Site Selection for Commercial Biofuel Production from Algae and Sugarcane, Using GIS Modelling in Queensland, Australia

Site selection for commercial biofuel production from algae and sugarcane, using GIS modelling in Queensland, Australia

Masoumeh Sedghamiz (BSc)

A thesis submitted for the degree of Master of Philosophy at

The University of Queensland in 2017

School of Geography, Planning and Environmental Management Abstract

A number of factors including, growing energy consumption and increasing fossil fuel price along with greenhouse gas emission concerns, have increased global attention to biofuel as a potential sustainable energy product. Even with a transition to electricity- driven transport (e.g. electric cars), high energy-density fuels will still be required for larger vehicles (planes, boats, trucks) and communities that are not connected to the electricity grid. While environmental benefits of biofuel have been considered in promoting the industry, existing first generation biofuel crops compete with agricultural land or biodiverse natural landscapes. Nevertheless, biofuel (in particular second and third generation biofuel) could be an economic and self-reliant regional energy product. Additionally, it would increase the level of services, quality of life and creation of employment, etc. for rural areas. In Australia, the total primary energy consumption is projected to grow by nearly 42 per cent until 2049-50, therefore the demand for energy is expected to continue to mount. Hence it is critical to investigate cost-effective investment and sustainability in Australia’s energy future.

The Australian state of Queensland with suitable broad land and climate, is generally well suited to biofuel production. Among various types of biofuel resources, microalgae are considered as one of the best feedstocks for sustainable biofuel generation, as they can be grown on non-arable land with nearly any source of water (fresh, brackish or saline) and CO2. Queensland has vast land resources with an area of 1,730,648 square kilometres and the total length of Queensland's mainland coastline is 6,973 km (4,333 mi) and the appropriate climate needed to produce algae as an alternative viable source of fuel.

Although QLD has vast land resources and suitable climatic condition for algae cultivation, there is a need to allocate the suitable sites according to climatic and environmental constraints and economic limitations such as land value, for sustainable and economic production of biofuel. For a cost-effective production, land value, biophysical parameters and access to resources and roads are critical criteria to consider for locating an algae farm. Therefore, the location decision is a priority to financial success in the biofuel industry.

In this case study, optimal commercial-scale biofuel production sites in Queensland were identified using a Multi-Criteria Analysis (MCA) tool, and a staged Geographical information System (GIS) analysis. In this study, the low-value regions with land use consideration were identified and proximity to roads mapped by Euclidean distance.

In another stage, they were combined with eco-climatic maps and ranked by their importance. Eventually, the MCA tool was employed to map the optimal locations. The outcomes of this study advance the techniques for biofuel site assessment and provide comprehensive and accurate results which can support the microalgae-based biofuel industry development in Queensland and evolve better management strategies for sustainable land use planning in the state. Two maps that resulted from these analyses are climate suitability and overall algae farming suitability. The first map shows the site suitability for commercial microalgae farms with biofuel production as their primary purpose, according to eco-climatic and land use criteria. The overall algae suitability shows the spatial distribution of microalgae production suitability levels.

Another part of this study includes the investigation for potential sites for cultivating both sugarcane and microalgae. The aim was to find out if algae could be produced in sugarcane production sites, in case of land use change considerations in the future. The GIS overlay technique has been adopted and the algae suitability map from Chapter 2 was overlayed on the sugarcane potential site map produced by Audit. Also, proximity to roads and CO2 resources were added to the analysis. The map result shows that, there is a lot of potential for cultivation both plants in north-western and eastern regions of Queensland with low value land.

III

Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

Publications during candidature

No publications.

Publications included in this thesis

No publications included.

Contributions by others to the thesis

No contributions by others.

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

Acknowledgements

This thesis could not have been completed without the assistance, advice and support of a number of people. Firstly, I’d like to thank my advisory team, Dr. David Pullar and Prof. Peer Schenk, for their support and advice throughout my postgraduate study. Staff at the School of Geography, Planning and Environmental Management provided administrative and technical support throughout my studies, particularly Judith Margaret Nankiville. I’d like to say a very big thank you to my friends and family for putting up with me and providing encouragement and moral support when I most needed it. Finally, a big thank to my husband, Mohammad for his unconditional love and support whose presence made a world of difference for me!

Keywords

Energy resources, GIS, MCA, biofuel, renewable energy, sustainable, eco-climatic, microalgae, land use, evaluation

Australian and New Zealand Standard Research Classifications (ANZSRC)

050205 Environmental Management (50%),

070108 Sustainable Agricultural Development (35%),

090608 Renewable Power and Energy Systems Engineering (15%)

Fields of Research (FoR) Classification

FoR code: 0502, Environmental Science and Management, 40% FoR code: 0803, Computer Software, 60%

VII

Table of Content

CHAPTER 1. Introduction 1.1 Background ...... 2 1.2 Thesis aim and Objectives and Questions ...... 6 1.3 Literature Review ...... 7 1.3.1 Challenges in Use/Demand for Energy Resource ...... 8 1.3.2 Renewable/Biofuel Energy ...... 9 1.3.3 ArcGIS evaluation methods ...... 12 1.3.3.1 Overview of MCA Approaches...... 13 1.3.3.2 Overview of Biofuel site selection studies ...... 16 1.4 Approach ...... 22 1.5 Thesis outline ...... 23

CHAPTER 2. Site selection for commercial microalgae cultivation using Multicriteria GIS modelling in Queensland, Australia 2.1 Introduction ...... 25 2.2 Study area and Materials ...... 29 2.2.1 Study area ...... 29 2.2.2 Algae site suitability ...... 32 2.2.3 Resource evaluation for biofuel production scale up ...... 33 2.2.3.1 Climatic variables ...... 34 2.2.3.2 Land use variables ...... 34 2.2.3.3 Economic variables ...... 35 2.2.4 Data sources ...... 36 2.3 Methodology ...... 38 2.3.1 Suitability analysis ...... 38 2.3.1.1 Overview of multi-criteria analysis ...... 38 2.3.2 Reclassification and suitability analysis ...... 40 2.4 Results…………………………………………………………………………………………………..41 2.4.1 Eco-climatic map ...... 43 2.4.2 Algae production suitability map ...... 36 2.5 Discussion ...... 47 2.6 Conclusion ...... 48

CHAPTER 3. Comparison of potential sites for microalgae and sugarcane as biofuel crops 3.1 Introduction ...... 50 3.1.1 Sugarcane industry in Qld ...... 50 3.1.2 Yes or no to continue sugarcane production ...... 53 3.2 Methods ...... 55 3.3 Results ...... 59 3.4 Discussion ...... 62 3.4.1 Cost effectiveness comparison of producing biofuel from algae and sugarcane ...... 64 3.5 conclusion ...... 67

CHAPTER 4. Synthesis and Conclusion 4.1 overview ...... 69 4.2 The contribution of biofuel production ...... 69 4.3 Limitation and future research ...... 70 4.4 Conclusion ...... 70 List of References ...... 72

