Abstract

This research delves into the transformative role of Artificial Intelligence (AI) in fostering sustainable technological advancements. As the urgency to address global environmental challenges intensifies, AI emerges as a pivotal catalyst for enhancing energy efficiency, optimizing resource management, and driving innovative solutions across critical sectors such as energy, healthcare, agriculture, and manufacturing. By integrating machine learning algorithms, data analytics, and predictive models, AI contributes to achieving global sustainability goals. This study explores AI’s contribution toward the United Nations Sustainable Development Goals (UN SDGs), highlighting key applications, the technological challenges faced, and offering policy recommendations. Through a combination of case studies, quantitative analysis, and literature review, the paper underscores AI's potential in shaping a greener, more sustainable future.

Keywords: Artificial Intelligence; Sustainable Development; Technological Innovation; Climate Risk Mitigation; SDGs; Smart Technologies; Predictive Analytics

References

1. Ajayi O., et al. “Social support and parental stress as factors predicting resilience among caregivers of autistic children”. PriMera Scientific Medicine and Public Health 4.2 (2024): 09-16.
2. Allen J and Brown S. “AI in renewable energy systems: A literature review”. Renewable Energy Journal 45.3 (2020): 334-350.
3. Anderson P and Thompson L. “The economic impact of AI on manufacturing industries”. Journal of Economic Perspectives 33.2 (2019): 89-108.
4. Brown K and Taylor M. “AI and industrial process optimization”. Journal of Manufacturing Science and Engineering 144.5 (2022): 45-62.
5. Brown T and Davis M. “Predictive maintenance in manufacturing: Leveraging AI for efficiency”. Journal of Industrial Engineering 27.3 (2021): 156-172.
6. Carter L and Brown H. “The role of AI in corporate sustainability strategies”. Business Strategy and the Environment 30.3 (2021): 1015-1030.
7. Chang X and Wang H. “IoT and AI integration for smart farming”. International Journal of Smart Agriculture 5.3 (2022): 203-220.
8. Davies E and Carter H. “Climate risk mitigation through AI: Challenges and opportunities”. Global Environmental Change 67 (2021): 102-118.
9. Evans R and Green S. “Artificial intelligence and global climate change strategies”. Environmental Research Letters 18.2 (2023): 240-256.
10. Garcia L and Johnson R. “Data privacy concerns in artificial intelligence”. Journal of Information Security 10.2 (2020): 213-229.
11. Gupta N and Sharma K. “Ethical frameworks for artificial intelligence”. AI and Society 36.1 (2021): 89-104.
12. Johnson MT and Nguyen SR. “Ethical challenges in AI integration: A global perspective”. Technology and Ethics Review 7.4 (2023): 112-130.
13. Kim J and Lee S. “Predictive analytics for energy efficiency: AI applications”. Energy Reports 6.1 (2020): 33-50.
14. Kumar R and Lee S. “AI innovations in precision agriculture: A sustainable approach”. International Journal of Agricultural Technology 9.1 (2022): 45-68.
15. Lee Y and Chen F. “The role of artificial intelligence in predictive diagnostics”. Journal of Healthcare Technology 16.2 (2022): 103-120.
16. Lewis J and Smith T. “The impact of AI on global labor markets”. Journal of Labor Economics 41.2 (2023): 245-268.
17. Martin L and Cooper D. “AI-driven diagnostic imaging in healthcare”. Journal of Clinical Imaging Science 11.1 (2021): 203-218.
18. Park S and Choi H. “Machine learning in precision agriculture: Benefits and challenges”. Agricultural Systems 190 (2023): 103-120.
19. Patel S and Singh R. “Addressing algorithmic bias in AI systems”. Journal of Artificial Intelligence Research 70 (2021): 1-20.
20. Roberts P and White J. “Overcoming barriers to AI adoption in emerging markets”. Development Economics Review 37.4 (2021): 102-118.
21. Singh P and Patel K. “AI-powered risk assessment tools in climate finance”. Journal of Financial Innovation 8.1 (2022) 89-104.
22. Singh R and Verma D. “AI applications in disaster risk management”. Disaster Prevention and Management 31.1 (2022): 52-69.
23. Smith AM and Patel R. “AI-enabled precision agriculture and its role in food security”. Agricultural Sustainability Journal 15.4 (2020): 341-357.
24. Smith JA and Doe J. “AI-powered solutions for climate risk mitigation”. Journal of Environmental Technology 12.3 (2023): 245-263.
25. Watson A and Greenfield S. “AI technologies in environmental monitoring”. Journal of Environmental Science and Technology 56.3 (2020): 189-210.
26. Wilson T and Martin R. “AI-powered healthcare innovations and patient outcomes”. Health Informatics Journal 26.4 (2020): 267-284.
27. World Bank. “AI infrastructure development in emerging economies”. Global Development Report (2021): 1-45.
28. Zhang W and Zhao Y. “AI-driven healthcare innovations: A systematic review”. Medical Informatics Review 29.1 (2020): 77-95.
29. Zhao L and Huang J. “AI's role in carbon emissions reduction”. Journal of Cleaner Production 345 (2023): 131048.
30. Zhou X, Li Y and Chen H. “Machine learning applications for energy optimization in smart grids”. Energy Policy 152 (2021): 112-124.

Citation

Sameera Fernandes. “The Role of Artificial Intelli gence in Accelerating Sustain able Technological Innovation". Clareus Scientific Science and Engineering 2.4 (2025): 32-40.

Copyright

© 2025 Sameera Fernandes. Licensee Clareus Scientific Publications. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.