Transformative technologies: A global Bibliometrci review of artificial intelligence in infrastructure governance and economic outcomes (2000–2024)

Alfonso Pellegrino, Alessandro Stasi

Article ID: 7560
Vol 8, Issue 14, 2024

VIEWS - 1715 (Abstract)

Abstract


This paper investigates the transformative role of Artificial Intelligence (AI) in enhancing infrastructure governance and economic outcomes. Through a bibliometric analysis spanning more than two decades of research from 2000 to 2024, the study examines global trends in AI applications within infrastructure projects. The analysis reveals significant research themes across diverse sectors, including urban development, healthcare, and environmental management, highlighting the broad relevance of AI technologies. In urban development, the integration of AI and Internet of Things (IoT) technologies is advancing smart city initiatives by improving infrastructure systems through enhanced data-driven decision-making. In healthcare, AI is revolutionizing patient care, improving diagnostic accuracy, and optimizing treatment strategies. Environmental management is benefiting from AI’s potential to monitor and conserve natural resources, contributing to sustainability and crisis management efforts. The study also explores the synergy between AI and blockchain technology, emphasizing its role in ensuring data security, transparency, and efficiency in various applications. The findings underscore the importance of a multidisciplinary approach in AI research and implementation, advocating for ethical considerations and strong governance frameworks to harness AI’s full potential responsibly.


Keywords


artificial intelligence; infrastructure governance; economic outcomes; sustainability; public policy; technology integration; urban development; ethical AI

