Strategic investment decisions in Zambia’s mining sector under a constrained energy system Bernard Tembo UCL Energy Institute University College London A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy January 2018 To my loving parents (both deceased) and their grandchildren. Declaration I, Bernard Tembo, confirm that the work presented in this thesis ismy own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Bernard Tembo January 2018 Acknowledgements To start, I would like to thank my wife, Fridah Siyanga-Tembo, for her support and for the many sacrifices she has had to make during the time I was reading for the PhD. Without her support and sacrifices, this jour- ney would have been extremely difficult if not impossible. To our children, Chisomo (Chichi) and Chigomezgo (Chigo), this is for you. I am grateful to my supervisors Prof. Neil Strachan and Dr Ilkka Keppo for their guidance and patience. To Neil, your character building words will forever be cherished. You may never know what you mean to me and my family. To my sponsor, UCL Institute for Sustainable Resources, without you this journey would not have even started. You gave me an opportunity to study at UCL under great supervisors. I am also indebted to Whole Sys- tems Energy Modelling Consortium (WholeSEM) who covered my tuition fees under their grant (EP/K039326/1). A big thank you to my colleagues at the UCL Energy Institute for their encouragement, advice and support; particularly, Pablo Carvajal Sarzosa, Jennifer Love, Maragatham Kumar (Maggie), Joel Guilbaud, Tashweka Anderson, Dayang Abu Bakar, Melissa Lott, Gabrial Anandarajah, Ivan Garcia Kerdan, Francis Li, Vig Seewoogoolam, Will Usher, Thuy Duong Khuu, Baltazar Solano-Rodriguez, Jennifer Cronin, Ukadike Nwaobi (Uka), Rob Liddiard, Kiran Dhillon, Mae Oroszlany, Andrew Smith to name but a few. Of course, not forgetting my Basketball team-mates: Sung-Min, Nick, Marius B., Marius P., Aman, Pip, Yair, Nawfal, Simon and other guys. To my family, Talingana Tembo, your support and confidence in my abili- ties is incomparable. My number #1 cheerleader, you are. To my mother- in-law, Agnes Kambikambi, my gratitude for all you have done for us is im- 6 | measurable. Let me just say, naonga chomene amama. To Samuel & Anali- cia Muzata, Kateula & Mulemba Sichalwe, Kangwa & Bukata Lubumbashi, Michael & Thelma Kapembwa, Fred & Lumba Ng’andu, Nzali Chella, Felix & Cindy Mwenge, Siloka Siyanga, Siwa Mwene, Claude Mwale, Benjamin & Ann Chilenga and Adrian & Mwango Ngoma, you guys are indeed a brother’s keeper. Finally and more importantly, in You, Oh Lord, I live, move and have my being. – Acts 17:28 Abstract This thesis studies the challenge of balancing between economic growth and social development that many developing countries are facing. The study sought to understand the impacts that these goals have on each other and how these impacts could be minimised. It looked at how clean energy access is modelled in developing countries and also how growth in Zam- bia’s mining sector would be impacted by meeting the government’s clean energy access targets in the residential sector. On one hand, increasing ac- cess to clean energy would lead to increase in energy demand, which would, in turn, imply increased capital investment in the energy supply system. This augmented investment means increase in energy prices which in turn would limit the growth of the mining sector (the backbone of the econ- omy). Limited growth implicitly means reduced funding for clean energy projects. Thus, in order to adequately capture these complex interactions, three bottom-up models were developed: energy demand, energy supply and mining models. The energy models sought to understand how energy demand would evolve by 2050 and how much capital investment would be required to meet this demand. The mining model focused on understanding how developments in the energy sector would impact strategic investment decisions in the mining sector. It was found that approaches used to study how households transition from one energy fuel to another in developing countries had significant conceptual errors. However, these errors could be minimised by using a bottom-up approach. Furthermore, it was found that while profit margins would reduce as a result of increase in energy prices, the impact of these prices on the firm’s production output was negligible - except if a firm is a marginal mine operation. The output was not impacted because mining firms make decisions based on thresholds and not marginal 8 | decrease in profits. Thus, even though reliable energy supply is critical in mining operations, the influence of energy price in investment decision making in Zambia’s mining sector is limited. The key decision variables in the sector were found to be copper price, grade and type of ore. Publications 1. Spalding-Fecher et al. 2017. Electricity supply and demand scenarios for the Southern African power pool, Energy Policy, DOI http://dx.doi.org/10.1016/j.enpol.2016.10.033. 