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Energy Scenarios for Cape Town - Technical Report 1

ANNEXURE B

Technical Report for

ENERGY SCENARIOS FOR CAPE TOWN:

Exploring the implications of different energy futures for the City of Cape Town up to 2050

Part of the DANIDA-funded City of Cape Town Climate Change Think Tank research initiative Also supported by the British High Commission Completed by:

ENERGY RESEARCH CENTRE


CONTENTS 1. ACRONYMS AND TERMS .......................................................................................................................... 4

2. PURPOSE OF DOCUMENT ......................................................................................................................... 7

3. BACKGROUND ....................................................................................................................................... 9

4. METHODOLOGY ................................................................................................................................... 10

4.1. OVERVIEW .................................................................................................................................. 10

4.2. PRECEDENT STUDIES ..................................................................................................................... 11

4.3. DATA PROBLEMS AND LIMITATIONS ................................................................................................. 12

4.4. MAIN ENERGY DATA SOURCES ........................................................................................................ 14

4.5. INTERVENTIONS AND COSTING ........................................................................................................ 15

4.6. CALCULATING SUPPLY-SIDE DATA USING LEAP .................................................................................. 16

4.7. KEY TO SCENARIOS IN LEAP ........................................................................................................... 19

5. CAPE TOWN ENERGY CONSUMPTION AND EMISSIONS OVERVIEW .................................................................. 23

6. RESIDENTIAL SECTOR ENERGY DATA ......................................................................................................... 27

6.1. DEMAND FOR ENERGY SERVICES ..................................................................................................... 27

6.2. ENERGY END USE ......................................................................................................................... 31


7. COMMERCIAL SECTOR ENERGY DATA ....................................................................................................... 41


7.2. ENERGY END USE ......................................................................................................................... 44


8. INDUSTRIAL SECTOR ENERGY DATA .......................................................................................................... 48


8.2. ENERGY END USE ......................................................................................................................... 50


9. LOCAL GOVERNMENT SECTOR ENERGY DATA ............................................................................................. 52


9.2. ENERGY END USE ......................................................................................................................... 52


10. TRANSPORT SECTOR ENERGY DATA ...................................................................................................... 55

10.1. DEMAND FOR ENERGY SERVICES ................................................................................................. 55

10.2. ENERGY END USE ..................................................................................................................... 55

10.3. INTERVENTIONS AND COSTING .................................................................................................... 60

11. SCENARIOS: PRIMARY ....................................................................................................................... 62

11.1. BUSINESS AS USUAL SCENARIO ................................................................................................... 62

11.2. NATIONAL LTMS SCENARIO ....................................................................................................... 63

11.3. OPTIMUM ENERGY FUTURE SCENARIO ......................................................................................... 66

12. SCENARIOS: SECONDARY .................................................................................................................... 70

12.1. PEAK OIL SCENARIO .................................................................................................................. 70

12.2. CARBON TAX SCENARIO ............................................................................................................. 71

12.3. DENSIFICATION SCENARIO .......................................................................................................... 73

12.4. ECONOMIC GROWTH SCENARIO .................................................................................................. 74

13. ENERGY SCENARIO MODELLING TILL YEAR 2050 .................................................................................... 75

13.1. BUSINESS AS USUAL ................................................................................................................. 75


13.2. SCENARIO COMPARISONS .......................................................................................................... 80

14. ENERGY SCENARIO MODELLING TILL YEAR 2025 .................................................................................... 86

14.1. BUSINESS AS USUAL ................................................................................................................. 86

14.2. SCENARIO COMPARISONS .......................................................................................................... 90

15. OTHER RESULTS AND INFORMATION .................................................................................................... 98

15.1. ENERGY EFFICIENCY .................................................................................................................. 98

15.2. CITY DENSIFICATION ............................................................................................................... 113

15.3. OTHER ................................................................................................................................. 119


1. ACRONYMS AND TERMS ACC African Centre for Cities BAU Business as Usual: describes a situation where no significant changes are made

and the status quo trends continue into the future. Capacity In terms of electricity, this refers to the amount of power (e.g. MW, kW, etc)

available at any one point in time. Cape Town Refers to the region governed by the City of Cape Town metropolitan

municipality Cape Town Energy and Climate Change Strategy

A document setting out a vision for the delivery and consumption of sustainable, environmentally sound energy; setting quantifiable targets for the core sectors of transport, electricity supply, residential, government, industry and commerce. Also referred to as “the Strategy.”

Cape Town Energy Futures Report

Comprises of initial energy modelling in order for the City to assess the implications of different future development paths for the energy sector

CBECS Commercial Buildings Energy Consumption Survey CC Think Tank Climate Change Think Tank CCGT Combined Cycle Gas Turbine CFL Compact Fluorescent Light CoCT City of Cape Town CO2 Carbon dioxide COP Coefficient Of Performance: the ratio of the change in heat at the "output" (the

heat reservoir of interest) to the supplied work CPTR Current Public Transport Records DANIDA Danish International Development Agency DEAT Department of Environmental Affairs and Tourism: now separated into the

Department of Environmental Affairs (DEA) and the Department of Tourism (DT) Degree-day A unit of measurement equal to a difference of one degree between the mean

outdoor temperature on a certain day and a reference temperature, used in estimating the energy needs for heating or cooling a building.

Demand In terms of electricity, this refers to the amount used (e.g. kWh, MWh, etc) by an entity over time.

Discount rate The interest rate used to determine the present value of future cash flows. The discount rate takes into account the time value of money (the idea that money available now is worth more than the same amount of money available in the future, because it could be earning interest) and the risk or uncertainty of the anticipated future cash flows (which might be less than expected).

DSM Demand Side Management DX Direct Expansion: HVAC (Heating, Ventilation and Cooling) coils that use direct-

expansion of refrigerants are commonly called DX coils. ECAP Energy and Climate Action Plan: lists current and future City of Cape Town

energy- and climate-related projects, with a prioritisation index End Use With regards to energy, it refers to the uses gained through energy services. For

example, electricity is the energy available in residential households, but the end


uses for which it is required includes water heating, cooking, refrigeration, heating, etc.

FBE Free Basic Electricity: residents using less than a set amount of kWh of electricity each month are allocated an amount of free electricity

EIA Energy Information Administration ERC Energy Research Centre from the University of Cape Town, South Africa Externality A side effect or consequence of an industrial or commercial activity that affects

other parties without this being reflected in the cost of the goods or services involved, e.g. externalities of a coal mining operation could include environmental damage, pollution (health and environmental impacts) and mining deaths.

GDP Gross Domestic Product: the total market value of all final goods and services produced in a country in a given year, equal to total consumer, investment and government spending, plus the value of exports, minus the value of imports

GGP Gross Geographic Product: the total value of all final goods and services produced within the boundaries of a country/specific region over a given year. In this report it refers to the area of Cape Town.

GIS Geographic Information Systems GJ GigaJoules: equates to 1,000,000,000 or 109 Joules (a unit of energy). GJ/hh/a GigaJoules per household per annum HFO Heavy Fuel Oil hh Household HVAC Heating, Ventilation and Cooling IES Integrated Environmental Solutions: a company that provides building energy

modelling software services IGCC Integrated Gasification Combined-Cycle Coal kJ kiloJoule: a unit of energy equivalent to 1,000 Joules kW kilowatt: 1,000 Watts of power kWh kiloWatt-hour: a unit of energy equivalent to one kiloWatt (1,000 Watts) of

power expended over one hour. LEAP Long-Range Energy Alternatives Planning: a computer-based modelling

programme, allowing for the examination of simulated possible future energy scenarios

LED Light-Emitting Diode: a very electricity efficient lighting option LIS Land Information System LPG Liquefied Petroleum Gas LTMS Long-Term Mitigation Scenarios Lux The Standard International unit of illumination; equal to one lumen per square

meter MW MegaWatt: 1,000,000 or 106 Watts of power MWh MegaWatt-hour: a unit of energy equivalent to one MegaWatt (1,000,000 or 106

Watts) of power expended over one hour. National LTMS Framework

National Long Term Mitigation Scenarios Framework: a Cabinet-endorsed policy framework that sets the strategic direction for climate action in South Africa.

O&M Operations and Maintenance


OCGT Open Cycle Gas Turbine OEF Optimum Energy Future Passenger-km A unit of measure representing the transport of one passenger over a distance

of one kilometre (Calculation: number of passengers x number of km = passenger-km)

PBMR Pebble-Bed Modular Reactor PDG Palmer Development Group Peak oil The point in time when the maximum petroleum extraction rate is reached,

after which the rate of production enters terminal decline PJ PetaJoule: equates to 1,000,000,000,000,000 or 1015 Joules (a unit of energy) PV Photovoltaic PWR Pressurised Water Reactor SAP Systems, Applications and Products SAPIA South African Petroleum Industry Association SEA Sustainable Energy Africa SNAPP Sustainable National Accessible Power Planning: a spreadsheet-based electricity

system model developed by WWF-South Africa and the Energy Research Center at the University of Cape Town.

State of Energy A report that provides information on a city’s current energy supply and use The City The City of Cape Town: refers to the metropolitan municipality that governs the

Cape Town region TJ TerraJoule: equates to 1,000,000,000,000 or 1012 Joules (a unit of energy) UCT University of Cape Town USA United States of America VRV Variable Refrigerant Volume W Watt: a unit of power ZAR South African Rands


2. PURPOSE OF DOCUMENT The Energy Scenarios for Cape Town project models and costs different energy futures for Cape Town. The project was undertaken by Sustainable Energy Africa and the Energy Research Centre (University of Cape Town), with support from the African Centre for Cities (University of Cape Town). The main findings of the work are presented in the report ‘Energy Scenarios for Cape Town: Exploring the implications of different energy futures for the city up to 2050’ (Sustainable Energy Africa, January 2011). This document is the technical annexure to the aforementioned report and is intended for those wanting to engage with the data, assumptions and methodologies used in the project. This work was undertaken as part of the research being conducted under the City’s DANIDA-funded Climate Change Think Tank (CC Think Tank), which consists of a partnerships between the African Centre for Cities, Sustainable Energy Africa and the City of Cape Town. The overall focus of the CC Think Tank Programme was to better understand and prepare for climate change, including both mitigation and adaptation aspects. Various projects, including the Energy Scenarios for Cape Town, were undertaken within this framework. The objectives of the Energy Scenarios for Cape Town project were to:

Clarify the optimum way forward for the energy sector in Cape Town, such that energy costs into the future are anticipated and minimised, job creation is maximised and environmental impact is reduced, with particularly emphasis on Cape Town meeting its responsibilities in terms of CO2 emissions reduction as per DEAT’s National Long-term Mitigation Scenarios (LTMS) Framework and the presidents carbon reduction commitments made at COP15 in Copenhagen.

Clarify the impact of different energy sector futures on City coffers

Develop a populated, functional and comprehensive LEAP (Long-Range Energy Alternatives Planning) model for Cape Town for use by the City and any researcher or organisation involved in City energy work

The outputs of the project were:

A representation of the “existing” (based on 2007 data) energy picture in Cape Town, through energy-related data collection

An up-to-date LEAP model for Cape Town, modelling and representing a reference scenario (the Business As Usual Scenario) and various alternative future energy scenarios

Details of energy, emissions (local and global) and cost implications of different future energy scenarios for Cape Town


A proposed set of interventions (energy efficiency, renewable energy, etc) for an optimum energy1 demand and supply mix; considering costs, social issues and other externalities in determining such a mix

o The mix of interventions should achieve the level of CO2 emissions specified in the LTMS

Action Plan o Overall public and private sector and externality costs where feasible, as well as the CO2

implications of each future energy scenario are modelled

Implications for City coffers of different future energy scenarios

Engagement with key city officials and councillors, as well as other key external stakeholders The function of this technical report is to discuss the data used and the assumptions made in the LEAP modelling exercises.

1 Refers to a situation where energy costs into the future are minimised, job creation is maximised, environmental impact

is reduced and Cape Town meets its emissions reduction targets as set out in the National LTMS Framework.


3. BACKGROUND The City of Cape Town was the first African city to establish an Energy and Climate Change Strategy (completed 2006); a document that sets out a vision for the delivery and consumption of sustainable, environmentally sound energy, and provides quantifiable targets in this regard for the core sectors of transport, electricity supply, residential, government, industry and commerce. It was also a leading city in the implementation and support of such a Strategy through the implementation of institutional reforms. The Strategy built on the City’s State of Energy report (first completed 2003, updated 2007), which provided a picture of energy supply and demand in Cape Town. Initial energy modelling (covered in the report labelled: Cape Town Energy Futures) was undertaken in 2005 in order for the City to assess the implications of different future development paths for the energy sector. In response to the Strategy and modelling report, the City developed an Energy and Climate Action Plan (ECAP), which was adopted by Council in May 2010. The ECAP is made up of 11 key objectives, further divided into programme areas consisting of individual projects, currently underway or planned, extending over a three year period. The projects were taken through an initial prioritisation process. However, additional information regarding consumption patterns, costs, trends, risks, etc, was required to underpin the ECAP and thereby verify the initial prioritisation, assist with the setting of targets, and extend the plan into the longer term. The data gathered was used to identify what was termed the Optimum Energy Future (OEF) Scenario, which provides a more in-depth energy sector analysis and projections than previously; based on an extended and up-to-date set of energy consumption data, supply mix options, costing and trends.


4. METHODOLOGY

4.1. OVERVIEW The project included a detailed energy data collection exercise; building on previous work carried out on the State of Energy Report for Cape Town, and the Cape Town Energy Futures Report on policies and scenarios for sustainable city energy development. The first step in any energy modelling process, such as in the Energy Scenarios for Cape Town project, is to develop a baseline of current energy use patterns. This information forms the foundation of all the modelling outputs that follow, and as such it is critical for it to be as accurate and meaningful as possible. Data was collected for the five sectors analysed within this project, namely:

Residential Sector: disaggregated according to electrified and non-electrified households and by income category.

Commercial Sector: included retail and office buildings, tourism activities, education facilities, hospitals and other non-industrial activities.

Industrial Sector: activities disaggregated into 1) Textiles, 2) Food and Beverage, 3) Non-Food Manufacturing sectors and 4) Other.

Local Government: covered all City of Cape Town municipal operations, including all public buildings, street and traffic lights, water and waste-water treatment, and the City’s vehicle fleet.

Transport: covered both freight and passenger transport, although they were modelled separately. Passenger transport included both private vehicle travel and public transportation associated with bus, mini-bus taxi and train use. Freight transport covered rail and road-based transport.

The Long-Range Energy Alternatives Planning (LEAP) simulation tool was used to examine the implications of a number of possible future energy scenarios for Cape Town from the base year of 2007 up to 2050. Each scenario contained a combination of specific energy efficiency interventions and supply mix options. The following primary scenarios were modelled:

Business As Usual Scenario: no significant change of course takes place and current growth trends continue

National LTMS Scenario: implementation of options in this scenario, such as the new nuclear supply, new renewable energy supply and energy efficiency interventions required to meet the nationally endorsed carbon reduction profile


Optimum Energy Future Scenario: the proposed optimum mix of energy efficiency interventions and low carbon supply options

Secondary scenarios were modelled based on a combination of the primary scenarios listed above:

Peak Oil Scenario cost implications: modelled by a yearly increase in liquid fuel prices (above inflation) of 5% from 2016 and 7% from 2020

Carbon Tax Scenario: a carbon tax of R100 per ton in 2007, escalating to R750/tonne by 20502, was modelled on the Cape Town Optimum Energy Future and Business As Usual Scenario.

