Assuring the Quality of your Upstream Supplier Data in Value Chain (Scope 3) Emissions

03 Nov 2022

Is the complexity of your supply chain and the lack of visibility in its data quality crippling your net zero ambitions?

Complex matrices of suppliers can make it difficult to collect reliable data

In the current sustainability landscape, organizations and individuals, particularly senior management are being criticized for being overly ambitious with their sustainability claims and falling short of them in practice. Companies can bridge the gap between promises and results by gathering reliable data that illustrates their current position in greenhouse gas (GHG) emissions. In today's climate, though, we need to go beyond understanding and reducing scope 1 and 2¹ emissions to tackling scope 3—value chain emissions.

Our whitepaper titled "The Road to Net Zero" explains the importance of scope 3 emissions when setting "net zero" targets.² "Net zero" is a global state that requires collective and collaborative efforts to achieve, and certain industries have their highest source of emissions from scope 3. In the apparel and textile industry, for example, some brands have found that scope 3 covers approximately 70 to 90% of their total emissions. The Carbon Disclosure Project (CDP) reported that supply chain GHG emissions are on average 11.4 times higher than operational emissions.³

Companies with a complete view of GHG emissions throughout their entire value chain have a tremendous opportunity to influence suppliers whose individual emissions add up to significant levels. However, complex matrices of suppliers can make this task difficult and complicated.

Collecting reliable and accurate data related to your company is one of the key obstacles to fully understanding the impact of your indirect emissions. Challenges include (i) having less influence and knowledge over the type of data the suppliers have and their methods of data collection, (ii) properly assessing their data quality, and (iii) filling in data gaps, possibly relying on assumptions and modelling.

Indirect emissions from purchased goods are related to extraction, production, and transportation of products. Emissions from a plastic toy, for instance, would include the plastic material, the coloring, the adhesives, the energy consumed during the manufacturing of the plastic, any upstream transportation, any waste generated, etc. More likely than not a supplier has multiple clients, and therefore would need to allocate information according to the products you have bought. For certain information, primary data like electricity bills could be available, which would provide greater confidence in data accuracy. Other information, such as levels of waste generated, may lack clear records and require assumptions.

Ultimately, the aim is to find data that can lead to reduction actions, rather than merely gathering information.

There is a growing number of data collection solutions, but do they ensure data integrity?

To achieve data integrity, you should pursue greater transparency from your upstream suppliers. Given the aforementioned challenges, helping your suppliers get started with data collection and capacity building are critical first steps to early success. It could be as simple as providing suppliers with GHG inventory data tracking tools, or data collection templates that are in the format you need. There is a growing amount of software in the market, too, that enables brand and supplier data integration and encourages record-keeping practices at the granular level necessary for complete GHG calculations. However, these tools only solve one part of the problem: data collection. They do not ensure data integrity, as they aren't data-quality management systems, nor solutions to transparently review the data source.

The challenge of finding good data explains the increasing demand for third-party assurance of value chain emissions and supplier data. As regulators, stakeholders, and oversight bodies continue to increase their mandates and require companies to report their value chain emissions, this demand will reach even greater heights in the coming years.

Assuring supplier data integrity through risk-based assessment

With the current levels of scrutiny on a company's GHG ambition and the practicality of its reduction roadmap, third-party verification is critical to ensuring the accuracy and comprehensiveness of supplier data, and to ensuring calculation methodologies are aligned with international GHG calculation criteria/standards.

The GHG Protocol Standard lists the "supplier-specific method" and "hybrid method" as the more-specific methods for calculating certain scope 3 emission categories.⁴ Assuring your supplier data cultivates widespread adoption of both of these methods, if not just the supplier-specific method.

It's important to remember that each supplier is unique—not just in terms of location, company size, and culture, but in terms of their production processes, product types, and maturity level when it comes to environmental and data quality management. Multiply that variety by all the suppliers your company has, and the task of reviewing the data can seem daunting. This is where a risk-based approach is helpful. A risk-based approach not only analyzes the supplier base to identify the ones at highest risk of providing erroneous or "low quality" data, but also provides a unique and holistic view that integrates sustainability and quality-related knowledge with data integrity assurance (including perspectives on scope 3 emissions, quality management, site audit, and supply chain management).

Helping suppliers establish a good foundation in data quality management

While the growing need for data collection and quality control is no small change for brands, it is no small change for suppliers, either. Many suppliers are inexperienced in supporting brands with their GHG accounting, so it is essential to maintain your supplier relationships while ramping up collection and quality control of Scope 3 insights.

With respect to national, regional, ethnic, and other differences, it is equally important to have someone with cross-cultural knowledge of sustainability who is familiar with the variety of supplier operations around the globe to support them in data collection and quality control.

Ultimately, data quality management is less about finding fault and more about facilitating growth, especially in suppliers that are at highest risk of providing poor-quality data. Cultivating good data quality management systems, effective habits, and strong knowledge and awareness leads to high-quality data that benefits not just the downstream companies, but the suppliers, too, in the collective journey to net zero.

We're Here to Help:

For more information about this topic, download our white paper, "Staying The Course in Your 'Net Zero' Journey".

To learn more about how we can support your business, visit: https://www.intertek.com/assuris/sustainability/.

References

1. The importance of scope 1 and 2 emissions for any company reporting their emissions or setting net-zero targets are straightforward and not covered here.

2. Scope 3 are indirect emissions that are a consequence of activities by the reporting company, but occur at sources that are owned or controlled by another entity.

3. CDP. (2022). "Engaging the chain: Driving speed and scale – CDP Global Supply Chain Report 2021".

4. The "supplier-specific method" collects product-level cradle-to-gate GHG inventory data from goods or services suppliers, whereas the "hybrid method" is a combination of supplier-specific activity data and secondary data, to fill in any existing data gaps.