||Integrated Study of Energy Consumption CO2 Emissions and Input-Output Life Cycle Assessment for the Electricity Sector in South Africa
||Department of Environmental Engineering
|| MOHAMED BEIDARI
Input-output life cycle assessment
South Africa (SA) is the most industrialized and developed country in Africa. According to British Petroleum (BP) Statistical Review of Energy (2014), coal accounted for 72% of South Africa's total primary energy consumption, followed by oil (22%), natural gas (3%), nuclear (3%), and renewables (less than 1%) in 2013. This dependency on coal put SA as the leading carbon dioxide emitter in Africa and the 13th largest in the world, according to the latest U.S. Energy Information Administration report (EIA, 2015). Coal combustion is generally more carbon-intensive than the burning natural of gas or petroleum for electricity. Coal represents more than 90% of the electricity generated in South Africa, which accounts for about 99% of CO2 emissions from electricity sector (IEA data, 2015). In 2013, the electricity sector was the largest source of SA’s CO2 emissions, accounting for about 60% of the SA total as shown in Figure 1. Carbon dioxide emissions from electricity have increased by 64% since 1990 as electricity demand has grown and as coal has remained the dominant source for generation. Therefore, the purpose of this study is firstly to evaluate the occurrence of decoupling of CO2 emissions from the Gross Domestic Product (GDP) in South Africa (SA) for the period of 1990 to 2012 by using the Organization for Economic Cooperation and Development (OECD) and Tapio methods, and to identify the primary CO2 emissions driving forces by the Kaya identity. Then, the Log Mean Divisia Index (LMDI) is applied to analyze the influence of the factors which ruled electricity generation-related CO2 emission in SA over the period 1990–2013. For further investigations, the input-output linkage and multiplier methods have been applied to investigate the interrelationships of the 18 sectors’ input-output tables for the years 1995, 2000, 2005, 2010 and 2012, and to measure the total impact of their energy commodity input coefficients and CO2 emissions output coefficients for the year 2012. Finally, the Input-Output Life Cycle Assessment (IO-LCA) was employed to evaluate the potential global warming and other environmental impacts from the electricity generation and related industry. The impact 2002+ model was adopted from SimaPro 7.3.3 to analyze environmental impacts. The methodology also explores the level of uncertainty of various impact categories. Monte Carlo simulation is used to analyze the uncertainties associated with Life Cycle Inventory (LCI), Life Cycle Impact Assessment (LCIA), and the normalization and weighting processes. The uncertainty of the environmental performance for major impact categories and damage categories are also calculated and compared.
The results from the decoupling investigation show a strong decoupling during the period of 2010–2012, which is considered as the best development situation. From 1994–2010 SA had a weak decoupling; while during the period 1990–1994, the development in SA presents an expansive negative decoupling state. The comparison of the OECD and Tapio’s methods shows well-correlated results but differs in their applications; however, the OECD method appears as the simpler one. The results of the Kaya identity demonstrate that the increase in population, GDP per capita and deteriorating energy efficiency were the main primary driving forces for the increase of CO2 emissions. Concerning the LMDI, results show that the electricity generation intensity effect plays the dominant role in decreasing CO2 emissions. However, the effect of economic activity is the major determinant that contributes to increasing CO2 emissions. Regarding the input-output linkage and multiplier analysis, results reveals that the electricity sector has a weak linkage with others sectors, which means it is mostly independent of other sectors. In another words, it does not induce and enable economic growth. Moreover, two sectors, namely Chemical and Petrochemical Industries and Basic Metals, were found as key sectors in SA’s economy in 1995, 2000 and 2012. In 2005 and 2010, only Chemical and Petrochemical Industries was the most important sector in SA. Additionally, Commercial and Public Services was the strongest forward linkage sector in SA. Our findings also showed that the electricity sector was the main direct monetary energy consumer and CO2 emitter and therefore the most dominant source in terms of energy and CO2 intensities among all the 18 sectors in SA. Furthermore, our investigation of the direct and indirect effects on energy consumption and CO2 emissions indicated that both total of direct energy consumption and CO2 emissions were higher than both total indirect energy consumption and CO2 emissions. Finally, the normalization results from IMPACT 2002+ demonstrates that the electricity power generation sector and the coal mining sector were respectively the two main sectors which had the highest environmental load during the study period. It also showed that the resources were the major environmental damages, followed by human health and climate change over those five years. In contrast, the ecosystem quality was barely affected, and their impact values were the lowest among those 4 damage categories over the study period. The cumulative of normalized impact values revealed that the respiratory inorganics, the non-renewable energy, and the global warming in that order were the most significant environmental impacts from the South Africa’s electricity generation sector. Results of direct and indirect effects revealed that the electricity sector had the highest direct impact on human health, followed by climate change and ecosystem quality in 1995, 2000, 2005, 2010 and 2012. In contrast, the resources impact was mainly caused from the indirect effect produced by other relevant sectors such as coal mining sector for three years. Results also pointed out that the Particulates, < 10 um mainly emitted from the electricity sector was the major substance that contributed to respiratory inorganics impact category and the human health damage category. The global warming impact category was largely caused by carbon dioxide from non-renewable energy which was as a result of the use of coal and unspecified energy at the power plant for the electricity generation. Thus, the electricity sector was the major emitter of carbon dioxide and had the highest share of the substances responsible of the global warming impact category. Moreover, the non-renewable energy is mainly caused by coal and unspecified energy with the coal mining and the electricity sectors being the main non-renewable energy consumers.
