Update README.md

README.md

@@ -147,7 +147,23 @@ This SFT approach enables Alpie-Core to deliver reliable, aligned, and context-a
 
 ## 8. Environmental Impact
 
-**Carbon Footprint**: 298-835 kg CO₂e (training)
+**Carbon Footprint**: We estimated the environmental impact of training Alpie-Core (32B) on 8× NVIDIA A100-80GB GPUs (Azure) by calculating carbon emissions from GPU energy consumption. The calculation follows the formula:
+CO₂e (kg) = Grid CO₂ Factor (kg/kWh) × Runtime (hours) × Power per GPU (kW) × Number of GPUs
+Training Parameters:
+Grid CO₂ Factor (Azure average): 0.364 kg CO₂e per kWh (source: Microsoft Sustainability Report)
+Runtime: 408 hours
+GPUs: 8× A100-80GB
+We report results under two assumption modes:
+Realistic mode (average training draw ≈ 250 W per GPU = 0.25 kWh/hr): 0.364 × 408 × 0.25 × 8 ≈ 298 kg CO₂e
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+Conservative mode (near TDP ≈ 700 W per GPU = 0.70 kWh/hr): 0.364 × 408 × 0.70 × 8 ≈ 835 kg CO₂e
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+Total training footprint ranges from ~298 kg CO₂e (realistic) to ~835 kg CO₂e (conservative worst-case)
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 ## 9. Use Cases