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This study addresses the urgent challenge of leveraging artificial intelligence (AI) to advance sustainable development during global crises, such as the COVID-19 pandemic, which amplified environmental, social, and economic challenges. The research problem focuses on understanding how AI can support the Sustainable Development Goals (SDGs), a set of essential global objectives established by the United Nations in 2015 to promote prosperity, social equity, and environmental protection by 2030. It also explores the applicability of the Environmental Kuznets Curve (EKC), a hypothesis suggesting that environmental degradation increases with early economic growth but declines after a certain income threshold, to AI-driven sustainability initiatives. Using a mixed-methods approach, including literature reviews, case studies, and quantitative analysis, the study investigates AI applications in health care, energy efficiency, Internet of Things (IoT)-enabled smart cities, industrial innovation, urban planning, and education. Key findings reveal that AI enhances energy optimization (e.g., 20% reduction in smart grid energy use) and urban resilience (e.g., 15% emission reduction via IoT traffic management) but faces barriers in equitable access, ethical implementation, and data privacy. The study underscores AI’s transformative potential to accelerate SDG progress while advocating for inclusive, ethical deployment to maximize sustainability benefits.

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Artificial Intelligence (AI): An Enhancement of Environmental Sustainability During the Pandemic in the Modern Era

  • 会议论文

  • 发表日期：

    2026年

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  • Sakshi Gautam,
  • Sanjay Tejasvee

摘要

This study addresses the urgent challenge of leveraging artificial intelligence (AI) to advance sustainable development during global crises, such as the COVID-19 pandemic, which amplified environmental, social, and economic challenges. The research problem focuses on understanding how AI can support the Sustainable Development Goals (SDGs), a set of essential global objectives established by the United Nations in 2015 to promote prosperity, social equity, and environmental protection by 2030. It also explores the applicability of the Environmental Kuznets Curve (EKC), a hypothesis suggesting that environmental degradation increases with early economic growth but declines after a certain income threshold, to AI-driven sustainability initiatives. Using a mixed-methods approach, including literature reviews, case studies, and quantitative analysis, the study investigates AI applications in health care, energy efficiency, Internet of Things (IoT)-enabled smart cities, industrial innovation, urban planning, and education. Key findings reveal that AI enhances energy optimization (e.g., 20% reduction in smart grid energy use) and urban resilience (e.g., 15% emission reduction via IoT traffic management) but faces barriers in equitable access, ethical implementation, and data privacy. The study underscores AI’s transformative potential to accelerate SDG progress while advocating for inclusive, ethical deployment to maximize sustainability benefits.