| dc.description.abstract | Urbanization is occurring fastest in developing countries, with the least developed countries
expected to have the highest population growth rates. Cities in these countries are going to
increasingly be important sites of energy demand. Potential future developments of energy
efficiency in developing urban areas and their impact on natural resources are often neglected.
Rwanda has ambitious targets to hook up thousands of households to the energy access
each year, and with the high projected economic growth forecast for the country,
demand for electricity by all economic sectors in Rwanda will almost certainly increase
for the foreseeable future. Energy efficiency is the key driver to meet with future demand.
The purpose of the study is to assess the impact of energy efficiency policies on future energy
demand in secondary cities. Modeling and scenario techniques through LEAP (Long Range
Energy Alternatives Planning System) software to predict the future energy demand. The results
showed that four energy sources predominate: electricity, charcoal, firewood and LPG.
However, there were urban households with greater diversity of energy sources in urban
households, also including electricity (97.89%), candles (44.21%) and liquefied petroleum gas,
or LPG (37.89%). Charcoal use is much higher at 82.11%, whereas firewood use is only
29.49%. The demand of modern fuels such as LPG, kerosene and electricity are increasing, and
the analysis shows that the demand of both LPG and electricity would increase significantly.
At Energy Efficiency Scenario will be led by mordern fuels at rate of 66.9% where LPG will
have 47% total demand and electricity with share of 19.9%. This study showed that with
effective implementation of the energy efficiency policies would save about 27.5% of energy
and reduce energy fuel demand in 2040. In order to minimize energy loss in urban households,
this research suggests that similar studies in the future may focus more on obtaining previous
data, if possible, the study could be conducted over years to use own primary data, or else
obtaining as many years’ data as possible to forecast the future energy consumption accurately | en_US |
