LIFE CYCLE AND SYSTEM DYNAMIC MODELLING AND ANALYSIS OF DOMESTIC PHOTOVOLTAIC PANELS

Transcription

1 AUTHOR: ANNIE MCCABE SUPERVISOR: DR. ANTHONY HALOG ENVM4200: HONOURS THESIS SUBMISSION LIFE CYCLE AND SYSTEM DYNAMIC MODELLING AND ANALYSIS OF DOMESTIC PHOTOVOLTAIC PANELS METHODOLOGY APPLICATION AS A CONTRIBUTION TO POLICY DESIGN October 2013

2 Statement of original authorship and independent research. I, Annie McCabe hereby declare that the work presented here in this thesis is, to the best of my knowledge, original and my own work, and that the material has not been submitted, either in whole or in part, at this or any other university, except as acknowledged in the text. Signature: Annie McCabe Dated: LCA and SD of Domestic PV for Policy Development 2

3 Contents Statement of original authorship and independent research Tables and Figures... 7 Acknowledgements... 9 Abstract Nomenclature Chapter 1 Introduction Rationale for research Research Questions Structure of Thesis Chapter 2 Literature Review and Background Life cycle analysis as a methodology Attributional and Consequential Life Cycle Analysis Overview LCA of PV Mono Crystalline Panels Manufacture of Mc-Si PV Crystals LCA of Photovoltaics Industrial Ecology Systems Thinking for Sustainability Environmental Policy Development: Principles and Problems Policy development in Australia Future Directions LCA and SD of Domestic PV for Policy Development 3

4 2.6.2 Assessments of Australia s Solar Policy Framework Knowledge Gaps within Literature Literature Review Summary Chapter 3 Life Cycle Analysis of a 3kWp Mono-Crystalline PV System Introduction: The System Methods Step 1 Goal and Scope Definition: Step 2: Life cycle Inventory Analysis Step 3. Impact Assessment Step 4 Uncertainty Analysis Interpretation Justification of Method Results: LCIA and Uncertainty Analysis Life cycle Impact Assessment Uncertainty Analysis Discussion Chapter 4 Sustainability Implications of Large Scale Uptake of Domestic Photovoltaics in Australia: Investigation through System Dynamic Modelling Sustainability and emerging technology Research Objectives Method LCA and SD of Domestic PV for Policy Development 4

5 4.3.1 Justification of Method Results Solar panels in Australia Causal loop representation and problem structuring System Dynamic Model Conclusions Chapter 5.0 Discussion: The merits of applying LCA and SD modelling in policy development Combined results: Consequences and Policy Evaluation The merits of applying LCA and SD modelling to Australia s renewable energy framework for sustainability The merits of applying LCA and SD modelling in overall environmental policy development for industry Conclusions: Research Questions Limitations to study: Future Applications References Appendix Appendix 1. Supplementary conversion information for impact assessment methods EcoIndicator 99 Hierarchist Version Ecological Footprint Method LCA and SD of Domestic PV for Policy Development 5

6 1.3 Cumulative Energy Demand with breakdown. (Energy by fuel source) V Appendix 2. Causal Loop Diagram Appendix 3. System Dynamic Model Data Sources and Code Data Sources Model Code Equations for Price Scenario Equations for No Price Scenario Equations for Tariff Scenario Equations for Rebate Scenario Equations for Price Stock Scenario Appendix 4. Statement of anticipated publication from this work Appendix 5. Submission to Journal of Cleaner Production LCA and SD of Domestic PV for Policy Development 6

7 Tables and Figures Chapter 2 Literature Review and Background Figure 1 Page 15 Basic Stages of LCA Figure 2 Page 18 Standard mc-si and m-si panels Figure 3 Page 19 Silicon PV Manufacture Stages Figure 4 Page 20 Life cycle Stages of PV Panel System Figure 5 Page 21 Embodied Energy Requirements by process Figure 6 Page 22 Planetary Boundaries for Safe Operating Figure 7 Page 26 Renewable Energy Policy Milestones Table 1 Page 17 Table of Previous LCA Studies Table 2 Page 20 LCI of mono-si wafer Table 3 Page 27 Australian Renewable Energy Framework Table 4 Page 28 State and Territory Feed-in Tariffs Chapter 3 Life Cycle Analysis of a 3kWp Mono-Crystalline PV System Figure 1 Page 33 Overview of LCA methodology Figure 2 Page 34 Unit process raw data for four modules of LCI Figure 3 Page 36 Network diagram of fossil carbon flow Figure 4 Page 40 Analysis using Eco- Indicator 99 Figure 5 Page 41 Analysis using IPPC 1990 method Figure 6 Page 42 Analysis using Raw Material Flows method Figure 7 Page 43 Analysis using Ecological footprints method Figure 8 Page 45 Analysis using Cumulative Energy Demand method Figure 9 Page 46 Monte-Carlo analysis of EcoIndicator 99 method Table 1 Page 34 Unit process raw data of 3kWp mc-si system Table 2 Page 35 Selected LCI fata from EcoInvent Dataset Table 3 Page 41 Selected process contributions for EcoIndicator 99 Table 4 Page 43 Selected process contributions of Raw Material Flows Table 5 Page 44 Selected process contributions of Ecological Footprints Table 6 Page 45 Selected Process Contributions by fuel type LCA and SD of Domestic PV for Policy Development 7

8 Chapter 4 System Dynamic Modelling and Analysis Figure 1 Page 53 Growth of Solar Panel MWh Figure 2 Page 53 Projection scenarios of solar panel uptake Figure 3 Page 54 CLD of PV Uptake in Australia Figure 4 Page 56 SD Model of Solar Panel Uptake in Australia Figure 5 Page 57 Uptake trends (no price scenario) Figure 6 Page 57 System Dynamic Model (price scenario) Figure 7 Page 58 Solar Ratio comparison between scenarios Figure 8 Page 58 Solar Ratio Comparison (no rebate) Figure 9 Page 59 Price scenario uptake trends Figure 10 Page 59 Surplus power and Wasted power ratio (price scenario) Figure 11 Page 60 Price trends in price scenario Figure 12 Page 60 Electricity Price Stock Scenario Figure 13 Page 61 Price trends in Electricity Price Stock Scenario LCA and SD of Domestic PV for Policy Development 8

9 Acknowledgements I would like to extend an immeasurable amount of gratitude to Anthony Halog for his continuous enthusiasm, encouragement and academic expertise throughout my thesis year. His supervision has given me a fantastic introduction to the world of research in industrial ecology and everything I have learnt this year will undoubtedly help me in any future endeavours, both in academia and on a professional level. I would also like to thank Dr Bradd Witt for his tireless efforts as program coordinator and in keeping our sanity. Finally, thanks go to my partner Sam for his understanding and feedback, my parents Lorelle and Michael for their unwavering support and love, and my twin brother Liam for his calm, logical and ever insightful words of wisdom. My honours experience has given me a strong respect for academic research and stimulated my interest in future exploration of a research orientated environmental management career.

10 Abstract Since the advent of mainstreamed industrial ecology within policy framework design, the applications of system analytics for emissions reduction have substantially grown. This study has applied the life cycle assessment (LCA) methodology in conjunction with system dynamic (SD) modelling to assess the merits of such a hybridized design approach in policy development. Whilst photovoltaic (PV) panels are operationally GHG emissions free and stationary, there are inherent emissions and environmental degradation potential within manufacture and waste stages. Using an LCI dataset sourced from the EcoInvent database for a 3kWp mono-crystalline slanted rooftop mounted system an LCA was carried out within LCA modelling software to yield life cycle impact assessment results regarding a number of indication areas such as land and resource use, human health and energy use. It was found that the predominant and most consistent impacts across the life cycle of a 3kWp system relate to the particular energy mix used within production and manufacturing stages. A prototype system dynamic model was then developed to assess the potential triple-bottom line impacts of extrapolated uptake trends of domestic solar PV in Australia. Through system dynamic modelling, a prototype stock and flow model was created, accommodating a number of scenarios involving pricing and policy changes. The system was modelled to a 50 year time horizon ( ) and used current population growth, electricity use and price, and PV uptake trends to model a simplified uptake scenario. A scenario with an annual feed-in tariff of 40c/kWh at years was created. This particular scenario found that as solar uptake increases (due to initially high disposable income and feed-in tariffs) disposable income for non-solar household decreases due to rising electricity prices (from price increases due to surplus power and wastage from grid capacity excess) which further drives panel uptake. Relative electricity price begins at 22c/kWh and concludes at 37c/kWh, in keeping with price projection scenarios of 33c/kWh for domestic electricity prices in 2050 (ROAM Consulting 2011). From SD modelling, it was found that, with current and projected install rates, domestic solar panel use for large scale electricity production is unlikely to achieve positive results regarding emissions reduction and penetrating a high enough energy supply for household use, unless in conjunction with other renewable power sources such as solar thermal or wind energy. The results of the LCA and SD model were then combined and compared against the policy framework regarding small-scale renewables and domestic PV systems in Australia to determine whether there is appropriate legislative coverage. An analysis of the merits of LCA and SD methodology application to policy development methodology was also carried out. LCA and SD of Domestic PV for Policy Development 10

11 Through policy analysis it was found that, whilst a reduction in rebates may be a positive step as prices of PV panels are reducing with regard to the proposed Direct Action Plan and future renewables legislation, the future legislation reduces ability to influence emissions from locally driven PV demand at a global level. It is also suggested that the plan reduces capacity for decoupling of economic growth from carbon emissions, including those industries contributing to solar PV efficiency improvements. Although there have been a number of studies working with LCAs of PV panels in various contexts, there is little synthesis of this produced data to aid in policy development. The policy framework for domestic PV technology in Australia has never been analysed using such a hybridized analytics approach before and this study will provide a base of knowledge that can be built upon through subsequent research. The ability to model impacts over the life cycle of a PV system over a set time horizon allows industry to better understand system impacts and policy makers to create a flexible and adaptive legislative framework with accurate emissions accounting. Nomenclature ALCA Attributional Life cycle Analysis mc-si Mono crystalline PV Photovoltaic kwp Killowatt Peak LCA Life cycle Analysis LCI Life cycle Inventory BOS Balance of System CED Cumulative Energy Demand EPBT Energy Payback Time EPI Environmental Policy Integration EROI Energy Return on Investment GHG Greenhouse Gas SD System Dynamic/s ISO International Organisation for Standardization LCA and SD of Domestic PV for Policy Development 11

12 Chapter 1 Introduction 1.1 Rationale for research Present rates of production and energy consumption have been facilitated through the exploitation of progressively depleting fossil fuels (Raugei 2012). For many years economists and scientists have speculated upon the advent of peak oil and rising electricity prices associated with the increasing cost of fossil fuel extraction, calling for renewable energy innovation (Campbell & Laherrere 1998; Murphy & Hall 2011). With increasing technical and environmental pressures due to effects of climate change, solar resources are growing in popularity as a readily available and well established technology with a relatively short implementation timeframe (Wright & Hearps 2010). Australia s position to exploit solar resources is one of the most advantageous in the world with the highest solar radiation per sq. metre of any country (Bahadori & Nwaoha 2013). As residential energy use has risen to comprise approximately 11% of Australia s total energy consumption, and concerns about anthropogenic climate change grow, there is increasing focus on legislating for domestic energy use reduction and efficiency, through solar renewable sources, to promote national energy use and emissions reduction in Australia (Stark et al. 2012; Wusthagen 2007; IEA 2012b; Bahadori & Nwaoha 2013). Solar energy uptake is predicted to reach 30% of Australia s electricity production by 2050 (CSIRO 2010). Compared to coal, for every gigawatt-hour of electricity produced through photovoltaics, approximately 1000 tonnes of CO 2 emissions are prevented (Fthenakis & Moskowitz 2000). Although there has been a relatively positive uptake of commercially available domestic photovoltaic (PV) panels, great inefficiencies within the manufacturing and disposal life cycles are yet to be fully addressed through legislative and system analysis approaches (Jungbluth et al 2008). The solar energy supply index from 1990 to 2010 (1990=100) rose to almost , indicating a substantial relative growth in solar energy utilization on a global level (IEA 2012a). Comparatively, wind and biofuel resources have only grown to a supply index of and 7500 respectively. Such large supply indicates both a high uptake and extensive increases in solar panel efficiency. It is crucial that a movement towards a renewable energy mix is approached with comprehensive emissions and resource accounting to ensure an effective transition concurrent with ideals of sustainable development and industrial ecology. Such high uptake highlights the need for both LCA and System Dynamic modelling for contribution to policy analysis and design for government and industry. Within renewable energy legislation, the need for an environmental management tool based more firmly in integrated systems is clear (Effendi & Courvisanos 2012). LCAs can be of great merit to policy development in identifying system inefficiencies, due to their ability to work

13 with a number of metrics and technical processes (Europen 1999). However, the inherent boundary setting requirements and thus limitations within the LCA methodology lend themselves to the application of System Dynamic (SD) modelling to model impacts across a timeframe, with the inclusion of triple bottom line variables. Although there have been a number of studies working with LCAs of PV panels in various contexts, there is little synthesis of this produced data to aid in policy development. With long term target setting for emissions reduction in Australia, the ability to model scenarios in a dynamic manner is vital in predicting future success of mitigation actions to avoid policy failure or oversight (Kenny & Arup 2013). The hybridization of LCA and SD methodologies for modelling impacts for PV panels within this thesis has never been approached as a research objective, particularly in reference to policy design and development. This research project proposes that LCA and System Dynamic analysis methodologies, whilst useful in an industrial context, may also lend itself to improving policy development approaches for domestic solar panel use in Australia (McCabe 2013). 1.2 Research Questions This thesis was framed around investigation of 4 key research questions: 1. What are the life cycle accountabilities of a domestic use solar panel system typical of one used in QLD Australia? 2. Is the legislative framework in Australia appropriately designed to account for resource flows and life cycle metrics of PV technology? 3. What are the effects to social, economic and environmental sustainability of solar panel use and production in Australia, identified through boundary extension using system dynamic modelling? 4. Can the attributional LCA framework and system dynamic modelling methodology be appropriately applied to policy formulation in Australia? Through investigation of the renewable energy policy framework and complexities of PV manufacture and use it is anticipated that this thesis will provide a substantial, unique knowledge contribution to the area of systems analytics for policy design in Australia. 1.3 Structure of Thesis This thesis will be structured through a review of literature for LCA, solar panel design, policy development and the Australian PV policy framework. Sections of the literature review have been selected and integrated from a previous academic submission to the School of Geography, Environmental Management and Planning in The subsequent 2 chapters on LCA and SD LCA and SD of Domestic PV for Policy Development 13

14 introduce the study, detail the methodologies separately and present the results. The results of these chapters were then combined within the discussion to determine coverage of the Australian policy framework, and to discuss the merits of LCA and SD methodology applications to policy development as a whole. Tables and Figures are numbered within individual sections. References for content within the Appendices are within their corresponding Appendix. A publication-ready journal paper is to be submitted from this thesis to the Journal of Cleaner Production (see Appendix 5) upon approval of my supervisor. LCA and SD of Domestic PV for Policy Development 14

