Setting science-based targets from utility data

A science-based target is only as credible as the inventory it is built on. The Science Based Targets initiative (SBTi) sets the rules for what counts as aligned with climate science, and more than 8,600 companies now have validated targets. But every one of those targets rests on a base-year footprint and a way to measure progress against it, and for most organizations that footprint is dominated by purchased energy. Get the utility data wrong and the target is wrong from day one.

This article walks through how SBTi targets are structured, what the buildings-sector rules add, and why the practical bottleneck is almost always the quality of your energy data rather than the math of the target itself.

Near-term targets: the absolute contraction approach

The most common near-term target uses the absolute contraction approach: cut absolute emissions by a fixed percentage every year. For alignment with 1.5 degrees Celsius, the SBTi Corporate Net-Zero Standard sets a minimum linear reduction of about 4.2 percent per year for Scope 1 and 2, derived from the pathway to roughly halve global emissions by 2030. Near-term targets typically cover a 5 to 10 year horizon from the base year.

That 4.2 percent is measured against your base-year emissions. If the base year is built from estimated bills or misattributed accounts, every future year is measured against a flawed anchor, and the reductions you report may not be real.

Net-zero targets: the long-term destination

A net-zero target under the SBTi standard requires deep absolute cuts, an emissions reduction of at least 90 percent by 2050 from the base year for most companies, with only the small residual then neutralized through permanent carbon removal. Net zero is not primarily an offsetting exercise; it is a decarbonization target with a narrow allowance for what cannot be eliminated. That makes measured, year-over-year energy reductions the core of the plan.

The buildings sector rules

Buildings are a large share of the problem. The SBTi notes the buildings sector is responsible for over a third of energy-related emissions, and it has published a buildings-sector target-setting framework with its own criteria. Companies whose building-related emissions exceed a defined share of their total footprint are expected to follow the buildings guidance rather than the generic cross-sector method, using physical-intensity pathways expressed per square metre.

That framing matters for data. A per-area intensity pathway needs consumption tied to floor area and to individual assets, which is a far higher data bar than a single company-wide total. You cannot report kilowatt-hours per square metre if you do not know which meter belongs to which building.

What good target data requires

To build and defend a science-based target you need a base-year inventory that is complete, correctly attributed and reproducible.

• Complete: every account and site captured, with no silent gaps papered over by estimates
• Correctly attributed: each meter tied to the right building, so intensity metrics are real
• Both Scope 2 methods available, since target tracking uses market-based while location-based shows the grid trend
• Auditable: every number traceable back to the source bill or interval file

From utility data to a defensible target

In practice, the path from raw utility data to a validated target is a data-engineering problem before it is a climate-strategy problem. You have to gather bills, interval data and Green Button files across many accounts and formats, reconcile them to sites and periods, catch estimates and errors, and produce a consumption dataset clean enough to anchor a decade of reporting. Most teams try to do this in spreadsheets and lose weeks to it every year.

MartinAI is built to remove that bottleneck. It reads and validates utility data, attributes it correctly, and produces structured consumption you can use to set a base year, measure the annual reductions your target requires, and produce the per-asset intensity metrics the buildings rules ask for. The target methodology is public. The hard part is the data, and that is the part we handle.