IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i19p10983-d649393.html
   My bibliography  Save this article

Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China

Author

Listed:
  • Ke Wang

    (Department of Civil and Architectural Engineering, Qingdao University of Technology, Shandong 273400, China)

  • Yafei Zhao

    (Building Information Technology Innovation Laboratory (BITI Lab), Solearth Architecture Research Center, Hong Kong 999077, China)

  • Rajan Kumar Gangadhari

    (Industrial Engineering and Manufacturing Systems, National Institute of Industrial Engineering, Mumbai 400087, India)

  • Zhixing Li

    (School of Design and Architecture, Zhejiang University of Technology, Zhejiang 310023, China)

Abstract

Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.

Suggested Citation

  • Ke Wang & Yafei Zhao & Rajan Kumar Gangadhari & Zhixing Li, 2021. "Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China," Sustainability, MDPI, vol. 13(19), pages 1-35, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10983-:d:649393
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/19/10983/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/19/10983/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Azadeh Sadeghi & Roohollah Younes Sinaki & William A. Young & Gary R. Weckman, 2020. "An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks," Energies, MDPI, vol. 13(3), pages 1-23, January.
    2. Yan, Jianghui & Liu, Jinping & Tseng, Fang-Mei, 2020. "An evaluation system based on the self-organizing system framework of smart cities: A case study of smart transportation systems in China," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    3. Mohammed T. Nuseir & Muhammad Farhan Basheer & Ahmad Aljumah, 2020. "Antecedents of entrepreneurial intentions in smart city of Neom Saudi Arabia: Does the entrepreneurial education on artificial intelligence matter?," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1825041-182, January.
    4. Jenni Viitanen & Richard Kingston, 2014. "Smart Cities and Green Growth: Outsourcing Democratic and Environmental Resilience to the Global Technology Sector," Environment and Planning A, , vol. 46(4), pages 803-819, April.
    5. Adriano Tanda & Alberto De Marco, 2018. "Drivers of Public Demand of IoT-Enabled Smart City Services: A Regional Analysis," Journal of Urban Technology, Taylor & Francis Journals, vol. 25(4), pages 77-94, October.
    6. Zheng He & Zhengkai Liu & Hui Wu & Xiaomin Gu & Yuanjun Zhao & Xiaoguang Yue, 2020. "Research on the Impact of Green Finance and Fintech in Smart City," Complexity, Hindawi, vol. 2020, pages 1-10, December.
    7. Aiping Wang & Weifen Lin & Bei Liu & Hui Wang & Hong Xu, 2021. "Does Smart City Construction Improve the Green Utilization Efficiency of Urban Land?," Land, MDPI, vol. 10(6), pages 1-18, June.
    8. David Mhlanga, 2021. "Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies?," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    9. Elvira NICA & Vladimir KONECNY & Milos POLIAK & Tomas KLIESTIK, 2020. "Big Data Management Of Smart Sustainable Cities: Networked Digital Technologies And Automated Algorithmic Decision-Making Processes," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 12(2), pages 48-57, June.
    10. Aleksandra Lewandowska & Justyna Chodkowska-Miszczuk & Krzysztof Rogatka & Tomasz Starczewski, 2020. "Smart Energy in a Smart City: Utopia or Reality? Evidence from Poland," Energies, MDPI, vol. 13(21), pages 1-19, November.
    11. André Luis Azevedo Guedes & Jeferson Carvalho Alvarenga & Maurício Dos Santos Sgarbi Goulart & Martius Vicente Rodriguez y Rodriguez & Carlos Alberto Pereira Soares, 2018. "Smart Cities: The Main Drivers for Increasing the Intelligence of Cities," Sustainability, MDPI, vol. 10(9), pages 1-19, August.
    12. Qiu, Changyu & Yi, Yun Kyu & Wang, Meng & Yang, Hongxing, 2020. "Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing," Applied Energy, Elsevier, vol. 263(C).
    13. Mauro Romanelli, 2020. "Towards Cities as Communities," Lecture Notes in Information Systems and Organization, in: Youcef Baghdadi & Antoine Harfouche & Marta Musso (ed.), ICT for an Inclusive World, pages 125-132, Springer.
    14. Tiago de Melo CARTAXO & Johana M. CASTILLA & Marcin DYMET & Kamrul HOSSAIN, 2021. "Digitalization and smartening sustainable city development: an investigation from the high north European cities," Smart Cities and Regional Development (SCRD) Journal, Smart-EDU Hub, vol. 5(1), pages 83-101, February.
    15. Khushboo Gupta & Ralph P. Hall, 2021. "Exploring Smart City Project Implementation Risks in the Cities of Kakinada and Kanpur," Journal of Urban Technology, Taylor & Francis Journals, vol. 28(1-2), pages 155-173, April.
    16. Shaohua Wang & Xianxiong Liu & Haiyin Wang & Qingwu Hu, 2018. "A Case Study on Spatio-Temporal Data Mining of Urban Social Management Events Based on Ontology Semantic Analysis," Sustainability, MDPI, vol. 10(6), pages 1-24, June.
    17. Sheng-Li Si & Xiao-Yue You & Hu-Chen Liu & Ping Zhang, 2018. "DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-33, January.
    18. Juhi Raghuvanshi & Rajat Agrawal & P. K. Ghosh, 2017. "Analysis of Barriers to Women Entrepreneurship: The DEMATEL Approach," Journal of Entrepreneurship and Innovation in Emerging Economies, Entrepreneurship Development Institute of India, vol. 26(2), pages 220-238, September.
    19. Wen, Yifan & Zhang, Shaojun & Zhang, Jingran & Bao, Shuanghui & Wu, Xiaomeng & Yang, Daoyuan & Wu, Ye, 2020. "Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data," Applied Energy, Elsevier, vol. 260(C).