15 Chapter 2 Literature Review and Background 2.1 Life cycle analysis as a methodology The use of LCA as an analytical tool in PV research has gained considerable clout in the past decade as increasing pressures of climate change and environmental stress call for efficiency advancements in the PV industry (Sherwani et al. 2010). Operating standards for industry are applied globally for the LCA process in tracking resource flows and identifying key emissions and pollutants from cradle to grave. Due to the fast-growing advances in PV efficiency, a systems analytics approach that allows for adaptive and iterative analysis has been found in the LCA methodology framework (Fthenakis et al. 2008). The ability to identify inefficiencies within each life-cycle stage of the PV system from resource extraction, manufacture, use and decommissioning, provides the decision maker with resource flow and energy expenditure information over time. Life cycle analysis of PV systems allows for elusive or hidden resource characteristics within the materials extraction and manufacture stages to be found, thus providing accountability and transparency across the life cycle of the system (Marimuthu & Kirubakaran 2013). Attributional life cycle analysis accounts for the immediate resource flow and allocation within a defined system boundary. The attributional life cycle analysis methodology only measures the direct unit processes involved within the PV system (Thomassen 2008). Life cycle analysis is an internationally standardized analytical framework for identifying resource use and emissions from the cradle to the grave of a system. Emerging from growing interest in sustainable development of products, LCA is used across many industries to satisfy legislative requirements for resource use and waste disposal efficiency and reporting (Schenck 2005). The standardized LCA Methodology follows a basic procedure shown in Fig. 1. Figure 1: Basic Stages of Standard LCA (Ito et al 2011) Figure 1 shows the basic process of LCA, with goal and scope definition including boundary definition, inventory analysis (after inventory data collection) and impact assessment. LCA and SD of Domestic PV for Policy Development 15

16 International standards governing life cycle analysis are ISO 14044, ISO and ISO These frameworks outline basic terms and procedures to comply with global standards for data collection and analysis within an LCA (ISO 2006a, ISO 2006b; ISO 2000) (McCabe 2013) Attributional and Consequential Life Cycle Analysis The two broad categories of LCA in published literature are Attributional and Consequential. These two methods of LCA are conceptually differentiated through their scope and boundary definitions. Attributional LCA measures the immediate static material flows within a clearly defined system boundary, whereas Consequential LCA takes into account external influences if demand for system products fluctuates (Earles & Halog 2010). This thesis topic will use the attributional life cycle Analysis due to time constraints, technical limitations and the abundance of attributional life cycle inventory data. One limitation within LCA methodology is the sheer amount of data required for appropriate and realistic analysis of a system, including data on some metrics that are notoriously hard to measure and quantify, e.g.: radon emissions. The condition to define a functional unit within ISO standards also requires that all LCI data be in consistent units, which can be a monumental task on temporal, academic and financial scales. Depending on the industry, the time taken to collect sufficient LCI data may exceed the pace of technological advances within the system, which can affect the validity and currency of the analysis. The methodological requirement for discrete boundary definition also limits an LCA in its ability to be analysed holistically, and on a real-world basis. Gaps in knowledge include a lack of studies surrounding the limitations of boundary definition (McCabe 2013). 2.2 Overview LCA of PV Due to the constantly advancing nature of PV research, following increases in efficiency with technology advancements, LCA studies yield important comparative data over time. A number of previous LCA of mc-si were researched and major metrics summarized in Table 1. LCA and SD of Domestic PV for Policy Development 16

17 Table 1: Summary of previous mc-si LCA C.O (Comparitive/Other Technologies), S.M (Single Metric), M.M (Multiple Metric), R (Review) (McCabe 2013). Mono-crystalline LCA studies Publication Authors Journal Topic Type Date Country Effici ency Power Rating Life time (years) EPBT GHG Emissions (g-co2/khhe) Kreith F, Norton P, Brown D. Energy CO2 emissions C.O 1990 vol. US kW 30 na (12) Shaefer H, Hagedorn G. Renewable Energy Hidden Energy Characteristics C.O 1991 vol. Germany W N/A 4-7 5,020kg CO2/kWp of P.V Powerplants. 2(2) Kato K, Murata A, Sakuta K. Solar Energy Materials and EPBT and CO2 emissions M.M 1997 vol. 47 Japan na 3kW Solar Cells Alsema E. Prog. Photovolt. Res. Appl. EPBT and CO2 emissions M.M 2000 vol. 8 Netherlands 14 NA NA Mathur J, Bansal NK, Wagner Energy Sources CED of renewable energies C.O 2002 vol. 24 India 13 35W 20 NA 64.8 HJ. Jungbluth N. Prog. Photovolt: Res. Appl. LCA from Eco-Invent Data M.M 2005 Switzerland 14.8 Muneer T, Younes S, Lambert MechE Journal of Power High latitude medium PV plant M.M 2006 vol. UK kW N, Kubie J. and Energy 220 Kannan R, Leong KC, Osman Solar Energy Case Study in Singapore M.M 2006 vol. Singapore kW R, Ho HK, Tso CP. 80(5) 8.9 Fthenakis, V. M.; Alsema, E Prog. Photovolt: Res Appl GHG and external costs M.M 2006 vol. 14 Europe 14% NA NA 2.7 NA Fthenakis V, Kim H. Energy Policy GHG emissions from PV and C.O 2007 vol. 35 USA 14% 30 ~40 Nuclear Life cycles. (4) Fthenakis VM, Kim HC, Alsema Environmental Science & Emissions M.M 2008 vol % E. Tech. Varun, Bhat I.K, Prakash R. Renewable and Sustainable LCA of Elec. Gen. Review R 2009 vol. Global 2.7kWp Energy Reviews 13(5) Lu L, Yang H.X. Applied Energy EPBT of rooftop PV S.M 2010 vol. Hong Kong 7-12 years 87(12) Ito M, Kudo M, Nagura M, Prog. Photovolt: Res Appl. Comparative of LCA of 20 PV C.O 2011 Japan kW (ground Kuokawa K. modules mounted) Raugei M, Fullana-i-Palmer P, Energy Policy EROI of PV panels S.M 2012 vol. 45 USA Fthenakis VM. Fthenakis V., Betita R., Shields European Photovoltaic Sol. LCA of High Performance M.M 2012 Philippines 15.4 M., Vinje R., Blunden J., J En. Conf. Systems, NEP and EPBT

18 The LCA field for PV analysis has been dominated by a small selection of authors and research groups over the past decade. Frankl et al (2005) published a number of metrics in the year 2005 for various mc-si LCA studies for both rooftop and ground-mounted systems located in Central and Southern Europe. The system lifetimes ranged from years with GHG emissions ranging from 36 to 76gCO2CO2e/kWh. Earlier studies by Pacca (2003) and later studies by Jungbluth et al (2009) and de Wild-Scholten and Alsema (2006) indicate a growth in lifetime from 25 to 30 years but no discernible decrease in GHG emissions or increase in performance ratio Mono Crystalline Panels Crystalline PV panels are the most common form of PV panel used on domestic scales, contributing to around 80% of the global market (Everts 2011). There are two forms of crystalline PV panels, mono-crystalline (Figure 2a) and multi-crystalline (Figure 2b). Monocrystalline panels are made from a singular cylindrical cut of silicon crystal, leaving an area of uncovered panel at the corner of each cut of ingot (Figure 2a). a. b. Figure 2: Standard Mono-Crystalline (a) and Multicrystalline (b) Solar Panels (CyberEnergy 2011; DirectIndustry 2013) Due to their composition and refining process, mono (or Singular) crystalline panels are more expensive, but have a higher power conversion than multi-crystalline panels (Kannan 2006) (McCabe 2013). Mono-crystalline PV panels are generally considered to have a higher peak energy efficiency compared to the other commercially available panels which are multicrystalline. However, the extra silicon refining required to produce a continuous mono-crystalline panel leads to a more energy and time intensive process which adds to cost and environmental impact of the system (Moller et al. 2005)

19 2.2.2 Manufacture of Mc-Si PV Crystals Raw material acquisition for mono-si systems requires mining of silica from quartz sand which results in indirect emissions of heavy metals such as cadmium, lead, mercury, arsenic and nickel, and particulates (Fthenakis et al. 2008). Mono crystalline PV panels are manufactured through the refining of quartz based silicon using the Czochralski process (Figure 3) (Fthenakis & Kim 2011). Solar grade silicon is produced through the melting of quartz sand in an arc furnace, forming Metallurgical Grade Silicon, which is subsequently purified and formed further using the Czochralski process (Kittner et al. 2013). The Czochralski process is a method of inducing crystallization in semiconductors (silicon), metals and salts (Abbaschian 2011). Creating Czochralski silicon requires high grade silicon to be heated to melting point (1450 o C) within a crucible (generally quartz or silica) and drawn out using a seed crystal (high purity silicon). The seed crystal is drawn out ( necking method ) slowly to incite homogenous constant crystalline growth into a cylindrical configuration (Dash 1958). These crystals are then cut into thin cylindrical discs up to 40cm in width (Figure 2a.), forming the largest silicon crystals available in the world (Porinni 2001). Figure 3 Basic Silicon PV Manufacture Stages (Fthenakis et. al. 2008) In order to act as an extrinsic semi-conducting material, silicon must be doped using elements from group 3 and 5 from the periodic table to produce either p-type or n-type conductivity respectively (Pfann 1966). Adding a particular dopant atom to the growing crystal allows conductive crystal growth. P-type conductivity is the most commonly used for solar panel production, utilizing boron as the dopant material; in n-type silicon phosphorous or arsenic are used (Porinni 2001; Xiao et al. 2012). Solid and liquid materials inputs within the wafer production stage of a mc-si cell include tap water, polycrystalline silicon, sodium hydroxide and hydrochloric acid (Table 2) (McCabe 2013). LCA and SD of Domestic PV for Policy Development 19

20 Table 2: LCI of mono-si wafer (Fthenakis et al. 2011) LCA of Photovoltaics LCA of PV panels have been performed with increasing consistency in the past decade (Table 1). Whilst PV panels are operationally GHG emissions free and stationary, there are inherent emissions and environmental degradation within manufacture and waste stages (Figure 4). Life cycle stages of a PV system are mining of raw materials (Quartz Sand), silicon processing, and manufacture of components and balance of system (BOS), use and decommissioning (Figure 4). Figure 4: Life cycle Stages of PV Panel System (Fthenakis & Kim 2011) LCA and SD of Domestic PV for Policy Development 20

21 As seen in Figure 4 there are material inputs and outputs across the entirety of a PV systems lifespan. Figure 5 shows the energy use breakdown by process. Figure 5: Embodied Energy Requirements by process (Jungbluth 2005) Figure 5 shows how the Czochralski process requires the greatest energy use due to the high temperatures required to produce molten silicon for purification, depending on manufacturing region energy required for this process is generally sourced from fossil fuel sources. The Czochralski process has been altered very little over the time since its first application in the 1950s (Teal & Bühler 1952), with embodied energy requirements remaining constant, posing management problems for energy use reduction. Additional to energy use requirements, a number of other metrics are used in LCA of solar PV panels. The Balance of System (BOS) is the system of all structures required for operation and mounting including cables, frame, inverter and connectors, and often provides an extra emissions process not always accounted for in traditional LCI data collection (Fthenakis & Kim 2011). As shown in Table 1, several LCA studies of mono-crystalline PV panels already exist. Recently, LCA of PV panels has evolved to incorporate a growing number of processes, as understanding of ecological impacts of industrial processes is increased. A typical mono crystalline PV system has a lifespan of 30 years for the panel, an EPBT of 2-4 years and a conversion efficiency of anywhere from 8.5% to 16% (Table 1) (McCabe 2013). 2.3 Industrial Ecology The theory of industrial ecology, alongside sustainable development is a relatively recent one. The concepts of systems thinking and interconnected complex system management by Forester in the 1950s and 60s fostered the understanding of the dependence and influence the industrial age had on life-supporting environmental processes (Nanas & Bellestri 2011). A publication LCA and SD of Domestic PV for Policy Development 21

22 introducing the idea was put forward by Robert Ayres in 1989, using the term industrial metabolism. Alongside a publication by Robert Frosch and Nicholas Gallopoulos introducing industrial ecology, it has encouraged the integrated use of the term in present-day academia (Ayres 1989; Frosch & Galloupoulos 1989). Embraced not only by academics but also by government and law makers, contemporary industrial ecology uses ideas of renewable energy, open energy use and ecosystem resilience to work towards goals of a closed energy-using system that utilizes recycling and diversity to encourage resilience (Duffy 2011).The idea of circular resource use is one proposed in contemporary approaches to industrial ecology. The term circular economy, purporting long term resource planning and energy efficiency, has become integrated within country policy during the past decade (UNEP 2010). Similar to a circular economy, the area of Industrial Ecology proposes the idea of a closed-loop system, whereby a waste-product could be used as a raw material source for another, such as wastewater-enabled bio-plastic production (Werner et al. 2002; Khardenavis et al. 2005). Planetary Boundaries are an emerging theory within Industrial Ecology, originally proposed by Johan Rockstrom et al. (2009), proposing 9 safe-operating levels for key earth-system processes, introducing a quantifiable goal-based methodology to the field (Figure 6). In terms of industrial ecology, the use of planetary boundaries as an indicator, particularly for that of global warming potential, may aid in designing more effective environmental practices incorporating the concept of the circular economy (McCabe 2013). Figure 6: Planetary Boundaries for Safe Operating (Rockstrom et al. 2009) LCA and SD of Domestic PV for Policy Development 22

23 Industrial Ecology seeks to quantify and assess the links between industrial processes and the natural environment (UNEP 2010). Policy frameworks for industrial operation and conduct have played a significant part in mainstreaming ideas of industrial ecology (Duffy 2011). The idea of a closed-loop system could be applied to sustainable production of solar PV by enabling more effective recycling and decommissioning practices. By applying ideas of industrial ecology to LCA of PV Panels, a more holistic approach can be made to triple bottom line policy development for renewable energies in Australia (McCabe 2013). 2.4 Systems Thinking for Sustainability Humans routinely fail to comprehend complexity and anticipate long-term consequences. Systems dynamicists try to overcome these weaknesses by developing computer-supported models that can account for multiple variables in non-linear relationships (Blythe 2013, p.1). The ability to model complex problems in a dynamic way has allowed for causal relationships to be understood to aid in decision making and policy development. Understanding complexity using system dynamic modelling allows for broad understanding of an issue without a prodigious or excessive amount of information clouding decision making abilities (Hjorth & Bagheri 2006). Linear causal thinking has historically been the school of thought in problem mitigation and solution development, where single causations are counteracted (Holling & Meffe 1996). However, as complex problem systems are increasingly prevalent (i.e. energy production systems involving a broad range of stakeholders and economic imperatives) linear causal thinking has become an ineffective and limited school of thought. System modelling, initially developed in the late 1950s to enhance understanding of industrial systems, allows for a contemporary interdisciplinary application of knowledge towards problem mitigation and management (Sterman 2000). Dynamically modelling and extrapolating trends to a relevant time horizon can help avoid unforseen/detrimental consequences at early stages of deployment or functioning of a system (Sarimveis et al. 2008). 2.5 Environmental Policy Development: Principles and Problems Contemporary policy design for environmental problems has focused predominantly upon the concept of sustainable development. As a relatable and almost universally adaptable notion, sustainable development has been extremely effective in mainstreaming environmental reform through rallying of public support for environmental issues (Helm 2000). However, Solow (1991) suggests that the less a person is acquainted with the idea of sustainability the more LCA and SD of Domestic PV for Policy Development 23

24 favourable it appears to them, highlighting the universally relatable but also somewhat arbitrary nature of the phrase. Difficulties that can arise within policy design, particularly for issues surrounding energy provision and implementation, revolve around the high level of uncertainty evolving from long time frames and the high initial financial input required for action (Huang et al. 1995). The inclusion of social, economic and political uncertainties adds further complexities to the decision making and policy design process (Hafkamp & Nijkamp 1986; Munda et al. 1994). Greening and Bernhow (2004) suggest that decision support frameworks for policy development in the energy sector require a flexibility that can transcend trans-boundary environmental issues. Hupps (1993) also highlights the need for flexibility within policy design, reasoning that the effectiveness of a methodological approach relies heavily upon its ability to accurately and comprehensively predict values close to a real-world situation. With the reasoning that a methodological approach that is theoretically closest to actuality, Hupps (1993) suggests the opportunity for side-effects and costs associated to be reduced substantially. Farrell et al. (2001) proposes a framework of 4 elements required for the success of policy design for assessment of environmental health, these are: Assessment context, science-policy interaction, participation in the assessment processes and assessment capacity. Essentially this framework highlights the importance of issue identification at a public/corporate level before participatory and inclusive policy development can occur. Richardson (1983) provides three arguments for public participation for environmental protection, these surround: 1. Democracy and the postliminary consequences of decisions by non-affected parties, i.e. Those who will bear the costs of certain policy decisions should have the solitary right to design and determine them. 2. Provision of dignity and a sense of inclusion fosters capacity for action and understanding of issues. 3. Public participation leads to more effective decisions through local knowledge and understanding of the constraints, complexities and limits to effective policy making. Through public understanding of the inherent difficulties of decision making and policy design, policy makers are aided in future endeavours through potentially increased public understanding. LCA and SD of Domestic PV for Policy Development 24

