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Job No. DTI / 1591 /GE
South AfricaModelling Report of
United Nations Environment ProgrammeP.O. Box 30552, Nairobi 00100 , Kenya
T: +254 (20) 762 1234F: +254 (20) 762 3927 E: [email protected]: www.unep.org
www.unep.org
Focus on Natural Resource Management, Agriculture, Transport and Energy Sectors
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Copyright © United Nations Environment Programme, 2013
This publication may be reproduced in whole or in part and in any form for educational or non-
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of the source is made. UNEP would appreciate receiving a copy of any publication that uses this
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No use of this publication may be made for resale or for any other commercial purpose whatsoever
without prior permission in writing from UNEP.
Citation
UNEP, 2013, Green Economy Scoping Study: South African Green Economy Modelling Report
(SAGEM) – Focus on Natural Resource Management, Agriculture, Transport and Energy Sectors
Disclaimer
The designations employed and the presentation of the material in this publication do not imply the
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concerning the legal status of any country, territory, city or area or of its authorities, or concerning
delimitation of its frontiers or boundaries. Moreover, the views expressed do not necessarily
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citing of trade names or commercial processes constitute endorsement.
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globally and in its own activities. This publication is printed on 100% recycled
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Photo credits
Department of Environmental Affairs, South Africa
Department of Energy, South Africa
Cover page: Blue solar batter cells , Salmonsdam Nature Reserve, Western Cape, South Africa – © Photosky/Shutterstock
June 2013
South African Green Economy Modelling Report (SAGEM)
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List of tables iiList of boxes iiList of figures iiList of acronyms iiiAcknowledgements iv
Foreword 1Key messages 41 Introduction 62 Country profile 7 2.1 Economic and social profile 7 2.2 Environmental profile 83 Transformation to a green economy 9 3.1 Green economy - Origins and definitions 9 3.2 Key green economy interventions 9 3.3 Understanding green economy in the South African
context 10 3.4 Sector priorities 11 3.5 Intervention priorities 134 Modelling a green economy for South Africa 14 4.1 Introduction 14 4.2 Overview of modelling approaches 14 4.3 Characteristics of T21 15 4.3.1 Transparency 15 4.3.2 Comprehensiveness 15 4.3.3 Policymaking guidance 16 4.3.4 Strategy comparison 16 4.3.5 Long-term perspective 16 4.3.6 User-friendliness 16 4.3.7 Environmental analysis 16 4.3.8 Partnership building 17 4.3.9 National development indicators 17 4.3.10 Continuous time-series output 17 4.3.11 Applications 175 Applying T21 to the South African Green Economy Modelling (SAGEM) 18 5.1 Introduction 18 5.2 Developing a green economy model for South Africa 19 5.2.1 Boundary 20 5.2.2 Time horizon 20 5.2.3 Scope 20 5.2.4 Modules 20 5.2.5 Data collection 20 5.3 Environment sphere 22
5.3.1 Natural resource management 22 5.3.2 Land 22 5.3.3 Water 22 5.3.4 Energy 23 5.3.5 Emissions 23 5.3.6 Minerals 23 5.4 Society sphere 23 5.4.1 Population 23 5.4.2 Education 24 5.4.3 Health 24 5.4.4 Public infrastructure (access to roads
and transport) 24 5.4.5 Employment 25 5.5 Economy sphere 25 5.5.1 Production (agriculture, services and industry) 25 5.5.2 Investment and households 27 5.5.3 Government 276 Scenario definitions and challenges 28 6.1 Scenario definitions 28 6.2 Challenges 297 Results of the simulation analyses 31 7.1 Baseline scenario (BAU) results 31 7.2 Results of green economy scenarios (GE2% and GETS) 33 7.3 Investment scenarios for key sectors 38 7.3.1 Natural Resource Management 38 7.3.2 Agriculture 40 7.3.3 Transport 40 7.3.4 Energy 408 Comparison of SAGEM and T21-World 42 8.1 Introduction 42
8.2 Comparison of the green economy analyses 42 8.2.1 Simulation of scenarios 42 8.2.2 Allocation of investments 42 8.3 Comparison of models 42 8.3.1 Selection of sectors 42 8.3.2 Structure of sectors 43 8.3.3 Data availability 43 8.4 Main results 449 Conclusions 46
References 48Notes 49Annex 1 51Annex 2 52
Contents
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List of tablesTable 1 Focus sectors prioritised by workshop
participants . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table 2 Medium- and long-term targets . . . . . . . . . . . . .Table 3 Green economy spheres and sectors . . . . . . . . . .Table 4 Modules of SAGEM . . . . . . . . . . . . . . . . . . . . . . Table 5 Data sources for SAGEM modules . . . . . . . . . . . Table 6 Allocation of green investment across sectors in
a green economy . . . . . . . . . . . . . . . . . . . . . . . .Table 7 Comparison of scenarios for the selected sectorsTable 8 Summary of the main indicators for 2012, 2020
and 2030 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 9 Main SAGEM indicators for the prioritized sectorsTable 10 Sources of date for T21-World models . . . . . . . .
List of boxesBox 1 Basic data on South Africa . . . . . . . . . . . . . . . . . Box 2 Comparison of economic and environmental
consequences of the BAU2% and GE2% scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
List of figuresFigure 1 Policy documents linked to green economy . . . . .Figure 2 Structure of T21 . . . . . . . . . . . . . . . . . . . . . . . . .Figure 3 The relationship between economic growth and
natural resources . . . . . . . . . . . . . . . . . . . . . . . .Figure 4 Causal loop of effect of population on water
demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure 5 Causal loop diagram – Effects of green economy
investment on agricultural production . . . . . . . . Figure 6 Comparison of real GDP in BAU with data . . . . .Figure 7 Employment in the key sectors in BAU scenarioFigure 8 Comparison of population in BAU with data . . . .Figure 9 Selected land use changes in the BAU scenarioFigure 10 Comparison of CO
2 emissions in BAU with data
Figure 11 BAU CO2 emissions in transport and power
sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure 12 Total employment in BAU, BAU2%, GE and
GE2% scenarios . . . .Figure 13 Potential job creation benefits with respect to a
high import scenario . . . . . . . . . . . . . . . . . . . . . Figure 14 Comparison of share of renewable energy in
BAU, BAU2%, GETS and GE2% scenarios . . . . . . Figure 15 Comparison of emissions in BAU, BAU2%, GETS
and GE2% scenarios . . . . . . . . . . . . . . . . . . . . .Figure 16 Comparison of centralised management and
cost-based scenario in electricity pricing in GETSFigure 17 Comparison of centralised management and
cost-based scenario in electricity pricing in GE2%Figure 18 Comparison of transport energy use in BAU,
BAU2%, GETS and GE2% scenarios . . . . . . . . . .
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List of annexesAnnex 1 Modelling symbols
Annex 2 Modules2.1 Water quantity provision with WfW module2.2 Potential electricity generation from invasive module2.3 Land module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.4 Water supply module . . . . . . . . . . . . . . . . . . . . . . . .2.5 Water demand electricity generation module . . . . . . 2.6 Coal electricity module . . . . . . . . . . . . . . . . . . . . . . .2.7 Nuclear electricity module . . . . . . . . . . . . . . . . . . . .2.8 Hydroelectricity module . . . . . . . . . . . . . . . . . . . . . . 2.9 Pumped storage electricity module . . . . . . . . . . . . . .2.10 Solar electricity module . . . . . . . . . . . . . . . . . . . . . .2.11 Wind electricity module . . . . . . . . . . . . . . . . . . . . . . 2.12 Electricity generation technology share module . . . .2.13 Electricity price module . . . . . . . . . . . . . . . . . . . . . .2.14 Electricity demand module . . . . . . . . . . . . . . . . . . . .2.15 Oil demand module . . . . . . . . . . . . . . . . . . . . . . . . .2.16 Gas demand module . . . . . . . . . . . . . . . . . . . . . . . .2.17 Air emissions module . . . . . . . . . . . . . . . . . . . . . . . . 2.18 Minerals module (a) . . . . . . . . . . . . . . . . . . . . . . . . .2.19 Minerals module (b) . . . . . . . . . . . . . . . . . . . . . . . . .2.20 Population module . . . . . . . . . . . . . . . . . . . . . . . . .2.21 Education module . . . . . . . . . . . . . . . . . . . . . . . . . .2.22 Health module . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.23 Access to roads module . . . . . . . . . . . . . . . . . . . . . .2.24 Transport module . . . . . . . . . . . . . . . . . . . . . . . . . . .2.25 Employment module . . . . . . . . . . . . . . . . . . . . . . . .2.26 Power sector employment module . . . . . . . . . . . . . .2.27 Agriculture module . . . . . . . . . . . . . . . . . . . . . . . . . 2.28 Industry module . . . . . . . . . . . . . . . . . . . . . . . . . . .2.29 Services module . . . . . . . . . . . . . . . . . . . . . . . . . . .2.30 GDP module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.31 Investment and household module . . . . . . . . . . . . .2.32 Government module . . . . . . . . . . . . . . . . . . . . . . . .
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List of acronymsASPO-USA Association for the Study of Peak Oil and Gas BAU Baseline scenario (business-as-usual)BAU2% Business-as-usual 2% scenarioCGE Computable General EquilibriumCRSES Centre for Renewable and Sustainable Energy StudiesCSP Concentrating Solar PowerDBSA Development Bank of Southern AfricaDC Disaggregated Consistency DEA Department of Environmental AffairsDME Department of Minerals and EnergyDoE Department of EnergyDTI Department of Trade and IndustryEIA Energy Information Administration FAO Food and Agriculture OrganizationGDP Gross Domestic ProductGE1% Green Economy 1% scenarioGE2% Green Economy 2% scenarioGER Green Economy ReportGETS Green Economy Target Specific scenarioGGND Global Green New DealGHG Greenhouse gasGW GigawattGWh Gigawatt hoursHA HectareIEA International Energy AgencyIRP Integrated Resource PlankW KilowattkWh Kilowatt hourME Macro-EconometricMFLE Male female life expectancyMI Millennium Institute
MIT Massachusetts Institute of TechnologyMW MegawattMWh Megawatt hourNDP National Development PlanNERSA National Energy Regulator of South AfricaNRM Natural Resource ManagementNT National TreasuryOECD Organisation for Economic Co-operation and DevelopmentPC Per capitaPGM Platinum Group MetalsPV PhotovoltaicRSA Republic of South AfricaSAGEM South African Green Economy Modelling reportSAM Social Accounting MatrixSAPS South African Police ServiceSARi South Africa Renewables initiativeSD System Dynamics SI Sustainability InstituteSTATS SA Statistics South AfricaTJ TerajoulesT21 Threshold 21UNDP United Nations Development ProgrammeUNEP United Nations Environment ProgrammeUNFCC United Nations Framework Convention on Climate ChangeUSGS United States Geological SurveyWEF World Economic ForumWfW Working for WaterWPP World Population ProspectsZAR South African Rand
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The United Nations Environment Programme commissioned an analysis of the opportunities and options for a green economy transition, focusing on selected key economic sectors in South Africa, at the request of the national Department of Environmental Affairs. The analysis was conducted with technical assistance from the Millennium Institute and the Sustainability Institute, in collaboration with the Centre for Renewable and Sustainable Energy Studies (CRSES) of Stellenbosch University. The United Nations Development Programme supported stakeholder consultations and capacity building activities related to the implementation of the project.
UNEP is grateful for the funding support provided by the European Commission.
The representatives of the following institutions led the project:
Department of Environmental AffairsDorah Nteo (Programme advocacy)Godfrey Nyakhulalini (Project coordination)Lindiwe Tshabalala (Project coordination)Mapula Tshangela (Policy alignment)
Millenium InstituteAndrea Bassi and Zhuohua Tan (Modelling and report preparation)
Stellenbosch University and the Sustainability InstituteAlan Brent and Josephine Kaviti Musango (Modelling and report preparation)
United Nations Development ProgrammeKibii KomenMaria Mbengashe
United Nations Environment ProgrammeMoustapha Kamal GueyeCecilia Kinuthia-Njenga
The following stakeholders participated and provided inputs that directed the project:
Agence française de développementFabio Grazi
Africa Institute of South AfricaNedson Pophiwa
British American Tobacco South AfricaNoxolo Mbana
City of TschwaneCaiphus Chauke, Benjamin Manasoe, Matlhodi Maphutha and Thembi Mkhabela
Council for Scientific and Industrial ResearchKristy Faccer, Maxwell Mapako and Henry Nuwarinda
Department of Agriculture, Forestry and FisheriesMatiga Motsepe and Nkosinathi Nomatiti
Department of Economic Development – Gauteng ProvinceLebohang Raliapeng
Department of Economic Development, Environment, Conservation and Tourism – North West ProvinceKgomotso Gaobepe
Department of Economic Development, Environment and Tourism – Limpopo ProvinceMachoene Lilly Maja and Carol Nkambule
Department of EnergyLindiwe Chauke
Department of Environmental AffairsAhmed Khan, Alfred Makhafula, Carina Malherbe, Wadzi Mandivenyi, Anna Mampye, Christo Marais, Nomusa Mbuyazi, Kiruben Naicker, Thembelihle Ndukwana, Xolisa Ngwadla, Thabang Nong, Faith Phooko, Guy Preston, Deborah Ramalope, Tlou Ramaru and Collins Ramavhona
Department of International Relations and CooperationSuhayfa Zia
Acknowledgements
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Department of Mineral ResourcesKgauta Mokoena
Department of Science and TechnologyHenry Roman and Blanche Ting
Department of Trade and IndustryZakhele Mdlalose and Witness Mokwana
Department of TransportJacob Dikgang and Shumani Mugeri
Development Bank of Southern AfricaThierry Giordano and Kate Rivett-Carnac
Economic Development DepartmentLebohang Mothiba, Luzuko Ndzuta, Khaya Qwabe and Khathutshelo Sikhitha
Endangered Wildlife TrustMarie Parramon-Gurney
Gauteng City Regional ObservatoryAlexis Schaffler
Industrial Development CorporationRiaan Joubert and Likeleli Makaulule
International Council for ScienceDaniel Nyanganyura and Richard Glover
International Labour OrganizationKarl Pleffen
Kowostone ConsultingAlfred Kowo
NEDBANKMarco Lotz
National TreasuryVsevolod Gorlach, Faaiqa (Salie) Hartley, Sharlin Hemraj, Muziwethu Mathema, Cecil Morden and Konstantin Makrelov
National Business InitiativeValerie Geen
Office of the Premier - Limpopo ProvincePhatu Letsoalo and Humphrey Mutangwa
South Africa National Biodiversity InstituteHeather Terrapon
Sustainability InstituteChristelle Beyers
The International Research Centre on Environment and DevelopmentFranck Lecocq and Hewu Waisman
The Presidency – National Planning Thembani Mabandla
Trade and Industrial Policy StrategiesPeet du Plooy and Gaylor Montmasson-Clair
Trade and Investment LimpopoMpho Shibambu
University of South AfricaGodwell Nhamo
World Wide Fund (South Africa)Malango Mughogho, Alexis Scholtz and Richard Worthington
Steven Stone, Moustapha Kamal Gueye, Joy Kim, Kofi Vondolia, Yacouba Gnegne and Simon Lobach of UNEP provided valuable and substantive comments and suggestions on the report. The final report was edited by Diwata Hunziker.
Original cover and layout design by Thomas Gianinazzi.
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1
In the past decade, natural resources, environmental risks and ecological scarcities have come to the centre of international attention, as it has become increasingly clear that these issues are of fundamental importance for achieving inclusive growth and enhancing social equity. The environmental crises that occur in different regions of the world represent not only a significant and historic challenge to our generation, but also an opportunity for change.
South Africa confronted the world financial and economic crisis of 2008 by launching a US$7.5 billion stimulus package, of which about 11 per cent, or US$0.8 billion, was allocated to environment-related themes, such as railways, energy-efficient buildings, and water and waste management. Furthermore, in May 2010, the South African government hosted a Green Economy Summit to set the stage for the formulation of a Green Economy Plan. More recently, in November 2011, the country announced a Green Economy Accord – a social pact between government, labour unions, private sector and civil society – to advance the country’s New Growth Path with the aim to create 300,000 new green jobs by 2020. These initiatives demonstrate a strong political will and government commitment to foster a transition to a more inclusive and sustainable path of growth and development.
The present study was undertaken at the request of South’s Africa Department of Environmental Affairs to assess potential opportunities and options to promote a green economy, with a focus on key economic sectors set out by South Africa’s National Development Plan – Vision 2030. Commissioned by UNEP, the assessment was led by Stellenbosch University and the Sustainability Institute of South Africa, in cooperation with the Millennium Institute. It presents a modelling exercise that compares a scenario of investments directed to “business-as-usual” (BAU) with scenarios of investments allocated to four critical sectors for a transition to a green economy in South Africa, namely: natural resource management, agriculture, transport and energy.
The findings from this assessment using the parameters of economic growth, poverty, employment, resource efficiency and climate change, show that the strengthening of natural resource management and environmental protection is fundamental for sustained economic growth and well-being.
We hope that this report will be a useful contribution to supporting the vision and leadership of South Africa in paving the way towards a green and inclusive economy in the context of sustainable development and poverty eradication in South Africa.
Ms Sylvie LemmetDirectorDivision of Technology, Industry and EconomicsUNEP
Foreword
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South Africa has set its short, medium and long-term vision for contribution towards an environmentally sustainable, climate-change resilient, low-carbon economy and just society. The vision is outlined in the Cabinet endorsed National Strategy for Sustainable Development and Action plan (2014), New Growth Path (2020) and National Development Plan (2030) supported by various sector policies and strategies including the integrated resource plan, industrial policy action plan, environment sector green economy implementation plan, national biodiversity strategy and action plan and the national climate change response white paper.
The first national green economy summit of 2010 committed the country to working together to pursue and explore the opportunities “towards a resource efficient, low carbon and pro-employment growth path”. This South African Green Economy Modelling (SAGEM) report contributes to building knowledge and understanding of the South African context. The cases within four sectors were selected to initiate the modelling exercise, based on the existing country commitments and targets. Firstly the natural resource management with the specific focus on Government’s working for water programme. Secondly the agriculture crop production and differentiates between production utilising conventional and organic fertilizer. Thirdly transport sector categorised into roads infrastructure and transport. Lastly the energy sector was categorised into energy production and energy demand.
The global economic crisis, depletion of natural resources, changing climate and high unemployment provide key signals that economic growth has to be redesigned in an integrated systems approach to sustainability. By adopting an integrated approach focus on the interaction of stocks and flows across sectors, the South African green economy modelling process examined the hypothesis that a correct management of natural resources does not necessarily imply accepting lower economic growth going forward. Instead, it explored the question of whether equal or higher growth could be attained with a more sustainable, equitable and resilient economy, in which natural resources would be preserved through more efficient use. By way of contrast, the green economy approach supports both growth and low-carbon development, by reducing emissions and conserving stocks in the short-term to profit from their healthier state in the future.
I would like to thank all the stakeholders for contribution that made this SAGEM report possible. I look forward to your continued support as we generate knowledge for national green economy in the context of sustainable development and poverty alleviation.
Ms Nosipho NgcabaDirector GeneralDepartment of Environmental AffairsGovernment of South Africa
Foreword
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The South African Green Economy Model (SAGEM), which is based on a system dynamics modelling approach, is primarily aimed at assessing the impacts of green economy investments in selected sectors pertaining to the South African economy. Based on planned targets and expenditures and/or costs of interventions, the model identifies the possible options and opportunities to achieving these targets. Four scenarios were defined: BAU, or business-as-usual; BAU2% representing a 2 per cent investment of gross domestic product in the BAU activities; GE2% representing an allocation of 2 per cent of gross domestic product in green economy sectors (natural resource management, agriculture, transport and energy); and GETS, which is a target-specific scenario aimed at identifying whether policy-makers can achieve the medium- to long-term targets following green economy interventions in the prioritised sectors.
Natural Resource Management (NRM) Green economy contributes to natural resource management, while maintaining agricultural land size.Investments in the green economy simulated in SAGEM will positively contribute to additional restored land without leading to a reduction in land requirements in the agriculture sector. The GE2% scenario reveals an additional 46.4 per cent restored land by 2030 and higher water availability.
Investments in NRM create jobs, while increasing water supply and making biomass available for power generation.Employment in the NRM sector occurs both in the restoration of the water ecosystem services and in the utilization of the biomass for energy. Most of the employment, however, arises from the alien species elimination. With the GETS scenario, employment is created for 701,000 people in 2030, while the GE2% scenario is projected to create jobs for 737,000 people – higher than BAU (568,000) and BAU2% (569,000) scenarios, respectively.
Agriculture
Investments allocated to the adoption of ecological agriculture practices (such as organic fertilizer use) provide a sustained increase of the yield per hectare, as opposed to the short-term gains from the use of conventional fertilisers.With these green economy investments, crop yield is projected to increase by 5.5 per cent and 23.9 per cent by 2030 for the GETS and GE2% scenarios, respectively.
While the increase in the yield per hectare reduces land requirements for agricultural crop production, the effect of population growth on agricultural land requirements is higher, which results in a net increase in land requirements.An additional benefit of avoided CO
2 emissions due to the use of organic fertilizer is observed in the GE2% scenario.
Transport
Green investment in the transport sector aims at improving energy efficiency, which in turn translates into reduced energy consumption in the sector.In the GETS scenario, investments in transport efficiency are not enough to reduce the energy demand by 9 per cent by 2015, as stipulated in the energy efficiency strategy of 2005.
The demand for energy in the transport sector is projected to grow because of the growth in GDP and population.A more aggressive investment scenario, such as the GE2% scenario, contributes to reduced energy consumption in the transport sector. The efficiency improvement in the GE2% scenario reaches 5.5 per cent by 2030. As a result,
Key messages
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transport energy use in the GE2% scenario is expected to decline relative to the BAU scenario, as well as CO2
emissions.
Energy demand and supply
A green economy contributes to energy efficiency and results in a reduction in energy demand and lowered investments in power supply.A 2 per cent (of GDP) investment in the green economy, which is distributed equally among all the sectors, including energy efficiency, results in a reduction of energy demand. In the power sector, this translates into a very significant reduction of coal power generation capacity. The reduction that could be achieved is comparable to the capacity of the large coal power stations that are under construction in South Africa (e.g., Medupi). A reduction of energy consumption in the transport sector would also be realized.
A green economy contributes to electricity diversification mix.With a green investment targeted to expand renewable electricity generation (GETS), the share of renewable energy would reach 24.4 per cent by 2030 in the GETS scenario and 16 per cent in the GE2% scenario. A more aggressive green economy investment intervention than GETS and GE2% would be required to achieve the stipulated target in the NDP (of 33 per cent by 2030).
National development
The green economy scenarios simulated in SAGEM reveal that a green economy stimulates economic growth.All green investments stimulate the growth of GDP relative to a BAU scenario. Investments in a green economy could realize a further growth in real GDP of over 2 per cent by 2030 relative to 2012. While this also translates into an increase in per capita income over the years, the growth target, stipulated in the National Development Plan (NDP), will not be realized and will require higher investments than the amounts simulated.
A green economy creates jobs and the extent of the employment increase is dependent on the investment option chosen.Overall, green economy investments simulated in SAGEM stimulate job creation relative to the BAU scenario. However, prioritizing investments in the energy sector will maximize the employment creation potential of a green economy (as simulated in the GETS scenario), due to the additional infrastructure requirements in this sector. If the investments are spread equally across all sectors, then the agriculture sector will deliver the highest employment creation potential.
Using the same assumption for additional investment spread equally across the four sectors, BAU2% appears to be comparable to GE2% in terms of economic output, but GE2% contributes to better environmental performance than BAU2%.The BAU2% scenario yields fewer overall benefits across the sectors than the GE2% scenario. While comparable, the GE scenario supports the environment and creates employment in sectors that support production (e.g., water). The green economy simulated in SAGEM is therefore able to deliver as much as the BAU case, but in a more sustainable manner.
A green economy contributes to the reduction of GHG emissions.A combined intervention in the energy sector to improve energy efficiency and diversify power supply with renewable energy, as well as other low carbon options in the various sectors, is projected to create relevant synergies in lowering GHG emissions.
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South Africa’s Department of Environmental Affairs (DEA), in partnership with the United Nations Environment Programme (UNEP) and with support from United Nations Development Programme (UNDP), embarked on the development of a green economy for South Africa, with technical assistance from the Millennium Institute and the Sustainability Institute, in collaboration with the Centre for Renewable and Sustainable Energy Studies (CRSES) of Stellenbosch University. The need for this modelling work has its beginnings in the first national green economy summit that was held in May 2010, which was aimed at catalyzing efforts towards a resource efficient, low carbon and pro-employment growth path. Subsequent to the summit, a roundtable and workshop were held, which aimed at identifying a shared approach, programmes and initiatives in support of South Africa’s transition to a green economy.
This report presents the modelling process and the results represented as the South African Green Economy Modelling (SAGEM) report that was developed to test national targets and the effects of investing in a green economy in South Africa. It affirms the current government’s support for a green economy. SAGEM was developed on the same basis as the model that underpins the UNEP Green Economy Report (GER) (UNEP, 2011). In essence, SAGEM utilised the Threshold 21 (T21) framework developed by the Millennium Institute.1 T21 provides
a framework for integrating several sectoral models, which were customised for the South African context (see Appendix A). The modelling traces the effects of a proportion of GDP on four of the nine core sectors prioritized to support a green economy for South Africa, namely: natural resource management, agriculture, transport and energy. These sectors were identified during a stakeholder workshop held on 1 February 2012 (see Section 3.4).
The report is divided into nine sections. Section 2 presents the country profile, followed by a discussion of green economy, in general, and an in-depth understanding of its origin in the South Africa context. The features of the T21 framework, which was utilised for the study, are presented in Section 4. This is followed by a discussion of the characteristics of SAGEM in Section 5, and the process of defining the scenarios and their underlying assumptions are discussed in Section 6. The modelling results are presented in Section 7 consisting of: (i) two business-as-usual scenarios, or BAU – where there is no additional investment – and BAU2% per cent, which consist of a 2 per cent of GDP investment in the BAU; and (ii) two green economy scenarios, which entail additional investment interventions on selected green economy sectors or programmes. This is followed by a discussion that compares GER results with SAGEM in Section 8. Finally, the conclusions of the study are presented in Section 9.
1 Introduction
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Area 1.219 million km2
Capitals Cape Town (Legislative); Pretoria/Tshwane (Administrative); Bloemfontein (Judicial).
Form of government Federal/unitary hybrid, comprising a central government and nine provincial governments.
Legal system Based on Roman Dutch Law and the 1996 Constitution.
Legislative branch Bicameral Parliament consisting of the National Council of Provinces and the National Assembly.
Judicial branch Constitutional Court; Supreme Court of Appeals; High Courts; and Magistrate Courts.
Currency ZAR 1 (South African Rand) = 100 centsUSD 1 = approximately ZAR 8 (2012)EUR 1 = approximately ZAR 11 (2012)
Languages There are eleven official languages: Afrikaans, English, IsiNdebele, IsiXhosa, IsiZulu, Sesotho, Northern Sotho, Setswana, SiSwati, Tshivenda and Xitsonga. English is the language of commerce, banking, government and official documentation.
Total GDP (2011) Approximately ZAR 3 trillion or US$408 billion (World Bank)
GDP per capita (2011) Approximately ZAR 60 000 or US$8 000 dollars (World Bank)
Real GDP growth (2011) 3.1 per cent
Exports Minerals, diamonds, metals and metal products, chemicals, foods and automotive components
Imports Machinery and transport equipment, manufactured goods, chemicals and oil
Main trading partners (in order of trade volume)
China, Germany, United States of America, Japan, India and the United Kingdom
2 Country profile
2.1 Economic and social profile
At the southern tip of Africa, with a population of around 51.8 million, the Republic of South Africa (RSA) is considered as an upper-middle income country, although there remain large disparities in income with a Gini coefficient of 0.70, ranked as being among the highest in the world. It is an emerging market with an abundant supply of natural resources ranking as the 3rd
largest mega-diverse country in the world. Out of the 144 economies cited in the 2012 world competitiveness index (WEF, 2012), South Africa rankings include scores by sustainability indicators (52nd), financial market development (3rd) and judicial independence (27th). Although considered good by regional standards, infrastructure (63rd) and the quality of electricity supply (94th) requires upgrading. Health and primary
education is ranked 132nd resulting from high rates of communicable diseases. On the other hand, South Africa does reasonably well in more complex areas such as business sophistication (38th) and innovation (42nd), benefitting from good scientific research institutions (34th) and strong collaboration between universities and the business sector in innovation (30th).
Growth was robust from 2004 to 2008 as South Africa reaped the benefits of macroeconomic stability and a global commodities boom. However, it began to slow in the second half of 2008 due to the impact of global financial crisis on commodity prices and demand.
At the end of 2007, South Africa went through a national electricity crisis because the state-owned electricity enterprise, Eskom, suffered supply problems with aged plants, necessitating “load-shedding” cuts
Box 1. Basic data on South Africa (Source: Grant Thornton, 2012)
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to residents and businesses in the major cities. In addition, daunting social and economic challenges remain from the apartheid era, in particular poverty, youth unemployment, lack of economic empowerment among the disadvantaged groups and shortage of public transportation.
