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DIY Spreadsheet Calculation

Using Utility Bills and a Spreadsheet to Calculate Your Carbon Footprint

Your community’s carbon footprint is a measure of the amount of greenhouse gases (GHG) you are contributing to the atmosphere. It is important to calculate your carbon footprint, because that is how you will be able to know (and to prove to others) that you are making progress toward eliminating your GHG emissions.

The Procedure

The idea behind this procedure is to convert the amount of fossil fuels and electricity you use into the corresponding amount of CO₂e. Having that information lets you focus on the energy sources that produce the most CO₂e; and, if you do the calculation on a yearly schedule, you can monitor how much progress is being made toward your goals.

To calculate the amount of CO₂e produced by each energy source, multiply the quantity of energy consumed by its emission factor—that is, the amount of greenhouse gas (in this example, CO₂e only) released per unit of energy. Emission factors are available from many sources, and values often differ slightly. This variation is not a concern, since the purpose of the calculation is to track trends over time rather than to achieve perfect accuracy. A reliable resource is this EPA Emission Factors for Greenhouse Gas Inventories PDF, which balances comprehensiveness with ease of use.

The only difficulty of this method is that emission factors are stated in a wide variety of units with which you may not be familiar e.g. lbs. CO₂e/gallon fuel, kg CO₂e/MMBtu, gram CO₂e/joule, which can become confusing. So, select a set of units and be consistent in their application. Fortunately, there are many websites that will aid you in maintaining consistent units. For example, using Energy conversion calculators – U.S. Energy Information Administration (EIA) you can calculate the MMBtu (millions of Btu) for a variety of energy sources.

An Example

To illustrate this approach let’s use a fictional senior living community: Havenwood is a 10,000 square foot community located in New England. In 2020 Havenwood consumed 4000 gallons of heating oil and 60,000 kWh of electricity. The table below is an example of this procedure.

Fuel

Units

Units Consumed

Energy Content (MMBTU)

kg CO₂e per gallon or (MWh for Electricity)

Total kg CO₂e

Metric Tons CO₂e

% Total CO₂e

% Total Energy

#2 Heating Oil

4000

Gallons

550

10.21

5,616

5.6

27.7

72.8

Electricity

60,000

kWh

205

244

14,640

14.6

72.3

27.2

Total

755

20.3

You can also calculate your Energy Use Intensity for a building by converting the quantity of each energy source used in that building into a common energy unit. In 2020 Havenwood’s EUI is:

755,000 kBtu/10,000 square feet = 75.5 kBtu/square foot

Tracking EUI alongside carbon emissions gives you additional insight into your community’s energy efficiency. Consider establishing EUI targets in tandem with greenhouse gas reduction goals to reinforce and align your sustainability efforts.

The limitations of this approach

  • Scope 3 Emissions Not Included: This method only covers direct (Scope 1) emissions from on-site fuel use and indirect (Scope 2) emissions from purchased electricity. It does not account for Scope 3 emissions, which include:
    • Upstream: food production and transport, staff commuting, building materials
    • Downstream: landfill waste, shipping, and mailings
  • Excludes Other GHGs: This example calculates only CO₂. as the combustion of fossil fuels produces only relatively small quantities of other GHGs (CH4 and N2O). Methane and nitrous oxide could be included by multiplying the GWP of each GHG times its emission factor as is done by more comprehensive approaches and software. However, if the same underlying data is the same all methods should produce the same result.
  • Weather Variability: Year-to-year differences in energy use for heating and cooling will be affected by weather. To accurately track progress, emissions should be normalized for weather which is a more advanced process involving heating and cooling degree days and statistical modeling. 
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