Why Data and Sustainability?

Climate change fundamentally is a physical challenge, so how is data relevant to addressing the climate crisis? Perhaps the unpalatable, somewhat tired, metaphor of data as the new oil should have alerted us.

Yet, as we steer our way through the shifty waters of mitigating climate change and adapting to it, and try to solve fundamental collective action problems, the need for clarity, alignment, agility and speed is higher than ever. And those are indeed the benefits of data well designed and leveraged. Trusted data and thoughtful metrics can define the collective goal post and cascade goals down to all relevant contributors, making every contributor clear about their impact. This is how we have already gone from a global carbon budget, to country contributions, sector-level pathways and company-level science-based targets. Accessible, relevant, data in fast feedback loops can also increase agility in the actions we take to decarbonize, or in allocating capital in the right places. More generally, the ability to measure progress but also to act against data in human or automated ways is indeed fundamental for us to progress at pace. 

In fact, in its 2023 Impact Study Chapter Zero has found that, while most corporate boards now consider climate to be of strategic importance (about 90% say it presents opportunity and innovation, and only 15% view it as primarily regulation-driven), data availability is the second most cited barrier to taking action (24%), only second to the need to trade off long term resilience against short term commercial imperatives.

At a higher level, we at Shyftr see science-backed policy and technology as the two leading forces in terms of climate progress: as regulators clearly signal the direction of travel and key technologies mature, i.e. become able to scale and be cost-effective, change becomes possible. We also see finance and corporate policies as key transmission mechanisms, spreading change across portfolios and supply chains. And consumers and employees crucially act as catalysts as their preferences shift. But for that to happen trusted data flows need to serve as the nervous system for action, sharing signals for consumers, employees and investors to base decisions on, and enabling key actors within organizations to coordinate their actions. We will develop this theory of change in subsequent posts. 

But, for now, to stay practical, what uses of data are we talking about? With evolving regulation, reporting today remains at the heart of the ESG teams’ priorities and usually represents a large proportion (up to 80%) of their time and bandwidth. So the big vision of data driving alignment and agility may sound like just that: a lofty vision hard to put in practice.

However, consider this: companies will have to wrangle many data sources, including external ones, to be able to calculate even just one of their ESG metrics. Let’s take emissions as an example. Emissions are calculated in equivalent CO2 tons by applying emission factors to activity data. Excel buffs can think of it as a giant ‘sumproduct’. For example, electricity in the UK has an average emission factor or 0.143 kg of CO2e per kWh (as per number provided by the UK government here), so if you use 40,000 KWH of electricity in a year (as a midsize business would), that would generate an estimated 5.7 tons of CO2e, a bit more than half of the average UK resident’s personal footprint (around 10 tons of CO2e). You immediately can see how this seemingly simple calculation can get more complex as you try to incorporate the shifting electricity mix that will depend on time and location of your connection to the grid. In windy weather near the large UK wind farms, the emission factor may fall significantly. And as the grid greens (this 0.143 was close to 0.5 back in 2013, just a decade ago), and you go for greener utilities and robust green tariffs, then you want to capture that change in the emission factor. The complexity is why an initiative like Perseus by Icebreaker 1 is amazing, aiming to unlock access to this data, at a granular resolution, for all SMEs in the UK.  But overall, companies who want to calculate their emissions at a level of granularity that allows them to really see the impact of changes they make, still spend a lot of manual effort, spreadsheets, and custom data requests across departments that are not used to providing this specific data. And those data sources oftentimes haven’t been cleansed or governed to the same degree as more routinely used sources.

So, what does that mean for our lofty vision? Well, what it means is that even just helping to solve the reporting challenge by identifying core internal and external datasets needed, cleansing them, governing them and connecting them, will potentially save many man-days of work in most companies. This in turn will free up resources to really play the change champion role that ESG teams are yearning to play.

And if you do this smartly, you can ensure that this data can also be used to drive decisions at the next level down, to actually help improve your ESG performance. That is the approach that Julien Weyl took as Head of Sustainability at Stuart. His focus from day 1 was to deliver the maximum level of impact possible, by really scaling sustainability and making it part of the fabric of the organization. He defined strategic objectives for sustainability, and then used the OKR (Objectives and Key Results) approach already in place at Stuart to cascade goals down across the organization and create a framework where everyone could contribute, and be held accountable for that contribution. And he is very clear on how critical data was to that process: ‘First, we had to understand the data very well, then be able to communicate this data and make it visible. For example, we provided data about carbon emissions within the dashboards teams were already using, all the way up to board discussions. We had to provide the data side-by-side with other business-relevant data, within the decision structures that already existed. That was really important and not that easy to do.’

Businesses also have opportunities to integrate ESG in their views of customer preferences and of market opportunities. They may even be able to unlock access to green financing instruments that will lower their cost of capital if they reach their ESG targets. But all of that requires trusted, shared data. It is no coincidence that a company like Holcim, that has put climate at the heart of its strategy and issued a $100m sustainability-linked bond based on its 2030 CO2e target, also has 7 full pages of data tables in its 2022 Sustainability Performance report.

Of course, software platforms can be of tremendous help on the journey, and there are now several well-funded carbon accounting, as well as ESG data management, software players. But even when adopting one of those solutions, fully owning your ESG data strategy and taking ownership of the relevant internal datasets is a critical step that helps smooth implementation and improve trust in the data. Rachel Delacour, CEO at the helm of Sweep, a leading carbon management platform, said it well when we first met: “Carbon management is a data problem and a network problem at the core. And companies where data experts are involved are the ones where we see the fastest progress.” 

We will stop here for today, just short of revealing the alternative data metaphor(s) we favor. So follow us on LinkedIn to hear that, and more. This is a vast topic we will tackle over many (shorter) coming posts. We hope today’s write-up gives you a flavor of why we at Shyftr are so passionate about this space, and believe that data in service of ESG really can boost your business’s strategy and competitive advantage as we transition to a low carbon economy.