25 Kemp (et al. 2008) suggests 6 problems associated with government intervention in policy development for sustainable development, these are: 1. Ambivalence about goals. Uncertainty and discourse regarding goal definition and scope often lead to a lack of consensus. 2. Uncertainty about long-term effects: The inability to predict long-term impacts of rapid decisions can lead to unintended environmental, economic and social consequences. 3. Distributed Control: Lack of communication within the governmental management hierarchy can lead to a blurring of objectives. 4. Political Myopia: To make substantial changes to policy structure, a period of time longer than most political cycles is required. 5. Determination of Short-Term Steps for Long-Term Change: Focus upon long-term results is hard to be achieved when devising actions for the short term. 6. Danger of lock-in: There is a high potential for irremediable decisions to become detrimental when devised with short term information for long term ambitions. Babrooke and Lindholm (1963) advocate an incrementalist model for policy design approach which requires less resources and analysis and also helps to combat the problem of long-term commitment to a particular solution. By avoiding fundamental temporal leaps of policy development, this incrementalist model incorporates concepts of adaptive management and flexibility into the policy design process. Romero (1996) and Van den Bergh et al. (2000) highlight the economy centric problem associated within policy design, suggesting that a number of important (and potentially positive) attributes of alternative energy policies are unable to be costed financially and are thus excluded from analysis. Alongside sustainable development, the concept of Environmental policy integration (EPI) became an influential force within environmental policy discourse in the late 20 th century. Kasperson and Owens (2009) argue that whilst there has been substantial growth in environmental thought over the past 50 years, in part due to the mainstreamed ideas of sustainable development and environmental assessment, there is still an implementation deficit when it comes to EPI. It is widely recognized that EPI is the shift necessary to ensure advancement within environmental policy making, providing proactive sector inclusive solutions. (Nilsson et al. 2009). EPI differs from environmental policy in that it highlights the need for LCA and SD of Domestic PV for Policy Development 25

26 integrated policy making within sectors, predominantly the construction of economic and environmental policy in parallel. The degree of integration in practice for policy-orientated research has been a topic of much interest, ultimately concluding a low degree of integration (Lenschow 2002; EEA 2005). Roberts (2011) discusses the detriment of scientific reductionism within environmental policy development, indicating that the understanding of a complex system can be severely limited with such an approach. System science based methods, such as system dynamics, LCA and agent based modelling are necessary to support environmental policy development. Roberts (2011) highlights the need for an approach to environmental policy development and decision making that can recognise a system as a whole, with many interrelated feedback loops. Stenzel (2000) assesses the merits of the ISO series in producing sustainable environmental outcomes. The assessment argues that command and control mechanisms are unable to capture complex systems in a holistic manner, with the ISO standards providing a systematic, preventative and proactive approach towards emissions management that does not encourage shifting of environmental burdens from one medium to another (such as groundwater to air). 2.6 Policy development in Australia Motivated by conclusions from the Rio Earth Summit in 1992, Australia s renewable energy policy framework has grown in scope and efficiency over the past two decades (Figure 7.) Figure 7: Renewable Energy Policy Milestones in Australia (IEA 2012b) Substantial change in solar legislation in the late 2000s came with the creation of the Department of Energy, Resources and Tourism, the Australian Solar Institute and the ratification of the Kyoto Protocol in However, the largest framework for renewable energy in Australia LCA and SD of Domestic PV for Policy Development 26

27 began in July 2011, enabled through the Clean Energy Package. The legislative package aimed to synthesise the existing renewable energy framework to work towards a carbon pricing mechanism and update of renewable energy efficiency (IEA 2012b). After a systematic review of documents published by Commlaw, the Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education, Department of Sustainability, Environment, Water, Population and Communities, and Department of Resources, Energy and Tourism, and State Dep. Clean Energy and Water Supply, Table 3 was produced to indicate a history of legislative policy and institutional frameworks for renewable energy. Table 3: Australian Renewable Energy Framework (McCabe 2013) Policy Schemes Departments and Institutes Renewable Energy (Electricity) Act 2000 The Small-scale Renewable Energy Scheme (SRES) including solar credits. (Closed 1 January 2013) Renewable Energy (Electricity) (Large-scale Generation Shortfall Charge) Act 2000 Renewable Energy (Electricity) (Small-scale Technology Shortfall Charge) Act 2010 Renewable Energy (Electricity) Regulations 2001 The Renewable Energy (Electricity) Amendment Act Renewable Energy (Electricity) Amendment (Transitional Provision) Regulations Renewable Energy (Electricity) Amendment (Transitional Provisions) Regulations 2009 United States- Australia Solar Energy Collaboration (USASEC) ($84m research funding) Solar flagships program (Reduced Funding) Renewable Energy Bonus Scheme Solar Hot Water Rebate (Closed 30 June 2012) QLD Solar Bonus Scheme (Reduced as of 2012) Queensland Renewable Energy Fund (Closed) Department of Industry, Innovation, Climate Change, Science Dep. Resources, Energy and Tourism Australian Centre for Renewable Energy Australian Renewable Energy Agency, Clean Energy Finance Coporation Sustainability, Environment, Water, Population and Communities Australia Solar Institute Clean Energy Act Package QLD Solar Initiatives Package (Closed) State Dep. Clean Energy and Water Supply Draft Energy White Paper 2011 Queensland Smart Energy Savings Fund (Closed) Mandatory Renewable Energy Target Renewable Energy Industry Development Plan Regulatory provisions on a Federal level have grown substantially in the past 5 years, with State (QLD) schemes being wound back with a change in leadership. Table 3 indicates a number of QLD schemes and initiatives that have been decommissioned within the past 1-2 years (shown in grey). Whilst Australia s Renewable Energy policy framework has a number of quantifiable targets and action plans, there is currently no formalised reporting or monitoring mechanism associated with them (IEA 2012a). By applying a standardized methodology for resource use accountability (LCA), this thesis could address this gap in knowledge and monitoring for PV panels. Limitations within Australia s renewable energy framework stem from inconsistencies of leadership, leading to policy lifespans being cut short. Common themes across renewable energy policy literature are incentivisation of renewable energy uptake and monetary emissions deterrents at an industry level (Clean Energy Package) (Table 3). There are fewer efficiency LCA and SD of Domestic PV for Policy Development 27

28 improvement incentives at an industry level, with quantifiable renewable energy targets not specifying quality (IEA 2012a). The Clean Energy Package was designed to provide a marketbased approach to emissions reduction through carbon pricing, provide decommissioning support for transitioning coal-fired power stations, such as Hazelwood power station, and to enable emerging renewable energy technologies to infiltrate the energy market and grid supply (Australian Government 2011). Renewable sources of electricity (such as hydroelectricity, wind and solar) do not emit greenhouse gases. (Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education 2012). Whilst this quote is specifically referring to the operation of renewables, production of equipment, as well as materials used for its production, emit greenhouse gases and other environmental emissions to air, water and land as shown through LCA. This quote from the National Greenhouse and Energy Reporting December Update demonstrates that legislation and monitoring programs in Australia do not currently address the emissions and environmental impacts of renewable energy technologies. This oversight could skew greenhouse gas and environmental accounting as Australia shifts to a renewable energy framework (CSIRO 2010) (McCabe 2013). Solar policy in Australia also includes the incentivisation of solar panel uptake through the use of feed in tariffs (Table 4). However, similar to Australian renewable energy schemes (table 3) many of the feed-in tariffs put in place in the past 5 years are now redundant. Table 4: State and Territory Feed-in Tariffs (Martin & Rice 2013) LCA and SD of Domestic PV for Policy Development 28

29 2.6.1 Future Directions Subsequent to a change in political leadership from the Australian Federal Election held on the 7 th September 2013 were a number of modifications to the renewable energy policy framework. The most substantial changes surrounded available funding for previous renewable energy support structures, as well as for the successor of the potentially superseded clean energy legislative package, the Direct Action carbon reduction plan. The emissions reduction potential of carbon sequestration and low targets proposed within the Direct Action plan have faced scrutiny from various scientific organisations since its announcement (Hannan 2013; Taylor 2013). As a funding body, the Direct Action plan has an inbuilt $3bn Emissions Reduction Fund to be directed towards the provision of soil carbon credits for sequestration efforts by landowners and industry (Lubke 2013). Among the proposed funding cuts for renewable energy programs is the Australian Renewable Energy Agency with reduced funding of $150m-$500m. This funding is, in part, set to be redistributed to fund the 1 Million Solar Roofs program, which aims to give support for one million additional homes with solar (either whole of house or hot water heating) power from 2013 levels by 2020 (Parkinson 2013). Still in its infancy, the Direct Action Plan circulates around a localised approach to carbon emissions reduction. The target of 5% carbon emissions reduction by 2020 is to be achieved from localized carbon emissions reduction, with no apparent framework for international efforts (Liberal Party of Australia 2013) Assessments of Australia s Solar Policy Framework The IEA (2012b) review of Australia s Energy Policy critiques the small scale renewable energy scheme indicating its benefits for emissions reductions but its drawbacks in terms of grid connection sustainability. Three recommendations were given for long term policy planning: 1. Develop a reporting mechanism for monitoring and regular evaluation of renewable energy-related projects and schemes. 2. Support the integration of large-scale renewables into the grid. 3. Collaborate with state governments to structure feed-in tariff planning. Regarding the implementation of tariffs a number of critiques and problems have arisen in Australia. Firstly, issues of equity for energy distributors with the introduction of a set tariff may become prevalent (Couture et al. 2010, Mendonca et al. 2010). A lack of industry-government consultation during planning and development stages of the small-scale solar bonus scheme in NSW has led to a lack of support for the scheme (IPART 2011). Martin and Rice (2013) suggest LCA and SD of Domestic PV for Policy Development 29

30 a need for transparent and early industry consultation to resolve issues of industry dissatisfaction. It is also suggested that in order to ensure economic sustainability with the development of a feed-in tariff scheme that schemes be integrated into business strategies for electricity distributors (Couture et al. 2010, Mendonca et al. 2010, Bull et al. 2011). Sivaraman and Horne (2011) suggest a number of administrative and policy development challenges associated with PV power generation. The lack of co-ordination between involved agencies, long-lead times and uncertainties associated with solar scheme approval, and a lack of locally based strategic planning agencies can all have adverse policy design and development impacts. 2.7 Knowledge Gaps within Literature Within this literature review a number of gaps in knowledge were identified. These include a lack of boundary extension in traditional LCA methodology, mainstreaming in Industrial Ecology, policy application in LCA of PV panels, and life cycle accountabilities in Australia s policy framework. By applying the LCA methodology to the renewable energy framework via mono crystalline solar PV panels in Australia and quantifying triple bottom line impacts through System Dynamic Modelling, this thesis aims to address these limitations and knowledge gaps. As Australia shifts towards renewable energy sources, the ability to quantify whole of life cycle emissions will enable a higher efficiency, lower emissions electricity framework for operations grounded in principles of industrial ecology Literature Review Summary This review has summarized and integrated a wide range of subject areas pertinent to the thesis topic in order to position objectives within a framework of contemporary and historical thought. Topics of sustainable development, impact accounting (LCA) and systems thinking are intrinsically linked to effective policy design through their shared objectives of a comprehensive and holistic (with particular reference to triple-bottom-line factors in concepts of systems thinking and sustainable development) approach to solving complex environmental problems. Whilst the LCA methodology has been well-established for use in accounting of PV life cycle environmental impacts, there are inherent boundary limitations that restrict capacity to determine impacts to the triple-bottom-line. For fast growing technologies, such as PV, the significant time investment in collection of comprehensive data for LCA can affect validity and currency of analysis, indicating a requirement for modelling techniques to help adapt the analysis to reflect possible future technological advancements. Systems thinking, based in ideas of sustainability and industrial ecology, has contributed to awareness of environmental impacts of industrial LCA and SD of Domestic PV for Policy Development 30

31 processes and application of understanding in action towards sustainable operational actions. Renewable energy targets within the small scale renewable legislation framework in Australia are proposed for the next decade, indicating the necessity for application of modelling for future forecasting scenarios. Critiques of the renewable energy framework have circulated around the requirement for more effective monitoring, collaborative decision making and efficient integration of power systems with the existing grid structures in Australia. Subsequent to review of environmental policy design problems and criticism, and the current Australian renewable energy framework, this thesis has developed a hybridized approach to systems analysis that utilizes LCA and SD modelling in order to assess the merits of such an approach in addressing policy design issues for government and industry. LCA and SD of Domestic PV for Policy Development 31

32 Chapter 3 Life Cycle Analysis of a 3kWp Mono-Crystalline PV System 3.1 Introduction: This chapter will detail the methods and findings of an LCA carried out for a 3kWp mc-si system. 3.2 The System The system studied is a 3kWp slanted-roof mounted installation, mono-crystalline panel system. The system is similar to one installed in Switzerland in the year 2000 and has an installed capacity of 711MWp. The system has a life expectancy of 30 years and consists of 22.8m2 of mc-si panels. Efficiency data is based upon comprehensive literature reviews within the panel market in the year 2005 (de Wild-Sholten & Alsema 2006). This particular mono-crystalline system was chosen due to its requirement for higher silicon refining, compared to polycrystalline panels, and because of the prevalence of similar capacity 3kWp installed systems within Australia. A roofmounted, slanted roof installation was chosen to include manufacturing processes for the mounting structure/frame. The cell efficiency of the panels was 14.4%, concurrent with previously stated efficiency levels, particularly for the past 3 years. Delivery of various system components via road transport was assumed to be 100km, including the transport of construction workers. It is also assumed that 20% of the produced panels were from overseas sources and would require international transport via ship. Inverters used for the system had a 15 year lifetime and a replacement after this time was included within the LCI. The use of recycled silicon is not included within the manufacturing stage of the LCA at the time of data collection there was no wide-spread decommissioning/recycling of solar panels as they reached their maximum lifespan, and thus no available recycled materials for use. A 2% replacement of damaged modules was included within the life cycle and a production loss of 1% due to handling. 3.3 Methods This methodology was applied in compliance with ISO standards 14040, and The steps of the method follow the framework for an ALCA modified from ISO (Figure 1). An LCA modelling software package was used to analyse the LCI information and perform an uncertainty analysis. LCA and SD of Domestic PV for Policy Development 32

33 Goal definition for thesis LCA 1. Goal and Scope Goal settings Scope definition for 3kWp system Scope settings Data Collection from EcoInvent Database Collected Dataset #1768 Basic Validation of Data using checks 2. LCI Analysis Validated Data Relation of data to unit process (within LCA software) Validated data per unit Relation of Data to Functional Unit 3. LCIA 4. Interpretation Related data to 3kWp functional unit Impact Assessment Choice of Methods Impact Assessment Calculations Classification Normalization Characterization Calculated LCIA graphical results Uncertainty Analysis Monte Carlo Analysis Uncertainty data Discussion Interpretation and Conclusions Figure 1: Overview of LCA methodology used Step 1 Goal and Scope Definition: Goal: The intended application of this LCA is to understand the environmental impacts of domestic solar panel usage in Australia and how policy can be changed with relevance to such impacts. Scope: The studied product system was a 3kWp slanted-roof mounted installation, mono-crystalline panel system installed in the year The function of the system is defined as the domestic production of photovoltaic electricity. The functional unit for the purposes of this study was defined as 3kWp. The system boundary for the system included all components for the manufacture, installation, energy use for the mounting, transport of materials and persons to construction, and disposal of components after end life (Figure 2). Data requirements for the LCA were all inputs and outputs from nature and to nature involved within the system boundary of the 3kWp system. LCA and SD of Domestic PV for Policy Development 33

