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Integrating Data-Based Strategies and Advanced Technologies with Efficient Air Pollution Management in Smart Cities

Author

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  • Seunghwan Myeong

    (Department of Public Administration, Inha University, Incheon 22212, Korea
    These authors contributed equally to this work.)

  • Khurram Shahzad

    (Department of Industrial Security & Governance, Inha University, Incheon 22212, Korea
    These authors contributed equally to this work.)

Abstract

The COVID-19 pandemic has demonstrated that creative leadership based on data and citizen volunteers is more significant than vaccines themselves, so this study focuses on the collaboration of sophisticated technologies and human potential to monitor air pollution. Air pollution contributes to critical environmental problems in various towns and cities. With the emergence of the smart city concept, appropriate methods to curb exposure to pollutants must be part of an appropriate urban development policy. This study presents a technologically driven air quality solution for smart cities that advertises energy-efficient and cleaner sequestration in these areas. It attempts to explore how to incorporate data-driven approaches and citizen participation into effective public sector pollution management in smart cities as a major component of the smart city definition. The smart city idea was developed as cities became more widespread through communication devices. This study addresses the technical criteria for implementing a framework that public administration can use to prepare for renovation of public buildings, minimizing energy use and costs and linking smart police stations to monitor air pollution as a part of an integrated city. Such a digital transition in resource management will increase public governance energy performance and provide a higher standard for operations and a healthier environment. The study results indicate that complex processes lead to efficient and sustainable smart cities. This research discovered an interpretive pattern in how public agencies, private enterprises, and community members think and what they do in these regional contexts. It concludes that economic and social benefits could be realized by exploiting data-driven smart city development for its social and spatial complexities.

Suggested Citation

  • Seunghwan Myeong & Khurram Shahzad, 2021. "Integrating Data-Based Strategies and Advanced Technologies with Efficient Air Pollution Management in Smart Cities," Sustainability, MDPI, vol. 13(13), pages 1-14, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7168-:d:582409
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    References listed on IDEAS

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    1. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    2. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
    3. Adeeb A. Kutty & Galal M. Abdella & Murat Kucukvar & Nuri C. Onat & Melih Bulu, 2020. "A system thinking approach for harmonizing smart and sustainable city initiatives with United Nations sustainable development goals," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(5), pages 1347-1365, September.
    4. Seunghwan Myeong & Yuseok Jung & Eunuk Lee, 2018. "A Study on Determinant Factors in Smart City Development: An Analytic Hierarchy Process Analysis," Sustainability, MDPI, vol. 10(8), pages 1-17, July.
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    Cited by:

    1. Hernández, José L. & de Miguel, Ignacio & Vélez, Fredy & Vasallo, Ali, 2024. "Challenges and opportunities in European smart buildings energy management: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    2. Wang, Zhan-ao & Samuel, Ribeiro-Navarrete & Chen, Xiao-qian & Xu, Bing & Huang, Wei-lun, 2023. "Central bank digital currencies: Consumer data-driven sustainable operation management policy," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

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