In calculating abatement for your aggregated small energy users project, you are bound by the choices you made before selecting control and treatment groups. For example, if you chose to use sub-method one for a population, you must calculate abatement for that population using sub-method one throughout the project.
You can use pro-rata energy consumption data for a measurement period if:
Other than sites affected by attrition, you must use all the sites you selected for the control and treatment group to calculate abatement. If a site is missed when you deliver goods or services as part of the activity, or if the small energy user opts out of receiving the goods and services, the site’s energy consumption must still be used in the calculation of treatment group emissions.
Use the following steps:
If the treatment group’s emissions are statistically significantly lower than those of the control group, calculate abatement for the population using the equation in section 42.
If the treatment group’s emissions are not found to be significantly lower than those of the control group—so you cannot reject the null hypothesis—abatement for that population in the measurement period is calculated to be zero (see the table in section 38).
The change in treatment group emissions is lower than the change in control group emissions if:
For example in a population where the business-as-usual trend results in emissions decreasing over time in both the treatment and control group, the change in pre and post treatment emissions will be a negative number for both groups. If the treatment has been effective the size of the change in the treatment group will be a smaller negative number (and a larger in absolute number).
If the difference between the control and treatment group is found to be statistically significant, calculate abatement using the equations in section 49.
If the change in the treatment group’s emissions is not lower than the control group’s emissions by a statistically significant amount—so you cannot reject the null hypothesis—abatement for that population in the measurement period is calculated to be zero (see the table in section 38).
You must then create a data set containing one entry for each site in the control or treatment group, with results for the site against the following variables:
Define a linear equation relating mean daily emissions at a site in the measurement period (the dependent variable) to the variables listed above (section 58).
Use linear regression, weighted by the number of days for which measured energy consumption data is used for the site in the measurement period, to calculate the value of the ‘treatment variable’. This lets you determine the effect of the treatment variable on emissions in the treatment group; it will be negative if emissions have declined compared to the control group.
Test the statistical significance of the effect of the treatment variable (section 56):
This is the final step in the calculations to determine the number of ACCUs that may be issued for a reporting period.
You may include more than one population in a project, and may choose to include two one-year measurement periods in a reporting period. If this is the case, the net abatement amount for the reporting period is found by adding abatement calculated for individual populations and measurement periods.
Calculating abatement for multiple populations in a single reporting period will require you to align the measurement periods for your populations. Alternatively, if the metering cycle for a whole population is different from the measurement periods that you are using for another population, you have the option of reporting the two populations separately as if they were separate projects (see part six).
This figure on the next page shows the sequence of decisions and calculations involved in an aggregated small energy users project. This example uses sub-method one to calculate the net abatement amount for the first reporting period for a project.
Figure one: Process to calculate net amount of abatement
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