Impact on Key Scope 3 Categories
To illustrate how these methodological differences change outcomes, consider a few major Scope 3 categories:
Purchased Goods and Services (Category 1)
Traditional Approach
When accounting for purchased goods and services, many tools and consultants use Environmentally-Extended Input-Output (EEIO) models or spend-based emission factors. For example, if $100,000 was spent on office furniture, they might multiply that by an average factor for the furniture manufacturing sector. This provides a rough estimate but can be quite misleading. Services in particular tend to "disappear" into these averages, $1 million spent on software development consulting might be given a generic professional services factor that doesn't reflect what that service actually entails. Such methods often overestimate emissions for services because they use economy-wide averages that include many upstream activities. The result is inflated totals that aren't actionable (you can't tell which vendor or activity is driving them). It also hides subcomponents, e.g. if a purchased product's emissions mostly come from steel vs. plastics vs. transport, the spend method won't tell you.
Bardo's Approach
Every purchased item or service is recorded with a specific activity and unit. Physical goods are measured in quantity (e.g. kg of material, units of product) and ideally tied to a product-level factor (if available). Services are unpacked into physical drivers, for example, an IT support contract might result in records for data center usage (kWh), business travel (if consultants traveled), and office space (if applicable). Bardo uses evidence-backed factors, if a supplier has an emissions disclosure, that is used; if not, a factor is constructed from similar data (and marked as such). The value of this approach is multi-fold: it tends to produce lower and more accurate emissions estimates than spend-based (because it avoids the double-counting and conservatism built into broad averages), and it surfaces levers for reduction. For instance, it might reveal that Supplier X has a much higher emission per dollar than Supplier Y for a similar service because Supplier X's activity (perhaps travel or energy use) is higher, this gives procurement a handle to negotiate or switch, which a spend-based number wouldn't show. Moreover, by separating embedded services (like freight or installation that came with a product purchase) into their proper categories, Bardo prevents Category 1 from being overstated. The end result is a Category 1 footprint that is evidence-based and verifiable, often smaller than a spend-based estimate, but also more credible to decision-makers.
Upstream Transportation and Distribution (Category 4)
Traditional Approach
This category covers emissions from transporting goods a company purchases (before they reach the company). If treated at all by incumbents, it's often done in a simplified manner, e.g. multiplying total logistics spend or tonnage by an average emission per ton-kilometer. Some companies even omit it, especially if those emissions were thought to be included in suppliers' Scope 1 (leading to confusion). A generic tool might assume a blanket emissions factor for any freight distance (ignoring mode differences), or it might not separate vendor shipping from the purchased goods category (meaning freight might be hidden in Category 1). For example, an average emissions factor per freight spend could overestimate emissions if a company mostly ships by sea (low carbon per ton-km) but the factor assumed a road freight mix. The lack of detail (route, mode, leg specifics) makes the results coarse and potentially inaccurate, giving little insight (you can't tell if air freight is the culprit or if most emissions are from last-mile trucking, etc.).
Bardo's Approach
All inbound logistics emissions are calculated at the level of individual shipments or routes. The data captured includes the mode of transport (air, sea, truck, rail), the distance or route (sometimes down to specific lanes, e.g. Shanghai to Rotterdam by sea, then Rotterdam to Berlin by truck), the weight/volume of goods, and class or utilization info if available. Emission factors are then applied that are mode- and route-specific – for instance, using a different factor for sea freight (which might be in gCO₂ per tonne-km) than for air freight. Standard frameworks like the GLEC Framework support this level of detail, and they classify generic per-km factors as "sufficient" while carrier or route-specific data is considered more accurate. Bardo either uses known factors (from GLEC, Smart Freight Centre, etc.) or constructs factors (if needed, say for a specific route) using credible data (like average fuel consumption of a truck per km and emissions per liter fuel). Crucially, Bardo unbundles freight emissions from goods: if a supplier's delivery is part of a purchase, that portion is carved out as Category 4. This yields a clear number for "upstream transport" that stands on its own. The value is that the company can now see, for example, we emitted 500 tCO₂ in upstream logistics, mostly from air freight from Asia. With a generic approach, they might only see an undifferentiated part of Category 1 or an averaged number. Knowing the breakdown enables actions like switching transport modes or optimizing routes. It also avoids over-counting: without separation, one might inadvertently count those same emissions under purchased goods. With Bardo's method, Category 4 is properly scoped. As a result, comparisons become meaningful, e.g., if one year you shift more shipments from air to sea, Category 4 will drop accordingly, reflecting an actual improvement. Generic methods might not capture that improvement clearly because their resolution is too low. In summary, Bardo's lane-level freight accounting leads to accurate, non-duplicative transport emissions and highlights logistics as a distinct area for efficiency gains, rather than burying it in other spend categories.