Densification Scenario: the creation of a denser city, modelled by decreasing costs for bus and rail systems in the transport sector and increasing occupancy

Economic Growth Scenario (high and low growth rates)

o High economic growth (3.6% energy growth, linked to a GGP growth of 4.6%) o Low economic growth (1.9% energy growth, linked to a GGP growth of 2.9%)3

4.2. PRECEDENT STUDIES Previous studies on energy data and energy scenarios modelling include:

Two State of Energy reports that were completed for the City of Cape Town. The first was commissioned in 2003 and compiled by Sustainable Energy Africa (SEA); the second, an update of the 2003 report, was compiled by the Palmer Development Group (PDG) in 2007.

The Cape Town Energy Futures report commissioned in 2005. The Energy Research Centre at the University of Cape Town developed policies and scenarios for sustainable city energy development, by simulating how energy might develop in Cape Town during the period from 2000 to 2020. This study refined the data presented in the first State of Energy report for use in its base year analysis.

The State of Energy in South African Cities compiled by SEA. Energy issues and energy consumption for a selection of cities in South Africa, including Cape Town, were assessed.

These precedent studies relied heavily on assumptions, due to a lack of disaggregated data. Aggregated energy data was available from the City of Cape Town’s electricity and air pollution departments for fuel use (some liquid fuel data, but more data available on other fuels) in the

2 These carbon tax figures are based on parameters used in South Africa’s Integrated Resource Plan (IRP)

3 The relationship between GGP growth and energy growth was taken from the 2010 national IRP parameter sheets.


industrial and commercial sectors. The data was used to estimate and disaggregate fuel use for the commercial, residential and industrial sectors. All reports discuss the lack of data and suggest mechanisms for improving data collection.

4.3. DATA PROBLEMS AND LIMITATIONS ELECTRICITY There are two electricity suppliers in Cape Town, namely the City of Cape Town and Eskom. The split is approximately 75% City of Cape Town and 25% Eskom. Municipal Electricity Sales Data The total sales data for the City was available in aggregated form, although it was disaggregated by tariff. In some cases the tariff provided a guide as to the sector for which it was aimed, but not in all cases. The larger power user tariffs, in particular, were not disaggregated by sector, e.g. disaggregated between commercial and industrial users. A further limitation to using sales data was that it was collected primarily for billing purposes. Actual energy consumption for a given period was not always accurate, particularly in the case of pre-paid customers, who may buy electricity for more than one year. It was anticipated that the SAP (Systems, Applications and Products) system, a data management tool introduced by the City of Cape Town to manage the billing of utilities, would provide a greatly improved database of disaggregated energy data. The SAP database has the potential to provide detailed data on energy consumption for around 75% of Cape Town’s electricity consumption; a huge resource if it could be exploited. However, detailed data was only available from the SAP database for the top 1,200 electricity consumers. This data had already been extracted by the City for its own purposes and was subsequently made available to this project. The data demonstrated that the municipality currently collects sufficient data to distinguish, in the majority of cases, between residential, commercial, government and industrial electricity customers, and to distinguish in many cases the building types within each sector. A limitation to the data was that many of the customers were registered as a body corporate, property fund or trust and in these cases the activity undertaken within the building was not clear. Access to a larger set of disaggregated data, beyond the top 1,200 consumers, was problematic. A request to the City’s SAP department for detailed data on all customers was lodged in November 2009. The data had still not been extracted from SAP by July 2010. It is likely that this is due to a combination of a lack of capacity and the time-consuming nature of data extraction from the SAP database. Eskom Electricity Sales Data


No data on Eskom customers within City boundaries was available. This is a significant limitation. It is likely that disaggregating energy sales by sector is not a priority for Eskom and, as such, the data is not easily accessible in the format required for the Cape Town LTMS analysis. Historically, Eskom has been reluctant to provide data, citing customer privacy concerns. At first attempt, there was an indication from Eskom that it was possible to geographically identify Eskom-supplied users, but when pursued further the information was not available. After several months of such pursuits the modellers moved to using projections based on Eskom data collected for the 2003 State of Energy Report for Cape Town. Details are discussed later on in this report. Further research into the geographical identification of electricity users is necessary. Using Geographic Information Systems (GIS) software could present a starting point for better electricity consumption monitoring and tracking over time. Fuels Other than Electricity for Non-Transport-Related Activities The database from the City’s air pollution department was used to estimate energy consumption of a number of fuels for non-transport-related purposes. This database is based on predictions of fuels used for burning purposes (burning requires a license from the City) rather than actual consumption and is only based on the predictions for a single month. In reality, fuel use may vary considerably month to month depending on variables such as climate, production processes and others. The air pollution database did not include data on LPG and non-transport petrol. These fuels had to be estimated from a database of total fuel sales for Cape Town. Commercial and Industrial Sector Size and Scope Real data on floor areas for commercial buildings was not available for this project. This data is collected by the valuations department within the City. It is understood that extracting the data is a lengthy and time-consuming process. This study employed the end-use proportional sector split from the National LTMS Study for the industrial sector, as no other information was available on the size and scope of the industrial sector in Cape Town. Ideally these proportions should be based on sub-sector specific studies of end-users. Commercial and Industrial Building Activity To gain a more accurate picture of energy consumption in the commercial and industrial sector, it is important to sub-categorise consumers by activity (e.g. health care, offices, food service, etc, in commercial; or mining, smelting, etc, in industrial). This is due to the fact that these sectors include a wide variety of buildings that use energy very differently depending on the activity undertaken in the building. Electricity sales data from the City’s SAP top consumers database (Table 1) indicated that the larger power users (Large Power LV, Large Power MV and Very Large Power from the table below) accounted for a substantial amount of City electricity sales. These customers are either large commercial or industrial users. This large consumption could be attributed to a specific building type or activity, but this is currently unknown. Table 1: City of Cape Town SAP top electricity consumers’ sales data

Sector Tariff Type Number of Customers Sales (ZAR) Customers (%) Sales (%)


Residential (Credit)

Domestic 1 83,378 1,254,978,857 14.4 % 13.6 %

Domestic 2 60,842 256,291,774 10.5 % 2.8 %

Commercial (Credit)

Small Power 1 25,360 1,464,037,150 4.4 % 15.8 %

Small Power 2 3,236 32,739,117 0.6 % 0.4 %

Large Power LV 1,015 690,877,687 0.2 % 7.5 %

Large Power MV 550 1,857,807,780 0.1 % 20.1 %

Very Large Power

54 1,308,515,738 0.0 % 14.2 %

Prepayment Domestic 1 106,946 1,201,435,338 18.4 % 13.0 %

Domestic 2 297,965 1,155,515,818 51.3 % 12.5 %

Small Power 1 192 6,533,316 0.0 % 0.1 %

Small Power 2 1,248 9,539,646 0.2 % 0.1 %

TOTAL 580,786 9,238,272,220 100.0 % 100.0 %

Transport data Transport data was the most difficult to come by and was the most incomplete of all the data sets. The fuel data was often not disaggregated in the detail required, e.g. for the commercial sector, diesel use was not disaggregated between transport-related and non-transport-related (mining, marine fishing, etc) activities. Specific data issues are dealt with in more detail in the transport chapter.

4.4. MAIN ENERGY DATA SOURCES The following table summarises data sources for fuel consumption estimates in Cape Town in 2007. Table 2: Sources for energy fuels data in Cape Town, 2007

Fuel Data Description Data Source

Electricity The City’s top consumers (30% of total municipal electricity sales): annual energy sales4, customer, tariff type and building LIS number5. This database did not include Eskom customers.

CoCT SAP Top Consumers database from the electricity department

The City’s municipal billing total electricity sales data (2007/08): total annual electricity sales figures and number of customers for 2007/08 (does not include customers supplied directly by Eskom)6

CoCT electricity department

Diesel For non-transport purposes: predicted consumption in litres per month

Air Pollution Management Database from CoCT Air Quality Monitoring Department

4 This data was made available with the understanding that the consumer identities remain confidential

5 The LIS number is a number generated by the City of Cape Town’s Land Information System to uniquely identify a

property. This number is seen as the most important identifier of a property. Once it has been allocated it can never be changed. 6 It was not possible to get accurate data on the Eskom-supplied areas of Cape Town. Based on conversations with the

electricity department at the City of Cape Town, it was determined that a 75%-25% City-Eskom split should be used for calculations in this report.


For transport purposes Fuel sales data (SAPIA)

Petrol For transport purposes Fuel sales data (SAPIA)

Wood For commercial and industrial purposes: predicted consumption in bags per month. The size of the bag was not given, but an entry in the notes in the Air Pollution Management Database referred to a 10kg bag. It was assumed that all bags were 10kg.

Air Pollution Management Database from CoCT Air Quality Monitoring Department

Coal Predicted consumption in kg per month Air Pollution Management Database from CoCT Air Quality Monitoring Department

Paraffin Predicted consumption in litres per month Fuel sales (SAPIA) and Air Pollution Management Database from CoCT Air Quality Monitoring Department

HFO Predicted consumption in litres per month Fuel sales (SAPIA) and Air Pollution Management Database from CoCT Air Quality Monitoring Department

LPG The proportion of LPG allocated to the industrial, residential and commercial sectors was advised to be approximately 25% residential, 25% commercial and 50% industrial.

Fuel sales (SAPIA) and personal communication with LPG industry members

4.5. INTERVENTIONS AND COSTING A set of interventions were modelled for each sector, in order to determine impacts on the energy use, carbon emissions and cost of differing future energy macro scenarios for Cape Town. Most macro scenarios modelled the same interventions, although the penetration rates for these interventions differed according to each scenario. The macro scenarios are discussed in more detail in the Scenarios section of the report. The cost of each intervention was modelled in LEAP by considering the capital cost of the intervention (duplicated whenever the end of the unit’s lifespan was reached) and the annual operation and maintenance costs. The number of units for each intervention was estimated by dividing the total annual consumption for each end use by the annual energy consumption per unit for the existing systems. For example, when considering a retrofit from incandescent lights to CFLs: Number of CFLs required = total annual consumption for lighting / annual energy consumption per incandescent light bulb The total cost of each intervention was calculated by adding the annualised capital cost difference7 to the energy cost of each technology. The capital cost difference was annualised over the lifespan of

7 The capital cost difference refers to the cost difference between the new technology and the old technology over the

lifespan of the product, e.g. an incandescent bulb costs R5 and lasts 1 year, while a CFL costs R20 and lasts 5 years. The cost of the incandescent bulbs over 5 years is R25 and the cost of the CFL over 5 years is R20. Therefore, the cost difference is -R5 for the CFL. This value was used to model a ‘new build’ scenario.


the technology. The energy cost was calculated by multiplying the amount of energy consumed by the technology by the tariff for the appropriate fuel. The equation in LEAP would be as follows: Annualised Cost (number of units x capital cost difference [ZAR], lifespan [years]) + Final Energy Intensity [GJ] x Key/Electricity Tariff [ZAR]

4.6. CALCULATING SUPPLY-SIDE DATA USING LEAP Due to the nature of the electricity supply in South Africa, it was difficult to model electricity supply at the municipal level for each of the future energy scenarios. In South Africa, electricity is supplied by a single national operator (Eskom). The electricity consumed in Cape Town is drawn directly from the national grid. It was decided to use the electricity demand of Cape Town to determine the amount of capacity (supply) required to meet that demand now and into the future. Unfortunately, because LEAP does not have iterative or optimising functions, this meant that some calculations needed to be done outside of the LEAP model, with the results being fed back into LEAP before the final calculations could be undertaken. The iteration is thus manual rather than automatic. A Microsoft Excel spreadsheet ‘Supply Tool.xls’8 (referred to as the Supply Tool from here onwards) was used for the external calculations. The LEAP user must first complete the demand side ‘current account’ (i.e. the 2007 electricity demand side picture for Cape Town) as well as all of the demand side scenarios (e.g. Business As Usual, etc) before undertaking any supply side calculations. If any changes are made to the demand side figures that would alter the total amount of electricity demand in any of the scenarios, the supply side figures would need to be recalculated. Once the total electricity demand for each scenario had been calculated in LEAP, these figures were used to calculate the required capacity to meet the demand. The capacity figures were calculated using the Supply Tool and entering the total annual electricity demand figures for the years (2007, 2010, 2020, 2030, 2040 and 2050) in the ‘demand’ tab of the Supply Tool. The Supply Tool was designed to calculate up to three scenarios at a time. The Supply Tool used the reserve margin (leave the default value of 15%, unless this has also been changed in the LEAP model) to calculate the total required capacity needed to meet the demand while still retaining the specified reserve margin. This was calculated by dividing the total annual electricity demand (in MWh) by the number of hours in a year and multiplying this figure by the reserve margin plus one, i.e. Capacity (MW) = demand (MWh) / hours in the year (365*24) x reserve margin plus one (1.15)

8 This spreadsheet can be obtained from Sustainable Energy Africa. Contact Mark Borchers at [email protected].

mailto:[email protected]


Figure 1: Entering electricity demand and reserve margin into the supply tool

It must be noted that LEAP is able to calculate the Peak Power Requirements (excluding reserve margin) in the same way as with the Supply Tool, but it was reasoned that it would be more intuitive for the user to calculate the required capacity from the actual electricity demand. The supply mix to be modelled in LEAP for each of three scenarios was entered on the ‘Supply’ tab. The Supply Tool used this data to produce the required ‘interp’ equations for insertion into LEAP. The equations were inserted into the 'Exogenous Capacity' field in LEAP for the relevant supply technology.

Figure 2: Enter supply mix for each scenario to calculate required 'interp' functions for LEAP

The ‘interp’ equations were copied into the correct scenarios in LEAP. Once all the exogenous capacities for each supply technology were entered into each scenario in LEAP, the model was run again to calculate the supply costs. By default, LEAP does not have a way of using the supply costs to influence the cost of electricity (i.e. an iterative optimising function). In this project, it was desired for the costs of various supply scenarios to be reflected in the cost of electricity. Once the supply figures were entered for all scenarios and the model was run successfully, the costs associated with each supply type were used to alter the electricity tariff, using the ‘Supply Costs’ tab in the Supply Tool. Total supply costs for each year (million ZAR) were entered into the relevant field of the Supply Tool. The Supply Tool provided a growth equation, which was copied into LEAP’s Key Assumption ‘Tariff Factor Elec’ function. Each scenario would have a slightly different tariff factor equation if the supply mixes are different.