VIII

List of Figures & Tables

Figure 1.1: The highest greenhouse gas emitter countries per capita 2010 ...... 2 Figure 1.2: Percentage change in emissions by sector, Australia, 1989-90 to 2012-13 ...... 3 Figure 1.3: World annual fuel ethanol production, 1975-2009 ...... 4 Figure2.1: Study area-Queensland government boundaries- Major climate classes and the average rain fall ...... 30 Figure 2.2: Queensland bio-industries map ...... 31 Figure 2.3: Essential factors for identifying optimal sites ...... 33 Figure 2.4: Radar plot of criteria used in eco-climatic MCA modelling ...... 42 Figure 2.5: suitability map according to eco-climatic and land use criteria ...... 43 Figure 2.6: Radar plot of criteria used in algae site selection MCA modelling ...... 45 Figure 2.7: Spatial distribution of microalgae production suitability levels ...... 46 Figure 3.1: Sugarcane worldwide distribution...... 51 Figure 3.2: Queensland Sugarcane production regions and gross value ...... ……..52 Figure 3.3: Percentage of current sugarcane land in each region ...... 53 Figure 3.4: Harvested sugar cane area and tonnage ...... 54 Figure 3.5: Queensland Sugarcane potential production sites ...... 57 Figure 3.6: The process of the final map production ...... 58 Figure 3.7: Queensland Algae and Sugarcane suitable production sites...... 58 Figure 3.8: Queensland Algae and Sugarcane suitable production sites close to the mills with economically land value ...... 61 Figure 3.9: Greenhouse gas emissions for cane sugar, showing contributing activities in Queensland………………………………………………………………………………………….63 Figure 3.10: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm with utilising purchased CO2……………………………………………………………………………………………………66 Figure 3.11: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm without utilising purchased CO2 ………………………… ...... 66 Table 1.1: An overview of MCA approaches studies ...... 15 Table 1.2: Biofuel Site Selection Studies ...... 21 Table 1.3: Requirement variables and data types ...... 22 Table 1.4. Schematic overview of thesis structure; each boxes indicate chapters comprised of research articles which individually address the main research questions of the study .. 23 Table2.1: Variables, data sources and type, ideal condition of criteria used in study ...... 37 Table 2.2: Frequencies, importance ranking and weight criteria calculated, using Meta – Analysis……………………………………………………………………………………...... 40 Table 2.3: Variables and Influence weighting use in Radar plot of Eco-Climate suitability map…………………………………………………………………………………………………..42 Table 2.4: Radar plot of algae site selection MCA modelling ...... 44 Table 3.1: Sugarcane production in major countries (by area harvest in 2008), according to FAO estimates ...... 51 Table 3.2: Limitation criteria used for assessing agricultural land suitability in Queensland..56 Table 3.3: Selected area suitable for sugarcane and algae farm………………………………61

List of Abbreviations used in the thesis

BOM: Bureau of Meteorology

AUDIT: The Queensland Agricultural Land Audit (the Audit) identifies land important to current and potential future agricultural production across Queensland. It aims to help Queensland better plan for future food and fiber production.

ArcGIS: Geographical Information System

ABARES: the Australian Bureau of Agricultural and Resource Economics and Sciences, the science and economics research division of the Department of Agriculture and Water Resources.

BREE: Bureau of Resources and Energy Economics

GGE: Greenhouse gas emission MCA: Multi-criteria analysis

Chapter1. Introduction

1.1 Background

Over the past decade, environmental issues such as, global warming along with increasing vulnerability due to oil dependencies, have contributed to the urgent need to research and discover alternative sustainable energy sources.

Australia was the most greenhouse gas emitter country in the world in 2010 and its emissions have increased 30.5% since 1990 (Fig 1.1). The main reasons for the increase in Australia’s emissions are stationary energy which includes emissions from direct combustion of fuels, predominantly in the manufacturing, mining, residential and commercial sectors – up by 43% and Transport emissions – up by 53.6% (Environment, 2013)(Fig 1.2). Rising concern about climate change and its necessary mitigation as well as the increasing awareness of the relationship between climate change and sustainability has driven notice to a search for a secure and clean source of energy. Biofuels have been put forward as one of a range of alternatives with lower emissions and a higher degree of fuel security (O'Connell et al., 2007; Seabra et al., 2011).

Figure 1.1: The highest greenhouse gas emitting countries per capita 2010

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Figure 1.2: Percentage change in emissions by sector, Australia, 1989-90 to 2012-13. Source: Department of the Environment estimates.

Liquid biofuels have recently attracted increased attention in Australia – as in other countries all over the world - as a major alternative to petroleum based transportation fuels (Fig 1.3). Their major benefits are: i) it is a renewable fuel, ii) may be grown so it is commonly available, iii) encourages regional development, iv) creates jobs in rural manufacturing, v) reduces greenhouse gas emission, and vi)it is biodegradability (Demirbas, 2009b; Keating and Carberry, 2010; Puri et al., 2012).

On the other hand, there may be negative impacts for biofuels: i) they compete with other agricultural crops and put pressure on demand for land (Harvey and Pilgrim, 2011), and ii) a growing Biofuel industry will affect the supply of feed grain for livestock, particularly in drought years and this will place upward pressure on the price of grain (O'Connell et al., 2007).

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20,000 18,000 16,000 14,000 12,000 10,000 8,000

6,000 MillionGallons 4,000 2,000 0 1975 1980 1985 1990 1995 2000 2005 2010

Figure 1.3: World annual fuel ethanol production, 1975-2009, source: www.earth- policy.org Source: F.O. Licht, World Ethanol and Biofuels Report.

There are different types of plants for producing biofuels. Among them, sugarcane is the most common crop for bioethanol; it has the advantage that it is already grown in Queensland and is a multi-profit product. However, it places land use pressures on agriculture as it requires land with fertile soils and high rainfall. Additionally, there are restrictions on choosing potential areas for growing sugarcane such as: i) climate requirements for minimum temperature and avoiding frost, and ii) economic requirements in terms of distance to processing mills (Audit, 2013). While cane growing provides direct economic benefits, environmental values are becoming increasingly important and should be considered (Mallawaarachchi and Quiggin, 2001).

Another alternative crop for biofuel production, is algae with these significant benefits: i) algae do not need to compete with valuable high intensity agricultural land and they can be grown on marginal or non-agricultural land so avoiding adverse impacts on land use and food prices (Gressel, 2008), ii) algae grow in fresh, brackish or even saline water (Prasad et al., 2014), iii) they can have a reduced greenhouse gas and energy footprint (Campbell et al., 2009).

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Recently, there has been a strong focus on reducing greenhouse gas emissions from aviation fuel and on limiting carbon emissions. So, bio- derived jet fuels have opportunities to take an essential part in eliminating those concerns. The aviation industry in Australia has aspirations to supply 5% of its domestic fuel use from biomass by 2020 (Graham et al., 2011; Murphy et al., 2015). Another advantage of microalgae is, its oil content which is up to 20-50% and can be extracted and used for biodiesel. In addition it can be refined to compounds that can replace jet fuel and is increasingly being used in attempts to reduce the environmental impacts of aviation and to ensure energy security in the industry (Fortier et al., 2014; Klein‐Marcuschamer et al., 2013b).

