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Why Excel is the best tool for carbon footprint measurement… for the first few years anyway.



There’s a plethora of tools out there to measure carbon footprints, from quick and free short assessments to comprehensive and dedicated software tools, but from our experience, the best way to measure your GHG emissions in the first few years, is by using simple Excel. Here’s why: 


  1. You’ll get the right emission boundary. 


One of the key challenges with measuring a carbon footprint is to know what to include and what to exclude. The rules for this are set out in the GHG Protocol guidance and working through this step by step to define your boundary and methodology (e.g. operational vs. financial control) enables you to record, in Excel, what emission sources you need to measure, and then work out how to measure them. Software tools in contrast either pre-determine what you have to measure (and usually miss out some relevant categories), or they determine what is included by what data you can provide, and the data you know you need to measure but don’t yet know how to, gets missed. 


  1. Data collection is less burdensome on the business.  


Collecting carbon data is typically an extra task asked of people who are already busy with their day job and it can be difficult to get their buy-in for this. Using Excel you have the freedom to accept this data in any format, thus allowing data owners the freedom to share it in a way that suits them best, e.g. from a document they already have available, or a system output they can readily generate. Software tools however need data to be entered into their system in a prescribed way. Of course, there may be two or three different options for how they’ll accept the data but, in our experience, it rarely lines up with the format in which the data is received.  


  1. Data gaps are clear and can be addressed in the most suitable way. 


In the first few years of data collection there will be many data gaps, such as months where meter readings weren’t known or air conditioning service records were lost, etc. It is important that these gaps are filled with a suitable emission estimation, otherwise the footprint could be significantly under-stated, and this requires some common sense to be applied. For example, if utility bills are missing from October to December, it would be better to estimate these from the following January to March use or the previous October to December use, rather than from July to September use which would likely have different weather patterns and thus heating/air conditioning use. Typically, software tools are not sophisticated in this area. Mostly, they don’t even identify missing information, and if they do, they don’t apply an estimate. In the few tools that do apply an estimate, it isn’t based on the intuitive logic that can be applied by a person using Excel. 


  1. Data entry errors are easier to spot. 


It’s normal that any data requiring manual human touch will have some errors, and this applies to most carbon data. Utility meter readings can be misread, units of data entry can be misunderstood, and fat fingers can mistype any number. When data is presented in Excel alongside other similar data (e.g. utility use by month over a number of properties; commuting distance by employee, etc.) it becomes easy to spot data outliers because of its presentation, but also because most office workers are highly experienced at using Excel for that exact purpose – conducting calculations and checking for errors. These same errors in carbon footprint software are harder to spot because people aren’t used to spotting software errors. Further, if a potential error is spotted (e.g. abnormal use), it’s easy in Excel to highlight that in a colour, investigate and resolve with a data change or add an explanatory comment as to why the usual use is correct. From our experience, carbon software tools lack this functionality. They may allow comments to be added to data points, but they have no easy way to summarise the data anomalies that humans can’t fix straight away but are working on checking/resolving.   


  1. Calculation errors are fewer.


Counterintuitive, but we believe calculation errors in Excel are fewer. To calculate carbon emissions the same basic process is always applied – a data input (e.g. kWh of electricity) is multiplied by a conversion factor (also known as emission factor) to produce the carbon emission value. No doubt carbon footprint software is infallible at multiplying two inputs together, but the calculation is much more likely to be incorrect because of the selection of the wrong conversion factor.  Errors here occur because either software pre-determines the conversion factor, or because it requires manual selection of the factor by the user.  


In the first scenario, where conversion factors are pre-determined, they will likely mainly be correct but if a company has any nuances, errors will appear. An example of this we have seen a few times is with companies operating largely in the UK but with perhaps one office abroad. This foreign office should use a different conversion factor for electricity, but a simple tool may not have this functionality or may not prompt the user to change the factor.  


