The importance of supplier specific emissions data

The purpose of this explainer is to highlight the importance of supplier specific emissions data for enabling and optimising greenhouse gas (GHG) emission reductions within GHG inventories. This includes the following key points:

• Supplier specific emissions data can unlock ‘hidden’ reduction opportunities not visible via alternative emissions calculation methods.
• Users of supplier specific emissions data can obtain greater accuracy, and value, in tracking the success of their reduction efforts.
• An evaluation of the quality and relevance of supplier specific emissions data must be applied, to help users contextualise its value for reduction efforts and tracking reductions over time.

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Introducing supplier specific emissions data

Supplier specific emissions data reflects data that is specific to the supplier's operation in the delivery of their customer/user contracted service or product. In order words, it should reflect the specificities of the activities performed to deliver the service or product contracted by the customer/user, as opposed to industry wide averages. For example, when you purchase a computer tablet you may want the emissions data directly and specifically associated with the manufacturing of that product, rather than generic, industry-wide computer tablet data that is an average combination of all industry suppliers.

Supplier specific emissions data is often provided in the form of intensity, or total emissions over a given period, of a product or service being supplied to the ‘user’ (refer appendix 1 for an overview of common methods).

Having supplier specific emissions data should enable the user (customer) to identify and action emissions reduction opportunities by gaining greater visibility of the value chain processes involved in the purchase of that product or service.

This explainer does not go into detail on how to obtain supplier specific emissions data. To learn more about this you can access our Explainer on supplier engagement here.

How supplier specific emissions data can be used

Supplier specific emissions data can provide 3 big benefits:

1. Quantification: improving the accuracy of the inventory, and accuracy of emission reduction performance. Such data can be used in all common types of inventories, such as organisation, product, events, and project inventories. It is important to acknowledge that supplier specific emissions data may be lower, or higher, than the alternative methods.

2. Optimising visibility of reduction opportunities: emission reductions can be achieved via two levers, behavioural (activity) and technology (intensity) changes (figure 1).

Figure 1. Reducing emissions via the activity and intensity lever, using transport as an example. With this example, the activity lever is the lever for reducing emissions by reducing the activity, i.e. travelling fewer km. But if still having to travel, the intensity lever is the lever for reducing emissions by traveling less intensively per km. Emission factors have a considerable role on the intensity lever.

Many alternative methods to the supplier specific method provide limited visibility and influence on the intensity lever. For example, emission factors under the ‘average data method’ typically have several variables (in the form of industry averages or similar) ‘baked in’ to the factor (figure 2), which the reporting organisation just has to accept. In this example, reductions via the intensity lever are limited to ‘organic’ decreases, reflecting the pace of the entire industry sector that the factor represents. National grid average electricity, road freight, and passenger air travel are examples of average data method emission factors in national emission factor publication datasets.

In contrast, some, or all the variables under a supplier specific method can reflect actual performance of the supplier (figure 2), hence increasing accuracy of the inventory. The supplier may, or may not be willing to disclose the variables specific to their operation, due to commercial sensitivity. If, however, the supplier is willing to disclose some or all the variables to their customers and/or other stakeholders, this presents an opportunity for collaborative efforts to influence the variables in a manner that results in reduced emissions for both the supplier and the customer.

Figure 2. Comparison of data sources for variables used within an 'industry average data method' (diagram on left) vs a 'supplier specific method' (diagram on right), using road freight as an illustrative example. Specificity and accuracy of the emissions data increase as more variables move from industry averages to supplier specific.

3. Informing reduction pathways: if the supplier is an industry leader, chasing their own Science Based reduction targets, the customer can build this into the development of their reduction pathways. i.e. the customer can anticipate a steeper downward trend on the emissions intensity, compared to alternative methods such as average data method which will often reflect the performance of the entire industry sector; whereby the downward trend in intensity will be shallower due to poor performers of the sector dragging down the average (figure 3. Conversely, in the absence of supplier specific data, the customer is essentially ‘missing out’ on being able to account for emission reductions equal to the gap between the industry average and supplier specific line.