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References

  1. Abràmoff, M. (2023). Considerations for addressing bias in artificial intelligence for health equity. NPJ Digital Medicine, 6(1). https://doi.org/10.1038/s41746-023-00913-9
  2. Adewusi, A. O., Okoli, U. I., Olorunsogo, T., Adaga, E., Daraojimba, D. O., & Obi, O. C. (2024). Artificial intelligence in cybersecurity: Protecting national infrastructure: A USA. World Journal of Advanced Research and Reviews, 21(1), 2263-2275. https://doi.org/10.30574/wjarr.2024.21.1.0313
  3. Allam, Z., Sharifi, A., Bibri, S. E., & Chabaud, D. (2022). Emerging trends and knowledge structures of smart urban governance. Sustainability, 14(9), 5275. https://doi.org/10.3390/su14095275
  4. Bai, X., Wang, H., Ma, L. Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nat Mach Intell 3, 1081–1089 (2021). https://doi.org/10.1038/s42256-021-00421-z
  5. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188-e194. https://doi.org/10.7861/fhj.2021-0095
  6. Bibri, S.E. Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning. Comput.Urban Sci. 1, 8 (2021). https://doi.org/10.1007/s43762-021-00008-9
  7. Bodó, B., Helberger, N., Eskens, S., & Möller, J. (2019). Interested in diversity: The role of user attitudes, algorithmic feedback loops, and policy in news personalization. Digital Journalism, 7(2), 206-229. https://doi.org/10.1080/21670811.2018.1521292
  8. Cihon, P., Maas, M., & Kemp, L. (2020). Fragmentation and the future: investigating architectures for international ai governance. Global Policy, 11(5), 545-556. https://doi.org/10.1111/1758-5899.12890
  9. Das, G., Li, S., Tunio, R., Jamali, R., Ullah, I., & Fernando, K. (2023). The implementation of green supply chain management (GSMC) and environmental management system (EMSems) practices and its impact on market competitiveness during covid-19. Environmental Science and Pollution Research, 30(26), 68387-68402. https://doi.org/10.1007/s11356-023-27077-z
  10. Deep, G., & Verma, J. (2023). Embracing the future: AI and ML transforming urban environments in smart cities. J. Artif. Intell, 5, 57-73. https://doi.org/10.32604/jai.2023.043329
  11. Deng, C., Zhang, B., Cheng, L., Hu, L., & Chen, F. (2019). Vegetation dynamics and their effects on surface water-energy balance over the Three-North Region of China. Agricultural and Forest Meteorology, 275, 79-90. https://doi.org/10.1016/j.agrformet.2019.05.012
  12. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
  13. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  14. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V. & Vayena, E. (2018). AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and machines, 28, 689-707. https://doi.org/10.1007/s11023-018-9482-5
  15. Gulson, K. N., & Sellar, S. (2019). Emerging data infrastructures and the new topologies of education policy. Environment and Planning D: Society and Space, 37(2), 350-366. https://doi.org/10.1177/0263775818813144
  16. Guo, F., Chang‐Richards, A., Wilkinson, S., & Li, T. (2014). Effects of project governance structures on the management of risks in major infrastructure projects: a comparative analysis. International Journal of Project Management, 32(5), 815-826. https://doi.org/10.1016/j.ijproman.2013.10.001
  17. Hameiri, S., and Jones, L. (2018). China challenges global governance? Chinese international development finance and the AIIB. International Affairs, 94(3), 573-593. https://doi.org/10.1093/ia/iiy026
  18. Harou, J. J., Matthews, J. H., Smith, D. M., McDonnell, R. A., Borgomeo, E., Sara, J. J., ... & Vicuña, S. (2020, April). Water at COP25: Resilience enables climate change adaptation through better planning, governance and finance. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 173, No. 2, pp. 55-58). Thomas Telford Ltd. https://doi.org/10.1680/jwama.173.2020.2.55
  19. Hashem, M., Chang, V., Anuar, N., Adewole, K., Yaqoob, I., Gani, A., … & Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management, 36(5), 748-758. https://doi.org/10.1016/j.ijinfomgt.2016.05.002
  20. Henisz, W., Levitt, R., & Scott, W. (2012). Toward a unified theory of project governance: economic, sociological and psychological supports for relational contracting. Engineering Project Organization Journal, 2(1-2), 37-55. https://doi.org/10.1080/21573727.2011.637552
  21. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: past, present, and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101
  22. Junaid, L., Bilal, K., Shuja, J., Balogun, A. O., & Rodrigues, J. J. (2024). Blockchain-Enabled Framework for Transparent Land Lease and Mortgage Management. IEEE Access. https://doi.org/10.1109/access.2024.3388248
  23. Kadefors, A., Lingegård, S., Uppenberg, S., Alkan-Olsson, J., & Balian, D. (2020). Designing and implementing procurement requirements for carbon reduction in infrastructure construction – international overview and experiences. Journal of Environmental Planning and Management, 64(4), 611-634. https://doi.org/10.1080/09640568.2020.1778453
  24. Kajo, M., Mwanje, S., Schultz, B., & Carle, G. (2021). Neural network-based quantization for network automation. arXiv preprint arXiv:2103.04764. https://doi.org/10.48550/arxiv.2103.04764
