Artificial intelligence affects the environment primarily through its significant energy consumption and associated carbon emissions. The development, training, and operation of AI models require massive computational power, which translates into high electricity usage. This power demand predominantly comes from data centers running high-performance computers, leading to large greenhouse gas emissions that contribute to climate change. For example, training a single large AI model can produce carbon emissions equivalent to hundreds of round-trip flights between two major cities. Overall, the information and communications technology (ICT) sector, including AI infrastructure, could reach up to 14% of global emissions by 2040 if current trends continue. Besides energy use, AI contributes to environmental harm through the generation of electronic waste (e-waste) as AI systems depend on specialized hardware that eventually becomes obsolete. This e-waste contains hazardous substances that can pollute soil and water if not properly managed, posing risks to human health and ecosystems. AI systems also impact natural ecosystems through applications like autonomous vehicles and delivery drones, which may increase resource consumption and waste. However, AI also holds potential for positive environmental impacts by helping to monitor biodiversity, forecast renewable energy production, manage resources more efficiently, and predict climate-related disasters. Despite these benefits, the overall environmental footprint of AI remains a critical concern, demanding improved energy efficiency, responsible e-waste management, and enhanced regulations to mitigate its negative impact. In summary, AI's environmental effects include:
- High energy use and carbon emissions from training and operating models in data centers.
- Large electronic waste generation from hardware.
- Potential disruption to ecosystems via certain AI applications.
- Opportunities to aid in climate change mitigation through data analysis and resource optimization.
Efforts to reduce AI's environmental impact focus on optimizing AI workflows for energy efficiency, measuring emissions transparently, and integrating sustainability into AI development and deployment processes.
