(2005) downscaled the GHG emissions. However, Gaffin et al. (2004) acknowledged many problems in the results due to the downscaling methodology applied and the lack of population scenarios for countries after 2050, which resulted in discontinuities in the population scenarios. The GDP results were abnormally high as a result of linear downscaling techniques used to determine country-level GDP. This was not plausible due to varying economic growth in countries within a region. Similarly Höhne and Ullrich (2005) used a linear downscaling technique to downscale GHG emissions to a country-level, which does not reflect differences in emissions in different countries within a region. The data produced were also inconsistent because socio-economic scenarios were not produced and related to the GHG data (van Vuuren et al., 2007).
Van Vuuren et al. (2007), aimed to provide a new dataset of consistent downscaled data, taking into account the discrepancies of the previous downscaling approaches. The study used the scenarios based on the Integrated Model to Assess the Greenhouse Effect (IMAGE) model, which was one of the six models used to develop emission scenarios by the IPCC (Nakicenovic, 2000a). Van Vuuren et al. (2007) developed downscaled SRES scenario data for 224 countries, illustrating population, GDP per capita and GHG emissions per capita. The data therefore reflect A1, A2, B1 and B2 scenarios for the 224 countries from 2000 – 2010.
For downscaling population scenarios, an external-input based downscaling approach was applied, based on more recent UN country-level data. For GDP and emissions, van Vuuren et al. (2007) used the partial convergence approach, which assumes that all countries‘ GDP and emissions per capita, within a region, will partially converge in a year out of the 2000-2100 scenario period. The rate of convergence varies according to the different scenarios and regions. The A1 and B1 scenarios will have high rates of convergence due to tendencies towards globalisation and the A2 and B2 scenarios have a low degree of convergence. After developing country-level data, based on the above methodology, van Vuuren et al.
(2007) state that the data can be downscaled from a national level to a grid level (0.5° x 0.5°) using a linear downscaling approach. This approach assumes that the rate of change within a grid of a country is the same as the changes in the country.
Alternative approaches can be used when downscaling to a grid-level or a city level, however this would require detailed information about the city or grid area. For example, in population projections, fertility, morality and migration patterns within a grid can be considered, however this minimises the transparency of the study. According to van Vuuren et al. (2007) GDP within a country will differ between rural and urban areas, especially in developing countries, therefore sub-national data will improve the scenarios.
The downscaling of the downscaled country-level IPCC scenarios was conducted in this study to enable a comparison between the SRES scenarios and the scenarios that were generated (Malone et al., 2004). The country-level data, based on van Vuuren et al.’s (2007) research were published on the Netherlands Environmental Assessment Agency website, and were used to downscale South African data on population, GDP and emissions to a Durban level. However, van Vuuren et al. (2007) did not downscale energy consumption to a national-level, which was important for this study, in order to determine the CO2
emissions in relation to energy consumption. Therefore the energy consumption for the GWC scenario of the LTMS was used and compared to a downscale of regional IPCC SRES data.
4.3.1 Data sources
The data for the downscaling were taken from:
Statistics South Africa: South African population statistics;
Netherlands Environmental Assessment Agency: South African population, gross value added (GVA) per capita and emissions per capita scenarios;
eThekwini Economic Development Unit: population data for Durban (2000 – 2008), data per economic sector for South Africa (2000 – 2007) and Durban (1997 – 2008);
GHG Data Collection and Emissions Inventory Report‘ (Antoni, 2007);
eThekwini Municipality State of Energy Report (Mercer, 2006).
4.3.2 Population
The first step towards the downscaling of South African data to a Durban scale was to review previous trends in population growth for both country and city. This purpose of this was to determine if the growth patterns in the city and country were similar. Data for South Africa‘s population from 2000-2007 were obtained from Stats SA and population data for Durban were obtained from Denny Thaver of the eThekwini Economic Development Unit. It was established, based on population data from the eThekwini Economic Development Unit, that Durban‘s population represents 6.94%-7% of South Africa‘s population. Therefore, a linear downscaling methodology was chosen, as described by Gaffin et al. (2004) and van Vuuren et al. (2006). The algorithm used to downscale from a South Africa to Durban level was kept simple, as van Vuuren et al. (2006) describes a good downscaling method as one which is transparent, consistent with local data and consistent with the original dataset. The algorithm is shown in equation (1) below.
PopDbn = PopSA * (ADbn/ASA) (1)
Where:
PopDbn represents Durban‘s population for the different years and scenarios.
PopSA is based on the South African downscaled data from van Vuuren et al. (2007) for the A1, A2, B1 and B2 scenarios.
ADbnrepresents population data for Durban‘s (Economic Development Unit, 2008).
ASA is equal to the population data for South Africa.
4.3.3 Gross value added
Previous GDP statistics of South Africa and Durban were compared in order to determine if growth trends are similar in both areas (Economic Development Unit, 2008). From 2000 to 2007, Durban contributed between 10.3 and 10.8% to South Africa‘s total GDP. Durban‘s GDP growth also correlates with that of South Africa, such that when South Africa‘s GDP increases, so too does that of Durban. This allowed for a linear downscaling of GDP from a South African level to a Durban level. The downscaled scenario data for South Africa provided by the Netherlands Environmental Assessment Agency, were in US $ per capita, whilst the data provided by the Economic Development Unit were in Rands, adjusted to constant
2000 prices. Therefore the growth rates of GDP per person for the South African data, for five-year intervals from 2000 - 2100 were used to determine the changes in GDP per capita for Durban. The outcome was then multiplied by the population outcome of the different scenarios to establish total GDP for the city.
4.3.4 GHG emissions
GHG emissions are a function of socio-economic driving forces such as population, economic growth and technological change, as discussed in Chapter 2. Van Vuuren et al. (2006) used the IPAT equation (discussed in Chapter 2) as the downscaling outline for GHG emissions, as emissions are determined by economic growth, population and technology. Hence, van Vuuren et al. (2007) downscaled South Africa‘s GHG emissions from the Africa Latin American Middle East (ALM) Region, using the IPAT Framework. However, a linear downscale was chosen for the estimation of GHG emissions in Durban, so that it follows the patterns of population and economic growth downscaled data.
The South African downscaled data were used to determine the changes in GHG emissions in Durban.
The South African and Durban emissions are likely to follow similar growth patterns, which are largely determined by national legislation and policy. For example, the Energy Efficiency Strategy of South Africa (DME, 2005) sets a target to reduce overall energy consumption by 12% by 2015. This target will have an impact on the energy consumption of Durban.