Abstract for: The Carbon Footprint of Large Language Models: The Case of ChatGPT

The rapid adoption of Large Language Models (LLMs), such as ChatGPT, has raised growing concerns about their escalating energy consumption, increasing carbon emissions, and the broader environmental impact of widespread AI deployment. This study utilizes System Dynamics Modeling (SDM) to analyze the environmental impact of Large Language Model (LLM) usage by simulating future trends in AI adoption, energy consumption, efficiency improvements, and carbon emissions. The model incorporates reinforcing and balancing feedback loops to capture the dynamic interactions between AI growth, technological advancements, and the transition to renewable energy sources, providing insights into sustainable AI deployment strategies. The findings highlight the critical need for proactive strategies for sustainable AI deployment. Key measures include advancing AI energy efficiency through technological innovation, expanding renewable energy infrastructure to offset carbon emissions, and implementing robust regulatory frameworks to manage AI-related environmental impacts. Without these interventions, the rapid growth of Large Language Models (LLMs) may lead to unsustainable energy consumption and increased carbon footprints, exacerbating climate challenges. The findings of this study emphasize the urgent need to balance AI innovation with environmental sustainability. While System Dynamics Modeling provides valuable insights, limitations include uncertainties in future AI adoption rates, energy efficiency advancements, and policy impacts. Future research should refine model assumptions, incorporate real-world energy data, and explore additional mitigation strategies. Policymakers and AI developers must collaborate to implement sustainable AI deployment while addressing these evolving challenges. Used Grammarly.