34 Silicon Crystalline Panel Electric Installation Balance of System Inverter Figure 2: Unit process raw data for the four main modules of the LCI combined Step 2: Life cycle Inventory Analysis The best available data was collected from the comprehensive EcoInvent database. It was not feasible both economically and temporally to gather LCI data for an Australian system within the scope of an 8 month honours thesis. At the time of this thesis completion there were no wellestablished Australian LCA bodies with available PV data, although the AusLCA database is currently in its development stage. The database number for the dataset was #1768. The unit process raw data for this dataset are show in Table 1. Resource extractions from nature and emissions to air, soil and water are included within the dataset (Table 2). In all, the dataset had 1296 data points for analysis. Table 1: Unit process raw data of 3kWp mc-si system LCA and SD of Domestic PV for Policy Development 34

35 Carbon dioxide, biogenic Carbon dioxide, fossil Heat, waste Unspecified noble gases, radioactive Radon-222 Water, Turbine Energy, potential in hydro. Energy, calorific value biomass Gravel Brown Coal High pop n density Table 2: Selected LCI data from EcoInvent Dataset oto Nature Air Soil Water kg Oils, forestry kg Hydrogen ocean unspecified 3 High pop n density High pop n density Low pop n density Low pop n density, long-term 2405kg Heat, waste industrial MJ Heat, waste river 55157MJ Silicon agricultural kg Sulfate Ground, longterm kBq Chloride unspecified kg Silicon Ground, long kBq Iron agricultural kg Calcium, ion From Nature In water m3 Natural gas In ground Nm3 In water 11684MJ Coal hard In ground kg biotic MJ Water, cooling In ground In ground In water m3 1000kg Crude oil In ground kg kg Wind power In air MJ term Ground, longterm 25827kBq MJ kg 158.9kg The functional unit for this LCA was chosen to be 3kWp, therefore the data is already related to this output. A network diagram was created using a 10% node-cut off to reflect fossil carbon emissions to create a 3kwp system (Figure 3). It demonstrates where the highest use of fossil carbon emissions are attributed within the life cycle of the panel. It can be seen that to make 23.4kg of Czochralski silicon, 2.21E3 kg of carbon equivalent were required up until that part of the life cycle. LCA and SD of Domestic PV for Policy Development 35

36 Figure 3: Network diagram of fossil carbon flow for one system at 10% cut-off. LCA and SD of Domestic PV for Policy Development 36

37 3.3.3 Step 3. Impact Assessment Impact assessment data manipulation requires 3 steps: 1. Classification: What products or resource uses will be within each impact category? 2. Characterization: Choosing equivalence factors for particular substances. 3. Normalization: Conversion for comparison by a set reference value (usually the average yearly environmental load/population: 1/yr) Within LCA software these steps are generally included, depending on method selection. For the purposes of this LCA, 5 impact assessment methods were chosen for use within the impact assessment stage: 1. Eco-Indicator 99 method, Hierarchist version. V Greenhouse IPPC distinguish main gases. (Greenhouse kg/co2eq) V Australian Raw Material Flows V Ecological Footprints Australian. (Global Footprint Average) V.1 5. Cumulative Energy Demand with breakdown. (Energy by fuel source) V2.01 Methods were chosen for purposes of sensitivity analysis and as a technique of quantifying impacts from the chosen dataset. Methods were selected for their ability to cover a number of impact areas from human health, land use and GHG emissions Method 1. Eco-Indicator 99 method, Hierarchist version. V2.08 This method uses a damage orientated approach, with weighting based upon damage types caused by impact groups. Damage categories of human health (expressed as Disability Adjusted Life Years), ecosystem quality (expressed as loss of species/area/time) and resource depletion (surplus energy needed for future fossil fuel extraction). Characterization factors are calculated at end-point level (damage). The hierarchist version was chosen as it is the most comprehensive analysis available within this impact assessment method. The hierarchist perspective is long-term, with substances only included if there is a consensus regarding their damage potential. Fossil fuels cannot be easily substituted. The Europe EI 99 H/A normalisation setting was chosen within this method. Developed by a body of scientists in 1997, this method links damage factors with existing life cycle inventories, such as the one used within this LCA (Goedkoop et al. 1998). Essentially, this method utilizes a scientific consensus upon impact and damage factors and integrates them with unit process data within LCI datasets to provide weighted and normalised data, easily comparable with other datasets. All outputs will use the unit of points, or Pt as an LCA and SD of Domestic PV for Policy Development 37

38 indicative value, rather than set values, again for ease of comparability (Frischknecht & Jungbluth 2007). Supplementary information for this method can be found in Appendix Greenhouse IPPC distinguish main gases. (Greenhouse kg/co2eq) V1.01 This method provides GHG emissions results with some disaggregation, based upon a 100year timeframe using IPCC default levels. The normalization setting chosen for this method was Greenhouse kg CO2eq Australian Raw Material Flows V 1.01 This method simply tracks the total mass flow of raw material and emissions based upon the summing of all elementary flows within the life cycle of the system. Theoretically the sum of resources and inputs within the system should equate to the sum of the outflows. However, due to discrepancies in data inclusion such as combustion of oxygen, this is not always the case Ecological Footprints Australian. (Global Footprint Average) V.1 Using the footprint methodology developed by Wakernagel et al. (1996) this method accounts for all land types used within the life cycle of a product. The ecological footprint is defined as the biologically productive land and water a population requires to produce the resources it consumes and to absorb part of the waste generated by fossil and nuclear fuel consumption (Huijbregts et al. 2008; Wackernagel et al. 1996). Implementation of this method for the dataset within the LCA software is fairly straightforward, using categories of land occupation combined with determined impact factors to yield footprints, therefore the certainty of data is high (Frischknecht & Jungbluth 2007). Data sets are normalised to the global average footprint in Impact factors and meta-information for this method can be found in Appendix Cumulative Energy Demand with breakdown. (Energy by fuel source) V2.01 This method simply provides breakdown of energy use through the life cycle of a system. It measures the energy mix of fossil fuels, nuclear, biomass, hydro and other renewables. Developed in the early 70s, this method has a long historical use and application (Boustead & Hancock 1979; Pimentel 1973). Whilst analysis of cumulative energy requirements is an acceptable starting point in life cycle thinking, it should be used in conjunction with other more comprehensive impact assessment methods for instance Eco-Indicator 99, such as within this LCA and SD of Domestic PV for Policy Development 38

39 LCA (Frischknecht & Jungbluth 2007). Model uncertainties for separate energy type indicators is relatively low when disaggregated (Frischknecht & Jungbluth 2007). Supplementary information for this assessment method can be found in Appendix Step 4 Uncertainty Analysis Because the data used within this LCA was collected over a decade ago, was based on the data available in the EcoInvent database and represents just one system, it is suitable to perform an uncertainty analysis to determine how representative a figure is for applicability within this study. The LCA software used allowed for absolute uncertainty analysis through Monte Carlo analysis of datasets, repeating comparisons within a specified uncertainty range. A Monte Carlo analysis was carried out using the EcoIndicator 99 impact assessment method using uncertainty parameters contained within the dataset. Monte Carlo analysis, like its famous casino namesake, is a statistical method of sensitivity analysis based upon random chance. For each sample within a dataset, random variants are generated and run a large number of times, yielding random outcomes for each output variable. Essentially the analysis is producing thousands of possible outcomes for a particular input, indicating how reliable and representative data are (Thomopoulos 2013) Interpretation The interpretation stage is essentially the summary and analysis of all results gained from inventory analysis and impact assessment according to the goal. This LCA study will incorporate the interpretation stage within the discussion section of this thesis as a whole Justification of Method The method used within this section was chosen as it follows the internationally recognised standard operating principles within ISO 14044, and for the collection and presentation of LCA data (ISO 2000; ISO 2006a; ISO 2006b). It is applicable as a chosen method within this thesis as it appropriately encapsulates the scope and outputs of a traditional LCA for the purposes of methodology assessment and integration for policy analysis. LCA and SD of Domestic PV for Policy Development 39

40 3.4 Results: LCIA and Uncertainty Analysis Life cycle Impact Assessment Eco-Indicator 99 method, Hierarchist version The Eco-Indicator 99 method, Hierarchist version was used to assess potential areas within the life cycle of the 3kWp system that could damage ecosystem, resource or human health (Figure 4). Figure 4. Analysis of 3kWp system using Eco-Indicator 99 (hierarchist), weighted. 1 pt is representative of one thousandth of the average yearly environmental load of one European inhabitant. Over the life cycle of the 3kWp system the areas of highest potential damage are the production of carcinogenic materials and respiratory inorganics (for human health) and fossil fuel use (resource depletion). Capacity for damage regarding climate change, eco-toxicity and mineral usage are secondary. Smaller damage capacities are seen for respiratory organics, radiation, ozone layer, eutrophication and land use. Process contribution data used for damage assessment were then tabulated (Table 3). In terms of ecosystem quality, the highest damage potential processes across the life cycle of the PV system were regarding the use and refining of copper and the disposal of sulfidic tailings. Similarly, the greatest impacts to human health also stemmed from disposal of sulfidic tailings and brown coal (respiratory inorganics)(lignite), and copper refining. LCA and SD of Domestic PV for Policy Development 40

41 Table 3: Selected process contributions for EcoIndicator 99 (Damage Assessment) Resources Process Unit 3kWp system Copper concentrate, at beneficiation/rer U MJ surplus Copper concentrate, at beneficiation/rla U MJ surplus Crude oil, at production onshore/rme U MJ surplus Crude oil, at production onshore/ru U MJ surplus Natural gas, at production onshore/ru U MJ surplus Ecosystem Quality Copper, primary, at refinery/rla U PDF*m2yr Disposal, sulfidic tailings, off-site/glo U PDF*m2yr Photovoltaic cell, single-si, at plant/rer U PDF*m2yr Ferrochromium, high-carbon, 68% Cr, at plant/glo U PDF*m2yr Disposal, spoil from lignite mining, in surface landfill/glo U PDF*m2yr Copper, primary, at refinery/id U PDF*m2yr Human Health Disposal, sulfidic tailings, off-site/glo U DALY Copper, primary, at refinery/rla U DALY Disposal, spoil from lignite mining, in surface landfill/glo U DALY Greenhouse IPPC distinguish main gases. (Greenhouse kg/co2eq) The IPPC method was used to assess the greenhouse gas emissions over the life cycle of the 3kWp system (Figure 5). Figure 5: IPPC 1990 method, distinguishing main gases. LCA and SD of Domestic PV for Policy Development 41

42 Results from analysis using the IPPC 1990 method show the predominant source of emissions due to carbon dioxide emissions at 5.092kCO2 e across the life cycle of the system. Emissions of methane, nitrous oxide and from land transformation are also within the life cycle of the system. Other represents all processes included but not characterised, such as the PV cell, liquid aluminium and tetraflouroethylene use at plant production Raw Material Flows (Australian) Raw Material Flows across the life cycle of the system were assessed within LCA software impact assessment (Figure 6). Figure 6: Raw Material Flows across the life cycle of the 3kWp system. kpt The kilopoint (1 kpt=1000 points) was derived by dividing the computed total environmental load in Europe by the number of its inhabitants. Raw materials use for the 3kWp system predominantly come from resource and raw material use from nature. Outputs to air including particulate matter and emissions are also significant material flows across the life cycle of the 3kWp system at ~5kPt. Among the processes requiring high resource input (excluding electricity input) are production of Czochralski silicon, decarbonized water and lignite (Table 4). This is due to the fossil fuel energy and refining required to produce solar grade silicon, indicating the use of fossil fuel sources and silicon refining as resource intensive manufacturing choices throughout the life cycle. The process of characterization has standardized values to give an indication of the relative resource contribution of each process within the life cycle of the PV system, not an accurate representation of actual resource uses. LCA and SD of Domestic PV for Policy Development 42

43 Table 4: Selected process contributions of Raw Materials Flows Method (Characterised) Inputs from nature Process Unit 3kWp system Transport, liquefied natural gas, freight ship/oce U kg Hot rolling, steel/rer U kg Sand, at mine/ch U kg Gravel, round, at mine/ch U kg Electricity, nuclear, at power plant pressure water reactor/de U kg Sulphuric acid, liquid, at plant/rer U kg Soda, powder, at plant/rer U kg Electricity, nuclear, at power plant pressure water reactor/ucte U kg Lignite, at mine/rer U kg Electricity, nuclear, at power plant pressure water reactor/fr U kg Water, decarbonised, at plant/rer U kg CZ single crystalline silicon, photovoltaics, at plant/rer U kg Outputs to air Hard coal, burned in power plant/pl U kg Hard coal, burned in power plant/es U kg MG-silicon, at plant/no U kg Flat glass, uncoated, at plant/rer U kg Hard coal, burned in power plant/de U kg Lignite, burned in power plant/de U kg Electricity, at cogen 1MWe lean burn, allocation exergy/rer U kg Ecological Footprints (Australian) The Ecological Footprints method was used to analyse the land uses across the life cycle of the 3kWp system (Figure 7). Figure 7: Ecological footprints and land use over the life cycle of the 3kWp system. Units are in ha/year. LCA and SD of Domestic PV for Policy Development 43

44 The ecological footprints over the life cycle of the 3kWp system are predominantly attributed to the use of land for energy production sources e.g. power plants. Consumed land is the land used occupied/built on during the life cycle (storage, plants etc) and is the second highest consumer of land resources. Land used for biofuel cropping and forest land was marginal and reliant upon the energy mix of the system dataset. Process contributions were highest for production of electricity, fossil fuel resource production (energy land) and factory creation (consumed land) (Table 5). Table 5. Selected process contributions for Ecological Footprints (Weighted) Energy Land Process Unit 3kWp system Natural gas, burned in power plant/it U ha a Lignite, burned in power plant/pl U ha a Natural gas, burned in industrial furnace low-nox >100kW/RER U ha a Hard coal, burned in power plant/pl U ha a Hard coal, burned in power plant/es U ha a Flat glass, uncoated, at plant/rer U ha a Hard coal, burned in power plant/de U ha a Lignite, burned in power plant/de U ha a Electricity, at cogen 1MWe lean burn, allocation exergy/rer U ha a Consumed Land Production plant crude oil, onshore/glo/i U ha a Lignite, at mine/rer U ha a Metal working factory/rer/i U ha a Residual material landfill facility/ch/i U ha a Electricity, hydropower, at reservoir power plant, alpine region/rer U ha a Operation, maintenance, road/ch/i U ha a Electricity, hydropower, at reservoir power plant, non alpine regions/rer U ha a Photovoltaic panel factory/glo/i U ha a LCA and SD of Domestic PV for Policy Development 44

45 Cumulative Energy Demand with breakdown The cumulative energy demand of the 3kWp system was investigated across its life cycle (Figure 8). Figure 8: Cumulative energy demand by fuel type across the life cycle of the 3kWp system. The majority of energy flows occur from fossil fuel, nuclear energy and hydroelectric power generation. The largest use of energy across the life cycle of the 3kWp system was through the use of coal, oil and gas during manufacturing, preproduction and disposal stages. Energy obtained from nuclear sources was also high due to the energy mix used in the EcoInvent database dataset sample system. Process contributions indicate a relatively even split between energy demand fuel types of lignite, natural gas and uranium ~10000 MJ (Table 6). Table 6: Selected Process Contributions by fuel type (weighted) Fossil Fuels-Coal Remaining processes MJ Hard coal, at mine/ru U MJ Hard coal, at mine/cpa U MJ Hard coal, at mine/rla U MJ Hard coal, at mine/au U MJ Hard coal, at mine/rna U MJ Hard coal, at mine/za U MJ Hard coal, at mine/eeu U MJ LCA and SD of Domestic PV for Policy Development 45