The particulate matter PM10 and the carbon dioxide were identified as air pollutants with major health and environmental concerns. The results of the IO-LCA specified that majority of these two substance emissions were emitted from the electricity sector. Thus, Eskom (SA’s electricity national distributor) and the SA’s government should take actions to reduce more PM10 and CO2 emissions by finding strategies to decrease the amount of coal consumed to produce electricity, or shift to renewable. However, other substances such as Sulfur dioxide, Arsenic, Barium, Nitrogen oxides, Ammonia, Dioxin, 2,3,7,8 Tetrachlorodibenzo-p-, Dinitrogen monoxide, Sulfur hexafluoride and Methane which are also pollutants with major health and environmental concerns, shouldn’t be ignored.
The results of the Monte Carlo simulation of the top 12 sectors showed that the standard deviations of the respiratory inorganics, global warming, climate change and human health do not widely vary from their means with coefficients of variation less than 32% overall. This point out some of level of consistency with the input data. The results also indicated that the score for non-renewable energy and resources have very high uncertainty compare to Global warming and Respiratory inorganics especially in 1995 and 2012 where the coefficients of variation were respectively 104% and 115%. That revealed the standard deviation varies widely from the mean. Finally, the simulation of Monte Carlo on Single score of the top 12 sectors presented a coefficient of variation less than 60% each year of the study, which means the standard deviation does not vary widely from the mean. Therefore, the results of this study revealed some of level of reliability with the input data overall. In addition to the results of this study, some potential suggestions on reducing the energy consumption and CO2 emissions deduced from this study are discussed.
LIST OF TABLES XIII
LIST OF FIGURES XVI
CHAPTER 1 INTRODUCTION 1
1.1 Research Background. 1
1.2 Research objective 11
1.3 Framework of the research 12
CHAPTER 2 METHODS AND LITERATURE REVIEW 15
2.1 Introduction 15
2.2 Decoupling 16
2.2.1 Literature review 16
2.2.3 Tapio's decoupling 22
2.2.4 Kaya Identity 24
2.3 Decomposition analysis 27
2.3.2 CO2 Emissions from Electricity Generation 31
2.3.3 Logarithmic Mean Divisia Index (LMDI) 32
2.4 Input-Output Analysis 36
2.4.1 Literature review 36
2.4.2 General framework of input-output analysis 39
2.4.3 Linkage effect analysis 47
2.4.4 Multiplier effect analysis 48
2.5 Life cycle assessment (LCA) 50
2.5.1 Input-output life cycle assessment (I-O LCA) 60
2.5.2 Literature review 63
2.5.3 I-O LCA calculation 67
2.5.4 Impact assessment model 69
2.5.5 Monte Carlo analysis 78
CHAPTER 3 DECOUPLING EFFECTS BETWEEN CARBON DIOXIDE AND GROSS DOMESTIC PRODUCT IN SOUTH AFRICA 82
3.1 Introduction 82
3.2 Data consolidation 83
3.3 Analysis of CO2 emissions 84
3.4 Decoupling analysis 87
3.5 Comparison of the methods 90
3.6 Kaya factors analysis 92
3.7 Summary 94
CHAPTER 4 DECOMPOSITION ANALYSIS OF CO2 EMISSIONS FROM COAL - SOURCED ELECTRICITY PRODUCTION IN SOUTH AFRICA 97
4.1 Introduction 97
4.2 Data consolidation 98
4.3 Trends of energy consumption and CO2 emissions from SA’s thermal power sector 98
4.4 Decomposition analysis 99
4.5 Policy Implications 105
4.6 Summary 107
CHAPTER 5 INTER-INSDUSTRY LINKAGES AND MULTIPLIER EFFECTS OF SOUTH AFRICA'S ELECTRICITY SECTOR 109
5.1 Introduction 109
5.2 Data consolidation 112
5.3 Inter-Industry linkages 113
5.4 Energy multiplier 116
5.5 CO2 multiplier 117
5.6 Direct and indirect effects of energy consumption 118
5.7 Direct and Indirect effects of CO2 emissions 118
5.8 Policy implications 119
5.9 Summary 120
CHAPTER 6 130
INPUT-OUTPUT LIFE CYCLE ASSESSMENT OF SOUTH AFRICA'S ELECTRICITY SECTOR 130
6.1 Introduction 130
6.1.1 Scope 131
