Why close enough isn’t good enough when it comes to carbon data

Each day, more companies join the global effort to combat the climate crisis, setting targets and committing to achieving net-zero carbon emissions. This year alone, 1,243 companies committed to a Science Based Target, and a further 1,019 set an SBT, even in the face of a recession.

To achieve these targets and reach net zero, companies must quickly and confidently decide how to reduce their emissions. Underpinning all of this is accurate carbon data. So how can companies increase their carbon accuracy?  

This article, contributed to by Emitwise’s Head of Environmental Data & Analytics, David Turner, will cover why accuracy is important in carbon accounting, how to calculate more accurate results, and the effort to value of increasing accuracy. But first, let’s define accuracy in the context of carbon accounting.

Defining accuracy

When we talk about accuracy in carbon accounting, we’re asking how close your carbon calculations are to the actual emissions your company and its value chain produce. Unless we find a way to physically measure the CO2 released by every activity on earth, we’re not going to know the actual amount. Instead, you need to ensure that the data powering carbon accounting calculations is as accurate as possible.

Carbon calculations are made up of four elements:

  1. Activity data, meaning data you collect on your business activities such as electricity to power office facilities, the goods and services you purchase, your employees travelling to work or for business
  2. A unit conversion factor, if required (for example, if you collect your fuel data in therms but need to convert to kWh, because there is no emission factor for therms) 
  3. The emission factor
  4. And the global warming potential value

Accuracy in carbon accounting looks to achieve the highest quality possible in those different data types, particularly the activity data and emission factors applied. We’ll discuss this in more detail later, but first, let’s remind ourselves why accuracy is vitally important in carbon accounting.

Why is accuracy important in carbon accounting?

Carbon accounting is the process of collecting, reporting, and presenting data on the greenhouse gas emissions (GHGs) resulting from a company’s operations and value chain. When done well, carbon accounting enables you to design realistic carbon reduction strategies and targets. Just like financial management, it’s all about data-led decision-making.

Accuracy is important to businesses taking carbon management seriously because they’ve seen inaccurate data lead to ineffective strategies that waste time and money.

Inaccuracy also risks eroding confidence in your company’s climate agenda from key stakeholders like the board, investors or employees. It can also damage your brand’s reputation with customers and consumers who consider unsubstantiated reduction activities and inaccurate carbon footprints as greenwashing.

How can you increase the accuracy of your carbon accounting results?

There are three focus areas for those looking to improve the accuracy of their carbon accounting results, input data, calculation approach and emission factors.

The calculation approach

It may seem strange that we’re starting with the calculation method rather than the data input, but there’s a method to this madness. In part, the accuracy of your calculation methodology defines the granularity of data you need to gather in the first place.

At the entry-level of calculations, we refer to spend data, easily accessible from financial and procurement systems. To increase your calculations’ accuracy, you may provide quantity level data all the way through to custom LCAs for individual products. We’ve written more about the calculation methodologies, particularly for Scope 3 emissions, here.

The GHG protocol offers guidance on various calculation methods that can be used to quantify the emissions for different scope categories. For example, mobile combustion has three different calculation methods.  

  1. Spend-based method
  2. Distanced-based method
  3. Fuel-based method

To increase the accuracy of your calculations, companies can try to move up the calculation method ladder for specific scope categories. This is most effective if you focus on the scope categories that correlate to the company’s most significant carbon hotspots. 

Improving input data

As we just said, the ability to use a specific calculation method is limited by the input data available, which we spoke about in a recent article on investor-grade carbon data.  

To achieve better quality data outputs, companies must often change how they manage their data internally to have the required inputs. For example, if you’re a company hoping to use the quantity-based method or hybrid method to calculate your purchased goods and services emissions, you will have to start recording the quantity of goods consumed alongside the spend on those goods. Doing this will move you towards the most specific and, therefore, most accurate calculation method.

Applying the right emission factor and the significance of Scope 3 

There’s so much in scope 3 that the data demands can feel enormous, especially as you need access to emission factors that cover the full range of activities. Scope 3 encompasses all the company’s interactions with its value chain, from buying goods to transporting them, sending their employees off on travel, selling goods, and having these goods used by consumers.

The best way to increase your accuracy across the board is to expand the range of emission factor sources you use.  Emission factors are one of the four main elements of carbon accounting calculations, so they play a critical role in the accuracy of your results. 

