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The data dilemma in tackling climate change

The data dilemma in tackling climate change

19-04-2021 | Insight
Curbing global warming means cutting emissions – but getting the right data is not so simple.
  • Thijs Markwat

Speed read

  • Emissions are classified as Scope 1, 2 or 3 to identify their source
  • Data faces problems of being backward looking and overlapping
  • The approach needs to be qualitative as well as quantitative
Climate investing: from urgency to solutions
Climate investing: from urgency to solutions
Read more

This can be seen in tracking the levels of emissions and particularly in trying to establish where they came from in the first place. It is essential to be able to identify the companies that are responsible for investment and engagement purposes.

To give a clearer idea of the source of emissions as they relate to companies, they are classified as either Scope 1, 2 or 3 emissions. In short, Scope 1 are those directly generated by a company; Scope 2 are created by the generation of the electricity or heat needed to make a product; and Scope 3 are caused by the entire value chain, including the end user of the product over its life cycle.

But it’s not simply a case of adding up tons of cubic meters of greenhouse gases – assuming that you could even access that information. There are three principal problems, stemming from the fact that by definition, any data acquired about anything is always historical.

Climate Series – don’t miss a thing
Climate Series – don’t miss a thing

Data looks backwards

“A fundamental problem of carbon footprint data is that it is backward looking, with an average time lapse of around two years. So, if we're staring down the barrel of carbon, then we're currently looking at the reality of 2019,” says Robeco’s climate data scientist Thijs Markwat.

“This means the data won’t tell you about the transition readiness of a company. What we really need is more forward-looking metrics. A carbon footprint as it is now doesn't tell me about whether the company is going to decarbonize in the future.”

Competing providers

The second problem is not that there isn’t enough data, but that it comes from multiple and overlapping sources that are often contradictory. “Scope 1 and 2 data is relatively easy to obtain, but there’s hardly any correlation on the scale of it from the different data providers,” says Markwat. “The real problem is that it's not measured, it's modelled. That means it's estimated.”

Furthermore, the scopes themselves do not tell us the whole story. For example, while a carmaker will produce relatively low Scope 1 and 2 emissions in making a petrol-driven car, the user of the vehicle would burn petrol over many years, causing very high Scope 3 emissions in exhaust fumes.

But the data challenges should not keep us from acting. “The lack of data is being used as an excuse by some to avoid tackling the issue head on,” says Markwat. “We need to be careful not to frame the entire issue as a data challenge; it’s more of an analytical challenge caused by the data itself. We know what the carbon-intensive sectors are, so we can act on that.”

Numerators versus denominators

The third issue is what metrics to use, as the current approach is largely quantitative when it needs to be qualitative as well. “The carbon footprint is the numerator, but then there's also the denominator,” says Markwat.

“So, do you look at companies in terms of their carbon footprint per sales, or per enterprise value? These factors make huge differences when EU law requires one thing and laws in other regions and countries require something different. There needs to be a more focused approach.”

This article is taken from our climate investing platform

Climate investing: from urgency to solutions


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