The BAU Scenario was based on the assumption that existing policies and strategies are implemented and that governments would do nothing more during the projection period to impact on energy consumption (IEA, 2008a). This scenario therefore reflects a development path that includes the current action plans of the city, which are continued into the future. The current action plans of the city aim to increase economic growth, provide jobs and housing and improve energy efficiency to an extent. The GDP of the city increases to a slightly lower level then the GWC scenario from R115.1 to R660.4 billion by 2050, which is six times the base year value. Population grows moderately from 3.3 million people to 5 million people by 2050, and the number of households doubles during this period.
Therefore, by 2050, the number of people per household decreases from 4 to 3.1.
The increase in GDP and the number of households impacts on the demand for energy and CO2e emissions in the future. Energy demand increases 3.9 times from the base year and CO2e emissions increase 3.5 times from the 2005 level (Fig. 5.4). The reason for a greater increase in energy demand as opposed to emissions is due to an improvement in energy efficiency and the quantity of renewable energy utilised in this scenario. The growth in energy demand and emissions is less than the economic growth of the city, which illustrates an overall decline in energy and carbon intensity in this scenario.
0 20 40 60 80
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Million tCO2e
Transport
Local Authority
Industrial and commercial sector Households
Figure 5.4: Energy demand (Million GJ) and CO2e emissions (Million tCO2e) for the BAU Scenario for the key sectors in the city from 2005 to 2050
The sector that has the highest energy demand and CO2e emissions is the industrial and commercial sector, followed by the transport sector, and thereafter the household and local authority sectors (Fig.
5.4). The energy demand and CO2e emissions per sector remains about the same, with some minor changes, for example the energy consumption per household decreases from 10% of total energy demand to 5% by 2050. This is explained by an increase in the energy efficiency and an increase in the use of SWH. The decrease in energy demand by the residential sector is met with an increase in energy demand in the industrial and commercial sector as a result of high GDP growth rates, which increases the demand for energy and CO2e emissions per sector.
Figure 5.5: Energy demand (Million GJ) and CO2e emissions (Million tCO2e) by energy source for the BAU Scenario for 2005 to 2050
If the city continues on a BAU development path, the dominant fuel type will continue to be liquid fuels, followed by electricity (Fig. 5.5). The highest emissions per energy type will still be electricity, due to the high emissions factor of electricity. The main difference between the energy sources/fuel type of the GWC and the BAU Scenarios is the increase in the use of renewable energy, in the BAU Scenario. Renewable energy and biomass, which is also considered to be a renewable energy source consisting of biofuels, wood and bagasse (DME, 2003), makes up 9% of total energy demand, by 2050. This is a 5% increase from the base year, where the only significant quantity of renewable energy consumed in the city was biomass, which represented 4% of total energy consumed (Fig. 5.6).
Figure 5.6: Change in composition of energy source from 2005 to 2050 for the BAU scenario Emissions in the national LTMS Current Development Plans increase about 3.2 times from the base year to 2050, which is a similar to the 3.5 times increase in the BAU Scenario. The reason for the similarity between the two scenarios is that the EMES is based on national energy efficiency and renewable energy targets.
Many other global and local scenarios have also developed BAU Scenarios to allow for a comparison with other development paths that include mitigation measures. On a global level the EIA and IEA Reference Scenario is also based on the assumption of a continuation of existing government policies into the future. By 2035, emissions in these scenarios increase 1.6 times from the base year (EIA, 2009 and IEA, 2008a). Similarly, the BAU scenario increases 1.8 times the base year level by 2035.
On a local level this scenario can be compared with other BAU Scenarios, such as Mexico City, London, New York City, Milan, California and Bangkok, as shown in Table 5.1. Table 5.1 illustrates the increase in emissions from the base year of the various cities to the target year in each case. The table also shows the normalised percentage increase in CO2e emissions, where the values have been normalised assuming a constant increase over the period so that they are directly comparable with the values for Durban. The target year for each city differed, therefore the increase in emissions for the various years was calculated for Durban. The increase in emissions from the base year to the target year is much greater in Durban in comparison with Milan, London, New York City and California, whilst the increase is very similar to the BAU Scenarios for Bangkok and Mexico City. This illustrates that even if existing targets are met, Durban is still far behind in reducing emissions as opposed to developed cities, whilst Durban‘s BAU emissions are similar to other developing cities.
This is because the rate of urbanisation is more rapid in developing countries as opposed to developed countries. Furthermore, developed countries have only recently begun developing climate mitigation strategies (Croci et al., 2009), whilst developed country strategy targets are far higher.
Table 5.1: Estimated BAU emission increases for other cities in comparison to that of Durban’s BAU Scenario using a base year of 2005
City Base
Year
Target Year
% Increase in CO2e emissions
Normalised
% increase in CO2e emissions
% Increase in Durban's CO2e for the relevant
target year
Mexico City (Croci et al., 2009)
2000 2012 11 low 25 medium 35 high
6 15 20
12
Milan
(Croci et al., 2009)
2005 2020 8 8 28
New York City (Bloomberg, 2007)
2005 2030 27 27 79
California
(Ghandan and
Koomey, 2005)
2000 2035 81 69 110
Bangkok
(Phdungsilp and Dhakal, 2009)
2005 2012 2050
~14
~240
14 240
12 250