Climate Impact:
AI-Powered Robotic Analytics for Steelmaking

Model Dashboard

Core metrics at a glance. Forecast year 2035 unless noted.

* Avoided emissions shown assume 1% market capture rate. Unit impact reflects model-computed values; small differences from displayed baseline minus solution are due to rounding in the underlying intensity data.

Projecting to Market Scale

At 1.37 billion tonnes of annual BF-BOF steel production (2035 forecast) and a unit impact of 0.077 t CO2e per tonne, at just 1% market capture, the avoided emissions would total approximately 1.1 million tonnes CO2e per year. Steel is one of the hardest-to-abate industrial sectors, making even incremental efficiency improvements valuable at scale.

The addressable market is limited to the BF-BOF (blast furnace - basic oxygen furnace) steelmaking pathway, which represents the traditional integrated route. Market size is relatively stable, growing marginally from 1,370.9 Mt (2034) to 1,373.4 Mt (2035). The unit impact remains constant at 0.077 t CO2e/t across the forecast period.

The technology deploys advanced robotics with sensors and AI-driven analytics to monitor critical steelmaking assets including blast furnaces, basic oxygen furnaces, and continuous casting machines. By detecting anomalies, measuring wear, and analyzing performance metrics, it enables predictive maintenance and data-driven energy conservation across the production process.

Key Findings

1. 1

   Small per-unit gain, massive installed base

   At 0.077 t CO2e per tonne (3.5% reduction), this is an incremental efficiency improvement rather than a step-change. But applied across 1.37 billion tonnes of BF-BOF steel production, even at 1% capture it translates to ~1.1 Mt CO2e of avoided emissions.

2. 2

   Applies to existing infrastructure in a hard-to-abate sector

   Steel production via the BF-BOF route is carbon-intensive and difficult to decarbonize. While breakthrough alternatives like green hydrogen DRI are under development, AI-powered analytics can deliver emissions reductions within the existing BF-BOF infrastructure without requiring a wholesale process change - a pragmatic near-term lever.

3. 3

   Stable reduction across the forecast period

   This technology maintains a steady 0.077 t CO2e/t reduction across the 2025-2035 forecast period, reflecting the consistent nature of energy conservation gains from analytics-driven optimization within the BF-BOF process.

4. 4

   Co-benefits beyond emissions

   The technology offers value beyond carbon reduction: predictive maintenance prevents catastrophic equipment breakdowns, minimizes downtime, and can produce higher quality steel with improved corrosion resistance. These operational benefits strengthen the economic case for adoption independent of carbon pricing.

Methodology & Data Provenance

Data Quality Assessment

References & Resources

• Koi Data & Methodology Overview
• Koi Avoided Emissions: Terms & Concepts
• World Steel Association - Steel Statistical Yearbook
• Full Model Datasheet (Koi platform)

Published by Rho Impact. Data sourced from the Koi Data Lake. Last updated March 2026.