Data Controls and Durability of Results

Ensuring that the carbon footprint results are credible year after year requires robust data controls. Here's how a managed approach like Bardo's compares to others:

Double-Counting Prevention Mechanisms

As discussed, the GHG Protocol categories themselves are designed to avoid overlap, but that assumes the practitioner correctly assigns things. Bardo encodes known overlap scenarios into rules. For example, if a certain category might feed into two Scope 3 categories, Bardo will have logic to allocate portions appropriately. A concrete case: purchased electricity – a naive approach could count it in Scope 2 and also count upstream fuel in Category 3 without linking, risking the addition of both (some older tools indeed double-counted grid emissions by mistake). Bardo's linking avoids that – Scope 2 and 3 are connected for energy. Another example: business travel – a flight could be paid via a travel agency (showing up under a vendor in spend) and also expensed by an employee; a tool might accidentally count both if not reconciled. Bardo's process would catch that via matching rules or manual review (human-in-loop) and ensure each flight is one entry. These kinds of collision detectors are not commonly found in simple carbon calculators. Consulting analysts might catch them if experienced, but it's ad hoc. By having systematic checks, Bardo's inventory is less prone to internal inconsistency, making it more durable under scrutiny.

Financial Reconciliation and Coverage Checks

A major aspect of durability is knowing that the inventory covers what it should. Bardo's integration with financial data means it can produce a coverage report: e.g. "95% of Accounts Payable spend was matched to an emission activity; the remaining 5% was deemed out of scope (with reasons like 'financial services – not in scope 3 per standard')". This level of rigor is not present in simpler tools. It ensures that if a new type of spend appears (say a new category of procurement), it doesn't slip through unaccounted. Everything is either counted or explicitly excluded with justification (which is needed if you claim an emission source is not relevant). This gives confidence that the numbers aren't omitting significant sources – a common problem in Scope 3 reporting is incomplete data. By tying to accounting records, Bardo essentially mirrors the approach of financial audits (completeness check). Other methods might rely on self-reported data collection (which can miss things inadvertently). The result is a more complete and hence durable dataset.

Version Control and Reproducibility

Each year's dataset in Bardo can be frozen for audit. If next year factors are updated, last year's can still be regenerated exactly. Traditional spreadsheets are notoriously hard to freeze – people often end up with a new workbook each year, and if a factor changes, they might overwrite the old one. Bardo instead maintains factor libraries with version IDs, so an audit trail might show "Factor X was updated from value 1.2 to 1.0 on date Y; previous inventory used version 1.2, new inventory uses 1.0, causing a drop of Z tons." This is extremely useful for audit and for internal explanation of changes. Generic platforms vary – some have audit logs, others not as transparent. But Bardo treats the methodology as evolving but trackable, which is important given how quickly emission factors and standards can change. It also means if an error is discovered (say an emission factor was misapplied), Bardo can correct it and re-issue results with clear disclosure. This kind of robust change management is something ingrained in financial systems and now making its way into carbon accounting; Bardo appears to embrace that fully.

Human QA and Continuous Improvement

Because Bardo is a managed service, there is an accountability loop – the Bardo team reviews outcomes with the client. If something looks off (e.g. emissions intensity increase from a supplier for no obvious business reason), they investigate whether it's a data issue or a real change. This helps catch anomalies, whether they are data errors or actual surprises that need action. Over time, this process improves the quality of the inventory (e.g. by prompting better data collection from a supplier, or adjusting a factor). In contrast, a purely automated tool might just output a result without context, or a consultant might flag issues but on a one-off basis. Having a partner continuously monitoring means the numbers remain decision-grade as the company grows or changes. It also builds internal knowledge for the company (they learn through each cycle what drives their footprint, rather than treating it as a black box).