6.1.2 Functional units 132
6.1.3 Data consolidation 132
6.2 Limitations and assumptions 133
6.3 Environmental impacts of the electricity generation sector 134
6.3.1 Environmental Impact Results 135
6.4 Summary 158
CHAPTER 7 CONCLUSIONS AND SUGGESTIONS 161
6.1 Conclusions 161
6.2 Suggestions 166
Appendix 1- OECD decoupling result (fix based year method) 191
Appendix 2- Tapio decoupling result (rolling based year method) 192
Appendix 3 Network on Single Score Impact on Environment of the top 12 sectors in 1995. 193
Appendix 4 Network on Single Score Impact on Environment of the top 12 sectors in 2000. 194
Appendix 5 Network on Single Score Impact on Environment of the top 12 sectors in 2005. 195
Appendix 6 Network on Single Score Impact on Environment of the top 12 sectors in 2010. 196
Appendix 7 Network on Single Score Impact on Environment of the top 12 sectors in 2012. 197
Appendix 8 Characterization of the top 12 sectors in 1995. 198
Appendix 9 Characterization of the top 12 sectors in 2000. 199
Appendix 10 Characterization of the top 12 sectors in 2005. 200
Appendix 12 Characterization of the top 12 sectors in 2010. 201
Appendix 13 Characterization of the top 12 sectors in 2012. 202
Appendix 14 Normalization of environmental damages of the top 12 sectors in 1995. 203
Appendix 15 Normalization of environmental damages of the top 12 sectors in 2000. 204
Appendix 16 Normalization of environmental damages of the top 12 sectors in 2005. 205
Appendix 17 Normalization of environmental damages of the top 12 sectors in 2010. 206
Appendix 18 Normalization of environmental damages of the top 12 sectors in 2010. 207
Appendix 19 Monte Carlo simulation results on Characterization of the top 12 sectors. 208
Appendix 20 Monte Carlo simulation results on Damage Assessment of the top 12 sectors. 209
Appendix 21 Monte Carlo simulation results on Normalization of the top 12 sectors. 210
Appendix 22 Monte Carlo simulation results on Weighting of the top 12 sectors. 211
Appendix 23 Monte Carlo simulation results on Single score of the top 12 sectors. 212
LIST OF TABLES
Table 1-1 Energy sector carbon dioxide emissions intensity and per capita in 2012. 5
Table 1-2 South Africa's power stations and nominal installed capacity (unit: megawatts). 8
Table 2- 1 Tapio’s decoupling states. 24
Table 2-2 Default emissions factors for stationary combustion in the energy industries (kg of greenhouse gas per TJ on a net calorific basis). 32
Table 2-3 LMDI formulae for decomposing changes in electricity generation-related CO2 emissions. 35
Table 2- 4 Input-output table structure 41
Table 2-5 Characterization damage factors of the impact categories in IMPACT 2002+ 75
Table 2-6 Normalization factor for four damage categories in IMPACT 2002+ 77
Table 2-7 Weighting factor for four damage categories in IMPACT 2002+ 77
Table 2-8 Statistical Distributions 79
Table 3 - 1 Total primary energy supply, total final energy consumption, total CO2 emission from fuels combustion and Gross Domestic Product (GDP) of SA. 86
Table 3 - 2 Tapio decoupling method results of SA. 89
Table 3 - 3 Carbon emissions and the Kaya identity components of SA (percentage changes per period). 94
Table 3 - 1 Total primary energy supply, total final energy consumption, total CO2 emission from fuels combustion and Gross Domestic Product (GDP) of SA. 86
Table 3 - 2 Tapio decoupling method results of SA. 89
Table 3 - 3 Carbon emissions and the Kaya identity components of SA (percentage changes per period). 94
Table 4 - 1 Decomposition of the changes in CO2 emissions in South Africa: 1990–2013. 104
Table 5 - 1 Sectors classification. 112
Table 5 - 2 Sectoral backward linkage effect for 1995, 2000, 2005, 2010 and 2012. 123
Table 5 - 3 Sectoral forward linkage effect for 1995, 2000, 2005, 2010 and 2012. 124
Table 5 - 4 Top 5 backward linkage sectors for 1995, 2000, 2005, 2010 and 2012. 125