46 Hard coal, at mine/weu U MJ Lignite, at mine/rer U MJ Fossil fuels - gas Natural gas, sweet, burned in production flare/mj/glo U MJ Polyethylene, HDPE, granulate, at plant/rer U MJ Ethylene, average, at plant/rer U MJ Natural gas, at production offshore/gb U MJ Natural gas, at production onshore/de U MJ Natural gas, at production offshore/nl U MJ Natural gas, at production onshore/nl U MJ Natural gas, at production offshore/no U MJ Natural gas, at production onshore/dz U MJ Natural gas, at production onshore/ru U MJ Nuclear Uranium natural, at open pit mine/rna U MJ Uranium natural, at underground mine/rna U MJ Uncertainty Analysis A Monte Carlo analysis was carried out on the dataset using the Eco-indicator 99 (h) method, as it is the most comprehensively spanning method performed within this LCA, using a Confidence Interval of 95% and repetitions (Figure 9.) The standard uncertainty values for each data point were included within the dataset. Figure 9: Monte-Carlo analysis of dataset using EcoIndicator 99 method (weighted). LCA and SD of Domestic PV for Policy Development 46

47 From the uncertainty analysis it can be seen that the largest degree of uncertainty within the dataset values for the EcoIndicator 99 method are regarding human health, with resource uses and ecosystem impacts having a lower degree of uncertainty. It is possible that, under this method of impact assessment, human health impacts from particulates and carcinogenic sources could have a much higher range considering the dataset. 3.5 Discussion Across the life cycle of the 3kWp system the largest impacts (land use, emissions production) appear to be associated with the energy mix used within the production and manufacturing stages. From analysis of the cumulative energy demand emissions intensive energy mixes were shown to have the highest proportion of energy use across the life cycle, bringing in to question the level of support that the PV manufacture industry could be providing to the fossil fuel industry. Carbon dioxide gas was also found to be the highest GHG emission across the life cycle. However, it is important to consider that the energy use of a product or system does not always yield a comprehensive picture of the impact on ecosystem goods and services. Biotic features that can contribute to eutrophication are obviously not reflected within energy use mix projections, but can still have a substantial impact to environmental systems (Frischknecht & Jungbluth 2007). Whilst the gas emissions to nature from fossil fuel use seem to be of highest contribution to global warming potential of the system, the inputs from nature of raw materials and energy indicate an area of resource pressure. The weighted human health impacts possible from the life cycle appear to revolve around a high level of carcinogenic material. This is most likely attributed to the small amount of Radon included within the emissions to nature section of the LCI. Radon is a carcinogenic gas formed during the break-down of uranium. However, the use of uranium as a substantial energy source for the manufacture stage of the system is location specific. Other potential contributors to carcinogenic impacts could be the use and emissions of cadmium and arsenic. Arsenic is used as a dopant element in manufacture of solar grade silicon (Xiao 2012). Cadmium is a by-product of the production of zinc (predominantly for BOS manufacture), with most PV manufacturing plants unequipped to capture cadmium emissions during smelting (Fthenakis et al. 2009). Cadmium is a known carcinogenic element, with many studies linking exposure to elevated risk of lung, prostate and kidney cancer occurrence in humans (Straif et al. 2009; Mandel et al. 1995; Sanchez et al. 1992). Exposure to arsenic through inhalation is also associated with higher risk of lung and throat cancer (Chiou et al. 1995; Rivara et al. 1997). Whilst the production of photovoltaics does involve the use of hazardous, potentially carcinogenic materials, standard conditions within factories regarding quality control and safety LCA and SD of Domestic PV for Policy Development 47

48 and protective equipment should reduce impacts to human health and environment in the short term (Fthenakis & Moskowitz 2000). However, long term occupied land may be adversely affected through the use of such hazardous materials, particularly if accidental conditions occur. LCA and SD of Domestic PV for Policy Development 48

49 Chapter 4 Sustainability Implications of Large Scale Uptake of Domestic Photovoltaics in Australia: Investigation through System Dynamic Modelling. 4.1 Sustainability and emerging technology Sustainable development as a concept poses a number of limitations within methodology and policy development for traditional researchers within the realm of science and technology. It is not a discrete or goal-based process and requires continuous and progressive re-evaluation for success. Renewable energy policy for photovoltaic panels is unique in that it ultimately is an individual investment decision whether to adopt the technology, though policy can encourage uptake through monetary incentivisation. Australia has shown a great increase in uptake of solar technology in the past decade (Bahadori & Nwaoha 2013). Due to the high amount of solar radiation available, Australia is in a reasonable position to exploit its solar resources (Australian Government Department of Agriculture, fisheries and forestry 2011). However, current trends show PV uptake is surpassing that of the electrical grid capacity, where negative impacts of incentivisation of uptake are already being seen (Ipakchi 2009; Flannery & Sahajwalla 2013). Emergent technologies in Australia create a complex policy development environment due to a lack of long-term historical data within a local context in considering future trends. Previous studies have highlighted projection scenarios for solar panel uptake in Australia, but have failed to consider impacts to the electrical grid associated with rapid uptake scenarios (AEMO 2012). Contemporary studies in to the effects of intermittency of PV panel uptake have highlighted the strong need for modelling and forecasting of solar uptake behaviour for grid regulation and adaptable energy management systems (Lew et al. 2010; Bebic 2008; Achilles et al. 2008). This study aims to begin to address these requirements by creating a prototype system dynamic model to base further potential impact hot-spots scenarios that may become significant as solar panel uptake in Australia becomes more prevalent, to inform policy understanding and development. 4.2 Research Objectives Whilst the environmental impacts of solar panels are relatively well documented and understood (Table 1, Chapter 2), the social and economic impacts of a rapid and commercially controlled uptake of solar panels in Australia have yet to be modelled on a scale that allows for grid capacity LCA and SD of Domestic PV for Policy Development 49

50 and pricing predictions. This study aims to create a prototype model capable of future manipulation to assess grid capacity and smart-grid efficacy. 4.3 Method A hybridized fieldwork-to-formalization design approach for the purposes of modelling within this thesis was adapted from methods developed by Hannon and Ruth (2001), Maani and Cavana (2000) and Spinuzzi (2003). The steps are as follows: 1. System Definition and Objectives of Modelling The objectives of the model were decided upon for the purpose of this thesis. The model was to provide a flexible prototype for further development in terms of data and causal loop intricacies. The system was determined as all metrics within an Australian context regarding domestic solar usage and other household metrics. 2. Problem Structuring: Desktop Research and data collection A desktop study of Australian solar uptake rates and other pertinent metrics was performed utilizing grey literature articles and news articles as supplementary information. 3. Causal Loop Modelling 3.1 Identification of key variables and causal relationships. Once the desktop study was performed main variables were selected and modelled within a causal loop diagram to determine feedback loops. Causal loop modelling has long been used as a method of problem structuring, linking and representation in various applications of published work, including supply chain modelling (Dhungel & Halog 2009; Campuzano & Mula 2011), business management (Kiani 2009), behavioural science (Schaffernicht 2010) and decision making sciences (Maruyama 1992). In this instance, causal loop modelling has been applied as a problem structuring mechanism for development of a dynamic system simulation model, as supported by Maani and Cavana (2000), for application in areas of energy efficiency and industrial ecology. LCA and SD of Domestic PV for Policy Development 50

51 4. Dynamic Modelling 4.1 Define control and state variables: Stocks and flows Control variables (stocks or accumulations) indicate a value that can accumulate, generally displaying the status of the overall system. Stocks of population, households with PV panels and electricity were determined as points for analysis within the model. State variables (flows) are the variables that control stocks through inputs, updated at each chosen time step (generally annually). A range of state variables were chosen subsequent to causal loop modelling with relevance to influencing or interrelated factors to the chosen stocks. Realms of publication that have utilized SD modelling to increase decision making capacity and understanding include the psychological sciences (Stevens & Zhang 2009), marketing (Edson & Figueiredo 2009), environmental sciences (Ford 1999) and power systems simulation (Padulles et al. 2000). The use of SD modelling as a method for decision making is supported by Blythe (2013), indicating that as software becomes increasingly more capable of modelling complex problems, the merits of SD modelling as a method are as pertinent and applicable as ever. 4.2 Collection of specific data Data was collected to determine parameters for the chosen stocks, utilizing Australian Bureau of Statistics (ABS), government and consultancy reports as main sources of metrics. Mathematical functions were then formed to communicate the connections between control variables and other parameters. 4.3 Select an appropriate time horizon 4.4 Initial simulation with base numbers for trend evaluation. 4.5 Validation Results were assessed for real-world feasibility, based upon current predictions and trends. The time horizon of 50 years was selected for simulation modelling. Agent based modelling simulation software was used to create the system dynamic model and causal loop simulation software utilized for causal loop modelling. In its current state, the prototype model created should act as a base for further sustainability modelling, not as a comprehensive and detailed simulation. LCA and SD of Domestic PV for Policy Development 51

52 4.3.1 Justification of Method Within the research area of analytics for environmental management and sustainability the concept of hybridizing methods for greater scope coverage is gaining considerable interest. Particular interest stems from industrial efficiency and cleaner production research fields where systems analysis is becoming pursued as an emerging technique for sustainability and innovation (Broman et al. 2013). As in Meadows (2007, p.7), where it is maintained that, Words and sentences must, by necessity, come only one at a time in linear, logical order. Systems happen all at one, this thesis seeks to support the merits of visualization in comprehension of complex problems over the exclusive use of words with the utilization of SD models and causal loop diagrams. This thesis has chosen to utilize the established system dynamic modelling method combined with the LCA method in the hope of furthering research and interest in to the merits of analytics integration. 4.4 Results Solar panels in Australia Projections from current trends indicate domestic photovoltaic solar systems are expected to provide 29% of Australia s total energy needs by Over the past decade solar PV installations have risen to provide over 5 million MWh to the grid (Figure 1) (Flannery & Sahajwalla 2013) Previous projection scenarios have been performed to predict the uptake of solar panels at varying levels of rapidity (Figure 2) (AEMO 2012). These uptake scenarios however do not take in to account impacts to the electrical grid associated with rapid uptake. Smart-grid programming, an emerging technology on a global scale, is being developed to counteract intermittency in grid supply and is expected to reduce grid overloading problems in the future. In order to effectively distribute smart-grid technology significant funding and planning, with dynamic long-term temporal thought is required. Solar power generation varies from traditional grid requirements in that is low in the morning, peaks at noon and reduces towards sunset, in a constant trigonometric pattern. This is diverged from load requirements which are generally highest in the evenings. Small fluctuations can also be attributed to cloud cover and weather variations (Sayeef et al. 2012). LCA and SD of Domestic PV for Policy Development 52

53 Figure 1: Growth of Solar Panel MWh (Flannery &Sahajwalla 2013) Figure 2: Projection scenarios of solar panel uptake (AEMO 2012) LCA and SD of Domestic PV for Policy Development 53

54 4.4.2 Causal loop representation and problem structuring A causal loop diagram has been developed to model the various causalities within the current state of solar panel uptake in Australia (Figure 3). Causal loop modelling graphically represents interelated variables and causalities within a complex system, indicating re-inforcing relationships between variables. + Positive/Same polarity - Negative/Opposite polarity Cause to effect linkage Delay mark Figure 3: Causal Loop Diagram of Photovoltaic Uptake in Australia (Appendix 2) Domestic solar panels are unique in that they are predominantly a consumer product, with an economic/income based imperative. There is a strong linkage between financial incentive and investment opportunities coupled with environmental concerns that drive domestic uptake of solar panels. Particularly where electricity prices are rising, there are two avenues that consumers may follow. A lack of disposable income due in part to heightened electricity prices may reduce a household s capacity to outlay the substantial initial investment required for solar panel purchase and installation (Willits 2013). Conversely, rising electricity prices could also create an incentive to invest in off-grid electricity sources such as domestic solar PV (Lloyd 2013; Sandiford 2012; Flannery & Sahajwalla 2013). Swansons law dictates that with every doubling of global manufacturing capacity the cost of solar photovoltaic cells required decreases by ~20% LCA and SD of Domestic PV for Policy Development 54

55 (Pethokoukis 2013). Due to monetary incentivisation and commercialisation of solar panel technology there is a perception that solar power uptake is an investment decision (Nanck 2012). With a substantial initial outlay of money, it is unlikely that an upgrade will be undertaken once higher efficiency technology advances become increasingly available. Additionally, as currently installed modules have a year lifespan and current uptake is strong it is likely upgrades will not occur for a substantial amount of time, until lifespan is approached. There is a variable efficiency of solar panels as they operate throughout the day (Lave 2012). Because power plant generators are designed to only run at a static capacity, large amounts of electricity and energy are lost (Hoff & Perez 2010). Large scale uptake of solar panels could have negative consequences surrounding voltage fluctuations, frequency regulation and grounding issues (Hoff & Perez 2010; Boyle 2007). Energy distributors that rely upon revenue from electricity rates for budget allocation may be impacted negatively as distributed domestic power becomes more popular. There is also limited historical experience with maintaining grid supply under current uptake levels of solar, whereby power utilities are not equipped to deal with adoption of new technologies that may compromise established power supply maintenance regimes (Sayeef et al. 2012; Katiraei & Aguero 2011). In some instances, solar panel uptake is already becoming restricted by commercial operators due to grid overload concerns (The Australian 2011). Economic issues that could arise with growth of solar extrapolated from current trends may be associated with increased financial costs as PV intermittency causes load variation. Change in utility monitoring practices and culture will incur increased cost, which may be passed on to consumers through an increase electricity prices (Sayeef et al. 2012). As solar PVs are a consumer product, issues of accountability could also arise regarding owner understanding and maintenance of voltage control and inverter operation if voltage limits are exceeded, leading to a dysfunctional, inefficient PV system (Sayeef et al. 2012). As solar panel use rises there will be a requirement for funding and long-term planning surrounding the electrical grid, if blackouts and efficiency problems are to be avoided System Dynamic Model All variables and sources used within modelling can be found in Appendix 3. An initial base system dynamic model was created to predict solar panel uptake to a time horizon of 50 years (2063) without external price or grid allocations (Figure 4). The model is based off a simplified population model from current rates of immigration, emigration, births and deaths to determine the number of households (at an average household of 2.6 people) available for installation in the LCA and SD of Domestic PV for Policy Development 55

56 future. Installation rates were then defined as a function of disposable household income and the number of available houses for installation. It is assumed that for each house to be considered installed would require a 2kWp system averaged over the population. A simplified demand and supply stock and flow model was used model electricity demand for both solar and non-solar originated energy sources. Figure 4: System Dynamic Model of Solar Panel Uptake in Australia (no price scenario) Figure 5 displays the basic uptake trends found within the simplified prototype model with price trends not included. It indicates that solar electricity supply rises in conjunction with population growth. As the installation rate remains constant (as this is a no price scenario) the number of installations grows linearly with houses without solar panels as saturation points are reached, and decomissioning of installations are staggered around a 25 year lifespan. According to the model, there is an expected population of 46.7 million in 2063, which is concurrent with high case scenario projections of approximately 48 million in 2063 by the ABS (BOS 2011). LCA and SD of Domestic PV for Policy Development 56

57 Figure 5: Uptake trends (no price scenario) Figure 6 shows the scenario including electricity price and financial incentives for uptake (price scenario). Basic trends display a causal loop of solar PV uptake increasing grid electricity supply, driving electricity prices for non-solar installed households, creating incentive, in conjunction with decreasing panel price trends, for solar panel uptake. Figure 6: System Dynamic Model (price scenario) Figure 6 shows the introduction of price incentives surrounding affordability of solar panels trending upward and electricity price rises (in part due to grid surplus increase). LCA and SD of Domestic PV for Policy Development 57