Figure 3. The importance of supplier specific emissions data for reduction pathway planning, using road freight as an illustrative example. The top line shows the actual industry average emissions (average data method), for 2019 through 2022. This top line shows only a small downward change of about 0.7% decrease. If we assume the rate of change continues, we can project the emission factors to track across on a very shallow downward trend to the across the decade (and beyond). Contrast this with an actual supplier-specific factor (supplier specific method) on the line underneath, which a) starts off on a lower intensity than the industry average, and b) if this supplier is an industry leader, and chasing their own Science Based reduction target, we can project a much steeper downward trend. Toitū is seeing this type of scenario occurring across lots of our members.

Quality and relevance considerations

Toitū recommends appropriate quality checks are applied when using supplier specific emissions data. We see the most important quality checks being in the areas of specificity, coverage, alignment to the relevant standard(s), and the extent of validation (of the methodology and calculation model) and verification (of the data flowing into the model).

Specificity

Specificity refers to where the data came from and the level of detail of the data (provenance and granularity). The more data that’s related to the specific supplier of the product or service, the higher the specificity. Table 1 gives more description on specificity levels. Supplier emissions data has higher specificity as we go from the general corporate level up to the product level. We would expect accuracy to increase with increasing specificity. However, this might not always be the case as shown by way of the following example:

A transport company that provides both passenger transport and cargo freight across the following modes: ferry, fixed-wing aircraft, helicopter, medium size bus. They may, for example, track their total emissions and divide that by the total kilometres travelled across all their transport modes to give an average emissions per km for their entire operations. If data is presented in this format, there’s no consideration of the portion of freight vs passengers. Additionally, there’s no allowance for customers that only use the bus service i.e. a bus service-only customer is essentially getting ‘punished’ with picking up extra emissions from the higher intensity helicopter and aircraft within the overall average per kilometre figure. This scenario would prompt the question of whether an industry average (average data method) for bus services would be more accurate than a supplier-specific average across multiple modes.

Table 1. Specificity levels

Coverage

Coverage refers to the extent of the ‘cradle to gate’ activities are included within the supplier specific emissions data. To ensure complete value chain inventory coverage, we see a few approaches to addressing scenarios where data is a subset of the cradle to gate, in order of priority:

1. Product level data: Ideally, supplier specific emissions data will be product level (i.e. the specific product being provided by the supplier) cradle to gate (top level in table 1), and transparent enough for users to ensure the cradle to gate coverage is aligned to their usage scenario.
2. Hybrid approach: In the absence of option 1, supplier specific emissions data should ideally be presented in a format that enables users to build a hybrid set of data to achieve full cradle to gate coverage. An example of how this can be done is by having supplier specific emissions data that covers ‘Tank to Wheel’ emissions, and then ‘bolting on’ industry averages for the ‘Well to Tank’. More examples of the concept of hybrid data coverage are shown in figure 4.
3. Comparability approach: supplier specific emissions data should reflect the same coverage as alternative methods. For example, if an average data method for road freight is limited to ‘Tank to Wheel’ emissions, then the supplier specific emissions data should also reflect Tank to Wheel emissions.
4. Materiality: If the data is still a subset of the full cradle to gate, balancing the level of coverage against materiality of the impacts of the activities not covered. For example, if road freight has warehousing and storage by third parties not covered in the data, a screening estimate and sense check can be applied to determine if this activity is material to the total freight service emissions.

Figure 4. Examples of a hybrid approach for forming sufficient cradle to gate coverage, by using combinations of calculation methods. The top example, product level data, is included for reference as being the ‘ideal approach’, not requiring any combination of methods to achieve full coverage.

