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<p>The accelerating impacts of climate change and resource depletion underscore the urgent need for sustainable technological interventions aligned with the United Nations’ Sustainable Development Goals (SDGs). This paper examines the convergence of the Green Internet of Things (Green IoT) and Artificial Intelligence (AI) as a transformative framework for advancing environmental sustainability. Green IoT leverages energy-efficient sensors, RFID systems, wireless sensor networks, and cloud-enabled infrastructures to monitor critical environmental parameters such as air and water quality, energy consumption, and waste generation with minimal ecological footprint. AI complements this ecosystem by applying advanced analytics, machine learning, and predictive modelling to convert raw IoT data into actionable intelligence. Together, these technologies enable real-time monitoring, anomaly detection, and adaptive resource optimisation, thus overcoming challenges of fragmented data systems, delayed responses, and inefficient resource allocation. The integration of Green IoT and AI has direct applications in achieving SDG 6 (clean water and sanitation) through intelligent water monitoring systems, SDG 7 (affordable and clean energy) via smart grid optimisation, SDG 11 (sustainable cities and communities) through urban mobility and energy management, and SDG 13 (climate action) via predictive climate risk assessment. By enabling data-driven decision-making, automated control, and cross-sector integration, this approach facilitates the design of resilient, low-carbon, and resource-efficient systems. The paper proposes a practical roadmap for policymakers, industries, and researchers to operationalise Green IoT-AI synergy, fostering a shift toward sustainable digital ecosystems and accelerating global progress toward the SDGs.</p>

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Integration of green IoT and artificial intelligence for advancing environmental sustainability and SDG indicators

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Swapnila Roy

摘要

The accelerating impacts of climate change and resource depletion underscore the urgent need for sustainable technological interventions aligned with the United Nations’ Sustainable Development Goals (SDGs). This paper examines the convergence of the Green Internet of Things (Green IoT) and Artificial Intelligence (AI) as a transformative framework for advancing environmental sustainability. Green IoT leverages energy-efficient sensors, RFID systems, wireless sensor networks, and cloud-enabled infrastructures to monitor critical environmental parameters such as air and water quality, energy consumption, and waste generation with minimal ecological footprint. AI complements this ecosystem by applying advanced analytics, machine learning, and predictive modelling to convert raw IoT data into actionable intelligence. Together, these technologies enable real-time monitoring, anomaly detection, and adaptive resource optimisation, thus overcoming challenges of fragmented data systems, delayed responses, and inefficient resource allocation. The integration of Green IoT and AI has direct applications in achieving SDG 6 (clean water and sanitation) through intelligent water monitoring systems, SDG 7 (affordable and clean energy) via smart grid optimisation, SDG 11 (sustainable cities and communities) through urban mobility and energy management, and SDG 13 (climate action) via predictive climate risk assessment. By enabling data-driven decision-making, automated control, and cross-sector integration, this approach facilitates the design of resilient, low-carbon, and resource-efficient systems. The paper proposes a practical roadmap for policymakers, industries, and researchers to operationalise Green IoT-AI synergy, fostering a shift toward sustainable digital ecosystems and accelerating global progress toward the SDGs.