South Africa’s economic policy is fiscally conservative but pragmatic, focusing on inflation targeting, maintaining a budget surplus and using state-owned enterprises to deliver basic services to low-income areas as a means to increase job growth and household income. The country’s main long-term social and economic challenges include a high rate of unemployment at 25.5 per cent (StatsSA, 2012) and the total number of people living with HIV/AIDS estimated at approximately 5.38 million in 2011 (StatsSA, 2011). On the other hand, according to the South African Policy Service crime statistics overview (SAPS, 2012), the level of serious crime has been reduced by 31.8 per cent (4,852 to 3,609 crime ratio per 100,000 of the population) from 2004/5 to 2011/12.
2.2 Environmental profile
According to the 2006 South Environment Outlook report (DEA, 2006), the key trends in respect of the state of South Africa’s natural resources include the following:
•South Africa has a relatively low annual rainfall and water is extracted from most of the country’s 22 major rivers to supply the growing number of domestic, agricultural and industrial users. It is estimated that national water requirements will exceed availability by 2025. This is exacerbated by the fact that water quality has been seriously compromised in many areas.
•There is limited agricultural land in South Africa. Of the country’s 122 million hectares total land surface,
it is estimated that only 16 million hectares, or 7.5 per cent, can be used for crop production. Soil erosion and degradation of agricultural land through overexploitation and inappropriate and unsustainable farming methods pose a threat to the country’s food security. Indeed, there are many issues that affect agricultural production, soil quality and erosion, as well as lack of infrastructure, but water is considered to be one of the most important.
•About 34 per cent of the country’s terrestrial ecosystems, 82 per cent of its main river ecosystems and 65 per cent of its marine biozones are threatened, while 50 per cent of its wetlands have already been destroyed and living marine resources are either maximally or over-exploited.
•There are elevated levels of a variety of pollutants in the atmosphere that, among other things, are leading to a growing incidence of respiratory problems.
The well-being of South Africans, as well as its economic development, is heavily reliant on the services that ecosystems supply such as air, food, water, energy, medicines as well as recreational, spiritual and cultural benefits. But measures of environmental sustainability show that the country has exceeded its ecological carrying capacity. The World Wide Fund for Nature’s Ecological Footprint for South Africa is 2.8 global hectares per person, compared to the world average of 2.2 global hectares per person, considering that the average for Africa is 1.2 hectares per person. In Africa, carbon represents the largest footprint component for five middle-income countries whose energy economies are dominated by fossil fuels, which accounts for over 50 per cent of South Africa’s footprint (WWF and AfDB, 2012). According to Greenhouse Gas Inventory South Africa, which was compiled under the United Nations Framework Convention on Climate Change (UNFCCC) in May 2009, the country’s emissions increased from 347 metric tons CO
2 equivalent (Mt CO
2e) in 1990 to 437 Mt
CO2e in 2000 (DEA, 2011).
9
3.1 Green economy – Origins and definitions
More than twenty years since the term “green economy” appeared in the “Blueprint for a Green Economy” (Pearce et al., 1989), interest in a green transition has evolved and intensified. As a result of the global market and financial crisis in 2008, calls were made in the global policy arena for a Global Green New Deal (GGND). This was the focus of a report commissioned by UNEP in 2009 (Barbier, 2010). The implementation of green economic action was described as a long-term strategy for moving national economies out of the crisis. The GGND set out three concrete objectives:
• Economic recovery; • Poverty reduction; and • Reduced carbon emissions and ecosystem
degradation.
The document proposed a framework for green stimulus programmes as well as supportive domestic and international policies, including support to least developed countries.
Following the GGND study, the Green Economy Report (GER) was published by UNEP in 2011. This document develops the concept of green economy, analyses its key sectors and identifies global as well as sectoral recommendations for action. At the visionary level, UNEP (2011) considers a green economy as “an economy that results in improved human well-being and social equity, while significantly reducing environmental risks and ecological scarcities”.
At the operational level, a green economy is seen as one whose growth in income and employment is driven by investments that:
• reduce carbon emissions and pollution;• enhance energy and resource efficiency; and• prevent the loss of biodiversity and ecosystem
services.
These include investments in human and social capital, recognizing the central position of human well-being and social equity as core goals promoted by income growth and increasing employment. This transition is guided by the economic analysis of current trends, risks and opportunities as well as by national experiences in applying more integrated policy tools effectively.
3.2 Key green economy interventions
A green economy aims to facilitate resilient, equitable and pro-employment development following a development path that reduces carbon dependency, promotes resource and energy efficiency and lessens environmental degradation. According to UNEP (2011), the priority areas for green policy-making could include:
• Addressing environmental externalities and existing market failures, where the production or consumption of goods and services has negative effects on third parties and the environment whereby the cost is not fully reflected in market prices;
• Limiting government spending in areas that deplete natural capital, such as subsidies that stimulate unsustainable production, resulting in the depletion of natural resource stocks and overexploitation;
• Promoting investment and spending in areas that stimulate a green economy, such as (a) innovation in new technologies and behaviours that are vital to green markets; (b) expansion of infrastructure that is required for certain green innovations to flourish; and (c) fostering infant green industries;
3 Transformation to a green economy
10
• Establishing a sound regulatory framework to channel investments into environmentally and socially valuable activities. A well-designed regulatory framework provides the legal basis on which government authorities can rely to monitor and enforce compliance, reduce regulatory and business risks and increase the confidence of investors and markets; and• Strengthening international frameworks that regulate
economic activity, including the international trading system, in driving a green economy.
Enabling conditions include the establishment of sound regulatory frameworks that would create rights and incentives that drive green economic activity, remove barriers to green investments, and regulate the most harmful forms of unsustainable behaviour, either by creating minimum standards, or by prohibiting certain activities entirely.
Furthermore, improved international governance is also a critical enabling condition. This can be enhanced through:
• Multilateral environmental agreements: regulating unsustainable economic activity with standards or prohibitions;
• International trade law enabling the flow of green goods, technologies and investments; and
• International investment frameworks: promoting sustainable over unsustainable investments.
Finally, a green economy greatly emphasises education and training as a means to meet the needs of a greener and more sustainable future, as providing relevant education and training opportunities for a new, green workforce will be critical to achieving a successful transition.
3.3 Understanding green economy in the South African context
According to the May 2010 Summit Report (DEA, 2010), South Africa views a green economy as a sustainable development path that is based on addressing the interdependence between economic growth, social protection and natural ecosystems. This view is in line with the country’s vision on sustainable development, which states that:
South Africa aspires to be a sustainable, economically prosperous and self-reliant nation-state that safeguards its democracy by meeting the fundamental human needs of its people, by managing its limited ecological resources responsibly for current and future generations, and by advancing efficient and effective integrated planning and governance through national, regional and global collaboration.
Table 1. Focus sectors prioritised by workshop participants
Priority Group
1 2 3
1 NRM* Energy Waste
2 Energy Transport Energy
3 Transport Agriculture Construction
4 Agriculture NRM* Transport
* Natural Resource Management refers to soil, water, ecosystems, etc., as well as extracted non-renewable (mineral and energy) resources.
National Development Plan, Vision 2030
National GreenEconomy Summitand programmes
reports
New Growth Path,Green Economy
Accord & Green JobsReport
Industrial PolicyAction Plan
National Strategy forSustainable
Development andAction Plan (NSSD1)
National ClimateChange Response
Policy
National Water Resource Strategy
Agriculture and Rural Development
Integrated ResourcePlan & Integrated
Energy Plan
Environmental FiscalInstruments (e.g.,
carbon tax, green fund)
National SkillsDevelopment
Strategy 3
2009-2014 Medium Term Strategic
Famework and 12Outcomes
National SkillsDevelopment
Strategy 3
2009 South African Framework for Responding to Economic Crisis
Transport and Human Settlement
10-Year Innovation and Global Research Plan
Figure 1. Policy documents linked to green economy
National GreenEconomy Summitand Programmes’
Reports
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The 2011 Cabinet endorsed the National Strategy for Sustainable Development and Action Plan, which identified five strategic priorities:
1. Enhancing systems for integrated planning and implementation;
2. Sustaining our ecosystems and using natural resources efficiently;
3. Towards a green economy;4. Building sustainable communities; and5. Responding effectively to climate change.
The transition to a green economy in South Africa is linked to many policies, strategies and plans (see Figure 1).
3.4 Sector priorities
For the purpose of the green economy modelling for South Africa, an inception meeting between DEA and UNEP was held at the DEA offices in September 2011 in order to discuss, among others, the sectors and key issues to be included in the customization and modelling. The four sectors for the modelling were chosen from the broader nationally prioritised nine green economy programmes:
1. Resource conservation and management; 2. Sustainable waste management practices; 3. Water management; 4. Environmental sustainability: greening & legacy;
major events & tourism, research, skills, financing and investments;
5. Green buildings and the built environment;6. Sustainable transport and infrastructure; 7. Clean energy and energy efficiency; 8. Agriculture, food production and forestry; and9. Sustainable consumption and production
Other issues that were discussed were the data requirements for the green economy modelling process and how the various stakeholders would provide support in data collection.
In order to refine and prioritise the modules for SAGEM, there was a need to interface with the policy-makers and stakeholders in other government departments, research institutions, civil society, and non-governmental institutions. This was achieved through a Green Economy Technical Workshop, which was organized by DEA, in partnership with UNEP, and facilitated by the Millennium Institute and the Sustainability Institute. Held on 1 February 2012 in Pretoria, the primary purposes of the workshop were to:
• Prioritise the areas of focus for SAGEM based on the eight prioritised sectors;
• Identify the South African specific targets for the green economy for the prioritised areas; and
• Identify the possible scenarios for consideration in the modelling.
The participants were organized in three groups to address the above objectives and reported their discussion in a feedback session.
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Sector Medium-term (2020) Long-term (2030)
Employment • 5 million additional jobs by 2020, with a total workforce of 18 million
• Direct green economy-related: 300 000 additional jobs, of which 80 000 in manufacturing and the rest in construction, operations and maintenance
• In the agricultural sector: 300 000 households in smallholder schemes by 2020; 145 000 jobs in agro-processing 2020; upgraded employment on commercial farms (currently total of around 660 000)
• Increase employment from 13 million in 2010 to 19 million in 2020 and to a total workforce of 24 million by 2030
• Direct green economy-related: Over 400 000 jobs
• Agriculture (rural): 643 000 direct and 326 000 indirect jobs
Education • 1 million students in FET colleges by 2014; from the current around half-a-million in 2010
• 30 000 additional engineers by 2014• 50 000 additional artisans by 2015
• Increase enrolment at university by at least 70 per cent by 2030, reaching a total enrolment of 1.62 million, as compared to 950 000 in 2010
• Produce 30 000 artisans per year
Economic growth • Growth rate of GDP should rise to between 4 and 7 per cent a year
• GDP should increase by 2.7 times in real terms, requiring average annual GDP growth of 5.4 per cent over the period. GDP per capita should increase from about ZAR 50 000 per person in 2010 to ZAR 110 000 per person in 2030, in constant prices.
Trade None stipulated • Export (in volume terms) grow by 6 per cent per year up to 2030 with non-traditional export growing by 10 per cent
Carbon emissions None stipulated • Peak GHG emissions reached by 2025
Energy mix • 33 per cent of new generation from renewables and 25 per cent from Nuclear; currently at around 0 per cent and 4 per cent respectively
• 20 000 MW of the total new generation (40 000 MW) from renewables
– energy demand • 1 million solar water heaters by 2014 • 5 million solar water heaters by 2030
– energy access None stipulated • 90 per cent access to grid electricity, with the remainder meeting their energy needs from off-grid sources.
Transport None stipulated • Public transport will be user-friendly, less environmentally damaging, cheaper and integrated or seamless
Water access None stipulated • 100 per cent access to clean water• Water demand in urban areas, 15 per cent
less than BAU
Table 2. Medium- and long-term targets
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Given the time constraints and the availability of data for the modelling process, the participants agreed to focus the SAGEM on four areas (see Table 1).
The criteria used by the participants to prioritise the key areas mainly revolved around aspects of employment and localisation, addressing inclusivity and inequality, attracting funding and improving competitiveness as well as the availability of data to support the analysis of the respective sectors. Energy and transport featured in all the top four priority lists of the groups; natural resource management and agriculture featured in two groups. Thus, the four areas that were finally selected were: natural resource management (NRM), agriculture, transport and energy.
With respect to prioritising the targets, the participants generally felt that it was challenging to quantify the targets, particularly for non-experts. It was therefore decided that the medium- to 2020 and long-term to 2030 targets should be defined based on the New Growth Path and the National Development Plan (NDP). These are summarized in Table 2.
A number of stakeholders suggested that more aggressive targets by 2030 should be used, if green economy is to be realised. Some examples given include: (i) universal access to 12 years of education; (ii) a cap at 400 million tonnes of carbon per annum; (iii) 50 per cent of energy derived from renewable resources; (iv) more than 80 per cent of cargo transported by rail; (v) more
than 15 per cent of the government’s vehicle fleet should be electric cars; and (vi) more than 25 per cent improvement in energy intensity. Sensitivity analysis for some of the suggested targets was therefore taken into account, which is discussed in Section 5.
3.5 Intervention priorities
In terms of defining appropriate scenarios, there was limited time for the participants to explore all possible scenarios. There were, however, some indications of the scenarios to be investigated in the modelling effort, including, among others:
i) South African economy grows between 4 and 7 per cent on average per year by 2030;
ii) A global ‘double-dip’ recession, as experienced in 2008, between 2012 and 2017;
iii) Global warming of between 4° and 6ºC; and iv) Depletion of natural resources, specifically a ‘peak
oil’ and ‘peak coal’ (for South Africa) by 2020.
Only two of the suggested scenarios above were taken into account in the modelling process. The first one is the global double dip scenario (ii), which was achieved through an exogenously determined effect of economic crisis on GDP. The depletion of coal reserves was incorporated into the modelling in order to account for (iv) as suggested by stakeholders.
Based on the outcomes of the stakeholder engagement workshop, it was clear that a green economy in the South African context refers to two inter-linked developmental outcomes for the South African economy, as stipulated in the Summit Report:
• Growing economic activity (which leads to investment, jobs and competitiveness) in the green industry sector; and
• A shift in the economy as a whole towards cleaner industries and sectors with a low environmental impact compared to its socio-economic impact.
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4.1 IntroductionDevelopment programs for national governments, particularly emerging and developing countries, primarily consist of large-scale public investments in infrastructure. In most cases, a standard cost-benefit analysis is used to assess the suitability of such investments. However, effects on the local society and environment are often not taken into account (Bassi, 2009).
To account for such effects, computer models have been used to perform integrated analysis of socio-economic-environmental systems.
4.2 Overview of modelling approaches
According to UNEP (2011), over the last 40 years, a variety of applied models and modelling methods have been developed to support national planning. Among those tools the most commonly used today include the following: Disaggregated Consistency (DC), Computable General Equilibrium (CGE), Macro-econometric (ME) and System Dynamics (SD) models. These models have proven useful in different degrees for various kinds of policy analyses, especially for mid-/short-term financial planning.
More specifically, CGE models are based on a matrix of flows concept, where actors in the economy interact according to a specified set of rules and under predetermined equilibrium conditions (Robinson et al.,1999). Initially conceived to analyse the economic impact of alternative public policies, e.g., those that work through price mechanism, such as taxes, subsidies, and tariffs, recent CGE models include social indicators (Bussolo and Medvedev, 2007) and environmental ones (OECD, 2008).
On the other hand, ME models are developed as combinations of macroeconomic identities and behavioural equations, estimated with econometric methods (Fair, 1993), which are largely used by national and international financial organisations to support short- and mid-term macroeconomic policy analysis, such as general fiscal and monetary policies.
DC models consist of a combination of spreadsheets representing the fundamental national macroeconomic accounts, and enforcing consistency among them. Well-known examples of this category of models include the World Bank’s RMSM-X (Evaert et al., 1990) and the International Monetary Fund’s FPF (Haque et al., 1990)that are mostly used to analyse the macroeconomic impact of adjustment programmes.
The three modelling methods described focus primarily on economic aspects and in general are not designed to support integrated, long-term planning exercises. However, recent global developments have stressed the importance of jointly addressing the economic, social, and environmental dimensions of development.
As a technique to analyse a variety of development issues (Saeed, 1998), including national policy analysis (Pedercini and Barney, 2010), the methodology of systems dynamics (SD), conceived in the late 1950s at the Massachusetts Institute of Technology (MIT), has greatly evolved over the last 60 years (see Forrester, 1961 and Forrester, 2007 for early and current examples on the use of this methodology). Specifically, the SD method has been adopted in various instances to analyse the relationship between structure and behaviour of complex, dynamic systems, where causal relationships are analysed, verified and formalised into models of differential equations (see Barlas, 1996), and their behaviour is simulated. The method uses a stock and flow representation of systems and is well-suited to jointly represent the economic, social and environmental aspects of the development process.
4 Modelling a green economy for South Africa
15
4.3 Characteristics of T21
Threshold 21 (T21) is a system dynamics national model that is specifically designed for planning and policy-making support (Bassi, 2009). It integrates economic, social, and environmental structures into one specialized tool. Thus, the model is organized into three macro sectors: society, environment and the economy.3 The sectors interact with each other as shown in Figure 2. Each application of the model is built around a core model – a set of main structures representing features of the socioeconomic-environmental systems that can be generalized across many countries – to which additional modules can be added to better portray specific aspects of the country being analysed. The characteristics of T21, as outlined in Perdercini (undated; 2009), are explained in Sections 4.3.1 to 4.3.11 below.
4.3.1 Transparency
Transparency indicates the degree of clarity and explicitness of a model’s structure and the related assumptions, which are fundamentally important for allowing civil society and the private sector to participate effectively in the analysis.
In T21, all variables constituting the structure of the model are accessible and generally well-documented. All the relationships between variables are evident since the structures of the different sectors are represented in stock and flow diagrams. Furthermore, the assumptions underlying the different modules are clearly stated, with the aim of stimulating the users’ confidence in the model as much as possible.
4.3.2 Comprehensiveness
Comprehensiveness refers to a model’s ability to represent the socioeconomic-environmental system of a region as a whole, by integrating in a single framework all the relevant aspects of development planning.
The T21 model was conceived explicitly to represent the social, economic and environmental system of a country as a whole. This goal has been achieved by utilising the complex structure that considers that any kind of human activity influences and is interdependent with the environment. Such perception, in turn, affects the economy and society in general. To have a rise in economic development, a country needs, for instance, an efficient workforce, which will need clean air and water to be healthy and productive. Conversely,
Figure 2. Structure of T21
16
intensive economic activities can pollute the environment, lowering the potential for further development.
4.3.3 Policymaking guidance
Policymaking guidance refers to a model’s usefulness for guiding policymaking; that is, the extent to which it provides relevant cross-sector policy insights to support long-term development planning.
The T21 model can be seen as an organic whole of many parts, in which every element influences and is influenced by the others, in a close network of feedback relationships. This kind of structure implies that every action taken locally in one sector will somehow influence the behaviour of other sectors, with a certain delay. Whilst testing a policy based on T21, the user is able to observe both its direct effects on the target sector and its indirect effects on the rest of the system in the long-term. The Causal Tracing feature offered by the system dynamics software enables the users to determine which components of a specific development plan have caused a particular change in one variable or indicator. This allows identification of leverage points of the system, which is a necessary condition for designing effective policies.
4.3.4 Strategy comparison
Strategy comparison refers to the extent to which a model allows for an easy comparison of alternative development strategies and supports the assessment of the feasibility of implementing strategies, both fiscally and institutionally.
T21 allows for the testing of different policies in different sectors and can estimate the costs and benefits they generate, both locally and within the system as a whole. Alternative development plans can be compared on the basis of their contribution to the achievement of different targets. Given the economic, social, and environmental resources available, the feasibility of implementing the potential policies in the short-, medium-, and long-term can be assessed.
4.3.5 Long-term perspective
A long-term perspective indicates the extent to which a model enables both short-term analyses (one to three
years) and long-term perspectives (up to 30 years) for national policy-making.
T21 allows to focus on short-, medium-, or long-term analysis by modifying the time horizon of the simulation. Past development of key variables that led to the current situation can be simulated, as well as the potential effect of any policy on future developments.
4.3.6 User-friendliness
User-friendliness refers mainly to the ability of a model’s interface to give the users an access to the main functions while using simple commands, to clearly display the output, and to help operators to find the information they need.
T21 model can be formatted in a simple and well-structured environment as well as organised in different windows that can be easily navigated using simplified command buttons on every page.
After the simulation has been run, the results can be reported in different forms, depending on the user’s needs. Generally, data relative to the national accounting system are presented in tables and the behaviour of most of the other indicators is reported on time graphs, which facilitates readability and comparison.
The Causal Tracing function can also be used to navigate the network of causal relationships between variables and to identify the effects of each policy on the system. The results of different simulation runs can be compared as this functionality is available in the system dynamics software.
4.3.7 Environmental analysis
Environmental analysis is the extent to which a model can calculate various environmental indicators for all scenarios and its usefulness for performing environmental impact assessments.
The environmental macro sector of T21 describes the mechanisms of pollution accumulation, the consumption of non-renewable resources and land use. Other sectors can be added to the core to better portray the specific situation of a country, including modules for analysing the dynamics of fossil fuel consumption, air pollution,
17
water pollution and GHGs. The resulting model structure considers the effects of human activities on the environment, and produces a wide range of indicators that are useful in evaluating the environmental effects of different policies.
4.3.8 Partnership building
Partnership building indicates the extent to which a model is able to produce structured outputs in standard formats that can help coordinate the participation of development partners.
T21 produces all the outputs required for the United Nations Development Assistance Framework, including indicators for environment (e.g., emissions) and society (e.g., education and poverty), for every year in the simulated future and for every scenario that is defined by the users.
Such features make T21 the ideal tool to foster the participation and collaboration of International Financial Institutions and other partners in the development plans of any country.
4.3.9 National development indicators
National development indicators refer to the modelling ability to estimate and display national development indicators for each scenario projected. The T21 model produces a wide range of aggregated indicators that
allow for the monitoring of the development process of the country.
4.3.10 Continuous time-series output
Continuous time-series output refers to the ability of the modelling to produce continuous time-series outputs for major variables, which are essential for the effective monitoring and evaluation of development programs.
To monitor and evaluate the state of the system in any moment of the simulation, T21 can produce time-series outputs for all variables of the model that users wish to control. The frequency at which each calculation is made and each value is reported can also be set to suit the specific demands and needs of a country.
4.3.11 Applications
Currently, more than 30 customized T21 models have been created and applied, both in developed and developing countries. The large size of the model implies a significant effort to customize the model for a specific country, particularly if, as suggested, local experts and policymakers are involved in the process. Once the structure is defined, the user needs to provide the necessary data inputs for the model. The additional modules can then be developed separately by different groups of experts, thus reducing the time required for customization.
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5.1 IntroductionT21 takes into account the stocks and flows of natural resources, which are the key drivers of a green economy, as well as the stocks and flows of capital and labour that are important in any long-term economic model. Stocks are accumulations of inflows and outflows (e.g., an area infested with invasive alien species is an accumulation of restoration and infestation). In a green economy, capital and labour are needed to develop and process natural resource stocks.
Three key factors transform natural resources into added economic value:
1. the availability of capital (which accumulates through investments and declines with depreciation);
2. labour (which follows the South African demographic evolution, especially the age structure, and labour force participation rates); and
3. stocks of natural resources (which accumulate with natural growth – when renewable – and decline with harvest or extraction).
Some of the examples of a direct impact of natural resources on GDP are the availability of water in the production sectors (e.g., agriculture, industry and services) and the availability of fossil fuels to power the capital needed for the operations of these sectors. In this respect, T21 accounts for both monetary and physical variables representing each sector in a coherent and consistent manner. Water stress and energy prices, which are endogenously determined, are some of the factors that also affect GDP (see Figure 3).
The analysis that was carried out in this study focused on the transition towards a green economy in South Africa, which is characterised by high resource-efficiency and low-carbon intensity. It assessed the needs for a short- to medium-term transition and evaluated the impacts of a longer-term green economic development. An emphasis is therefore naturally put on stocks because they define the state of the system, as highlighted by projections of many key indicators for sustainability, which are categorised according to economic, social and environmental indicators. In concrete terms, the study aims at analysing the dynamic complexity of the social, economic, and environmental
characteristics of the South African context with the goal of evaluating whether green investments can create synergies and help move the country toward green economy goals: resilient economic growth, job creation, low-carbon development and resource efficiency.
Longer-term sustainable growth is related to the sustainable management of natural resources, such as water, land and fossil fuels. Increasing the efficiency of use and curbing waste of such resources would reduce the decline of stocks, or even support their growth in certain cases. In this respect, understanding
Fossil fuels,water, forest
Resource efficency
Demand ofnatural resources
GDP
Figure 3. The relationship between economic growth and natural resources
Supply ofnatural resources
5 Applying T21 to the South African Green Economy Modelling (SAGEM)
19
the relationship between stocks and flows is crucial (e.g., the concentration of emissions in the atmosphere may keep increasing, even if yearly emissions are kept constant or decline; or carbon concentrations will decline only if yearly emissions are below the natural sequestration capacities of forests and land).
The economic growth of recent decades, which profited from the contribution of natural resources, did not allow stocks to regenerate (as has been illustrated by the Millennium Ecosystem Assessment). For instance, according to EIA, oil production has reached its peak and is declining in most countries (EIA, 2009), and global peak oil, although much debated, is expected to take place within the next two or three decades (ASPO-USA, 2010; IEA, 2009). In addition, water is becoming scarce and water stress is projected to increase. Water supply would be satisfying only 60 per cent of world demand in 20 years, in a BAU scenario (McKinsey, 2009), which in turn will make significant changes in consumption (and demand) patterns necessary.
Agriculture has seen increasing yields, primarily owing to the use of chemical fertilisers (FAO, 2009), which reduced soil quality (Muller and Davis, 2009) by almost 10 per cent relative to the 1970 level. Agricultural intensification did not curb the growing trend of deforestation, which remains at 13 million hectares per year during the 1990-2005 period (FAO, 2009).
There has been a long-standing perception among both the general public and policymakers that the goals of economic growth, environmental protection and national energy security involve a complex set of trade-offs (Brown and Huntington, 2008; CNA 2007; Howarth and Monahan, 1996). By adopting an integrated approach focused on the interaction of stocks and flows across sectors, the South African green economy modelling process examined the hypothesis that a correct management of natural resources does not necessarily imply accepting lower economic growth going forward. Instead, it explored the question of
whether equal or higher growth could be attained with a more sustainable, equitable and resilient economy, where natural resources would be preserved through more efficient use. This initial framework is in contrast with a variety of sectoral reports focused on energy and climate change mitigation scenarios. By way of contrast, a green economy approach supports both growth and low-carbon development by reducing emissions and conserving stocks in the short-term. As such, in the future, the country could profit from their improved state.
5.2 Developing a green economy for South Africa
Integrating the concept of a green economy into a formal model calls for a mathematical model that integrates economic and physical dimensions of the social, economic and environmental systems being analysed. This is due to the cross sectoral and dynamic nature of the green economy concept.
Within this context, SAGEM was developed to explore the transition to a green economy for South Africa, with special attention to its ability to meet low carbon growth, resource efficiency and pro-job development targets.
Accelerating environmental, economic, social and technological change challenges the decision and policymakers to learn at a faster rate, while at the same time the complexity of the environment in which we live continues to increase (Sterman, 2000). Many of the challenges that humanity faces today are related to unforeseen side effects arising from actions in the past. Effective development planning thus requires extending the current discourse of analysis by incorporating the dynamic complexity of the systems being studied and their interrelationships with other sub-systems.
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This is the approach that was undertaken in the development of SAGEM, based on the T21 framework.
5.2.1 Boundary
The key variables that were considered essential in catalyzing the green economy agenda in South Africa were calculated endogenously in the model. These include, among others, the variables of the prioritized areas, that is, natural resource management, agriculture, energy and transport.
5.2.2 Time horizon
The South African green economy modelling (SAGEM) was developed to evaluate the impact of green economy investment on medium- to long-term environmental, economic and social development issues. Given the data availability at a national scale, the time horizon for modelling begins in 2001 and extends to 2030, which is in line with the current NDP time horizon. The simulation could also be easily extended further in the future if need be. The historical trends from 2001 to 2010 were utilised to ensure that the modelling replicates the characteristics of the behaviour of the issues investigated in SAGEM.
5.2.3 Scope
SAGEM represents the South African national environmental, social and economic spheres without disaggregation at provincial or cities level.
5.2.4 Modules
In a broad sense, SAGEM was divided into fourteen sectors (see Table 3) and 31 modules (see Table 4).
The sub-sectors represent the economy, society and environment interactions implicated by the green economy investment that was investigated for the South African context in the identified sectors: natural resource and management, agriculture, energy and transport.
A brief description of each of the sectors, sub-sectors and their respective modules for SAGEM is presented inthe next section.
5.2.5 Data collection
Based on the outcome of the workshop, the Sustainability Institute and Millennium Institute collected data in consultation with, and assistance from, the Department of Environmental Affairs, National Treasury, Department of Trade and Industry, and Development Bank of Southern Africa, among others. The data that were collected covered the various sectors and were obtained from various sources (see Table 5).