Figure 3: Calculating tariff factor using total supply costs

Finally, once the tariff factor for each scenario was entered into LEAP, the model was run for the last time. The results of this run presented the final demand, the final supply and all associated costs. Supply-Side Costing All supply costs (capital, operation and maintenance), as well as efficiencies and availabilities, were taken from the SNAPP9 tool. Below is a summary table of electricity supply costs extracted from SNAPP. Table 3: Electricity supply costs of various supply technologies

Technology Type 2008 Capital cost (R/kW)

Fixed O&M (R/kW)

Variable O&M (R/MWh)

Efficiency (fraction)

Availability (fraction)

Lifetime (years)

Existing coal (large) 7064.8 198.5 8.1 0.349 0.870 50

Existing coal (small) 7064.8 274.8 9.6 0.282 0.803 50

Open Cycle Gas Turbine diesel

5722.2 101.0 29.7 0.315 0.930 25

Nuclear Pressurised Water Reactor

37444.8 750.9 4.0 0.327 0.837 40

Hydro (existing) 0 130.2 0 1 0.150 100

Landfill gas 21075.9 953.0 0.083 0.250 0.855 25

Biomass 31212.0 537.6 55.97 0.353 0.902 25

Supercritical coal 20145.2 229.6 28.71 0.370 0.857 30

Wind 30% 16423.6 252.7 0 1 0.3 20

Wind 25% 16423.6 252.7 0 1 0.25 20

Solar Thermal Central Receiver

67460.5 473.6 0 1 0.6 30

Solar Thermal Parabolic Trough

44973.7 473.6 0 1 0.4 30

Solar Photovoltaic 50041.9 97.430286 0 1 0.23 15

Combined-Cycle Gas 8763.4 104.1 17.2 0.474 0.9025 25

9 SNAPP - Sustainable National Accessible Power Planning Tool developed by the Energy Research Centre of the University

of Cape Town


Turbine

Nuclear Pebble-Bed Modular Reactor

35491.5 153.8 26.0 1 0.837 50

Integrated Gasification Combined-Cycle Coal

23406.7 322.5 24.3 0.389 0.857 30

Pumped storage 13009.5 57.8 0 0.729 0.223 50

4.7. KEY TO SCENARIOS IN LEAP All scenarios modelled in LEAP can be obtained from Sustainable Energy Africa10. For those who are interested in working with the LEAP data, an explanation key of all LEAP scenarios can be found below: A) BAU: Business as Usual The Business As Usual Scenario models current growth trends unchanged into the future. No energy interventions are included; only a continued escalating growth rate associated with the different sectors. B) POL: Policy The Policy Scenario models the potential emissions levels if all current South African energy policies were implemented as planned. The reduction in emission levels in this scenario alone are not enough to move South Africa to the emissions levels required by science. C) HIGE: High Economic Growth with EE The High Economic Growth with Energy Efficiency Scenario is based on the Business as Usual Scenario, but models a higher energy demand growth rate of 3.6% (linked to a GGP growth rate of 4.6%) and some residential, commercial, local government and transport energy efficiency interventions. No industrial sector energy efficiency interventions are included. D) LOW: Low Growth The Low Economic Growth Scenario is based on the Business as Usual Scenario, but models a lower energy demand growth rate of 1.9%, linked to a GGP growth rate of 2.9%. E) HIG: High Growth The High Economic Growth Scenario is based on the Business as Usual Scenario, but models a higher energy demand growth rate of 3.6%, linked to a GGP growth rate of 4.6%. F) OEF: Optimum Energy Future The Optimum Energy Future Scenario is the preferred energy future scenario. It includes energy efficiency interventions across all sectors and a larger proportion of electricity generated from renewable (municipal waste, solar thermal, wind, hydro) and nuclear sources.

10

contact Mark Borchers at [email protected] or 021 702 3622)



G) OEFP: Optimum Energy Future Peak Oil The Optimum Energy Future Peak Oil Scenario models the same drivers as the Optimum Energy Future Scenario, but it also includes a high growth rate in liquid fuel prices: 5% per annum from 2016 onwards and 7% per annum from 2020 onwards, as opposed to the 0.01% per annum in all other scenarios. H) OEFB: Optimum Energy Future with BAU Supply The Optimum Energy Future with BAU (Business as Usual) Supply Scenario models energy efficiency interventions across all sectors, but includes a Business As Usual Scenario electricity supply mix, i.e. it projects current electricity supply trends into the future. I) NAT: National LTMS The National LTMS Scenario represents the electricity supply options, including new nuclear and renewable energy, and energy efficiency interventions, required to meet the nationally endorsed carbon reduction profile. J) OEFBE: OEF with BAU Supply and Electricity Efficiency only The Optimum Energy Future with Business As Usual Supply and Electricity Efficiency Only Scenario models a Business As Usual electricity supply mix (e.g. no change in current trends of electricity supply options added) with electricity efficiency interventions across all sectors. No fuel efficiency interventions (e.g. efficient use of LPG, paraffin, diesel, etc) are included. K) OEFD: Optimum Energy Future Densification The Optimum Energy Future Densification Scenario models the effects of a denser city on the Optimum Energy Future Scenario (see (F) above), through a decrease in the demand cost of buses and rail in the passenger transport sector. L) OEF1: OEF Base The Optimum Energy Future Base Scenario is actually only a copy of the Business As Usual Scenario. It was created in order to assess the cumulative impact of individual energy efficiency interventions on a Business As Usual Scenario. Each of the Scenarios below (OEF2-OEF20) models the same factors as the scenario that comes before it, except that it adds one of its own efficiency interventions on top of those modelled in previous scenarios. For example,

“OEF2: OEF residential lighting” (see below) models the same factors as OEF1 (a Business as Usual Scenario), but also models energy efficient lighting in the residential sector;

“OEF3: OEF low income ceilings” (see below) models the same factors as OEF2 (Business as Usual, along with energy efficient lighting in the residential sector), but also models the effect of the installation of ceilings in low income residential households;

and so forth… i) OEF2: OEF residential lighting The Optimum Energy Future Residential Lighting Scenario models a Business as Usual Scenario, but with energy efficient lighting in the residential sector.


ii) OEF3: OEF low income ceilings The Optimum Energy Future Low Income Ceilings Scenario models OEF2 (see above), but includes the effects on energy use through the installation of ceilings in low income households. A ceiling lowers the space heating and cooling requirements of a house. Most current low income houses do not have ceilings installed. iii) OEF4: OEF high income cooking The Optimum Energy Future High Income Cooking Scenario models OEF3 (see above), but also includes cooking energy efficiency interventions in high income households. iv) OEF5: OEF residential fridges The Optimum Energy Future Residential Fridges Scenario models OEF4 (see above), but also includes energy efficient fridges in the residential sector. v) OEF6: OEF residential water heating The Optimum Energy Future Residential Water Heating Scenario models OEF5 (see above), but also includes energy efficient water heating (solar water heaters or heat pumps) in the residential sector. vi) OEF7: OEF commercial HVAC The Optimum Energy Future Commercial HVAC Scenario models OEF6 (see above), but also includes energy efficient heating, ventilation and cooling systems in the commercial sector. vii) OEF8: OEF commercial water heating The Optimum Energy Future Commercial Water Heating Scenario models OEF7 (see above), but also includes energy efficient water heating (solar water heaters or heat pumps) in the commercial sector. viii) OEF9: OEF commercial lighting The Optimum Energy Future Commercial Lighting Scenario models OEF8 (see above), but also includes energy efficient lighting in the commercial sector. xi) OEF10: OEF industrial machine drives The Optimum Energy Future Industrial Machine Drives Scenario models OEF9 (see above), but also includes energy efficient machine drives in the industrial sector. x) OEF11: OEF industrial HVAC The Optimum Energy Future Industrial HVAC Scenario models OEF10 (see above), but also includes energy efficient heating, ventilation and cooling systems in the industrial sector. xi) OEF12: OEF industrial lighting The Optimum Energy Future Industrial Lighting Scenario models OEF11 (see above), but also includes energy efficient lighting in the industrial sector. xii) OEF13: OEF industry fuel (excl. elec) 15% efficiency The Optimum Energy Future Industry Fuel Efficiency Scenario models OEF12 (see above), but also includes non-electric fuel (LPG, paraffin, coal, diesel, etc) efficiency in the industrial sector.


xiii) OEF14: OEF government buildings The Optimum Energy Future Government Buildings Scenario models OEF13 (see above), but also includes energy efficient buildings (through efficient water heating, lighting, HVAC, etc) in the local government sector. xiv) OEF15: OEF government street lights The Optimum Energy Future Government Street Lights Scenario models OEF14 (see above), but also includes energy efficient street lighting in the local government sector. xv) OEF16: OEF government traffic lights The Optimum Energy Future Government Traffic Lights Scenario models OEF15 (see above), but also includes energy efficient traffic lights in the local government sector. xvi) OEF17: OEF government fleet The Optimum Energy Future Government Fleet Scenario models OEF16 (see above), but also includes a fuel efficient transport fleet (efficient petrol and diesel vehicles) in the local government sector. xvii) OEF18: OEF freight modal shift The Optimum Energy Future Freight Modal Shift Scenario models OEF17 (see above), but also includes a modal shift from road to rail in the freight transport sector. xviii) OEF19: OEF private vehicle efficiency The Optimum Energy Future Private Vehicle Efficiency Scenario models OEF18 (see above), but also includes fuel-efficient vehicles (efficient petrol and diesel cars, and hybrid and electric cars) in the passenger transport sector. xix) OEF20: OEF public transport modal shift The Optimum Energy Future Public Transport Modal Shift Scenario models OEF19 (see above), but also includes a modal shift from private to public transport in the passenger transport sector.


5. CAPE TOWN ENERGY CONSUMPTION AND EMISSIONS OVERVIEW The 2007 baseline energy picture of Cape Town is dominated by the transport sector, which consumes approximately 50% of all energy; followed by the residential (18%), commercial (17%) and industrial (14%) sectors. The Figure 4 represents the energy consumption per sector in Cape Town for 2007, the base year. Figure 5 represents the energy consumption per fuel in Cape Town for 2007.

Energy Consumption per Sector in Cape Town, 2007

Residential

18%

Commercial

17%

Local Government

1%

Industrial

14%

Transport

50%

Figure 4: Energy consumption per sector in Cape Town, 2007


Energy Consumption per Fuel Type in Cape Town, 2007

Electricity

40%

Petrol

30%

Paraffin

2%

Wood

0%

Diesel

22%

Coal

2%

HFO

3%

LPG

1%

Figure 5: Energy consumption per fuel type in Cape Town, 2007

Figure 6 shows the CO2 emissions associated with each of the sectors in 2007. It should be noted that although the transport sector consumes 50% of the energy in Cape Town, it is only responsible for 27% of the carbon emissions. This is due to emissions associated with different types of fuels and, in particular, the fact that South Africa’s electricity is largely coal-generated, which renders it very carbon intensive.


Figure 6: Carbon emissions per sector in Cape Town, 2007


Table 4: Energy consumption breakdown by fuel types (in GJ/annum) and sector for Cape Town, 2007

Electricity LPG Paraffin Wood Coal Petrol Diesel HFO Total %

Residential 21,470,320 428,183 1,556,795 31,065 23,486,363 18 %

Commercial 19,817,344 428,183 90,449 29,574 274,483 66,171 18,393 20,724,597 16 %

Industrial 6,205,371 856,366 1,201,683 1,074 2,781,510 2,693,120 4,068,976 17,808,100 14 %

Government 1,107,851 205,065 368,897 1,681,813 1 %

Transport 806,654 38,238,074 25,310,526 64,355,254 50 %

TOTAL 49,407,540 1,712,732 2,695,528 61,713 3,055,993 38,443,139 28,438,714 4,087,369 127,902,728 100 %

% 39 % 1 % 2 % 0.05 % 2 % 30 % 22 % 3 %


6. RESIDENTIAL SECTOR ENERGY DATA

6.1. DEMAND FOR ENERGY SERVICES The energy use indicator for the residential sector was the total number of households. Number of households The Community Survey (Stats SA, 2007) estimated a total number of households in Cape Town of 902,278; based on a sample of households, scaled up. However, the General Household Survey (Stats SA, 2007) reported 947,870 households. The Community Survey appeared to underestimate the number of households. The General Household Survey figure is closer to that reported by the City; hence the number of households given by the General Household Survey was used. All other residential data used in this report was drawn from the Community Survey. Household/population growth rates The official population growth projections used by the city are given below. The overall official population growth figure used is 3%11. Because studies indicate that household size is decreasing on average over time, household growth will be slightly higher than population growth. LEAP has used a 4% housing growth. Table 5: Projected population using medium growth rates by population

Population Group

2001 2006 2011 2016 2021 2026 2031

Asian Num 47,252 57,742 67,388 75,546 82,334 88,383 93,541

% 1.50% 1.63% 1.76% 1.89% 2.00% 2.10% 2.20%

Black Num 984,452 1,225,695 1,387,606 1,496,267 1,581,397 1,653,399 1,703,802

% 31.21% 34.56% 36.32% 37.43% 38.39% 39.29% 40.03%

Coloured Num 1,454,346 1,572,766 1,655,042 1,697,148 1,711,661 1,712,078 1,698,536

% 46.11% 44.34% 43.32% 42.45% 41.55% 40.68% 39.91%

White Num 668,188 690,851 710,811 728,756 744,113 754,584 759,977

% 21.18% 19.48% 18.60% 18.23% 18.06% 17.93% 17.86%

Total Num 3,154,238 3,547,055 3,820,847 3,997,718 4,119,504 4,208,444 4,255,857

% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

The growth of the informal sector is less straightforward because good data is not available. Two sources of data were used:

11

Pers Comm Karen Small, Head: Strategic Information Analysis and Research, City of Cape Town


SOURCE 1: ‘Informal Dwelling Count for Cape Town (1993-2005)’ (Rodrigues, Gie, Haskins, June 2006). This data shows a growth of 10.9% p.a from 1993 to 2005. The growth trend seems to be slowing.

SOURCE 2: ‘Are informal settlements a housing solution? Is anyone responsible or accountable for informal settlements? What is the future of informal settlements in Cape Town? And other leading questions.’ Gerry Adlard 2008 Conference paper. Here a current total figure of 400 000 informal dwellings is shown (150 000 informal and rest backyard shacks), representing a growth of 16.9% p.a. from 1993 to 2010.

While the latter source indicates a very high growth rate, it may be the most reliable as data gathering appears to have been more comprehensive than for other studies. Although there is still a significant amount of uncertainty in informal sector growth rates, LEAP has used 13% from 2007, dropping to 8% from 2010. Households by income bands The Community Survey reports income in 12 bands. These were further grouped into four main income bands:

Low Income: R 0 – R 38,400

Medium Income: R 38,401 – R 153,600

High Income: R 153,601 – R 614,400

Very High Income: R 614,410+ The percentage of households in each of these is shown in the figure below.


Household Breakdown according to income categories for

Cape Town

32%

12%29%

3%

20%

4%

low income formal

low income informal

medium income formal

medium income informal

high income

very high income

Figure 7: Percentage of households by income group

City electricity sales data The residential electricity consumption for 2007/08 by the 549,860 City of Cape Town customers was 4,068.5 GWh.12 In addition, Eskom also sells to residential customers in Cape Town. However, it was not possible to obtain the number of Eskom residential customers or the amount of electricity sold by Eskom to the residential sector. The total amount of electricity sold and the number of customers in residential sector, for Eskom and the City, had to be estimated. The table below shows the residential tariff structure for City of Cape Town customers and the number of customers on each tariff. The amount of electricity sold in MWh divided by the number of customers in each tariff group gives the average energy consumption per customer in MWh. Multiplying this value by 1,000 results in values for average energy consumption per customer in kWh. Table 6: City of Cape Town residential customer tariffs

Tariff Customers Energy (Annual MWh)

Energy / customer / month (kWh)

Domestic 1 Credit 73,271 985,714 1,121

Domestic 2 Credit (FBE) 45,861 223,854 407

Domestic 2 Credit (no FBE) 23,408 145,450 518

Domestic 3-phase 18 463 2,143

12

Electricity sales data from CoCT electricity department


Domestic with Off-Peak Combination 287 133,112 38,650

Domestic Cluster with Off-Peak 9 3,604 33,373

Domestic Cluster 183 26,999 12,295

Domestic 1 Prepaid 101,920 1,181,504 966

Domestic 2 Prepaid (FBE) 230,341 867,042 314

Domestic 2 Prepaid (no FBE) 74,312 496,551 557

Domestic 3–phase 250 4,176 1,392

TOTAL DOMESTIC 549,860 4,068,470 617

Number of electrified households Households were categorised as electrified or non-electrified. This was deduced from households reporting whether electricity was used as a main source of fuel for lighting in the Community Survey. Generally, if a low-income house is electrified the first and cheapest appliance usually made to run on electricity is lighting. It was found that 94% of households were electrified. Due to the fact that the figure for total number of households used in this report was gained from the General Households Survey, which states a higher number of households than the Community Survey, the number of electrified households (as reported in the Community Survey) was up-scaled proportionally to be in line with the General Households Survey figures, i.e. 94% of all the households in the General Households Survey were assumed to be electrified. Households in Cape Town received electricity from Eskom or the City on either credit meters or prepaid meters. The number of electrified households was larger than the number of customers on record due to the sales of electricity from one household to another (e.g. to households in backyard shacks) and to supply to grouped customers (e.g. a block of flats). The customers were divided into low, medium, high and very high income groups according to the Community Survey income bands. Table 7: Split of households by income group and electrification

No. of households Electrified % Non-electrified % Total %

Low income 382,889 40.4 % 52,194 5.5 % 435,083 45.9 %

Medium Income 284,989 30.1 % 10,615 1.1 % 295,604 31.2 %

High Income 182,115 19.2 % 0 0 % 182,115 19.2 %

Very High Income 35,068 3.7 % 0 0 % 35,068 3.7 %

Total 885,061 93.4 % 62,809 6.6 % 947,870 100 %

The average electricity use intensities for customers per income band are shown in the table below. Table 8: Average electricity consumption for households in each category

Household Type Number of Households Average KWh/month

Low Income 382,889 220

Medium Income 284,989 528

High Income 182,115 930

Very High Income 35,068 1,033

The City distinguishes between customers that receive free basic electricity (FBE) on credit or pre-payment meters. In 2007/08, there were 45,861 customers on credit meters receiving FBE, with an


average consumption of 406.76 kWh per month, and 230,341 customers on pre-payment meters receiving FBE, with an average consumption of 329 kWh per month. The number of customers receiving FBE, who are not on the City’s database was 143,284; estimated based on the assumption that 69% of households not supplied by the City were low-income households that received FBE. Total electricity consumption Total electricity consumption in the residential sector in 2007/08 was 19 TJ. This value was gained from the estimated number of customer/electrified households (as calculated above) and the energy intensities (kWh per customer) for customers in each income band.