Although biofuel has a lot of advantages, but the production cost needs to be compatible with petroleum based fuels. The ability to produce and integrate large volumes of biofuels cost-effectively and sustainably are primary concerns of which policy makers should be aware (Sims et al., 2011). But, there are a range of barriers to perform large size cost-effective biofuel production as many factors involve in.

According to the statistics of an Audit report by The Queensland Department of Agriculture, Forestry and Fisheries, Queensland has the potential to increase the land use for sugarcane from 0.33 to 4.06 %. It means in this state there is an enormous opportunity for growing sugarcane. Based on technology of using waste resources needed for growing algae, they can also be generated by cane growing and sugar processing (Prasad et al., 2014). Hence, it could be beneficial environmentally and economically to identify the specific and suitable regions with favourable biophysical and climatic condition for both sugarcane and microalgae.

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1.2 Thesis Aim, Objectives and Questions

In this study, Queensland was selected as the study area with huge development opportunities for economical investment in commercial bioenergy production, considering its suitable climatic and economic factors(Queensland Goverment, 2017) (Picture 1.1). As an economical advantage for Queensland, algae based biofuel production projects can make use of vast arable land across the state that is naturally unsuitable for crop production. The expected large gap between future demand and potential domestic supply in Queensland requires expanding biofuel production in areas which have the land and the climate needed to produce raw feedstocks on a large scale.

The aim of the current study is investigating the most suitable locations for allocating biofuel farms for long term economic and environmental sustainability, before any investment for large-scale biofuel production.

Picture 1.1: Study area: Queensland, Australia.

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The specific objectives of this research were to:

I. To evaluate availability of suitable lands for crops for bioethanol production according to land uses, eco-climatic parameters. II. Assessing the land use implications comparing sugarcane and algae for bioethanol production according to economic efficiency and land use concerns. III. Updated map results for future sustainable land use planning in Qld.

To obtain the objectives of the study, the research questions below have been investigated:

1. How do different criteria which influence algae production along with land-use and socio-economic factors determine the production location of algae? 2. How can suitability modelling can be adopted to allocate a commercial production site for microalgal biofuel? 3. How does sugarcane production affect the Qld environment and can algae be a substitute energy crop to mitigate these effects? 4. Are there any locations which are now under sugarcane production and also suitable for algae production that can be considered for land-use change consideration?

1.3 Literature Review

A large body of literature exists on biofuel production in terms of theoretical, technical, environmental, economics and implementation to support the basis for development of government policy and/or industry investment (Brennan and Owende, 2010; Hu et al., 2008; Li et al., 2008; Lundquist et al., 2010).

The majority of these studies consider economic factors as the most common criteria in their modelling, based on biomass type/resources and final market analysis.

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Recently there is growing interest to examine how these opportunities vary across space and ideal land use suitability allocation (Borowitzka et al., 2012; Coleman et al., 2014; Das and Salam, 2014; Klise et al., 2011; Maxwell et al., 1985; Quinn et al., 2012).

The literature review consisted of results from search using following search strings: biofuel and environment; energy and demand; renewable energy and economy; spatial allocation and GIS. The following section discussed about the related studies in: (1) Challenges in use/demand for energy resources issues; (2) renewable/biofuel energy; (3) ArcGIS evaluation methods and its applications in environmental management scope.

1.3.1 Challenges in Use/Demand for Energy Resources Issues

Demand for energy is expected to grow during the next few decades in Australia (Geoscience Australia and BREE, 2014) and the energy consumption is projected to increase by 63 per cent by 2029-30 (Commonwealth of Australia, 2007). Australia’s combined dependency on crude oil and fuel imports for transport has grown from around 60% in 2000 to over 90% today (Biofuels Association of Australia, 2014) and it is projected to increase to 76% by 2030 (ABARE, 2010; Geoscience Australia and BREE, 2014). On the demand side, for long term supply and price stability, there is concern over whether Australia is resilient to future fuel security challenges. Therefore, alternate fuels, particularly those that are potentially in plentiful supply in Australia, are the obvious option to improving our fuel dependence on both the supply and demand side (Blackburn, 2013).

From an environmental perspective, greenhouse gas emissions (GGE) in Australia have grown by 24.7%, since 1990, which is mainly caused by the electricity and transport sector (Department of Environment, 2015). Rising concerns about climate change and increasing awareness of the relationship between climate change and sustainability urged the Australian government to develop the Clean Energy Future plan. The plan is directly aimed at mitigating the impacts of climate change by ambitiously targeting to cut GGE by at least 5 per cent compared with 2000 levels by 2020 (Commonwealth of Australia, 2007; Commonwealth of Australia, 2011).

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Recent increases in demand for petroleum based transportation fuels (i.e. aviation) and their GGE emissions encourage industry to support the development of drop-in renewable fuels (Elmoraghy and Farag, 2012; Klein-Marcuschamer et al., 2013). Biofuels have direct, fuel-cycle GGE emissions that are typically 30 – 90 % lower per kilometre travelled than those for gasoline or diesel fuels (IPCC, 2014).

By taking appropriate action towards clean energy, Australia can look forward to protecting environment and long term economic prosperity. Achieving that, the Federal Government has recently established a ‘‘Clean Energy Finance Corporation’’ which will invest AUD$10 billion in developing renewable energy, and low-pollution and energy efficient technologies (Puri et al., 2012).

Al in all, the necessity to study new sources of clean energy, is undoubted a way to address energy demands and mitigate GGE concerns. The supply sources locations for alternative energy is also vital important to be considered and the sio- economic factors are inevitable to be neglected.

1.3.2 Renewable/Biofuel Energy

Growing environmental and energy concerns have led to consideration of alternative energy sources based on production of biofuel in Australia (Puri et al., 2012). In Australia there are large scale opportunities available that appear to offer a range of environmental and social benefits, in addition to commercial bioenergy (Stucley et al., 2012). According to Ramachandra and Shruthi (2007), for regional energy supply independence it is vital for countries to search for renewable, alternate and non- polluting sources of energy. Biofuel production is also suited to rural and remote areas with the potential of significantly promoting their economic and development. As long as sustainability and reduction of greenhouse gas emission, biofuels offer the potential to increase the level of services for rural population and creation of employment (Demirbas, 2009a; Gheewala et al., 2011). Consequently, through the efficient use of locally available bioenergy sources the quality of life in rural areas can be improved (Ramachandra and Shruthi, 2007).

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Biofuel feedstocks divide into 4 broad categories: (1) high-efficiency feed stocks (e.g. palm oil, sugar cane); (2) moderate- efficiency feedstocks (e.g. corn, soybean, rapeseed, sugar beet); (3) feedstocks under development (e.g. sweet sorghum, Jatropha); and (4) dedicated energy feedstocks (e.g. switchgrass, miscanthus, short rotation crops, algae, waste) (Elbehri et al., 2013). According to O'Connell et al. (2007), the key crops which are currently use for bioethanol production in Australia are sugarcane, molasses, wheat, barley, maize and sorghum, while future potential biofuel production based on Jatropha, Pongamia, Moringa, Hura crepitans and algae is under research.

