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.