6.2. ENERGY END USE The Community Survey contained the main fuel data for the end uses of cooking, lighting and space heating. The main fuels reported were electricity, LPG, paraffin, wood, coal and candles. In the lower- and medium-income households, the fuel use split for heating water was assumed to be the same as for cooking. It was assumed that all high- and very high-income households had electric geysers for heating water.

Figure 8: Lighting by fuel source and household income type in Cape Town (note scale of y-axis)


Figure 9: Cooking by fuel source and household income type

Figure 10: Space heating by fuel source and household income type

Figure 11: Water heating by fuel type and household income type

Energy intensities are reported below for lighting, water heating, space heating, cooking, refrigeration and other electric end uses. Lighting Energy Intensity


The energy intensity of lighting used a bottom-up calculation based on an assumed average number and type of lights in each income group. It is very important to note that these are estimates and may or may not be how households use electricity for lighting. The estimates represent the best figures that can be obtained given the poor data available. The ratio between CFL and incandescent lights in households was based on the number of CFLs distributed per household and purchased by households during the Eskom Demand Side Management (DSM) programme rollouts in the Western Cape, which took place in 2006. During the rollout, 336,000 households received 3.5 million CFLs. It was assumed that each lamp was used for an average of 4.5 hours per day in low and medium income households and for 3 hours per day in high to very high income households. The high and very high income households were assumed to have more lamps on average than the low and medium income households. The number of lights and hours of use was based on the Monitoring and Verification surveys13 of savings in the CFL rollout campaign in Cape Town. The number of lights was cross-checked against the average number of rooms recorded per households in the Community Survey and the estimated number of lights per room. The hours of use in the high and very high income categories were estimated to be lower, due to the fact that these households had more rooms and would not use all the lighting all the time. The low and medium income categories contained CFLs and incandescent lights, whilst the high and very high income categories had these lighting types along with “other” lighting (e.g. halogen down-lighters, other fluorescents, etc). In the high and very high income group, it was assumed that “other” lighting contributed 38% and 34% respectively14 towards the energy intensity. The table below shows the assumptions relating to the average number of lights, average number of light hours, and the ratio of CFLs to incandescent lights. Table 9: CFL and incandescent lighting assumptions

Household Type Average Number of Lights per Households

Average Number of hours per light

Percentage CFLs

Low Income 4.11 4.5 39.98%

Medium Income 7.94 4.5 39.98%

High Income 11.46 3 39.98%

Very High Income 13.61 3 39.98%

13

These surveys were not published, but information was obtained through personal communication. 14

Calculated by subtracting the estimated amount of CFL and incandescent lighting from the estimated total amount of electricity used for lighting.


The energy intensities for each fuel source are shown below.15 In the case of gas energy intensity values, it was assumed that the lighting levels were the same as that of paraffin. Relative appliance efficiencies were then used to calculate efficiencies for gas lighting. Table 10: Energy intensity of light in GJ/hh/a

GJ/hh/a Electricity LPG Paraffin Candles

Low Income 1.02 2.95 3.44 2.05

Medium Income 1.97 5.69 6.64 3.95

High Income 4.00

Very High Income 4.36

Cooking Energy Intensity Cooking intensities for low income households were taken from Cowan et al (2008)16 and were based on measured energy consumption for meals cooked on different appliances using electricity, paraffin and LPG. The medium, high and very high income electricity intensity for cooking was based on an average percentage of electricity used for cooking and assumed that households would heat additional water for tea/coffee (3 litres per household). Assuming households in the medium and high income categories had similar requirements for cooking (i.e. the useful energy required was the same); appliance efficiencies were used to convert electricity intensities to these household categories to intensities for cooking with LPG and paraffin. For wood use, it was assumed that households used around 2kg for cooking per day. Table 11: Energy intensity of cooking

GJ/hh/a Electricity LPG Paraffin Wood

Low Income 2.85 3.80 8.17 3.73

Medium Income 3.03 5.38 6.06 3.73

High Income 4.01 6.47

Very High Income 4.53 7.30

Space Heating Energy Intensity The energy intensity of electrical space heating was calculated based on the average increase in energy consumption over the winter months (see figures 12 and 13)17 and the number of households that reported using electricity as the main fuel for space heating. The Community Survey reports that in electrified households the proportion of energy used for space heating is 77% for low income, 90% for middle income and 92% for high and very income.

15

Source of intensity figures for paraffin and candles: Simmonds, G. and Mammon, M. 1996. “Energy services in low-income urban South Africa: A quantitative assessment.” EDRC (Energy and Development Research Centre) reports, University of Cape Town. 16

Full source: “Alleviation of Poverty through the Provision of Local Energy Services (APPLES): (Project no. EIE-04-168). Project Deliverable No. 17: Identification and demonstration of selected energy best practices for low-income urban communities in South Africa” by Bill Cowan of the Energy Research Centre at UCT. December 2008. 17

Source: City of Cape Town electricity sales data.


The energy intensity was cross-checked against the percentage of energy attributed to space heating in households by other studies. The intensity of space heating using other fuels was calculated assuming a useful energy demand18 for space heating and appliance efficiencies. The electricity consumption profile for households on pre-paid and credit meters are shown in the figures below.

200

400

600

800

1000

1200

1400

July October January April July October January April

Co

nsu

mp

tio

n k

Wh

/hh

ld/m

nth

Domestic 1 Prepaid Domestic 2 Prepaid Domestic 2 Prepaid - No FBE

Figure 12: Electricity profile from the City of Cape Town sales data for households on prepaid meters

18

Useful energy demand is a measure of energy service that is produced by a device. Most often it is calculated as (final energy x efficiency)


0

200

400

600

800

1000

1200

1400

1600

1800

July

Sep

tem

ber

Nove

mbe

r

Janu

ary

Mar

chM

ay July

Sep

tem

ber

Nove

mbe

r

Janu

ary

Mar

chM

ay

co

nsu

mp

tio

n (

kW

h/h

hld

)

Domestic 1 credit Domestic 2 credit Domestic 2 credit no FBE

Figure 13: Electricity profile from the City of Cape Town sales data for households on credit meters

Table 12: Energy intensity for space heating

GJ/hh/a Electricity LPG Paraffin Wood

Low Income 1.0 1.4 1.4 0.3

Medium Income 2.3 3.0 3.1 0.4

High Income 2.4 3.3 3.3 0.6

Very High Income 2.8 3.7 0.6

Water Heating Energy Intensity Water heating intensities were based on an assumption of average litres of water heated per day for the purpose of bathing19 and the efficiency of water heating. Calculation used: Water heating intensity = mass x specific heat capacity x change in temperature/efficiency The table below shows the assumed average water consumption of households in each income category. The reason for the difference in water consumption between the high and very high income households is the relatively small sample number of people in very high income households recorded in the Community Survey. Table 13: Energy intensity for water heating

Electricity (GJ/hh/a)

LPG (GJ/hh/a) Paraffin (GJ/hh/a)

Wood (GJ/hh/a)

Average (litres/hh/day)

19

Source: Community Survey (Stats SA, 2007)


Low Income 2.8 3.6 4.4 8.5 18

Medium Income (Electrified)

9.2 7.1 8.5 8.5 120

Medium Income (Non-Electrified)

7.1 8.5 8.5 17

High Income 17.0 201.6

Very High Income 16.6 197.4

Refrigeration Energy Intensity Refrigeration ownership by income group and assumed intensity is shown in the table below. The refrigeration intensities are based on a low-efficiency, smallish fridge for low-income households; a slightly better efficiency medium-sized fridge for middle income households; and a moderately efficient middle to large fridge for high and very high income households. The data was obtained from international websites, but since South Africa has no efficiency standards for appliances, it was assumed that South African fridges were at the lower to moderate end of the efficiency scale. Table 14: Refrigeration ownership and intensity

Household Type Percentage of households with a refrigerator

Assumed intensity (GJ/hh/a)

Low Income 66 % 2.14

Medium Income 92 % 3.56

High Income 99 % 5.7

Very High Income 99 % 5.7

Summary: Household Energy Intensity by End Use The figure below shows a comparison of average daily electricity consumption in kJ per households by end use for all household types.

0.00

20000.00

40000.00

60000.00

80000.00

100000.00

120000.00

140000.00

low medium high very high

kJ/h

hld

/day

lighting cooking heating w ater heating refrigeration other

Figure 14: Estimated average daily electricity consumption by end use for residential households (kJ/hh/day)


6.3. INTERVENTIONS AND COSTING Table 15: Interventions and costing for low income electrified housing

Lighting Water Heating

Existing System Incandescent lighting Conventional geyser

Intervention Replacement of incandescent lighting with CFLs

Efficient Water Heating (with timer where applicable)

Number of units per house hold 4.11 (average) 1

W / unit of existing system 100 W 3 kW

W / unit of intervention 20W 3 kW

Operating hours 4.5 hours per day Conventional geyser = 4 hours / day Efficient water heating = 1.36 hours/ day

Lifespan (number of years before it will need to be replaced)

Incandescent = 1 year CFL = 5 years

Conventional = 10 years Efficient water heating = 10 years

Cost / unit of existing R 5 per incandescent R6 100 per unit

Cost / unit of intervention R 20 per CFL R 13 000 per unit

Table 16: Interventions and costing for medium income electrified housing



Intervention Replacement of incandescent lighting with CFLs


Number of units per house hold 7.94 (average) 1

W / unit of existing system 100 W 3 kW

W / unit of intervention 20W 3 kW

Operating hours 4.5 hours per day Conventional geyser = 4 hours / day Efficient water heating = 1.36 hours/ day


Incandescent = 1 year CFL = 5 years


Cost / unit of existing R 5 per incandescent R 6,100 per unit

Cost / unit of intervention R 20 per CFL R 13,000 per unit

Table 17: Interventions and costing for high income electrified housing



Intervention Replacement of incandescent lighting with CFLs Replacement of “other” lighting with more efficient alternatives



Number of units per house hold 11.46 (average for incandescent and CFL) 4 (other lighting)

1

W / unit of existing system 100 W – incandescent 50 W – “other”

3 kW

W / unit of intervention 20W – CFL 5 W – “other efficient”20

3 kW

Operating hours 3 hours per day Conventional geyser = 4 hours / day Efficient water heating = 1.36 hours/ day


Incandescent = 1 year “Other” = 2 years CFL = 5 years “Other Efficient” = 6 years


Cost / unit of existing R 5 per incandescent R 25 per “other” light

R 7,200 per unit

Cost / unit of intervention R 20 per CFL R 175 per “other efficient”

R 16,000 per unit

Table 18: Interventions and costing for very high income electrified housing



Intervention Replacement of incandescent lighting with CFLs Replacement of “other” lighting with more efficient alternatives


Number of units per house hold 13.61 (average for incandescent and CFL) 4 (other lighting)

1

W / unit of existing system 100 W – incandescent 50 W – “other”

3 kW

W / unit of intervention 20W – CFL 3 kW

20

In the residential sector “other efficient lighting” consists of mainly LED lighting


5 W – “other efficient”

Operating hours 3 hours per day Conventional geyser = 4 hours / day Efficient water heating = 1.36 hours/ day


Incandescent = 1 year “Other” = 2 years CFL = 5 years “Other Efficient” = 6 years


Cost / unit of existing R 5 per incandescent R 25 per “other” light

R 8,000 per unit

Cost / unit of intervention R 20 per CFL R 175 per “other efficient”

R 18,300 per unit


7. COMMERCIAL SECTOR ENERGY DATA

7.1. DEMAND FOR ENERGY SERVICES The energy use indicator for the commercial sector was gross floor area in square meters (m2). The gross floor area of the commercial building sector was used to define the energy intensity of each fuel in GJ/m2. It was necessary to distinguish between building stock that existed before 2007 and building stock constructed during and after 2007, in order to enable a distinction between energy efficiency interventions applied to new buildings and to existing buildings. Total floor area was estimated by approximating an average value for lighting energy per m2. Lighting energy was selected, as it could be assumed that all commercial buildings will have lighting of some description. Average energy used for lighting The average lighting value of 61.4 kWh/m2 per annum was obtained from the CBECS (Commercial Buildings Energy Consumption Survey) database of existing buildings’ energy consumption21. This was verified against the value of 58.7 kWh/m2 per annum provided by Virtual Environment Software from IES (Integrated Environmental Solutions), 1994; an approved building simulation software package used to produce energy certificates. The IES value is based on the following assumptions:

Lighting level: 500 lux

Power: 3.75 W/m2/100 lux

Energy: 18.75 W/m2

Profile: Lighting in use 7am-7pm on weekdays only Calculating total electricity use The City provided detailed sales data from their SAP database on their top 1,200 electricity consumers in Cape Town. The data was disaggregated into the following sub-sectors: industrial, commercial, government, domestic and unknown. The information extracted from the SAP Top Consumers database was used to guide a number of assumptions in disaggregating the total electricity consumption data for the commercial and industrial sectors in Cape Town. The SAP Top Consumers data provided sufficient information to be able to identify 66% of the top 1,200 electricity customers as belonging to certain sub-sectors.

21

Source: EIA (Energy Information Administration Independent Statistcs & Analysis), 2003a. Commercial buildings energy consumption survey (CBECS) Table E6A. U.S. Department of Energy: Washington DC. Retrieved 23/06/2010 from http://www.eia.doe.gov/emeu/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03

http://www.eia.doe.gov/emeu/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03


The City provided total electricity sales data for 2007. This data provided no information on customers and billing names, but did provide the total annual energy consumption data for each tariff type. The dataset identified the tariff relating to the domestic and government sector. It was assumed the remaining tariffs related to the industrial and commercial sectors. It was assumed that the following tariff types were used exclusively by commercial customers:

Credit – small power 1 and small power 2

Pre-payment – small power 1 and small power 2 The large power user data was more complex, as industrial and commercial customers were aggregated. To disaggregate this data, it was divided between the commercial and industrial sectors in the same proportions as the SAP Top Consumers data on the top electricity consumers. Data on large power users was therefore provided both in the City of Cape Town municipality total sales summary for 2007/08 and the City of Cape Town SAP Top Consumers data. A number of tariffs types are available to large power users. Although some of the tariffs names differed between the total electricity sales data and the SAP Top Consumers data, it was assumed that all the customers listed in the top consumer database were duplicated in the total sales data. The government sub-sector electricity data was extracted from the total sales data and the remainder was divided between the commercial and industrial sectors using the proportions calculated from the SAP Top Consumers database (figure 15).

