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This chapter considers data management tools and best practices for using primary data to estimate the impact reduction potential of interventions and track progress in the supply chain.
The right data can make tracking progress easier and more accurate. It has enough detail to provide your company with an accurate picture of your progress, but not so much that it creates unnecessary complexity and cost without adding value. What constitutes the right data, however, will vary depending on the company and circumstances.
In this chapter, we’ll look at the different types of data for tracking progress, how to determine which is best suited for your company, when and how to make the switch to supplier-specific data, and what to think about when integrating such data in your footprint.
Before diving into what supplier specific data is, it’s important to clarify that in environmental accounting, data often refers to two things, activity data or emissions factors. Activity data represent the direct information about a process, for instance the energy consumption used to roast coffee. Emissions factors on the other hand, are computed using activity data, either statistical or specific, to provide the impact of that process.
Most companies rely on generic, or secondary, emission factors – pulled from databases such as ecoinvent, Gabi, or Quantis’ WFLDB and WALDB. These factors reflect statistical practices and trends at the global, national or regional level, making them efficient for assessing overall emissions and spotting major hotspots, but not specific to any one company or value chain. Examples of secondary emission factors include country level electricity impact, relying on energy grid mixes, or a jurisdictional crop emission factor relying on average fertilizer use and deforestation in that given region.
Supplier-specific data, on the other hand, is data collected directly by a company or its partners along the value chain that reflects the actual activities or process inputs used by suppliers. It can be obtained in a number of ways, most commonly through supplier data collection files or directly at the source, for instance in agriculture with field measurement, using techniques such as remote sensing tools or various sampling approaches. Just like secondary data can have different levels of granularity, supplier specific data can be at different levels. For industrial data, that data could be at the company level, site level or even more granular going to the production line or batch level. For agriculture, you will have data at the sourcing region, land management unit or farm, or even at the plot within the farm.
Each type of data and level of granularity will bring various benefits and challenges, it’s therefore critical to determine the type of data that will be relevant to reach your objectives.
Secondary data, while less precise, is easily accessible and can help you draw conclusions about your own impacts, including where the main sources of emissions lie in your company’s value chain, and where you should focus your efforts to get the best results. Secondary data may be preferable for companies that:
More precise and accurate data may become necessary the further a company advances along its transformation journey – particularly for reporting on annual progress towards emissions targets. Supplier-specific data, which by nature is more accurate and granular, may reveal that a company’s impact is lower or more significant than previously believed. However, as collecting supplier-specific data is a resource-intensive process, it’s important to understand if and where it is likely to add value before making the switch.
Beyond these questions, companies should strongly consider transitioning to supplier-specific data in these two situations:
Improving supplier performance
Have you or your suppliers implemented actions that will lead to a reduction of emissions? Do you plan to report this progress? Collecting supplier-specific data will be the most effective way to reflect this progress. Beyond specific actions, supplier-specific data can also be used to identify suppliers’ emissions performance and support them in their transition.
Accounting for removals
If you plan to account for emissions reductions and removals, you’ll need to collect supplier-specific data, which is mandatory under the draft GHG Protocol for Land Sector and Removals. Indeed, GHGP LSRS requires companies to use data that reflects actual projects in their supply chain, proved through relevant Chains of Custody and calculated using soil sampling or models calibrated specifically to the local conditions.
If you determine that collecting supplier-specific data is the most appropriate course of action for your company, there are a few important things you’ll need to consider:
While you ideally want to collect supplier-specific activity data which will allow you to draw clear conclusions and have the full transparency you need, you may receive only a supplier-specific emissions factor without much insight on the drivers behind that data. If this happens, you’ll need to ask the supplier to provide information about the scope and the underlying assumptions that went into the emissions factor, to ensure both consistency with your existing emission factors, and credibility of the data, especially if there are significant variances with the generic emissions factors.
The granularity of the data you should collect will depend on the use for that data. If you are looking to refine a specific supplier specific emission factor because of an intervention you have implemented on a specific farm with that supplier, farm level will be your direction. If this is something your supplier did across all their operations, you might be looking at something more high level representing a broader scope. Note that data accuracy and transparency will typically increase with the level of granularity but will come with more data and emissions factors to manage. So just like finding a balance between secondary and supplier-specific information, you’ll want to find the right level of granularity to bring value without increasing complexity too much.
Data Quality
The quality of your data matters. It’s vital to take steps to ensure that the data you’re collecting is as high quality, relevant and consistent as possible, such as providing trainings for those responsible for gathering data and performing a deep quality review of the data once collected.
Consistency
As pointed out in the chapter on rebaselining, data must be consistent across your footprint. So, if your company decides to plug a new supplier-specific data-generated emission factor into your footprint, you’ll need to check that the scope, underlying assumptions and methodologies are aligned to determine if rebaselining is required.