Microalgae in particular have gained wide attention as a potential source of biofuel. This commodity suits non-arable lands, utilises virtually any source of water,

may uptake waste CO2 sources and produces many profitable by-products, alongside co-benefits of GGE mitigation. Furthermore, microalgae are superior in productivity compared with plant crops in land area requirement and water consumption for cultivation feedstock. They are considered as a reliable and continuous supply of fuel due to their high oil content and continual-harvest characteristics (Brune et al., 2009; Li et al., 2008; Pate et al., 2011; Pittman et al., 2011; Schenk et al., 2008; Singh et al., 2011). In commercial plants one of the following four technologies is typically used to cultivate algae: 1. extensive ponds (lagoons); 2. raceway and circular ponds; 3. tubular photo bioreactors; 4. fermenters (where algae are grown on organic substrates in the dark). Among these systems, open ponds are the most widely used for commercial large-scale outdoor microalgae cultivation (Borowitzka, 2013; Borowitzka et al., 2012; Schenk et al., 2008).

In practice, to date, the lowest cost of commercially produced microalgal oil is still much higher than the reasonable medium-term price target to become cost competitive with petroleum diesel (Borowitzka et al., 2012; Stephens et al., 2010). One of the key factors for a technically and economically viable biofuel resource is that, it should be competitive and cost less than petroleum fuels (Brennan and Owende, 2010). The challenges to reach that goal and the fundamental barriers to development of the biofuel industry are water and nutrients availability, harvesting methods and high costs of oil extraction, land value, land availability, facilities cost, existing land use, proximity

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to resources and infrastructures, climate requirements, government policies and supports (Singh et al., 2011; Stephens et al., 2010).

While other energy sources are concentrated in a limited number of countries, renewable energy resources can be produced over wide geographical areas. However, there are many possible scenarios of bioenergy production, and the options vary with geographical location (Davis et al., 2011). Moving towards large scale production of biofuel, needs choosing the most proper crops and the suitable location according to the eco-climatic and socio-economic considerations(Maxwell et al., 1985).

The major requirements of growing biofuel crops are water resources availability, suitable temperature and slope (Borowitzka et al., 2012; Brennan and Owende, 2010; Pate et al., 2011; Prasad et al., 2014; Quinn et al., 2012; Quinn et al., 2013; Wigmosta et al., 2011). The lack of each one of those would be a significant spatial limitation for allocating a biofuel farm. As Panichelli and Gnansounou (2008) indicated, biomass to energy projects are highly geographically dependent and the plant’s profitability can be strongly influenced by its location (Panichelli and Gnansounou, 2008). Notably, resources for land, water and climate provide different regions with widely contrasted agricultural potentials (Harvey and Pilgrim, 2011). Hence, it is preferable for biofuel farm sites to be located in areas with suitable biophysical and climate characteristics. These considerations are helpful for desirable economic biofuel project investment. In addition the suitability of farming locations depends on other potential positive effects, such as reduction of run-off, soil erosion and sedimentation in rivers and dams, together with increased water retention (Gheewala et al., 2011).

However, biofuels industry could have some major shifts on agriculture, food industry and notably on land use, depends on where it is located and the type of crop (O'Connell et al., 2007). So, the other issue for expanding biofuel production is focussing on land use competition with food (Goldemberg et al., 2008) and land value pressure (Coggan et al., 2008), so the key questions are what land and where? It has been suggested that, energy crops such as algae that can be grown on less productive or marginal lands has the potential to lead to a marked reduction in competition for land between energy and food over the coming decade (Harvey and Pilgrim, 2011).

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One of the land use implications for growing biofuel crops in marginal lands, is increasing opportunities outside agriculture which leads to high land price (Strijker, 2005) and investment in other profitable industries like tourism. Ecological and environmental issues are the other concerns in biofuel production in those areas (Cai et al., 2010). Therefore, minimum risk to food security, loss and degradation of habitat, biodiversity and other environmental damages are among the main aspects that will determine the sustainability of the Biofuel project (Group and Management, 2009; Nhantumbo and Salomão, 2010).

Although the scientific literature indicates very high potential productivity for biofuel crops in laboratory, it is not certain if this could be achieved in practice on an industrial scale. Hence the need to address the potential, available locations where climate and biophysical parameters are suitable for commercial cultivation of biofuels is vitally important. Considering the constrain factors, the investment of biofuel industry requires precise investigation of allocation and optimal geographical locations for potential biofuel farms.

Therefore, this specific study on regional environmental conditions for growing biofuel crops is a fundamental for maximising the benefit of bioenergy production regionally. The result would readily enable assessment of how much potential-suitable land might be located in a region which leads towards better management of land use as a core element of any biofuel investment project.

1.3.3 ArcGIS Evaluation Method

This section provides an overview of MCA approaches and an overview of biofuel site selection studies. It contains summary of related studies in both topics which were guidance for this research.

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 1.3.3.1 Overview of MCA Approaches

Geographical information systems (GIS) been widely adopted in decision making in land use allocation, site, and route selection problems, with the privilege of helping the decision makers to assign priority weights to decision criteria, evaluate the suitable alternatives, and visualize the results of choice (Carver, 1991; Malczewski and Rinner, 2015). GIS provides the decision-maker with a powerful set of tools for the manipulation and analysis of spatial information. A method adopted for approaching many spatial problems, such as site selection or land use allocation, which require the decision- maker to consider multiple criteria in order to choose the best alternative, is Multi Criteria Evaluation (MCE) method (Hajkowicz et al., 2000; Jankowski, 1995).

MCE is commonly achieved by Boolean overlay or Weighted Linear Combination procedure. In the first method, all criteria are reduced to logical statements of suitability and then combined by means of one or more logical operators such as intersection (AND) and union (OR). While, in the second method continuous criteria (factors) are standardized to a common numeric range, and then combined by means of a weighted average (Eastman et al., 1998; Malczewski, 2004).

Multiple-criteria decision analysis (MCDA) is a family of techniques that aid decision makers in formally structuring multi-faceted decisions and evaluating the alternatives(Greene et al., 2011). Combining GIS and MCDA for land planning involves many tasks including data gathering and structuring, and computation of criteria using spatial analysis and simulation (Joerin et al., 2001). The main steps in Multi-criteria analysis are criteria selection, determining criteria weights according to the relative importance of criteria, the acceptable alternatives are ranked by MCDA methods with criteria weights and finally, the alternatives’ ranking is ordered and the process is ended (Wang et al., 2009).

Weighted overlay analysis is one of the effective techniques in MCE for land use suitability mapping and analysis with multiple criteria based decision-making purpose. Weighted overlay analysis is a component of spatial modelling using spatial multicriteria

evaluation, which assigns more importance to some criteria over others(Malczewski, 2004; Malczewski and Rinner, 2015).