Fuel- and Energy-Related Activities (Category 3)
Traditional Approach
Category 3 (often shorthand "Upstream energy") includes emissions from producing fuels and energy that the company consumes (and which aren't already counted in Scope 1 or 2). Many companies have historically ignored this category or simply lumped it with Scope 2. For example, some organizations report only their Scope 2 electricity emissions (direct combustion at power plant) and not the upstream fuel extraction, refining, and transport, or the grid transmission losses. Some tools may not calculate it separately, leading to incomplete Scope 3 reporting. Alternatively, they might include a generic uplift (e.g. "add 10%" to account for well-to-tank emissions) but not document it well. This makes the audit trail weak (it's hard to show where that 10% came from) and can cause double counting issues if not careful (especially if the electricity provider also reports something). Inconsistent treatment of Category 3 means some companies end up with Scope 3 that isn't truly complete or comparable.
Bardo's Approach
All upstream emissions from energy are calculated explicitly. For each energy purchase that the company makes (electricity, steam, fuels, etc.), Bardo will record the Scope 2 emission (or Scope 1 for direct fuel use) and then also record the upstream portion in Category 3. For electricity, this means including emissions from fuel extraction, processing, and the efficiency losses in generation/transmission. For purchased fuels, it includes the well-to-tank emissions (like drilling, refining, and transporting gasoline before it's burned). These are well-documented factors (often provided in lifecycle databases or by agencies like DEFRA or EPA as "Scope 3 fuel emissions factors"). Bardo links these to the same activity so one can see the relationship. The result is Scope 3 Category 3 is fully accounted. This matters for completeness (ensuring the inventory captures all indirect energy impacts). It also improves Scope 2 reporting accuracy, by not blending upstream into Scope 2, Bardo keeps Scope 2 strictly as per protocol (location-based or market-based emissions from electricity use), which is important for compliance and targets, while still giving the company the insight into total impact. In practical terms, a company might find their purchased electricity caused 1,000 tCO₂ in Scope 2 and an additional 100 tCO₂ in Category 3 upstream, knowledge that could inform decisions like sourcing from a renewable energy provider (which would cut both numbers). Incumbent approaches that ignore the 100 tCO₂ would underestimate the benefit of such actions or misattribute those emissions. Bardo's method ensures no piece of the energy lifecycle is missing, and everything is correctly classified. This leads to cleaner audit outcomes (as auditors can verify that upstream energy is reported in the right place and not omitted) and avoids double count (since each part is in only one category). Moreover, it feeds better internal decisions: companies get credit for improvements like reducing transmission losses (via maybe localized generation) because that would show up as reduced Category 3, which many basic reports would not even track.
Capital Goods (Category 2)
Traditional Approach
Capital goods (equipment, machinery, buildings a company purchases) are often handled with simplified methods. Many rely on generic emissions factors (e.g. "per $ spent on machinery" or a one-time LCA for an "average" machine). This can lead to large one-off spikes in emissions when a big asset is bought, without clarity on what's driving it or how to manage it. Additionally, capital expenditures often bundle various things: the equipment itself, its shipping, installation services, maybe construction materials, etc. A consultant might manually disaggregate these if detailed, but often they get lumped together or dumped entirely in Category 2 for convenience. This results in mixed categories (e.g. installation energy might really belong in Cat 3, freight in Cat 4, but all got counted as Cat 2). It also typically accounts for the full cradle-to-gate emissions of the asset in the purchase year, which is correct per GHG Protocol reporting, but companies then struggle to explain year-over-year changes (one year shows a huge jump because they bought a factory, the next year nothing, making trends uneven). Traditional accounting often doesn't offer a solution to this beyond commentary in reports.
Bardo's Approach
For each capital asset, Bardo creates an activity profile that includes specifics like the model/type of asset, its capacity or size, key materials (if known), and even the commissioning or manufacturing year. If available, a product-specific LCA for that asset or a similar one is used (e.g. if buying vehicles, use the manufacturer's carbon footprint per vehicle). If not available, Bardo will build an estimate from the Bill of Materials, known industry data, and other proxies (documenting the approach). Importantly, Bardo unbundles the capital project into pieces: the manufacturing of the asset (Category 2), the transportation of it to the site (Category 4), the installation services (Category 1 if contractors are used), and any significant energy used in commissioning (Category 3, perhaps). This means Category 2 (capital goods) in the report only reflects the actual embodied emissions of the equipment itself, making it comparable across years and investments. The value here is clarity: if one year you bought more equipment but hired fewer contractors, you'd see Category 2 go up, Category 1 go down, etc., which is truthful to what happened, whereas a simplistic method might have lumped it all. Another key feature Bardo provides is a dual view for capital goods: the GHG Protocol requires you to report the full impact in the purchase year (cradle-to-gate of a machine in the year you bought it), but management might prefer to amortize that impact over the life of the asset for internal analysis (since a machine will be used for, say, 10 years). Bardo can supply both views – a compliance view (all emissions in purchase year) and a management view (spread over the asset's life), with a reconciliation between them. This helps finance and sustainability teams explain why, for example, emissions intensity per product went up in a year due to a one-time capital purchase, and how that will normalize. Traditional tools rarely support that kind of analysis. Overall, Bardo's handling of capital goods results in more consistent and explainable reporting, avoids category mix-ups, and allows companies to see the impact of their investments clearly (e.g. comparing one type of machine's footprint to another's when making purchasing decisions, which a generic $-based factor would not enable).

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