unep green economy modelling focus on natural resource management, agriculture, transport and energy...

1

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

2

Copyright © United Nations Environment Programme, 2013

This publication may be reproduced in whole or in part and in any form for educational or non-

profit purposes without special permission from the copyright holder, provided acknowledgement

of the source is made. UNEP would appreciate receiving a copy of any publication that uses this

publication as a source.

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

expression of any opinion whatsoever on the part of the United Nations Environment Programme

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

represent the decision or the stated policy of the United Nations Environment Programme, nor does

citing of trade names or commercial processes constitute endorsement.

UNEP promotes environmentally sound practices

globally and in its own activities. This publication is printed on 100% recycled

paper, using vegetable inks and other eco-friendly practices. Our distribution policy aims

to reduce UNEP’s carbon footprint.

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)

5

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

6

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 . . . . . . . . . .

ii

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 . . . . . . . . . . . . . . . . . . . . . . . .

Page

1012202126

2930

364144

7

35

1115

18

24

253132323233

33

33

34

38

38

39

39

Page51

525456586062646668707274767880828486889092949698

100102104106108110112114

7

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

iii

8

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

iv

9

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.

v

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

3

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

4

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

4

5

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.

5

6

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

7

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)

8

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

11

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.

12

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

13

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.

14

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.

18

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.

20

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

21

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

25

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.

28

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

30

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

31

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.

48

Association for the Study of Peak Oil and Gas (ASPO-USA) (2010). Peak Oil Basics. (www.aspousa.org/index.php/peak-oil/peak-oil-202/ Accessed 29 October 2012)

Barbier, E.B. (2010). A Global Green New Deal: Rethinking the Economic Recovery. New York: Cambridge University Press.

Bassi, A.M. (2009). An Integrated Approach to Support Energy Policy Formulation and Evaluation. PhD Dissertation. System Dynamics Group, Department of Geography, University of Bergen, Norway.

Brown, S. P. A., & Huntington, H. G. (2008). Energy Security and Climate Change Protection: Complementarity or Tradeoff? Energy Policy, 36(9): 3510–3513.

Bussolo, M., & Medvedev, D. (2007). Challenges to MDG Achievement in Low Income Countries: Lessons from Ghana and Honduras. World Bank Policy Research Working Paper 4383, Washington DC.

CNA Corporation. (2007). National Security and the Threat of Climate Change. Alexandria, VA.

Coyle, R. G. (1996). System Dynamics Modelling. London: Chapman and Hall.

Department of Environmental Affairs. (2011). National Strategy for Sustainable Development and Action Plan. Pretoria.

_____. (2010). Summit Report. Pretoria.Department of Environmental Affairs and Tourism. (2006).

South Africa Environment Outlook – A report on the state of the environment. Pretoria.

Energy Information Administration. (2009). International Energy Statistics. (tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm (Accessed 29 October 2012)

Evaert, L., García-Pinto, F., & Venutre, J. (1990). A RMSX-X Model for Turkey, Volume 1. Policy, Research, and External Affairs working paper no. WPS 486. World Bank.

Fair, R. C. (1993). Testing Macroeconometric Model. The American Economic Review, 83:287-293.

Food and Agriculture Organization of the United Nations – FAO. (2009). World Agriculture: Towards 2030/2050. Rome.

Forrester, J. W. (1961). Industrial Dynamics. Cambridge, MA: Productivity Press.

Forrester, J. W. (2007). System Dynamics – the Next 50 Years. (obssr.od.nih.gov/issh/2012/files/Forrester%202007b.pdf

(Accessed 29 October 2012)Grant Thornton. (2012). A guide to establishing a

presence in South Africa. Benmore: Grant Thornton South Africa.

Haque, N.U., Khan, M.S., & Montiel, P. (1990). Adjustment with Growth: Relating the Analytical Approaches of the IMF and the World Bank. Journal of Development Economics, 32: 155-179.

Howarth, R. B., & Monahan, P.A. (1996). Economics, Ethics and Climate Policy: Framing the Debate. Global and Planetary Change, 11:187-199.

McKinsey & Company, & 2030 Water Resources Group. (2009). Charting Our Water Future. Washington, DC.

Muller, A., & Davis, J. S. (2009). Reducing Global Warming: the Potential of Organic Agriculture. Policy Brief, no. 31.5.2009, Rodale Institute.

Organisation for Economic Co-operation and Development – OECD. (2008). Environment Outlook to 2030. Paris.

Pedercini M. Potential Contributions of Existing Computer-Based Models to Comparative Assessment of Development Options. University of Bergen, Norway.

_____. (2009). Modelling Resource-Based Growth for Development Policy Analysis. PhD Dissertation. University of Bergen, Norway.

References

49

Pedercini, M., & Barney, G.O. (2010). Dynamic Analysis of Interventions Designed to Achieve Millennium Development Goals (MDG): The Case of Ghana. Socio-Economic Planning Sciences, 44:89-99.

Pearce, D.W., Markandya, A., & Barbier, E.B. (1989). Blueprint for a Green Economy. London: Earthscan.

Roberts, N. H., Anderson, D. F., Deal, R. M., Grant, M. S., & Shaffer, W. A. (1983). Introduction to Computer Simulation: the System Dynamics Modelling Approach. Reading, MA: Addison-Wesley.

Robinson, S., Yunez-Naude, A., et al. (1999). From Stylized to Applied Models: Building Multisector CGE Models for Policy Analysis. The North American Journal of Economics and Finance, 10: 5-38.

Saeed, K. (1998). Towards Sustainable Development: Essays on System Analysis of National Policy. Aldershot, UK: Ashgate Publishing Company.

South African Policy Service (SAPS). (2012). Crime Situation in South Africa. Pretoria.

Statistics South Africa (StatsSA). (2012). Quarterly labour force survey: Quarter 3 (July to September 2012), Pretoria.

Statistics South Africa (StatsSA), 2011. Mid-year population estimates 2011, Pretoria.

Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modelling for a Complex World. New York: McGraw-Hill/Irwin.

UNEP. (2011). Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication. Nairobi: UNEP.

World Economic Forum (WEF), 2012. The Global Competitiveness Report 2012-2013, Geneva: WEF.

World Wide Fund for Nature and African Development Bank. (2012). Africa Ecological Footprint Report. Gland: WWF/AfDB.

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

tInves

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>

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



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>



<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>

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>



<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.

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.

unep green economy modelling focus on natural resource management, agriculture, transport and energy...

Government & Nonprofit

green economy analyses

green economy origins

green economy spheres

green economy scoping

allocation of green

sectors ina green economytable

south africancontext

sectors table