What Good Land-Sector Accounting Actually Looks Like

Understanding the Land Sector and Removals Standard requirements is one thing. Knowing whether your methods are actually good enough to meet them is another.

In Part 2 of our conversation with Zander Dale of Schneider Electric (SE) Advisory Services, we get into the methodology: what determines when to opt for Tier 3 modeling, what good accounting looks like in practice, and how to evaluate and deploy digital MRV platforms. In case you missed it, you can find Part 1 here.

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Regrow: The Guidance lays out the IPCC's Tier 1/2/3 framework for method selection, where higher tiers require more data but produce more accurate, lower-uncertainty estimates. In practice, what determines whether a company should invest in a Tier 3 approach versus staying at Tier 1 or 2?

Zander: For me it comes down to three things, materiality, decision-use, and how much credibility you need from the number.

Tier 1 and Tier 2 can be perfectly fine for screening, early inventories, immaterial categories, or where the data just isn't there yet. They get you moving and show you where more effort is worth it. But where emissions or removals are material, where you want to track a specific intervention, or where the result will feed target progress, assurance or an external claim, Tier 3 becomes far more important.

The test we always come back to is: what decision will this number support?

If it's "we need a rough estimate for a first inventory," Tier 1 or 2 is probably fine. If it's "we want to compare suppliers, claim progress from regenerative practices, report removals, allocate investment, or show FLAG progress," then average factors are not good enough.

And Tier 3 isn't really about scientific sophistication for its own sake. It's about whether the method is specific enough to the geography, land use, practices and carbon pools that actually matter. It should reduce uncertainty and improve decision-usefulness, but it also has to be transparent, repeatable and auditable.

Regrow: Regrow's work with General Mills is cited directly in the Guidance as an example of Tier 3-level precision (page 74!).  What do you think that case study get right about what "good" looks like? Is there anything you would add or push further on?

Zander: What I think the case study gets right is that it deals with the world companies actually operate in, rather than assuming perfect data or perfect traceability from day one.

Most companies working across agricultural supply chains have to deal with partial traceability, mixed sourcing regions and uneven supplier data. The spring wheat example is useful because it shows how accounting quality can still be improved at scale, using sourcing-region data, remote sensing, modeling, calibration and uncertainty analysis. That is exactly the direction companies need to move in.

Good land-sector accounting is not just about replacing one emission factor with another. It is about building a method that is linked to real sourcing patterns, updated over time, calibrated to local conditions, and useful for decision-making.

Where SE Advisory often comes in is helping companies build the operating model around that technical approach. For example, how do you define the sourcing boundary? How do you evidence attributable productive land? What data supports calibration and validation? How is uncertainty explained? How are emissions and removals reported separately? And how does the company make sure the outputs are usable for governance, reporting, supplier engagement and assurance?

So I would say the case study is a strong example of what good looks like. The next step is making sure companies can take that level of technical precision and wrap it in the governance, documentation and decision-making processes needed for real implementation.

Regrow: That operating model question is exactly where a lot of companies get stuck. For those without in-house scientific capacity, how do they evaluate whether a vendor's approach is actually compliant with what the Guidance requires?

Zander: I'd look for three things: transparency, methodological alignment and auditability.

What's helpful about Regrow’s approach, and similar data-led platforms, is that it moves the conversation beyond generic emission factors. For land-sector accounting, that is where companies increasingly need to go.

They need methods that can reflect sourcing geographies, production conditions, land-management practices and uncertainty, rather than relying on broad averages that may not reflect what is actually happening in the supply chain.

From an SE Advisory Services perspective, the key is making sure that the technical output is also usable for governance, reporting and decision-making. That means being clear on which accounting categories are covered, what data sources sit behind the method, how calibration and validation have been handled, how uncertainty is quantified, and how the outputs trace back to a geography, commodity, supplier group or land area.

The questions I would encourage companies to ask are not intended to catch vendors out. They are the questions that help make sure the outputs are used in the right way:

• Does the method align with the relevant GHG Protocol land-sector accounting categories, rather than only producing one blended “net” number?
• Can it distinguish land-use change, land-management emissions, production emissions, removals, leakage and biogenic product emissions where relevant?
• What empirical or region-specific data has been used for calibration and validation?
• How is uncertainty quantified, and how are conservative assumptions applied where uncertainty is high?
• What level of physical traceability is required, and what happens where traceability is incomplete?
• Can the method be repeated year-on-year without creating artificial changes from methodological updates?
• Can the outputs be supported by a clear audit trail of source data, assumptions, calculations and QA/QC?

For me, that is what good looks like: not just a more sophisticated calculation, but a method that gives the company confidence in what the result means, where it can be used, and where caution is still needed.

A strong platform can be very useful for screening, hotspot analysis, supplier engagement and performance tracking. If companies want to use the outputs for removals reporting, target progress or external claims, they then need to make sure the evidence base, governance and assurance trail are strong enough for that specific use case. That is often where we come in: connecting the technical method to the accounting treatment, internal controls, claims position and implementation roadmap.

Regrow: It's worth addressing technology directly. Where do digital MRV platforms and software partners fit into land-sector accounting and insetting programs, and what should companies have in place before adopting them?

Zander: For companies serious about land-sector accounting, a digital MRV platform isn't optional. It's how you make the work repeatable, auditable and scalable. Supplier data incorporation, field-level monitoring, activity tracking, dashboards, year-on-year reporting consistency... doing that manually isn't feasible at scale.

The way to get the most out of a platform like Regrow is to come in with your fundamentals clear: program objectives, sourcing boundaries, priority commodities, supplier maturity and data requirements. That's not a reason to delay. It's how you make sure the platform is doing the right work from day one rather than having to retrofit it later.

Get that foundation in place and the technology becomes a genuine multiplier: easier to justify internally, easier to scale, and far more useful for governance, reporting and supplier engagement over time.

Getting the methodology right is the foundation. But even the most sophisticated approach means little if it can't withstand external scrutiny. In Part 3, we walk through what auditors will actually flag once companies start reporting under the Standard, how to govern insetting programs without double counting, and where the line between insetting and offsetting really sits.