Artificial Intelligence and Large Language Models in Energy Systems and Climate Strategies: Economic Pathways to Cost-Effective Emissions Reduction and Sustainable Growth

Abstract

Artificial intelligence (AI) and large language models (LLMs) are revolutionizing energy systems and climate strategies by enhancing efficiency, enabling precise resource management, and supporting sustainable economic growth. This work explores the integration of AI across diverse domains including urban energy optimization, industrial automation, transport electrification, renewable energy forecasting, grid management, and circular economy practices. It highlights AI’s role in improving emissions monitoring, carbon markets, and policy compliance while addressing economic imperatives such as cost reduction, return on investment, and labor market transformations. Challenges related to the energy consumption and carbon footprint of AI technologies, ethical considerations, data privacy, and scalability are examined alongside strategies for mitigation and responsible governance. Emerging technologies and next-generation AI models are identified as key enablers for advancing green innovation and digitalization, with emphasis on interoperability, equitable access, and long-term resilience. The synthesis underscores the necessity of interdisciplinary collaboration and adaptive policy frameworks to maximize AI’s contributions toward achieving global sustainability targets and fostering a resilient low-carbon economy. Keywords: Artificial Intelligence in Energy Systems, Climate Economics, Energy Efficiency, Carbon Emissions Reduction, Sustainable Economic Growth, AI and Renewable Integration, Large Language Models, Green Technology Economics, Environmental Policy Modeling, Cost Optimization in Climate Strategy