Alignment to relevant standards

Supplier specific emissions data should align with the accounting requirements of standards relevant to the user. For example, the common standards relevant for organisation inventories is ISO14064-1:2018 and the GHG Protocol, hence data users compiling an organisation inventory should align to the relevant requirements of these. In contrast, data users compiling a product level inventory should align to the relevant requirements of common product standards, ISO14067:2018 and PAS 2050. For both organisation and product inventories, the supplier data should be presented at a suitable level of granularity that enables the user to collate the emissions under the relevant accounting categories of the standard.

Extent of validation and verification

Supplier specific emissions data will typically be generated using a multivariable calculation model⁶, with an underlying methodology. Ideally, the methodology and model should undergo suitable external review, or validation, to give confidence to users that the data being generated is sufficiently reflecting the stated specificity, coverage, and alignment to accounting standard(s).

An important complementary component to the methodology and model validation is regular (typically annually) verification of:

• The activity data set(s) being fed into the model (including completeness, accuracy, geographic appropriate time period, relevance).
• The underlying ‘base’ emission factors residing in the model. For example, a road freight model is likely to have a diesel emissions factor, based on emissions per litre, that drives the calculations for emissions from their specific fuel consumption activity. These underlying factors need to be the best match⁶ for the specific supplier; including geographic and suitability for the time period, greenhouse gases (e.g. CO₂, CH₄, HFCs) covered, and extent of activities covered. Note that some, or all the underlying base factors in the model may also be supplier specific. For example, a road freight supplier using TTW and/or WTT emissions intensity data from their specific biodiesel supplier.

Achieving confidence in supplier specific emissions data

As already noted, supplier specific emissions data that’s aligned to relevant accounting standards, validated, and verified, can give confidence to data users.

Additionally, Toitū is seeing the development of emerging global initiatives such as the Pathfinder Framework, and the Carbon Call. Both initiatives are heavily grounded on the concept of fostering an efficient, interoperable exchange of supplier data up and down supply chains, in a consistent format.

Within Toitū, we have embarked on two of our own programme level initiatives:

1. ‘Pre-appraised’ supplier registry: a checklist-based approach to sense check and inform verifiers and other users of the data quality. Further checks as part of the standard audit process might still apply, hence pre-appraised suppliers are not treated as “pre-verified” in the Toitū programme.
2. ‘Carbon Compatible Reporting’ certification: a thorough review of the reporting to determine if the data is aligned to with the ISO 14064-1 and GHG Protocol. Only certified Carbon Compatible Reports can be treated as “pre-verified” in the Toitū carbon programme.

These two Toitū initiatives are designed for Toitū members being certified under the Toitū carbon programme. Toitū acknowledges that the acceptance of such data being used for other[3] intended use(s) and user(s) will be subject to the particular requirements of those users. However, Toitū encourages dialogue from other users, in order to fully understand the Toitū initiatives and ideally gain acceptance of these (beyond the Toitū carbon programme).

The key objective of Carbon Compatible Reporting (CCR) certification is to give verifiers confidence that the data generated by a CCR certified supplier is acceptable as ‘pre-verified’, regardless of the magnitude of the emissions relative to the total inventory.

The key difference between the two initiatives is the Pre-appraised registry is a light touch assessment of the data being generated by a supplier. Checks include: state of methodology and model validation, state of verification of input data flows, suitability of data to end users (i.e. type of inventory the data is contributing to), and level of specificity. This type of data feeding into an end users inventory may need verification checks at a level reflective of the magnitude of the emissions relative to the total inventory. From our Toitū programme experience for example, if it represents <1% of the total inventory, the verifier may need to apply minimal scrutiny, using a ’tertiary’ or ‘secondary’ verification level check. In contrast, if it represents 20% of the inventory, the verifier may need to apply more intensive scrutiny under a ‘primary’ level check, that is additional to the pre-appraised checks recorded in the registry.

Managing impacts on historic inventories

Moving up to supplier specific calculation methods results in emissions being incomparable across an inventory time series. The key point here is that hopping across methods over time is not an appropriate way to demonstrate emission reductions. The reason for this, by way of example, is that the supplier specific emissions data intensity across the time series is likely to be different from the alternative methods (such as the average data method or spend based method), as shown in Figure 5.

