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Avoided emissions are the greenhouse gas (GHG) reductions that result from a climate solution being deployed, measured by comparing what emissions would have occurred under a business-as-usual scenario (the counterfactual baseline) against what actually occurs when the solution is in use. They represent the forward-looking climate impact of a technology or product, as distinct from historical carbon accounting.

Avoided emissions are calculated by subtracting the lifecycle emissions of a climate solution from the emissions of the counterfactual (what would have happened without that solution). The calculation incorporates market penetration projections, grid emissions factors, lifecycle emissions data, and the time horizon over which the solution operates. Koi automates this process using a validated data lake and the CRANE methodology, producing decision-grade forecasts in minutes rather than months.

A counterfactual baseline is the projected emissions scenario that represents the world without a given climate solution. It is the reference point against which avoided emissions are measured. Establishing a credible counterfactual requires selecting an appropriate incumbent technology or practice, a geographic scope, and a market penetration timeline. Koi's methodology, rooted in CRANE, provides structured guidance for counterfactual selection aligned with Project Frame and WBCSD standards.

The CRANE Tier is Koi's free, permanent access level with no trial period and no time limit. It gives users access to the CRANE open methodology for forecasting avoided emissions of climate technologies, backed by Prime Coalition. Users can build up to three independent impact models with full data lake access, transparent exports, and team collaboration. It is the same methodology trusted by 6,000+ users since 2020, now running on Koi's AI-accelerated engine.

Koi's methodology is built on CRANE, the Climate Risk and Neutrality Evaluation framework created by Rho Impact, Prime Coalition, Greenometry, and Evergreen Climate Innovations. It uses a bottom-up, technology-specific approach: each model starts from a counterfactual baseline, applies lifecycle emissions data from verified sources (IEA, SBTi, U.S. LCA Commons, peer-reviewed literature), and projects avoided emissions across a defined market penetration curve. Results are auditable and aligned with Project Frame.

Koi draws on a validated data lake that includes emissions factors and lifecycle data from the International Energy Agency (IEA), Science Based Targets initiative (SBTi), U.S. Life Cycle Assessment Commons, IPCC reports, and peer-reviewed scientific literature. Market benchmarks are sourced from industry databases and Rho Impact's proprietary research spanning close to a decade of climate technology analysis.

Koi's data infrastructure covers 10,000+ climate technology models spanning energy, transportation, buildings, agriculture, industry, and land use. Each model encodes a counterfactual baseline, technology emissions profile, and market penetration data. Together, these are the three inputs required to produce an avoided emissions forecast.