  25. Kashyap, S., Morse, K. E., Patel, B., & Shah, N. H. (2021). A survey of extant organizational and computational setups for deploying predictive models in health systems. Journal of the American Medical Informatics Association, 28(11), 2445-2450. https://doi.org/10.1093/jamia/ocab154
  26. Kelly, C., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1). https://doi.org/10.1186/s12916-019-1426-2
  27. Kim, S., Kim, J., & Kim, D. (2020). Implementation of a blood cold chain system using blockchain technology. Applied Sciences, 10(9), 3330. https://doi.org/10.3390/app10093330
  28. Klímová, B., & Ibna Seraj, P. M. (2023). The use of chatbots in university EFL settings: Research trends and pedagogical implications. Frontiers in Psychology, 14, 1131506. https://doi.org/10.3389/fpsyg.2023.1131506
  29. Kondylakis, H., Kalokyri, V., Sfakianakis, S., Marias, K., Tsiknakis, M., Jiménez-Pastor, A., … & Lekadir, K. (2023). Data infrastructures for ai in medical imaging: a report on the experiences of five eu projects. European Radiology Experimental, 7(1). https://doi.org/10.1186/s41747-023-00336-x
  30. Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024). Artificial intelligence‐driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development, 32(3), 2253-2267. https://doi.org/10.1002/sd.2773
  31. Lobova, S., Bogoviz, A., & Alekseev, A. (2022). Sustainable ai in environmental economics and management: current trends and post-covid perspective. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.951672
  32. MacRae, D. (2021). Toward Benevolent AGI by Integrating Knowledge Graphs for Classical Economics, Education, and Health: AI Governed by Ethics and Trust-Based Social Capital. In Technological breakthroughs and future business opportunities in education, health, and outer space (pp. 163-186). IGI Global. https://doi.org/10.4018/978-1-7998-6772-2.ch010
  33. Maiyya, S., Zakhary, V., Agrawal, D., & Abbadi, A. (2018). Database and distributed computing fundamentals for scalable, fault-tolerant, and consistent maintenance of blockchains. Proceedings of the VLDB Endowment, 11(12), 2098-2101. https://doi.org/10.14778/3229863.3229877
  34. McKenzie, M., & Gulson, K. N. (2023). The incommensurability of digital and climate change priorities in schooling: An infrastructural analysis and implications for education governance. Research in Education, 117(1), 58-72. https://doi.org/10.1177/00345237231208658
  35. Meng, Q., Chen, Y., Kumari, S., & Chen, C. (2023). Toward a secure smart-home IoT access control scheme based on home registration approach. Mathematics, 11(9), 2123. https://doi.org/10.3390/math11092123
  36. Minkkinen, M., & Mäntymäki, M. (2023). Discerning between the “easy” and “hard” problems of AI governance. IEEE Transactions on Technology and Society, 4(2), 188-194.
  37. Mkhongi, F. A., & Musakwa, W. (2022). Trajectories of deagrarianization in South Africa− Past, current and emerging trends: A bibliometric analysis and systematic review. Geography and Sustainability, 3(4), 325-333. https://doi.org/10.1016/j.geosus.2022.10.003
  38. Mobayo, J., Aribisala, A., Yusuf, S., & Belgore, U. (2021). The awareness and adoption of artificial intelligence for effective facilities management in the energy sector. Journal of Digital Food Energy & Water Systems, 2(2). https://doi.org/10.36615/digitalfoodenergywatersystems.v2i2.718
  39. Muduli, K., Kusi-Sarpong, S., Yadav, D.K. et al. An original assessment of the influence of soft dimensions on implementation of sustainability practices: implications for the thermal energy sector in fast growing economies. Oper Manag Res 14, 337–358 (2021). https://doi.org/10.1007/s12063-021-00215-x
  40. Nikitas, A., Michalakopoulou, K., Njoya, E. T., & Karampatzakis, D. (2020). Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era. Sustainability, 12(7), 2789. https://doi.org/10.3390/su12072789
  41. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
  42. Paik, H., Xu, X., Bandara, H., Lee, S., & Lo, S. (2019). Analysis of data management in blockchain-based systems: from architecture to governance. IEEE Access, 7, 186091-186107. https://doi.org/10.1109/access.2019.2961404
  43. Park, A. and Li, H. (2021). The effect of blockchain technology on supply chain sustainability performances. Sustainability, 13(4), 1726. https://doi.org/10.3390/su13041726
  44. Posinasetty, B., Chauhan, N., Yadav, N., Walke, S., Raj, N., & Aggarwal, S. (2023). A Novel Paradigm In Health Care Knowledge Management For Integrated Component For Accountable Government. Journal of Informatics Education and Research, 3(2).
  45. Qin, C., Guo, B., Shen, Y., Tao, L., Yun, Z., & Zhang, Z. (2020). A secure and effective construction scheme for blockchain networks. Security and Communication Networks, 2020, 1-20. https://doi.org/10.1155/2020/8881881
  46. Ritwik, G., Sestili, C., Vazquez-Trejo, J., & Gaston, M. (2018). Focusing on the big picture: insights into a systems approach to deep learning for satellite imagery. 2018 IEEE International Conference on Big Data (Big Data), Seattle, USA. https://doi.org/10.1109/bigdata.2018.8621941
  47. Ruiz Rivadeneira, A. M., Dekyi, T., & Cruz, L. (2023). OECD Infrastructure Governance Indicators: Conceptual framework, design, methodology and preliminary results (No. 59). OECD iLibrary. https://doi.org/10.1787/95c2cef2-en
