References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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).
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629