Figure 15: Proportional split between commercial and industrial electricity sales and customers calculated from the SAP Top Consumers database


Small power users’ sales data was taken directly from the total electricity sales summary 2007/08 and is summarised in the table below. Based on discussions with the City, it was assumed that all consumers on the small power tariffs were commercial customers, although there were potential exceptions to this rule. Table 19: Summary of total municipal electricity sales for small power users

CoCT Small Power Sales Number of Customers GJ/annum

Credit - Small Power 1 24,770 5,397,064

Credit - Small Power 2 3,453 134,201

Prepayment - Small Power 1 223 26,003

Prepayment - Small Power 2 1,434 40,580

The City of Cape Town supplied 75% of the electricity in Cape Town and Eskom supplied the remainder22. Eskom electricity sales were estimated from this data and the average electricity sales per customer value calculated from the City sales by tariff data. The City of Cape Town served a total of 584,289 customers in 2007. Taking this figure to be 75% of the total served, it was calculated that Eskom served 194,746 customers. The total estimated number of Eskom customers was divided between sectors using the same proportions as those calculated for the City sales by tariff data. Average electricity consumption per customer for the commercial sector was calculated from the City sales by tariff data. This value was used to estimate the total electricity sales by Eskom. It was estimated that 10,354 Eskom commercial customers consumed 4,954,336 GJ in 2007. Calculating total floor area Total floor area was estimated by dividing the total energy consumption estimated for lighting (in kWh) in 2007/2008 by average lighting energy per m2. The total floor area was calculated as follows:

Step 1 Data: total estimated annual electricity consumption for the commercial sector (City of Cape Town 2007/08: municipality total sales summary and the SAP Top Consumers data)

5,498,860,679 kWh/annum

Step 2 Data: percentage of energy used for lighting (CBECS) 37 %

Step 3 Calculation: electricity used for lighting - 37% (Step 2) of total estimated annual electricity consumption (Step 1)

2,034,578,451 kWh/annum

Step 4 Data: estimated average lighting load (CBECS) 61.4kWh/m2 per annum

Step 5 Calculation: divide electricity used for lighting (Step 3) by estimated average lighting load (CBECS)

33,136,457 m2

Other fuels used

22

Source: Integrated analysis for Cape Town’s resource flows: energy sector. SEA, June 2007.


A number of other fuels were considered in addition to electricity. These are listed below along with a description of the analysis undertaken to estimate annual consumption for each fuel. Table 20: Summary of annual energy consumption by fuel (excluding electricity) for the commercial sector

Fuel GJ/Annum Calculation

Wood 29,574 Calculated from monthly estimates provided by the City's Air Pollution Management Database Coal 274,483

Diesel 66,171

LPG 428,183 Industry 50%, Residential 25%, Commercial 25% (G. Tatham)23 Domestic consumption was calculated using a bottom-up approach described in the Residential Consumption Data Analysis section. The industry and commercial sectors’ consumption was estimated pro-rata from this figure.

Paraffin 90,449 Commercial and Industrial consumption = Total (2,848,927) - Residential (1,556,795) Commercial and industrial consumption was allocated in the same proportion as the monthly paraffin consumption estimates from the City’s Air Pollution Management Database Commercial 7%; Industrial 9%

HFO 18,393 Total aggregated commercial and industrial consumption: 4,087,369 GJ/annum Commercial and industrial consumption allocated in the same proportion as monthly HFO consumption estimates from the City’s Air Pollution Management Database Commercial 0.45%; Industrial 95.55%

7.2. ENERGY END USE Data source Data on energy consumption by end use in the commercial sector is in short supply globally. In South Africa, data on end use consumption is very limited. The most comprehensive database of energy by end use in a variety of commercial buildings is collected in the USA for the Commercial Buildings Energy Consumption Survey (CBECS). The CBECS is a national sample survey undertaken on a quadrennial basis in the USA. The survey collects information on the stock of USA commercial buildings, along with the buildings’ energy-related characteristics, energy consumption and expenditures. Commercial buildings are defined as including all buildings in which at least half of the floor space is used for a purpose that is not residential, industrial and/or agricultural.

23

Tatham, G., 2010. Telephone communication on 30 March 2010. Investment manager specialising in LPG. Director of Wild Orchard. [email protected].



The CBECS end use data has been calculated based on a combination of surveys of commercial buildings in the USA and computer simulation. The methodology is described in detail on the USA Energy Information Administration (EIA) website and essentially consists of the following four steps:

Step 1: Regressions of monthly consumption on degree-days to establish reference temperature for the engineering models

Step 2: Engineering modelling by end use

Step 3: Cross-sectional regressions to calibrate the engineering estimates and account for additional energy uses

Step 4: Reconciliation of the end-use estimates to the CBECS total building energy consumption

The CBECS data on energy consumption by end use is presented in a number of ways, including energy consumption by end use for different commercial building types and energy consumption by end use for different climatic regions on degree-day information. The energy consumption by end use characteristics of commercial facilities vary widely depending on the activities undertaken within the buildings. Energy by end use Insufficient information was available to divide the entire commercial sector into building activities (e.g. health care, offices, food service, etc). An estimation of the energy consumption by end use was applied to the entire sector. In reality, energy consumption by end use will vary considerably depending on building activity. The proportion of energy consumption by each end use was taken from the CBECS data on end use electricity consumption presented for different climatic regions (EIA, 2003b)24. The climate zone selected included regions that experienced fewer than 2,000 cooling degree-days a year and fewer than 4,000 heating degree-days a year. Over the last 12 months (penned in 2009) Cape Town had 1,101 cooling degree-days and 1,643 heating degrees-days (BizEE Software Ltd, 2010); falling into this climate band. Figure 16 shows the end use split used in this analysis. In the final analysis, cooling and ventilation were merged; as were refrigeration, computers, office equipment and other.

24

EIA (Energy Information Administration Independent Statistcs & Analysis), 2003b. Commercial buildings energy consumption survey (CBECS) Table E5A. U.S. Department of Energy: Washington DC. Retrieved 23/06/2010 from http://www.eia.doe.gov/emeu/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03

http://www.eia.doe.gov/emeu/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03


Figure 16: Electricity consumption by end use in the commercial sector of Cape Town (CBECS)

Other fuels (except Diesel and LPG) were categorised based on detail provided in the Cape Town Energy Futures Report. Diesel end use was split in the same proportions as that of electricity, because the majority of diesel usage in the commercial sector was shown, by the comments from the consumer in the Air Pollution Management Database, to be for back-up generation purposes (CoCT, 2007a)25. Table 21: Summary of uses of different fuels by building types and use within the commercial sector

Fuel Main Building Types Typical Uses Dominant use assumed in this study

Diesel Office, lodging, hospital, public assembly, education, food services , hotels

Electricity generation (back-up)

Same split as electricity: mainly lighting and HVAC

Wood Restaurant, food sales, public assembly Aesthetic heating, cooking

Heating

Coal Hospitals, food services Heating, cooking (boilers) Heating

Paraffin Lodging, offices, education, other Cooking Cooking

HFO Education, Hospital, Other Heating (boilers) Heating

LPG No data in the Air Pollution Management Database

Cooking in hotels, restaurants and hospitals as well as some heating

Cooking, Heating

25

City of Cape Town, 2007a. Air quality survey 2007 (PREMISE). City of Cape Town


7.3. INTERVENTIONS AND COSTING Table 22: Interventions and costing for the commercial sector

Cooling Water Heating Lighting

Existing System DX split systems with COP of 2.75

Conventional electricity geyser (200 litres, no insulation)

50% of existing light fittings are low energy lamps. Remaining 50% is 40W fluorescent lamps with magnetic ballasts

Intervention Replace all with VRV systems with COP of 4 (32% more efficient)

Replace all water heaters with a Solar Water Heater (200 litres with back-up element and timer)

For 50% of light fittings, replace 2x40W fluorescent lamps with 1 x electronic ballast and 2 x 36W lamps

Number of units 1 m2 treated areas (assumes an average cooling load of 180 W/m2)

1 geyser 2 lamps and 1 ballast

W / unit of existing system

0.65 w (average cooling load / COP)

3 kW 0.096 w

W / unit of intervention 0.045w (average cooling load / COP)

3 kW 0.058 w

Operating hours 12 hours a day 261 days a year

Conventional geyser = 4 hours/ day Efficient water heating = 1.36 hrs /day 261 days a year

12 hours a day, 261 days a year

Lifespan (number of years before it needs replacing)

Existing unit = 7 years Intervention = 10 years


Existing unit = 1.4 years Intervention = 3 years

Cost / unit of intervention

R 950 per m2 R 15,141 R 172

Cost / unit of existing R 700 per m2 R 10,000 R 100


8. INDUSTRIAL SECTOR ENERGY DATA

8.1. DEMAND FOR ENERGY SERVICES Energy consumption by sub-sector It was not possible to disaggregate the industrial sector into specific activities by meters squared or by the number of buildings, due to a lack of data available on the industrial sector in Cape Town. According to the Cape Town Energy Futures report26, the dominant industrial energy activities in Cape Town in 2000 were Pulp and Paper, Food and Beverage, Textiles and other key industries such as plastics, glass, cardboard, cement and metal works. The City’s Air Pollution Management Database was composed of a sample of 761 users across Cape Town. The City’s electricity consumptions SAP database comprised 1,200 of the top electricity users. From the sample of the top electricity users, 290 could be classified as industrial users based on the types of activities conducted. The two databases were matched on the basis of business name; providing energy consumption figures for industrial users in Cape Town. Implicit to the Air Pollution Management Database was a “Nature of Business” Code. The table below shows the relevant codes for industrial users, as well as basic descriptive statistics on the number and the percent of observations from each sub-sector within the industrial sector. The percentage of occurrences reflected in the table was employed to determine the distribution of industrial sub-sectors in Cape Town. Table 23: Nature of business for industry Cape Town

Nature of Business Code

Nature of Business Percent Sector

100 Bakeries 4.94% Food and Beverage

110 Beverage / Canner / Bottler 1.23 % Food and Beverage

120 Butcher 4.94 % Food and Beverage

140 Commercial Building 1.23 % Food and Beverage

170 Food Processing Plant 18.52 % Food and Beverage

220 Manufacturing: Clothing 9.88 % Textiles

240 Manufacturing: Non-Food 19.75 % Non-Food Manufacturing

250 Manufacturing: Textiles 19.75 % Textiles

270 Manufacturing: Food 12.35 % Non-Food Manufacturing

320 Scheduled Industries 4.94 % Other

400 Other 2.47 % Other

26

Winkler, H., Borchers, M., Hughes, A., Visagie, E., & Heinrich, G. ,2005. Cape Town Energy Futures: Policies and scenarios for sustainable city energy development. Energy Research Centre: University of Cape Town. Retrieved on 05/11/2009 from http://www.erc.uct.ac.za.ezproxy.uct.ac.za/Research/publications/05WInkler%20etal%20-%20CT%20energy%20futures.pdf

http://www.erc.uct.ac.za.ezproxy.uct.ac.za/Research/publications/05WInkler%20etal%20-%20CT%20energy%20futures.pdf

http://www.erc.uct.ac.za.ezproxy.uct.ac.za/Research/publications/05WInkler%20etal%20-%20CT%20energy%20futures.pdf


Total 100 %

The figure below shows the distribution of these sub-sectors of the industrial sector, namely Food and Beverage, Textiles, Non-Food Manufacturing and Other. It is clear from the sample observed that the industrial sector predominantly operates in the Food and Beverage sub-sector, following closely by Textiles and Non-Food Manufacturing.

Distribution of Industrial Sub-Sectors

43%

29%

21%

7%

Food and Beverage

Textiles

Non-Food Manufacturing

Other Industry

Figure 17: Distribution of industrial sub-sectors in Cape Town, 2007

Energy consumption by fuel type The industrial sector alone was responsible for 6,205,371 GJ (13%) of Cape Town’s electricity demand in the 2007 base year. The table below shows the distribution of energy consumption. It is clear that electricity is the dominant source of energy to the industrial sector. Table 24: Energy consumption in GJ in 2007

Electricity LPG Paraffin Wood Coal Diesel HFO

6,205,371 856,366 1,201,682 1,074 2,781,510 2,693,120 4,068,975

With the exception of the Food and Beverage sector, where 5% of total energy consumption is electricity, an average of 35% of industrial energy consumption is met through electricity. The table below shows the breakdown of energy consumption per industrial sub-sector in GJ/year. Table 25: Sub-sector proportion of total energy consumption, 2007

Electricity (%) LPG (%) Paraffin (%) Coal (%) Diesel (%) HFO (%) Total (GJ)

Textiles 10 9 20 2 50 4,946,781

Food and Beverage

5 9 11 14 22 32 5,033,467

Non-Food Manufacture

54 9 15 23 6,680,963


Other 97 3 1,860,352

TOTAL (GJ) 6,205,371 856,366 1,201,682 2,781,510 2,693,120 4,068,975 17,808,099

8.2. ENERGY END USE The National LTMS was used to determine a reasonable proportional split of end use activities for the industrial sector. The table below shows proportional electricity consumption by end use. In all sub-sectors, the “Other Machine Drives” tend to be the dominant electricity end use. Table 26: Percentage of electricity consumption by end use for the industrial sector

Percentage Textiles Food and Beverage Non-Food Manufacture

Other

Indirect uses (boiler fuel)

1 % 2 % 2 %

Process heating 5 % 4 % 7 % 13 %

Process cooling, refrigeration

7 % 24 % 3 %

Compressed Air 10 % 8 % 26 % 7 %

Other Machine Drive

50 % 44 % 55 % 36 %

Electro chemical processes

32 %

Other process use 1 % 1 %

Sum of HVAC, Support and Transport

17 % 10 % 4 % 5 %

Facility Lighting 9 % 8 % 5 % 4 %

8.3. INTERVENTIONS AND COSTING Table 27: Interventions and costing for the industrial sector

Motors HVAC Lighting

Existing System Various range of inefficient motors

Inefficient HVAC systems Inefficient incandescent, halogen and mercury vapour lights

Intervention Replace with efficient motors – typically 7% more efficient

Replace with efficient HVAC systems, typically 30% more efficient

Replace with efficient CFL and sodium lighting, typically 33% more efficient

Number of units 1 1 1

GWh / year/ industry type of existing system

Food and Beverages 211 471.33 Textiles 359 485.07




Non-Food Manufacturing 3 202 840.96 Other 1 055 388.27

Non-Food Manufacturing 145 583.68 Other 89 239.62

Non-Food Manufacturing 218 375.52 Other 71 478.51

GJ / year of intervention Food and Beverages 196 668.34 Textiles 334 321.12 Non-Food Manufacturing 2 978 642.09 Other 981 511.09

Food and Beverages 18 503.74 Textiles 84 029.63 Non-Food Manufacturing 144 127.84 Other 88 347.22

Food and Beverages 14 168.58 Textiles 30 106.87 Non-Food Manufacturing 146 311.60 Other 47 890.60


12 hours a day 261 days a year






Cost / unit of existing Not Available R700/square meter Not available

Additional cost / unit of intervention

R14700 differential cost between efficient and inefficient 22kW motors, with an annual energy saving of 9056 kWh (129524 kWh-inefficient; 120468 kWh-efficient)

R950 per square meter (Differential cost of R250/square meter)

Differential cost of R250 per unit


9. LOCAL GOVERNMENT SECTOR ENERGY DATA

9.1. DEMAND FOR ENERGY SERVICES The City of Cape Town, as the local government authority, is assessed as an individual sector. It is the single biggest user of energy in Cape Town, consuming 1% of the total energy. The City controls, or has a direct impact, on a host of functions and activities in Cape Town. It is responsible for providing services to the population of 3.4 million people in Cape Town. The main activities that use energy within the functions of the City of Cape Town are:

The vehicle fleet of the City (both petrol and diesel). An estimated 6,000 vehicles are owned and operated by the City, including heavy trucks, waste removal vehicles and passenger vehicles.

