Integrated Study of Energy Consumption CO2 Emissions and Input-Output Life Cycle Assessment for the Electricity Sector in South Africa

  • 裴 牧和

Student thesis: Doctoral Thesis


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
Date of Award2017 Dec 8
Original languageEnglish
SupervisorLiang-Ming Whang (Supervisor)

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