Table 5 - 5 Top 5 forward linkage sectors for 1995, 2000, 2005, 2010 and 2012. 125
Table 5 - 6 Electricity sector inter-industry linkages for 1995, 2000, 2005, 2010 and 2012. 126
Table 5 - 7 Energy consumption, monetary energy consumption and energy multiplier for 2012. 126
Table 5 - 8 CO2 emissions, monetary CO2 emissions factor and CO2 multiplier for 2012. 127
Table 6 - 1 Characterization of South Africa’s electricity sector for years 1995, 2000, 2005, 2010 and 2012. 137
Table 6 - 2 Characterization of South Africa’s Coal mining sector for years 1995, 2000, 2005, 2010 and 2012. 138
Table 6 - 3 Environmental damage categories of the top 12 sectors. 140
Table 6 - 4 Respiratory inorganics total major substances of the top 12 sectors. 144
Table 6 - 5 Respiratory inorganics total major substances of the electricity sector. 145
Table 6 - 6 Global warming (climate change) total major substances of the top 12 sectors. 145
Table 6 - 7 Global warming (climate change) total major substances of the electricity sector. 146
Table 6 - 8 Non-renewable energy (resources) total major substances of the top 12 sectors. 147
Table 6 - 9 Non-renewable energy (resources) total major substances of the coal mining sector. 148
Table 6 - 10 Non-renewable energy (resources) total major substances of the electricity sector. 149
Table 6 - 11 Human health total major substances of the top 12 sectors. 150
Table 6 - 12 Human health total major substances of the electricity sector. 151
Table 6 - 13 Weighting of South Africa’s electricity sector. 154
Table 6 - 14 The direct and indirect environmental impact of South Africa’s electricity sector. 154
LIST OF FIGURES
Figure 1 - 1 Total primary energy supply of South Africa by fuel types. 4
Figure 1 - 2 Share of total CO2 emissions from fossil fuels burned and others energy sources in South Africa. 4
Figure 1 - 3 Share of South Africa CO2 emissions by sector in 2013 (IEA data, 2015). Other sectors include industrial waste and non-renewable municipal waste. 9
Figure 1 - 4 Generation mix and CO2 intensity of Eskom and per countries (Source: Eskom, IMF, 2010 World Energy Outlook, BCG analysis). 10
Figure 1 - 5 Research Framework 14
Figure 2 - 1 Schematic presentation of the environmental impact assessment mechanism explaining the general modeling of a substance emission through a series of impacts leading to damages to the environmental areas of protection. Source: Hauschild and Potting, (2005) 55
Figure 2 - 2 Overall scheme of the IMPACT 2002+ framework, linking LCI results via the midpoint categories to damage categories. Source: Jolliet et al., (2003). 74
Figure 3 - 1 Trend of GDP, CO2 emissions, Total Primary Energy Supply and Total Final Energy Consumption of SA. 85
Figure 3 - 2 Trend of GDP, CO2/GDP, TFC/GDP and decoupling factor in South Africa.Decoupling factor is defined as 1 – (EP/DF)To/(EP/DF)T where EP = environmental pressure and DF = driving force. Decoupling occurs when the value of the decoupling factor is between 0 and 1. Source: OECD, Eurostat (2002). 89
Figure 4 - 1 Share of energy consumption structure and the CO2 emissions from South Africa’s electricity power generation. 103
Figure 4 - 2 LMDI decomposition results of CO2 emissions per-capita from electricity generation in SA (1990–2013). 104
Figure 5 - 1 Trend of GDP, Total Final Energy Consumption and CO2 emissions (Source: consolidation from data of IEA (2017) and World Bank (2017 111
Figure 6 - 1 Normalized environmental damages for years 1995, 2000, 2005, 2010 and 2012. 152
Figure 6 - 2 Normalization of the total environmental impacts for years 1995, 2000, 2005, 2010 and 2012. 152
Figure 6 - 3 Normalization of the environmental impacts from electricity sector for years 1995, 2000, 2005, 2010 and 2012. 153
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