In utilizing the data, the approach was to first use nationally available data, or to use expert-based documents in South Africa. In cases where these forms of information were not available, the internationally best available data, such as the World Development Indicators and data of the International Energy Agency, were utilised. Where data were entirely unavailable, assumptions were made based on the experience of the Millennium Institute pertaining to the GER; these assumptions are discussed in greater detail in Section 6.
The availability of data only allowed the simulation to start in 2001, and the modelling period was set to 2030.
Table 3. Green economy spheres and sectorsSpheres
Environment Society Economy
Sectors • Natural Resource Management
• Land• Water• Energy• Emissions• Minerals
• Population• Education• Health• Employment• Public infrastructure
• Production• Investment and
households• Government
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Environment sphere Society sphere Economy sphere
Natural Resource Management sector1. Water quantity provision with WfW2. Potential electricity generation from invasive species
Land sector3. Land
Water sector4. Water demand and supply5. Water requirements in electricity generation
Energy sector• Energy production
6. Electricity supply – coal7. Electricity supply – nuclear8. Electricity supply – hydro9. Electricity supply- pumped storage10. Electricity supply – solar11. Electricity supply – wind12. Electricity technology generation share13. Electricity prices• Energy demand
14. Electricity demand 15. Oil demand16. Gas demand
Emissions17. Emissions from different sectors- Power sector- Industry- Transport- Agriculture
Minerals18. Mining- Coal- Gold and uranium- PGM- Other
Population sector19. Population
Education sector20. Education
Health sector21. Access to basic health
Employment sector22. Employment in different sectors- Industry employment- Agriculture employment- Services employment- Employment from NRM (invasive alien)23. Power sector employment
Public infrastructure sector24. Transport25. Access to roads
Production sector26. Agriculture27. Industry28. Services29. GDP
Households and investment sector30a) Household accounts30b) Banks
Government sector31. Government accounts
Table 4. Modules of SAGEM
22
5.3 Environment sphere
The environmental sphere of the SAGEM consists of six sectors and 18 modules (see Table 4) described below.
5.3.1 Natural resource management
The natural resource management represents the environmental and biodiversity protection programmes, with a specific focus on the WfW programme. The sector is classified into two modules: the first module calculates water quantity provision with the WfW programme4 (see Annex 2.1), while the second one estimates the potential electricity generation from invasive species (see Annex 2.2). WfW is one of the country’s strategic programmes aimed at reducing the area of land under invasive alien species, which is essential in maintaining the ecological integrity in terms of restoring the water flows. In addition, the programme has plans for value-added activities from invasive alien biomass, in particular electricity generation, which is the key value-added activity that was investigated in this module.
The variables in blue font are those that were extracted from other modules and used in the current modules.5 The variables in red font are those that were determined in the current module and used in other modules.6
5.3.2 Land
The land module represents land use in South Africa and includes forest land, cropland, agricultural land, conservation land, land covered by invasive alien species. and other types of land (see Annex 2.3), and considers the land use changes for the different land types within the constraint of land availability.
5.3.3 Water
The water sector consists of two modules: water supply (see Annex 2.4) and water demand (see Annex 2.5). The water supply module represents the yearly water supply from renewable resources. It is utilised to estimate the water stress index, which influences the production sectors. Water demand from production sectors, as well as domestic and municipal demand,
23
is also estimated in this module. In the case of water demand, specific attention is given to the water requirement for electricity generation, including coal, wind, solar, nuclear and biomass energy.
5.3.4 Energy
The energy sector is categorised into energy production and energy demand.7 Energy production consists of electricity supply (which is further categorised into coal, nuclear, wind, pumped storage, hydro and solar), electricity generation share and electricity prices. The electricity supply from coal (see Annex 2.6) represents the capacity of a coal electricity plant, the amount generated given the capacity factor and the potential for coal capacity reduction within given energy-efficiency measures. It was assumed that the required electricity generation from coal is the difference between the total electricity demand and the total electricity generation from other electricity technologies.
In a similar manner, other electricity supply modules, mainly nuclear, hydro, pumped storage, solar and wind (see Annex 2.7 to 2.11), represent the electricity generation from electricity technologies other than coal. Electricity production is influenced by investments (installed capital capacity). Electricity production is computed taking into account the demand and production capacities. Gross electricity demand is calculated by the sum of retail sales and distribution, distribution and transmission losses as well as electricity net exports. Subtracting the gross electricity demand from the electricity generation from renewables, nuclear, hydro and pumped storage yields the coal electricity demand for electricity production.
The technology share module estimates the proportion in which each electricity generation technology contributes to the total electricity supply (see Annex 2.12). In the case of electricity prices, this module (see Annex 2.13) describes the electricity prices, which are taken as exogenous. This assumption is reasonable for South Africa because the electricity prices are regulated by NERSA and projected exogenously based on assumptions for NERSA’s determination on different electricity growth after 2013. They are considered as: (i) BAU – 10 per cent; (ii) average growth – 5 per cent; and (iii) slow growth – 2.5 per cent. Relative electricity
prices have a major influence on production sectors, which in turn influence GDP and investments.
Energy demand, on the other hand, consists of electricity demand (see Annex 2.14), oil demand (see Annex 2.15) and gas demand (see Annex 2.16), representing the drivers of energy demand in the medium- and long-term. Electricity demand estimates future electricity dynamics by different electricity users, and is driven by GDP, population and electricity prices. Oil demand is influenced by an exogenously determined oil price since South Africa imports approximately 64 per cent of its oil consumption requirements.8 At present, gas production and consumption still plays an insignificant role in the South African energy market. The gas demand module is therefore assumed to be influenced only by GDP.
5.3.5 Emissions
The air emission module estimates CO2 emissions from
the different sectors (see Annex 2.17) that comprise industry – which is categorised as electricity and non-electricity industry – as well as transport, agriculture, residential and services CO
2 emissions. The annual CO
2
emission is endogenously determined in the modelling.
5.3.6 Minerals
The mineral module represents the main mining activities, mainly coal, gold and PGM, and tracks both proven and unproven mineral reserves. The module further calculates the corresponding energy demand and employment generated from these sectors (see Annex 2.18 and 2.19).
5.4 Society sphere
The societal sphere of the SAGEM consists of five sectors and seven modules (see Table 4), which are discussed in the following sections.
5.4.1 Population
The population module represents the population of South Africa and was categorized according to sex (male and female) and age groups (see Annex 2.20). The module is generally used to dynamically estimate
24
the factors that influence population (fertility rate and birth rate) and the way in which population influences environmental, social and economic indicators in other modules such as water demand, energy demand and GDP. For example, a growing population results in an increase in domestic water demand, which in turn increases total water demand. With an increasing total demand, the water stress index subsequently increases, implying a reduction in the water reserve margin relative to the demand. Consequently, a rising water stress index negatively influences the production sectors (agriculture, industry and services), which in turn influences the size of the country’s GDP. Finally, GDP, in particular per capita income, has an influence on fertility rate and life expectancy, which in turn determines the level of population in the country (see Figure 4).
The main output of the sub-model is population, which was compared with the nationally available population data.
5.4.2 Education
The module (see Annex 2.21) represents the advancement of the population through the education system, from school age children (both primary and high school) to becoming a literate population. The module is categorised according to the South African educational system consisting of seven years in primary school and five years in high school. Government
expenditure in education and per capital income is assumed as the main influences of entrance to school.Generally, the module is utilised to estimate the access to education and the literacy rate. These, in turn, are utilised to estimate the broader socio-economic factors such as availability of labour, population and GDP.
5.4.3 Health
The health module (see Annex 2.22) aims to represent the access of basic health care based on government expenditure on health. While access to health has an influence on fertility and life expectancy, the sparse data for this sector did not allow for the modelling to be linked to other modules.
5.4.4 Public infrastructure (access to roads and transport)
This sector is subdivided into two categories: roads infrastructure and transport. In the case of the roads module (see Annex 2.23), the process of road construction is estimated as influenced by government expenditure on transport and communication as well as the unit cost of roads construction. The process is aimed at providing an estimation of access to roads, which has an influence on the production sectors.
The transport module (see Annex 2.24), on the other hand, estimates the volume of roads, air and rail
transport, which are categorised according to goods and passenger transport. The travel volumes are calculated by multiplying the initial value of 2001 to the effects of GDP and population. It is assumed that travel volumes increase as GDP and population increases. The associated emissions, employment and energy use from these transportation modes is also estimated. These are calculated based on the CO
2 emissions, employment
and energy consumption factors for each of the transport modes, which are exogenously determined.
Figure 4. Casual loop of effect of population on water demand
Water stressindex
Population
Per capita income
Water supply
Agriculture
Water demand
GDP
Fertility rateLife expectancy
Services Industry
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5.4.5 Employment
This represents employment created throughout all economic activities (see Annex 2.25). The accumulation of capital in the main production sectors (agriculture, industry and services) is considered important in driving the growth in employment. The estimation of employment was disaggregated from other sectors. For instance, employment from ecosystems restoration, for NRM, with a specific focus on the WfW programme; and employment from mining, transport and power sectors (see Annex 2.26). Employment in all the sectors tends to adjust over time to the demand, whilst recognising that employment cannot be more than the supply of labour force.
5.5 Economy sphere
The economic sphere of the SAGEM consists of three sectors and six modules (see Table 4), which are discussed in the following sections.
5.5.1 Production (agriculture, services and industry)
This sub-sector represents the agriculture, services, and industry sectors that are utilised to calculate the gross domestic income.
The agriculture module (see Annex 2.27) includes crop production and differentiates between production utilising conventional and organic fertilizer. The agricultural production is based on the Cobb-Douglas production function, where land, labour and capital are the main factors of production, and are influenced by water availability, electricity prices, literacy rate and access to roads. Growth in the agriculture production is dependent on these factors of production.
Being a production sector, agriculture has an influence on macroeconomic indicators related to a green economy, as illustrated in the causal loop diagram in Figure 5 above. An investment in ‘resource conservation’ and ‘agriculture capital’, will lead to an increase in ‘agricultural production’ with a consequent increase in GDP with opportunities for further investments.
Figure 5. Causal loop diagram – Effects of green economy investment on agricultural production
GDP
Organicfertilizer
Agricultureproduction
E�ective crop yield per ha
Natural crop yieldper ha
Agriculturecapital
Agriculture labour
Water stress
Fertilizer use
Investment
Waterdemand
Watere�ciency
Population
Harvestedarea
Resourceconservation
and restoration Invasive alien species
land
Chemicalfertilizer
26
Sphere Data source
Environment
NRM- water quantity provision with WfW Various documents from SA experts on Working for Water Programme
NRM – potential electricity generation
from invasive alien species
Various documents from SA experts on Working for Water Programme
Land STATS SA; World Bank Database (World Development Indicators); various documents on invasive alien land
Water (demand and supply) Water stress index
Water requirements in electricity
generation
DME / DoE Digest of Energy Statistics; Evans et al., 2009
Electricity supply- coal STATS SA; DME / DoE Digest of Energy Statistics
Electricity supply- nuclear STATS SA; DME / DoE Digest of Energy Statistics
Electricity supply- hydro STATS SA; DME / DoE Digest of Energy Statistics
Electricity supply- pumped storage STATS SA; DME / DoE Digest of Energy Statistics
Renewable energy – solar DME/DoE Digest of Energy Statistics; IRP 2010; SARi documents; Information on Engineering News
Renewable energy – wind DME / DoE Digest of Energy Statistics; IRP 2010; SARi documents; Information on Engineering News
Electricity technology generation share STATS SA; DME / DoE Digest of Energy Statistics; IRP 2010
Electricity prices DME / DoE Digest of Energy Statistics; NERSA
Electricity demand STATS SA; DME / DoE Digest of Energy Statistics; World Bank Database (World Development Indicators);
International Energy Agency
Oil demand DME / DoE Digest of Energy Statistics
Gas demand DME / DoE Digest of Energy Statistics
Air emissions International Energy Statistics; World Bank Database (World Development Indicators)
Mining STATS SA Minerals statistics; World Bank Database (World Development Indicators)
Society
Population STATS SA; World Bank Database (World Development Indicators)
Education STATS SA; World Bank Database (World Development Indicators)
Health (access to basic health) World Bank Database (World Development Indicators)
Roads (access to roads) Various South Africa documents; World Bank Database (World Development Indicators)
Employment STATS SA; World Bank Database (World Development Indicators); Green jobs report
Power employment SARi documents; International Energy Agency
Economy
Agriculture STATS SA; World Bank Database (World Development Indicators)
Industry STATS SA; World Bank Database (World Development Indicators)
Service STATS SA; World Bank Database (World Development Indicators)
GDP STATS SA; World Bank Database (World Development Indicators)
Investment and Household STATS SA; South African Reserve Bank
Government STATS SA; South African Reserve Bank
Transport DME / DoE Digest of Energy Statistics; World Bank Database (World Development Indicators)
Table 5. Data sources for SAGEM modules
27
These investments may be in ‘health and education’, which will increase the ‘population’ and ‘labour force’, which can then also boost ‘agricultural production’; or it can also increase ‘education levels’ that will improve ‘labour productivity’, as well as ‘agricultural production’. Another option is to channel investments into ‘pollution control’, which can improve ‘life expectancy’ and associate benefits to the rest of the economy, or directly improve ‘agricultural production’. Many other casual loops are possible for this and other sectors.
Similarly, the industry and services modules (see Annex 2.28 and 2.29) represent the industry and services production respectively, and employ the Cobb-Douglas production function. Their production is also influenced by water availability, electricity prices, access to roads and the education level.
The GDP module (see Annex 2.30) shows the accounting relationships in the calculation of the major income-related indicators. These include the real GDP, which is influenced by the production sectors and the per capita income among others. GDP is one of the main outputs of the economic sector and the simulation results were similarly compared with the historically available data.
5.5.2 Investment and households
This represents the accounts of how the flows from the various economic production sectors determine the investment in the country and the household income. The investment arises from both private and public sectors, which is then allocated to the various production sectors. On the other hand, the household income is divided into consumption and savings. The savings part eventually becomes part of the investment.
Annex 2.31 represents the accounts of how the flows from the various economic production sectors determine the investment in the country and the household income.
5.5.3 Government
The government module (see Annex 2.32) shows the various sources of government revenue including taxes, grants and interest, which are received both domestically and from abroad. The module also shows the government expenditure allocation to the various sectors.
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6 Scenario definitions and challenges
SAGEM has been used to simulate green investment scenarios that aim to promote economic growth, job creation and a transition towards low carbon development and resource efficiency in comparison to the baseline scenario. The development of the scenarios was guided by the outcomes of the discussions of the workshop (see Section 3.4), which suggested the need to identify the scenarios based on the existing targets in the different South African documents that are supporting the green economy.
A combination of three criteria was used in the identification and selection of green economy scenarios, as follows:
1. The available targets in the existing policy and strategy documents were used (see Figure 1).
2. In cases where the targets were not available, the planned expenditure for projects that are being evaluated, or are already planned, was used.
3. Where targets or expenditure were not available, speculative assumptions on specific interventions were used.
There was enough information regarding the targets and investment costs for the energy sector, and this was used in the modelling. For instance, the IRP 2010 provides electricity capacity targets up to 2030; the South Africa Renewables initiative (SARi) has documents estimating employment in different energy technologies; and the International Energy Agency (IEA) provides the capital and operational costs for the different energy technologies. This was the same data that was used in setting the target-specific green economy investment.
For the case of transport, the IEA carbon abatement costs for the whole sector were used due to the unavailability of cost multipliers. For the agriculture sector, speculative assumptions were used on an intervention where the agricultural arable land is converted to ecological agriculture, which uses organic
instead of chemical fertilizer. This target followed similar assumptions in the GER, which used estimations of cost interventions per hectare.
The NRM sector used estimates from the WfW programme regarding the costs of interventions. These include the costs of clearing the invasive alien species, establishing a biomass electricity generation plant, and the associated employment.
6.1 Scenario definitions
A particular interest in the analysis of the green economy for South Africa was the evaluation of the broader impacts of investments in the selected sectors and their respective indicators. It was therefore crucial to explore how such investments would be allocated in the South African context.
Two types of scenarios were thus developed for the SAGEM analysis (also refer to Box 2). The first one comprises the BAU scenarios, which assume a general continuation on the current investment in the economy in the areas of natural resource management, energy sectors, and agriculture and transport sector. These scenarios were defined as BAU and BAU2%. The BAU2% scenario allocates an additional 2 per cent of GDP investments to the business-as-usual activities.
The second type of scenario comprises the green economy scenarios, which assume an active government intervention in the identified four sectors in order to encourage shifts towards low carbon, resource-efficient and pro-employment development. Two green economy scenarios are defined as GETS and GE2%. These two green economy scenarios represent an additional investment of 2 per cent of the total real GDP, and a priority-driven investment respectively, in the four selected sectors.
29
The GE2% assumes that the investment is allocated equally across the four selected sectors. While a variety of additional investments equally distributed could be analysed,9 the GE2% was considered more coherent and relevant.
GETS was developed based on the target expenditure and goals for the different sectors of interest. In this scenario, all the options are weighted differently based on their investment prioritisation and targets in various policy and strategy documents, for instance, in the NRM sector. The targets for clearing invasive alien species in the WfW programme were used; for the case of energy sector, the IRP 2010 electricity capacity targets were used; and for agriculture, the targets for increasing land based on the NDP were used, but it was assumed that this land would be used for organic instead of chemical fertiliser. For the transport sector, the targets for improving efficiency were used. Thus, the proportion of green investment varied over time, and differed across the sectors, with the allocation driven on the prioritisation of targets (see Table 6).
The process of scenario analysis began by first simulating a base scenario (BAU) and a BAU2%. This was followed by green economy scenarios which explored the effects of allocating a proportion of GDP to green economy sectors and provided the outcomes of such interventions. In order to ensure consistency in the comparison of the results, GETS was compared with BAU while GE2% was compared with BAU2%.
A comparison of the baseline and the green economy scenarios for the four prioritised areas are shown in Table 6.
Based on the suggestions from the stakeholder workshop, some additional scenarios and sensitivity analysis were also carried out. These included the analysis of:
• Invasive alien species clearance impacts due to uncertain cost of clearing invasive species; and
• Employment impacts with 90 per cent of manufactured components imported in the renewable power sector.
6.2 Challenges
The modelling process does not address the sources of funding for a green economy, but assumes an allocation of 2 per cent of the GDP is made. While sources of funding, such as the reallocation of funds, may be available in the medium- to long-term, in the short-term, the government needs to embark on strategies to provide resources for the green economy.
The specific responsibilities of the different agents in transitioning the green economy were also not explicitly addressed. However, the key contribution of SAGEM is its dynamic nature, cross-sectoral analysis and endogenous feedback loops (Roberts et al., 1983) within the various spheres, sectors and modules.
SectorShare of green investment
GE2% (%)
Share of green investment
GETS (%)
Natural resource management 20 0.32
Agriculture 20 0.15
Transport 20 4.70
Energy 20 94.30
Industry resource efficiency 20 0.47
Share of GDP 2 0.70
Table 6. Allocation of green investment across sectors in a green economy
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Sector and objectiveBaseline scenarioBAU and BAU2%
Green economy scenario 2% (GE2%)
Green economy target specific scenario (GETS)
Natural resource management
Decrease the land cover infested with invasive alien species
Less aggressive investment in restoration of land under invasive alien species
An equal allocation of investment in the clearing of the invasive alien species
Target specific on investment requirement to clear the invasive alien species in the WfW programme
Agriculture
Increasing the yield and land under agricultural production
Extensive utilisation of chemical fertiliser
An equal allocation of investment to the use of organic fertiliser
Target specific to the amount of land using organic fertilisers. Assumes that the expansion of land as in the National Development Plan will use organic fertiliser
Transport
Improving energy efficiency in transport sector
No investment in energy efficiency
An equal allocation of investment to improving transport sector efficiency
An aggressive investment in transport expansion and energy efficiency in the sector. This was equivalent to 16 per cent of energy efficiency by 2030
Energy
Diversification of power energy mix
Investment in coal electricity, with minimal renewable energy. Investment in Kusile and Medupi included in the BAU. This also includes the committed renewable energy development (Wind, CSP and Solar PV)
Assumes equal allocation of investments to all the renewable energy specified in IRP 2010. This includes the new built plans to renewable energy development
This is a priority expansion of renewable power generation as specified in IRP 2010
Table 7. Comparison of scenarios for the selected sectors
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7 Results of the simulation analyses
7.1 Baseline scenario (BAU) results
The baseline scenario replicates the historical trend over the period 2001-2010 and assumes no fundamental changes in the policy or external conditions in the years leading up to 2030. This scenario was set up and calibrated to reflect the baseline projections for the various existing sectoral models presented in Table 3.
The real GDP10 is observed to grow over the simulation period (see Figure 6) due to the growth of the production sectors, namely: services, industry and agriculture. The result of the GDP simulation was compared with STATS SA data. For the past projections, the simulation results perform well compared with the STATS SA data, with an R-square of 94.8 per cent and an average point-to-point standard deviation of 0.21 per cent.
Between 2012 and 2030, the contribution for these production sectors to the GDP is expected to increase by 68 per cent for industry, 44 per cent for services and 14 per cent for agriculture. Overall, this represents an increase in real GDP of 50.2 per cent in 2030 relative to 2012.
The growth in the production sectors also corresponds to the employment opportunities that these sectors offer, with industry still providing much of the employment. BAU shows a 24 and 36 per cent increase in employment in the services and industry sectors, respectively. On the other hand, agriculture shows a drop in employment of about 14 per cent in 2030 relative to 2012.
Total employment covers all the sectors. This was disaggregated for the industry sector, which also provides employment in the mining and power sectors. Natural resource management is also contributing to employment, resulting from BAU allocation of some
amount in the WfW programme. When considering the employment creation of the specific sectors, the transport sector shows an increase in employment by 2.3 times in 2030 relative to 2012 (see Figure 7).
Similarly, the population grows from 51.7 million in 2012 to 61.4 million in 2030 (see Figure 8). Simulation from 2001 to 2010 matches the historical data from STATS SA, with an R-square of 97.9 per cent and an average deviation of 0.09 per cent. While births are projected as declining, life expectancy is increasing.
Energy demand, including electricity, oil and gas, are projected to grow due to population and GDP growth. In 2030, demand is projected to reach 121,643 thousand TJ for oil, 112,783 TJ for gas and 232,644 GWh for electricity. Coal-generated electricity remains relatively high, only declining from 91.7 per cent in 2012 to 83.6 per cent in 2030 due to the introduction of renewable energy that is already committed in the BAU scenario. It should, however, be noted that the demand for electricity is unmet for some years in this scenario.
ZAR(in billion)
BAU Data
Figure 6. Comparison of real GDP in BAU and data
3 500
3 000
2 500
2 000
1 500
1 000
2001 2005 2013 2021 202520172009 2029
32
In a similar manner, water demand is projected to increase due to population and GDP growth, where the demand reaches 13,955 billion litres, representing an 8 per cent increase relative to 2012. With South Africa facing water stress and receiving only an average of 500 mm rainfall per annum, an increase in the water demand obviously increases the water stress index. Therefore, there is a need for sustainable water management practices, in order to avoid compromising the already stressed water resources.
In terms of land use changes (see Figure 9), the cropland will increase to 16.5 million hectares by 2030, representing a 9 per cent growth relative to 2012. This expansion, although marginal, is due to the increasing food demand from the growing population.
The area infested by the invasive alien species is observed to increase by 28 per cent in 2030 relative to 2012. While the WfW programme is incorporated in the BAU, the rate of spread of invasive alien species is greater than the rate of restoration – hence the increase in the invasive alien species land. With similar programmes and targets in the BAU on the Working for Woodlands programme, forestland is observed to increase by 0.9 per cent per annum from 2012 to 2030. Similarly, settlement land also grows by 1 per cent on average, reaching 2.14 million hectares. On the other hand, the growth in livestock land, as well as area infested by invasive alien species, decreases by 2 per cent and 6 per cent respectively.
The simulation result of the annual CO2 emissions fits
well with the World Development Indicators data, with an R-square of 80 per cent and an average deviation of 0.09 per cent (see Figure 10). The annual CO
2 emissions
are observed to be relatively increasing, reaching 475 billion kilograms by 2030. This is a result of the increasing energy demand, population and GDP. The key sectors contributing to the increasing emissions are the energy, mainly due to power generation, and transport sectors (see Figure 11).
The power generation sector contribution to CO2
emissions is projected to reach 297 million tons. Initially, the emissions from the power generation are shown to increase due to the planned coal generation that was modelled in the BAU scenario. In addition, the renewable energy generation that is committed in the
Figure 7. Employment in the key sectors in BAU scenario
600 000
500 000
400 000
300 000
200 000
100 000
0
PersonNRM Power sector Transport Mining industry
Figure 8. Comparison of population in BAU and data
Person(in millions)
BAU Data
65
60
55
50
45
40
35
30
2005 2010 2015 2020 2025 2030
90
80
70
60
50
40
30
20
10
0
HA(in millions)
Crop Forest Invasive species Livestock
Figure 9. Selected land use changes in the BAU scenario
33
Ton(in millions)
BAU Data
550
500
450
400
350
300
250
200
Figure 10. Comparison of CO2 emissions in BAU and data
IRP 2010 was modelled as part of the BAU scenario, which explains why CO
2 emissions are growing at a
decreasing rate. On the other hand, the emissions increase in the transport sector follows a different trend, with a growth rate of 1.6 per cent per annum, between 2012 and 2030. The share of the transport sector in CO
2 emissions is thus projected to rise from 11.1
per cent in 2012 to 13 per cent in 2030.
7.2 Results of green economy scenarios (GE2% and GETS)
An additional investment of 2 per cent GDP in green economy has impacts on the environment, society and economy. A summary of the main impacts of green economy scenarios in relation to BAU and BAU2% are presented in Table 8.
The green investment scenarios show that promoting investment in the key areas of low carbon development and ecosystem services management provides a sustained growth in the longer term. At the same time, the green scenarios show a reduction in emissions, a reduced dependence on conventional fuels, in particular the power sector, and a sustained utilisation of natural resources.
The growth in real GDP in the green economy scenarios is projected to reach ZAR 2.867 billion in GE2% and ZAR 2.907 billion in GETS. These exceed the projections in the BAU scenarios, which were projected to be ZAR 2.850 billion in BAU and ZAR 2.879 billion in BAU2%. The lower GDP growth projected in the BAU and BAU2% scenarios relative to GETS and GE2% is attributed to strained, depleted or polluted natural resources. The green economy scenarios build up resilience in the environment, economy and society, which is partly observed in, for instance, improved water stress index, reduced reliance on coal power generation and increased ecosystem services as a result of the removal of invasive species (see Box 2).
The green economy scenarios also stimulate total employment (see Figure 12), where GE2% contributes more to the total employment relative to other scenarios, reaching 28.347 million persons in 2030. When considering the contribution of specific sectors to employment, the two green investment scenarios provide a different picture (Table 8).
350
300
250
200
150
100
50
0
Ton(in million)
Transport Power
Figure 11. BAU CO2 emissions in transport and power sectors
2005 2010 2015 2020 2025 2030
Figure 12. Total employment in BAU, BAU2%, GE and GE2% scenarios
Person(in millions)
30
25
20
15
10
5
0
BAU BAU2% GETS GE2%
2005 2010 2015 2020 2025 2030
34
% GETS GE2%
25
20
15
10
5
0
Figure 13. Potential job creation benefits with respect to a high import scenario in GE
In the BAU scenario, the jobs created in the power sector assume that 100 per cent of the manufacturing and assembling of renewable energy plants will take place in South Africa.
When accounting for the possibility of importing 90 per cent of the components, the potential domestic jobs avoided from undertaking a less optimistic approach is significant. With 100 per cent power components manufactured and assembled in the country, GETS potentially creates, on average, 12 per cent more jobs between 2012 and 2030 than a situation where 90 per cent of components are imported.
On the other hand, the GE2% scenario potentially creates, on average, 10.5 per cent more jobs between 2012 and 2030, with manufacturing and assembling of power sector components occurring in the country. In the case of the GETS scenario, there is a wide fluctuation in the short- and medium-term, which results from the differences in the prioritisation of renewable energy technology development (see Figure 13).
The GE2% scenario contribution to jobs created in NRM is higher than those generated in the GETS scenario (see Table 8). In addition, both the green economy scenarios yield higher job creation in NRM relative to BAU and BAU2%. The job creation in NRM is mainly from the restoration of the invasive alien species (see Table 8), employing 701,000 persons and 737,000 persons in GETS and GE2% respectively. Jobs are also generated in the development of electricity generation from biomass, but only play a minor role compared to the total potential of jobs creation in NRM.
The GETS and GE2% scenarios also contribute to the diversification of the energy mix in the power sector. According to IRP 2010, the target is to achieve 17.8 per cent of newly built electricity generation from renewables. In the GETS scenario, this is achieved by 2030 (see Table 8), as this share reaches 24.4 per cent. In the GE2%, renewable energy constitutes 16 per cent of total electricity generated.
On the other hand, BAU and BAU2% scenarios have a lower share of renewable energy, which is 8.2 per cent for both scenarios. Thus, the GE2% scenario results in almost double the share of renewable energy relative to the BAU and BAU2% scenarios (see Figure 14). Other documents, such as the NDP, have prescribed a higher target of 33 per cent of new generation from renewables. Such targets would require a more aggressive green economy investment intervention than GETS and GE2%.