Why These Controls Matter

All these controls mean that the Scope 3 numbers generated are not only accurate for one report, but can be consistently relied upon and audited over time. As regulations like CSRD and the SEC rules demand audit-ready data, this robustness is crucial. A "quick & dirty" spend-based footprint might suffice for a voluntary report one year, but as soon as assurance is required, the lack of audit trail or controls could become a problem. Bardo's approach is essentially preparing companies for that audit scrutiny from the start, by design. A future audit of emissions will examine the end-to-end calculation process, data completeness, and prevention of double counting – exactly the areas Bardo has focused controls on. This gives confidence that Bardo's results would pass muster in an external review, whereas a spreadsheet built by a consultant might require significant extra documentation to be audit-ready.

Conclusion and Common Concerns

To conclude this methodology, it's useful to address a few common objections or misconceptions that often arise when moving from incumbent approaches to a more advanced activity-based approach like Bardo's:

"We already follow the GHG Protocol; isn't that enough?"

GHG Protocol provides the framework, but how you follow it can differ. Spend-based tools also claim GHG Protocol compliance; the difference with Bardo is in the rigor of data quality within that framework. Bardo's approach is still fully aligned with GHG Protocol (in fact, it's using the Protocol in "strict mode", meaning clearly delineating categories, using hybrid data methods as recommended, etc.). The ledger approach simply implements the Protocol at a more detailed level, ensuring completeness and accuracy that generic implementations might miss. Essentially, Bardo doesn't change what you report, it changes the fidelity and trustworthiness of the numbers you report, all within GHG Protocol's rules. So it's not a departure from GHG Protocol – it's an enhancement in data quality and auditability while staying within that standard.

"We only need a high-level number for annual reporting; why go to this effort?"

It might seem easier to just get a single total for Scope 3 for the sustainability report. However, consider the risks and lost opportunities of an inflated or dubious number: If it's too high (due to crude estimates), the company might be allocating budget or attention inefficiently, or worse, making decisions that hurt business (imagine avoiding outsourcing because the generic emissions factor made it look bad, even though a detailed analysis might show it's efficient). If it's inaccurate, and later auditors check it, the company could face compliance issues or restatements. Furthermore, a single number with no detail doesn't help in reducing that number – it's hard to manage what you don't measure properly. A detailed ledger might sound like overkill if one only cares about disclosure, but it actually saves time and cost in the long run. The ledger can be reused and updated easily each year, whereas starting from scratch or dealing with inconsistent data can become a yearly headache (and cost center). Also, regulations are moving toward requiring more than just a number – they'll want to see how you derived it and that you have internal control over it. Investing in a proper system now can avoid fire drills later.

"Our services (or our supply chain) are too complex to model in detail."

This is a fair concern; getting data for every little thing is hard. But that's precisely why an approach like Bardo's, which is highlhy automated, managed and iterative, is useful. It doesn't demand perfect data on day one. Yes, it's challenging to gather data for, say, a legal services firm's footprint. But Bardo use ai powered agents to do extensive research and constructing reasonable models (office use, IT use, etc.) which is order of magnitude better than a flat spend factor, and then it flags that for improvement (maybe next time get actual data on that law firm's operations). Over time, even services can be refined (or at least kept transparent about uncertainty). The worst approach is to just throw up hands and assign one generic factor – that often does the most harm by overstating or misallocating emissions (and provides zero insight). By tackling services, one can find efficiencies (perhaps discovering that a cloud provider in one region has a much lower footprint than in another, influencing IT choices). Complexity is a reason to choose a solution built to handle complexity, rather than to avoid doing it.

"This sounds like a lot of change and work for our team."

It might seem daunting to adopt a new system, but if it's a managed service, the internal workload is actually lower than trying to do it yourself. Bardo explicitly states "you do not run software". That means no new tool for your team to learn and operate day-to-day. The primary tasks for the company are: provide data access (which IT/finance does), review results (which is much easier than creating them), and collaborate on edge cases (which is minor effort compared to building everything). So rather than burdening the team, it frees them to focus on interpretation and action. Bardo will guide the process – so the change is more about mindset (trusting a more data-driven process) than about heavy operational load. Once the initial setup is done, each cycle should be routine and faster. In fact, many organizations find that once this data is available, internal interest grows – finance might want to integrate carbon metrics into their planning, procurement might weave it into supplier scorecards, etc., which increases the utility of the sustainability team's work without a proportional increase in their labor, because the data flows are largely automated.