Electricity for the City buildings, depots and other public buildings.

Electricity for street lighting and traffic signals within the Cape Town area.

Electricity for waste water treatment and bulk water supply.

City of Cape Town Electricity Consumption by Operations

31%

16%

16%

33%

4%

Waste Water Treatment Works

Bulk Water Supply

Buildings

Steet Lighting

Traffic Signals

Figure 18: City of Cape Town electricity consumption by municipal operations

9.2. ENERGY END USE


The City of Cape Town electricity department provided data on electricity consumption for municipal operations, including municipal buildings, waste water treatments works, bulk water supply, street lights and traffic signals. Information on fuel use by the City’s vehicle fleet was provided from the Technical Services Vehicle Fleet and the Electricity Department Vehicle Fleet of the City’s transport department. The information on bulk water supply and municipal buildings is estimated, as detailed information for these sectors is not available. The facilities and operations, which are Eskom supplied, have not been included due to a lack of data. It is assumed that this electricity use amount will be negligible. Systems are currently being implemented to address the capturing of electricity data within municipal buildings, bulk water supply operations and Eskom-supplied areas. Table 28: City of Cape Town operations electricity consumption

Operation kWh/annum Percentage

Waste Water Treatment Works27

81,650,000 31 %

Street Lighting28 91,752,080 34 %

Traffic Signals29 10,110,924 4 %

Bulk Water Supply30 41,404,828 15.5 %

Buildings31 41,404,828 15.5 %

Total 266,322,660 100 %

Table 29: Fuel consumption for municipal vehicle fleet

Vehicle Fleet Diesel (litres/annum) Petrol (litres/annum)

Special Technical Services Vehicle Fleet32 8,647,829.21 5,470,697.23

Electricity Department Vehicle Fleet33 1,137,996 714,902

Total 9,785,825.21 6,185,599.23

9.3. INTERVENTIONS AND COSTING Table 30: Interventions and costing for the local government sector

HVAC Lighting Street Lighting Traffic Lights

Existing System DX split systems with COP of 2.75

50% of existing light fittings are low energy lamps. Remaining 50% is 40W fluorescent

87% of all street lights are mercury vapour

90% of all traffic lights are incandescent and halogen lamps

27

Source: Waste Water Electricity Accounts (2007/08) 28

Summary of Electricity Sales for 2006/07 29

Summary of Electricity Sales for 2006/07 30

Remainder (31%) from total municipal use split between bulk water and buildings. The City of Cape Town will be implementing a system to measure consumption. 31

Remainder (31%) from Total Municipal use split between bulk water and buildings. The City of Cape Town will be implementing a system to measure consumption. 32

Fuel and On-Board Computers Management Key Performance Indicators 2007/08 33

City of Cape Town Electricity Department Fuel Consumption 2007/08


lamps with magnetic ballasts

Intervention Replace all with VRV systems with COP of 4 (32% more efficient)

For 50% of light fittings, replace 2 x 40 W fluorescent lamps with 1 x electronic ballast and 2 x 36 W lamps

Replace mercury vapour lights with CFL lights (50% more efficient)

Replace with LED lights (80% more efficient)

Number of units 1 m2 treated areas (assumes an average cooling load of 180 W/m2)

2 lamps and 1 ballast

1 1

W / unit of existing system

0.65 W (average cooling load / COP)

0.096 W 100 W 100 W

W / unit of intervention

0.045 W (average cooling load / COP)

0.058 W 50 W 20 W







Existing unit = 1.4 years Intervention = 3 years

Existing unit = 3 years Intervention = 7years


Cost / unit of existing

R 700 per m2 R 100 R 140 R 1,150

Cost / unit of intervention

R 950 per m2 R 172 R 70 R 950


10. TRANSPORT SECTOR ENERGY DATA

10.1. DEMAND FOR ENERGY SERVICES Two separate sub-sectors of the transport sector were researched, namely freight transport and passenger transport. Data on the freight transport sub-sector is presented in tonne-km moved on an annual basis, whilst passenger transport is presented in passenger-km travelled on an annual basis. Overall demand for 2007 for each transport sub-sector is summarised as follows:

Freight sub-sector: 10,970,995,893 tonne-km

Passenger sub-sector: 47,358,082,153 passenger-km Energy demand for both sub-sectors by fuel is broken down as follows: Table 31: Energy demand by passenger and freight transport sub-sectors

Fuel use 2007 (PJ) Passenger Transport Freight Transport

Diesel 16.4 8.5

Electricity 0.8

Gasoline 39.3

Total 56.5 8.5

10.2. ENERGY END USE

PASSENGER TRANSPORT The passenger transport analysis was conducted using passenger-km as the unit to measure the different modes of transport. Calculations in this sense are based on the number of people travelling and how far they travelled. Passenger-km comprehensively represents the use of the different modes of transport, whereas the number of passengers travelling on a specific mode would not give an accurate and complete representation. For this study, the passenger sector is broadly considered to be the combination of public and private transport modes being used in Cape Town. However, it should be noted that for this study the private and public passenger data could not be disaggregated from commercial vehicle use, and the figures presented, with the exception of rail, do not necessarily present an accurate picture. Results are presented below: Table 32: Passenger-km calculations based on OEF 2.8 data for LEAP model

Mode Passenger-km

Rail 13,052,631,579


Minibus 6,926,352,705

Bus 4,432,770,880

Car (petrol) 14,091,942,820

Car (diesel) 8,616,534,105

Total 47,120,232,089

Figure 19: Transport modal share in terms of passenger-km in Cape Town, 2007

Rail Cape Town’s commuter trains use electricity as their energy source. A detailed analysis of each rail line was performed to determine the passenger-km and fuel intensity of the Cape Town rail system. The distance between each station was calculated. The 2007 Rail Census34 included the number of people boarding and alighting at each stop. Using this information, the number of passengers riding between each stop was calculated and multiplied by the distance between each stop to obtain passenger-km. The sum of the passenger-km between each stop was calculated for each line in the Cape Town rail network. This total was multiplied by two, based on the assumption that most passengers made return trips on a given day. Fuel intensity was determined by using a calculated conversion factor to convert the km of each line into kWh, based on the number of coaches that run on the line. It was assumed that the rail system operated with either 8 or 11 coaches. Conversion factors of 22 and 35.76 kWh/km35, respectively, were multiplied by the number of trains on a line. This number was then multiplied by the rail line distance and divided by the total passenger-km one-way to get the kWh/passenger-km. A weighted average of the kWh/passenger-km of all of the lines was taken to find a kWh/passenger-km value for the entire rail system. The factors are as follows: Passenger-km 13,052,631,579

kWh/Passenger-km 0.017,125,849

34

City of Cape Town 35

South African Rail Commuter Corporation, 2008


Liquid fuel analysis SAPIA (South African Petroleum Industry Association) data for 2007 provides the following figures for diesel and petrol: Litres PetaJoules (PJ)

Petrol 1,159,602,430 39.43

Diesel 754,400,601 27.91

0.1 PJ of the petrol component is not used for transport, while 2.7 PJ of the diesel component is used by industry. This leaves the following allocation for transport: Litres PetaJoules (PJ)

Petrol 1,155,882,353 39.3

Diesel 681,081,081 25.2

This remaining fuel had to be divided between the various modes of transport within Cape Town. Calculations were based on liquid fuel information obtained from 2006 SAPIA data, which was more disaggregated than the 2007 data. This data made it possible to separate private use from commercial and government use, in order to get a clearer picture of the modal split. It must be noted that the commercial component also includes local marine fishing, agriculture and mining and could not be disaggregated into fuel use for non-transport applications. This figure will require revision into the future. For now, the commercial component assumes all fuel is used for transport, and was distributed amongst the various modes. Descriptions of how the passenger-km for each liquid fuel-based transport mode were calculated follow: Minibus To determine minibus passenger-km and fuel intensity, it was necessary to make some assumptions. Since the amount of petrol consumed by minibuses was unknown, it had to be calculated based on the known information about minibus trips. The number of minibus trips a day was 55,988 and the average trip length was 13 km, based on the City of Cape Town’s Current Public Transport Records (CPTR) report. The report also indicated that there were approximately 7,467 minibus vehicles operating in the fleet, including unregistered vehicles. Each minibus can hold approximately 15 people. By comparing the known number of minibus passengers a day (332,407 people) to the number of possible passengers when the minibuses are full (maximum of 15 people riding on each trip), it was determined that minibuses operate on average at 40% of capacity, or with 6 passengers. The total number of trips, average trip distance and number of passengers were then used to calculate the total passenger-km, i.e. Total passenger-km = number of trips x average trip distance x average no of passengers.


The assumption that minibuses run at 8 km/litre determined the total litres of petrol that minibuses consumed a year. Petrol (litres) 22,749,187.5

Km 181,993,500

Passenger-km 1,091,961,000

Litres/Passenger-km 0.020,833,333

GJ/Passenger-km 0.000,718,390

As these calculations contain certain assumptions, and are dependent on the City’s CPTR report as being accurate, the results can only be considered indicative. It is not inconceivable that the figure presented is substantially incorrect. The LEAP modelling has included diesel minibuses in addition to this calculation. These have been assumed to have the same average number of passengers as petrol taxis, but have a lower energy intensity due to the assumption that diesel minibuses run at 11.5km/litre. The total fuel consumption was modified to tie up with the SAPIA 2007 figures, and include the commercial fuel data omitted from the 2006 calculation above. The final minibus table for the Optimum Energy Future model therefore read as follows: Passenger-km 6,926,352,705

Petrol Litres/Passenger-km 0.020,833,333

GJ/Passenger-km 0.000,718,390

Bus It was assumed that the SAPIA 2006 figure for the amount of public transport diesel sold in Cape Town was consumed entirely by the Golden Arrow bus fleet. Based on the knowledge that this fleet carries 197,444 passengers a day, makes 5,777 trips a day36 and takes approximately 90 seats a bus, it was determined that buses run at 38% capacity, or an average of 34 passengers over a full working day. The total number of trips, average trip distance and number of passengers were used to calculate the total passenger-km. The assumption that buses run at 3 km/litre determined the total kilometers that buses travel a year. Diesel (litres) 27,620,542

Km 82,861,626

Passenger-Km 2,817,295,284

Litres/Passenger-Km 0.009,803,922

GJ/Passenger-Km 0.000,377,073

36

Source: CPTR, CoCT


The total fuel consumption was then modified to tie up with the SAPIA 2007 figures, and include the commercial fuel data omitted from the 2006 calculation above. Passenger-Km 4,432,770,880

Litres/Passenger-Km 0.009,803,922

GJ/Passenger-Km 0.000,377,073

Private Vehicle The figures for private vehicle use were calculated based on the balance of petrol and diesel fuel remaining from the 2007 SAPIA data. Petrol and diesel efficiencies were set at 10km/l and 16km/l respectively. Both vehicles have an average of 1.4 passengers per trip. This data can be summarised and synthesised as follows: Diesel (litres) 386,486,486

Petrol (litres) 1,020,588,235

Passenger-km (diesel) 8,638,114,185

Passenger-km (petrol) 14,093,765,249

GJ/Passenger-Km (diesel) 0.0016596

GJ/Passenger-Km (petrol) 0.0024624

FREIGHT TRANSPORT There is very little data around freight transport in Cape Town. 2006 SAPIA data does provide some information around freight fuel use by Transnet (rail-diesel) and for road haulage. However, it is not clear how much of the sizeable commercial component also applies to this area. For this study it was assumed that 60% of the 2006 commercial supply of diesel would be allocated to freight transport, and added to the existing Transnet and road haulage figures. The energy intensity figures for rail and road were taken from research done for the Gauteng Energy Strategy, and the share of tonne-km for each mode was utilised from the same study, assuming that the nature of freight transport is consistent throughout the country’s city areas. Diesel (litres) 70,270,270

GJ/Tonne-Km (diesel-rail) 0.001,659

GJ/Tonne-Km (diesel-road) 0.002,462


Freight Breakdown according to Transport Mode for Cape

Town, 2007

77%

23%

Road Freight

Rail Freight

Figure 20: Freight breakdown according to transport mode for Cape Town, 2007

10.3. INTERVENTIONS AND COSTING Table 33: Interventions and costing for transport sector

Transport modal share Private transport (car) Public transport (bus, minibus, rail)

Existing system 50% of passenger km are through public transport

Inefficient petrol and diesel cars

Inefficient minibuses and buses. Rail service.

Intervention Increase percentage to 60%

Efficient petrol and diesel cars (both 9% improvement in energy use per pass km), hybrid (40% improvement) and electric cars (80% improvement)

Diesel minibuses and efficient BRT, both 30% more efficient per pass km. Additional rail capacity

Passenger-km 47,358,082,153 22 731 879 433 24 626 202 719

GJ / passenger-km of existing system

Petrol: 0.0024624 Diesel: 0.0016596

Minibus: 0.00071856 Bus: 0.00037692 Train: 0.00006156

GJ / passenger-km of intervention

Efficient petrol : 0.002240784 Efficient diesel : 0.001510236 Hybrid: 0.00147744 Electric car: 0.000504

Diesel minibus: 0.000502992 BRT: 0.000263844 Train: 0.00006156





Cost / passenger km of existing

Petrol: 69c Diesel: 48c

Minibus: 16c Bus: 8c Rail: 1c

Cost / passenger km of intervention

Efficient petrol : 65c Efficient diesel : 46c Hybrid: 90c Electric car: 84c

Diesel minibus: 10c BRT: R2.61(including new infrastructure) BRT: 6c (excluding new infrastructure) New rail: R1.61 (including new infrastructure) New rail: 1c (excluding new infrastructure)


11. SCENARIOS: PRIMARY Scenarios should be understood as a series of ‘what if’ questions, e.g. what if all buildings had CFL light fittings? Scenarios do not predict the future; nor is any one scenario considered more likely than another. LEAP was used to model and explore the implications of different future energy scenarios in Cape Town. The implications for energy use, emissions (both local pollutants and greenhouse gases) and development are of particular interest. The cost implications of different scenarios were covered as far as was feasible, but further work in this area would be useful. More detailed analysis on the business cases for the different scenarios would need to be undertaken, for example not only including capital and operation costs of the technologies, but also other retrofit programme costs (management, labour etc) where appropriate. The Primary Scenarios chapter covers three supply mix options:

Business As Usual scenario

National LTMS scenario

Optimum Energy Future scenario The table below shows the electricity generation costs and the supply mixes associated with three of the above scenarios. The Business As Usual Scenario is predominantly coal-based; the National LTMS Scenario includes new nuclear and new renewables and the Optimum Energy Future focuses on renewable electricity with a smaller proportion of nuclear. Table 34: Generation mixes and costs for different scenarios in 2050

Business As Usual Optimum Energy Future National LTMS

% R/kWh % R/kWh % R/kWh

Municipal Waste 0 % R 0.44 3 % R 0.44 2 % R 0.44

Solar Thermal Electricity 0 % R 1.50 8 % R 1.50 10 % R 1.50

Wind 9 % R 1.00 26 % R 1.00 20 % R 1.00

New Nuclear 2 % R 0.69 9 % R 0.69 32 % R 0.69

New Fossil Base 82 % R 0.42 48 % R 0.42 32 % R 0.42

New mid and peak (Gas Turbines) 5 % R 3.40 4 % R 3.40 4 % R 3.40

Existing Hydro 2 % R 0.10 2 % R 0.10 0 % R 0.10

Existing mid and peak (Gas Turbines) 0 % R 3.40 0 % R 3.40 0 % R 3.40

Existing Base (coal) 0 % R 0.20 0 % R 0.20 0 % R 0.20

Existing Nuclear 0 % R 0.69 0 % R 0.69 0 % R 0.69

Average Generation Costs R 0.62 R 0.80 R 0.85

11.1. BUSINESS AS USUAL SCENARIO The Business As Usual Scenario models current growth trends unchanged into the future. No energy interventions are included; only a continued escalating growth rate associated with the different sectors.


