Abstract:
This thesis analysed investments in electricity generation by employing a combination of Autoregressive Distributed Lag (ARDL) technique Mean
Variance Portfolio (MVP) analysis. With annual time series data on Ghana
from 1970 to 2009, the study used the ARDL technique to analyse investment,
cost and demand equations.
First, the investment equation examined the relationship between
investment in electricity generation, interest rate, output, price, and demand
uncertainty. Second, the cost equation investigated the effect of output,
interest rate, wage rate, and capacity utilisation on cost of electricity
generation. Third, the demand equation focused on the impact of price of
electricity, price of light crude oil, income, population, and consumption
efficiency on demand for electricity. Finally, the thesis used the MVP analysis
to evaluate the cost risk of Ghana5s projected electricity generation mix fbr
2015.
The results showed that investment in electricity generation depends
on interest rate, output, price, and demand uncertainty. Besides, the results
demonstrated that output, interest rate and capacity utilisation significantly
affect the cost of electricity generation. Again, the study confirmed that price
of electricity, income, population and consumption efficiency have a
significant effect on demand for electricity. Finally, the thesis established that
increase in the share of renewable energy reduces the cost risk of electricity
generation mix. The results implied that increase in electricity price can
increase investments in electricity generation in Ghana.
