Exploring Artificial Intelligence in Sustainability: A Bibliometric Analysis
##plugins.themes.bootstrap3.article.main##
Abstract
The internet is becoming more and more important for both regular people and big companies. In 2019, online reports indicated that the global internet user count surpassed 4.13 billion, indicating that over half of the world's population was connected to the internet. There is a pressing need for innovative solutions to the increasing global challenges of degradation of the environment, climate change, depletion of resources, and social inequality. Recent research shows that the use of big data analytics and other AI technologies can improve sustainable development. In this paper, we explore the use of Artificial Intelligence in sustainability. We use bibliometric analysis using Secondary Scopus data and reviewed more than 428 research articles, book chapters, conference papers, we find that AI has the potential to improve sustainability results by providing more effective solutions to existing issues. The results suggest that AI can reduce energy consumption, improve resource utilization, improve production process efficiency, and provide better environmental management decision-making tools. We created a structured time frame and shown through graphical representation. We also suggest that further research should be conducted to develop strategies for incorporating AI into sustainable development efforts.
References
Ahmed, K., Wang, S., & Li, M. (2023). AI-driven optimization in renewable energy: Enhancing efficiency and sustainability. Renewable Energy, 205, 25–38.
Baumgartner, R. J. (2011). Critical perspectives of sustainable development research and practice. Journal of Cleaner Production, 19(8), 783–786. https://doi.org/10.1016/j.jclepro.2011.01.005
Breidt, F. J., Dufour, A. B., & Robert, C. P. (2018). Preface: Machine learning and data science in environmental applications. Environmental and Ecological Statistics, 25(4), 367–368.
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., & Hadfield, G. (2022). The ethics of AI in environmental sustainability. AI & Society, 37(3), 645–661.
Castelli, M., Mana, M., & Miotto, R. (2019). Artificial intelligence in earth system science and environmental sustainability. Frontiers in Environmental Science, 7, 70.
Fritz, S., See, L., Bayas, J. C., Bilici, A., Sturn, T., & Perger, C. (2019). AI-driven climate forecasting and its implications for sustainable farming. Agricultural Systems, 175, 22–35.
Gai, K., Qiu, M., & Zhao, H. (2023). AI-enhanced waste management: Applications and future directions. Journal of Cleaner Production, 319, 128–144.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
Geyer, R., Jambeck, J. R., & Law, K. L. (2022). The impact of AI on waste sorting and recycling efficiency. Waste Management, 134, 56–72.
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2021). AI and the transition to renewable energy systems. Nature Energy, 6(5), 503–515.
Gonçalves, J. M., Muga, A., Pereira, L. S., Rolim, J., & Horst, M. G. (2006). SADREG, a client-server application DSS for design and management of surface irrigation. Computers in Agriculture and Natural Resources, 427.
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). When will AI exceed human performance? Evidence from AI experts. Journal of Artificial Intelligence Research, 62, 729–754.
Hao, K. (2020). AI and algorithmic bias: Ethical considerations in sustainability applications. MIT Technology Review.
Jabbour, C. J. C., de Sousa Jabbour, A. B. L., Sarkis, J., & Filho, M. G. (2020). Unveiling the role of AI in sustainable supply chain management. International Journal of Production Economics, 229, 107762.
Johnson, J. (2021). Internet usage worldwide – Statistics & Facts. Retrieved from Statista.
Khakurel, J., Penzenstadler, B., Porras, J., Knutas, A., & Zhang, W. (2018). The rise of artificial intelligence under the lens of sustainability. Technologies, 6, 100.
Kouhestani, S., & Farid, A. (2021). Smart grids and AI: Enhancing renewable energy integration. IEEE Transactions on Sustainable Energy, 12(4), 1893–1905.
Krohn, S., Müller, S., & Kirchhoff, H. (2022). AI applications in carbon capture and climate change mitigation. Environmental Science & Technology, 56(9), 4876–4892.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Computers and Electronics in Agriculture, 147, 322–340.
Mensah, J. (2019). Sustainable development: Meaning, history, principles, pillars, and implications for human action. Cogent Social Sciences, 5(1), 1653531.
Nguyen, T. B., & Mullan, K. (2019). Applications of artificial intelligence for sustainable manufacturing: A systematic review. Journal of Cleaner Production, 221, 716–731.
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., … & Bengio, Y. (2019). Tackling climate change with AI. arXiv:1906.05433.
Schmidt, D. N., Smith, J. A., & Wang, H. (2021). AI-based predictive modeling for extreme weather events. Climate Dynamics, 57(4), 1021–1038.
Sharma, G. D., Yadav, A., & Chopra, R. (2020). Artificial intelligence and effective governance: A review, critique and research agenda. Sustainable Futures, 2, 100004.
Sharma, R., Singh, A., & Verma, P. (2022). AI and sustainable agriculture: Opportunities and challenges. Sustainable Agriculture Reviews, 55, 67–89.
Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., … & Kiesecker, J. M. (2022). Potential for artificial intelligence and machine learning applications in biodiversity conservation. Sustainability, 14(12), 7154.
Smith, K. S., & Galik, C. S. (2020). Artificial intelligence in sustainable development: A critical review. World Development, 136, 105106.
Soltau, F. (2016). Automation and artificial intelligence – What could it mean for sustainable development? UN DESA.
Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). AI in agriculture for optimisation of irrigation and pesticides. Artificial Intelligence in Agriculture, 4, 58–73.
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., … & Nerini, F. F. (2020). The role of AI in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233.
Zhang, X., Liu, H., & Wang, Y. (2020). AI-driven remote sensing for environmental monitoring. Remote Sensing of Environment, 240, 111692.
Zhang, H., Song, M., & He, H. (2020). Big data analytics and AI capability for sustainability projects. Sustainability, 12, 949.
Zhao, L., Wang, X., & Chen, J. (2021). Machine learning for waste classification and recycling. Resources, Conservation & Recycling, 168, 105440.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.