Assumptions The following key assumptions are used in the Business As Usual Scenario:

Growth in number of formal households (electrified) of 1.7% per annum

Growth in number of informal households (non-electrified) of 13% per annum from 2007; 8% per annum from 2010

Growth in energy (including electricity) consumption of 2.9% per annum (corresponds to 3.4% GDP escalation)

Costing o All costs are real (in today’s Rands) o Discount rate set at 5% o Liquid fuel costs linked to inflation

Transport: growth in number of private vehicles is 3.4% per annum Electricity supply mix

Figure 21: Business As Usual Scenario electricity supply mix

11.2. NATIONAL LTMS SCENARIO


The National LTMS Scenario represents the electricity supply options, including new nuclear and renewable energy, and energy efficiency interventions, required to meet the nationally endorsed carbon reduction profile. The interventions highlighted in the National LTMS report have been scaled down for the Cape Town situation. Energy Efficiency Interventions The following interventions and penetration rates were modelled in the National LTMS Scenario: Table 35: Energy efficiency interventions modelled in the National LTMS Scenario

Sector Interventions for Cape Town LTMS Scenario

Penetration Rates

Residential Energy efficient lighting in low, medium, high and very high income households

Low Income (CFL): 2007, 40%; 2015, 15%; 2035, 90%; 2050, 100% Medium Income (CFL): 2007, 40%; 2015, 50%, 2035, 85%, 2050, 100% High Income (CFL): 2007, 40%; 2015, 50%; 2035, 20%; 2050, 10% High Income (other efficient): 2007, 0%; 2015, 10%; 2025, 50%; 2050, 70% Very High Income (CFL): 2007, 40%; 2015, 60%; 2035, 30%; 2050, 0% Very High Income (other efficient): 2007, 0%; 2015, 10%; 2035, 50%; 2050, 70%

Energy efficient water heating technologies implemented in medium, high and very high income households: either solar water heaters or heat pumps

Medium Income: 2007, 40%; 2035, 85%; 2050, 100% High Income: 2007, 0%; 2035, 50%; 2050, 70% Very High Income: 2007, 0%; 2035, 50%; 2050, 70%

Geyser blanket and efficient showerheads in medium, high and very high income households


Commercial Efficient HVAC systems in new and existing buildings

2007, 0%; 2025, 15%; 2035, 30%; 2050, 75%

Efficient water heating technology (either solar water heaters or heat pumps) in new and existing buildings

2007, 0%; 2025, 20%; 2035, 40%; 2050, 60%


Efficient lighting37 implemented in new and existing buildings

2007,50%; 2025, 70%; 2035, 85%; 2050, 100%

Industrial Efficient machine drives installed, where feasible

2007, 0%; 2015, 10%; 2035, 20%; 2050, 35%

Efficient HVAC systems implemented

2007, 0%; 2025, 10%; 2035, 20%; 2050, 35%

Energy efficient lighting38 options implemented

2007, 0%; 2025, 25%; 2035, 45%; 2050, 60%

Local Government

Efficient lighting and HVAC systems in government buildings

Lighting: 2007, 50%; 2025, 60%; 2035, 75%; 2050, 90% HVAC: 2007, 0%, 2025, 35%, 2035, 60%, 2050, 75%

Street lighting: replacement of mercury vapour lamps with high pressure sodium lamps

2007, 13%; 2025, 55%; 2035, 85%; 2050, 90%

Traffic lights: replacement of incandescent and halogen lamps with LED lamps

2007, 10%; 2025, 35%; 2030, 80%; 2050, 100%

Vehicle fleet: improved fuel efficiency through the purchase of more efficient diesel and petrol options and the implementation of behavioural changes

Efficient diesel: 2007, 0%; 2025, 50%; 2035, 48%; 2050, 55% Efficient petrol: 2007, 0%, 2025, 15%, 2035, 25%, 2050, 27%

Freight Transport

Shifting freight transport from road- to rail-based transport

2007, 23%; 2025, 30%; 2035, 34%; 2050, 40%

Passenger Transport

Improved fuel efficiency of private vehicles and the inclusion of hybrid and electric vehicles in the private vehicle mix

Efficient diesel: 2007, 0%; 2015, 5%; 2035, 10%, 2050, 10% Efficient petrol: 2007, 0%; 2015, 5%; 2035, 15%; 2050, 50% Electric vehicles: 2007, 0%; 2025, 2%; 2035, 10%; 2050, 15% Hybrid vehicles: 2007, 0%, 2025, 5%, 2035, 7%, 2050, 10%

Improved public transport vehicle efficiency, including a shift to diesel mini-bus taxis and more efficient buses

Efficient minibus: 2007, 10%; 2025, 41%; 2035, 56%; 2050, 75% Efficient buses: 2007, 0%; 2025, 23%; 2035, 35%; 2050, 50%

A modal shift from private vehicles to public transport

2007, 52%; 2025, 60%; 2035, 66%; 2050, 72%

37

In the commercial sector “efficient lighting” refers mainly to efficient fluorescent lighting 38

In the industrial sector “efficient lighting” refers mainly to the conversion from Mercury Vapour to High Pressure Sodium Lamps for area lighting


Electricity supply mix

Figure 22: National LTMS Scenario supply mix (new fossil, new nuclear and renewables electricity base load)

The National LTMS electricity supply mix includes cleaner (supercritical) coal, renewables and new nuclear as the key components of its supply mix. It specifies that nuclear is to make up 27% of electricity supply by 2030, either from Pebble Bed Modular Reactors (PBMR) or from conventional Pressurised Water Reactors (PWR). Although new nuclear supply is part of the national LTMS mix, it needs to be approached with caution due to the common occurrence in nuclear projects of construction delays, long lead-in times and large cost overruns. There is also a lot of public contention around nuclear energy. The National LTMS Scenario is based on the assumption that no new nuclear capacity can be commissioned before 2013, with the first PBMR commissioned in 2013 and the PWR following in 2015. However, the deadlines for such a commissioning schedule have passed, as nuclear lead-in times are typically well over 5 years.

11.3. OPTIMUM ENERGY FUTURE SCENARIO This Scenario is considered an optimum way forward for Cape Town. It builds on the existing Cape Town ECAP Energy Vision in that it achieves or promotes the following:


Significant employment creation

A vibrant and efficient economy that is robust in a carbon-constrained future

Lowers overall cost of energy for the City, without compromising energy services provided

A carbon profile for Cape Town in line with national and international obligations

A ‘green’ city The core motivations for the Optimum Energy Future Scenario and its set of associated interventions are embodied in the following key issues:

Proceeding along a Business As Usual Scenario poses significant risks, including:

o Vulnerability in a carbon-constrained future o Vulnerability to peak oil o High energy expenditure for Cape Town’s occupants o An increasingly inefficient economy o Reduced jobs in the energy sector o Losing any marketing advantage around being a ‘green’ city

The overall cost of the Optimum Energy Future Scenario to Cape Town’s inhabitants is slightly higher than the Business As Usual Scenario, mainly due to the costs associated with substantial public transport infrastructure, but the efficiency gains and economic benefits resulting from the interventions far outweigh the extra costs.

The cost of an electricity supply mix that includes a strong component of renewable energy is higher than Business As Usual (mainly coal-based), but not significantly higher.

Nuclear electricity supply is part of the National LTMS mix, but needs to be approached with caution due to the common occurrence in nuclear projects of construction delays, long lead-in times and large cost overruns. There is also a large amount of public contention around nuclear energy.

All electricity efficiency interventions that are recommended for implementation in the residential, commercial, industrial and local government sectors are financially sensible and pay themselves back over the lifetime of the implementation programme. This results in a more efficient economy.

A high renewable energy supply component forms part of a robust future; resulting in a significant increase in jobs created, although this will require the proactive development of a renewable energy industry to maximise job-creation in Cape Town.

Energy Efficiency Interventions Table 36: Energy efficiency interventions included in the Optimum Energy Future Scenario

Sector Interventions for Optimum Energy Future Scenario

Penetration Rates


Residential Energy efficient lighting in low, medium, high and very high income households

Low Income (CFL): 2007, 40%; 2015, 15%; 2035, 90%; 2050, 100% Medium Income (CFL): 2007, 40%; 2015, 50%, 2035, 85%, 2050, 100% High Income (CFL): 2007, 40%; 2015, 50%; 2035, 20%; 2050, 10% High Income (other efficient): 2007, 0%; 2015, 10%; 2025, 50%; 2050, 70% Very High Income (CFL): 2007, 40%; 2015, 60%; 2035, 30%; 2050, 0% Very High Income (other efficient): 2007, 0%; 2015, 10%; 2035, 50%; 2050, 70%

Energy efficient water heating technologies (solar water heaters or heat pumps) in medium, high and very high income households


Geyser blanket and efficient showerheads in medium, high and very income households


Commercial Efficient HVAC systems in new and existing buildings

2007, 0%; 2025, 15%; 2035, 30%; 2050, 75%

Efficient water heating technology (either solar water heaters or heat pumps) in new and existing buildings

2007, 0%; 2025, 20%; 2035, 40%; 2050, 60%

Efficient lighting implemented in new and existing buildings

2007,50%; 2025, 70%; 2035, 85%; 2050, 100%

Industrial Efficient machine drives installed, where feasible

2007, 0%; 2015, 10%; 2035, 20%; 2050, 35%

Efficient HVAC systems implemented 2007, 0%; 2025, 10%; 2035, 20%; 2050, 35%

Energy efficient lighting options 2007, 0%; 2025, 25%; 2035, 45%; 2050, 60%

Local Government

Efficient lighting and HVAC in government buildings

Lighting: 2007, 50%; 2025, 60%; 2035, 75%; 2050, 90% HVAC: 2007, 0%, 2025, 35%, 2035, 60%, 2050, 75%

Street lighting: replacement of mercury vapour lamps with high pressure sodium lamps

2007, 13%; 2025, 55%; 2035, 85%; 2050, 90%

Traffic lights: replacement of incandescent and halogen lamps with LED lamps

2007, 10%; 2025, 35%; 2030, 80%; 2050, 100%

Vehicle fleet: improved fuel efficiency through the purchase of more efficient

Efficient diesel: 2007, 0%; 2025, 50%; 2035, 48%; 2050, 55%


diesel and petrol options and the implementation of behavioural changes to support further fuel efficiency

Efficient petrol: 2007, 0%, 2025, 15%, 2035, 25%, 2050, 27%

Freight Transport

Shifting freight transport from road- to rail-based transport

2007, 23%; 2025, 30%; 2035, 34%; 2050, 40%

Passenger Transport

Improved fuel efficient of private vehicles and the inclusion of hybrid and electric vehicles in the private vehicle mix

Efficient Diesel : 2007, 0%; 2015, 5%; 2035, 10%, 2050, 10% Efficient Petrol : 2007, 0%; 2015, 5%; 2035, 15%; 2050, 50% Electric Vehicles : 2007, 0%; 2025, 2%; 2035, 10%; 2050, 15% Hybrid Vehicles : 2007, 0%, 2025, 5%, 2035, 7%, 2050, 10%

Improved public transport vehicle efficiency, including a shift to diesel mini-bus taxi and more efficient buses

Efficient Minibus : 2007, 10%; 2025, 41%; 2035, 56%; 2050, 75% Efficient Buses : 2007, 0%; 2025, 23%; 2035, 35%; 2050, 50%

A modal shift from private vehicles to public transport

2007, 52%; 2025, 75%; 2035, 80%; 2050, 87%

Electricity supply mix

Figure 23: Optimum Energy Future Scenario electricity supply mix


12. SCENARIOS: SECONDARY The scenarios covered in this chapter consist of further thought experiments run on a combination of the primary scenarios given in the above chapter. These scenarios include:

Peak Oil Scenario

Carbon Tax Scenario

Densifications Scenario

Economic Growth Scenario

12.1. PEAK OIL SCENARIO Peak oil will have potentially huge financial implications for the economy. A radical modal shift from private to public transport is needed to combat its effects. While it is uncertain when global oil production will peak, and what the post-peak rate of oil reserve depletion will be, available evidence suggests that the global oil production rate could decline between 2007 and 2020, with a significant risk of rapid decline thereafter, resulting in increasing and unstable prices. This Scenario was modelled to illustrate potential impacts rather than as an accurate prediction of peak oil. As oil is an input into most economic activities (including food production) and forms the basis of the modern transport system, an oil shortage will have significant impacts on all aspects of the economy, not just on the transport sector. South Africa imports approximately 66% of its oil. Most of the remainder comes from SASOL’s coal to liquid fuel plants, which is incredibly carbon-intensive.


Figure 24: Total costs (demand and supply) for Optimum Energy Future Scenario with and without peak oil cost implications

The impact of an increase in oil price can be seen in the figure above. The costing information used in this graph shows that there is a significant increase in the cost of the energy system associated with peak oil of 50% to 100% by 2050. This magnitude of cost increase would be devastating to the economy.

12.2. CARBON TAX SCENARIO A high carbon emissions level in the future is likely to impact on economic competitiveness. A carbon tax on electricity generation has been mooted and the impact of a tax of R100 per tonne in 2007, escalating to R750/tonne in 205039, could have serious direct financial implications to a fossil fuel-based supply mix. A carbon tax was modelled on the Business As Usual Scenario and the Optimum Energy Future Scenario, so as to illustrate clearly the cost implications of inaction where the energy sector is concerned.

39

These carbon tax figures are based on parameters used in South Africa’s Integrated Resource Plan (IRP)


Figure 25: Impact of a carbon tax on total cost of energy system in Cape Town as a deviation from what costs would have been without a carbon tax


Figure 26: Impact of a carbon tax on total cost of energy system in Cape Town

12.3. DENSIFICATION SCENARIO The densification of a city increases public transport feasibility; therefore playing a key role in moving towards a low carbon city. Low-density urban sprawl has had a particular impact on Cape Town’s city perimeter to the north, east and northeast; resulting in increased dependence on private vehicles and a less energy-efficient city. Other impacts include a loss of valuable agricultural land, increased commuting times, increased pollution and the loss of some natural resource areas and cultural landscapes. Public transport is an essential component of a sustainable, low carbon city; yet providing such services is unviable in low-density cities. Experience in South American cities indicates that the costs of public transport are double per passenger-km in sprawling cities compared with dense cities. Further information and graphs on the relationship between city density and energy use and costs can be found under the chapter “Other Results and Information: City Density.” The Densification Scenario models a shift in public transport occupancy levels from 30% in 2007 to 60% in 2035 (modelled in LEAP by decreasing the demand costs of bus and rail systems). The creation


of a denser reduces the cost per passenger-km and brings down to manageable levels the capital requirements for an effective public transport system.

Figure 27: Cost for Optimum Energy Future compared to a higher densification scenario

The figure above shows that the Densification Scenario costs are significantly less than the Optimum Energy Future Scenario for the same level of service to city inhabitants. In 2030, it equates to a saving of R10 billion, while in 2050, R40 billion would be saved. Strong support for densification is important for a sustainable city.

