Artificial Intelligence for a Sustainable Ecosystem: Opportunities, Structural Challenges, and Regulatory Pathways

DOI

10.2991/978-94-6239-791-0_28How to use a DOI?

Keywords

Artificial Intelligence (AI); Climate Change Mitigation; Sustainability Solutions; Predictive Environmental Analytics

Abstract

Climate change is a universal challenge. The escalating cases of extreme weather conditions worldwide, such as forest fires, drought, and intense rains, remind us of the increasing danger to our survival. The Conference of Parties (COP) 21 identified a maximum global warming limit of 1.5 degrees Celsius. Although there has been a considerable rise in efforts to curb climate change, we have surpassed the set limit. This places humans in an unanticipated dilemma and calls for seizing new possibilities for immediate actions to counter climate change. The tremendous developments in Artificial Intelligence (AI) carry enormous promise. AI’s role in accelerating decision-making to address climate change will be examined in this paper. The role of AI can be described as being one of an amplifier because it enables pattern recognition, predictive problem solving, and optimization of complex systems. In addition, AI has the capacity to coordinate, with the level of precision and at a larger scale, which is not currently available in efforts to address climate change. AI is fast becoming a part of sectors vital for environmental sustainability, giving individuals the ability to transform their practices from reactive environmental management to proactive sustainability.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Deepshikha Sharma
AU  - Rahi Alhat
AU  - Shruti Das
PY  - 2026
DA  - 2026/10/09
TI  - Artificial Intelligence for a Sustainable Ecosystem: Opportunities, Structural Challenges, and Regulatory Pathways
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 382
EP  - 392
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_28
DO  - 10.2991/978-94-6239-791-0_28
ID  - Sharma2026
ER  -