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In this analysis, the pixel (cell) of feature classes of a particular thematic layer is assigned with numeric weight values to combine mathematically to produce a new value to the corresponding pixels in the output layer. The weighted overlay analysis applies a common scale values to the multiple thematic layers to produce an output layer (Kaliraj et al., 2015).

To meet the objective, the multiple thematic layers have been analysed using an algorithm of weighted overlay analysis in ArcGIS environment (Esri, 2011). This technique was used for suitability analysis in this study for spatial multicriteria evaluation. Different variety of land suitability studies have been performed using multicriteria evaluation approach (Charabi and Gastli, 2011; Garmendia and Gamboa, 2012; Hajkowicz and Collins, 2007; Hajkowicz, 2008; Perpiña et al., 2013; Zhu et al., 2001). A list of studies using multicriteria evaluation approach, GIS application are listed in Table 1.1.

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Table 1.1: An overview of MCA approaches studies Stefan Hajkowicz & Kerry Collins Definition for MCA A Review of Multiple Criteria Analysis for Water Resource 2007 Types of MCA Techniques Planning and Management Types of MCA Applications Eneko Garmendia, Gonzalo Determining Weights in social MCE Weighting social preferences in participatory multi-criteria Gamboa evaluations

Stefan A. Hajkowicz , Geoff T. Applied five generic MODS weighting methods to weight six economic, An Evaluation of Multiple Objective Decision Support McDonald & Phil N. Smith environmental and social criteria Weighting Techniques in Natural Resource Management

Stefan A. Hajkowicz Application of MCA in multi-stakeholder environmental Supporting multi-stakeholder environmental decisions management decisions Weighted summation method Randal Greene, Rodolphe Devillers, Multiple-criteria decision analysis approaches different methods GIS-Based Multiple-Criteria Decision Analysis Joan E. Luther and Brian G. Eddy Jacek Malczewski, Claus Rinner Spatial analysis approach Multicriteria Decision Analysis in Geographic Information Science Jacek Malczewski overview techniques for GIS based land-use suitability mapping and, GIS-based land-use suitability analysis: a critical overview and identify the challenges and prospects of GIS-based land-use suitability analysis STEPHEN J. CARVER An introduction to multi-criteria evaluation Principals and techniques Integrating multi-criteria evaluation with geographical information systems Wang et al., 2009 Reviewed the corresponding methods in different stages of multi- Review on multi-criteria decision analysis aid in sustainable criteria decision-making for sustainable energy, i.e., criteria selection, energy decision-making criteria weighting, evaluation, and final aggregation Esri (2011) Spatial analyst, weighted overlay technique. Weighted Overlay. Joerin et al., 2001 Land suitability analysis for housing was realised for a small region of Using GIS and outranking multicriteria analysis for land-use Switzerland. suitability assessment van Haaren and Fthenakis, 2011 A method of site selection for wind turbine farms in New York State, GIS-based wind farm site selection using spatial multi-criteria based on a spatial cost–revenue optimization analysis (SMCA): Evaluating the case for New York State Charabi and Gastli, 2011 GIS-based spatial multi-criteria evaluation approach, to assess the land PV site suitability analysis using GIS-based spatial fuzzy multi- suitability for large PV farms implementation criteria evaluation Garmendia and Gamboa, 2012 Address the critical ―compression‖ phases of participatory multi- Weighting social preferences in participatory multi-criteria criteria evaluation (MCE) processes and explore the issue of criteria evaluations: A case study on sustainable natural resource weighting management

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 1.3.3.2 Overview of Biofuel site selection studies

Several studies have been done in order to access the potential sites for allocating biofuel farms, considering different parameters and methods (Batten et al., 2011; Borowitzka et al., 2012; Das and Salam, 2014; Milbrandt and Jarvis, 2010; Wigmosta et al., 2011). Similarly, the majority of these studies identify the importance of site selection in terms of resource availability (Quinn et al., 2012). A list of studies for allocating biofuel production site are listed in Table 2.1.

Walmsley et al. (1999) used NRMtools decision support framework to integrate economic assessment of alternate land allocation strategies with spatial land use allocation technologies to generate spatially land use patterns on the basis of economic optima and land use objectives. The main input criteria of the model were areas of land in different land use suitability classes, commodity prices and production costs, then the model determined an optimal economic expansion and allocated the total sugar cane of a catchment across different land use classes (Walmsley et al., 1999).

Zhu et al. (2001) have adopted Multi-criteria modelling and GIS to evaluate different land allocation scenarios for sugarcane production along with the values of stakeholders. The model evaluated the feasibility of land for sugarcane based on the rank orders of importance of criteria using SMARTER technique. For case-study region in Lower Herbert Catchment in Queensland with high effective sugarcane industry, land use constrained and allocation criterion maps were produced. It has been represented that how the result might be different under using various allocation criteria and the importance of defining precise land use constrain and allocation criteria, however the model isn’t applicable for multiple land allocation and the selected allocation criteria were limited to slope, distance to mills and roads (Zhu et al., 2001).

Mallawaarachchi and Quiggin (2001) provided a method for analysing economic – environmental trades-off in land allocation for sugarcane. The main purpose of Cane Land Allocation Model-Herbert (CLAM-Herbert) was to investigate the socially optimal strategy for allocating land at regional level between sugarcane, other production and conservation.

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The regional value of available land and site characteristic such as slope and elevation and the opportunity of using that land were the main factors of the model. As the result the model classified land to good, average and marginal according to the cane production price (Mallawaarachchi and Quiggin, 2001). The model didn’t consider climatic parameters and water availability in its assumptions despite of being highly important and necessary to be investigated.

Perpiña et al. (2013) applied a GIS-MCA technique for identification of suitable sites for locating biomass plants in Utiel-Requena, Spain. They investigated the influence of selection of factors and criteria such as slope, crop type, land use, transport cost and socio-economic factors in evaluation of potential sites. Mapping economic, environmental and social aspects of land showed the least to the most suitable sites for bioenergy plants. Furthermore, they performed the sensitivity analysis for the different factors involved in MCE, which showed strong influence of chosen criteria such as physiography and crop types in the model result (Perpiña et al., 2013).

Das and P. Abdul Salam performed Suitability analysis, using Geographic Information System (GIS) to develop a generic methodology for the inspection and assessment of microalgae cultivation potential over a province in Thailand. Their study included two stages: Stage 1) comprises of examining the availability of the site considering all the factors influencing the cultivation of microalgae. Stage 2) depicts the theoretical calculation of the potential of biomass from microalgae. There considered several criteria for the implementation of algae cultivation unit like climate, water, land, nutrients and carbon supply, as this all factors affect the quality of the production as well as quantity (Das and Salam, 2014).

In another study, the identification of the optimum sites for industrial-scale microalgae biofuel production using a GIS Model, performed by Algae R&D Centre, Murdoch University, WA, Australia. In terms of the criteria, their climatic considerations include the amount of incoming solar radiation, minimum daily temperatures, length of the growing season, the amount of precipitation and evaporation, and the frequency and intensity of severe storms. Land requirements, consisted of large tracts of level topography with workable soils are important as well as land that can be purchased for a reasonable price. Furthermore, impact on cultural values, environmental sensitivity

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and the economic viability of production considered in their study. In addition, CO2 and nutrient availability in the form of nitrogen and phosphorus included in the effective factors(Borowitzka et al., 2012).

