Environmental Impact

Version 1.0 Published: June 3, 2026 Updated: Quarterly

TL;DR — The Short Version

AI generation uses energy. Code generation is lightweight; image and video generation are heavier. We offset what we can and are working toward carbon-neutral operations by 2027.

~0.02 kWh
Per code generation request
~0.15 kWh
Per image generation request
~0.8 kWh
Per video generation request

1. Carbon Footprint by Format

We estimate energy consumption per generation type based on our infrastructure and AI provider reports. These are approximate figures and vary with model size and infrastructure efficiency:

FormatEst. Energy/RequestEst. CO₂-eqNotes
Code0.02 kWh~8g CO₂Lightweight inference on efficient models
Image0.15 kWh~60g CO₂GPU-based generation, image models
Music0.1 kWh~40g CO₂Audio model inference
Video0.8 kWh~320g CO₂Highest energy use due to compute
App (code + preview)0.05 kWh~20g CO₂Multiple code generations + image preview

CO₂-equivalent estimates assume US average grid intensity of ~400g CO₂/kWh. Estimates updated quarterly as model efficiency improves.

2. Carbon Offsets & Commitments

We purchase carbon offsets equivalent to our estimated operational energy use (hosting, compute, office operations). Offsets are sourced from verified projects (Gold Standard, Verra). We are committed to transitioning to carbon-neutral operations by Q2 2027 through renewable energy procurement.

3. Infrastructure Efficiency

Skylina runs on cloud infrastructure that is continuously optimized for efficiency. Our hosting providers use renewable energy for a portion of their operations. We evaluate and select infrastructure partners based on their renewable energy commitments.

4. Model Efficiency

We choose AI models that balance capability with energy efficiency. We monitor energy consumption per generation and will report changes as our model stack evolves. Updates to this disclosure will be made within 30 days of any material change to our generation infrastructure.

5. Calculation Methodology

Energy estimates are derived from: (a) AI provider transparency reports (Anthropic publishes energy usage data); (b) our own infrastructure monitoring (cloud compute billing data); (c) industry benchmarks for comparable model sizes. We review and update estimates quarterly.

6. Contact

Questions about our environmental impact: env@skylina.polsia.app

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