Green economy investments provide an opportunity for achieving the target of diversifying the energy mix and job creation, as well as CO
2 emissions reduction. The
priority for the development of renewable energy in the GETS scenario leads to decreasing annual emissions, which will reach 415 million tons of CO
2 by 2030 (see
Figure 15). On the other hand, the GE2% scenario shows a lower reduction of emissions, with emissions projected to reach 461 million tons of CO
2 by 2030. The
Figure 14. Comparison of share of renewable energy in BAU, BAU2%, GE and GE2% scenarios
BAU BAU2% GETS GE2%%
2012 2020 2030
25
20
15
10
5
0
35
The approach used to simulate the green economy investment in South Africa consisted of an analysis of additional investment in green economy and BAU activities. In order to ensure consistency, the impact of allocating 2 per cent of GDP to BAU2% activities is compared with the 2 per cent additional investment in a green economy, all else being equal. The BAU2% scenario was developed to emphasise the outcomes of a green economy, as compared to a similar investment allocated to BAU activities.
The result shows that, using the same assumption for additional investment, BAU appears to yield similar results for economic growth and employment, but worse results concerning the environment.
Comparing the GDP for the two scenarios, it appears that investing in a green economy is not worthwhile in the short-term. However, in the medium- to long-term, the GE2% GDP rises towards 2020 and 2030, getting closer to the GDP projected in the BAU2% scenario. Moreover, the GE scenarios result in environmental benefits such as reduced CO
2 emissions. Air emissions for BAU2% reach 477 million tonnes of CO
2, while that of GE2% is 461 million tonnes.
Other environmental benefits in the GE2% scenario include a reduced water stress index, which reaches 2.82 per cent in 2030, compared to the BAU2% which is about 1.1 times more than GE2% scenario.
Furthermore, the GE2% scenario also creates slightly more employment relative to BAU2%, which in 2030 reaches 28.347 million persons, while the BAU2% creates employment for 27.853 million persons in 2030.
The share of renewable electricity generation in the GE2% scenario is about two times more than the BAU2% scenario. This is projected to reach 16 per cent in 2030 in the GE2% scenario, while the BAU2% scenario reaches 8.2 per cent.
Comparing the results of the BAU2% and GE2% scenarios, it is clear that across all sectors the BAU2% scenario yields fewer overall benefits than the GE2% scenario. Comparatively speaking, the GE2% scenario supports the environment more and creates jobs in sectors that support production (e.g., water).
A green economy is therefore projected to be able to deliver as much as a BAU case, but in a more sustainable manner. By considering extreme conditions in terms of the cost for clearing invasive species, it is observed that a relatively large area is restored in the GE2% scenario relative to the BAU2% scenario.
Sensitivity analysis for invasive alien species land with uncertain cost of clearing invasive species for GE2% and BAU2%
Sensitivity analysis for water savings from restoration with uncertain cost of clearing invasive species for GE2% and BAU2%
Box 2. Comparison of economic and environmental consequences of the BAU2% and GE2% scenarios
Million ton/year
5E+11
4.8E+11
4.6E+11
4.4E+11
4.2E+11
4E+11
3.8E+11
3.6E+11
BAU2% GE2%
2005 2010 2015 2020 2025 2030
Invasive alien species land 50% 75% 100%GE2%
95%Invasive alien species land
BAU2%50% 75% 100%95%
2001 2008 2016 2023 2030
1 M
0
2 M
3 M
4 M
2001 2008 2016 2023 2030
1.75 M
1 M
2.25 M
3.25 M
4 M
Time (year) Time (year)
Water gains from restoration 50% 75% 95% 100%
GE2%Water gains from restoration
BAU2%50% 75% 100%95%
2001 2008 2016 2023 2030
20 B
0
40 B
60 B
80 B
Time (year)
2001 2008 2016 2023 2030
100 B
0
200 B
300 B
400 B
Time (year)
36
Main indicators UNIT2012 2020 2020 2030
UNIT Main indicatorsBAU BAU2% GETS GE2% BAU BAU2% GETS GE2% BAU BAU2% GETS GE2%
Economic sector Environmental sector
Annual CO2 emission Million ton/year 425 425 425 425 488 489 469 471 475 477 415 461 Million ton/year Annual CO
2 emission
Emissions intensity Ton/GWh 1 523 1 523 1 523 1 523 1 477 1 477 1 378 1 419 1 404 1 404 831 1 275 Ton/GWh Emissions intensity
Share of emissions in transport % 11.1 11.11 11.11 11.11 11.5 11.5 11.9 11.4 13.0 13.1 15.0 12.9 % Share of emissions in transport
Water demand Million ton/year 12 982 12 983 12 982 12 975 15 205 15 216 15 281 14 839 13 955 13 984 14 221 12 857 Million ton/year Water demand
Water stress index % 2.85 2.85 2.85 2.85 3.34 3.34 3.35 3.26 3.06 3.07 3.12 2.82 % Water stress index
Forest land Million ha 1.95 1.95 1.95 1.95 2.14 2.14 2.14 2.14 2.26 2.26 2.26 2.26 Million ha Forest land
Crop land Million ha 15.16 15.16 15.16 15.16 15.76 15.76 15.76 15.76 16.53 16.53 16.53 16.53 Million ha Crop land
Invasive alien species land Million ha 1.89 1.89 1.89 1.85 2.11 2.11 1.82 1.46 2.43 2.43 1.73 0.99 Million ha Invasive alien species land
Oil demand Thousand TJ 80 943 80 960 80 949 80 957 99 037 99 486 99 151 99 796 121 643 122 863 122 383 124 063 Thousand TJ Oil demand
Gas demand TJ 75 965 75 981 75 971 75 978 92 353 92 759 92 456 93 039 112 783 113 877 113 448 114 961 TJ Gas demand
Electricity demand GWh 208 673 208 685 208 662 208 667 221 211 221 471 213 526 213 842 232 644 233 152 222 146 221 662 GWh Electricity demand
Share of coal % 91.7 91.7 91.7 91.7 88.6 88.6 82.1 83.8 83.6 83.6 46.1 73.0 % Share of coal
Share of nuclear % 5.4 5.4 5.4 5.4 4.5 4.5 4.4 5.8 4.6 4.6 26.9 7.6 % Share of nuclear
Share of pumped storage % 1.7 1.7 1.7 1.7 2.7 2.7 2.7 2.7 2.7 2.7 1.9 2.5 % Share of pumped storage
Share of solar % 0 0 0 0 2.5 2.5 5.2 3.4 7.6 7.6 12.9 9.7 % Share of solar
Share of wind % 0 0 0 0 0.7 0.7 4.6 2.3 0.5 0.5 11.4 4.2 % Share of wind
Share of hydro % 1.2 1.2 1.2 1.2 1.0 1.0 1.0 1.0 1.0 1.0 0.7 0.9 % Share of hydro
Share of renewable energy % 0 0 0 0 3.2 3.2 9.8 6.7 8.2 8.2 24.4 16.0 % Share of renewable energy
Economic sector Economic sector
Real GDP Billion ZAR 1 896 1 897 1 897 1 897 2 320 2 331 2 323 2 338 2 850 2 879 2 867 2 907 Billion ZAR Real GDP
Additional investment Billion ZAR 0 38 13 37 0 47 14 46 0 58 20 57 Billion ZAR Additional investment
Real GDP per capita ZAR 36 655 36 663 36 658 36 662 41 156 41 344 41 204 41 473 46 432 46 895 46 714 47 352 ZAR Real GDP per capita
Agriculture production Billion ZAR 42 42 42 42 42 43 42 47 48 48 48 56 Billion ZAR Agriculture production
Industry production Billion ZAR 507 507 507 507 668 671 669 672 855 863 860 867 Billion ZAR Industry production
Services production Billion ZAR 1 149 1 149 1 149 1 149 1 368 1 374 1 369 1 376 1 650 1 667 1 659 1 680 Billion ZAR Services production
Industry efficiency improvement % 0 0 0 0 0 0 5.9 5.9 0 0 8.1 9 % Industry efficiency improvement
Transport efficiency improvement % 0 0 0 0 0 0 0 4.4 0 0 0 5.5 % Transport efficiency improvement
Social sector Social sector
Total population millions 51.74 51.74 51.74 51.74 56.38 56.38 56.38 56.38 61.38 61.38 61.38 61.38 Millions Total population
Total employment Thousand people 22 320 22 320 22 670 23 190 24 585 24 607 24 891 25 404 27 753 27 853 27 974 28 347 Thousand people Total employment
NRM employment Thousand people 439 439 734 1 087 493 493 737 968 568 569 701 737 Thousand people NRM employment
Agriculture employment Thousand people 1 254 1 254 1 310 1 476 1 155 1 155 1 194 1 470 1 084 1 083 1 116 1 380 Thousand people Agriculture employment
Industry employment Thousand people 4 601 4 601 4 601 4 601 5 215 5 222 5 217 5 221 6 237 6 265 6 250 6 269 Thousand people Industry employment
Services employment Thousand people 15 126 15 126 15 126 15 126 16 748 16 763 16 753 16 762 18 794 18 863 18 826 18874 Thousand people Services employment
Mining employment Thousand people 573 573 573 573 556 556 556 556 535 533 534 533 Thousand people Mining employment
Power sector employment Thousand people 17 17 17 17 15 15 32 23 12 12 19 21 Thousand people Power sector employment
Table 8. Summary of main indicators for 2012, 2020 and 2030
37
Main indicators UNIT2012 2020 2020 2030
UNIT Main indicatorsBAU BAU2% GETS GE2% BAU BAU2% GETS GE2% BAU BAU2% GETS GE2%
Economic sector Environmental sector
Annual CO2 emission Million ton/year 425 425 425 425 488 489 469 471 475 477 415 461 Million ton/year Annual CO
2 emission
Emissions intensity Ton/GWh 1 523 1 523 1 523 1 523 1 477 1 477 1 378 1 419 1 404 1 404 831 1 275 Ton/GWh Emissions intensity
Share of emissions in transport % 11.1 11.11 11.11 11.11 11.5 11.5 11.9 11.4 13.0 13.1 15.0 12.9 % Share of emissions in transport
Water demand Million ton/year 12 982 12 983 12 982 12 975 15 205 15 216 15 281 14 839 13 955 13 984 14 221 12 857 Million ton/year Water demand
Water stress index % 2.85 2.85 2.85 2.85 3.34 3.34 3.35 3.26 3.06 3.07 3.12 2.82 % Water stress index
Forest land Million ha 1.95 1.95 1.95 1.95 2.14 2.14 2.14 2.14 2.26 2.26 2.26 2.26 Million ha Forest land
Crop land Million ha 15.16 15.16 15.16 15.16 15.76 15.76 15.76 15.76 16.53 16.53 16.53 16.53 Million ha Crop land
Invasive alien species land Million ha 1.89 1.89 1.89 1.85 2.11 2.11 1.82 1.46 2.43 2.43 1.73 0.99 Million ha Invasive alien species land
Oil demand Thousand TJ 80 943 80 960 80 949 80 957 99 037 99 486 99 151 99 796 121 643 122 863 122 383 124 063 Thousand TJ Oil demand
Gas demand TJ 75 965 75 981 75 971 75 978 92 353 92 759 92 456 93 039 112 783 113 877 113 448 114 961 TJ Gas demand
Electricity demand GWh 208 673 208 685 208 662 208 667 221 211 221 471 213 526 213 842 232 644 233 152 222 146 221 662 GWh Electricity demand
Share of coal % 91.7 91.7 91.7 91.7 88.6 88.6 82.1 83.8 83.6 83.6 46.1 73.0 % Share of coal
Share of nuclear % 5.4 5.4 5.4 5.4 4.5 4.5 4.4 5.8 4.6 4.6 26.9 7.6 % Share of nuclear
Share of pumped storage % 1.7 1.7 1.7 1.7 2.7 2.7 2.7 2.7 2.7 2.7 1.9 2.5 % Share of pumped storage
Share of solar % 0 0 0 0 2.5 2.5 5.2 3.4 7.6 7.6 12.9 9.7 % Share of solar
Share of wind % 0 0 0 0 0.7 0.7 4.6 2.3 0.5 0.5 11.4 4.2 % Share of wind
Share of hydro % 1.2 1.2 1.2 1.2 1.0 1.0 1.0 1.0 1.0 1.0 0.7 0.9 % Share of hydro
Share of renewable energy % 0 0 0 0 3.2 3.2 9.8 6.7 8.2 8.2 24.4 16.0 % Share of renewable energy
Economic sector Economic sector
Real GDP Billion ZAR 1 896 1 897 1 897 1 897 2 320 2 331 2 323 2 338 2 850 2 879 2 867 2 907 Billion ZAR Real GDP
Additional investment Billion ZAR 0 38 13 37 0 47 14 46 0 58 20 57 Billion ZAR Additional investment
Real GDP per capita ZAR 36 655 36 663 36 658 36 662 41 156 41 344 41 204 41 473 46 432 46 895 46 714 47 352 ZAR Real GDP per capita
Agriculture production Billion ZAR 42 42 42 42 42 43 42 47 48 48 48 56 Billion ZAR Agriculture production
Industry production Billion ZAR 507 507 507 507 668 671 669 672 855 863 860 867 Billion ZAR Industry production
Services production Billion ZAR 1 149 1 149 1 149 1 149 1 368 1 374 1 369 1 376 1 650 1 667 1 659 1 680 Billion ZAR Services production
Industry efficiency improvement % 0 0 0 0 0 0 5.9 5.9 0 0 8.1 9 % Industry efficiency improvement
Transport efficiency improvement % 0 0 0 0 0 0 0 4.4 0 0 0 5.5 % Transport efficiency improvement
Social sector Social sector
Total population millions 51.74 51.74 51.74 51.74 56.38 56.38 56.38 56.38 61.38 61.38 61.38 61.38 Millions Total population
Total employment Thousand people 22 320 22 320 22 670 23 190 24 585 24 607 24 891 25 404 27 753 27 853 27 974 28 347 Thousand people Total employment
NRM employment Thousand people 439 439 734 1 087 493 493 737 968 568 569 701 737 Thousand people NRM employment
Agriculture employment Thousand people 1 254 1 254 1 310 1 476 1 155 1 155 1 194 1 470 1 084 1 083 1 116 1 380 Thousand people Agriculture employment
Industry employment Thousand people 4 601 4 601 4 601 4 601 5 215 5 222 5 217 5 221 6 237 6 265 6 250 6 269 Thousand people Industry employment
Services employment Thousand people 15 126 15 126 15 126 15 126 16 748 16 763 16 753 16 762 18 794 18 863 18 826 18874 Thousand people Services employment
Mining employment Thousand people 573 573 573 573 556 556 556 556 535 533 534 533 Thousand people Mining employment
Power sector employment Thousand people 17 17 17 17 15 15 32 23 12 12 19 21 Thousand people Power sector employment
38
BAU and BAU2% projections reach 475 million tons of CO
2 and 477 million tons of CO
2 by 2030, respectively.
As a result of reduced emissions and increased power generation in the green economy scenarios, the emissions intensity in a green economy scenario is reduced relative to the BAU scenarios. Emissions intensity is projected to reach 831 tons per GWh for GETS and 1,275 tons per GWh for the GE2% scenario.
The NDP also stipulates a target of achieving a per capita real income of ZAR 110,000 per person by 2030. This target is not achieved in all scenarios by 2030, but the green economy scenarios yield relatively higher
per capita income relative to the BAU scenarios. The projected per capita income is ZAR 46,714 per person for the GETS scenario and ZAR 47,352 per person for the GE2% scenario. On the other hand, the BAU and BAU2% scenarios per capita income projections in 2030 reach ZAR 46,432 and ZAR 46,895 per person, respectively.
Green economy investments are thus observed to provide numerous benefits in terms of resource use, energy consumption and emissions reduction. These reductions result from investments in energy efficiency, which reduce energy demand, and ultimately decrease the coal capacity requirements for electricity generation. It is observed that in a GE2% scenario, a potential cumulative energy demand reduction of 3,648 MW is achieved by 2030. This is comparable to the largest coal plants that are being constructed in South Africa (i.e. the Medupi coal plant, which will have a capacity of 4,788 MW).
The BAU scenario for electricity prices follows a centralised management approach, where NERSA is the regulating agent for electricity prices in South Africa. In the modelling exercise, a cost-based approach scenario was also tested, where the price of the renewables and electricity remains constant while the price of coal increases. This is based on the expectation that, in the longer term, the price of the coal-generated electricity will increase while that of renewable energy will decrease (with significant capacities). With such a scenario (GE2% and GETS), the cost of electricity will go down and the productivity of agriculture, industry and services will go up, which will ultimately increase the economic growth (GDP) (see Figures 16 and 17).
7.3 Investment scenarios for key sectors
The investment scenarios for each of the key sectors of focus in the South African context are discussed further in the following sections.
7.3.1 Natural Resource Management
The projected average investment in NRM from 2012 to 2030 is ZAR 52 million for the GETS scenario and ZAR 9.4 billion for the GE2% scenario. The investment is
Figure 15. Comparison of emissions in BAU, BAU2%, GE and GE2% scenarios
500
480
460
440
420
400
380
360
2010 2015 2020 2025 2030
BAU BAU2% GETS GE2%
Ton/year(in millions)
Figure 16. Comparison of centralised management and cost-based scenario in electricity pricing in GETS
ZAR(in billions)
Real GDP-GETS Real GDP-GETS cost-based scenario
3 500
2 500
3 000
2 000
1 500
1 000
500
0
2010 2020 2030
2 867
2 323 2 329
2 912
1 742 1 742
39
directed at the expansion of the programme of WfW, which mainly focuses on the improvement of the water ecosystem services, through the elimination of invasive alien species.
Due to inadequate information in this sector, an assumption was made that, with NRM increased investment in the GE2% scenario, more effort would be put in place to increase the rate at which the invasive alien species is cleared, albeit to a certain threshold.The additional benefit of reducing the area infested with invasive alien species is the improvement in the ecological water flow. The invasive alien species land is projected to decrease by 1.09 times in the GETS scenario and 1.87 times in the GE2% scenario, reaching 1.73 million hectares and 0.992 million hectares, respectively.
Thus, in 2030, the targeted investment that prioritises restoration of natural resources (GETS) results in an additional 28.8 per cent restored land relative to the BAU scenario, therefore reducing the size of the land under invasive alien species. Similarly, the GE2% scenario results in an additional restored land of 59 per cent relative to the BAU2% scenario. Another current consideration is the use of biomass resulting from invasive alien species to generate electricity, although this is not a primary priority. The projected electricity generation in 2030 is 30 MWh per year and 4,870 MWh per year for the GETS and GE2% scenarios, respectively. The case for the BAU scenario is about five times less than the GETS scenario.
Water gains from restoration of land infested with invasive alien species reach 113 million tons and 242 million tons for the GETS and GE2% scenarios, respectively. This improvement is mainly the result of water gains from the restoration process since invasive species consume more water relative to the indigenous species. The water savings thus provide a source of renewable water resources, reducing water stress. In addition, only the GE2% scenario provides an added benefit of decreasing the water stress index, which, in 2030, is expected to reach 2.82 per cent. The BAU and BAU2% scenarios’ water stress indexes are projected to reach 3.06 and 3.07 per cent, respectively.
The improved water stress index in the GE2% scenario is the added benefit of reducing water demand, which is projected to reach 12,857 million tons by 2030, while the other scenarios reach 13,955 million tons,
13,984 million tons and 14,221 million tons for the BAU, BAU2% and GETS scenarios, respectively.
The results in the NRM sector have a critical implication for water resource management of a water-scarce country such as South Africa. Concretely, a focus is needed on both water supply (or) gains and water savings (through demand reduction) that can be achieved, for instance, by reducing coal power generation.
Employment in the NRM sector occurs both in the restoration of the water ecosystem services and in the utilisation of biomass for energy. Most of the employment, however, arises from alien species elimination. With a GETS scenario, employment is
Figure 18. Comparison of transport energy use in BAU, BAU2%, GE and GE2% scenarios
1 400 000
1 000 000
1 200 000
800 000
600 000
400 000
200 000
0
2001 2005 2009 2013 2017 2021 2025 2029
TJ/yearBAU BAU2% GETS GE2%
Figure 17. Comparison of centralised management and cost-based scenario in electricity pricing in GE2%
ZAR(in billions)
3 500
2 500
3 000
2 000
1 500
1 000
500
0
1 742 1 742
2 338 2 343
2010 2020 2030
2 907 2 930
Real GDP-GE2% Real GDP-GE2% cost-based scenario
40
created for 701,000 people in 2030 while the GE2% scenario is projected to create 737,000 jobs. The employment opportunities created in the green economy scenarios are higher than what is projected in the BAU (568,000 persons) and BAU2% (569,000 persons) scenarios.
7.3.2 Agriculture
Green investment scenarios in the agricultural sector allocate an additional investment of ZAR 24 million and ZAR 9.4 billion in the GETS and GE2% scenarios, respectively. These investments are directed to organic fertilizer use. Such investments provide a sustained increase of the yield per hectare, as opposed to the short-term gains from the use of conventional fertilisers. With these green economy investments, the crop yield is projected to increase by 5.5 per cent and 23.9 per cent by 2030 for the GETS and GE2% scenarios, respectively. The small difference for the massive increase in investments in the GETS and GE2% scenarios is due to water stress index, which is a dominant factor influencing the crop yield. Due to the increase in the yield per hectare, this is expected to result in the reduction of land requirements for agricultural crop production. However, the effect of population growth on the agricultural cropland requirements is higher than the cropland reduction from the increase in yield per hectare due to ecological agriculture. An additional benefit of avoided CO
2 emissions due to the use of
organic fertilizer is observed in the GE2% scenario. In 2030, this is close to ten times the reduction in CO
2 emissions relative to the BAU, BAU2% and GETS
scenarios (see Table 9).
7.3.3 Transport
Green investment in the transport sector is aimed at improving energy efficiency, which in turn translates into reduced energy consumption in the sector. The investment allocated in transport is on average ZAR 0.7 billion in the GETS scenario and ZAR 9.4 billion in the GE2% scenario. In the GETS scenario, efficiency investments in the transport sector are not enough to
reduce the energy demand by 9 per cent by 2015, as stipulated in the energy efficiency strategy of 2005. The demand still continues to increase over the simulation period. Thus, in the GETS scenario, there is no visible improvement observed in the energy intensity, given the fact that the demand for energy will continue to grow as a result of GDP and population growths.
A more aggressive investment scenario, such as GE2% scenario, is observed to contribute to the reduced energy consumption in the transport sector (see Figure 18). The efficiency improvement in the GE2% scenario reaches 5.5 per cent by 2030. As a result, transport energy use, as well as CO
2 emissions, is expected
to decline in the GE2% scenario relative to the BAU scenario.
7.3.4 Energy
Green investments in this sector allocate ZAR 33.2 billion and ZAR 94 billion to the GETS and GE2% scenarios, respectively (see Table 9). These investments are mainly aimed at energy supply, in particular at the diversification of the energy mix in electricity generation.
In the GETS scenario, the projected share of the electricity mix for the following sources of energy supply is: 26.9 per cent for nuclear; 1.9 per cent for pumped storage; 0.7 per cent for hydroelectric; 12.9 per cent for solar; 11.4 per cent for wind; and 46.1 per cent for coal.
In the case of the GE2% scenario, the share of electricity mix is projected to be 7.6 per cent for nuclear, 2.5 per cent for pumped storage, 0.9 per cent for hydro, 9.7 per cent for solar, 4.2 per cent for wind and 73 per cent for coal.
Similarly, these investments also aim at improving energy efficiencies in the transport and industry, commerce and residential sectors. Improvements in the energy efficiency are projected to decrease energy demand in the various energy use sectors (transport, industry, residential, agriculture and commerce).
41
Prio
rity
sect
ors
2012
2020
2030
UN
ITBA
UBA
U2%
GET
SG
E2%
BAU
BAU
2%G
ETS
GE2
%BA
UBA
U2%
GET
SG
E2%
Nat
ural
reso
urce
man
agem
ent
Addi
tiona
l inv
estm
ent i
n N
RMM
illio
n ZA
R0
041
7 36
00
051
9 18
90
063
11 4
12
Elec
trici
ty g
ener
atio
n fro
m
biom
ass
MW
h/ye
ar0
00
01
113
2 26
41
130
4 87
0
Inva
sive
alie
n sp
ecie
s la
ndM
illio
n ha
1.9
1.9
1.9
1.9
2.1
2.1
1.8
1.5
2.4
2.4
1.7
1.0
Wat
er g
ains
from
rest
orat
ion
Mill
ion
ton/
year
2626
2629
4343
6399
6161
113
242
Agric
ultu
re
Agric
ultu
re a
dditi
onal
in
vest
men
tBi
llion
ZAR
00
07
00
09
00
011
Ferti
lizer
con
sum
ptio
nTh
ousa
nd to
n/ye
ar93
493
493
493
41
093
1 09
31
093
1 09
31
321
1 32
11
321
1 32
1
Chem
ical
ferti
lizer
use
Thou
sand
ton/
year
743
743
740
9390
990
990
510
91
227
1 22
71
221
132
Org
anic
ferti
lizer
use
Thou
sand
ton/
year
128
128
128
135
110
110
114
1 08
813
213
213
81
452
Ferti
lizer
CO 2 e
miss
ion
Ton/
year
7 43
37
433
7 39
693
49
092
9 09
29
045
1 09
312
266
12 2
6612
209
1 32
1
Tran
spor
t
Tran
spor
tatio
n ad
ditio
nal
inve
stm
ent
Mill
ion
ZAR
00
606
7 36
00
075
39
189
00
928
11 4
12
Indu
strie
s ad
ditio
nal i
nves
t-m
ent
Mill
ion
ZAR
00
617
360
00
759
189
00
9311
412
Empl
oym
ent t
rans
port
sect
orTh
ousa
nd p
eopl
e31
031
031
031
040
240
340
240
452
452
952
753
3
Tran
spor
t ele
ctric
ity u
seTJ
/yea
r4
848
4 84
84
848
4 84
85
266
5 27
45
268
5 04
95
767
5 78
75
779
5 49
0
Tran
spor
t oil
use
Thou
sand
TJ/y
ear
963
963
963
963
1 14
51
151
1 14
71
103
1 26
71
280
1 27
51
222
CO2 em
issio
ns fr
om tr
ansp
ort
Thou
sand
ton/
year
47 0
6447
068
47 0
6547
068
55 9
3556
182
55 9
9953
874
61 8
6762
507
62 2
4959
683
Ener
gy
Ener
gy a
dditi
onal
inve
stm
ent
Billi
on Z
AR0
012
70
013
90
018
11
Emiss
ions
from
pow
er s
ecto
rM
illio
n to
n/ye
ar30
130
130
130
133
733
732
132
529
729
724
229
1
Coal
ele
ctric
ity g
ener
atio
nGW
h/ye
ar18
1 10
118
1 10
118
1 10
118
1 10
120
1 93
420
2 04
019
1 09
619
4 32
317
6 53
717
6 53
713
4 27
017
1 25
3
Nuc
lear
ele
ctric
ity g
ener
atio
nGW
h/ye
ar10
711
10 7
1110
711
10 7
1110
291
10 2
9110
291
13 3
209
789
9 78
978
379
17 2
99
Rene
wab
le e
nerg
y el
ectri
city
ge
nera
tion
GWh/
year
4343
4343
7 37
57
375
22 8
8015
294
17 2
3517
235
71 0
8636
529
Tabl
e 9.
Mai
n SA
GEM
indi
cato
rs fo
r the
prio
ritiz
ed s
ecto
rs
42
8 Comparison of SAGEM and T21-World
8.1 Introduction
Both SAGEM and the T21-World model, which was developed for UNEP’s Green Economy Report (GER), use the T21 family of models as starting point. T21 is a comprehensive planning tool that integrates social, economic and environmental dimensions of national development into a single framework of analysis. SAGEM is based on T21, but is further customised for the South African context to represent the key issues and policy interests of the country, as defined in the initial stakeholder workshop of the project.
A comparison of the major differences between SAGEM and the T21-World model, in terms of the analysis and the structure of the model itself, is discussed in the following sections.
8.2 Comparison of the green economy analyses
8.2.1 Simulation of scenarios
Both SAGEM and T21-World analyse a green economy transition by simulating investments that target low carbon development, resource efficiency and natural resource conservation. Both models are used to simulate an investment reaching 2 per cent of GDP, which are compared to a BAU scenario with an additional investment – BAU2% – in conventional activities. The SAGEM analysis also includes an additional scenario, called GETS, which is driven by existing planned investments and goals, prioritised by government. The GETS scenario allows for an estimation of the investment costs required to achieve the desired goals for different sectors, while the 2 per cent scenarios are used to evaluate to what extent the GE scenario competes with a BAU case for a variety of indicators across all sectors. In this respect, the GETS scenario is a more realistic and probably more useful
scenario to inform decision-making, while the 2 per cent investment case is more illustrative of the concept, as well as the pros and cons, of a green economy strategy.
8.2.2 Allocation of investments
Despite the similarity in the assumption of total investments in the 2 per cent case, the sectoral allocation of such investments differs considerably between SAGEM and T21-World. In the GER analysis, GE scenarios have been developed for a total of twelve sectors in order to cover a broad range of aspects of the potential green economy transition at a global scale and provides a broad range of considerations, by sector, on what a green economy is and how it can be operationalized. Furthermore, the modelling work for the GER was developed over the course of two years, while only a few months were available for SAGEM. In this respect, SAGEM should be seen as a pilot project that would require more extensive follow-up work to make the best of the integrated nature of the model to support decision-making. Regarding the allocation of investments across sectors in SAGEM, the stakeholders selected four key sectors: natural resource management, energy, agriculture and transport.