Due to challenges around resources availability for algae production, Prasad et al. (2014) mapped regional hotspots for growing algae according to the availability of nutrient resource requirements. They also quantified potential regional biomass production based on the limiting resources in those regions (Prasad et al., 2014). The suitability map generated considering both available waste nutrients and eco-climatic parameters showed the most suitable areas for establishment of algal ponds in Queensland with the potential production of 309 ML of biodiesel which is 5% of Queensland’s 2011 diesel oil sales.

Milbrandt and Jarvis in a study, provide understanding of the resource potential in India for algae biofuels production and assist policymakers, investors, and industry

developers in their future strategic decisions. They considered climate, water, CO2, other nutrients, and land as the critical resources for algae production systems in India and used GIS technology to analyse the collected information and visualize the results.

The study considered stationary CO2 sources in areas where these facilities coincide with other inputs necessary for algae growth or conditions that meet the engineering, economic, environmental, and social requirements for this technology (Milbrandt and Jarvis, 2010).

To answer “Where” at a greater geographical scale, Batten et al. (2011) investigated suitable locations for algal production globally in APEC economies for the sustainable production of biofuels. They developed a geographical information system (GIS) based model to rank the potential algal site and their production based on solar

radiation, CO2 sources and available land. The model output suggested that the most preferred sites in Australia are on its marginal coastline, however in Queensland there are several areas of inexpensive, marginal land near the coastline that could be good for growing algae (Batten et al., 2011).

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Moreover, a variety of studies incorporate site selection with economic factors for suitable large scale production location. For instance, Quinn et al. (2012) generated

a dynamic map based on economic evaluation for CO2 transport distance, and land resource data for algal production in several key regions of the USA. The validated

growth model predicted biomass production and CO2 economic evaluation. The GIS land availability was defined based on land classification and maximum slope. The results of both models are dynamic maps illustrating current production locations and corresponding productivity potential (Quinn et al., 2012).

In Queensland a variety of land evaluation and classification approaches has been used as a basis of protecting agricultural land and supporting the agricultural sector since the early 1990s, including the lapsed State Planning Policy 1/92, statutory regional planning, the Strategic Cropping Land framework and more recently through the Agricultural Land Audit, draft State Planning Policy 2013 and reforms to the Vegetation Management Act of 2013. As a part of goal to identify and plan for additional future food production land in the state, the Queensland Land Audit (The Audit) mapped current and potential land uses across the state for different land use classifications of various crops, including sugar cane. As a part of goal to identify and plan for additional future food production land in the state, the Queensland Land Audit used certain principles have been identified as applicable in Queensland .these principals draw on the Food and Agriculture Organization (FAO) Framework for Land Evaluation (FAO, 1976; FAO, 1983) which has been the primary approach used worldwide (Department of Natural Resources and Mines and the Department of Science, 2013). The Audit mapped current and potential land uses across the state for different land use classifications of various crops, including sugar cane. The Queensland Land Use Mapping Program (QLUMP) provided the current land-use datasets used in the Audit. Land potential was determined by the Audit through an approach largely based on the established Agricultural Land Classification for strategic planning in Queensland published in Guidelines for Agricultural Land Evaluation in Queensland (Audit, 2013). The Audit uses a desktop based method analysing existing datasets or data developed from existing datasets, and presenting them using existing tools and expert knowledge in a Geographic Information System.

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The Audit combined the spatial datasets such as, socio-economic and climatic data to identify and map agricultural land use potential. The results showed the potential area for growing sugarcane in Queensland is almost 7 million hectares or 4.1 percent of the state while Current land use Is 0.33 percent of the state. It has been indicated that in this study, access to a sugar mill is an important consideration in determining the potential for land to be used for growing sugarcane (Audit, 2013).

In spite of biofuel research inputs to date, producing algal biofuel at a national supply scale is still an unfulfilled vision in Australia and studies are dominated by the production process and cost at the plot-level (Li et al., 2012). To our knowledge, there has been no study using mixed criteria (biophysical, economic and environmental) in Queensland to identify the suitable sites for larger scale biofuel production, which may be more cost-effective. Hence, this study can cover the gap knowledge between actual and potential land use for biofuel production. Therefore, as a priority for detailed further investigation, this study based on reliable basic information, would obtain important results such as the land use for bioethanol production regarding to eco-climatic, economic criteria and land use factors. The criteria used in this study included: temperature, sunshine, rainfall, evaporation, wind speed (climatic), land value, transportation cost, labour costs (economic) and ownership, land cover, agriculture, wasteland, forest, industrial, slope, cultural value (land use). Suitability analysis performed on these key factors would locate the potential site for biofuel production in QLD.

This research would be the first study which identify the most suitable sites for biofuel farms with the greatest potential for long term economic and environmental sustainability, as a basis for any investment in large scale biofuel production in Queensland, Australia.

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Table 1.2: Biofuel Site Selection Studies

MAXWELL, E. L., FOLGER, G. & Climate, land, and water resource requirements of Resource evaluation and site selection for microalgae production HOGG, S. E. 1985 microalgae production systems (MPS) were examined systems relative to construction costs, operating costs, and biomass productivity. FARRELL, J. & SARISKY-REED, V. Identifying challenges in the production of economically National Algal Biofuels Technology Roadmap 2010 viable, environmentally sound biofuels. LUNDQUIST, T. J., WOERTZ, I. C., Assesses the economics of microalgae biofuels A realistic technology and engineering assessment of algae QUINN, N. & BENEMANN, J. R. production through an analysis of five production biofuel production 2010 scenarios. WIGMOSTA, M. S., COLEMAN, A. Providing a detailed screening of required on‐site land National microalgae biofuel production potential and resource M., SKAGGS, R. J., HUESEMANN, M. and water requirements demand H. & LANE, L. J. 2011 KLISE, G. T., ROACH, J. D. & The model uses spatially referenced data for nitrogen study of algal biomass potential in selected Canadian regions PASSELL, H. D. 2011 and phosphorous, CO2, land cover and solar insolation to identify optimal locations. QUINN, J. C., CATTON, K. B., Geographical Assessment of Microalgae Biofuels Geographical assessment of microalgae biofuels potential JOHNSON, S. & BRADLEY, T. H. Potential Incorporating Resource Availability growth incorporating resource availability 2012 system. Borowitzka et al. 2012 Site targeting approaches used to identify appropriate Identification of the optimum sites for industrial-scale microalgae locations for algal biofuel production facilities. biofuel production in WA using a GIS model Zhu et al. (2001) Sugarcane land allocation modelling which integrates Integrating Multi-Criteria Modelling and GIS for Sugarcane Land Multicriteria and GIS Allocation Perpiña et al. (2013) Identifying suitable areas for locating biomass plants Multicriteria assessment in GIS environments for siting biomass using MCA-GIS method plants Das and Salam, 2014 Reviews and develop a generic methodology for Development of a Generic Methodology for assessment of microalgae cultivation potential site. Assessment of Microalgae Cultivation Potential Using GIS Prasad et al., 2014 Mapping the availability of the three inputs for algal Facilitating access to the algal economy – mapping waste cultivation (N, P and CO2) together with climatic and land resources to identify suitable locations for algal farms in use considerations Queensland

Milbrandt and Jarvis, 2010 Understanding of the resource potential in India for algae Resource Evaluation and Site Selection for Microalgae Production biofuels production in India.

