4.2 Scenario-based approach
4.2.3 Input of data into the model
Local Authority
The data used for the local authority sector were taken from the GHG Inventory 2005/2006. This sector was divided into municipal buildings, street and traffic lights, water and sewage, vehicle fleet and distribution losses. Waste is not included as the scope of the project involved focusing on emissions as a result of energy consumption.
Transport
The fuel consumption for road transport was taken from the GHG Inventory, and is based on the total fuel sold within the municipal area. This was not divided further into end-uses due to a lack of data on vehicle kilometres travelled by vehicle type within the municipal area. The methodology applied is consistent with the Tier 1 approach as stipulated in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006).
Fuel consumed for marine transport was obtained from the National Ports Authority , which includes jet fuel, diesel and petrol. The main activities that fall within this sector are dredging, the tug boats and the container sector. The jet fuel consumed by the harbour helicopters was also included in the marine fuel consumption (Appendix 2.2).
Lastly, estimates for air transport fuel consumption were taken from the GHG Inventory 2005/2006.
Limitations and assumptions
Assumptions, based on information gained from informal interviews with key informants, were made in order to distribute these fuels into subsectors. The assumptions were:
Heavy fuel oil is utilised only within the manufacturing sector to operate boilers,
75% of total LPG is consumed in the industrial and commercial sector and 25% in the residential sector. This was based on conversations with Andrew Marquard, Bob de Lange and Steven Ferreira (Table 4.2).
20% of total paraffin consumption in Durban is consumed in the industrial and commercial sector, whilst the remaining 80% is consumed in the residential sector.
The transport services subsector consists solely of air, rail and water transport, due to difficulties in allocating liquid fuel consumption for freight and passenger transport by road. Freight and passenger transport was incorporated in the transport sector.
The LEAP system is a tool created by the Stockholm Environmental Institute (SEI) that allows for energy planning and GHG emissions mitigation. LEAP is an integrated physical accounting and simulation tool, which was chosen because it is very flexible and easy to use, due to its similarity to other Windows programmes (Heaps, 2008). LEAP allows users to input the data in various formats and does not require any specific disaggregation of data in order to generate results. The tool consists of three main divisions, which are energy demand, energy transformation and resources (Taviv et al., 2008); however, the tool allows the user to work with only one division if necessary. It is also flexible in that it can be used for global to local level planning and analysis and to simulate short to long term energy projections (EnergyScape, 2007; Heaps, 2008; Phdungsilp, 2006). The other advantage of LEAP is that it has a large database, which consists of emission factors for different fuel types and technologies, that are specific to South Africa. This is due to the involvement of the ERC, at the University of Cape Town, in the development of the tool (Phdungsilp, 2006).
Scenario analysis is a key feature of LEAP, which allows users to create a range of storylines to illustrate how energy demand and GHG emissions may change over time. The scenarios are based on a variety of different assumptions of population growth, economic growth, technological change, fuel mix and other drivers. ―Scenarios are developed by asking ‗what if‘ questions‖, for example what would happen if the population of the city stabilises (Phdungsilp, 2006: 12). The scenario analysis function allows policy makers to create different scenarios and evaluate what the best options are based on energy consumed, environmental impacts and costs (Heaps, 2008). The LEAP interface consists of a series of different views, each depicting different aspects of the energy analysis, as shown in Figure 4.2. The analysis view consists of a data tree structure, which can be divided into a number of main branches and sub-branches depending on the level of detail required. The Key Assumptions Branch consists of basic information that is related to the energy consumption of an area. The next branch is the Demand Branch, which consists of all sectors related to energy consumption. The energy demand for the various sectors identified in Section 4.2.3 was input into the Demand Branch of the tool. The other main branches are Transformation, Resources and Non Energy Sector Effects; however, these were not used for the study, as Durban does not generate its own electricity and resources and non energy sector effects are was not part of the study.
Figure 4.2: The LEAP interface – the Analysis View Source: Heaps (2008)
The tool calculates energy consumption and environmental loadings based on baseline data such as Activity Level, Final Energy Intensity and Environmental Loading that are input into the tool. The total energy consumed and CO2e emissions are calculated based on the equations below:
Total Energy Consumption = Final Energy Intensity x Activity Level (4) Total CO2e Emissions = Total Energy Consumption x Emissions/Unit Energy Consumed (5)
Activity Level: Measures the activity associated with a particular demand, for example the activity level for the manufacturing sector can be GDP output.
Final Energy Intensity: The energy required by the particular activity level, for example the energy required to generate a unit of GDP in the pulp and paper sector.
Environmental Loading: measures the environmental impact of the energy consumed (EnergyScape, 2007). The CO2e emission factors are recorded as the amount of CO2eemitted per unit of energy consumed.
Views Tree
Data Tabs
Once the baseline data are entered into the tool, scenarios can then be created using the ‗Manage Scenarios‘ button. LEAP has a series of functions to assist in the development of scenarios, such as the interpolate function, the end-year value, or the growth rate function, to illustrate how energy consumption will change in the future.
In order to calculate energy demand and GHG emissions many different methodologies can be applied using the tool. The activity level for energy demand and GHG emissions were calculated as a function of economic growth for the industrial and commercial sector, number of households for the residential sector and total consumption for the transport sector. The emission factors for the different fuel types and activities were taken from the LEAP Technology and Environmental Database for some fuel types, which included coal, Sasol MRG, AvGas, diesel, petrol and heavy fuel oil. The emission factors for the other fuel types were manually input into the tool, for example the emissions for electricity, which is updated by Eskom on a regular basis.
Once the scenarios have been created the user may then generate results by clicking on the ‗Results‘
button on the ‗Views‘ toolbar. The results (shown in Figure 4.3) can be displayed as charts or tables and in any acceptable unit of measurement. The results can also be viewed by fuel type, scenarios or branches, which are by sectors or subsectors. By providing users with a flexible interface, it makes it easier to visually see the implications for the different scenario options on future energy demand and GHG emissions.
Figure 4.3: The LEAP interface – Results View Source: Heaps (2008)