8.3 Comparison of models
8.3.1 Selection of sectors
In terms of the structure of the two models, which have key social and economic sectors in common (e.g., population and sectoral production), each model also has its unique sectors. This is due to the high degree of customization of the models, which are tailored around a specific set of issues (and sectors) to be analysed, and a geographical context (national vs. global). Data availability also defines the level of detail included in the model, and the presence of specific sectors for which relevance and data may be peculiar for one or the other model.
43
In concrete terms, SAGEM includes mining of primary minerals in South Africa (which are not included in the T21-World model) and takes into account the important role of the mining sector in the country’s economy, as well as energy consumption and natural resource exploitation. The NRM sector is also unique to SAGEM and allows for the analysis of investment interventions in an especially relevant area for South Africa. Finally, in the economic sphere of the model, the government and households’ accounts were added to represent more fully the System of National Accounts (SNA) and Social Accounting Matrix (SAM), for which global data are not available.
On the other hand, SAGEM is not equipped with sectors included in T21-World that the stakeholders did not select, or for which data are not available (e.g., waste recycling and reuse).
8.3.2 Structure of sectors
The sectors included in both SAGEM and T21-World do not necessarily share the same variables and equations. Since the SAGEM is fully customized to the South African context, the level of detail represented in T21-World could not always be achieved (due to both data and time constraints).
This is the case of the energy sector, for instance, where the production of fossil fuels (i.e. oil, gas and coal) is not modelled with the same detail as in the T21-World model because of the high level of uncertainty surrounding world fossil fuels production, as opposed to the higher stability of the coal and mining sectors for South Africa. In addition, the SAGEM energy sector emphasizes primarily electricity (power demand and supply), as this is a key issue currently facing the country. Furthermore, SAGEM’s electricity supply sectors, which are highly customized for South Africa, represent production, generating capacity, costs and employment in plant construction and operations – all of which were disaggregated by energy source into
coal, nuclear, wind, pumped storage, hydro and solar, excluding those sources that are mentioned in T21-World but are not used in the context of South Africa. The potential generation from biomass is further represented to estimate the impacts of green economy investments.
Another example is the water sector, where the representation of three major categories of water resources (i.e. surface water, groundwater and reservoir) in T21-World had to be simplified for SAGEM due to a lack of data on water stocks and flows as well as water consumption. On the other hand, SAGEM has more details on interventions that could improve water supply, such as water gains from land restoration (clearing of invasive alien species), one of the NRM interventions analysed in the study. Finally, SAGEM includes the estimation of water requirements for power generation separately from other water requirements, an indicator that is not included in T21-World.
8.3.3 Data availability
Both T21-World and SAGEM are developed based on statistical information available for each sector, which is used for model calibration and validation. Thus, the availability of data would largely influence the level of detail a model can provide, as well as the modelling approach employed.
The T21-World model is supported by historical data and baseline projections at both global and national levels. Global level data by sector, often highly disaggregated, have been collected from a range of international databases and reports (see Table 10).
In some cases, data at the regional or national levels are used, such as the value of natural resources used to estimate natural capital.
With data available from 1970 for most of the key variables, T21-World simulates almost 40 years of
44
trends (from 1970 to around 2010) for model validation, which allows reliable projections of the future up to 2050. In the case of SAGEM, data on earlier periods were lacking and consequently, simulation could only start in 2001. In addition, due to the shorter historical period simulated for validation, the modelling period was set to 2030. Moreover, some of T21-World data are not available for South Africa.
As listed in the data collection section above, data on the most essential social and economic indicators (e.g., population, education, healthcare, road infrastructure, GDP and the three production sectors) have been collected from both domestic (e.g., STAT SA) and international sources (e.g., World Bank’s World Development Indicators database). However, the detailed data on fishery and forestry sectors, as well as poverty and Human Development Index, are not available for SAGEM.
On the other hand, the data on government expenditure allocated to various functions in South Africa allows for the inclusion of the government sector in SAGEM, while T21-World does not include a government sector at the global level.
Larger data gaps mainly exist in various environmental sectors. In the case of the water sector, national data on water demand and water stress index are available and thus modelled in SAGEM. However, data on the stocks of South African water sources are not available from the FAO database for T21-World. On the other
hand, data on the value of additional water availability from interventions (such
as the removal of invasive alien species) is available for South Africa and modelled in SAGEM, but not in T21-World. Additional information has been collected related to water gains from land restoration from a number of documents.
Similarly, with regard to non-renewable natural resources, data on the stocks of oil and gas, as well as main exploration and supply flows, have been collected from EIA and USGS for T21-
World based on data, but they are are not available for South Africa. The total stock of coal, however, is provided by reports on minerals for South Africa, while the level of detail in SAGEM is limited due to the lack of disaggregated information (e.g., discovered versus undiscovered reserves). Mineral data is available on the stocks and flows of gold and PGM, along with relevant employment and economic benefits, which are included in SAGEM, but not in T21-World.
In the energy sector of SAGEM, a range of detailed data is available on the key issue of electricity, including electricity production by energy source from national sources, costs and load factors of electricity production, and employment in power plant construction and operations. Data of all these variables are available disaggregated by energy source. On the demand side, national data on South Africa’s electricity, oil and gas demand by end-users are available from national data sources, and a calculation of energy and transport data has been conducted to remove inconsistency of data from different sources. The information on total or sectoral coal demand, however, is not available, and thus is not included in SAGEM.
8.4 Main results
Due to the aforementioned differences in the availability of data, the definition of key issues by stakeholders and selection of key sectors to be analysed, as well as the results produced by SAGEM and GER, are not directly
Sector Source of data
Population • World Population Prospects (WPP) (UNPD, 2009)
Economy • World Development Indicators (WDI) (WB, 2009)• Environmental Outlook to 2030 (OECD, 2008)
Agriculture • FAOSTAT (FAO, 2010)
Forests • State of World’s Forests (FAO, 2009)
Water • AQUASTAT (FAO, 2009)• Charting Our Water Future report (McKinsey, 2009)
Biofuels • Sustainable Production of Second Generation Biofuels (IEA, 2010)
Transport • IEA’s Mobility Modelling Model and Transport, Energy• CO
2 report (IEA, 2009)
Table 10. Sources of data for T21-World models
45
comparable on a sector by sector basis. On the other hand, the results are generally consistent concerning the social, economic and environmental impacts of interventions.
As discussed above, the simulation period for SAGEM is shorter than for T21-World due to the availability of data. Thus, SAGEM estimated results till 2030 under various scenarios, while longer-term projections up to 2050 can be calculated with T21-World.
Due to the different key sectors selected by the stakeholders, the indicators in GER sectors that are not prioritised for this study, and are not essential to other sectors, are not calculated in SAGEM. These include variables related to waste collection and disposal and the disaggregation of agriculture (into livestock, fishery and forestry production). On the other hand, the amount of government expenditures (total and disaggregated into various functions) are analysed in SAGEM, which are linked to other economic sectors, and also used as drivers for a number of social and environmental sectors, such as land protection, mining, among others. The indicators on restoration of land under invasive alien species and on mining of primary minerals are calculated in SAGEM only, as they are considered as key issues for South Africa, but not at the global level.
Considering the common sectors in the two models, stocks of renewable resources cannot be estimated by SAGEM due to lack of data. For example, the stocks and most supply flows of water cannot be evaluated as it was done in GER. However, the model estimates the amount of water gains from land restoration under invasive alien species and how it is influenced by green economy investments, along with the employment created and area of land restored, variables which are not included in GER.
With regard to non-renewable natural resources, the stocks and main supply flows of oil and gas fuels are not estimated in SAGEM due to data and time constraints. On the other hand, the model calculates stocks and exploration flows of coal, gold and PGM in the mining sector. The latter two resources are only estimated in SAGEM in order to take South Africa’s important mining sector into account. SAGEM further calculates the contribution of mining to employment and the economy, which is not among the key issues analysed in GER.
Concerning the energy sector, electricity is SAGEM’s main focus. A series of results for electricity has been calculated, including production, generating capacity and employment, all of which are further categorised by energy source. The sources are customized for South Africa according to coal, nuclear, wind, pumped storage, hydro, solar and biomass.
On the demand side, the consumption of resources are calculated by sector for both models, while the end-using sectors in SAGEM are selected and customized for the context of South Africa. Due to the importance of the electricity sector in South Africa, water demand is also driven by the amount used for electricity generation, creating a water-electricity nexus; whereas in T21-World, water used for electricity generation is not listed apart from water demand by other industries. In terms of the demand for energy, the additional components of primary minerals in SAGEM allow for the estimation of mining sector demand for electricity, oil and gas, as part of total demand for each of these fuels. In terms of coal, however, while the mining sector demand is calculated, the demand for coal by other sectors, as well as the total demand for coal, can only be estimated due to lack of data. As such, total fossil fuel demand or primary energy demand cannot be evaluated in SAGEM. Total emissions from fossil fuels are calculated in both models, and the impact of green economy interventions is consistent.
In terms of socio-economic results, employment by sector is estimated in both analyses, and shows consistent projections. The sectors in SAGEM are selected to account for the key issues in South Africa that have been identified by stakeholders, namely, agriculture, industry (including mining), services, electricity, transport, and natural resource management, while the main focus of GER is broader.
Other GER social results, which are not estimated in SAGEM, include poverty level and HDI, for lack of sufficient data for South Africa. In addition, GER evaluates the changes in natural capital (values of fossil fuels, forest and fish stocks), which is not included in the SAGEM analysis. This is mainly because the stocks of fossil fuels are not estimated due to lack of information, while fish stocks were not prioritised as key issues by stakeholders. In addition, the estimation also requires data on economic values of these natural resources, which are not available for South Africa.
46
9 Conclusions
The South African Green Economy Modelling (SAGEM) report is primarily aimed at assessing the impacts of green economy investments in selected cases within the identified sectors pertaining to the South African economy. Based on planned targets and expenditures and/or costs of interventions, the modelling identifies the possible opportunities to achieving these targets. Four scenarios were defined: BAU, or business-as-usual; BAU2% representing a 2 per cent investment of GDP in the BAU activities; GE2% representing an allocation of 2 per cent of gross domestic product in green economy sectors (energy, natural resource management, agriculture and transport); and GETS, which is a target-specific scenario aimed at identifying whether policy-makers can achieve the medium- to long-term targets following green economy interventions in specific cases within these sectors.
The two green economy scenarios (GE2% and GETS) clearly show that green economy investment interventions have positive impacts on the main indicators representing the transition of South Africa to a green economy.
The BAU2% scenario was aimed at emphasising the outcomes of a green economy, when a same investment allocation is directed to BAU activities rather than green investment. It was clearly shown that, using the same assumptions for additional investment, the BAU scenario appears to yield roughly the same economic benefits as the GE scenario, but underperforms when it concerns environmental performance. In other words, while the GE2% and BAU2% scenarios are aimed at allocating a similar proportion of GDP to different investments, the results show the added benefits of the GE2% scenario in the long-term. This includes not only sustained economic growth, but also increased value in the natural resources through the reduction of emission intensity, water stress index, reduced energy and water demand as well as the restoration of natural capital. For, the projected reduction in coal capacity requirements is 3,648 MW by 2030 in the GE2% due to investments in energy efficiency. This is comparable to the largest coal plants in South Africa under construction (e.g., Medupi, 4,788 MW).
It should, however, be noted that although synergies can be found, the specific green economy interventions simulated do not allow the achievement of all the targets set by the government simultaneously. Thus, there are some investment allocations that tend to provide more employment, while others tend to reduce emissions more. Finally, as a recommendation, the modelling could be used to test several different investment allocation options depending on the indicators that are considered to be of higher priority.
47
48
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1 www.millennium-institute.org2 www.sustainabilityinstitute.net3 This is based on a Social Accounting Matrix and on Cobb-Douglas
production functions.4 Water quantity and water quality are two different ecosystem
services, though related. Given that South Africa is a water scarce country, the main threat of the invasive alien species is that of water quantity. The WfW programme has thus mainly focused on improving water quantity (or availability); this is then also the focus of SAGEM. Nevertheless, the importance of water quality impacts is not disregarded; at this stage, there are not well-founded scientific facts on the extent or seriousness of the impact of invasive alien species on water quality that could be used for SAGEM.
5 Usually regarded as input variables.6 Idem.7 It should be noted that the energy sub-model is estimated using
a variety of endogenous inputs (e.g., GDP and population) and exogenous inputs (e.g., the case of electricity price).
8 http://www.energy.gov.za/files/petroleum_frame.html9 For instance, in GER, a 1% green economy scenario was analyzed.10 Based on 2005 data.
Notes
50
Annexes
Annex 1 Modelling symbols 51 Annex 22.1 Water quantity provision with WfW module 52
2.2 Potential electricity generation from invasive module 54
2.3 Land module 56
2.4 Water supply module 58
2.5 Water demand electricity generation module 60
2.6 Coal electricity module 62
2.7 Nuclear electricity module 64
2.8 Hydroelectricity module 66
2.9 Pumped storage electricity module 68
2.10 Solar electricity module 70
2.11 Wind electricity module 72
2.12 Electricity generation technology share module 74
2.13 Electricity price module 76
2.14 Electricity demand module 78
2.15 Oil demand module 80
2.16 Gas demand module 82
2.17 Air emissions module 84
2.18 Minerals module (a) 86
2.19 Minerals module (b) 88
2.20 Population module 90
2.21 Education module 92
2.22 Health module 94
2.23 Access to road module 96
2.24 Transport module 98
2.25 Employment module 100
2.26 Power sector employment module 102
2.27 Agriculture module 104
2.28 Industry module 106
2.29 Services module 108
2.30 GDP module 110
2.31 Investment and household module 112
2.32 Government module 114
51
Annex 1. Modelling symbols
There are three symbols that are used in system dynamics modeling; a stock, a flow (can be inflow or outflow), and a link (arrow).
A stock is a foundation of any system and it is like a store; an accumulation of material or information that has been built up over time. Stocks change over time through the actions of a flow. Flows are filling and draining.
As an illustration, a mineral deposit is a stock, which is diminished through mining. The arrow, often regarded as an information link, as shown above, implies that the information in the stock influences the value of the flow. The cloud on the flow symbol represents either a source (for an inflow) or sink (for an outflow); that is, where the flow originated and vanished.
Stock
Inflow Outflow
LINK
52
Accu
mula
tedre
store
d lan
dRe
stora
tion r
ate
Harve
stable
biom
ass
per a
nnum
Yield
per h
a
Prop
ortio
n of in
vasiv
e in
mou
ntaino
us or
ripar
ianar
eas
Invas
ive al
ien in
mou
ntaino
us or
ripar
ianar
ea
Prop
ortio
n of m
ounta
inous
ripar
ian ar
ea re
store
d
Resto
red m
ounta
inous
and r
iparia
n are
as
Redu
ced w
ater y
ield b
yinv
asive
alien
spec
ies
Redu
ced w
ater y
ield
per h
a
Amou
nt of
water
savin
gsfro
m re
stora
tion
Wate
r gain
s fro
mre
stora
tion
Econ
omic
value
of w
ater
savin
gs fro
m re
stora
tion
Low
econ
omic
value
scen
ario
<Kilo
gram
s per
cubic
mete
r of w
ater>
<Inva
sive a
lien
spec
ies la
nd>
Aver
age c
ost o
f clea
ring
invas
ive al
ien sp
ecies
Initia
l ave
rage
cost
ofcle
aring
per h
aRe
alisti
cally
harve
stable
area
with
inva
sive s
pecie
sCo
st of
clear
inginv
asive
alien
spec
iesCh
ange
s in c
ost o
finv
asive
alien
clea
ranc
e
Aver
age c
ost
grow
th ra
teInv
asive
alien
spec
iesinv
estm
ent re
quire
men
ts
<Res
torati
onra
te>
Oper
ating
cost
ofm
aintai
ning
resto
red l
and
Chan
ges i
n ope
ratin
gco
st of
main
taina
nce
Initia
l ope
ratin
g cos
t of
main
tainin
g res
tored
land
Time t
o adju
stinv
estm
ent
Emplo
ymen
t in th
ere
stora
tion o
f inva
sive a
lien
land
Emplo
ymen
t in th
e ope
ratin
gan
d main
tainin
g res
tored
land
Total
emplo
ymen
t inre
stora
tion o
f land
with
invas
ive pl
ants
Aver
age e
mplo
ymen
t inre
stora
tion p
er ha
Aver
age e
mplo
ymen
t inm
aintai
nanc
e of re
store
d lan
dpe
r ha
<Res
torati
onra
te>
Time t
o adju
stem
ploym
ent
Total
cost
of inv
asive
clear
ance
per h
a
<Res
torati
on ra
te to
other
land
>
<Inva
sive a
lien
spec
ies la
nd>
<Res
torati
on ra
te to
other
land
>
Anne
x 2.
1
W
ater
qua
ntity
pro
visi
on w
ith W
fW m
odul
e
53
The water quantity provision module is aimed at estimating the amount of
water savings that results from the restoration activities of the Working for
Water (WfW) programme. In addition, the total employment from restoring
invasive alien land and costs associated with the restoration are also estimated.
The module consists of three stocks namely: accumulated restored water cost of
clearing invasive alien species (capital cost) and operating cost of maintaining
the restored land. The accumulated restored land is driven by the changes in
the rate of restoration. Using an exogenously determined proportion of the
restored mountainous areas, the amount of the water savings from restoration
is estimated. On the other hand, the changes in costs influences both capital
and operations costs. Similarly, the growth in cost of restoring invaded land is
driven by an exogenously determined growth rate. Since this value is uncertain,
sensitivity analysis taking into account of a low and high average growth rate in
costs was carried out. The endogenously determined restoration rate, together
with the coefficient of the average employment restoration estimates the total
employment generated.
54
Anne
x 2.
2 P
oten
tial e
lect
ricity
gen
erat
ion
from
inva
sive
s m
odul
e
Biom
ass r
equir
emen
ts pe
rM
W of
elec
tricity
gene
ratio
n
Elec
tricity
capa
city
poten
tial u
sing b
iomas
s
Elec
tricity
gene
ratio
nfro
m bi
omas
s
Num
ber o
f day
spe
r yea
r
Conv
ersio
n fac
tor
MW
to M
Wh
Emplo
ymen
t in bi
omas
sele
ctrici
ty ge
nera
tion
Num
ber o
f jobs
per M
W in
cons
tructi
on m
anufa
cturin
gan
d ins
tallat
ionTim
e to a
djust
emplo
ymen
t in bi
omas
s
Num
ber o
f jobs
per M
W in
oper
ation
s and
main
tenan
ce
Biom
ass p
lant
capa
city
Biom
ass c
apac
ityco
nstru
ction
Biom
ass p
lant
depr
eciat
ion
<WfW
value
add g
reen
inves
tmen
t>Bi
omas
s cap
acity
from
gree
n inv
estm
ent
Capit
al co
st for
biom
ass p
lant
Biom
ass p
lant
lifesp
an
Total
biom
ass
requ
irem
ents
Time t
o adju
stca
pacit
y pote
ntial
<Tim
e>
<WfW
BAU
inves
tmen
t>
Biom
ass c
apac
ityfor
BAU
<Tim
e>
Prop
ortio
n of W
fW on
value
add
<Tim
e>
Elec
tricity
gene
ratio
nfro
m bi
omas
s GW
h
<MW
h to G
Wh>
55
The potential electricity generation from invasives module estimates
the amount of electricity generation from biomass harvested in the WfW
programme and the respective employment in the biomass electricity
generation. The module consists of one stock, namely biomass plant capacity,
which increases with due to construction of the biomass capacity and decreases
with depreciation of the biomass electricity plant. The main driver for the
biomass capacity construction is investments in the value added activities
of the WfW programme. The electricity generated is thus dependent on the
capacity of the biomass electricity plant, its conversion factor and the number
of days the plant is in operation. Employment created in the biomass electricity
generation is estimated given the exogenously determined coefficient of
employment in constructing, manufacturing and installation of the biomass
electricity plant, and in the operations and maintenance.
56
Anne
x 2.
3 L
and
mod
ule
<Tota
lpo
pulat
ion>
Fore
stpla
ntatio
nsIni
tial fo
rest
planta
tions
Crop
land
Fore
st to
crop
Initia
l cro
p lan
d
Time t
o con
vert f
ores
tto
crop l
and
Lives
tock l
and
Initia
l live
stock
land
Othe
r land
Initia
l othe
r land Tim
e to c
onve
rt stoc
klan
d to o
ther la
nd
Settle
men
t land
Initia
l sett
lemen
tlan
d
Othe
r land
tose
ttlem
ent
Time t
o con
vert
other
land
Desir
ed se
ttlem
ent
land
Chan
ge in
the d
esire
dse
ttlem
ent la
nd
Othe
r land
tofor
est
Desir
ed fo
rest
planta
tions
Chan
ge in
desir
edfor
est p
lantat
ions
Aver
age t
ime t
om
ature
fore
sts
Aver
age l
ife of
agric
ultur
e lan
d
Desir
ed cr
op la
nd
Chan
ge in
the d
esire
dcro
p lan
d
Othe
r land
to cr
op
Time t
o con
vert
other
to cr
opDe
sired
settle
men
tlan
d per
capit
a
Chan
ge in
the d
esire
dsto
ck la
nd
Total
land
<Sett
lemen
t land
>
<For
est
planta
tions
>
<Live
stock
land
>
<Othe
r land
>
<Cro
p lan
d>Ot
her la
nd to
lives
tock
Crop
land
to ot
her
land
Stoc
k to o
ther
land
<Cha
nge i
n the
desir
ed cr
op la
nd>
Relat
ive ag
ricult
ure
land
<Initia
l cro
p lan
d>
<Initia
l live
stock
land>
Land
per p
erso
n
Desir
ed liv
estoc
klan
d
Time t
o con
vert o
ther
to sto
ck la
nd
<Tota
lpo
pulat
ion>
Elas
ticity
of ag
ricult
ure l
and
dem
and t
o pop
ulatio
n
Arab
le lan
d and
perm
anen
t cro
psSh
are o
f ara
ble la
nd an
dpe
rman
ent c
rops
<Tim
e>
Invas
ive al
iensp
ecies
land
Initia
l inva
sive a
lien
spec
ies la
nd
Rate
of sp
read
tooth
er la
nd
<Inva
sive a
lien
spec
ies la
nd>
<Rela
tive t
otal
popu
lation
>
Initia
l agr
icultu
re la
ndde
man
d
<Rela
tive t
otal
popu
lation
>
Initia
l live
stock
land
dem
and
Elas
ticity
of liv
estoc
k lan
dde
man
d to p
opula
tion
Prop
ortio
n of in
vasiv
einv
estm
ent
Effec
t of in
vestm
ent ra
tioon
spre
ad ra
te
Resto
ratio
n rate
tooth
er la
nd
Spre
ad to
othe
rlan
d
Time t
o con
vert o
ther
land t
o live
stock
Cons
erva
tion l
and
Initia
l con
serva
tion
land
Othe
r land
toco
nser
vatio
n
Conv
ersio
n fra
ction
ofoth
er la
nd to
cons
erva
tion
<Con
serva
tion
land>
<WfW
wate
r and
biod
iversi
tygr
een i
nves
tmen
t>Inves
tmen
t in in
vasiv
esp
ecies
land
clea
ranc
e<Cos
t of c
learin
ginv
asive
alien
spec
ies>
<Env
ironm
ental
prote
ction
expe
nditu
re>
Inves
tmen
t total
cost
ratio
WfW
BAU
inves
tmen
t<P
ropo
rtion o
f WfW
onva
lue ad
d>Inves
tmen
t ininv
asive
clea
ring
Total
cost
of cle
aring
invas
ive la
nd
Effec
t of in
vestm
ent ra
tioon
spre
ad ta
ble
<GRE
EN E
CONO
MY
SWITC
H>Invas
ive al
ien sp
ecies
land c
leare
d
Resto
ratio
nfra
ction
57
The land module represents the land size in South Africa and the dynamics of
the various categories of land uses in the country. The total land size of the
country is 1,214,470 km2, which is divided into seven competing uses namely:
forest plantations; crop land; livestock land; conservation land; settlement
land, other land and invasive alien species land. Thus, there are seven land use
stocks. Most of the land uses stocks could be converted from their current use
to other land use and vice versa. Exceptions are settlement and conservation
land, where the assumption made was that the land cannot be converted to
other land once they are in these states of land use. The invasive alien species
land was separated from other land in order to account for the changes in the
land use with the restoration of infested land. The South African government’s
WfW programme is key to converting the invasive infested land to other land,
which is dependent on the investment allocated to the programme and the cost
implicated in the restoration. On the other hand, settlement land, crop land
and livestock land are mainly influenced by the population dynamics. Despite
the dynamic changes in the seven land uses, the total land size in South Africa
is maintained throughout the simulation analysis.
58
Anne
x 2.
4
Wat
er s
uppl
y m
odul
e
<Tota
l land
>
Cros
s bor
der
inflow
Prec
ipitat
ion
Aver
age
prec
ipitat
ion
Cubic
mete
r of w
ater p
erm
m of
rain
per h
ectar
e
Kilog
ram
s per
cubic
mete
r of w
ater
Wate
r res
ource
sint
erna
lly pr
oduc
ed
Total
rene
wable
wate
rre
sour
ces
Frac
tion o
f rain
evap
orati
ng im
med
iately
Prod
uctio
nwa
ter de
man
d
Dom
estic
and m
unici
pal
water
dem
and
Total
wate
r dem
and
Initia
l PC
water
dem
and
Wate
r dem
and p
erun
it pro
duce
d
Effec
t of e
duca
tion o
nwa
ter us
e effic
iency
table
Wate
r stre
ssind
ex
Effec
t of in
com
e on P
Cwa
ter de
man
d tab
le
Rene
wable
wate
rre
sour
ces a
vaila
blepe
r cap
ita
Effec
t of w
ater s
tress
onac
cess
to cl
ean w
ater ta
ble
Acce
ss to
clea
nwa
ter
Per c
apita
wate
rde
man
d <Tota
lpo
pulat
ion>
<Rea
l GDP
>
<Ave
rage
adult
litera
cy ra
te>
<Wate
r gain
s fro
mre
stora
tion>
<Tota
l wate
r req
uirem
ent in
electr
icity
gene
ratio
n in k
g>
<Rela
tive P
Cre
al GD
P>
59
Water is one of the input resources required in the country’s economic production. This module is mainly used
to estimate the availability of water relative to its demand. The total water demand is categorised into (i)
production water demand, arising from three main production activities (agriculture, industry and services); (ii)
water requirement in electricity generation and (iii) domestic and municipal water demand – the sum of these
different water demand calculates the total water demand. The other total of renewable water resources is
determined by the water resources that is produced internally in South Africa, as well as cross-border inflows
and water gains from restoration activities. Both total water demand and total renewable water resources are
utilised to estimate the water stress index, which in turn influences the production activities and hence the
economic growth.
Water is one of the input resources required in the country’s economic
production. The water supply module is mainly used to estimate the
availability of water relative to its demand. The total water demand is
categorised into (i) production water demand, arising from three main
production activities (agriculture, industry and services); (ii) water requirement
in electricity generation and (iii) domestic and municipal water demand – the
sum of these different water demand calculates the total water demand. The
other total of renewable water resources is determined by the water resources
that is produced internally in South Africa, as well as cross-border inflows and
water gains from restoration activities. Both total water demand and total
renewable water resources are utilised to estimate the water stress index, which
in turn influences the production activities and hence the economic growth.
60
Anne
x 2.
5
Wat
er d
eman
d el
ectr
icity
gen
erat
ion
mod
ule
<Wind
elec
tricity
gene
ratio
n in G
Wh>
<Sola
r elec
tricity
gene
ratio
n in G
Wh>W
ater u
se co
al pe
rkW
h
Wate
r use
coal
Wate
r use
nucle
arpe
r kW
h
Wate
r use
nucle
ar
Wate
r use
pum
ped
stora
ge pe
r kW
h
Wate
r use
pum
ped s
torag
ean
d hyd
roele
ctrici
ty
Wate
r use
wind
per
kWh
Wate
r use
wind
Wate
r use
solar
per k
Wh
Wate
r use
solar
Total
wate
r req
uirem
ent in
electr
icity
gene
ratio
n
Initia
l wate
r use
from
electr
icity
gene
ratio
nRelat
ive w
ater u
se fro
mele
ctrici
ty ge
nera
tion
<Nuc
lear e
lectric
ityge
nera
tion>
<Hyd
ropo
wer
gene
ratio
n>
<Pum
ped s
torag
eele
ctrici
ty ge
nera
tion>
<Con
vert
GWh/k
Wh>
<Con
vert
GWh/k
Wh>
<Con
vert
GWh/k
Wh>
Total
wate
r req
uirem
ent in
electr
icity
gene
ratio
n in k
g
Conv
ert li
treto
kg
<Effe
ctive
elec
tricity
gene
ratio
n fro
mco
al in
GWh>
61
Electricity generation contributes significantly to water use. The water demand electricity generation module estimates the amount of water that is
required for electricity generation for the different generation technologies. It
makes use of endogenously estimated electricity generation from coal, nuclear,
pumped storage, hydro, solar and wind as well as their respective coefficients
of water use per unit of electricity generation. Water use for the specific
electricity generating technology is thus estimated by multiplying the electricity
generated by that technology at each time (year) with the coefficient of unit
water use of that technology. The total water use in electricity generation
is the sum of all water use from the various technologies. The total water
requirement in electricity generation further becomes part of the total (overall)
water demand, which in turn determines the water stress index given the
current water availability.
