Sustainable AI in environmental economics and management: Current trends and post-COVID perspective

Lobova, Svetlana V.; Bogoviz, Aleksei V.; Alekseev, Alexander N.

doi:10.3389/fenvs.2022.951672

Cited by 7 publications

(2 citation statements)

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“…This exploration delves into the sustainable application of AI in environmental economics and management, its role in scientific discovery for a sustainable future, and the mitigation of heavy metal pollution, highlighting the transformative potential of AI in fostering environmental resilience and advancing sustainable practices. Lobova, Bogoviz, and Alekseev (2022) provide a comprehensive analysis of sustainable AI's role in environmental economics and management, particularly in the context of the post-COVID-19 era. Their study emphasizes the critical importance of AI in studying its impact on ecological economics, highlighting the need for sustainable AI as smart technology to maintain stable and balanced economic systems.…”

Section: Future Trends In Ai For Environmental Conservationmentioning

confidence: 99%

“…These studies collectively underscore the future trends in AI for environmental conservation, showcasing AI's potential to revolutionize environmental economics and management, scientific discovery, and pollution mitigation. Lobova, Bogoviz, and Alekseev (2022) provide a foundational perspective on sustainable AI's role in environmental management, while Gomes (2023) and Miller et al (2023) extend the discussion to scientific discovery and pollution mitigation, respectively. Together, these works highlight the transformative impact of AI on environmental sustainability, emphasizing the need for innovative approaches and interdisciplinary collaboration to harness AI's full potential in addressing ecological challenges.…”

Section: Future Trends In Ai For Environmental Conservationmentioning

confidence: 99%

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Artificial intelligence in environmental conservation: evaluating cyber risks and opportunities for sustainable practices

Uwaga Monica Adanma,

Emmanuel Olurotimi Ogunbiyi

2024

Comput. sci. IT res. j.

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This study explores the integration of Artificial Intelligence (AI) into environmental conservation efforts, aiming to assess AI's transformative potential in enhancing sustainability practices. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, reports, and case studies from 2014 to 2024, focusing on the application of AI in biodiversity preservation, climate change mitigation, and sustainable resource management. The methodology hinges on a comprehensive search strategy, adhering to strict inclusion and exclusion criteria to ensure the relevance and quality of the literature analyzed. Key findings reveal that AI significantly contributes to environmental conservation by optimizing resource management, improving predictive analytics for biodiversity conservation, and facilitating advanced monitoring and analysis to mitigate environmental impacts. However, the deployment of AI technologies also presents ethical and cybersecurity challenges, necessitating robust frameworks for responsible use. The study underscores the importance of interdisciplinary collaboration, stakeholder engagement, and the development of ethical AI solutions to address these challenges effectively. Finally, AI holds immense promise for advancing environmental sustainability efforts. Strategic recommendations include fostering partnerships across disciplines, prioritizing ethical considerations in AI development, and enhancing AI literacy among conservationists. Future research directions emphasize the need for innovative AI applications in conservation and addressing the socio-technical complexities of integrating AI into environmental strategies. This study contributes valuable insights into leveraging AI for a sustainable and resilient future, highlighting the critical balance between technological advancements and ethical considerations. Keywords: Artificial Intelligence (AI), Environmental Conservation, Sustainability, Cyber Risks.

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Section: Future Trends In Ai For Environmental Conservationmentioning

confidence: 99%