If you’re reliant on using Defra, the US EPA, or other government databases, these, unfortunately, don’t go into a huge amount of depth or breadth in terms of the activities that are covered. You will have to explore alternative data sources, such as LCA databases like GaBi, to access more emission factors.

There’s also a wide range of sector-specific databases. Depending on your sector, you may want to start purchasing licenses to these databases that cover the activity types you do.

Database licenses can be expensive; altogether, you could be looking at $100,000’s. So partnering with tools like Emitwise can be helpful for many companies, as third-party carbon accounting providers often have licenses to a much broader and more detailed database of emission factors.

How can you attribute the most accurate emission factor to your calculations?

To find the best emission factor, you need to do something called emission factor selection. There’s no officially defined process for how to do this, but in principle, it involves pairing an activity data point with the emission factor that best represents it.

Generally, emission factor selection will involve consulting different emission factor databases and then using a set of criteria to select an emission factor most representative of the activity type.

This criterion may include;

  • The technical correlation: Is the emission factor technically aligned with the activity type, e.g., is it the specific fuel type and grade?
  • The temporal correlation: How recent is the data, and is it timely enough to compare to those of the activity?
  • Geographical representativeness: Does the emission factor data’s geography match the activity’s location? If so, it’s probably more representative.

Lets look at a working example

Chief Sustainability Officer George Nash works for a UK-based property services provider, Capital Improvements LTD. They’ve just completed their baseline assessment and discovered that Scope 1, mobile combustion emissions account for 50% of their total emissions due to their fleet of vans.

For their baseline assessment, George used fuel-spending claims as their activity data and used the spend-based approach.

George knows that reducing emissions from their fleet will be essential to cutting company-wide emissions and achieving their targets. In the near term, he’s invested in company-wide fuel-efficiency driving training as they transition to a fully electric fleet in the long term.

The distance-based approach will not capture the impacts of his fuel-efficiency driving programme, so George has invested in a centralised fleet management system that captures data on the fuel consumption and fuel type in each company vehicle. Thanks to this change in their internal data landscape, George can use the fuel-based methods and start to see the payoffs of the fuel-efficiency training in emissions reductions. 

Assigning emission factors

Once he has captured his fuel consumption data, the next step for George is pairing this data with the correct emission factor. If George has data on a van consuming 35 litres of 100% mineral diesel, he’ll need to find an emission factor that best represents this.

To do this, he’ll first consult different emission factor databases. In this case, George checks Defra, Gabi, and US EPA. They all have an emission factor for diesel which seems reflective of his van consuming diesel, so he needs to rely on the criteria to tell him which one to pick.

  1. The technical correlation check: is the emission factor the right diesel grade? Is it a different grade? Is it a very average grade? George can find an emission factor for the correct 100% mineral diesel in the Defra and EPA databases.
  2. The temporal correlation check: how recent is the data, or do the dates on which the data was prepared to compare to those of the activity? The Capital Improvement’s van completed this trip in 2020, with a diesel emission factor in both databases for 2020.
  3. Geographical representativeness check: does the geography of the emission factor data match the location of the activity? If so, it’s probably more representative.  George knows the van is operating in the UK, so he’ll choose the Defra database as it’s likely to be more representative.

Doing this at scale can be challenging, which is where emerging carbon accounting technologies can help. In the past, sustainability managers have been burdened with finding and pairing the best emission factors to spreadsheets full of activity data.

Now, technology like Emitwise can ingest activity data and pair it with the most representative emission factor at speed, saving companies time and allowing them to identify reduction initiatives faster.

Who benefits from accurate carbon data?

So, who benefits from all this effort? First and foremost, the reporting company. Ultimately, if you’re serious about setting targets and reducing emissions, you need an accurate baseline to demonstrate the impact of your initiatives and reduction activities on your value chain. You also need accurate emissions data continuously to track progress and pivot reduction activities where needed.

Good quality carbon accounting data means you’re more confident sharing your results with shareholders, customers and competitors, protecting your brand from greenwashing accusations.

But the benefits are not limited to your company alone.

Sharing these results fundamentally improves how other companies in your ecosystem understand and accelerate their own climate action, enabling customers and consumers to confidently make lower carbon choices.

Altogether, accurate carbon management underpins a successful triple bottom line- for people, planet and profit.