62
Anne
x 2.
6
Coal
ele
ctric
ity m
odul
e
Initia
l total
capa
city
instal
led
Sales
capa
city
ratio
<Elec
tricity
net
expo
rts>
Elec
tricity
requ
irem
ent in
MW
h
Conv
ersio
n Gwh
to M
wh
<Tim
e>
Requ
ired c
apac
ity Instal
led ca
pacit
yre
quire
d
<Tim
e>
<Sha
re of
coal>
Ratio
prod
uctio
n to
capa
city o
f coa
l elec
tricity
Coal
electr
icity
capa
city
requ
irem
ents
<Tim
e>
<Elec
tricity
lose
s>
Time t
o adju
st co
alca
pacit
y
Poten
tial e
ffecti
veco
al ca
pacit
y
Poten
tial c
umula
tive
coal
capa
city r
educ
tion
Poten
tial e
lectric
ityde
man
d red
uctio
n
MW
redu
ction
cost
in ZA
RCo
al ca
pital
cost
inUS
D pe
r MW
<Offic
ialex
chan
ge ra
te><T
ime>
<Tota
l sec
tors
electr
icity
dem
and>
<Sale
s cap
acity
ratio
>
<Elec
tricity
lose
s ove
r total
electi
ricy g
ener
ation
>Ef
fectiv
e elec
tricity
gene
ratio
n fro
m co
al
Effec
tive e
lectric
ityge
nera
tion f
rom
coal
in GW
h
Conv
ert M
Wh t
oGW
h
Coal
plants
cons
tructi
on
Plan
ned c
oal d
evelo
pmen
tIR
P 20
10 co
mm
itted
Coal
cost
in ZA
Rpe
r MW
Futur
e coa
linv
estm
ent
Coal
capit
al co
st
<Tim
e><E
xcha
nge r
ate>
<Fue
l and
ener
gyex
pend
iture
>
Prop
ortio
n on e
nerg
yex
pend
iture
coal
ener
gy
Plan
ned c
oal p
ower
inves
tmen
t
<Tim
e>
Coal
capa
city f
rom
alloc
ated i
nves
tmen
t
Coal
capa
city g
apCo
al en
ergy
capa
city
Coal
ener
gyco
nstru
ction
Depr
eciat
ion co
alca
pacit
y
Coal
capit
allife
time
<Tim
e>
<Coa
l ene
rgy
capa
city>
<Ene
rgy e
fficien
cygr
een i
nves
tmen
t>
Conv
ersio
nfac
tor co
al Coal
electr
icity
gene
ratio
n
<Tim
e><H
ours
per d
ay> <Day
s per
year
>
<MW
h to G
Wh> Po
tentia
l coa
lge
nera
tion
Unit c
onve
rsion
MW
h
<Effe
ct of
electr
icity
dem
and
supp
ly on
coal
gene
ratio
n>
63
Currently, coal electricity generation contributes about 92 per cent of the
total electricity generation and it is utilised to cater for the base load demand.
In addition, electricity generation from coal contributes significantly to GHG
emissions. This module estimates the amount of electricity generation from coal
and the potential coal capacity reduction resulting from the energy efficiency
measures. The module thus consists of two stocks, coal energy capacity and the
potential cumulative capacity reduction. The coal capacity is increased through
coal plant construction and decreased due to depreciation. The planned coal
plant investment as stipulated in the IRP 2010 is accounted for. Investments
in energy efficiency can result in the reduction of coal plant requirements, or
rather avoided coal capacity, as a result of efficiency measures. The difference
between coal capacity and the cumulative capacity reduction estimates the
effective coal capacity, which in turn is used to estimate the effective electricity
generation from coal. The coal electricity generation estimated in this module
is utilised in other modules to estimate, among others,: (i) the share of this
technology’s contribution to the overall electricity generation; (ii) the resulting
emissions from coal; (iii) employment created; and (iv) water use in coal
electricity generation.
64
Anne
x 2.
7 N
ucle
ar e
lect
ricity
mod
ule
Nucle
ar pl
ant
capa
city
Nucle
ar pl
ant
cons
tructi
on
Nucle
arde
prec
iation
rate
Initia
l nuc
lear
instal
led ca
pacit
y Conv
ersio
n fac
tornu
clear
Nucle
ar ca
pacit
yuti
lizati
on fa
ctor ta
ble
Nucle
ar el
ectric
ityge
nera
tion
Deco
miss
ioned
nucle
ar ca
pacit
yNu
clear
plan
tde
prec
iation
Plan
ned n
uclea
rde
velop
men
t IRP
2010
new
built
Nucle
ar co
st in
ZAR
per M
W
Nucle
arca
pital
cost
<Tim
e> <Exc
hang
e rate
>
Nucle
ar en
ergy
cons
tructi
on tim
e
<Nuc
lear g
reen
inves
tmen
t>
Nucle
ar ca
pacit
y fro
mgr
een i
nves
tmen
t
Futur
e nuc
lear e
nerg
yinv
estm
ent
<Tim
e>
65
Nuclear power is similarly used to cater for base load demand in South Africa.
This module is used to estimate the amount of electricity generated from nuclear
energy. The module thus consists of two stocks, nuclear plant capacity and de-
commissioned nuclear capacity. The nuclear capacity is increased through plant
construction and decreased due to depreciation. The planned nuclear plant
investment as stipulated in the IRP 2010 is accounted for. The nuclear electric-
ity generation estimated in this module is utilised in other modules to estimate
among other things: (i) the share of this technology contribution to the overall
electricity generation; (ii) the resulting emissions from nuclear; (iii) employment
created; and (iv) water use in nuclear electricity generation.
66
Anne
x 2.
8
Hyd
roel
ectr
icity
mod
ule
Hydr
opow
erca
pacit
yHy
drop
ower
cons
tructi
on
Hydr
opow
erge
nera
tion
Hydr
opow
erco
nstru
ction
table
<Tim
e>
Aver
age h
ydro
powe
rlife
span
Initia
l hyd
ropo
wer
instal
led ca
pacit
y
Conv
ersio
n fac
torhy
drop
ower
Conv
ersio
n fac
tortab
le hy
drop
ower
<Tim
e>
Deco
miss
ioned
hydr
opow
er ca
pacit
yHy
dro p
ower
depr
eciat
ion
67
The hydroelectric module is used to estimate the amount of electricity
generation from hydropower. The module thus consists of two stocks, hydro
plant capacity and decommissioned hydro capacity. The hydro capacity is
increased through plant hydropower construction and decreased due to
depreciation. Being a water scarce country, hydropower development is not a
major electricity generation in the country. The major hydropower contribution
to the energy mix is through imports from the neighbouring countries. This
was, however, not taken into account since the consideration for the modelling
process is on the hydroplant investments planned within the country. Micro
and mini-hydro projects are also stipulated to be developed although the
planned development over the years is not explicit in the IRP 2010. Similarly,
the hydroelectricity generation estimated in this module is utilised in other
modules to estimate, among other things: (i) the share of this technology’s
contribution to the overall electricity generation; (ii) the resulting emissions
from hydro; (iii) employment created; and (iv) water use in hydroelectricity
generation.
68
Anne
x 2.
9
Pum
ped
stor
age
elec
tric
ity m
odul
e
Conv
ersio
n fac
torpu
mpe
d stor
age
Pum
ped
stora
geca
pacit
yPu
mpe
d stor
age
cons
tructi
on
<Tim
e>Pu
mpe
d stor
age
aver
age l
ife
Initia
l pum
ped s
torag
eins
talled
capa
city
Conv
ersio
n fac
tor ta
blepu
mpe
d stor
age
<Tim
e>
Pum
ped s
torag
eco
nstru
ction
table
Pum
ped s
torag
eele
ctrici
ty ge
nera
tion
Deco
miss
ioned
pum
ped s
torag
eca
pacit
yPu
mpe
d stor
age
depr
eciat
ion
69
The pumped storage electricity module is used to estimate the amount of
electricity generation from pumped storage. It consists of two stocks, pumped
storage capacity and decommissioned pumped storage capacity. The pumped
storage capacity is increased through power plant construction and decreased
due to depreciation. The two pumped storage plants that are currently under
construction were taken into account in the module. Similarly, the pumped
storage electricity generation estimated in this module is utilised in other
modules to estimate among other things: (i) the share of this technology
contribution to the overall electricity generation; (ii) the resulting emissions
from pumped storage; (iii) employment created; and (iv) water use in pumped
storage electricity generation.
70
Anne
x 2.
10
Sol
ar e
lect
ricity
mod
ule
Solar
elec
tricity
gene
ratio
n in G
Wh
CSP
cost
in ZA
Rpe
r MW
Solar
elec
tricity
gene
ratio
n in M
Wh
Poten
tial e
lectric
ityge
nera
tion C
SP
<Tim
e>
Futur
e CSP
inves
tmen
t
CSP
cons
tructi
ontim
e
Solar
aver
age
lifetim
e
Capa
city f
actor
solar
PV
<Hou
rs pe
r day
>
<Day
s per
year
>
<MW
h to G
Wh>
<Unit
conv
ersio
n>
CSP
capit
al co
st
<Tim
e>
<Pote
ntial
electr
icity
gene
ratio
n CSP
>
Deco
miss
ioned
CSP
capa
city
CSP
disca
rd
CSP
plant
unde
rco
nstru
ction
CSP
plant
cons
tructi
on st
art
Solar
new
capa
city
Aver
age C
SPco
nstru
ction
time
CSP
capa
city
Plan
ned C
SPde
velop
men
t IRP
2010
com
mitte
d
Plan
ned s
olar P
VIR
P 20
10 ne
w bu
ilt
<Exc
hang
e rate
>
<CSP
gree
ninv
estm
ent>
Solar
PV
cost
in ZA
Rpe
r MW
Poten
tial e
lectric
ityge
nera
tion s
olar P
V<T
ime>
Futur
e sola
r PV
inves
tmen
t
Solar
PV
cons
tructi
on tim
e
Solar
PV
aver
age
lifetim
e
<Hou
rs pe
r day
><D
ays p
er ye
ar>
<Unit
conv
ersio
n>So
lar P
V ca
pital
cost
<Tim
e>
Deco
miss
ioned
solar
PV
capa
city
Solar
PV
disca
rd
Solar
PV
plant
unde
rco
nstru
ction
Solar
PV
plant
cons
tructi
on st
art
Solar
PV
new
capa
city
Aver
age s
olar P
Vco
nstru
ction
time
Solar
PV
capa
city
<Exc
hang
e rate
>
<Sola
r PV
gree
ninv
estm
ent>
Solar
PV
capa
city f
rom
gree
n inv
estm
ent
<Tim
e>
CSP
capa
city f
rom
gree
n inv
estm
ent
<Tim
e>
Capa
city f
actor
solar
CSP
<Pote
ntial
electr
icity
gene
ratio
n sola
r PV>
71
The solar electricity module estimates the amount of electricity generation from
two solar energy technologies, namely, concentrated solar power (CSP) and
solar photovoltaic (PV). Currently, these technologies are non-existent in the
South African energy mix. However, IRP 2010 has stipulated the development
of these solar technologies and processes are underway to bring these into
the electricity generation mix. The module thus consists of three stocks for
each of the solar technology namely: (i) CSP plant under construction and solar
PV plant under construction; (ii) CSP capacity and solar PV capacity; and (iii)
decommissioned CSP capacity and decommissioned solar PV capacity. Both
technologies are increased through power plant construction and decreased
due to depreciation. The planned solar PV and CSP development as per the
IRP 2010 were taken into account in the module. Similarly, the solar electricity
generation estimated for both technologies is utilised in other modules to
estimate among other things: (i) the share of these technology contribution to
the overall electricity generation; (ii) the resulting emissions from solar PV and
CSP; (iii) employment created; and (iv) water use in solar PV and CSP electricity
generation.
72
Anne
x 2.
11
Win
d el
ectr
icity
mod
ule
Hour
s per
day
Wind
elec
tricity
gene
ratio
n in G
Wh
Wind
O &
M co
st in
ZAR
per M
WW
ind el
ectric
ityge
nera
tion i
n MW
h
Poten
tial e
lectric
ityge
nera
tion w
ind<T
ime>
Wind
cons
tructi
ontim
e
Wind
capit
al life
time
Days
per y
ear
MW
h to G
Wh
Wind
capa
city
factor
Unit c
onve
rsion
<Offic
ial ex
chan
gera
te>W
ind O
& M
cost
inUS
D pe
r MW
<Tim
e><P
otenti
al ele
ctrici
tyge
nera
tion w
ind>
Deco
miss
ioned
wind
capa
city
Wind
disc
ard
Wind
plan
tun
der
cons
tructi
onW
ind pl
ant
cons
tructi
on st
art
Wind
new
capa
city
Aver
age w
indco
nstru
ction
time
Wind
capa
city
Initia
l wind
capa
city
Plan
ned w
indde
velop
men
t IRP
2010
com
mitte
d
Wind
cost
in ZA
Rpe
r MW
Futur
e wind
inves
tmen
t
Wind
capit
al co
st
<Tim
e>
<Exc
hang
e rate
>
<Wind
gree
ninv
estm
ent>
Wind
capa
city f
rom
gree
n inv
estm
ent
<Tim
e>
73
The wind electricity module estimates the amount of electricity generation
from wind. Currently, wind plays a very minor role in South Africa’s energy
mix, where only a demonstration plant is in existence (Darling Wind Power).
IRP 2010 has, however, stipulated further development of wind and processes
are underway to bring this technology into the electricity generation mix. The
module thus consists of three stocks for each of the wind technology namely: (i)
wind plant under construction; (ii) wind capacity; and (iii) decommissioned wind
capacity. Wind capacity is increased through power plant construction and
decreased due to depreciation. The planned wind power development as per
the IRP 2010 was taken into account in the module. Similarly, wind electricity
generation estimated is utilised in other modules to estimate, among others: (i)
the share of this technology’s contribution to the overall electricity generation;
(ii) the resulting emissions from wind electricity generation; (iii) employment
created; and (iv) water use in wind electricity generation.
74
Anne
x 2.
12
Ele
ctric
ity g
ener
atio
n te
chno
logy
sha
re m
odul
e
Elec
tricity
net
expo
rtsDo
mes
ticall
ypr
oduc
ed el
ectric
ity
Gene
ratio
n dist
ributi
on an
dtra
nsm
ission
loss
es
Elec
tricity
lose
s
<Tim
e>
Total
elec
tricity
dem
and
Requ
ired t
otal
electr
icity
gene
ratio
n
Elec
tricity
lose
s ove
r total
electi
ricy g
ener
ation
Requ
ired e
lectric
ityge
nera
tion f
rom
coal
Desir
ed el
ectric
ityge
nera
tion f
rom
rene
wable
s
Rene
wable
elec
tricity
gene
ratio
n gapTo
tal re
newa
bleele
ctrici
ty ge
nera
tion
Shar
e of n
uclea
rpo
wer
Shar
e of
hydr
opow
er
Shar
e of p
umpe
dsto
rage
powe
r
Shar
e of re
newa
blepo
wer
Shar
e of c
oal
<Wind
elec
tricity
gene
ratio
n in G
Wh>
<Sola
r elec
tricity
gene
ratio
n in G
Wh>
<Nuc
lear e
lectric
ityge
nera
tion>
<Hyd
ropo
wer
gene
ratio
n>
<Pum
ped s
torag
eele
ctrici
ty ge
nera
tion>
<Tota
l sec
tors
electr
icity
dem
and>
<Coa
l elec
tricity
gene
ratio
n>
Total
elec
tricity
gene
ratio
n
Shar
e of
solar
powe
rSh
are o
fwi
nd po
wer
Total
rene
wable
elec
tricity
and h
ydro
powe
r gen
erati
on
Shar
e of b
iomas
s<E
lectric
ity ge
nera
tion
from
biom
ass G
Wh>
75
The electricity generation technology share module endogenously
estimates the share of each of the technologies to the electricity generation
mix throughout the simulation period, that is up to 2030. The technologies
considered are coal, nuclear, pumped storage, hydro, solar, wind and biomass.
The information on the electricity generated for each of the technologies is
obtained from the respective modules that estimate the generation. The share
of the specific technology in a specific year is given as electricity generation
by the technology divided by the sum of the electricity generated from all the
technologies. The module further estimates the required electricity generation
from coal, which is the difference between the total electricity required and
the sum of electricity generation from other technologies excluding coal.
What makes up the total required electricity generation is the total electricity
demand. Total electricity demand is the sum of demand from the various
electricity consumers, net exports and electricity losses.
76
Anne
x 2.
13
Ele
ctric
ity p
rice
mod
ule
Relat
ive el
ectric
itypr
iceEl
ectric
ity pr
ice on
dem
and e
ffect
Aver
age h
ydro
electr
icity
gene
ratio
n cos
t
Aver
age w
ind el
ectric
ityge
nera
tion c
ost
Hydr
oelec
tricity
gene
ratio
n cos
t table
Wind
elec
tricity
gene
ratio
n cos
t table
Aver
age s
olar e
lectric
ityge
nera
tion c
ost
Solar
elec
tricity
gene
ratio
n tab
le
<Exc
hang
e rate
>
<Tim
e>
<Tim
e>
<Tim
e>
Aver
age c
oal e
lectric
ityge
nera
tion c
ost
Coal
electr
icity
gene
ratio
n cos
t table
Aver
age n
uclea
rele
ctrici
ty ge
nera
tion c
ost
Nucle
ar el
ectric
ityge
nera
tion c
ost ta
ble
<Tim
e>
<Sha
re of
coal
powe
r> <Sha
re of
hydr
opow
er>
<Sha
re of
nucle
ar po
wer>
<Sha
re of
pum
ped
stora
ge po
wer>
Aver
age e
lectric
itypr
oduc
tion c
ost
<Sha
re of
solar
powe
r>
<Sha
re of
wind
powe
r>
Initia
l ave
rage
elec
tricity
prod
uctio
n cos
tRe
lative
aver
age
electr
icity
prod
uctio
n cos
t
Aver
age e
xcha
nge
rate
Coal
electr
icity
gene
ratio
nco
st bio
mas
s tab
leAv
erag
e biom
ass
electr
icity
gene
ratio
n cos
t
<Sha
re of
biom
ass>
<Tim
e>
Hydr
opow
erge
nera
tion c
ost fr
actio
n
Wind
elec
tricity
gene
ratio
n cos
t frac
tion
Solar
elec
tricity
gene
ratio
n fra
ction
Nucle
ar el
ectric
ityge
nera
tion c
ost fr
actio
n
Coal
electr
icity
gene
ratio
nco
st bio
mas
s fra
ction
ELEC
TRIC
ITYPR
ICE
SWITC
H
<ELE
CTRI
CITY
PRIC
E SW
ITCH>
77
The electricity price module endogenously estimates the average electricity
production cost which is used as a proxy for electricity price. The key
variables in the estimation is the exogenously determined electricity
generation costs for all the technologies, which follows NERSA’s
determination on electricity growth after 2013; and the share of electricity
generation from each technology, which is endogenously determined. The
average electricity production cost is the sum of average cost of electricity
generation for each technology, multiplied by the respective share of each
technology to the electricity generation mix. Using the average production
cost as a proxy for electricity price, the relative electricity price is used to
influence other modules, such as the production modules (agriculture,
industry and services) and electricity demand by different users.
78
Anne
x 2.
14
Ele
ctric
ity d
eman
d m
odul
e
Effec
t of e
lectric
ity pr
iceon
dem
and
Secto
ral e
lastic
ity of
electr
icity
cons
umpti
onto
price
Secto
ral e
lastic
ity of
electr
icity
cons
umpti
on on
GDP
<Rela
tive r
eal
GDP>
Effec
t of G
DP on
electr
icity
dem
and
Effec
t of g
rowt
h on
electr
icity
dem
and
<Tim
e><T
otal
popu
lation
> Initia
l total
popu
lation
Relat
ive to
talpo
pulat
ionEf
fect o
f pop
ulatio
n on
electr
icity
dem
and
Secto
ral e
lectric
ityde
man
d
Initia
l sec
toral
electr
icity
cons
umpti
on
Total
secto
rsele
ctrici
ty de
man
d
Conv
ert T
Jto
GWh
<Mini
ng an
d qua
rrying
electr
icity
use>
<Tra
nspo
rtele
ctrici
ty us
e>
Mini
ng an
d qua
rrying
electr
icity
use G
Wh
Tran
spor
t elec
tricity
use G
Wh
<Effic
iency
impr
ovem
ent ra
tioind
ustry
from
gree
n inv
estm
ent>
Elec
tricity
cons
umpti
on
<Tota
l elec
tricity
gene
ratio
n>
Elec
tricity
dem
and
over
supp
ly
<Effic
iency
facto
rpo
wer g
ener
ation
>
<Tota
l elec
tricity
dem
and>
<Elec
tricity
price
onde
man
d effe
ct>
Effec
t of e
lectric
ity de
man
dsu
pply
on co
al ge
nera
tion
Indica
ted su
pply
dem
and r
atio
79
The electricity demand module estimates the total electricity demand by the
various electricity users. There are two main purposes of electricity demand
sub-model: firstly, to calculate the electricity demand and secondly, to provide
inputs of the required electricity generation capacity. The demand for electricity
sub-model calculates the electricity demand for industry, transport, agriculture,
commerce and residential sectors, which are the key electricity users in South
Africa.
The electricity demand is modelled as an endogenous variable and is influenced
by changes in endogenous GDP, electricity prices and efficiency in the electricity
consumption due to improved technology. This in turn affects electricity
production from the different technologies.
80
Anne
x 2.
15
Oil
dem
and
mod
ule
Rail p
asse
nger
ener
gy us
e
Rail f
reigh
t ene
rgy
use
Air tr
ansp
ort
pass
enge
rs ca
rried
Initia
l air t
rans
port
pass
enge
rs ca
rried
Air tr
ansp
ort
oil us
e
Othe
r tran
spor
toil
use
Elas
ticity
of ai
rpa
ssen
ger tr
avel
to GD
PAi
r ene
rgy u
se pe
rpa
ssen
ger ta
ble
Rail g
oods
trans
porte
d
<Rela
tive r
eal
GDP>
Initia
l othe
r tran
spor
ten
ergy
use
Elas
ticity
of w
ater tr
ansp
ort
ener
gy us
e to G
DP<R
elativ
e rea
lGD
P>
Rail e
nerg
y use
per
millio
n km
ton
Initia
l rail g
oods
trans
porte
d
Elas
ticity
of ra
il goo
dstra
nspo
rted t
o GDP
<Rela
tive r
eal
GDP>
<Rela
tive t
otal
popu
lation
>
Elas
ticity
of ai
r pas
seng
ertra
vel to
popu
lation
Rail p
asse
nger
sca
rried
Initia
l rail
pass
enge
rs ca
rried
Elas
ticity
of ra
ilpa
ssen
gers
carri
edto
GDP
<Rela
tive t
otal
popu
lation
>
Elas
ticity
of ra
il pas
seng
ers
carri
ed to
popu
lation
Rail e
nerg
y use
per
millio
n km
pass
enge
r
Rail t
rans
port
electr
icity
use
Rail t
rans
port
oil us
e
Rail t
rans
port
ener
gy us
e
Elec
tricity
shar
e of ra
iltra
nspo
rt ene
rgy u
se
<Rela
tive r
eal
GDP>
Road
trave
l per
vehic
le
Initia
l road
trave
lpe
r veh
icle
Elas
ticity
of ro
ad tra
vel
per p
asse
nger
to G
DP
Elas
ticity
of ro
ads t
rave
l per
pass
enge
r to ro
ad de
nsity
<Rela
tive r
eal
GDP>
Road
s veh
icle e
nerg
y use
per m
illion k
m tra
nspo
rt
Road
trans
port o
ilus
e
Road
trans
port
gas u
se
Road
trans
port
ener
gy us
e<T
ime>
Desir
ed m
otor
vehic
le sto
ck
Moto
rve
hicles
Vehic
le sa
lesVe
hicle
dispo
sal
Initia
l des
ired m
otor v
ehicl
eow
nersh
ip pe
r cap
ita
Aver
age v
ehicl
elife
time
Initia
l moto
rve
hicles
<Tota
lpo
pulat
ion>
Desir
ed m
otor v
ehicl
eow
nersh
ip pe
r cap
ita
Vehic
le sa
lesad
justm
ent ti
me
Elas
ticity
of ve
hicle
owne
rship
to GD
P
<Rela
tive r
eal
GDP>
Road
trans
port v
olum
ein
millio
n km
<Rela
tive k
m of
road
s per
ha>
<Moto
r veh
icles
>
<Time>
<Time>
Ener
gy ef
ficien
cytab
le
<Time>
<Time>
Tran
spor
toil
useTran
spor
tga
s use
Rail t
rans
port e
mplo
ymen
t
Road
trans
port e
mplo
ymen
t
Air tr
ansp
ort e
mplo
ymen
t
Road
trans
port
emplo
ymen
t facto
r
Air tr
ansp
ort
emplo
ymen
t facto
r
Rail t
rans
port
emplo
ymen
t facto
r
Tran
spor
t sec
tor em
ploym
ent
<Time>
<Time>
Tran
spor
t em
ploym
ent in
prim
ary m
odes
Shar
e of e
mplo
ymen
t inpr
imar
y mod
es<T
ime>
Tran
spor
tele
ctrici
ty us
eTo
tal tra
nspo
rten
ergy
use
Oil s
hare
of ro
ads
ener
gy us
e
<Effic
iency
impr
ovem
ent ra
tiotra
nspo
rt fro
m gr
een
inves
tmen
t> <Effic
iency
impr
ovem
ent ra
tiotra
nspo
rt fro
mgr
een i
nves
tmen
t>
<Effic
iency
impr
ovem
ent
ratio
trans
port f
rom
gree
n inv
estm
ent>
<Roa
d tra
nspo
rt volu
me
in m
illion k
m>
<Rail
pass
enge
rsca
rried
>
<Air t
rans
port
pass
enge
rs ca
rried
>
<Rail
good
stra
nspo
rted>
<Rela
tive a
vera
geoil
price
>
Elas
ticity
of oi
l pric
e on
road
trans
port o
il use
81
The oil demand module estimates the total oil demand by the various liquid
fuel users, which include industry, commerce, residential, agriculture, non-
energy oil use and non-specified oil use. The total oil demand is the sum of
oil use by different users. Oil price and GDP are the main factors influencing
changes in oil demand.
82
Anne
x 2.
16
Gas
dem
and
mod
ule
GDP
gas d
eman
dpa
ram
eter in
dustr
y
Indus
try ga
s use
<Tim
e>
Non-
spec
ified g
asus
e
Resid
entia
lga
s use
Total
gas d
eman
d
Gas d
eman
d inte
nsity
non-
spec
ified u
se
<Tim
e>
Gas d
eman
dint
ensit
y res
identi
al
<Tra
nspo
rtga
s use
><M
ining
and
quar
rying
gas u
se>
<Rea
l GDP
>
Com
mer
cega
s use
Gas d
eman
dint
ensit
y com
mer
ce
<Tim
e>
83
Gas plays a minimal role in the South African energy market. This module
estimates the total gas demand by the various gas users. These are categorised
as: industry, commerce, residential, mining and quarrying, transport and non-
specified gas use. The total gas demand is the sum of gas use by different
users. Only GDP is the main driver influencing changes in gas demand.
84
Anne
x 2.
