Treefera's Environmental Intelligence capability exists to answer one question faster: is this land worth committing capital to, and what will it yield?

This release addresses the two analytical bottlenecks that have historically made that question expensive to answer at the first-mile evaluation stage — ACR Dynamic Baselining V1.4 for Improved Forest Management forest carbon projects, developed under the ACR (American Carbon Registry), one of the leading voluntary carbon registries in the US, and ARR Land Eligibility Screening for ARR (Afforestation, Reforestation and Revegetation) under Verra's VM0047. The details that follow are technical. The underlying logic is not: earlier data, at lower cost, before the capital decision. The most significant change in this release is a removal, not an addition.

Improved Forest Management (ACR) Dynamic Baselining V1.4

Improved Forest Management (ACR) credits developed under the ACR methodology hold CCP approval from the ICVCM, a recognition of high integrity that is translating into rising buyer demand for this project type. Baseline analysis no longer requires a completed accuracy assessment. Previously, developers needed ground-truth shapefiles – the spatial reference files that map harvested areas on the ground – prepared, uploaded and validated before analysis could run. That placed the platform's most valuable output after the decision it was most needed to inform. That requirement is now removed.

Analysis can now be initiated without ground-truth data, with results delivered in under four hours. The accuracy assessment remains available as an optional enhancement for production-stage projects where registry verification requires it, but it no longer gates access to the deterministic baseline output. For a developer evaluating a pipeline of IFM candidate sites before committing to full feasibility on any of them, the platform is now usable at the stage where it matters, before the capital decision, not after it.

Anew Climate, one of the largest forest carbon developers in the US, ran this workflow across their portfolio. Their technical team processed 300+ comparable properties in under 48 hours — work previously estimated to take months using prior methods. The analysis produced a 3.3% increase in baseline harvest intensity on comparable properties, translating directly to additional credits per year over the lifespan of the project, and an F1 score of 0.55 on harvest detection against a typical benchmark of 0.4. The accuracy difference is what makes the baseline defensible at verification.

V1.4 also introduces comparable location selection functionality. Identifying comparable properties – the reference commercial forests against which a project's harvest intensity is benchmarked under ACR IFM v2.1 – has previously required developers to research and select candidates manually. The functionality surfaces candidate comparable properties within the workflow, reducing preparation burden and compressing the time from project boundary to baseline output, replacing what has typically required slower and more expensive bespoke consultancy work. Methodology documentation, comparable property selection rationale and data sources are packaged as structured outputs ready for verifier submission.

The broader April Improved Forest Management (ACR) Dynamic Baselining release

V1.4 ships alongside three further capabilities that enable portfolio-scale Improved Forest Management (ACR) Dynamic Baselining workflows for the first time.

Bulk Polygon Upload supports ingestion of 200 or more polygons in a single batch, with automatic splitting, geometry validation and coordinate reference system normalisation. Developers managing a pipeline of candidate sites no longer need internal GIS or DataOps resource to prepare location data before analysis can begin.

Metadata Import enables project-specific attributes, registry ID, project type, ownership structure and custom classification tags, to be ingested and persisted at location level. Analysis outputs then map directly to a customer's internal portfolio management systems, removing the manual reconciliation step that has added overhead between Treefera results and operational decisions.

Default Harvest Configuration introduces streamlined default harvest intensity assumptions within the baseline workflow, reducing setup complexity for projects that do not require custom parameterisation.

Together, these four capabilities enable a workflow that was not possible under V1.3: a project developer can ingest a full pipeline of candidate Improved Forest Management sites, run early-stage baseline analysis across all of them without ground-truth data and identify which sites warrant full feasibility investment, before committing resource to any of them.

ARR Land Eligibility Screening

The same early-stage logic that shapes V1.4 applies to ARR.

ARR Land Eligibility Screening addresses the question that precedes baselining in every ARR project: whether the land qualifies. Under Verra's VM0047 methodology, project activity must occur in areas with less than 10% pre-existing woody biomass cover and no forest classification for the past ten years. Weather & climate volatility has accelerated land-use change across many candidate regions, making historical deforestation evidence both more important and harder to assemble quickly. Assembling that spatial evidence has historically meant bespoke consultancy work and multi-week turnaround. The tool runs VM0047-aligned eligibility checks at the plot level using satellite-derived land cover classification and 10-year historical deforestation analysis, producing a structured pass/warn/fail result with the spatial evidence layer attached.

What this means in practice

The addressable market for both capabilities has expanded.

V1.3 addressed developers who had already committed to a project and needed to run a production-grade baseline efficiently. That use case is still well-served, and V1.4 improves it. But V1.4 and ARR Land Eligibility Screening together address developers who are one stage earlier, evaluating whether a project is worth pursuing, before the resource commitment that full feasibility entails.

For carbon finance teams making portfolio allocation decisions, the cost per qualified site falls and the time to investment decision compresses. For origination teams running a broad pipeline, the volume of sites that can be evaluated before committing to feasibility rises. The analytical bottleneck and the commercial bottleneck in first-mile IFM and ARR development are usually the same constraint. This release addresses both.

Two questions gate every early-stage carbon project: does this land qualify, and what will the baseline look like? A developer with a pipeline of candidate sites can now run both before committing to full feasibility on any of them. Neither requires ground-truth data. Both return results in a single working session. The constraint is no longer analytical capacity at the first-mile evaluation stage.