The University of Queensland | School of Geography, Planning and Environmental Management 21 1.4 Approach

Outside of the necessary nutrient requirements (Verdoodt and Van Ranst, 2006), the importance of seasonal and regional climatic parameters influence on crops growth is not negligible (Elbehri et al., 2013; Wigmosta et al., 2011).In this research Three main factors which are land use factors, climatic factors and economic factors (Klise et al., 2011; Singh and Gu, 2010) were evaluated. Biophysical growth requirement, land use and socio-economic data obtained from Queensland state government and national databases sources (ANU, ABARE, ABS and BOM) and then matched against Queensland local government authority boundaries (Table1.3).

Table 1.3: Requirement variables and data types Variable Data Type

Climatic Temperature, Sunshine, Rainfall, Evaporation, Humidity

Economic Land Value, Transportation cost, Labour costs

Land Use Ownership, Land cover, Agriculture, Wasteland, Forest, Industrial, Slope, Cultural value

The methodology for this study, divides into two objectives, suitability and evaluation. In this thesis, I employed ArcGIS application and Multi-criteria analysis (MCA) for evaluation and developing map data layers though spatial modelling, identifying the suitable land for algae production. Multicriteria evaluation technique was used to perform suitability analysis. Within ArcGIS software package, the ‘Overlay toolset’ in the ‘Spatial Analyst toolbox’ includes three tools that support suitability modelling and site selection: weighted overlay, weighted sum, and fuzzy overlay. In this study, weighted overlay tool has been adopted to find the potential sites (Malczewski and Rinner, 2015). This approach described in chapter two in details.

In the third chapter, map overlay technique used to investigate the potential site for both algae and sugarcane production. In this section, algae suitability map from chapter two overlayed on sugarcane potential site map produced by Audit. The aim was to find

out if algae could be produced in sugarcane production sites in case of land use change consideration in future.

1.5 Thesis Outline

This thesis includes four chapters which are shown in a schematic overview (Table 1.4). The first chapter consists of a brief description of the problem and the motivation of the study, the research aim and the objectives followed by a literature review section which presents the past studies and the knowledge gaps which this research intends to address. The following two chapters (2-3) are presented as a set of publication-ready articles that each address the research objectives. Chapter two addresses the first and second research aims and reviewed biofuel/algae growth and production literature and also different methods of suitability evaluation models. In this chapter an algae suitability map was produced and the influence and the strength of each factor on the suitability model were presented. The third and fourth research questions were addressed in chapter three. In this chapter, a comparison of the sugarcane production locations with the algae suitability map with the option of co-location were studied. The last chapter of this thesis concludes with a synthesis of the previous chapters and a general discussion on this study.

Table 1.4. Schematic overview of thesis structure; each boxes indicate chapters comprised of research articles which individually address the main research questions of the study.

CHAPTER1 •General introduction Research aim and objectives Study area.

• Site selection for commercial microalgae production using CHAPTER2 multicriteria GIS modelling in Queensland, Australia. •Assessing the land use implications comparing sugarcane and CHAPTER3 algae for bioethanol production according to economic efficiency and land use concerns.

CHAPTER4 Synthesis and Conclusion. .

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CHAPTER 2. Site selection for commercial microalgae cultivation using multicriteria GIS modelling in Queensland, Australia

The University of Queensland | School of Geography, Planning and Environmental Management 24 2.1 Introduction

There is greater global attention on the potential of biofuel as a sustainable energy source; influential factors include: growing energy consumption, increasing fossil fuel prices and mounting concerns over greenhouse gas emissions. In Australia biofuels may be economically produced on rural land without competing with agriculture or conservation, and may potentially provide economic opportunities for development and employment. Total primary energy consumption is projected to grow by nearly 42 percent by 2050 in Australia, therefore the demand for energy is expected to continue to mount. Hence it is critical to investigate cost-effective investment for Australia’s energy future and for achieving its government legislated Renewable Energy Targets.

Our study focuses on finding suitable land for growing microalgae as a biofuel resource as it is considered as one of the best feedstocks for sustainable biofuel generation. This research investigated suitable areas in the state of Queensland for growing microalgae; Queensland was chosen because of its land areas, favourable growing conditions and available data on land values for economic assessment. This study considers a number of criteria as part of a GIS land suitability analysis, including biophysical parameters affecting growth, climatic and environmental constraints, site access and remoteness along with land values.

The outcomes of this research advance the techniques for biofuel site assessment and provide comprehensive and accurate results which can support the microalgae-based biofuel industry development in Queensland and evolve better management strategies for sustainable land use planning in the state.

Recently liquid biofuel has attracted huge interest as an alternative source for transportation fuel as it is similarly energy efficient than fossil-derived fuel and has low emissions. Energy security, mitigating greenhouse gas emission, biodegradability and socio-economic opportunities for rural areas are significant advantages of using biofuels (Batten et al., 2011; Campbell et al., 2009; Demirbas, 2009b; Keating and Carberry, 2010; Puri et al., 2012).

However, current first generation biofuel crops, such as sugarcane (for bioethanol) and oil palm (for biodiesel) often stand in direct competition with food production and/or conservation of previous biodiverse landscapes, such as tropical rainforests. Microalgae in particular have gained wide attention as a potential source of biofuel. This commodity suits non-arable lands, utilises virtually any source of water,

may uptake waste CO2 sources and produces many profitable by-products, alongside co-benefits of GGE mitigation. Furthermore, microalgae are superior in productivity compared with plant crops in land area requirement and water consumption for cultivation feedstock. They are considered as a reliable and continuous supply of fuel due to their high oil content and continual-harvest characteristics (Brune et al., 2009; Li et al., 2008; Pate et al., 2011; Pittman et al., 2011; Schenk et al., 2008; Singh et al., 2011). However, to establish microalgae as a successful biofuel crop in Australia or elsewhere, production costs must be considerably reduced. Identifying suitable locations for their cultivation can contribute to this goal.

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In 2009, the Australian government legislated a Renewable Energy Target (RET) of 20 per cent by 2020 in-line with its national plan for a clean energy future (Commonwealth Of Australia, 2014) . Currently only 0.5% of Australia transport fuel is supplied from biomass (ABARE, 2010; Geoscience Australia and BREE, 2014). The expected large gap between future demand and potential domestic supply requires expanding viable economic biofuel production in areas which have the land and the climate needed to produce raw feedstocks on a large scale. In practice, to date, the lowest cost of commercially produced microalgal oil is still much higher than the reasonable medium-term price target to become cost competitive with petroleum diesel (Borowitzka et al., 2012; Stephens et al., 2010).