17
Air
emis
sion
s m
odul
e
<Wind
elec
tricity
gene
ratio
n in G
Wh>
<Sola
r elec
tricity
gene
ratio
n in G
Wh>
CO2 e
miss
ions c
oal
per k
Wh
Emiss
ions f
rom
coal
Conv
ert
GWh/k
Wh
CO2 e
miss
ions
nucle
ar pe
r kW
h
Emiss
ions f
rom
nucle
ar
<Con
vert
GWh/k
Wh>
CO2 e
miss
ions p
umpe
dsto
rage
per k
WhEm
ission
s fro
m pu
mpe
dsto
rage
and h
ydro
powe
r
CO2 e
miss
ions w
indpe
r kW
h
Emiss
ions f
rom
wind
CO2 e
miss
ions s
olar
per k
Wh
Emiss
ions f
rom
solar
<Con
vert
GWh/k
Wh>
Emiss
ion fro
mpo
wer s
ector
Initia
l GHG
emiss
ion fro
mele
ctrici
ty ge
nera
tionRe
lative
air e
miss
ion fro
mele
ctrici
ty ge
nera
tion
<Nuc
lear e
lectric
ityge
nera
tion>
<Hyd
ropo
wer
gene
ratio
n>
<Pum
ped s
torag
eele
ctrici
ty ge
nera
tion>
Cum
ulativ
e air
emiss
ions
Annu
al CO
2em
ission
sAi
r em
ission
sde
com
posit
ion
Emiss
ions
deco
mpo
sition
facto
r
CO2 e
miss
ions p
er Z
ARof
indus
try pr
oduc
tion
exclu
ding e
lectric
ity
CO2 e
miss
ions f
rom
indus
try ex
cludin
g elec
tricity
<Indu
stry
prod
uctio
n>
Emiss
ions f
rom
non-
electr
icity
gene
ratio
n
CO2 e
miss
ions f
rom
resid
entia
l com
mer
cial s
ector
spe
r cap
ita
CO2 e
miss
ions f
rom
trans
port p
er un
it ene
rgy
used
CO2 e
miss
ions f
rom
othe
rse
ctors
per Z
AR of
GDP
CO2 e
miss
ions f
rom
trans
port
CO2 e
miss
ions f
rom
resid
entia
l com
mer
cial
secto
rs
CO2 e
miss
ions f
rom
other
secto
rs
<Tota
l tran
spor
ten
ergy
use>
<Rea
l GDP
><T
otal
popu
lation
>
<Tim
e>
<Tim
e><T
ime>
<Tim
e>
Fertil
izer C
O2em
ission
facto
rFe
rtilize
r CO2
emiss
ion
<Che
mica
lfer
tilizer
use>
CO2 e
miss
ion fro
mag
ricult
ure
Total
indu
stry C
O2em
ission
s
<Em
ission
from
powe
r sec
tor>
Indus
trial e
miss
ionab
atem
ent fr
actio
n
Indus
trial e
miss
ionab
atem
ent re
ducti
onfra
ction
<Tim
e>
Indus
trial a
batem
ent
fracti
on in
dicate
d<In
dicate
d gre
en ec
onom
yinv
estm
ent fr
actio
n>
Indus
trial e
miss
ionab
atem
ent
<Effic
iency
impr
ovem
ent ra
tioind
ustry
from
gree
ninv
estm
ent>
Emiss
ions
inten
sity
<Tim
e>
Shar
e of e
miss
ions i
ntra
nspo
rt
<Coa
l elec
tricity
gene
ratio
n><T
otal e
lectric
ityge
nera
tion>
<Ann
ual C
O2em
ission
s>
85
The air emissions module calculates the carbon emission equivalents from
various sources. Air emissions dynamics, by its nature, is complex and
influenced by the characteristics of the emission source. The module consists
of one stock, cumulative air emissions. This is increased by the annual CO2
emissions and decreased by air emissions decomposition. There are different
sources of CO2 emissions that are used in the estimation of the annual
emissions. These include emissions from the power sector and other industries,
excluding power sector, agriculture, transport, residential and commerce
sectors. Thus, the sum of emissions from the different sources provides the
estimate of the annual CO2 emissions. Generally, power sector emission is
considered as the largest contributor of the total CO2 emissions in South
Africa. The total emission from this sector is the sum of the annual electricity
generated by each technology, multiplied by each technology respective of life
cycle CO2 emissions.
86
Anne
x 2.
18
Min
eral
s m
odul
e (a
)
Mini
ng in
dustr
y tota
lem
ploym
ent
Ratio
of m
ining
value
adde
d ove
rop
erati
ng su
rplus
Nom
inal m
ining
prod
uctio
nM
ining
prod
uctio
n
<GDP
defla
tor>
<Tim
e>
Mini
ng w
age
per p
erso
n
Mini
ng sa
laries
and w
ages
<Tim
e>
<Gold
and u
raniu
m or
em
ining
emplo
ymen
t>
<Othe
r mini
ngem
ploym
ent>
<Coa
l extr
actio
nem
ploym
ent>
<Gold
extra
ction
volum
e>
<PGM
extra
ction
volum
e>
<Coa
l extr
actio
nvo
lume>
Prim
ary m
inera
lex
tracti
on vo
lume
Othe
r mine
ral
extra
ction
volum
eSh
are o
f prim
ary m
inera
l intot
al m
ining
extra
ction
volum
e
Mini
ng to
talex
tracti
on vo
lume
Mini
ng se
ctor
incom
e per
ton
Mini
ng se
ctor
incom
e
Mini
ng co
sts ex
cludin
gsa
laries
and w
ages
per to
n
Mini
ng co
sts ex
cludin
gsa
laries
and w
ages
Mini
ng se
ctor
expe
nditu
re
Mini
ng op
erati
ngsu
rplus
<Tim
e>
<Tim
e>
Mini
ng an
d qua
rrying
electr
icity
use
Mini
ng an
d qua
rrying
coal
use
Mini
ng an
d qua
rrying
gas u
se
Mini
ng an
dqu
arryi
ng oi
l use
Mini
ng an
d qua
rrying
total
ener
gy us
e
Initia
l mini
ngpr
oduc
tionRe
lative
mini
ngpr
oduc
tion
Coal
shar
e of m
ining
and
quar
rying
ener
gy us
e
Oil s
hare
of m
ining
and
quar
rying
ener
gy us
e
Gas s
hare
of m
ining
and
quar
rying
ener
gy us
e
Mini
ng an
d qua
rrying
total
ener
gy us
e per
unit
mini
ng pr
oduc
tion
Elec
tricity
shar
e of m
ining
and q
uarry
ing en
ergy
use
<Tim
e> <Tim
e>
87
The minerals module (a) shows the section of the minerals module that
estimates employment from mining, mining production and mining energy
use, that is oil, gas and electricity. The mining employment is categorized into
gold and uranium ore mining employment, coal extraction employment and
other mining employment (PGM). The employment estimates are based on
the mining extraction volumes for each mining category and their respective
employment coefficients. The primary extraction volumes are used to estimate
the economic value of the mining activities, which in turn is utilised to estimate
the amount of energy used in the mining sector. Some of the variables
estimated in this module, such as mining industry employment, are used in
other modules.
88
Anne
x 2.
19
Min
eral
s m
odul
e (b
)
Undis
cove
red
gold
reso
urce
sPr
oven
gold
rese
rves
Gold
disco
very
Gold
extra
ction
volum
e
Initia
l und
iscov
ered
gold
reso
urce
sIni
tial p
rove
ngo
ld re
serve
s
Gold
expo
rtsGo
ld ex
port p
rice
per to
n
Gold
and u
raniu
m or
e mini
ngem
ploym
ent p
er to
n gold
prod
uced
Frac
tion o
f gold
reso
urce
sex
tracte
d per
year
<Ava
ilabil
ity of
labou
r force
>Go
ld ex
tracti
onca
pital
lifetim
e
Gold
and u
raniu
m or
em
ining
emplo
ymen
t
Gold
expo
rtvo
lume
Shar
e of g
oldex
porte
d
Gold
extra
ction
capit
alRe
al go
ld ex
tracti
onfix
ed in
vestm
ent
Gold
extra
ction
capit
alde
prec
iation
<Mini
ng m
anufa
cturin
g and
cons
tructi
on ex
pend
iture
>
Shar
e of g
old ex
tracti
on in
mini
ng in
vestm
ent
Initia
l gold
extra
ction
capit
al
Gold
extra
ction
fixed
inves
tmen
t
<GDP
defla
tor>
<Tim
e>
Relat
ive go
ldex
tracti
on ca
pital
Gold
disco
very
table
Undis
cove
red
PGM
reso
urce
sPr
oven
PGM
rese
rves
PGM
disc
over
yPG
M ex
tracti
onvo
lume
<Tim
e>
Initia
l und
iscov
ered
PGM
reso
urce
sIni
tial p
rove
nPG
M re
serve
s
PGM
expo
rtsPG
M ex
port p
rice
per to
n
Othe
r mini
ng em
ploym
ent
per to
n PGM
prod
uced
Frac
tion o
f PGM
reso
urce
s extr
acted
per y
ear
<Ava
ilabil
ity of
labou
r force
>
PGM
extra
ction
capit
al life
time
Othe
r mini
ngem
ploym
ent
PGM
expo
rtvo
lume
Shar
e of P
GMex
porte
d
PGM
extra
ction
capit
alRe
al PG
M ex
tracti
onfix
ed in
vestm
ent
PGM
extra
ction
capit
al de
prec
iation
Shar
e of P
GM ex
tracti
onin
mini
ng in
vestm
entInitia
l PGM
extra
ction
capit
al
PGM
extra
ction
fixed
inves
tmen
t<G
DP de
flator
><T
ime>
Relat
ive P
GMex
tracti
on ca
pital
Undis
cove
red
coal
reso
urce
sPr
oven
coal
rese
rves
Coal
disco
very
Coal
extra
ction
volum
e
Initia
l und
iscov
ered
coal
reso
urce
s
Initia
l pro
ven
coal
rese
rves
Coal
expo
rtsCo
al ex
port p
rice
per to
n
Coal
mini
ng em
ploym
ent
per to
n coa
l pro
duce
d
Frac
tion o
f coa
l reso
urce
sex
tracte
d per
year
<Ava
ilabil
ity of
labou
r force
>
Coal
extra
ction
capit
al life
time
Coal
extra
ction
emplo
ymen
t
Coal
expo
rtvo
lume
Shar
e of c
oal
expo
rted
Coal
extra
ction
capit
alRe
al co
al ex
tracti
onfix
ed in
vestm
ent
Coal
extra
ction
capit
alde
prec
iation
Shar
e of c
oal e
xtrac
tion i
nm
ining
inve
stmen
t
Initia
l coa
l extr
actio
nca
pital
Coal
extra
ction
fixed
inves
tmen
t<G
DP de
flator
>
<Tim
e>
Relat
ive co
alex
tracti
on ca
pital
<Tim
e>
Mini
ng fix
edinv
estm
ent
Ratio
of m
ining
inve
stmen
t ove
rgo
vern
men
t exp
endit
ure i
n mini
ng,
man
ufactu
ring a
nd co
nstru
ction
<Tim
e>
<Tim
e>
<Tim
e>
Total
gold
stock
PGM
disc
over
ytab
le
Coal
disco
very
table
<Tim
e>
Total
PGM
stoc
k
Total
coal
stock
Frac
tion o
f gold
reso
urce
sex
tracte
d per
year
table
<Tim
e>
Frac
tion o
f PGM
reso
urce
sex
tracte
d per
year
table
Frac
tion o
f coa
l reso
urce
sex
tracte
d per
year
table
<Tim
e>
<Mini
ng fix
edinv
estm
ent>
<Mini
ng fix
edinv
estm
ent>
89
The minerals module sketch (b) shows the section of the minerals module that estimates the primary
extraction of the mineral resources and their respective extraction capital. The primary mineral extraction
is categorised into coal, gold and PGM. Each of the mineral extraction consists of two stocks namely:
undiscovered mineral resources and proven mineral resources. The proven reserves are influenced by
changes in the mineral discovery and the minerals extraction volumes. As mentioned earlier, the ex-
traction volumes are used to estimate the employment created in the mining industry. The extraction
capital for each mining industry consists of one stock, which is influenced by the fixed investment (which
increase it) and depreciation of capital (which decreases it).
The minerals module (b) shows the section of the minerals module that
estimates the primary extraction of the mineral resources and their respective
extraction capital. The primary mineral extraction is categorised into coal,
gold and PGM. Each of the mineral extraction consists of two stocks namely:
undiscovered mineral resources and proven mineral resources. The proven
reserves are influenced by changes in the mineral discovery and the minerals
extraction volumes. As mentioned earlier, the extraction volumes are used to
estimate the employment created in the mining industry. The extraction capital
for each mining industry consists of one stock, which is influenced by the fixed
investment (which increases it) and depreciation of capital (which decreases it).
90
Anne
x 2.
20
Popu
latio
n m
odul
e
Popu
lation
Birth
sDe
aths
Initia
l pop
ulatio
n
Death
rate
Sexu
ally a
ctive
female
Prop
ortio
n of fe
male
babie
sAg
e spe
cific
fertilit
ydis
tributi
on ta
bleAg
e spe
cific
fertilit
ydis
tributi
on
Life e
xpec
tancy
Total
fertil
ity ra
te
Death
rates
table
Total
popu
lation
Popu
lation
coho
rtsh
ift
<Tim
e><T
IME
STEP
>
Natur
al fer
tility De
sired
fertil
ityra
te
Initia
l des
ired
fertilit
y rate
Effec
t of e
cono
mic
cond
itions
on de
sired
num
ber o
f chil
dren
table
Effec
ts of
econ
omic
cond
itions
on fe
rtility
rate
Total
birth
s
Total
death
s
Total
sexu
ally a
ctive
female
Perce
ived i
ncom
e per
capit
a ppp
Offic
ial ex
chan
gera
te
Norm
al life
expe
ctanc
y
MFL
E dif
feren
ce
Aver
age l
ifeex
pecta
ncy
Crud
e migr
ation
rate
table
Net m
igrati
on
<Tim
e>
Total
migr
ation
Effec
t of e
duca
tion o
npr
opor
tion u
sing
contr
acep
tive t
able
Contr
acep
tive
prev
alenc
e<A
vera
ge ad
ultlite
racy
rate>
<Rea
l GDP
per c
apita
>
Norm
al life
expe
ctanc
y tab
le Loca
l con
dition
sad
justm
ent fa
ctor
Time f
or in
com
e cha
nges
to aff
ect li
fe ex
pecta
ncy
Scho
ol ag
epo
pulat
ion<R
elativ
e PC
real
dispo
sable
inco
me>
<Tim
e>
Adult
popu
lation
Relat
ive lif
eex
pecta
ncy
Initia
l life
expe
ctanc
y
Popu
lation
grow
th
91
The population module is important since the variables estimated were used to
estimate some social, environmental and economic indicators in other modules.
The module represents the South African population, which is categorized
according to sex and age groups. These include, among others, primary school
age, working age and adult age. The module consists of one stock variable
– population – which influences three rate variables namely: births, deaths
and net migration. It is estimated that the birth and death variables are both
influenced by the economic conditions, which in turn influence the level of the
population stock. Changes in population influence variables in other modules,
such as water demand, energy demand and GDP.
92
Anne
x 2.
21
Educ
atio
n m
odul
e
Stud
ents
Entra
nce r
ate
<Pop
ulatio
n>
Prop
ortio
n of s
choo
l age
willin
g to g
o to s
choo
l
Drop
-out
rate
Drop
-out
rate
fracti
on
<Initia
lpo
pulat
ion> Tim
e to c
omple
teon
e gra
de
Initia
l pro
portio
n of
child
ren i
n sch
ool
Youn
g lite
rate
popu
lation
Com
pletio
n rate
Time t
o bec
ome
adult
Adult
liter
atepo
pulat
ionBe
com
ing ad
ultLit
erate
adult
death
s
<Initia
lpo
pulat
ion>
Initia
l ave
rage
litera
cy ra
te
Adult
crud
e dea
thra
te
<Pop
ulatio
n><D
eaths
>
Aver
age a
dult
litera
cy ra
te
<Pop
ulatio
n>
Total
popu
lation
insc
hool
age
Total
stud
ents
Effec
t of in
com
e on
willin
gnes
s to a
ttend
table
Poten
tial n
ewstu
dents
Total
popu
lation
of sc
hool
age s
tuden
t not
in sc
hool
Time t
aken
to en
rol
stude
ntAv
erag
e gro
ssint
ake r
ate
<Pop
ulatio
n>
GDP
defla
tor
Real
educ
ation
expe
nditu
re<T
ime>
Aver
age e
duca
tion
expe
nditu
reTim
e for
educ
ation
expe
nditu
re to
impa
cton
servi
ces
Frac
tion a
lloca
ted to
prim
ary e
duca
tion
Aver
age c
ost p
erstu
dent
Scho
ol sy
stem
capa
city
Vaca
nt pla
ces
at sc
hool
Educ
ation
expe
nditu
repe
r stud
ent
Initia
l edu
catio
nex
pend
iture
per s
tuden
t
Relat
ive ed
ucati
onex
pend
iture
per s
tuden
t
Effec
t of e
duca
tion
expe
nditu
re on
drop
-out
Effec
t of e
duca
tion
expe
nditu
re on
drop
-out
table
Initia
l dro
p-ou
tfra
ction
<PC
real
dispo
sable
inco
me>
<Edu
catio
nex
pend
iture
>
Relat
ive av
erag
e adu
ltlite
racy
rate
93
Figure 25. Education module sketch
Similar to the population module, the education module is also important
since the variables estimated are useful in influencing social, economic and
environmental indicators. The module shows in a systematic manner the
movement of the population from school-age to adulthood. The module is
categorised into three stocks: student, young literate population and adult
literate population. Entrance and dropout rates determine the number of
population of students (pupils – from primary school to matric). The factors
influencing entrance rate includes the number of population who are of
school-age (7 years), income and vacant places at school. Students move
to be part of the young literate population once they complete their higher
education, and then on to adult literate population when their age as part of
the young population lapses. Average adult literacy rate, which is estimated
in this module, is utilised in other modules, such as population and production
modules (agriculture, industry, services).
94
Anne
x 2.
22
Hea
lth m
odul
e
<Hea
lthex
pend
iture
>
<GDP
defla
tor>
Real
healt
hex
pend
iture
Healt
h bud
get fo
r new
centr
es co
nstru
ction
Healt
h cen
tres
Healt
h cen
treco
nstru
ction
Healt
h cen
tredis
rupti
onAv
erag
e cos
tpe
r hea
lth ce
ntre
<Time>
Popu
lation
serve
d with
healt
h car
ePo
pulat
ion pe
rhe
alth c
entre
Initia
l pop
ulatio
n per
healt
h cen
tre
Relat
ive po
pulat
ionpe
r hea
lth ce
ntre
<Tota
lpo
pulat
ion>
Area
cove
red b
yhe
alth c
entre
sProp
ortio
n of a
rea
cove
red b
y hea
lth ce
ntre
Acce
ss to
the b
asic
healt
h car
ePo
pulat
ionde
nsity
table
Area
cove
red
per h
ealth
centr
e
Wor
king h
ealth
centr
esRu
nning
cost
per
healt
h cen
tre
Initia
l runn
ing co
st pe
rhe
alth c
entreHe
alth c
entre
sru
nnnin
g cos
t Prop
ortio
n of w
orkin
ghe
alth c
entre
s
Time f
or ru
nning
costs
to aff
ect fo
nctio
ning o
fhe
alth c
entre
<Tota
l land
>
Healt
h cen
treav
erag
e life
95
Figure 26. Health module sketch
The health module is aimed at estimating access to health centres by the South African population. It consists
of one stock – health centres – which is increased by construction and decreased by disruption (due to depre-
ciation). The factors influencing heath centres construction is the health expenditure that is allocated to heath
centre construction and the associated average costs of construction. There are two main variables estimated
in this module: the number of population per health centre and access to the basic health care based on the
area covered by the health centres relative to the total land of South Africa. Given that the data on the health
centres was sparse, the estimates from this module were not linked to other modules.
The health module is aimed at estimating access to health centres by the
South African population. It consists of one stock – health centres – which is
increased by construction and decreased by disruption (due to depreciation).
The factors influencing heath centres construction is the health expenditure
that is allocated to heath centre construction and the associated average costs
of construction. There are two main variables estimated in this module: the
number of population per health centre and access to the basic health care
based on the area covered by the health centres relative to the total land
of South Africa. Given that the data on the health centres was sparse, the
estimates from this module were not linked to other modules.
96
Anne
x 2.
23
Acce
ss to
road
s m
odul
e
Road
unde
rco
nstru
ction
Func
tionin
g roa
dRo
ad co
nstru
ction
starts
Road
com
pletio
nRo
ad di
srupti
on
Real
trans
porta
tion a
ndco
mm
unica
tions
expe
nditu
re
Road
main
tenan
ceco
stBu
dget
for ro
adco
nstru
ction
Initia
l cos
t of m
aintai
ning
road
s per
km
Aver
age r
oad
cost
per k
m
Fulfill
ed fra
ction
of ro
adm
ainten
ance
Aver
age r
oad l
ifewi
thout
main
tenan
ce
Time t
o com
plete
road
Effec
t of m
ainten
ance
on ro
ad lif
e
Effec
t of m
ainten
ance
on ro
ad lif
e tab
le
Km of
road
s per
ha
Initia
l km
ofro
ads p
er ha
Relat
ive km
ofro
ads p
er ha
Initia
l road
cost
per k
m
<Tota
l land
>
<Tra
nspo
rt and
com
mun
icatio
nsex
pend
iture
><G
DP de
flator
><T
ime>
Elas
ticity
of ro
adde
nsity
to co
st
Cost
of m
aintai
ning
road
s per
kmCh
ange
s in t
he co
st of
road
main
tenan
ce
Cost
of ro
adm
ainten
ance
grow
th ra
te
Aver
age b
udge
ttim
e
97
Figure 27. Access to road module sketch
The access to roads module is utilised to estimate access to roads
infrastructure. The module consists of three stocks: the first two are road
construction and functioning roads, which systematically shows the dynamics
involved in the road development, and the third stock is the cost of maintaining
roads per kilometre, which shows the changes in cost over time for maintaining
roads. Starting the construction of roads increases the stock of roads that
are under construction. Once they are completed, these move to the stock of
functioning roads. Functioning roads, however, are decreased by disruptions
resulting from depreciations. Maintaining the roads decreases disruptions as
this increases the average road life. The relative kilometre of roads per hectare,
which is the measure of road access, is utilised in other modules such as the
production sectors (agriculture, industry and services).
98
Anne
x 2.
24
Tran
spor
t mod
ule
Rail p
asse
nger
ener
gy us
e
Rail f
reigh
t ene
rgy
use
Air tr
ansp
ort
pass
enge
rs ca
rried
Initia
l air t
rans
port
pass
enge
rs ca
rried
Air tr
ansp
ort
oil us
e
Othe
r tran
spor
toil
use
Elas
ticity
of ai
rpa
ssen
ger tr
avel
to GD
PAi
r ene
rgy u
se pe
rpa
ssen
ger ta
ble
Rail g
oods
trans
porte
d
<Rela
tive r
eal
GDP>
Initia
l othe
r tran
spor
ten
ergy
use
Elas
ticity
of w
ater tr
ansp
ort
ener
gy us
e to G
DP<R
elativ
e rea
lGD
P>
Rail e
nerg
y use
per
millio
n km
ton
Initia
l rail g
oods
trans
porte
d
Elas
ticity
of ra
il goo
dstra
nspo
rted t
o GDP
<Rela
tive r
eal
GDP>
<Rela
tive t
otal
popu
lation
>
Elas
ticity
of ai
r pas
seng
ertra
vel to
popu
lation
Rail p
asse
nger
sca
rried
Initia
l rail
pass
enge
rs ca
rried
Elas
ticity
of ra
ilpa
ssen
gers
carri
edto
GDP
<Rela
tive t
otal
popu
lation
>
Elas
ticity
of ra
il pas
seng
ers
carri
ed to
popu
lation
Rail e
nerg
y use
per
millio
n km
pass
enge
r
Rail t
rans
port
electr
icity
use
Rail t
rans
port
oil us
e
Rail t
rans
port
ener
gy us
e
Elec
tricity
shar
e of ra
iltra
nspo
rt ene
rgy u
se
<Rela
tive r
eal
GDP>
Road
trave
l per
vehic
le
Initia
l road
trave
lpe
r veh
icle
Elas
ticity
of ro
ad tra
vel
per p
asse
nger
to G
DP
Elas
ticity
of ro
ads t
rave
l per
pass
enge
r to ro
ad de
nsity
<Rela
tive r
eal
GDP>
Road
s veh
icle e
nerg
y use
per m
illion k
m tra
nspo
rt
Road
trans
port o
ilus
e
Road
trans
port
gas u
se
Road
trans
port
ener
gy us
e<T
ime>
Desir
ed m
otor
vehic
le sto
ck
Moto
rve
hicles
Vehic
le sa
lesVe
hicle
dispo
sal
Initia
l des
ired m
otor v
ehicl
eow
nersh
ip pe
r cap
ita
Aver
age v
ehicl
elife
time
Initia
l moto
rve
hicles
<Tota
lpo
pulat
ion>
Desir
ed m
otor v
ehicl
eow
nersh
ip pe
r cap
ita
Vehic
le sa
lesad
justm
ent ti
me
Elas
ticity
of ve
hicle
owne
rship
to GD
P
<Rela
tive r
eal
GDP>
Road
trans
port v
olum
ein
millio
n km
<Rela
tive k
m of
road
s per
ha>
<Moto
r veh
icles
>
<Tim
e>
<Tim
e>
Ener
gy ef
ficien
cytab
le
<Tim
e>
<Tim
e>
Tran
spor
toil
useTran
spor
tga
s use
Rail t
rans
port e
mplo
ymen
t
Road
trans
port e
mplo
ymen
t
Air tr
ansp
ort e
mplo
ymen
t
Road
trans
port
emplo
ymen
t facto
r
Air tr
ansp
ort
emplo
ymen
t facto
r
Rail t
rans
port
emplo
ymen
t facto
r
Tran
spor
t sec
tor em
ploym
ent
<Tim
e>
<Tim
e>
Tran
spor
t em
ploym
ent in
prim
ary m
odes
Shar
e of e
mplo
ymen
t inpr
imar
y mod
es<T
ime>
Tran
spor
tele
ctrici
ty us
eTo
tal tra
nspo
rten
ergy
use
Oil s
hare
of ro
ads
ener
gy us
e
<Effic
iency
impr
ovem
ent ra
tiotra
nspo
rt fro
m gr
een
inves
tmen
t> <Effic
iency
impr
ovem
ent ra
tiotra
nspo
rt fro
mgr
een i
nves
tmen
t>
<Effic
iency
impr
ovem
ent
ratio
trans
port f
rom
gree
n inv
estm
ent>
<Roa
d tra
nspo
rt volu
me
in m
illion k
m>
<Rail
pass
enge
rsca
rried
>
<Air t
rans
port
pass
enge
rs ca
rried
>
<Rail
good
stra
nspo
rted>
<Rela
tive a
vera
geoil
price
>
Elas
ticity
of oi
l pric
e on
road
trans
port o
il use
99
The transport module is used to estimate a number of variables such as
transport energy use (electricity, oil and gas) and employment in the transport
sector. Transport is categorised into three modes – roads, rail and air – and the
commodities transported are passengers and goods/cargo. In the case of roads
transportation, the number of vehicles is estimated, where this is a stock, to be
increasing with vehicle sales and declining due to vehicle disposal. The volume
of passengers and goods are thus estimated, which in turn is used to calculate
energy use by the different forms of transportation. Investment in transport
efficiency has an influence in energy use in the sector. Similarly, employment
in the transport sector is determined by the volume of passengers and goods
handled in each of the transport mode. Energy use and employment from
transport variables estimated are utilised in other modules.
100
Anne
x 2.
25
Em
ploy
men
t mod
ule
Agric
ultur
e em
ploym
ent
per h
ectar
e
Total
adult
popu
lation
Labo
urpa
rticipa
tion r
ate
Labo
ur fo
rce
Elas
ticity
labo
ur fo
rcepr
essu
re on
emplo
ymen
t
Agric
ultur
eem
ploym
ent
Agric
ultur
e ind
icated
land l
abou
r rati
oNe
t agr
icultu
rehir
ing
Initia
l agr
icultu
reem
ploym
ent
Agric
ultur
elab
our d
eman
d
Time t
o hire
inag
ricult
ure
Indica
ted ag
ricult
ure
emplo
ymen
t leve
l
Avail
ability
of la
bour
force
Effec
t of e
duca
tion o
nind
icated
land
labo
ur ra
tioag
ricult
ure
Elas
ticity
of ag
ricult
ure
capit
al lab
our r
atio t
oye
ars o
f sch
oolin
g
Initia
l indic
ated
agric
ultur
e lan
d lab
our
ratio
Capit
al pe
r ha o
fha
rveste
d are
a Initia
l cap
ital p
er ha
ofha
rveste
d are
a
Relat
ive ca
pital
per h
a of
harve
sted a
rea
Elas
ticity
of la
bour
dem
and t
o mec
haniz
ation
Effec
t of la
bour
force
pres
sure
on ag
ricult
ure
emplo
ymen
t
Labo
ur fo
rce av
ailab
leto
agric
ultur
e
Avail
able
labou
r force
per h
ectar
e
Initia
l ava
ilable
labo
urfor
ce pe
r hec
tare
Relat
ive av
ailab
le lab
orfor
ce pe
r hec
tare
<Cap
ital
agric
ultur
e>
<Rela
tive a
vera
ge ad
ultlite
racy
rate>
Total
dem
and
labou
r
<Har
veste
d are
a>
<Adu
ltpo
pulat
ion>
<Rela
tive a
vera
ge ad
ultlite
racy
rate>
<Cap
ital
servi
ces>
<Cap
ital in
dustr
y>
Usua
l indu
stry
emplo
ymen
t
Usua
l ser
vices
emplo
ymen
t
Initia
l indu
stry
emplo
ymen
t Initia
l ser
vices
emplo
ymen
t
Net s
ervic
eshir
ing
Indus
try la
bour
dem
and
Indus
try in
dicate
dca
pital
labou
r rati
o
Servi
ces l
abou
rde
man
d
Servi
ces i
ndica
tedca
pital
labou
r rati
o
Time t
o hire
inind
ustry
Time t
o hire
in se
rvice
sNet in
dustr
y hirin
gInd
icated
indu
stry
emplo
ymen
t leve
l
Indica
ted se
rvice
sem
ploym
ent le
vel
Effec
t of e
duca
tion o
nind
icated
capit
al lab
our r
atio
indus
try
Effec
t of e
duca
tion o
nind
icated
capit
al lab
our r
atio
servi
ces
Elas
ticity
of in
dustr
yca
pital
labou
r rati
o to
year
s of s
choo
ling
Elas
ticity
of se
rvice
s cap
ital
labou
r rati
o to y
ears
ofsc
hooli
ng
Initia
l indic
ated i
ndus
tryca
pital
labou
r rati
o
Initia
l indic
ated s
ervic
esca
pital
labou
r rati
o
Indus
tryem
ploym
ent
Servi
ces
emplo
ymen
t
<Ava
ilabil
ity of
labou
r force
>
Total
emplo
ymen
t
Unem
ploym
ent
<Time>
<Usu
al ind
ustry
emplo
ymen
t>
<Usu
al se
rvice
sem
ploym
ent>
Effec
t of fi
nanc
ial cr
isis
on G
DP0
Shar
e of a
rable
land
with
agric
ultur
al cro
ps
<Time>
<Time>
<Tota
l em
ploym
ent in
resto
ratio
n of la
nd w
ithinv
asive
plan
ts>
<Em
ploym
ent in
biom
ass
electr
icity
gene
ratio
n>
<Cro
p lan
d>
<Tota
l pow
er se
ctor
emplo
ymen
t>
<Che
mica
lfer
tilizer
use>
<Org
anic
fertiliz
er us
e>
Total
fertil
zer u
se
<Indic
ated g
reen
econ
omy
inves
tmen
t frac
tion>
Ratio
of or
ganic
toch
emica
l fertil
izer u
se
Agric
ultur
e em
ploym
ent
from
orga
nic fa
rming
Frac
tiona
l add
itiona
lem
ploym
ent d
ue to
shift
toor
ganic
farm
ing
Addit
ional
emplo
ymen
t due
to sh
ift to
orga
nic fa
rming
Total
agric
ultur
eem
ploym
ent
<Tra
nspo
rt sec
torem
ploym
ent>
<Mini
ng in
dustr
y tota
lem
ploym
ent>
<Usu
al se
rvice
sem
ploym
ent>
101
Employment is generated as a result of different activities taking place in the
economy. This module estimates some of these employments, particularly
from the production sectors (agriculture, industry and services). Each of the
employment in the production sector is represented by one stock, representing
employment in the specific sector, which is influenced by net hiring in the
specific sector. The labour demand in each of the production sector is
determined by the capital investment in each production sector. The total
employment in the economy is also estimated in the module as the sum of
all the employment, which include employment in following: power sector,
biomass electricity generation, transport, restoration of invasive alien species,
mining, agriculture, services and industry.
