THE PRO AND CONS OF AI ON THE ENVIRONMENT.

Negative Environmental Impacts

1. High Energy Consumption
   • Training large AI models (like GPT or image generators) requires massive amounts of electricity — sometimes comparable to powering small towns for weeks or months.
   • Data centers that run these models consume large amounts of energy for both computation and cooling.
2. Carbon Emissions
   • The energy used often comes from fossil fuels, leading to substantial carbon footprints.
   • For instance, training a single large AI model can emit as much CO₂ as five cars over their lifetime, depending on where the electricity comes from.
3. Resource Extraction
   • Manufacturing chips (GPUs, TPUs) and servers requires rare earth metals, water, and chemicals — contributing to mining pollution and resource depletion.
4. E-Waste
   • As AI hardware becomes outdated quickly, electronic waste increases, adding pressure on recycling systems and landfill sites.

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🌱 Positive Environmental Potentials

1. Energy Optimization
   • AI can make data centers, factories, and power grids more efficient by predicting and reducing energy waste.
   • Example: Google uses AI to cut cooling energy use in its data centers by up to 40%.
2. Climate Modeling and Forecasting
   • AI improves climate prediction, extreme weather forecasts, and carbon cycle modeling, helping scientists and policymakers act faster.
3. Sustainable Agriculture
   • AI can monitor soil, water, and crop health to reduce fertilizer and pesticide use, leading to more eco-friendly farming.
4. Wildlife and Ecosystem Protection
   • AI-driven drones and sensors can detect illegal logging, poaching, and deforestation in real time.
5. Smart Cities and Transport
   • AI helps optimize traffic flow, public transport, and energy grids, reducing fuel use and emissions in urban areas.

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