Baseline Scenario: Modelling the World Without a Project

Baseline scenario is the modelled description of how emissions or removals would evolve in the absence of the project, used as the reference against which a project's climate impact is measured. Within MRV & Verification, the idea is foundational: the way it is defined quietly determines how the whole market behaves.

Developers build the baseline from historical data, trends, and assumptions about future activity and policy, following the approved methodology. Measured project emissions are then compared with the baseline to determine the reductions credited.

The baseline is the reference that gives a reduction its meaning, because a reduction only exists relative to what would otherwise have happened. A credible baseline is therefore the anchor of credit quality. Because Baseline scenario links technical detail to market behaviour, small errors in how it is handled can grow into large gaps in trust and value.

A useful way to think about Baseline scenario is as a bridge between climate science and finance: the science defines what a genuine outcome looks like, while finance decides whether that outcome gets funded and repeated at scale.

Regulators, standards bodies, and market participants each bring a different lens to Baseline scenario, which is why shared definitions and reliable records matter so much. When everyone works from the same facts, disputes shrink and confidence grows.

Discussions of Baseline scenario often surface the same tension between ambition and rigour, and the most durable solutions are those that treat transparency as a design requirement rather than an afterthought.

Practical experience with Baseline scenario tends to reward patience and discipline: the organisations that document their assumptions, keep an audit trail, and revisit their methods are the ones that keep credibility when questions are asked.

As carbon markets mature, Baseline scenario is shifting from a niche technical concern to a mainstream one, shaping diligence checklists, disclosure expectations, and the way one credit or claim is weighed against another.

Because the baseline describes a counterfactual, it is inherently uncertain and can be set too generously, inflating credits, or too strictly, penalising good projects. Methodologies use conservative defaults and periodic updates to manage this.

CarbonFi's verification checks the plausibility of the assumed baseline against measured project data, aiming to keep the counterfactual grounded in evidence. CarbonFi's model, which pairs an AI-driven digital MRV layer with an on-chain registry and marketplace, is built so that Baseline scenario can be handled with transparency and traceability from end to end.