    20. Frick, Karen Trappenburg PhD & Kumar, Tanu PhD & Mendonça Abreu, Giselle Kristina & Post, Alison PhD, 2021. "Benchmarking “Smart City” Technology Adoption in California: Developing and Piloting a Data Collection Approach," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3797p0ws, Institute of Transportation Studies, UC Berkeley.
    21. Ming-Lang Tseng & Chun-Wei Remen Lin & Raditia Yudistira Sujanto & Ming K. Lim & Tat-Dat Bui, 2021. "Assessing Sustainable Consumption in Packaged Food in Indonesia: Corporate Communication Drives Consumer Perception and Behavior," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    22. Aleksandra Radziejowska & Bartosz Sobotka, 2021. "Analysis of the Social Aspect of Smart Cities Development for the Example of Smart Sustainable Buildings," Energies, MDPI, vol. 14(14), pages 1-14, July.
    23. Kinga Kijewska & Witold Torbacki & Stanisław Iwan, 2018. "Application of AHP and DEMATEL Methods in Choosing and Analysing the Measures for the Distribution of Goods in Szczecin Region," Sustainability, MDPI, vol. 10(7), pages 1-26, July.
    24. Richard Peto & Daniel Tokody, 2019. "Building and Operating a Smart City," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 17(3-A), pages 476-484.
    25. Karin Pfeffer & Hebe Verrest, 2016. "Perspectives on the Role of Geo-Technologies for Addressing Contemporary Urban Issues: Implications for IDS," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 28(2), pages 154-166, April.
    26. Chaiyan Jettanasen & Panapong Songsukthawan & Atthapol Ngaopitakkul, 2020. "Development of Micro-Mobility Based on Piezoelectric Energy Harvesting for Smart City Applications," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    27. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
    28. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    29. Yafei Zhao & Paolo Vincenzo Genovese & Zhixing Li, 2020. "Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI," Future Internet, MDPI, vol. 12(2), pages 1-18, February.
    30. Hazel Si Min Lim & Araz Taeihagh, 2018. "Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications," Energies, MDPI, vol. 11(5), pages 1-23, April.
    31. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    32. Eunil Park & Angel P. Del Pobil & Sang Jib Kwon, 2018. "The Role of Internet of Things (IoT) in Smart Cities: Technology Roadmap-oriented Approaches," Sustainability, MDPI, vol. 10(5), pages 1-13, May.
    33. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    34. Scott B. Kelley & Bradley W. Lane & Benjamin W. Stanley & Kevin Kane & Eric Nielsen & Scotty Strachan, 2020. "Smart Transportation for All? A Typology of Recent U.S. Smart Transportation Projects in Midsized Cities," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(2), pages 547-558, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Titov Sergei & Trachuk Arkady & Linder Natalya & RD Pathak & Danny Samson & Zafar Husain & S Sushil, 2023. "Digital transformation enablers in high-tech and low-tech companies: A comparative analysis," Australian Journal of Management, Australian School of Business, vol. 48(4), pages 801-843, November.
    2. Ke Wang & Ziyi Ying & Shankha Shubhra Goswami & Yongsheng Yin & Yafei Zhao, 2023. "Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment," Sustainability, MDPI, vol. 15(15), pages 1-42, August.
    3. Yanhong Guo & Yifang Dong & Xu Wei & Yifei Dong, 2023. "Effects of Continuous Adoption of Artificial Intelligence Technology on the Behavior of Holders’ Farmland Quality Protection: The Role of Social Norms and Green Cognition," Sustainability, MDPI, vol. 15(14), pages 1-17, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shadi Shayan & Ki Pyung Kim & Tony Ma & Tan Hai Dang Nguyen, 2020. "The First Two Decades of Smart City Research from a Risk Perspective," Sustainability, MDPI, vol. 12(21), pages 1-20, November.
    2. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
    3. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    4. Marcin Janusz & Marcin Kowalczyk, 2022. "How Smart Are V4 Cities? Evidence from the Multidimensional Analysis," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    5. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    6. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    7. Gul Shah Sabary & Lukáš Durda & Arif Ibne Asad & Aleksandr Kljuènikov, 2023. "Key motivational factors behind Asian immigrant entrepreneurship: A causal relationship analysis employing the DEMATEL approach for Germany," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(1), pages 287-318, March.
    8. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    9. Zhaocheng Li & Yu Song, 2022. "Energy Consumption Linkages of the Chinese Construction Sector," Energies, MDPI, vol. 15(5), pages 1-13, February.
    10. Tatiana Tucunduva Philippi Cortese & Jairo Filho Sousa de Almeida & Giseli Quirino Batista & José Eduardo Storopoli & Aaron Liu & Tan Yigitcanlar, 2022. "Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review," Energies, MDPI, vol. 15(7), pages 1-38, March.
    11. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    12. Saidjon Shiralievich Tavarov & Pavel Matrenin & Murodbek Safaraliev & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    13. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    14. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    15. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    16. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    17. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    18. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    19. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    20. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10983-:d:649393. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.