Figure 5. How a time series of emissions data from a range of calculation methods can be comparable in certain conditions. Trajectories are hypothetical for illustrative purposes only. The dotted lines indicate the quantified reductions achieved between two points in time.

Another key point though, is that ‘organic’ reductions in the emissions intensity within the same method, is an appropriate way to demonstrate reductions. A caveat to this statement is that the accuracy of the quantified reduction is typically expected to be greater via the supplier specific method than alternative methods because it will be closer to the ‘real’ emissions of the product or service being reported in the inventory.

This leads to the next key point, that data users compiling inventories need to recognise that historic inventory recalculations may sometimes be beneficial. The need for recalculations can be guided by materiality. For example, if the methodology change results in the inventory changing by >5% relative to the total, a recalculation is performed.

To confirm, the need for recalculations related to method changes is anticipated to be limited to Scope 3, indirect emission sources of the value chain. Scope 2 has its own unique requirements in relation to tracking progress under a location-based method or market based method. We expect that in most cases, Scope 1, direct sources will be quantified using the best available method right from the initial base year onwards, hence no need for method changes. For example, a vehicle fleet will usually be calculated using litres consumed multiplied by the appropriate emissions factor per litre. In this example, even if half the fleet is switched to electric vehicles this is a genuine reduction within the bounds of the same method, i.e. less fuel consumed; likewise, a switch to biofuel would still reside in the same calculation method provided the biofuel is calculated via litres consumed.

When recalculations are necessary, the ideal outcome is to recalculate the entire inventory series via the newly adopted method. For example, if a 2023 inventory adopts supplier specific emissions data, the same supplier data series should be applied back to the initial base year inventory (e.g. year 2020). However, if the supplier data series is not available back to base year, this poses a consistency and comparability challenge, especially for tracking progress against reduction targets. Toitū uses a ‘split base year’ approach for this scenario. This is illustrated in Figure 6. Zooming into Scope 3 category 1 as an example:

• The base year and year 1 inventories were both dominated by the spend method.
• In year 3 the inventory quantified a materially significant portion of emissions under the average data method, and data via this method was unavailable for applying back into the base year and year 1 inventories, hence a new base year for this category is set (‘base year 2’).
• In year 5, the inventory calculated a significant portion of emissions under the supplier specific method, and data via this method was available for applying back as far as the year 2 inventory, hence ‘base year 2’ remains for this category, but is now tracked against the recalculated inventory series comprising of data from the supplier specific method.

Throughout all historic changes, Toitū still requires reductions across time series that have used the same calculation method. For example, for Scope 3 category 1 in Figure 6, reductions should be achieved:

• between the base year and end of year 2, prior to switching to the average data method.
• From year 3 onwards, reductions should be achieved firstly via the average data method,
• and then via the supplier method once the recalculated time series is completed.

Except for scenarios where there’s a major material change within a subcategory or specific emission source of an accounting category, we typically expect the split base year approach to be applied at the category level, such as Scope 3 category 1, Scope 3 category 2, etc.

Figure 6. Tracking reduction performance under recalculation scenarios (illustrative example). For Scope 3 category 1 (purple box), reductions need to be tracked between the base year and the end of year 2, prior to a significant switch from spend to average data method. From year 3 to year 4, reductions should be achieved via the average data method. From year 4 onwards reductions should be tracked via the supplier method.

Conclusion

Supplier specific emissions data reflects primary data emissions data specific to the supplier. Used with appropriate quality and relevance checks to ensure it’s fit-for-purpose, this type of data can improve the accuracy of the inventory, and accuracy of emission reduction performance. It also serves to optimise the visibility of reduction potential by unlocking ‘hidden’ opportunities not visible via alternative emissions calculation methods. This leads to better-informed reduction pathway planning.

To see this report with footnotes, sources, and appendix access the pdf here: Download Report