102
Anne
x 2.
26
Pow
er s
ecto
r em
ploy
men
t mod
ule
Solar
powe
rem
ploym
ent
Hydr
opow
erem
ploym
ent
Wind
powe
rem
ploym
ent
Solar
powe
r con
struc
tion e
mplo
ymen
t
Hydr
opow
er co
nstru
ction
emplo
ymen
t
Wind
powe
r con
struc
tion e
mplo
ymen
t
Solar
oper
ation
emplo
ymen
t
Hydr
opow
er op
erati
on em
ploym
ent
Wind
oper
ation
emplo
ymen
t
Coal
powe
rem
ploym
ent
Nucle
ar po
wer
emplo
ymen
t
coal
powe
r con
struc
tion e
mplo
ymen
t
Nucle
ar po
wer c
onstr
uctio
n em
ploym
ent
Coal
oper
ation
emplo
ymen
t
Nucle
ar op
erati
on em
ploym
ent
Conv
entio
nal th
erm
alpo
wer e
mplo
ymen
t
Total
powe
r sec
torem
ploym
ent
Rene
wable
emplo
ymen
tov
er po
wer s
ector
emplo
ymen
t
Rene
wable
powe
rem
ploym
ent
Rene
wable
ener
gyav
erag
e job
inten
sity
Foss
il fue
l pow
erav
erag
e job
inten
sity
Hydr
opow
er co
nstru
ction
emplo
ymen
t facto
r
Hydr
opow
erm
anufa
cturin
gem
ploym
ent fa
ctor
Nucle
ar co
nstru
ction
emplo
ymen
t facto
r
Nucle
ar m
anufa
cturin
gem
ploym
ent fa
ctor
Wind
cons
tructi
onem
ploym
ent fa
ctor
Wind
man
ufactu
ring
emplo
ymen
t facto
r
Coal
ener
gy co
nstru
ction
emplo
ymen
t facto
r
Coal
ener
gym
anufa
cturin
gem
ploym
ent fa
ctor
Hydr
opow
er op
erati
on an
dm
ainten
ance
emplo
ymen
tfac
tor
Nucle
ar op
erati
on an
dm
ainten
ance
emplo
ymen
tfac
tor
Wind
oper
ation
and
main
tenan
ce em
ploym
ent
factor
Coal
ener
gy op
erati
on an
dm
ainten
ance
emplo
ymen
tfac
tor
<Time>
<Time>
<Time>
<Time> <Time>
<Time><U
nit ye
ar>
Solar
cons
tructi
onem
ploym
ent fa
ctor
Solar
man
ufactu
ring
emplo
ymen
t facto
rSo
lar op
erati
on an
dm
ainten
ance
emplo
ymen
tfac
tor
Rene
wable
and n
uclea
rem
ploym
ent o
ver p
ower
secto
rem
ploym
ent
Rene
wable
and n
uclea
rpo
wer e
mplo
ymen
t
<Nuc
lear p
lant c
apac
ity>
<Nuc
lear p
lant
cons
tructi
on>
<Hyd
ropo
wer c
apac
ity>
<Hyd
ropo
wer
cons
tructi
on>
<Coa
l plan
tsco
nstru
ction
>
<Wind
capa
city>
<CSP
capa
city>
<Effe
ctive
elec
tricity
gene
ratio
n fro
m co
al>
<CSP
plan
t und
erco
nstru
ction
>
<Wind
plan
t und
erco
nstru
ction
>
<Coa
l ene
rgy
capa
city>
Wind
cons
tructi
onem
ploym
ent fa
ctor
10%
impo
rts
Solar
cons
tructi
onem
ploym
ent fa
ctor
10%
impo
rts
IMPO
RTS
SWITC
H
<Tota
l rene
wable
elec
tricity
and h
ydro
powe
r gen
erati
on>
<Unit
year
>
<Unit
year
>
<Unit
year
>
<Unit
year
>
103
The power sector employment module is dedicated to estimate the total
employment in the power sector only, due to additional electricity generation
plant. The employment estimated refers to the various electricity generation
technologies, namely: coal, nuclear, CSP, solar PV, wind and hydro. The jobs
generated are further categorised into construction jobs and manufacturing
jobs. Each of the specific electricity generation technology corresponds
exogenously to determined coefficients of jobs. The total power sector
employment is mainly used in the employment module discussed in Annex 2.25.
104
Anne
x 2.
27
Agric
ultu
re m
odul
e
Labo
r elas
ticity
onyie
ld
Per c
apita
cerea
lpro
ductio
n
Yield
Crop
s prod
uctio
nin
tons
Capit
alag
ricult
ure
Avera
ge life
agric
ulture
capit
al
Depre
ciatio
nag
ricult
ure
Initia
l cap
ital
agric
ulture
Harve
sted a
reaCr
op in
tensity
index
Agric
ulture
capit
alela
sticity
Effec
t of w
ater s
tress
onag
ricult
ure pr
oduc
tivity
Initia
l yiel
d
Effec
t of in
frastr
uctur
e den
sityon
prod
uctivi
ty ag
ricult
ure
Total
facto
r prod
uctivi
tyag
ricult
ure Effec
t of e
duca
tion o
npro
ductiv
ity ag
ricult
ure
Effec
t of h
ealth
onpro
ductiv
ity ag
ricult
ure
Initia
l wate
r stre
ssind
ex
Elastic
ity of
yield
towa
ter st
ress
Elastic
ity of
produ
ctivity
tolife
expe
ctanc
y agri
cultu
reEla
sticity
of pro
ductiv
ity to
educ
ation
agric
ulture
Elastic
ity of
produ
ctivity
toinf
rastru
cture
dens
ity ag
ricult
ure
Agric
ulture
gros
sca
pital
forma
tion
Initia
l agri
cultu
reem
ploym
ent p
er he
ctare
Relat
ive ag
ricult
ureem
ploym
ent p
er he
ctare
Capit
al pe
r ha
Initia
l cap
ital
per h
a
Relat
ive ca
pital
per h
a
Integ
ration
produ
ctivity
effec
t
<Wate
r stre
ssind
ex>
<Tota
lpo
pulat
ion>
<Rela
tive av
erage
adult
literac
y rate
>
<Rela
tive km
ofroa
ds pe
r ha>
<Agri
cultu
re em
ploym
ent
per h
ectar
e>
<Inve
stmen
tag
ricult
ure>
<Harv
ested
area
>
<GDP
defla
tor>
<Time>
Crop
s valu
e add
edpe
r ton t
able
Agric
ulture
produ
ction<T
ime>
<Rela
tive life
expe
ctanc
y>
Time f
or wa
ter st
ress t
oaff
ect a
gricu
lture
produ
ctivity
Crop
s valu
e add
edpe
r ton <C
rop la
nd>
<Harv
ested
area
>Initia
l harv
ested
area
Relat
ive ha
rveste
dare
aEla
sticity
of fer
tilizer
use
on ha
rveste
d area
Effec
t of h
arves
ted ar
eaon
fertil
izer u
se
Fertil
izer
cons
umpti
on
Initia
l fertil
izer
Fertil
izer
cons
umpti
on pe
r ha
Addit
ional
cost
fororg
anic
fertiliz
er
Orga
nic fe
rtilize
rinc
rease
Addit
ional
cost
ZAR
Exch
ange
rate
<Time>
Actua
l orga
nicag
ricult
ure in
creas
e
Frac
tion o
f orga
nic in
creas
eto
fertiliz
er co
nsum
ptionNe
t frac
tion c
hemi
cal
fertiliz
er us
e
Initia
l frac
tion o
fch
emica
l fertil
izer u
se
Indica
ted fra
ction
chem
ical fe
rtilize
r use
<Tim
e>
Frac
tion c
hemi
cal
fertiliz
er us
eEff
ect o
f oil p
rice o
nfer
tilizer
use
Proje
cted r
elative
oil pr
iceOi
l pric
e tab
leOi
l pric
e
<Time>
Initia
l oil p
rice
Relat
ive av
erage
oil
price
Elastic
ity of
oil pr
ice on
chem
ical fe
rtilize
r use
Fertil
izer u
seby
type
Chem
ical fe
rtilize
rus
e
Initia
l che
mica
lfer
tilizer
use
Orga
nic fe
rtilize
rus
e
Initia
l orga
nicfer
tilizer
useRe
lative
chem
ical
fertiliz
er us
e Relat
ive or
ganic
fertiliz
er us
e
Effec
t of fe
rtilize
r use
on yi
eld
Elastic
ity of
yield
toch
emica
l fertil
izer
Elastic
ity of
yield
toorg
anic
fertiliz
er<E
ffect
of fer
tilizer
use
on yi
eld>
<Orga
nic fe
rtilize
rinv
estm
ent>
Frac
tion
increa
se
Time t
o adju
stch
ange
Prop
ortion
ofch
emica
l fertil
iser
<Rela
tive el
ectric
itypri
ce>
Elastic
ity of
produ
ctivity
topri
ce ag
ricult
ure
Effec
t of e
lectric
ity pri
ce on
produ
ctivity
agric
ulture
105
The agriculture module one of the production modules that estimate
agricultural production. It is based on the Cobb-Douglas production function,
which assumes capital and labour as the main factors of productivity.
Agriculture capital investment is modelled as a stock, which increases with
investments and decreases due to depreciation. Amount of agriculture capital,
employment in agriculture (labour) and total factor productivity influences the
yield in this production sector. A number of variables endogenously estimated
in other modules have an influence on the total productivity in agriculture.
These were also taken into account and include water stress index, electricity
price, access to roads, literacy rate and life expectancy. Yield in agriculture
is also determined by the type of farming practices, that is where they follow
organic farming or conventional farming using chemical fertilisers. The
agriculture production estimated in this module is utilised in the GDP module to
estimate GDP.
106
Anne
x 2.
28
Indu
stry
mod
ule
Capit
al ind
ustry
Indus
try ca
pital
elasti
city
Aver
age l
ifeca
pital
indus
try
Depr
eciat
ion in
dustr
y
Initia
l cap
ital
indus
try
Non-
mini
ngind
ustry
prod
uctio
n
Relat
ive ca
pital
indus
try
Relat
ive in
dustr
yem
ploym
ent
Initia
l non
-mini
ngind
ustry
prod
uctio
nTo
tal fa
ctor
prod
uctiv
ity in
dustr
y
Real
indus
try gr
oss
capit
al for
mati
on Effec
t of e
duca
tion o
npr
oduc
tivity
indu
stry
Effec
t of h
ealth
onpr
oduc
tivity
indu
stry
Effec
t of in
frastr
uctur
ede
nsity
on pr
oduc
tivity
indus
tryEl
astic
ity of
prod
uctiv
ity to
life ex
pecta
ncy i
ndus
try
Elas
ticity
of pr
oduc
tivity
toed
ucati
on in
dustr
yEl
astic
ity of
prod
uctiv
ity to
infra
struc
ture d
ensit
y ind
ustry
<Indu
stry
emplo
ymen
t><In
itial in
dustr
yem
ploym
ent>
<Inve
stmen
tind
ustry
>Ind
ustry
prod
uctio
n
<GDP
defla
tor>
<Mini
ngpr
oduc
tion>
<Time>
<Rela
tive a
vera
ge ad
ultlite
racy
rate>
<Rela
tive l
ifeex
pecta
ncy>
<Rela
tive k
m of
road
s per
ha>
Effec
t of c
risis
onGD
P ind
ustry
<Time>
<Eco
nom
ic va
lue of
wate
rsa
vings
from
resto
ratio
n>
Effec
t of e
lectric
ity pr
ice on
prod
uctiv
ity in
dustr
y
Effec
t of w
ater s
tress
onind
ustry
prod
uctiv
ity
Elas
ticity
of to
wate
rstr
ess t
o ind
ustry
<Wate
r stre
ssind
ex>
Time f
or w
ater s
tress
toaff
ect in
dustr
y pro
ducti
vity
<Initia
l wate
r stre
ssind
ex>
Relat
ive in
dustr
ypr
oduc
tion
Initia
l indu
stry
prod
uctio
n
Elas
ticity
of pr
oduc
tivity
to pr
ice in
dustr
y
<Rela
tive e
lectric
itypr
ice>
Elasti
city o
f wate
r str
ess t
o serv
ices
107
The industry module is also a production module that estimates industry
production. It also follows the Cobb-Douglas production function, which
assumes capital and labour as the main factors of productivity. Industry capital
investment is modelled as a stock, which increases with investments and
decreases due to depreciation. The amount of industry capital, employment
in industry (labour) and total factor productivity influences the yield in this
production sector. A number of variables endogenously estimated in other
modules have an influence on the total productivity in industry. These were
also taken into account and include water stress index, electricity price, access
to roads, literacy rate and life expectancy. Industry production estimated
includes both mining and non-mining production and is utilised in the GDP
module to estimate the GDP.
108
Anne
x 2.
29
Serv
ices
mod
ule
Capit
alse
rvice
s
Servi
ces c
apita
lela
sticit
y
Aver
age l
ife ca
pital
servi
ces
Depr
eciat
ion se
rvice
s
Initia
l cap
ital
servi
ces
Servi
ces
prod
uctio
n
Servi
ces g
ross
capit
alfor
mati
on
Relat
ive se
rvice
sca
pital
Relat
ive se
rvice
sem
ploym
entInitia
l ser
vices
prod
uctio
n
Total
facto
rpr
oduc
tivity
servi
ces
Effec
t of h
ealth
onpr
oduc
tivity
servi
ces Effec
t of e
duca
tion o
npr
oduc
tivity
servi
ces
Effec
t of in
frastr
uctur
ede
nsity
on pr
oduc
tivity
servi
ces
Elas
ticity
of pr
oduc
tivity
tolife
expe
ctanc
y ser
vices
Elas
ticity
of pr
oduc
tivity
toed
ucati
on se
rvice
sEl
astic
ity of
prod
uctiv
ity to
infra
struc
ture d
ensit
y ser
vices
<Rela
tive a
vera
ge ad
ultlite
racy
rate>
<Rela
tive k
m of
road
s per
ha>
<Ser
vices
emplo
ymen
t>
<Initia
l ser
vices
emplo
ymen
t>
<Inve
stmen
tse
rvice
s><G
DP de
flator
>
<Time>
<Rela
tive l
ifeex
pecta
ncy>
Effec
t of c
risis
GDP
servi
ces
<Time>
Elas
ticity
of pr
oduc
tivity
to pr
ice se
rvice
sEf
fect o
f elec
tricity
price
onpr
oduc
tivity
servi
ces
Effec
t of w
ater s
tress
onse
rvice
s pro
ducti
vity
Elas
ticity
of to
wate
rstr
ess t
o ser
vices
<Wate
r stre
ssind
ex>
Time f
or w
ater s
tress
toaff
ect s
ervic
es pr
oduc
tivity
<Initia
l wate
r stre
ssind
ex>
Relat
ive se
rvice
spr
oduc
tion
<Rela
tive e
lectric
itypr
ice>
Elasti
city o
f wate
r str
ess t
o serv
ices
109
The services module is one of the production modules that estimate services
production. It is also follows Cobb-Douglas production function, which
assumes capital and labour as the main factors of productivity. Services
capital investment is modelled as a stock, which increases with investments
and decreases due to depreciation. Amount of services capital, employment
in services (labour) and total factor productivity influences the yield in this
production sector. A number of variables endogenously estimated in other
modules have an influence on the total productivity in services. These were
also taken into account and include: water stress index, electricity price, access
to roads, literacy rate and life expectancy. Services production estimated is
utilised in the GDP module to estimate GDP.
110
Initia
l real
GDP Re
al GD
P
Relat
ive re
al GD
P
Real
GDP
grow
th ra
te
Initia
l real
GDP
grow
th ra
te
Time t
o mea
sure
GDP
grow
th
Real
GDP
per c
apita
<Tota
lpo
pulat
ion>
Relat
ive P
C re
alGD
P
Initia
l real
GDP
per c
apita
<Ser
vices
prod
uctio
n>
Real
GDP
factor
price
Ratio
of G
DP fa
ctor p
rice
to m
arke
t pric
es
Real
GDP
mar
ket p
rice
<Time>
<Agr
icultu
repr
oduc
tion>
<Indu
stry
prod
uctio
n>
Anne
x 2.
30
GD
P m
odul
e
111
Th GDP module estimates the dynamics of GDP, which is determined by gross
capital formation and depreciation of the capital in the production sectors
(agriculture, industry and services). Thus, the real GDP at factor prices is the
South African Rand (ZAR) value of the three production sections, which in turn
is converted to market prices when multiplied with the conversion ratio. Other
variables estimated in this module are real GDP per capita and real GDP growth
rate. Real GDP and real GDP per capita are key variables that are utilised in
most of other modules.
112
Anne
x 2.
31
Inve
stm
ent a
nd h
ouse
hold
mod
ule
Nom
inal G
DPDi
spos
able
incom
ePC
real
dispo
sable
incom
e
Priva
teco
nsum
ption
Priva
te sa
ving
Relat
ive P
C re
aldis
posa
ble in
com
e
Initia
l PC
real
dispo
sable
inco
me
Prop
ensit
y to
cons
ume
Elas
ticity
of pr
open
sity t
osa
ve to
inco
me
Initia
l pro
pens
ity to
save
Priva
te cu
rrent
trans
fers t
able
Priva
te cu
rrent
trans
fers
Prop
ensit
y to
save
<Time>
<Time>
<Time>
Relat
ive no
mina
lGD
P
Initia
l nom
inal
GDP
<Tota
lpo
pulat
ion>
<Sub
sidies
and
trans
fers>
<Inter
est
paym
ents>
<Rea
l GDP
><G
DP de
flator
>
Hous
ehold
sre
venu
e
<Dom
estic
reve
nue>
<GDP
defla
tor>
Savin
gsPr
ivate
dom
estic
inves
tmen
tInv
estm
ent s
aving
ratio
Savin
gsdis
burse
men
t tim
e
Initia
l inve
stmen
tsa
ving r
atio
Desir
ed do
mes
ticfin
ancin
gLe
nding
togo
vern
men
t
Total
inve
stmen
t
Priva
te ca
pital
and
finan
cial tr
ansfe
rs tab
le
Desir
ed sh
are o
f fina
ncing
from
dom
estic
sour
ces t
able <Time>
<Time>
<Priv
ate sa
ving>
<Gov
ernm
ent to
tal ne
tbo
rrowi
ng>
<Gov
ernm
ent
inves
tmen
t>
Frac
tion o
f inve
stmen
tag
ricult
ure Frac
tion o
finv
estm
ent in
dustr
y
Frac
tion o
finv
estm
ent s
ervic
es
Inves
tmen
tag
ricult
ure
Inves
tmen
tind
ustry Inv
estm
ent
servi
ces
Initia
l frac
tion
inves
tmen
t agr
icultu
re
Initia
l frac
tion
inves
tmen
t indu
stry
Initia
l frac
tion
inves
tmen
t ser
vices
Elas
ticity
of ag
ricult
ure
inves
tmen
t to P
C GD
P
Elas
ticity
of in
dustr
yinv
estm
ent to
PC
GDP
Elas
ticity
of se
rvice
sinv
estm
ent to
PC
GDP
Sum
of in
vestm
ent
shar
es
Norm
alize
d agr
icultu
reinv
estm
ent fr
actio
n
Norm
alize
d ind
ustry
inves
tmen
t frac
tion
Norm
alize
d ser
vices
inves
tmen
t frac
tion
<Rela
tive P
C re
alGD
P>
<Gre
en ec
onom
yinv
estm
ent>
Frac
tion o
f GE
to tot
alinv
estm
ent
BAU2
<Indic
ated B
AU2%
inves
tmen
t>
113
The investment and household module estimates the household accounts
and total investment, and calculates a number of output variables for use in
some of the above discussed modules. These include relative per capita and
real disposable income, nominal GDP and private saving, and total investment.
The total investment is endogenously estimated using endogenous private
domestic investment, partly exogenous government investment and the
exogenous private capital, and financial transfers time series. Total investment
is a key variable in estimating the investment in the production sectors, which
ultimately calculates the GDP.
114
Anne
x 2.
32
Gov
ernm
ent m
odul
e sk
etch
Tax r
even
ue
Dire
ct tax
reve
nue
Gran
ts an
d othe
rre
venu
e tim
e ser
ies
Gran
ts an
d othe
rre
venu
esTo
tal go
vern
men
tbu
dget
Gove
rnm
ent to
tal ne
tbo
rrowi
ng
Gove
rnm
ent
non-
discre
tiona
ryex
pend
iture
Gove
rnm
ent
discre
tiona
ry ex
pend
iture
Inter
est o
n pub
licde
btSu
bsidi
es an
dtra
nsfer
s
Educ
ation
expe
nditu
re
Taxe
s on i
ncom
e and
profi
t table
Desir
ed de
ficit a
s sha
reof
GDP
table
PC su
bsidi
es an
dtra
nsfer
s tab
le
Healt
hex
pend
iture
Envir
onm
ental
prote
ction
expe
nditu
re
Desir
ed ed
ucati
on ex
pend
iture
as sh
are o
f disc
retio
nary
budg
ettab
le Desir
ed he
alth e
xpen
ditur
e as
shar
e of d
iscre
tiona
ry bu
dget
table
Desir
ed en
viron
men
tal pr
otecti
onex
pend
iture
as sh
are o
fdis
cretio
nary
budg
et tab
le
Total
gove
rnm
ent
purch
ases
Gove
rnm
ent
expe
nditu
re
<Tim
e>
Taxe
s on i
ntern
ation
altra
de ta
ble<T
ime>
<Tim
e>
<Tim
e>
<Tota
lpo
pulat
ion>
Gove
rnm
ent
inves
tmen
t
Gove
rnm
ent
cons
umpti
on
Desir
ed ge
nera
l ser
vices
expe
nditu
re as
shar
e of
discre
tiona
ry bu
dget
table
Desir
ed ho
using
expe
nditu
re as
shar
e of d
iscre
tiona
ry bu
dget
table
Socia
l sec
urity
and
welfa
re ex
pend
iture
Prote
ction
servi
ces
expe
nditu
re
Mini
ng, m
anufa
cturin
g and
cons
tructi
on ex
pend
iture
Hous
ingex
pend
iture
Gene
ral p
ublic
servi
ces
expe
nditu
re
<Tim
e>
Taxe
s on g
oods
and
servi
ces t
able
Taxe
s on g
oods
and
servi
ces
Inter
est p
aym
ents
Inter
natio
nal tr
ade
taxes
Indire
ct tax
es
Othe
r tax r
even
uetab
le
Othe
r tax r
even
ue
Othe
r indu
stries
expe
nditu
re
Desir
ed ot
her in
dustr
iesex
pend
iture
as sh
are o
fdis
cretio
nary
budg
et tab
le
Fuel
and e
nerg
yex
pend
iture De
sired
fuel
and e
nerg
yex
pend
iture
as sh
are o
fdis
cretio
nary
budg
et tab
le
Desir
ed ag
ricult
ure,
fores
try an
dfis
hing e
xpen
ditur
e as s
hare
ofdis
cretio
nary
budg
et tab
le
Agric
ultur
e, for
estry
and
fishe
ries e
xpen
ditur
e
Desir
ed m
ining
, man
ufactu
ring a
ndco
nstru
ction
expe
nditu
re as
shar
e of
discre
tiona
ry bu
dget
table
Tran
spor
t and
com
mun
icatio
nsex
pend
iture
Desir
ed tra
nspo
rt and
com
mun
icatio
ns ex
pend
iture
assh
are o
f disc
retio
nary
budg
et tab
le
Desir
ed ge
nera
l pub
lic se
rvice
sex
pend
iture
as sh
are o
fdis
cretio
nary
budg
et tab
le
Desir
ed pr
otecti
on se
rvice
expe
nditu
re as
shar
e of
discre
tiona
ry bu
dget
table
<Nom
inal G
DP>
<Nom
inal G
DP>
Dom
estic
reve
nue
<Nom
inal G
DP>
Inter
est o
n for
eign
debt
table
Inter
est o
n dom
estic
debt
table
<Tim
e>
Recre
ation
cultu
re an
dre
ligion
expe
nditu
re
Desir
ed re
creati
on cu
lture
and
relig
ion ex
pend
iture
as sh
are o
fdis
cretio
nary
budg
et tab
le
Cons
umpti
on sh
are o
fgo
vern
men
t exp
endit
ure
115
The government module shows the South African government accounts, which
are based on the social national accounts. The government expenditure in the
different sectors (e.g., education, fuel and energy expenditures) is dependent
on exogenously determined time series of the desired share of the expenditure
in each of these sectors and the amount of the government discretionary
expenditure. The discretionary expenditure is the difference between the total
government budget and the government non-discretionary expenditure. Non-
discretionary expenditure is the sum of subsidies and transfers, interest on
foreign debt and interest on domestic debt.
116
117
For more information, contact:UNEP DTIE
Economics and Trade Branchchemin des Anémones 11-13
1219 Châtelaine/GenevaSwitzerland
T: +41 (0)22 917 82 43F: +41 (0)22 917 80 76
www.unep.org/greeneconomy
Job No. DTI / 1591 /GE
South AfricaModelling Report of
United Nations Environment ProgrammeP.O. Box 30552, Nairobi 00100 , Kenya
T: +254 (20) 762 1234F: +254 (20) 762 3927 E: [email protected]: www.unep.org
www.unep.org
Focus on Natural Resource Management, Agriculture, Transport and Energy Sectors
For more information, contact:National Department of Environmental Affairs
For attention: Directorate National Sustainable Development
Private Bag 447, Pretoria 0001South Africa
T: +27 12 310 39 11F: +27 12 322 26 82
Call Centre: +2786 111 24 68www.environment.gov.za
South Africa’s Department of Environmental Affairs views a green economy as a sustainable development path that is based on addressing the interdependence between economic growth, social protection and natural ecosystems. At its National Summit held in May 2010, the resolutions included a commitment for national coordinated efforts in pursuing and exploring opportunities “towards a resource efficient, low carbon and pro-employment growth path”.
In contributing to knowledge generation, this modelling study is primarily aimed at assessing the impacts of green economy investments in selected four sectors pertaining to the South African economy. Based on planned targets and expenditures and/or costs of interventions, the modelling identifies possible options and opportunities to achieve these targets. Four scenarios are defined: BAU, or business-as-usual; BAU2% representing a 2 per cent investment of gross domestic product in the BAU activities; GE2% representing an allocation of 2 per cent of gross domestic product in green economy sectors (natural resource management, agriculture, transport and energy); and GETS, which is a target-specific scenario aimed at identifying whether policy-makers can achieve the medium- to long-term targets following green economy interventions in the prioritised sectors.
The United Nations Environment Programme (UNEP)commissioned an analysis of the opportunities and options for South Africa’s transition to a green economy, focusing on four of its nine sectors, at the request of the national Department of Environmental Affairs. The analysis was conducted with technical assistance from the Millennium Institute and the Sustainability Institute, in collaboration with the Centre for Renewable and Sustainable Energy Studies (CRSES) of Stellenbosch University. The United Nations Development Programme supported stakeholder consultations and capacity building activities related to the implementation of the project. UNEP is grateful for the funding support provided by the European Commission.