58 Years Solar Ratio Years Solar Ratio A third scenario involving an 10 year annual injection of a rebate of $10000 was introduced at years 25 to 30 (Figure 7) Solar Ratio No Price Solar Ratio Price Figure 7: Solar Ratio comparison between No Price, Price and Rebate scenarios. Figure 7 shows the solar ratio comparison between the no-price, price and rebate scenarios. The initial effect of disposable income is higher in the no-price model. The price model appears to remain robust to a rebate scenario, which could be attributed to falling trends in feed in tariffs cancelling the effect of a rebate. A fourth scenario introducing a feed-in tariff increase (with no rebate) over years 25 to 35 was run (Figure 8) Solar Ratio No Price Solar Ratio Price Solar Ratio Feed In Tarrif Figure 8: Solar Ratio Comparison (no rebate) No Price, Price and Tariff Scenarios. Interestingly all scenarios at conclusion have a solar infiltration of approximately 50% of the household market, indicating a saturation point. The model also indicates a higher infiltration with tariff setting rather than an upfront rebate. LCA and SD of Domestic PV for Policy Development 58

59 Figure 9: Price scenario uptake trends From Figure 9 it can be seen that as installations peak around the 40 year mark, houses without solar panels decline. Figure 10 graphically represents linear growth of surplus power and wasted power ratios as solar panel uptake increases. Figure 10: Surplus power and Wasted power ratio (Price scenario) LCA and SD of Domestic PV for Policy Development 59

60 Figure 11: Price trends in price scenario. Figure 11 demonstrates that as solar uptake increases (due to initially high disposable income and feed-in tariffs) disposable income for non-solar household decreases due to rising electricity prices (from price increases due to surplus power and wastage from grid capacity excess) which further drives panel uptake. Disposable household income concludes at $34,751, an approximately $4000 decrease on 2013 levels. Houses with solar panels commence at houses, concluding at 8,700,104 houses in Relative electricity price begins at 22c/kWh and concludes at 37c/kWh in 2063, in keeping with price projection scenarios of 33c/kWh for domestic electricity prices in 2050 (ROAM Consulting 2011). An additional scenario including electricity price as a supply and demand stock was also formulated to add an extra level of complexity to the model (Figure 12). Figure 12: Electricity Price Stock Scenario LCA and SD of Domestic PV for Policy Development 60

61 This price stock scenario adds a more complex modelling mechanism to determine relative electricity price. Rather than displaying a strictly linear increase, relative electricity price now shows high initial growth, levelling out over the 50 year timeframe, now reaching 38c/kWh in 2063, 1c higher than price scenario predictions (Figure 13). Figure 13: Price trends in Electricity Price Stock Scenario With this added level of complexity, the number of houses with solar panels concludes at 9,030,425 households in 2063, a higher infiltration than that of the price scenario. Disposable household income begins at $39,410 and concludes at $34,344, indicating the electricity price stock scenario has a slight impact on initial and concluding disposable income when compared to the price scenario. 4.4 Conclusions Domestic solar panel use for large scale electricity production is unlikely to achieve positive results regarding emissions reduction and achieving an energy supply to satisfy all household consumption, unless alongside other renewable power sources such as solar thermal or wind energy. There is a saturation point that domestic PV will reach, with a higher level of solar utilization to be gained from large-scale solar plants. Many problems associated with solar panel uptake surround excess power supply back on to the grid, driven by feed-in tariff incentives and environmental mindfulness. It is possible that smart grid technology may become feasible in the future that could mitigate problems associated with grid capacity overload. Further opportunities would be to adapt the model to take in to account adaptable grid accountabilities with smart grid technology, and also whether problems of intermittency may impact upon solar panel uptake. Because solar panel uptake is predominantly a commercial investment enterprise it is hard to LCA and SD of Domestic PV for Policy Development 61

62 legislate for long-term grid planning. In order to reap the appropriate benefits from domestic solar, significant structural planning (involving grid allocations) will have to be carried out in a time conservative manner, before problems of intermittency and grid-overloading become prevalent. LCA and SD of Domestic PV for Policy Development 62

63 Chapter 5.0 Discussion: The merits of applying LCA and SD modelling in policy development 5.1 Combined results: Consequences and Policy Evaluation The LCA results indicate high resource reliance upon fossil fuel-based energy and land use for production and manufacture of a standard domestic PV system. Human health impacts from emissions of respiratory inorganics also stem from extraction and disposal of fossil fuels for energy provision (lignite spoil in surface landfill; Chapter 3, Table 3). If solar household saturation reaches its predicted energy provision of 29% by 2050 (Flannery & Sahajwalla 2013), or follows the model s prediction of near 50% households with solar, the consequences for nonrenewable resource demand, emissions and land use over this time could lead current PV technologies to become an inefficient form of renewable energy in terms of sustainable growth. However, it appears that most of the life cycle impacts across the life cycle of the PV life cycle relate to the particular energy mix of the manufacturing region surrounding fossil fuel based electricity production. This indicates a strong capacity for global emissions reduction in overseas manufacturing if policy for domestic PV uptake is formed in conjunction with support of large scale renewables for overseas energy use (particularly in regions where manufacturing plants are situation). If impacts across the life cycle of a single 3kWp PV are extrapolated to parallel the trends found within the prototype model, the consequences for environmental emissions and resource use should be reflected within policy action for resource use and sustainable development of renewables implementation. Similarly, if trends are to follow those of 29% power provision (Flannery & Sahajwalla 2005) or those predicted within the model (~50% household saturation), Australia s domestic energy needs will also require support from renewable technologies such as wind or concentrated solar plants to penetrate a high enough energy supply with significant GHG emissions reduction from domestic energy use. If domestic renewables policy in Australia is to succeed in reduction of emissions it will have to focus on large-scale renewables for small-scale domestic use, not just PV technology investments. With projected high-case scenario population growth to reach approximately 46.7 million people in 2063, or around 18 million households (Chapter 4, Figure 5) and current domestic energy use already a significant proportion of national energy use (Stark et al. 2012) it is likely that future domestic energy requirements will necessitate significant material and manufacturing inputs from overseas sources. As significant support for local R&D and manufacturing of small scale renewables is not strongly legislated within the current policy framework, resource uses and LCA and SD of Domestic PV for Policy Development 63

64 degradation from manufacture will inherently be shifted to overseas sources, and support for environmental and social impacts through domestic policy should be accounted for in long-term trade arrangements and policy for renewables production and planning. As it currently stands, the proposed Solar Roofs program is likely to divert funding from ARENA, the body that was to financially support the commercialization of a new PV cell technology produced by UNSW that drastically reduces emissions and cost through bypassing of the energy intensive silicon refining stage and improves upon conversion efficiency (up to 21-23%)(Hannam 2013). From the results of raw materials impact assessment, the use of Czochralski refined silicon has a high raw material usage (Table 4, Chapter 3). Such resource intensive manufacturing practices of refining could be mitigated through the development of similar emerging PV cell technologies. It is unlikely, from the lifetime of a PV panel and the results of the SD modelling, that support for additional domestic PV panels deferring funds from efficiency improvement projects will enable a movement towards a lower emission domestic energy supply. Inevitable improvements in efficiency of solar panels are unlikely to be reflected within uptake scenarios in Australia if policy is focused upon supporting already high uptake, rather than supporting R&D of efficiency improvements. Diverting funding from R&D projects could also limit opportunity to establish information and technology trade relationships with countries that supply manufacture for the PV panels used in Australia, and thus limit ability to influence the energy mix used within manufacture of the PV panels used in Australia. From the prototype model, current uptake trends and other basic projections (Parmeter 2012) it is highly possible that PV uptake will reach two million homes well before 2020 even with no intervention strategies. As a general criticism, by simply attempting to reach one million extra households with either solar power or hot water heating this plan uses simple addition which could skew solar roof installation numbers and yield higher results. The plan also reduces capacity to reward homes with both PV panels and solar hot water. Similarly, while the Emissions Reduction Fund proposed within the anticipated Direct Action plan has the capacity to incentivize uptake of renewables, it may lead to the detriment in Australian based R&D of renewables being outsourced overseas. By providing no marketplace incentive, the fund essentially reduces any capacity for the decoupling of economic growth from carbon emissions, including those industries contributing to solar PV efficiency improvements. Monetary incentives are based upon carbon sequestration through reforestation of land, with no apparent incentive to move towards a higher mix of renewables. Budget allocations for the plan are ostensibly inflexible, further indicating a requirement for appropriate modelling and prediction LCA and SD of Domestic PV for Policy Development 64

65 techniques to assess the feasibility of the plan in reduction of carbon emissions whilst remaining within budgetary constraints (Kenny & Arup 2013). Reducing the rebates for solar panels may actually be a positive policy step as prices of solar panels are drastically reducing. Additionally, the prototype model predicts that whilst panel infiltration is initially higher in the short term with a feed in tariff increase, it has little effect upon uptake over the long term (Figure 8, Chapter 4). This finding is concurrent with current patterns of uptake continuing to increase regardless of significant decreases in feed in tariff incentives (Vogler & Hall 2013). Through quantification of the major resource flows and identification of significant impacts to environmental and human health from resource extraction (respiratory inorganics: sulfidic tailings from copper mining and lignite from brown coal) and materials refining (arsenic and cadmium emissions), a consideration for the boundary inclusive social and environmental impacts of PV manufacture can be gained. This understanding should be applied to policy development for international emissions abatement and in identification of areas of leverage for improving international social sustainability from Australian driven demand for PV resources. However, the Direct Action plan has indicated a reduced capacity and importance placed on overseas carbon credit trading, removing any incentive for overseas PV manufacturing companies to reduce pressures of Australia s high need for solar panel uptake. From the results of the LCA it is clear that energy mix is a high contributor to impacts across the life cycle of PV manufacture. With weakened capacity to influence overseas manufacturing processes the legislative framework in Australia is reducing its ability to positively reduce emissions from locally driven PV demand at a global level. The trend in renewable energy policy for Australia seems to be focused upon domestic based renewables for carbon emission reduction, rather than large scale renewables for all sectors. This may mean that as Australia moves towards a high domestic solar electricity supply with no emphasis on large scale renewable energy reform, resource pressures and emissions will continue to compound, and these will not be reflected within emissions accounting as renewable energy is not currently included within accounting measures. The use of a feed-in tariff raises problems of equity regarding households that require higher energy consumption (home businesses and retirees etc.). A tariff that relies upon importexport also increases uncertainty of payback times for PV systems, and reduces the ability to account for emissions saved as metering does not account for energy mix. Rather than encouraging energy efficiency, tariff provision may skew electricity use to when PV systems are LCA and SD of Domestic PV for Policy Development 65

66 not producing, i.e. Early evening peak residential time, placing further pressure on the electrical grid. There also appears to be no well-established PV recycling scheme in Australia which will need to be addressed within the next years as PV lifetimes begin to end. 5.2 The merits of applying LCA and SD modelling to Australia s renewable energy framework for sustainability. The transient nature of the solar policy framework as well as tarrif provision included in Tables 3 and 4 (Chapter 2) are a drawback to comprehensive long-term domestic renewable energy planning in Australia (Martin & Rice 2013). The LCA and SD methodologies may lend themselves towards policy development, through maintenance of a comprehensive understanding of the life cycle impacts of a technology paired with the ability to model impacts along an extended timeframe. If consequences of supporting a particular solar technology or scheme in the long term are better understood within the policy development and review stages, particularly financial contribution aspects, schemes may have an increased chance of remaining effective and relevant in the long term, even in changing political climates. Within current emissions accounting in Australia there is no accountability for renewable energy emissions (CSIRO 2010). However with the use of LCA and SD methodology application in policy design it is possible that emissions from renewable energies can be accounted for (particularly those produced overseas) and reported within traditional emissions accounting to provide a more effective and inclusive understanding of Australia s emissions status as we move towards a less fossil fuel intensive energy mix. Critical evaluation of renewable energy policy design by the IEA (2012b) highlights the potential uncertainty surrounding grid connection aspects and their sustainability. Recommendations for long-term support of renewables in grid planning, intergovernmental collaboration for feed-in tariff development and an established evaluation and monitoring scheme for renewables were also made. LCA and SD methodologies could assist in contributing to fulfilling these recommendations by the IEA in terms of providing a methodology for inclusive and flexible collaboration within varying tiers of government through SD modelling, helping to supplement and contribute to understanding of consequences within grid and tariff planning. Use of the LCA methodology may contribute to structuring of an effective reporting and monitoring scheme, with a more accurate focus upon whole of life cycle resource use and emissions for proposed and operational renewable energy projects. LCA and SD of Domestic PV for Policy Development 66

67 Issues arising from questions of equity and engagement in tariff and policy development for power distribution companies suggested by Couture et al. (2010), Mendonca et al. (2010) and the Independent Pricing and Regulatory Tribunal (2011) may be alleviated with increased engagement and long-term understanding of impact modelling facilitated through the use of SD modelling developed collaboratively between government and industry. If a framework for collaboration using industry and government imperatives and goals was developed to include the use of SD modelling with utilization of company and governmental information, problems of engagement and inclusion may be alleviated. Such a framework may also mitigate issues of transparency in policy development suggested by Martin and Rice (2013). Specifically in terms of PV power utilization, the problems outlined by Sivaraman and Home (2011) could also be lessened through the use of the LCA and SD methodologies, creating opportunity for cross-governmental interests to be incorporated in locally based strategic planning, and reducing uncertainty associated with PV scheme and project approval through long-term modelling of potential scenarios. In terms of future applications, the LCA and SD methodologies may also lend themselves in assessing the emissions reductions targets and goals purported by the Direct Action Plan. In essence, the LCA and SD methodologies are constructed within concepts of industrial ecology. Utilizing ideas within industrial ecology such as circular economy and closed-loop systems could lend themselves to ensuring sustainability within environmental policy development. LCA methodology provides an opportunity for raw material flows and wastes used in creation of new solar technologies to be accounted for in policy development (such as in an institutionalised PV recycling scheme). This technique can be expanded to other industrial applications, particularly regarding large scale renewable energy technologies. 5.3 The merits of applying LCA and SD modelling in overall environmental policy development for industry. Policy design for energy typically requires high initial financial investment and long time frames (Huang et al ). The use of SD modelling along with comprehensive life cycle data can assist in modelling of energy scenarios to support justification of investment and policy decisions, and also help to avoid unintended consequences of policy choices over an extended timeframe. LCA and SD of Domestic PV for Policy Development 67

68 System dynamic modelling can allow for inclusion of triple bottom line impacts that add complexity to decision making processes (Munda et al. 1994; Hafkamp & Nijkamp 1986). Greening and Bernow (2004) suggest the need for flexibility within decision support systems. The comprehensive understanding of a production process, combined with the ability to adaptively predict various scenarios along an extended timeline can allow for multiple decision analysis processes to assess the merits of different analytics approaches (e.g. Multi-criteria decisions analysis and Cost utility-analysis). Romero (1996) and Van den Bergh et al. (2000) suggest that energy utilities that aren t costed are usually not included within the assessment process for energy supply decisions analysis. SD and LCA can assist in the comprehensive modelling of triple bottom line impacts (both positive and negative) to allow for the merits of particular energy policies to be fully recognised without economic imperative. Hupps (1993) suggested that the closer a methodology is to predicting resource use and emissions in actuality, the more effective it can be. In its essence, LCA is the practice of comprehensively defining the emissions and resource uses across the entirety of a life cycle of a process and therefore lends itself to the use in policy design. The four steps proposed by Farel et al. (2001) highlight the need for stakeholder participation in environmental assessment and decision making. The applications for the use of LCA within a company are well documented in terms of gains in resource use efficiency, however as an analytical approach for participatory policy development LCA and SD used in conjunction could allow for individual, company based scenario planning. Richardson (1983) proposed three arguments for public participation in policy making. LCA inherently requires participation from corporate sources within its methodology to satisfy data requirements. Allowing corporate industry to adaptively model their production processes has the dual merit of providing awareness of the consequences of product design, including corporate understanding of social responsibility, but also an understanding and recognition for the complexity of the policy design process. The six problems associated with government transition proposed by Kemp et al. (2008) particularly regarding uncertainty about long-term effects, determination of short-term steps for long-term change and danger of lock-in, can be remediated through the use of adaptive design techniques within SD modelling to assist in reducing the uncertainties of commitment to long term policy decision as suggested by Sarimveis et al. (2008) and Sterman (2000). LCA and SD of Domestic PV for Policy Development 68

