Data as a cornerstone of a successful sustainability strategy

A data-driven approach to sustainability

When we assist clients on their sustainability journey, we always begin with a critical first step: gathering all the necessary data. To evaluate your environmental impact and identify carbon reduction opportunities, having accurate information is essential. Low-quality data will result in an over- or underestimation of emissions, meaning unclear reduction pathways, unclear KPI’s, potentially higher costs for compensation and a negative perception compared to competitors. The higher the quality of your data, the more tailored and effective your carbon reduction strategy. In today’s corporate sustainability landscape, this foundation is crucial, yet challenging to achieve.  

Our approach? A long-term, phased plan, beginning with a comprehensive CO₂ analysis. This analysis helps us pinpoint areas for improvement in both sustainability impact and data quality. With these insights, we establish the framework for your sustainability strategy. Next, we focus on refining data quality. 

Collecting data with a CO₂ analysis

Our starting point is often a CO₂ analysis. This allows us to assess the ecological impact of your organisation, throughout its entire value chain, and uncover areas for improvement. Although only focussing on the environmental pillar of an ESG (economic, social and governance) strategy, this is a time-proven gateway to effective sustainable action. We also know from experience that charting carbon emissions is a big hurdle for many organisations, due to the complex nature of the process. Which is why we usually start there. 

The four levels of data quality

Accurate CO₂ measurement relies heavily on the quality of your data. We can identify four distinct levels of data quality, ranging from the strongest (supplier-specific data) to the weakest (financial data). To meet CSRD standards, data quality must reach at least the average data level (3rd level below). 

1: Supplier-specific data 

Supplier-specific data are the emission data for a specific product or service, provided by the supplier, that cover the entire life cycle up to the point of purchase. The market is gradually shifting toward relying on supplier-specific data, a development that simplifies the data collection process significantly. By obtaining this data directly from suppliers, the need for extensive data collection is greatly reduced. Supplier-specific data is also the most accurate, offering a robust foundation for crafting a focused and impactful carbon reduction strategy.  

2: Hybrid data 

Hybrid data are a combination of average and supplier-specific data. This combination can be useful when for instance a supplier can provide you with the Scope 1 and 2 emissions of their product, but no data is available on the waste that’s generated during the production process. The waste impact is then calculated based on sector averages. This data level is quite complex as it entails data from your suppliers for Scope 1 and 2, and factors for specific elements. Which is why hybrid data is often not available in many organisations. 

3: Average data  

When using average data, we are able to calculate your emissions based on the physical data of activities or products, multiplied with the relevant emission factors.  

4: Financial data  

When using financial data, we are only able to calculate your emissions based on the financial value of a product or service, multiplied by a financial-based emission factor.  

Emission factors are coefficients that represent the amount of greenhouse gas emissions produced per unit of activity, product, or expenditure. They allow companies to estimate emissions associated with various activities or goods, even when direct measurement isn’t feasible. Emission factors are most often sourced from established databases, government agencies, and industry-specific research. 

Improving data quality 

While a CO₂ analysis usually provides sufficient information to make a good first assessment of your impact in terms of carbon emissions, it wouldn’t be unusual for a few datapoints to require further optimisation.  

Precise information is not necessarily always available for every part of the value chain, and if it is, the quality might sometimes not suffice. 

In the table below, you can see a realistic representation of data categories and their respective quality level for a service company. The category with the largest share of emissions, scope 3 upstream purchased goods and services, has the lowest data quality. Which means an inaccurate and distorted view of actual impact in terms of emissions, and consequently, potentially ill-suited reduction measures and higher compensation costs. 

Let’s look at this from another angle. In the example below, a cement production company measured carbon emissions using financial versus average and supplier-specific data. As you can see, the financial-based data results in measured carbon emission levels that are almost four times higher than the actual supplier-specific data! 

It doesn’t mean action can’t yet be taken in the meantime, but it simply indicates that a few metrics are best improved upon in the long term. This is important because low data quality often results in an over– or underestimation of your carbon emissions. The more specific and accurate the data, the better we can make the assessment of your environmental impact. Which can lead to: 

• A more tailored and effective impact reduction strategy 

• CSRD (Corporate Sustainability Reporting Directive) compliance: the directive demands, at least, average-quality data. Financial data does not meet this requirement. Even if your company is not directly affected by the CSRD, your clients might, which means they require qualitative data from you. 

• An improved competitive position: sustainability has become a USP in and of itself, often positively influencing purchasing decisions. So, if you provide a sustainable product or service, or have undertaken significant efforts to reduce your carbon footprint, you’d be smart to broadcast this. Using high-quality data for this adds the necessary transparency and credibility to your green claims. 

Next steps 

After the first analysis is done, and the lines for your future sustainability strategy have been drawn, we are only at the beginning of the journey. Some potential next steps: 

• Improving carbon emission data quality by acquiring more detailed information from your suppliers  

• Repeating the CO₂ analysis on a yearly basis to track progress and to further improve data quality 

• If you’re a production company: conducting a life cycle assessment, complementing the impact assessment from the CO₂ analysis with a broader scope of information. An LCA examines the entire lifecycle of a product, from growing the raw material to disposal of the final product at the end-of-life. It also covers more environmental impact categories than just global warming potential (of which CO₂ is a subcategory) 

Need help? 

Sustainable ambitions, but need help gaining insight into your organisation’s impact and tackling the challenges that come with data collection? Reach out to us!