AI-Driven Solutions for a Low-Carbon Transition: Evaluating Effectiveness and Limitations in Climate Change Mitigation

  • Xinyi Huang Shenzhen College of International Education, China
Keywords: low-carbon economy, artificial intelligence, climate change, carbon neutrality, greenhouse effect, renewable energy


Climate change, primarily caused by human activities, poses a significant global challenge. Countries worldwide are integrating efforts to combat climate change through initiatives such as the Paris Agreement and setting targets to reach net-zero emissions by 2050. This paper explores the potential of artificial intelligence (AI) as a promising solution to address climate change, particularly through the analysis of mass data. AI can aid in environmental decision-making processes, optimize renewable energy use, and accelerate the global transition to a low-carbon economy. Using public data from the OECD, the study investigates the effectiveness of AI in promoting a low-carbon economy by examining its impact on greenhouse gas emissions, carbon footprint, investment in research and development, renewable energy production, and recycling rates. The findings suggest that AI has been considerably effective in supporting the growth of renewable energy and recycling while restraining gas emissions and carbon footprint. However, the study also identifies potential limitations, such as the carbon release from AI itself, and suggests further improvements to AI models.


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