69 Hjorth and Bagheri (2006) highlighted the importance of SD modelling in both reducing complexity but also including complex relationships in understanding of systems. EPI could benefit from the application of LCA and SD in integrated sectors for environmental and economic policy design to incorporate two complex systems together. Policy making in EPI is interested in the applications and opportunities for environmental considerations to be built in to the process. LCA, with its environmental imperative, has a high capacity for integration within industrial efficiency regimes, particularly regarding company production rates and economic gains. LCA and SD based policy designed through EPI could be presented to manufacturers as a method of reducing material use. The potential for an integrated software tool that combines LCA and SD is highly needed if this approach is to be successful and user friendly for use by industry, consultancy and government agencies. Blythe (2013) also indicates the merits of SD modelling and causal loop modelling software as a valuable method of interaction and problem solving within complex systems. Whilst lending its methodological applicability to design of policy, SD and LCA may also act as a pragmatic catalyst and proponent for EPI thinking in systemic change without the need for reactionary responses to drastic effects of inaction. In other words, the LCA and SD methodologies could be applied towards a preventative approach for EPI introduction into policy making rather than as an intervention for an environmental disaster that has already occurred. Kingdon (1995) and Lebow (1984) discuss the concept of policy windows as a process where EPI assimilation may occur as a result of dramatic incidents (e.g. Chernobyl) or long-term evolution of an environmental situation (e.g. Change in energy market configuration). The LCA and SD methodologies used within this thesis could be applied as preventative decision making tools to contribute to knowledge and perhaps insight policy windows for change. Failing inaction as a result of ignored predicted modelled outcomes, as is often the case, SD modelling and LCA methodologies could be applied to modelling scenarios for policy window predictions. These scenarios would require substantial understanding of social willingness to change and extent of catalysts that could potentially precipitate action. Holling and Meffe (1996), referring to the merits of SD modelling in policy development, suggested that linear causal thinking dominated historical problem solving. The merits of a reductionist approach to policy making have also been criticised by Roberts (2011). Reductionism, whilst theoretically approachable and manageable has serious limitations when planning for policy development. The ability to view a system not only throughout its life cycle as with an LCA, but also across a wide variety of projected scenarios particularly the ability to LCA and SD of Domestic PV for Policy Development 69

70 include social variables, including their feedback loops shows the merits of applying these two methodologies within decision making processes. Taking an incrementalist approach towards policy development is ideal, but not always feasible considering time constraints and public and corporate pressure for extensive policy decisions. However, bearing in mind the considerable reforms that will inevitably need to occur to ameliorate the many environmental problems that face decision makers today, the incrementalist method does substantially limit a policy frameworks ability to focus on long term changes, particularly within transient government control situations. The use of SD modelling to predict impacts of policy decisions could help reduce the risk of abandoning the incrementalist policy model in favour of more drastic and long-term policy decisions required to ameliorate many environmental problems. Achieving environmental objectives and innovative practice at an industry level are often facilitated through voluntary, self-regulated actions as opposed to regulatory provisions (Grabosky & Gant 2000; Hibbitt & Kamp-Roelands 2002). Non-regulatory provisions may also enhance industry s flexibility in practice, maintain competitiveness and reduce governmental enforcement costs (Borkey, Glachant & Lévêque 1998). In keeping with an assessment by Stenzel (2000) the LCA methodology, as a mechanism under the ISO standards, can supplement legislative provisions for emissions reduction, providing a way of integrating domestic and global standards of environmental protection. The SD methodology was initially devised to aid in efficiency assurance for industrial processes, and the LCA methodology is at its essence a mechanism for production accounting (Sterman 2000). The incentives towards enhancing production efficiency and cost reduction within LCA and SD methodologies lend themselves for use in self-regulated, voluntary environmental practice which could have merits towards reducing company costs and environmental damage, whilst maintaining competitive practices and flexibility of operations. 5.4 Conclusions: Energy mix choices and provision are intimately linked with notions of sustainability. However, in the pathway to a renewable energy electricity supply, the impacts of this movement must be determined. This paper has demonstrated how a hybridized analytical approach to systems analysis can aid in environmental policy integration with industrial processes. Australia is at the cusp of an energy provision revolution and it is at this point that extensive forward planning and comprehensive understanding of our energy requirements is crucial. With the available LCA and SD of Domestic PV for Policy Development 70

71 techniques for system analytics not well established for policy makers in the past, we are in the rare position to be able to comprehensively plan for energy reform Research Questions 1. What are the life cycle accountabilities of a domestic use solar panel system typical of one used in QLD Australia? This thesis quantified the resources used across the life cycle of a 3kWp mc-si PV system, determining the accountabilities of energy mix used within manufacturing and production stages to be fossil fuel dependent and a potential point of policy leverage regarding land use, resource and emissions reductions. For a system typically produced in an European/Asian geographic area, such as the system studied within the LCA and those utilized in Australian domestic situations, there is high potential for nuclear originated carcinogenic emissions. 2. Is the legislative framework in Australia appropriately designed to account for resource flows and life cycle metrics of PV technology? From analysis of the current and proposed renewable energy framework it is unlikely the current and expected emissions and resource uses across the life cycle and uptake of PV systems will be accounted for within policy planning for grid capacity and emissions accounting. 3. What are the effects to the triple bottom line of solar panel use and production in Australia, identified through boundary extension using system dynamic modelling? It is possible that with increased and unchecked PV uptake there may be some detriment to electricity prices, maintenance cost and grid supply certainty. Projected uptake trends indicate the requirement for long-term grid planning, which will require significant financial investment in the long term. 4. Can the attributional LCA framework and system dynamic modelling methodology be appropriately applied to policy formulation in Australia? From the utilization of both LCA and SD methodologies within this thesis it is possible that both may be appropriately applied towards environmental policy development towards a more sustainable legislative framework. Both methodologies could enhance industry-government collaboration and understanding of complex systems, to counteract and correct potential detriments within policy design for long term planning, particularly in industrial contexts. LCA and SD of Domestic PV for Policy Development 71

72 5.4.2 Limitations to study: The Direct Action plan proposed is still in its infancy and time will be required to flesh out the details within the framework, which may increase or decrease its effectiveness as an emissions reduction mechanism. This study was limited by the inability to collect primary, geographically pertinent data within the constraints of an honours thesis. The SD model created, due to time and resource availability has limited capabilities as a full representation of the uptake scenario in Australia, but provides a prototype framework for further development in assessing uptake trends of PV panels in Australia Future Applications It is hoped that the results from this thesis will lend themselves to research in a higher degree form for other emerging renewable energies and system analytics approaches within Australia. Conducting a consequential LCA is also a possible extension of this honours thesis. The expansion and complication of the SD model may also be a potential topic for further research. LCA and SD of Domestic PV for Policy Development 72

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88 Appendix Appendix 1. Supplementary conversion information for impact assessment methods. 1.1 EcoIndicator 99 Hierarchist Version Normalisation :Normalisation is performed on damage category level. Normalisation data is calculated on European level, mostly based on 1993 as base years, with some updates for the most important emissions. Weighting :In this method weighting is performed at damage category level (endpoint level in ISO). A panel performed weighting of the three damage categories. For each perspective, a specific weighting set is available. The average result of the panel assessment is available as weighting set. Weighintg and Normalization assumptions (Frischknecht and Jungbluth 2007). All supplementary information is taken from the SimaPro Database Manual Methods Library (PRE 2008; PRE Consultants 2008, SimaPro Database Manual, PRE Consultants, v.2.2, Netherlands) LCA and SD of Domestic PV for Policy Development 88

89 1.2 Ecological Footprint Method Impact factors of the ecological footprint for the main categories (Frischknecht and Jungbluth 2007) EcoSpold meta information of the ecological footprint implemented in ecoinvent data v2.0 LCA and SD of Domestic PV for Policy Development 89

90 1.3 Cumulative Energy Demand with breakdown. (Energy by fuel source) V2.01 Implementation method explanations (Frischknecht & Jungbluth 2007) LCA and SD of Domestic PV for Policy Development 90

91 LCA and SD of Domestic PV for Policy Development 91

92 Impact factors for the cumulative energy demand implemented in ecoinvent data v2.0 EcoSpold Meta Information for CED method. Further concise information can be found in: Frischknecht, R & Jungbluth, N 2007, Implementation of Life Cycle Impact Assessment Methods, Swiss Centre for Life cycle Inventories, available online: < LCA and SD of Domestic PV for Policy Development 92

93 Appendix 2. Causal Loop Diagram LCA and SD of Domestic PV for Policy Development 93

94 Appendix 3. System Dynamic Model Data Sources and Code This section will detail data sources and equations used for variables within each system dynamic model scenario. Section 2.2 displays the raw equation coding from the system dynamic modelling software. 3.1 Data Sources Table 1: Data Sources and Equations for SD model variables Variable Type Equation/Value Source No Price Scenario Population Stock {people} ABS 2013a Approximate figure taken from ABS population clock at time of model development. Immigration rate Converter {people/people/year} ABS 2012c Immigration Inflow Population*Immigration_Rate - {people/year} Emigration rate Converter {people/people/year} ABS 2012c Emigration Outflow Population*Emigration_Rate {people/year} - Birth Rate Converter {people/people/year} ABS 2012d Births Inflow Population*Birth_Rate {people/year} - Death Rate Converter {people/people/year} ABS 2012e Deaths Outflow Population*Death_Rate{people/year} - Households Converter Population/2.6 {households} ABS 2013 Houses without Converter ABS(Households- - solar panels Houses_with_solar_panels) Houses with solar Stock {panels} Vorath panels/ Systems 2013 installed Conveyer Transit Time: ABS((NORMAL(25,10))) Install rate Converter ABS((Installs_per_disposable_income)*Disp - osable_household_income) {installs/household} Installations Inflow ABS(Houses_without_solar_panels*Install_ - Rate) Installs per Converter /44000 {installs/dollars} ABS 2012a disposable income Disposable Converter {dollars/year/household} ABS 2012 household income Solar Ratio Converter Houses_with_solar_panels/Households - NonSolar elec use Converter Actual_Electricity_demand/Houses_withou - per house t_solar_panels {kwh/household) Elec Use Converter Actual_Electricity_demand+Solar_Elec_Sup - ply LCA and SD of Domestic PV for Policy Development 94

95 Solar Elec Supply Converter Houses_with_solar_panels*2200 {kwh/year} Jungbluth et al Assuming average of 2kWp systems used. Electricity Stock Actual_supply - Actual Supply Inflow 10^20 - Arbitrary large value. Indicates ability for coal/centralised power generation to fill electricity needs where solar cannot.will always exceed predicted demand for the next 50 years. Surplus Power Outflow Actual_supply-Actual_Electricity_demand - Actual Electricity Outflow *440*(Households/ ) - - demand Solar_Elec_Supply {kwh/year} Price Scenario Affordability of Converter TIME {dollars/kwh) solar panels Graphical Function Affordability/Time: Feed in tariff Converter TIME Graphical Function Tariff/Time: Vogler & Hall 2013 Relative Electricity Price Converter Wasted_power_ratio*( *TIME+0. 22) AEMC 2013 Assuming price rise of ~1%/year based on current rates accounting for inflation Electricity Price used for Queensland average LCA and SD of Domestic PV for Policy Development 95

96 Disposable Household Income Converter (Non_Solar_electricity use_per_house*rel ative_electricity_price) {dollars/year/household} Install Rate Converter ABS((Installs_per_disposable_income)*Disp osable_household_income)*affodability_of _solar_panels*(relative_electricity_price+f eed_in_tariff)/0.22 {installs/household} Actual Suppy Inflow *440*(Households/ ) Energy values include natural gas, electricity and centralised renewable energy. Rebate Scenario Rebate Converter STEP(10000,25)-STEP(10000,35) ABS 2012 AEMC 2013 ABS2012b - ($10000 arbitrary number chosen to indicate injection of finances. ) Disposable Converter Rebate- Household Income (Non_Solar_electricity use_per_house*rel ative_elec_pric) {dollars/year/household} Install Rate Converter ABS((Installs_per_disposable_income)*Disp osable_household_income)*affodability_of _solar_panels*(relative_elec_pric+feed_in _tariff)/0.22 {installs/household} Tariff Scenario Tariff Increase Converter STEP(0.4,25)-STEP(0.4,35) - Total Tariff Converter Feed_in_tariff+Tarrif_Increase - Install Rate Converter ABS((Installs_per_disposable_income)*Disp osable_household_income)*affodability_of _solar_panels*(relative_elec_pric+total_t ariff)/0.22 {installs/household} Price Stock Scenario Actual Electricity Stock Price 0.30 {dollars/kwh} Change in Price Biflow ((Wasted_power_ratio- 1)/time_to_adjust_price ) - Difference_between_actual_and_suggested Time to adjust Converter 1 {year} price Difference between actual and suggested Expected price Relative Electricity Price Converter Converter Converter Assumed 1 year for price change actualisation Actual_Electricity_Price-Expected price 0.30 {dollars/kwh} Actual_Electricity_Price ABS 2012 AEMC 2013 AEMC 2013 AEMC AEMC 2013 LCA and SD of Domestic PV for Policy Development 96

97 3.2 Model Code Equations for Price Scenario LCA and SD of Domestic PV for Policy Development 97

98 3.2.2 Equations for No Price Scenario LCA and SD of Domestic PV for Policy Development 98

99 3.2.3 Equations for Tariff Scenario LCA and SD of Domestic PV for Policy Development 99

100 3.2.4 Equations for Rebate Scenario LCA and SD of Domestic PV for Policy Development 100

101 3.2.5 Equations for Price Stock Scenario LCA and SD of Domestic PV for Policy Development 101

102 References: ABS 2013a, Population clock, viewed October 2013, 9ef7e25faaca2568a900154b63?OpenDocument ABS 2013, Households and Families, viewed May 2013, 69C65. ABS 2012a, Australian Social Trends, Sep 2012, viewed June 2013, ABS 2012b, Year Book Australia, 2012, viewed June 2013, 0Features~Energy%20use~201 ABS 2012c, Migration, Australia, , viewed April 2013, A94?opendocument. ABS 2012d, Births, Australia, 2012, viewed June 2013, ABS 2012e, Deaths, Australia, 2011, viewed June 2013, ABS 2012, Measures of Australia's Progress: Summary Indicators, 2012, viewed June 2013, 12~Main%20Features~National%20income~16. AEMC 2013, AEMC Electricity Price Trends report released, Australian Energy Market Commission, Sydney, (viewed June 2013, Price-Trends-Report-53b9d f-467e-bf8a-b74d69d696d7-0.PDF). Jungbluth, N Stucki, M & Frischknecht, R 2009, Photovoltaics, ecoinvent report no. 6-XIII, Swiss Centre for Life Cycle Inventories, Dubendorf, Switzerland. (viewed March 2013, Vorath S 2013, Solar milestone: 1,000,000 PV systems installed in Australia, viewed June 2013, Vogler, S & Hall, P 2013, Cut in feed-in tariff from 44 cents to 8 cents has little impact on demand for solar panels, viewed July 2013, tariff-from-44-cents-to-8-cents-has-little-impact-on-demand-for-solar-panels/story-fnihsrf LCA and SD of Domestic PV for Policy Development 102

