LOW-CARBON CITY SCENARIOS FOR DKI JAKARTA TOWARDS 2030 754.4 CONCLUSION AND REMARKS
ExSS GAMS modelling was conducted to explore the development paths
for DKI Jakarta that will lead to low-carbon emission development in the
energy sector by 2030. These development paths are expected to earn
low-carbon city status for DKI Jakarta in the future. No specifi c defi nition
regarding the number of low-carbon cities currently exists.
GDP growth measures used in this modelling study will result in a fi ve-
fold increase in GDP in 2030 in comparison with 2005 levels. There will
also be shift in sectoral GDP product levels, where the commercial (ter-
tiary industry) share will increase from 66 % to 71 %, which is in line with
the expectation that a capital city like DKI Jakarta will rely more heavily on
its commercial sector than its manufacturing industry and other sectors.
Notably, energy intensity levels found in the commercial sector are not as
high as those found for the manufacturing sector. Thus, such a shift will
decrease growth rates for energy demand. Electricity is the main energy
type used in this sector. Mitigation actions in this sector and in other sec-
tors that use electricity (industry and residential) can only be applied to
the end-user side, as mitigation on the power generation side falls outside
of DKI Jakarta’s jurisdiction.
Mitigation actions for DKI Jakarta have been explored using the
Provincial RAD GRK of DKI Jakarta ( 2012 ) as reference materials.
The selected mitigation actions will result in emission reductions of 26
million tons of CO 2 in 2030 relative to BaU scenario emissions levels.
This reduction is equivalent to 19 % of the BaU level for 2030. For
purposes of comparison, the RAD target is 30 %. Although reduction
percentages resulting from mitigation approaches used in the study
are lower than the RAD levels, they are similar to the RAD targets in
terms of the magnitude of GHG emission reductions. The percentage
difference is likely attributable to higher BaU levels found in this study
that resulted from higher economic growth assumptions compared
with those used for the RAD. The high BaU GHG emission results are
also attributable to additional parameters related to energy consump-
tion (e.g., changes in economic growth will affect lifestyle habits that
involve high levels of energy consumption, more electricity devices in
households, moving from conventional cooking devices (gas-fuelled)
to modern electric stoves).