2. Tembo, B., 2016. Modelling of capital investment decisions. System Dynamics Conference, Delft, The Netherlands. https://www.systemdynamics.org/assets/conferences/2016/index.html 3. Tembo, B., 2016. Modelling of energy efficiency in Copper mining industry. IAEE Conference, Bergen, Norway. http://www.iaee.org/proceedings/article/13683 4. Spalding-Fecher et al. 2016. The vulnerability of hydropower production in the Zambezi River Basin to the impacts of climate change and irrigation developments, Mitig. Adapt. Strateg. Glob. Change, DOI 10.1007/s11027-014-9619-7. 5. Tembo, B., 2015. Trade-offs in Zambia’s Energy System: Identifying key drivers. WholeSEM Conference, Cambridge, UK. http://www.wholesem.ac.uk/wholesem-events-repository/annual- conf-15 6. Milligan, BM et. al., 2014. (as an academic contributor). 2nd GLOBE Natural Capital Accounting Study: Legal and policy developments in twenty-one countries. GLOBE International, UCL Institute for Sustainable Resources. http://discovery.ucl.ac.uk/1432359/ 10 | 7. Spalding-Fecher, R., et al., 2014. Climate Change and Upstream Development Impacts on New Hydropower Projects in the Zambezi Project: Report for Climate and Development Knowledge Network, January 2014. http://www.erc.uct.ac.za/groups/esap/current/esap-zambezi1 8. Tembo, B. and Merven, B., 2013. Policy options for the sustainable development of Zambia’s electricity sector. Journal of Energy in Southern Africa, 24(2), pp.16–27. http://www.scielo.org.za/pdf/jesa/v24n2/02.pdf Contents Contents 11 List of Figures 17 List of Tables 21 Glossary 25 1 Introduction 29 1.1 Research context . 31 1.1.1 Research questions . 33 1.1.2 Contribution to knowledge . 34 1.2 Thesis outline . 35 2 Industry context 37 2.1 Resources and reserves . 38 2.2 State of global copper industry . 43 2.2.1 State of the Zambian copper industry . 47 2.3 Chapter summary . 51 3 Literature review: Review of energy and mining models 53 3.1 What is a model? . 54 3.1.1 Modelling paradigms . 55 3.1.2 Bottom-up model frameworks . 56 3.1.3 Uncertainty and risks in models . 57 3.1.4 Sensitivity analysis . 59 3.2 Studies on energy use and modelling . 60 12 | Contents 3.2.1 Uncertainty in the energy model . 61 3.2.2 Industrial energy use and modelling . 61 3.2.3 Energy demand in the copper industry . 68 3.2.4 Energy efficiency investments . 71 3.2.5 Energy systems in developing countries . 76 3.3 Studies on copper industry . 83 3.3.1 Uncertainty in the copper industry . 84 3.3.2 Material production modelling . 87 3.3.3 Production costs modelling . 90 3.3.4 Valuation of copper reserves . 93 3.3.5 Copper price modelling . 94 3.3.6 Feedback relationships within a firm . 97 3.4 Linkage of energy and mining systems . 98 3.5 Chapter summary . 99 4 Literature review: Investment decision making 101 4.1 Investment decision making in firms . 102 4.1.1 Strategic decision making process . 103 4.1.2 Identification of strategic issues . 106 4.1.3 Decision effectiveness . 107 4.2 Choice paradigms . 109 4.2.1 Decision environment . 111 4.2.2 Rationality and bounded rationality . 111 4.2.3 Analytic techniques and their criticism . 116 4.3 Modelling decision making process . 119 4.3.1 Characteristics of a system dynamics model . 120 4.3.2 Decision making in SD models . 121 4.3.3 Model validation process . 124 4.4 Decision making research context . 126 4.5 Chapter summary . 128 5 Modelling of Zambia’s energy system 129 5.1 Demand model . 130 Contents | 13 5.1.1 Energy consumption in Zambia . 133 5.2 Supply model . 145 5.2.1 Average generation cost of electricity . 149 5.3 Scenarios . 150 5.4 Chapter summary . 155 6 Modelling of strategic investment decisions 157 6.1 Introduction . 157 6.2 Identification of decision processes . 158 6.2.1 Key interview findings . 160 6.3 Mining model . 162 6.3.1 Data . 163 6.3.2 General Assumptions . 164 6.3.3 Framework of a mining firm . 165 6.3.4 Material module . 168 6.3.5 Financial module . 173 6.4 Method for SD model analysis . 181 6.5 Chapter summary . 183 7 Results and discussion 185 7.1 Energy system results . 185 7.1.1 Energy demand . 186 7.1.2 Energy supply . 198 7.1.3 Sensitivity analysis on the supply model . 210 7.2 Mining model results . 218 7.2.1 Indicative production scenarios . 218 7.2.2 Identification and impacts of key drivers . 224 7.2.3 Impact of increasing access to clean energy . 239 7.2.4 Impact of energy efficiency investments .