103 Appendix 4. Statement of anticipated publication from this work An abstract formed from this thesis was conditionally accepted for presentation at the LCA XIII conference in Orlando, Florida in An abstract was submitted for review for the Journal of Cleaner Production Special Volume on Carbon Emissions Reduction and a paper developed for submission. Submission of full, peer review ready paper: February LCA and SD of Domestic PV for Policy Development 103

104 Appendix 5. Submission to Journal of Cleaner Production An abstract for the Journal of Cleaner Production has been submitted for a special volume on GHG emissions reduction. The following paper is to be submitted once abstract approvals have been released. Combining Life cycle Assessment and System Dynamics for Analysing the Carbon Emission Reduction Potential and Policy Framework of Photovoltaic Cells (Solar Panels) in Australia. Annie McCabe*, Anthony Halog School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane Qld 4072, Australia A R T I C L E I N F O A B S T R A C T Keywords: System Dynamics Photovoltaic Life Cycle Assessment Policy Since the advent of mainstreamed industrial ecology within policy framework design, the applications of system analytics for emissions reduction have substantially grown. This study has applied the life cycle assessment (LCA) methodology with system dynamic (SD) modelling, to assess the merits of such a hybridized design approach in policy development. Whilst PV panels are operationally GHG emissions free and stationary, there are inherent emissions and environmental degradation within manufacture and waste stages. Using a life cycle inventory (LCI) dataset sourced from the EcoInvent database for a 3kWp mono-crystalline slanted rooftop mounted system, an LCA was carried out within LCA modelling software to yield life cycle impact assessment results regarding a number of indication areas such as land and resource use, human health and energy use. It was found that the main impacts across the life cycle of a 3kWp system relate to the particular energy mix used within production and manufacturing stages. A prototype system dynamic model was then developed to assess the potential triple-bottom line impacts of extrapolated uptake trends of domestic solar PV in Australia. Through system dynamic modelling, a stock and flow model was created, accommodating a number of scenarios involving pricing and policy changes. From SD modelling, it was found that with current and projected install rates that domestic solar panel use for large scale electricity production is unlikely to achieve positive results regarding emissions reduction and penetrating a high enough energy supply for household use, unless alongside other renewable power sources such as solar thermal or wind energy. The results of the LCA and SD model were then combined and compared against the policy framework regarding small-scale renewables and domestic PV systems in Australia to determine whether there is appropriate legislative coverage. The ability to model impacts over the life cycle of a PV system over a set time horizon allows for industry to better understand system impacts and policy makers to create a flexible and adaptive legislative framework with accurate emissions accounting. *Corresponding author. address: annie.mccabe@uqconnect.edu.au (A. McCabe)

105 1. Introduction Present rates of production and energy consumption have been facilitated through the exploitation of progressively depleting fossil fuels (Raugei 2012). For many years economists and scientists have speculated upon the advent of peak oil and rising electricity prices associated with the increasing cost of fossil fuel extraction, calling for renewable energy innovation (Campbell & Laherrere 1998; Murphy & Hall 2011). With increasing technical and environmental pressures due to effects of climate change, solar resources are growing in popularity as a readily available and well established technology with a relatively short implementation timeframe (Wright & Hearps 2010). Australia s position to exploit solar resources is one of the most advantageous in the world with the highest solar radiation per sq. metre of any country (Bahadori & Nwaoha 2013). Solar energy uptake is predicted to reach 30% of Australia s electricity production by 2050 (CSIRO 2010). Compared to coal, for every gigawatt-hour of electricity produced through photovoltaics, approximately 1000 tonnes of Co2 emissions are prevented (Fthenakis & Moskowitz 2000). Although there has been a relatively positive uptake of commercially available domestic photovoltaic (PV) panels, great inefficiencies within the manufacturing and disposal life cycles are yet to be fully addressed through legislative and system analysis approaches (Jungbluth et al. 2008). The solar energy supply index from 1990 to 2010 (1990=100) rose to almost , indicating a substantial relative growth in solar energy utilization on a global level (IEA 2012a). Comparatively, wind and biofuel resources have only grown to a supply index of and 7500 respectively. Such large supply indicates both a high uptake and extensive increases in solar panel efficiency. It is crucial that a movement towards a renewable energy mix is approached with comprehensive emissions and resource accounting to ensure an effective transition concurrent with ideals of sustainable development and industrial ecology. Such high uptake highlights the need for both LCA and System Dynamic modelling for contribution to policy analysis. Within renewable energy legislation, the need for an environmental management tool based more firmly in integrated systems is clear (Effendi & Courvisanos 2012). LCAs can be of great merit to policy development in identifying system inefficiencies, due to their ability to work with a number of metrics and technical processes (Europen 1999). The inherent boundary requirements and thus limitations within the LCA methodology lend themselves to the application of System Dynamic (SD) modelling to model impacts across a timeframe, with the inclusion of triple bottom line variables. Although there have been a number of studies working with LCAs of PV panels in various contexts, there is little synthesis of this produced data to aid in policy development. The hybridization of LCA and SD methodologies for modelling impacts for PV panels within this paper has never been approached as a research objective, particularly in reference to policy design and development. This research paper proposes that LCA and System Dynamic analysis data, whilst useful in an industrial context, may also lend itself to improving policy development approaches for domestic solar panel use in Australia. 2. Methodology 2.1 LCA Methodology This methodology was applied in compliance with ISO standards 14040, and regarding solar panels. The steps of the method follow the framework for an ALCA (Attributional Life Cycle Analysis) modified from ISO (Figure 1). An LCA modelling software package was used to analyse the LCI information and perform an uncertainty analysis Goal and Scope Definition Goal: The intended application of this LCA is to understand the environmental impacts of domestic solar panel usage in Australia and how policy can be changed with relevance to such impacts. Scope: The studied product system was a 3kWp slantedroof mounted installation, mono-crystalline panel system installed in the year The function of the system is defined as the domestic production of photovoltaic electricity. The system boundary for the system included all components for the manufacture, installation, energy use for the mounting, transport of materials and persons to construction, and disposal of components after end life (Figure 2). Data requirements for the LCA were all inputs and outputs from nature and to nature involved within the system boundary of the 3kWp system.

106 2. Characterization: Choosing equivalence factors for particular substances. 3. Normalization: Conversion for comparison by a set reference value (usually the average yearly environmental load/population: 1/yr) Within LCA software these steps are generally included, depending on method selection. For the purposes of this LCA, 5 impact assessment methods were chosen for use within the impact assessment stage: 1. Eco-Indicator 99 method, Hierarchist version. V2.08 Fig. 1. Overview of LCA methodology used 2. Greenhouse IPPC distinguish main gases. (Greenhouse kg/co2eq) V Australian Raw Material Flows V Ecological Footprints Australian. (Global Footprint Average) V.1 Fig. 2. Unit process raw data for the four main modules of the LCI combined Life cycle Inventory Analysis The best available data was collected from the comprehensive EcoInvent database. It was not feasible both economically and temporally to gather LCI data for an Australian system within the scope of this paper. At the time of this paper s publication there were no well-established Australian LCA bodies with available PV data, although the AusLCA database is currently in its development stage. The database number for the dataset was #1768. Resource extractions from nature and emissions to air, soil and water are included within the dataset. In all, the dataset had 1296 data points for analysis Functional Unit The functional unit for this LCA was chosen to be 3kWp, therefore the data is already related to this output Impact Assessment Impact assessment data manipulation requires 3 steps: 5. Cumulative Energy Demand with breakdown. (Energy by fuel source) V2.01 Methods were chosen for purposes of sensitivity analysis and as a technique of quantifying impacts from the chosen dataset. Methods were chosen for their ability to cover a number of impact areas from human health, land use and GHG emissions. Method 1. Eco-Indicator 99 method, Hierarchist version. V2.08 This method uses a damage orientated approach, with weighting based upon damage types caused by impact groups. Damage categories of Human health (expressed as Disability Adjusted Life Years), ecosystem quality (expressed as loss of species/area/time) and resource (surplus energy needed for future fossil fuel extraction). Characterization factors are calculated at end-point level (damage). The hierarchist version was chosen as it is the most comprehensive analysis available within this impact assessment method. The hierarchist perspective is long-term, and substances are only included if there is consensus regarding their damage potential. Fossil fuels cannot be easily substituted. The Europe EI 99 H/A normalisation setting was chosen within this method. 1. Classification: What products or resource uses will be within each impact category? LCA and SD of Domestic PV for Policy Development 106

107 Greenhouse IPPC distinguish main gases. (Greenhouse kg/co2eq) V1.01 This method provides GHG emissions results with some disaggregation, based upon a 100year timeframe using IPCC default levels. The normalization setting chosen for this method was Greenhouse kg CO2eq. Australian Raw Material Flows V 1.01 This method simply tracks the total mass flow of raw material and emissions based upon the summing of all elementary flows within the life cycle of the system. Theoretically the sum of resources and inputs within the system should equate to the sum of the outflows. However, due to discrepancies in data inclusion such as combustion of oxygen, this is not always the case. Ecological Footprints Australian. (Global Footprint Average) V.1 Using the footprint methodology developed by Wakernagel et al. (1996) this method accounts for all land types used within the life cycle of a product. Data is normalised to the global average footprint in CO2 emissions within the LCI are only accounted for within energy land. Cumulative Energy Demand with breakdown. (Energy by fuel source) V2.01 This method simply provides breakdown of energy use through the life cycle of a system. It measures the energy mix fossil fuels, nuclear, biomass, hydro and other renewables Uncertainty Analysis Because the data used within this LCA was collected over a decade ago, was based on the data available in the EcoInvent database and represents just one system, it is suitable to perform an uncertainty analysis to determine how representative a figure is for applicability within this study. The LCA software used allowed for absolute uncertainty analysis through Monte Carlo analysis of datasets, repeating comparisons within a specified uncertainty range. A Monte Carlo analysis was carried out using the EcoIndicator 99 impact assessment method using uncertainty parameters contained within the dataset. 2.2 SD Modelling Methodology A hybridized design approach for the purposes of this paper adapted from Hannon and Ruth (2001) and Maani and Cavana (2000) was taken towards modelling. The steps are as follows: System Definition and Objectives of Modelling The objectives of the model were decided upon for the purpose of this LCA. The model was to provide a flexible prototype for further development in terms of data and causal loop intricacies. The system was determined as all metrics within an Australian context regarding domestic solar usage and other household metrics Problem Structuring: Desktop Research and data collection A desktop study of Australian solar uptake rates and other pertinent metrics was performed utilizing grey literature articles and news articles as supplementary information Causal Loop Modelling Identification of key variables and causal relationships. Once the desktop study was performed main variables were selected and modelled within a causal loop diagram to determine feedback loops Dynamic Modelling Define control and state variables: Stocks and flows Control variables (stocks or accumulations), indicate a value that can accumulate, generally displaying the status of the overall system. Stocks of population, households with PV panels and electricity were determined as points for analysis within the model. State variables (flows) are the variables that control stocks through inputs, updated at each chosen time step (generally annually). A range of state variables were chosen subsequent to causal loop modelling with relevance to influencing or interrelated factors to the chosen stocks. Collection of specific data Data was collected to determine parameters for the chosen stocks. Mathematical functions were then formed to communicate the connections between control variables and other parameters. LCA and SD of Domestic PV for Policy Development 107

108 Select an appropriate time horizon The time horizon of 50 years was selected for simulation modelling. Validation Results were assessed for real-world feasibility, based upon current predictions and trends. Agent based modelling simulation software was used to create the system dynamic model and causal loop simulation software utilized for causal loop modelling. In its current state, the prototype model created should act as a base for further sustainability modelling, not as a comprehensive and detailed simulation. 3. Results 3.1 LCA Results Fig. 3. Analysis of 3kWp system using Eco-Indicator 99 (hierarchist), weighted. 1 pt is representative of one thousandth of the average yearly environmental load of one European inhabitant. Table 1. Selected process contributions for EcoIndicator 99 (Damage Assessment) Eco-Indicator 99 method, Hierarchist version The Eco-Indicator 99 method, Hierarchist version was used to assess potential areas within the life cycle of the 3kWp system that could damage ecosystem, resource or human health (Figure 3). Over the life cycle of the 3kWp system the areas of highest potential damage are the production of carcinogenic materials and respiratory inorganics (for human health) and fossil fuel use (resource depletion). Capacity for damage regarding climate change, eco-toxicity and mineral usage are secondary. Smaller damage capacities are seen for respiratory organics, radiation, ozone layer, eutrophication and land use. Process contribution data used for damage assessment were then tabulated (Table 1). In terms of ecosystem quality, the highest damage potential processes across the life cycle of the PV system were regarding the use and refining of copper and the disposal of sulfidic tailings. Similarly, the greatest impacts to human health also stemmed from disposal of sulfidic tailings and brown coal (lignite), and copper refining. Greenhouse IPPC distinguish main gases. (Greenhouse kg/co2eq) The IPPC method was used to assess the greenhouse gas emissions over the life cycle of the 3kWp system (Figure 4). LCA and SD of Domestic PV for Policy Development 108

109 high resource input (excluding electricity input) are production of Czochralski silicon, decarbonized water and lignite (Table 2). This is due to the fossil fuel energy and refining required to produce solar grade silicon, indicating the use of fossil fuel sources and silicon refining as resource intensive manufacturing choices throughout the life cycle. The process of characterization has standardized values to give an indication of the relative resource contribution of each process within the life cycle of the PV system, not an accurate representation of actual resource uses. Fig. 4. IPPC 1990 method, distinguishing main gases. Table 2. Selected process contributions of Raw Materials Flows Method (Characterised) Results from analysis using the IPPC 1990 method show the predominant source of emissions due to carbon dioxide emissions at 5.092kCO2 e across the life cycle of the system. Emissions of methane, nitrous oxide and from land transformation are also within the life cycle of the system. Other represents all processes included but not characterised, such as the PV cell, liquid aluminium and tetraflouroethylene use at plant production. Raw Material Flows (Australian) Raw Material Flows across the life cycle of the system were assessed within LCA software impact assessment (Figure 5). Ecological Footprints (Australian) The Ecological Footprints method was used to analyse the land uses across the life cycle of the 3kWp system (Figure 6). Fig. 5. Raw Material Flows across the life cycle of the 3kWp system. kpt The kilopoint (1 kpt=1000 points) was derived by dividing the computed total environmental load in Europe by the number of its inhabitants. The raw materials use for the 3kWp system predominantly come from resource and raw material use from nature. Outputs to air including particulate matter and emissions are also significant material flows across the life cycle of the 3kWp system at ~5kPt. Among the processes requiring LCA and SD of Domestic PV for Policy Development 109

110 Fig. 6. Ecological footprints and land use over the life cycle of the 3kWp system. Units are in ha/year. The ecological footprints over the life cycle of the 3kWp system are predominantly attributed to the use of land for energy production sources e.g. power plants. Consumed land is the land used occupied/built on during the life cycle (storage, plants etc) and is the second highest consumer of land resources. Land used for biofuel cropping and forest land was marginal and reliant upon the energy mix of the system dataset. Process contributions were highest for production of electricity, fossil fuel resource production (energy land) and factory creation (consumed land) (Table 3). Fig. 7. Cumulative energy demand by fuel type across the life cycle of the 3kWp system. The majority of energy flows occur from fossil fuel, nuclear energy and hydroelectric. The largest use of energy across the life cycle of the 3kWp system was through the use of coal, oil and gas during manufacturing, preproduction and disposal stages. Energy sourced from nuclear sources was also high due to the energy mix used in the EcoInvent database dataset sample system. Process contributions indicate a relatively even split between energy demand fuel types of lignite, natural gas and uranium ~10000 MJ (Table 4). Table 4. Selected Process Contributions by fuel type (weighted) Table 3. Selected process contributions for Ecological Footprints (Weighted) Cumulative Energy Demand with breakdown The cumulative energy demand of the 3kWp system was investigated across its life cycle (Figure 7). LCA and SD of Domestic PV for Policy Development 110