871 | Kutty A.A.; Kucukvar M.; Abdella G.M.; Bulak M.E.; Onat N.C. | Sustainability Performance Of European Smart Cities: A Novel Dea Approach With Double Frontiers | Sustainable Cities and Society, 81 (2022) |
| 56976 | 0.87 | Mutambik I. | The Sustainability Of Smart Cities: Improving Evaluation By Combining Mcda And Promethee | Land, 13, 9 (2024) |