Documentation
Companies should always document the overall data collection process as well as the source of the data in the event that any major changes occur.
Collecting supplier-specific data can be a tedious process. To ensure you’re setting yourself up for success from the get-go, we’ve outlined the four essential steps companies should take after identifying the type of data they need to properly collect, process and utilize supplier-specific data.
Many factors need to be taken into consideration when defining these needs. For example: Do you need activity data vs EFs? What is the scope of the data? At what cadence? What granularity?
When defining this need, it’s not enough to consider your current needs, you also need to anticipate your needs in the future.
When collecting supplier-specific data, transparency and standards alignment will also be critical. It ensures scientific and strategic integrity. Understanding the methodology and assumptions behind supplier-specific data is critical for interpreting results, finding and fixing errors, and ensuring updates can be made.
Some of the key methodological pieces to look for:
As mentioned, companies can collect data either from their suppliers, or in agriculture, go directly through the supply chain and collect it from the farms with data collection tools. Suppliers will often rely on such tools to collect data at scale from farms. The decision might be driven by the type of actions you are trying to track, for instance, a series of interventions you have implemented directly at the farm level might drive you to go directly with farm level tools. On the other hand, reflecting practices from your suppliers will benefit from working with them to collect data directly.
If the decision is to go through a third-party tool
Tools can help companies leverage supplier-specific data to generate emissions factors that are more representative of their supply chains. But if the quality of the data going into the tool is poor, so will the results it produces.
To ensure you choose the most appropriate tool for your needs now and in the future, there are a few important things to consider:
Before collecting supplier-specific data, whether through suppliers or with the use of external tools, it’s critical to provide training to the people who will collect the data on:
These people can be the buyers in the case of a supplier angle, or the sustainability team in the case of a third-party tool. Beyond the internal teams, if asking the suppliers to provide that data, they will also need to understand why you’re starting this journey and might also need training – especially when considering smaller size suppliers. Note that buyers’ primary responsibility is to buy raw materials or finished products following certain quality and price specs. Therefore, sustainability teams should provide enough support to ensure the right data is collected.
For example, when collecting information on energy consumption, special attention should be given to: regional preference for units (MJ, kWh, Nm3); data representing either an annual average or a shorter cycle; and the assumption behind the energy mix (e.g., CH4/H2 ratio for natural gas).
Though these parameters may seem simple and straightforward, they’re a common source of error in supplier-specific data collection that can have significant implications for the overall quality of data and the results they produce – and buyers can’t be expected to know about these specific climate-related details.
In addition to the training, data collection can be time-intensive and needs to be done repeatedly (ideally every year) in order for companies to track progress effectively, so it’s a big ask for already busy producers, suppliers and farmers. Providing insight into the purpose of the data collection, as well as incentives – financial or otherwise – can bolster buy-in and engagement, helping strengthen relationships between companies and their supply chain stakeholders, as well as ensuring more accurate and robust data collection.
Collecting data can take time. Ensure enough time is allotted for that process, and if going through suppliers, ensure you provide them with the right support and enough time to provide you with robust data.
Throughout the data collection process, keep track of best practices and key learnings developed during the data collection process to shape future collection efforts.
Once the data has been collected, it needs to undergo a thorough review to ensure consistency and address any errors. All critical information should be filled in and correct (e.g., all numbers and units are plausible, best practices reflect discussions you had with your suppliers or farmers). We also recommend doing a cross-check or benchmark between the different survey answers and external sources (e.g., IEA, FAO or environmental databases such as WFLDB, ecoinvent) to see if answers make sense.
For example, if the data you receive from your suppliers indicates their packaging impact is 3 kg CO2e per kg of pack, but the generic information you had previously showcases something closer to 12 kg CO2e per kg of pack, there is likely an issue in one of the emission factors and a discussion should take place. Similarly, looking at farm level data collection tools, if one farm harvests 20 tons of a given crop and another farm of the same size and located in the same region harvests 200 tons of the same crop, there could be an order of magnitude data error for one of the farms.
If collected as activity data, processing the data into the company’s footprint should only happen once the collected activity data has been deemed to be of sufficient quality and following rebaselining principles. If data quality is not ensured, results will be biased, which will negatively impact all the work that follows.
When transitioning from secondary to supplier-specific data you should anticipate that there may be some immediate impacts on your footprint with variations going both ways. Though companies may be concerned that these variations, especially when they showcase an increase in emissions that could put them at a competitive disadvantage with peers using secondary data, the additional knowledge brought by supplier-specific data will enable them to identify key areas for improvement – effectively tackling impacts and focusing resources where they’ll count.