12.4. ECONOMIC GROWTH SCENARIO Scenarios of low and high economic growth, linked to lower and higher energy demand, were modelled in order to test the sensitivity of a scenario to a change in economic growth rates. The graph below includes the Business as Usual and Optimum Energy Future Scenarios, as well as a High Growth and Low Growth Scenario. These scenarios are based on the following assumptions:

Both High and Low Growth Scenarios are based on the Business as Usual Scenario

The Low Growth Scenario models an energy demand growth rate of 1.9%, linked to a GGP of 2.9%40

The High Growth Scenario models an energy demand growth rate of 3.6%, linked to a GGP of 4.6%

40

For both low and hi growth rates, the relationship between GGP growth and energy consumption growth were taken from the 2010 National IRP parameter sheets.


Note: the dip in the graph at 2008 is indicative of the recession.

Figure 28: Energy demand in Business as Usual Scenario for current, low and high economic growth rates; compared with energy demand in Optimum Energy Future Scenario

13. ENERGY SCENARIO MODELLING TILL YEAR 2050 This chapter represents the energy impacts modelled by LEAP of the various energy scenarios till the year 2050. Note: the dip in energy demand and emissions levels between 2008 and 2010 represents the effect of the economic recession.

13.1. BUSINESS AS USUAL Greenhouse gas emissions


Figure 29: Greenhouse gas emissions for all sectors in Business As Usual Scenario

Figure 30: Greenhouse gas emissions for Business As Usual Scenario (excluding transport sector)


Figure 31: Greenhouse gas emissions for transport sector only in Business As Usual Scenario

Energy demand

Figure 32: Energy demand for all sectors in Business As Usual Scenario


Figure 33: Energy demand for all sectors in Business As Usual Scenario for the years of 2007, 2014, 2025 and 2050

Figure 34: Energy demand for Business As Usual Scenario (excluding transport sector)


Figure 35: Energy demand for Business As Usual Scenario (excluding transport sector) for the years of 2007, 2014, 2025 and 2050

Figure 36: Energy demand for transport sector only in Business As Usual Scenario


Figure 37: Energy demand for transport sector only in Business As Usual Scenario for the years of 2007, 2014, 2025 and 2050

13.2. SCENARIO COMPARISONS Greenhouse gas emissions


Figure 38: Greenhouse gas emissions for Business As Usual, National LTMS and Optimum Energy Future

Figure 39: Greenhouse gas emissions for Business As Usual, National LTMS and Optimum Energy Future (excluding transport sector)


Figure 40: Greenhouse gas emissions for transport sector only in Business As Usual, National LTMS and Optimum Energy Future

Energy demand


Figure 41: Energy demand for Business As Usual, National LTMS and Optimum Energy Future (excluding transport sector)

Figure 42: Energy demand for transport sector only in Business As Usual, National LTMS and Optimum Energy Future


The figure below shows that while electricity efficiency has a substantial impact on reducing total energy demand, the biggest shift would be realised when transport efficiency, including a modal shift from private to public transport, is implemented.

Figure 43: Impact of efficiency on total Business As Usual demand

Energy Costs


Figure 44: Total end user expenditure for Business As Usual, National LTMS and Optimum Energy Future (excluding transport sector expenditure)

Figure 45: Total end user expenditure for transport sector only in Business As Usual, National LTMS and Optimum Energy Future


14. ENERGY SCENARIO MODELLING TILL YEAR 2025 This chapter graphically represents the various future energy scenarios modelled by LEAP till the year 2025. This was done in order to show the detail that may be lost on the longer modelled projections that run till the year 2050. It also indicates that the implementation of energy interventions would still have substantial impacts in the nearer future; not just in the far future. Note: the levelling off of energy demand and emissions levels between 2008 and 2010 represents the effect of the economic recession.

14.1. BUSINESS AS USUAL Greenhouse gas emissions

Figure 46: Growth in greenhouse gas emissions in all sectors for Business As Usual Scenario


Figure 47: Greenhouse gas emissions for transport sector only in Business As Usual Scenario

Energy Demand

Figure 48: Energy consumption for all sectors in Business As Usual Scenario


Figure 49: Energy consumption for all sectors in Business As Usual Scenario for years 2007, 2015, 2020 and 2025

Figure 50: Energy consumption for Business As Usual Scenario (excluding transport)


Figure 51: Energy consumption for Business As Usual Scenario (excluding transport) for years 2007, 2015, 2020 and 2025

Figure 52: Energy consumption for transport sector only in Business As Usual Scenario


Figure 53: Energy consumption for transport sector only in Business As Usual Scenario for years 2007, 2015, 2020 and 2025

14.2. SCENARIO COMPARISONS Greenhouse gas emissions The Optimum Energy Future carbon emissions are in keeping with national and international obligations (figure 54), as set out in the National LTMS. The transport sector contributes a considerable amount to the overall emissions profile.


Figure 54: Greenhouse gas emissions for Business As Usual, National LTMS and Optimum Energy Future

Figure 55: Greenhouse gas emissions for Business As Usual, National LTMS and Optimum Energy Future (excluding emissions from transport sector)


Figure 56: Greenhouse gas emissions for transport sector only in Business As Usual, National LTMS and Optimum Energy Future

Energy Demand


Figure 57: Energy demand for Business As Usual, National LTMS and Optimum Energy Future (including transport sector)

Figure 58: Energy demand for Business As Usual, National LTMS and Optimum Energy Future (excluding transport sector)


Figure 59: Energy demand for transport sector only in Business As Usual, National LTMS and Optimum Energy Future

Energy Costs The Optimum Energy Future Scenario (OEF) results in similar overall energy expenditure as the Business As Usual Scenario (BAU), without compromising energy service delivery. Excluding the enormous public transport investments usually included in the OEF, the overall expenditure of the OEF is less than BAU. National government may contribute towards much of the public transport investment needed; thus significantly reduce the burden on the City and making the OEF clearly beneficial for Cape Town in financial terms, in addition to the other benefits.


Figure 60: Total end user expenditure for Business As Usual, National LTMS and Optimum Energy Future (includes costs for transport infrastructure investment, energy efficiency interventions and electricity supply mix)


Figure 61: Total end user expenditure for Business As Usual, National LTMS and Optimum Energy Future, excluding all transport sector costs (transport efficiency and transport infrastructure investment)

Figure 62: Total end user expenditure for Business As Usual, National LTMS and Optimum Energy Future; including transport efficiency costs, but excluding transport infrastructure investment costs


Figure 63: Total end user expenditure for transport sector only for Business As Usual, National LTMS and Optimum Energy Future (does not include costs associated with energy efficiency interventions or electricity supply mix)


15. OTHER RESULTS AND INFORMATION This chapter contains additional graphs and information on the factors that influence the present and future energy outlook.

15.1. ENERGY EFFICIENCY Cost effectiveness The modelled cumulative costs of electricity efficiency interventions displayed shows that almost all these interventions were financially sensible.

Figure 64: Cumulative net savings from electricity efficiency interventions up to 2025

Impact on sectors

The bars represent cumulative net savings (i.e. considering capital costs and electricity savings) of electricity efficiency interventions.

Low-inc residential

Commercial

Govt

Mid-hi inc residential


Figure 65: Impact of energy efficient HVAC, water heating and lighting (as included and modelled in Optimum Energy Future) on commercial sector energy demand


Figure 66: Impact of energy efficient public buildings, lighting and transport fleet (as included and modelled in Optimum Energy Future) on local government sector energy demand

Steps towards Optimum Energy Future The figure below illustrates the steps that need to be taken to move from the Business As Usual (BAU) Scenario towards the Optimum Energy Future (OEF) Scenario. These include electricity efficiency, transport efficiency and a supply mix containing a larger proportion of renewable supply sources.


Figure 67: Generalised impact of energy efficiency and renewable energy electricity supply mix on greenhouse gas emissions (smoothed curve)

Costs versus emissions reduction The graphs below show the “bang for buck” of energy efficiency interventions in different sectors. The best interventions are those that save a lot of money and reduce carbon emissions by a large amount. All energy efficiency intervention costs and carbon savings were modelled according to the implementation rate and penetration level of that given for the Optimum Energy Future Scenario. Key for terms used:

COM: Commercial

HI: High Income Residential

HVAC: Heating, Ventilation and Cooling

IND: Industrial

LG: Local Government

LI: Low Income Residential

MI: Medium Income Residential

PAS: Passenger

VH: Very High Income Residential

Water: refers to water heating, e.g. solar water heaters or heat pumps The below figure indicates that the overall “big win” energy efficiency interventions include efficient lighting and HVAC in the commercial sector; efficient water heating, lighting and refrigeration in the residential sector; and efficient motors in the industrial sector.


Figure 68: Costs and carbon savings of energy efficiency interventions implemented across all sectors up till 2015

R 0 R 20,000,000 R 40,000,000 R 60,000,000 R 80,000,000

0

50,000

100,000

150,000

200,000

250,000

300,000

LI lightingLI fridgeMI lighting

MI fridge

MI water

HI lighting

HI fridge

HI water

VH lightingVH fridge

VH water

COM HVAC

COM water

COM lighting

IND motors

IND HVACIND lighting

LG lightingLG HVAC

LG street lights

LG traffic signals

All Sectors

2015

Saving (R)

Ca

rbo

n (

T)

Commercial buildings – lighting,

HVAC

Residential water heating

Residential lighting

Residential fridges

Industrial motors


Figure 69: Costs and carbon savings of energy efficiency interventions implemented across all sectors up till 2025

R 0 R 100,000,000 R 200,000,000 R 300,000,000 R 400,000,000 R 500,000,000

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

LI lightingLI fridge

MI lightingMI fridge

MI water

HI lighting

HI fridge

HI water


VH water

COM HVAC

COM water

COM lighting

IND motors


LG lightingLG HVAC

LG street lights

LG traffic signals

All Sectors

2025

Saving (R)

Ca

rbo

n (

T)


Figure 70: Costs and carbon savings of energy efficiency interventions implemented in the residential sector up till 2015

R 0 R 20,000,000 R 40,000,000 R 60,000,000 R 80,000,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000


MI lighting

MI fridge

MI water

HI lighting

HI fridge

HI water

VH lighting

VH fridge

VH water

Domestic Sector

2015

Saving (R)

Ca

rbo

n (

T)


Figure 71: Costs and carbon savings of energy efficiency interventions implemented in the residential sector up till 2025

R 0 R 100,000,000 R 200,000,000 R 300,000,000 R 400,000,000 R 500,000,000

0

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MI lighting

MI fridge

MI water

HI lighting

HI fridge

HI water


VH water

Domestic Sector

2025

Saving (R)

Ca

rbo

n (

T)


Figure 72: Costs and carbon savings of energy efficiency interventions implemented in the local government sector up till 2015 (government buildings are represented by LG HVAC and LG Lighting)

R 0 R 5,000,000 R 10,000,000 R 15,000,000

0

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LG street lights

LG traffic signals

Local Government

2015

Saving (R)

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Govt streetlights

Govt traffic signals

Govt buildings


Figure 73: Costs and carbon savings of energy efficiency interventions implemented in the local government sector up till 2025

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LG lightingLG HVAC

LG street lights

LG traffic signals

Local Government

2025

Saving (R)

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T)


Figure 74: Costs and carbon savings of energy efficiency interventions implemented in the commercial sector up till 2015

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COM water

COM lighting

Commercial

2015

Saving (R)

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Figure 75: Costs and carbon savings of energy efficiency interventions implemented in the commercial sector up till 2025

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0

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COM HVAC

COM water

COM lighting

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2025

Saving (R)

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Figure 76: Costs and carbon savings of energy efficiency interventions implemented in the industrial sector up till 2015

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IND motors


Industry

2015

Saving (R)

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Figure 77: Costs and carbon savings of energy efficiency interventions implemented in the industrial sector up till 2025

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0

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400,000IND motors


Industry

2025

Saving (R)

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T)


Figure 78: Costs and carbon savings of energy efficiency interventions implemented in the transport sector up till 2015

-R 7,000,000,000 -R 6,000,000,000 -R 5,000,000,000 -R 4,000,000,000 -R 3,000,000,000

0

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800,000PAS Transport

Transport

2015

Saving (R)

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Figure 79: Costs and carbon savings of energy efficiency interventions implemented in the transport sector up till 2025

15.2. CITY DENSIFICATION The figures below graphically illustrate the effect of increased city densification on transport mode use, energy consumption, cost and emissions.

-R 20,000,000,000 -R 15,000,000,000 -R 10,000,000,000 -R 5,000,000,000

0

500,000

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3,000,000PAS Transport

Transport

2025

Saving (R)

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Figure 80: Energy consumption per capita as a function of city density41

Figure 81: Transport-related energy consumption and urban densities42

41

Source: Newman, P. and J. Kenworthy (1999) Sustainability and Cities: Overcoming Automobile Dependence, New York: Island Press.


Figure 82: Car use per capita and urban density in global cities, 199043

42

Source: Newman and Kenworthy, 1999, p110 43

Kenworthy and Laube, 1999


Figure 83: Transport greenhouse gas emissions vs. population and employment density, 200644

Figure 84: Emissions by percentage of public transport mode45

44

Source of graph still to be established. 45

Source of graph still to be established.


Figure 85: Costs of public transport as a function of city density46

Poor households are usually located on city margins and must spend a large part of their income on transport services to reach the city centre where the bulk of job opportunities reside (see figure below). A denser city would bring them closer to work opportunities and decrease travelling costs.

46

Data used from presentation given by Eduardo Vasconcellos to the UCT Urban Transport Research Group, 2007.


Figure 86: Percentages of households, by income bracket, that spend more than 20% of their income on public transport47

Distinction must be made between “good” and “bad” densification. The poor reside in extremely dense informal communities. This is also not a sustainable or fair situation. The figure below graphically represents Cape Town population densities. Areas of high density are associated with informal settlements.

47

Source: Lisa Kane analysis of National Household Travel Survey, 2006

Percentage of Cape Town households spending >20%

of income on public transport

0%

5%

10%

15%

20%

25%

30%

35%

40%

0-R1000 R1001-R3000 R3001-R6000 R6001 +

Income quartiles

Perc

en

tag

e o

f h

ou

seh

old

s


Figure 87: Population densities in Cape Town48

15.3. OTHER Required by science The figure below displays the discrepancy between emissions levels for energy demand growth without constraints and that required by science (and the National LTMS endorsed by Cabinet) to keep dangerous climate change effects to a minimum. The “current development plans” path displays what the emissions levels would be if all current South African energy policies were implemented as planned. These policies alone are not enough to move South Africa to the emissions levels required by science.

48

Source: City of Cape Town


Figure 88: Emissions futures for South Africa for growth without constraints, current development plans and required by science

Household growth City service delivery planning and budgeting will need to consider the fact that the informal, largely un-electrified household sector is currently growing fast (see earlier for discussion on household growth and informal sector growth). It will place increasing demands on the City’s ability to provide services and will contribute little to revenue.


Figure 89: Growth projections in household numbers49

Electricity supply costs Renewable electricity supply option costs will come down in future due to the fact that they are new technologies. As they undergo the learning curve, further research and development drops prices over time (figure below).

49

Source: 2007 base year data projected into the future for BAU scenario

Low income electrified

Med income (elec)

Hi income (elec)

Household growth projectionsShowing the potential growth in the

informal sector if current trends continue

Low income unelectrified(informal)


Figure 90: Generation costs of electricity supply options over time50

Energy consumption in residential sector

50

Source: ERC SNAPP tool


Figure 91: Energy (includes all energy forms, not just electricity) consumption per income group51

51

Source: Base year data for OEF 2.8 LEAP model

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