GREEN ECONOMY AND HOLISTIC PLANNING BY ARTIFICIAL INTELLIGENCEAPPLICATION

Stevovic Ivan, Hadrovic Sabahudin and Jovanovic Mihailo
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Stevovic Ivan: INNOVATION CENTRE OF THE FACULTY OF MECHANICAL ENGINEERING, UNIVERSITY OF BELGRADE, BELGRADE, REPUBLIC OF SERBIA
Hadrovic Sabahudin: INSTITUTE OF FORESTRY, KNEZAVISESLAVA 3 BELGRADE, REPUBLIC OF SERBIA
Jovanovic Mihailo: FACULTY OF MANAGEMENT HERCEG NOVI,MELJINE ADRIATIC UNIVERSITY BAR, MONTENEGRO

Annals - Economy Series, 2024, vol. 5, 184-188

Abstract: Artificial intelligence (AI) is the main toolin the green economy development, as contemporary methodology addressing environmental sustainabilitywith all opportunities, problems and challenges. This manuscript provides a systematic overview of the advantages and drawbacks associated with AI deployment in green sectors. On the one hand, IT such as machine learning, data analytics, and optimization algorithms offer immense potential to enhance resource efficiency, optimize energy systems, and facilitate sustainable decision-making processes. By analyzing large datasets and identifying patterns, AI can optimize energy consumption, streamline waste management, and accelerate the transition to renewable energy sources with the goal of green economy. However, the widespread adoption of AI in the green economy also raises concerns regarding data privacy, algorithmic bias, and job displacement. Additionally, the energy-intensive nature of AI training and computing poses environmental challenges, potentially offsetting the environmental benefits gained from its implementation. Therefore, while AI holds promise as a tool for advancing environmental sustainability in the green economy, careful consideration of its implications is necessary to maximize its positive impacts and mitigate potential risks.

Keywords: artificial intelligence; green economy; environmental sustainability; holistic planning methodology; sustainable decision-making processes. (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:cbu:jrnlec:y:2024:v:5:p:184-188

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