In the second scenario, where the user selects the conversion factors, this on face value seems as prone to errors as Excel, where the user also has to select their own conversion factors, but it isn’t, because of the volume and presentation of conversion factors in these systems. Dedicated carbon software systems often have thousands of different emission factors available to users (and tend to promote this fact!) yet we nearly always use less than 200, and often far less. Identifying the right conversion factor to use is complex but for UK companies is made vastly simpler by the annual publication of the UK Government Conversion Factors. This Excel data set is presented in an intuitive way, using clearly labelled tabs and tables, coupled with detailed explanations and examples of which factors to apply. The document literally guides you to the right factor, and with a standard format consistently used year-on-year, use gets easier over time. Software tools in contrast largely rely on users selecting an emission factor from an extremely long list of labels that they have chosen to call the conversion factors. Whilst no doubt aiming to be clearly labelled, it can be challenging to find the factor wanted from a list that is vast (is it “diesel car” or “car, diesel”, for example) and for non-experts, understanding the nuances of application is harder when the numbers aren’t presented next to related emission factors. Of course, some tools do try to guide users around these issues by filtering what they can see but in all the software data sets we’ve reviewed, we’ve always found conversion factor selection errors. 


  1. Data process improvements can be made more readily.   


The methodology for measuring emissions will change over time, particularly for high emission sources. An obvious example is around business travel. A common way to calculate this initially is by estimating travel distance from travel spend (i.e. by applying an average £ per mile), and then using a conversion factor to convert distance to carbon. As data collection gets more sophisticated this might be adapted to consider car type (e.g. a different average £ per mile for diesel vs. petrol vs. electric vehicles), then it may evolve to measure mileage instead of spend (e.g. from odometers), and eventually a method to capture fuel purchased may be used (e.g. litres from fuel cards). Going through this journey of emission specificity takes time but is important to track emission reductions. When using Excel, amends in method can be readily made, but changes to software, particularly where API feeds have been established, is more difficult.  


  1. Functionality is not limited. 


Excel has endless options for calculations but carbon software is usually limited in functionality to some extent. Two specific areas we see this in are in relation to calculating scope 2 electricity emissions, and scope 3 category 11 in-use emissions.  

For scope 2 emissions, these usually need to be measured in two ways, the location-based method (i.e. standard way) and the market-based method, which reflects supplier/tariff-specific emissions. For any company seeking to use green energy to reduce their electricity emissions the market-based method is imperative. From our experience, carbon software tools often don’t allow for the market-based calculation, or if they do, they don’t have the emission factors needed (i.e. supplier-specific ones) and don’t prompt users to source these themselves, instead just using the default factors, which are an allowable but overestimate emissions.  


For scope 3 category 11 emissions, these relate to in-use emissions and we find that software tools are rarely sufficiently tailorable to remove the need for additional Excel calculations. This is because their calculation is so bespoke to the company and products sold. For example, for a housebuilder these are calculated based on the annual designed gas and electricity use for each home multiplied by 60 years, with electricity emissions reduced at a rate reflective of grid decarbonisation. For another electrical product manufacturer, the product energy use per hour would need to be scaled by likely use in a year (time), then for product lifespan (which will vary per product), and also consider grid decarbonisation. For companies with many product lines or unique products, and particularly those with some data gaps, these calculations get very complex very quickly. 

 


As we’ve highlighted, carbon software tools have a number of limitations, particularly around usability and accuracy. However, once a clear method has been established and honed for a few years in Excel many of these issues, e.g. selection of emission factors, missing data, etc. will fall away. At this point, using a software tool can be extremely beneficial, reducing data collection time though automated API feeds, and increasing visibility of emissions performance and reduction potential. If you’re still on that data journey though, stick with what we know works – Excel. 


If you’ve already done an Excel-based analysis and want an expert to cast an eye over it, submit this to one of our free ‘ask an ESG expert’ sessions (see here). Alternatively, contact us here if you’d like us to provide a quote for measuring your GHG emissions for you… in Excel of course!  

 

Authored by Caroline Johnstone. 

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