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Устойчивое развитие

Цифровой след.

Наш подход к измерению и сокращению углеродных и энергетических затрат программного обеспечения, инфраструктуры и внедрений.

Действует с · 1 May 2026 Версия · 3.2 Применяется к · lenscorp.ai и все продукты LENS Языки · Русский (резюме) · Английский (каноническая версия)
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Резюме политик, навигация и метаданные локализованы. Полный юридический текст ниже отображается в канонической английской версии, если для юрисдикции еще не поддерживается отдельный перевод.

01Summary

We measure the carbon and energy footprint of our offices, our cloud compute and our business travel using the methodology in section 02. We do not publish a quarterly figure on this page. The current numbers are compiled internally and are available on request from solutions@lenscorp.ai.

02Methodology

We follow the GHG Protocol Corporate Standard and ISO 14064-1:2018. Operational control boundary. Cloud emissions calculated via AWS Customer Carbon Footprint Tool and Azure Emissions Impact Dashboard, cross-checked with the SCI specification (greensoftware.foundation). These figures are compiled internally and have not been externally assured.

03Scopes 1, 2, 3

  • Scope 1 (direct) — small. Our offices use district heating; we operate no fleet.
  • Scope 2 (purchased energy) — offices in 8 cities. 74% of office grid electricity is matched with renewables; HQ Gurugram is on a green-power tariff.
  • Scope 3 (value chain) — the largest share. Dominated by cloud compute (74% of Scope 3) and business travel (18%).

04Compute & data

Energy per inference is a design constraint for us: it is measured for each release and it is one of the inputs to whether a model ships. We do not publish per-product figures, because the number depends on the hardware, the input resolution and the workload of each deployment. Deployment-specific measurements are available to customers on request.

05How we reduce

  • Smaller models. We benchmark distilled / quantised variants for every release. Default to the smallest that hits accuracy targets.
  • Right-sized inference. Edge deployment where the workload allows; cloud only where centralisation actually saves energy.
  • Region-aware scheduling. Batch jobs are routed to regions with the cleanest grid mix that hour.
  • Hardware reuse. Min. 4-year refresh cycle on laptops; refurbished where possible.

06Offsets

We offset what we cannot reduce, but only with verified high-quality removals — currently biochar and enhanced rock weathering, sourced via Frontier and Puro.earth. We don’t buy avoidance credits. The retired-credits ledger is available on request from solutions@lenscorp.ai.

07Annual report

We prepare our disclosure with reference to ISSB IFRS S2. A copy is available on request from solutions@lenscorp.ai.

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