  48. Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2018). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135. https://doi.org/10.1080/00207543.2018.1533261
  49. Saeed, M. R., Abdullah, M., Zoraiz, M., Ahmad, W., Naeem, M. A., Akram, Q., & Younus, M. (2023). Impact of Artificial Intelligence and Communication Tools in Veterinary and Medical Sciences: AI in Health Sciences. In AI and Its Convergence With Communication Technologies (pp. 181-211). IGI Global. https://doi.org/10.4018/978-1-6684-7702-1.ch007
  50. Sehgal, R. and Dubey, A. (2019). Identification of critical success factors for public–private partnership projects. Journal of Public Affairs, 19(4). https://doi.org/10.1002/pa.1956
  51. Selim, A. (2021). Managerial smart governance model and indicators as an evaluation methodology to promote public-private partnership in infrastructure projects. Port-Said Engineering Research Journal, 0(0), 0-0. https://doi.org/10.21608/pserj.2021.54652.1082
  52. Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., ... & O'Brien, C. (2020, January). " The human body is a black box" supporting clinical decision-making with deep learning. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 99-109). https://doi.org/10.1145/3351095.3372827
  53. Shi, L., He, Y., Onishi, M., & Kobayashi, K. (2018). Efficiency analysis of government subsidy and performance guarantee policies in relation to PPP infrastructure projects. Mathematical Problems in Engineering, 2018, 1-11. https://doi.org/10.1155/2018/6196218
  54. Shuford, J. (2024). Interdisciplinary perspectives: fusing artificial intelligence with environmental science for sustainable solutions. JAIGS, 1(1), 106-123. https://doi.org/10.60087/jaigs.v1i1.87
  55. Sira, M. (2024). Potential of advanced technologies for environmental management systems. Management Systems in Production Engineering, 32(1), 33-44. https://doi.org/10.2478/mspe-2024-0004
  56. Stix, C. (2021). Actionable principles for artificial intelligence policy: three pathways. Science and Engineering Ethics, 27(1), 15. https://doi.org/10.1007/s11948-020-00277-3
  57. Takeda, I., Yamada, A., & Onodera, H. (2021). Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture. Computer methods in biomechanics and biomedical engineering, 24(8), 864-873. https://doi.org/10.1080/10255842.2020.1856372
  58. Tian, B., Wang, Z., Li, C., & Fu, J. (2021). Can relational governance improve sustainability in public-private partnership infrastructure projects? an empirical study based on structural equation modeling. Engineering Construction & Architectural Management, 30(1), 19-40. https://doi.org/10.1108/ecam-04-2021-0333
  59. Vempati, S., & Nalini, N. (2024). Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation. Journal of Electrical Systems, 20(3), 1420-1429. https://doi.org/10.52783/jes.3052
  60. Venkatesh, V., Kang, K., Wang, B., Zhong, R., & Zhang, A. (2020). System architecture for blockchain based transparency of supply chain social sustainability. Robotics and Computer-Integrated Manufacturing, 63, 101896. https://doi.org/10.1016/j.rcim.2019.101896
  61. Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business process management journal, 26(7), 1893-1924.
  62. Wang, D., Fang, S., & Li, K. (2019). Dynamic changes of governance mechanisms in mega construction projects in china. Engineering Construction & Architectural Management, 26(4), 723-735. https://doi.org/10.1108/ecam-03-2018-0137
  63. Wang, L., Jiao, S., Xie, Y., Mubaarak, S., Zhang, D., Liu, J., … & Li, M. (2021). A permissioned blockchain-based energy management system for renewable energy microgrids. Sustainability, 13(3), 1317. https://doi.org/10.3390/su13031317
  64. Wang, N., Ma, M., & Liu, Y. (2020). The whole lifecycle management efficiency of the public sector in PPP infrastructure projects. Sustainability, 12(7), 3049. https://doi.org/10.3390/su12073049
  65. Wang, X. and Cui, X. (2022). Ppp financing model in the infrastructure construction of the park integrating artificial intelligence technology. Computational Intelligence and Neuroscience, 2022, 1-10. https://doi.org/10.1155/2022/6154885
  66. Wibowo, A. and Alfen, H. (2015). Government-led critical success factors in PPP infrastructure development. Built Environment Project and Asset Management, 5(1), 121-134. https://doi.org/10.1108/bepam-03-2014-0016
  67. Williamson, B. (2024). The social life of AI in education. International Journal of Artificial Intelligence in Education, 34(1), 97-104. https://doi.org/10.1007/s40593-023-00342-5
  68. Yiğitcanlar, T., & Cugurullo, F. (2020). The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint From the Lens of Smart and Sustainable Cities. Sustainability. https://doi.org/10.3390/su12208548
  69. Yiğitcanlar, T., Mehmood, R., & Corchado, J. (2021). Green artificial intelligence: towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability, 13(16), 8952. https://doi.org/10.3390/su13168952
  70. Yoshino, N. and Pontines, V. (2015). The 'highway effect' on public finance: case of the star highway in the Philippines. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2697322
  71. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22-32. https://doi.org/10.1109/jiot.2014.2306328
  72. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629


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