Collecting data in itself does not generate impact. What truly matters is distinguishing between results that stem from methodological changes – such as moving from secondary to supplier-specific data or switching data collection tools – and those that reflect actual emissions reductions from targeted interventions. Methodological shifts may improve accuracy, traceability, and monitoring, but they should not be mistaken for real reductions.
Keep in mind that if methodological change represents more than 5% of total base-year emissions, rebaselining will be necessary. However, we recommend rebaselining the specific EFs as soon as supplier specific data is collected to enable tracking and claiming progress.
Switching from secondary to supplier-specific data to measure the progress and changes generated by an intervention at a supplier level.
The graphic illustrates that:
One question we often receive is how to manage switching from one supplier to the other. While it might not be something we recommend pushing as an initial way to drive change, it can sometimes either be the only solution due to a supplier refusing to make changes or can be the result of usual procurement cycles or sourcing availability. In either case, while globally this might not lead to a change of emissions, at the corporate level, it does represent a potential change in emissions, and if the new supplier is more efficient or has more environmentally friendly practices than the former one, then yes, it should count as progress. However, in order to claim this progress, note that you will ensure you have data enabling a robust comparison between the two suppliers’ performance, for instance activity data of good quality from both, or EFs modeled following consistent methodologies and scope. Without this, making such comparison and claim will be difficult and could result in credibility issues.
Once you have received your supplier-specific emission factors, you’ll need to review them to understand what information they provide. When doing so, you should ask yourself a few questions to ensure your interpretation is accurate.
Another key challenge companies face is the variability of results comparing data from different suppliers or generated by different third-party tools. Differences in scope (e.g., inclusion or exclusion of carbon sequestration), assumptions, models, or default values can all lead to inconsistent outcomes. For this reason, it is critical to understand the driver behind these changes, whether they reflect a difference in methodology or an actual emissions delta from interventions.
Ultimately, progress tracking depends on ensuring that reported reductions are “real” and not artifacts of calculation. Solely switching from secondary to supplier-specific data, or from one tool to another, will not qualify as a reduction. To maintain transparency and credibility, companies must understand what drives the shifts, applying the appropriate processes and clearly communicate why results may vary to ensure only true progress is reported.
The following practices will help you ensure that the transition from secondary to supplier-specific data is a smooth one.
Data collection: a means to an end
Collecting extensive supplier-specific data can be an expensive and time-consuming exercise, especially if it doesn’t highlight improvements or bring additional insight. Before, during and after supplier-specific data collection, it’s important to keep the purpose – your “why” of data collection – top of mind: reducing your environmental impacts and tracking your progress. In this context, supplier-specific data is useful to better understand the reality on the ground, prioritize, forecast and finally track progress.
Focusing solely on refining data, without a true purpose in mind, you risk expending valuable resources (cost, time, personnel) without a clear outcome and detracting attention away from the actions necessary to drive business transformation and alignment with planetary boundaries.
Manage expectations.
Communicate clearly, and as early as possible, to internal stakeholders, particularly those in the C-suite, that the numbers will evolve when data collection techniques are updated and that such changes might not represent effective increase or decrease of emissions. C-suite executives may not be accustomed to continuously changing baselines. Communicating these changes upfront can help prevent difficult discussions later.
Establish a clear data collection and quality assurance process.
If you don’t outline best practices and processes for collecting data, you could end up with data that is inconsistent and unusable. Take the time to develop clear, concise guidelines that include information on identifying and clarifying data sources, standard operating procedures for data collection, data quality requirements and third-party quality assurance requirements.
Increase transparency over your supply chain.
Ultimately, your company’s ability to access (and benefit from) supplier-specific data is directly related to the level of transparency you have over your supply chain. Working to improve supply chain traceability and maintaining an up-to-date map of suppliers will put your company in a better position to (a) identify and assess the impact of its sourcing activities (energy consumption, land-use changes, etc.) and (b) switch progressively from secondary to supplier-specific data as your suppliers start implementing emission reduction initiatives.
Be cautious of how you interpret results
Tracking progress can be a complex exercise, especially when moving to supplier specific information. Bear in mind that results might vary from one supplier to another due to methodological specificities, but also that supplier specific data shouldn’t be compared to secondary data. Rebaseline with the supplier specific data and use this updated baseline to track your emissions effectively.
We have answers. Get in touch with our team today and let us guide you through the solutions that might help you on your journey toward a sustainable supply chain.
Pierre Collet – Global Footprint Lead, France
Tereza Lévová – Data Team Lead
Alexi Ernstoff – Sustainability Principal, Land + Agriculture
Marcial Vargas-Gonzalez – Global Science + Innovation Lead
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