Inequality and Climate Change Inégalité et changement climatique

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Caetano and Thurlow: The Socioeconomic Implications of Renewable Energy 15 Measuring Economy-wide Impacts Structure of South African Economy and Labour Markets Table 2 outlines the structure of the South African economy and labour market in 2007. South Africa has a dominantly services-based economy, with services accounting for over 66 per cent of gross domestic product (GDP) and approximately two-thirds of employment. The electricity sector is a relatively small sector, with a contribution of around 1.8 per cent of GDP and 0.3 per cent of employment. Historically cheap electricity prices coupled with a mineral-rich country has aided the development of energy-intensive sectors in the economy. For this reason, we believe that the importance of the electricity sector is understated when looking at the direct contribution to GDP; the indirect effects of changes in the electricity sector are more pronounced given the forward linkages associated with the sector. Table 1.2: Structure of South Africa’s Economy and Labour Market (in percentage) Share of Total Exports/ Output Imports/ Output GDP Employment Exports Imports Total GDP 100,00 100,00 100,00 100,00 11,21 15,28 Agriculture 3,11 3,74 2,64 0,95 11,14 5,65 Industry 30,77 29,08 83,73 84,22 21,49 27,53 Mining 8,83 8,79 33,41 10,47 65,07 40,75 Coal Mining 1,59 1,61 4,49 0,21 43,82 4,31 Manufacturing 16,83 15,88 48,75 72,47 16,55 30,04 Petroleum 1,15 0,20 2,17 3,67 8,41 21,84 Electricity 1,81 0,31 1,57 1,29 15,22 14,43 Coal-fired 1,63 0,28 - - - - Nuclear 0,15 0,02 - - - - Hydro 0,02 0,01 - - - - Construction 2,70 3,93 - - - - Services 66,12 67,18 13,63 14,83 3,11 3,91 Source: South Africa 2007 social accounting matrix (own calculations) Eskom is the state utility and runs a monopoly in the electricity sector, generating approximately 95 per cent of the electricity used in South Africa and an estimated 45 per cent of the electricity used in Africa (Eskom 2011). Electricity generation is highly reliant on the use of coal, which remains the cheapest generation option given that South Africa is a coal-rich country. There was not much diversity in terms of electricity generation in 2007, with approximately 93 per cent of electricity generated by coal-fired plant; 1.8GW (5 per cent) generated by Koeberg, Africa’s first nuclear power station; and the remainder generated mainly from hydropower (Eskom 2011).
16 Inequality and Climate Change Description of the Static E-SAGE Model A number of CGE models have contributed to the local policy making process in areas including trade strategy, income distribution, and structural change in low-income countries. There are several features of this class of models that make them suitable for this type of analysis (Arndt, Davies, & Thurlow 2011). Firstly, the structure of CGE models ensures that all economy-wide constraints are respected and provide a theoretically consistent framework for welfare and distributional analysis (Arndt, Davies, & Thurlow 2011). Secondly, CGE models simulate the functioning of a market economy, and provide a platform for analysis on how different economic conditions affect markets and prices (Arndt, Davies, & Thurlow 2011). One of the drawbacks of this type of modelling, however, is that the credibility of the results is highly dependent on the accuracy of the data and assumptions made when calibrating the model. It is possible to mitigate this limitation through transparency and disclosing the assumptions made and data used in building the economy-wide model. The South African General Equilibrium (SAGE) model used in this analysis is derived from neoclassical tradition originally presented in the seminal work by Dervis, de Melo, & Robinson (1982). A number of extensions and adaptations have been made to this framework including the ability for producers to produce more than one commodity and the explicit treatment of transaction costs (Lofgren, Harris, & Robinson 2001). The dynamic-recursive energy extension to the SAGE model, developed by Channing Arndt, Rob Davies and James Thurlow (2011) is used in this paper. The SAGE model was extended to reflect the detailed structure and workings of South Africa’s energy sector. In addition, the model was developed further to capture a detailed factor demand for the electricity sector. The SAGE model is a dynamic recursive model; in simple terms a sequence of static model runs that are solved to simulate the passing of time. The static model is solved ‘within-the-period’ with the use of non-linear equations that are solved simultaneously to capture linkages that exist in the real economy. This is followed by a ‘between-period’ run where a number of parameters are updated according to exogenous behavioural changes over time as well as the results from the previous static run. The E-SAGE model simulates the period between 2010 and 2030 and each static run represents one year. There are 46 productive sectors, or activities, identified within the model; as well as six factors of production including, capital, crop land and labour. Labour is disaggregated further into four factors by level of education – primary, middle, secondary, tertiary. The production schedule for a sole producer is provided for simplicity, although, in reality, the SAGE model contains 46 sectors, each of which are assigned a representative producer. The behaviour of the representative producer is such that they will maximise profits subject to a given set of input and output
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Inequality and Climate Change Inégalité et changement climatique

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