| dc.description.abstract | Greenhouse gas (GHG) emissions are one of the main contributors to climate change.
Several mitigation measures such as carbon tax, carbon budgets and sectoral emissions targets
(SETs) are used to reduce GHG emissions. These mitigation measures require long-term plans,
detailing how reduction outcomes or targets will be achieved.
Carbon emissions forecasting is vital to ensure effective carbon mitigation measures. Forecasted
emissions give a good indication of whether a facility’s emissions targets will be met and if more
stringent mitigation measures are required.
Several forecasting models have been developed to predict emissions at a country-specific level.
However, mitigation policies aim to reduce carbon emissions at a facility level. Thus, a forecasting
model is required to predict carbon emissions for South African facilities.
An emissions forecasting model was developed based on the basic forecasting model
development steps; focussing on problem definition, data identification, preliminary analysis,
choosing and fitting models, and using and evaluating the model.
Different forecasting models were evaluated, whereafter the models best suited for the data were
used. The time-series variables, material input and production data, were estimated using the
error-trend-seasonality (ETS) forecasting model, the activity data was calculated using
multivariable linear regression, and carbon emissions were predicted using linear regression. The
developed forecasting model is then validated and tested on an industrial facility in South Africa.
After testing and validating the model, the model is used to predict the baseline material input,
production, activity data and ultimately the emissions. The developed model forecasted the fuel
combustion and process emissions within a 6% error. The fugitive emissions were forecasted
within a 23% error.
Using the baseline forecast, the facility’s emission roadmap was incorporated into the forecast to
predict the emissions when mitigation measures are implemented. Next, the forecasted emissions
are compared to the allowable emissions to determine whether the facility is likely to meet its
emission targets.
The chosen facility aims to reduce its Scope 1 and Scope 2 emissions by 30% by 2030. To
achieve this target, an emissions reduction of 18.5% is required by 2025. By only implementing a
10% reduction in material input, a 9% reduction in Scope 1 emissions is forecasted. Thus, the
implementation of additional mitigation measures are required to ensure the facility meet its
emission reduction targets.
In addition to using long-term forecasts to determine if a facility will meet its emission targets,
short-term forecasting can be used for setting up carbon budgets. During the first carbon tax
phase, companies can receive an additional 5% allowance on their carbon tax liability when
participating in the carbon budget system. This amounts to a combined total of about R 2.3-billion
in avoided carbon tax liability when considering six South African industries.
Ultimately, the developed forecasting models can be used to develop accurate mitigation plans
for any South African industrial facility. | en_US |