One of the key factors for a technically and economically viable biofuel resource is that, it should be competitive and cost less than petroleum fuels (Brennan and Owende, 2010). The cost of petroleum-based fuels may go up in the future as fossil fuel reserves decline and policies are put in place to account for the hidden costs associated with GGE and fuel combustion pollution that pose significant threats to human health, food security, biodiversity and degradation of natural ecosystems.

However, the commercial production of algae is limited due to challenges around water and nutrients availability, harvesting methods and high costs of oil extraction (Prasad et al., 2014; Singh et al., 2011; Stephens et al., 2010). Other factors, such as land value, land availability, facilities cost, existing land use, closures to resources and infrastructures, climate requirements, government policies and supports have also been reported as fundamental barriers to development of the biofuel industry (Borowitzka, 2013; Coleman et al., 2014; Mata et al., 2010; Maxwell et al., 1985; Wigmosta et al., 2011).

A large body of literature exists on microalgae production in terms of theoretical, technical, environmental, economics and implementation to support the basis for development of government policy and/or industry investment (Brennan and Owende, 2010; Hu et al., 2008; Li et al., 2008; Lundquist et al., 2010).

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The majority of these studies consider economic factors as the most common criteria in their modelling, based on biomass type/resources and final market analysis. Recently there is growing interest to examine how these opportunities vary across space and ideal land use suitability allocation (Borowitzka et al., 2012; Coleman et al., 2014; Das and Salam, 2014; Klise et al., 2011; Maxwell et al., 1985; Quinn et al., 2012). However, only a few researchers address the overall relations and constraints among economic, climate and land use factors using available government datasets and national databases.

The present study integrates state-wide scale data about various factors and performs spatial analyses for feasibility evaluation and location optimisation based on methods developed by (Shi et al., 2008). We use a Geographical Information System (GIS) and methods for Multi-Criteria Analysis (MCA) for mapping and analysis as these have been used in many energy facility sitting studies (Baban and Parry, 2001; Malczewski, 2004; Wang et al., 2009).

Our study develops a geographical land suitability model to locate feasible spatial locations for microalgae production, considering factors for land availability, land cost and existing land use, climatic variables intrinsic to algae growth and transportation infrastructure proximity. Each criterion was weighted based on its importance to productivity and cost-effectiveness. This suitability model provided an economic assessment of the feasible locations with accurate and updatable map results which address the gaps in knowledge between actual and potential land use.

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The results are discussed in terms of implications for better management strategies in sustainable land use planning in Queensland and support for decision making and further work towards a sustainable bio-economy.

2.2 Study Area and Materials

The following sections provide an overview of the study area and biofuel development in Queensland, Australia, the essential criteria, data scale and sources used in spatial information system (GIS) and the multi criteria analysis technique, based on climatic, land use and economic resources evaluation. This study is both a suitability and optimality analyses as we consider cost-effective parameters such as land value, transportation in our modelling to optimise the locations for scaling up biofuel production.

2.2.1 Study Area

This study is conducted across the state of Queensland. It is the second largest state in Australia including 1.9 million square kilometres of land and over 4.5 million citizens. Queensland has sub-tropical and tropical climate. The maximum daily average number of bright sunshine hours across the state is 8 hours and the average mean annual temperature is 21°C in the south to 27°C in the north. Fourteen Statistical Divisions in Queensland are wet in marginal land along the east side with average annual rainfalls of 1000 mm in the south and 3200 mm in the north and dry to semi-dry towards the inland west with average annual rainfalls of less than 200 mm (Bureau of Meteorology, 2015)(figure 2.1).

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Figure2.1: Study area-Queensland Government Boundaries (source: Queensland Government) and Major climate classes and the average rain fall (source; Bureau of Meteorology, 2015)

The state economy is primarily based on strong mining, agriculture, tourism, construction, manufacturing and financial services sectors. Queensland's main exports are coal, metals, meat and sugar. Towards inland Queensland, development pressure and land value begins to decrease along with vast areas with lesser degree of interest in all industry sectors. This provides huge opportunities for economical investment in commercial bioenergy production throughout the study area along with the suitable climatic and economic factors.

Over the past decade, the ambition to secure the fuel supply and mitigate GGE based on renewable energy has grown in Australia. A number of sustainable energy options have been initiated, although algal biofuel production is a rather recent option in Queensland. Recently, several innovative research projects within Queensland's renewable energy industry are being carried out with collaborations between private companies, universities and research institutes in Queensland.

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Additionally, state funding programs and private industry supports increased the level of biofuel activity over last few years. Bio-industries map (Figure 2.2) showed more detailed of major projects and sites in Queensland.

Figure 2.2: Queensland Bio-industries map (source: Queensland (Department of State Development, Infrastructure and Planning, 2013).

The University of Queensland (UQ)’s Algae Energy Farm, the Solar Bio-fuels Consortium, and the UQ-led Jet fuel and Culturing Facility of North Queensland (NQAIF) are the main institutes involved in research on algal biofuel in Queensland (Li et al., 2012). In 2003/2004, the NQAIF was established within the School of Marine and Tropical Biology at James Cook University (JCU) through funding by the ARC, JCU and the Australian Institute for Marine Science.

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NQAIF is the first tropical microalgal research facility in the world and they have screened many algal strains that are promising for microalgae biorefinery processing, including the production of biofuel. Their research areas of interest included freshwater, marine environments, and a range of paleoclimate studies using fossil diatoms to identify microalgal strains suitable for biotechnological and environmental applications (Li et al., 2012).

In 2011, the UQ-led Jet fuel initiative was established with the aim to evaluate the potential of environmentally friendly aviation-fuel production sourced, among others, from microalgae. Collaborators include Boeing, Virgin Australia Airlines and US-based green energy company Amyris , which link with UQ’s biofuel research and biofuel initiatives of the Queensland Sustainable Aviation Fuels Initiative program(Li et al., 2012).

2.2.2 Algae Site Suitability

This study focuses exclusively on open pond cultivation of algae (Schenk et al., 2008). To identify the best location for commercial biofuel production, the methodology supports two objectives/stages: Stage (1) - an evaluation of suitable lands availability for biofuel production according to land uses, eco-climatic parameters. Stage (2) - suitability modelling and finalising the optimal land selection based on cost of land, proximity to transportation and labour cost and evaluating the effect of those costs on the location allocation. Both stages adopted a GIS approach.

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2.2.3 Resource Evaluation for Biofuel Production Scale up

A major barrier in scaling up biofuel operation is affordability of the production (Farrell and Sarisky-Reed, 2010). Successful commercial production of biofuel requires choosing the most suitable locations, according to eco-climatic and socio-economic considerations. The eco-climatic factors have a critically impact on biofuel productivity and ultimately cost-effectiveness of the production. Socio-economic criteria consider the land use and availability of affordable land for scaling up biofuel production.

Three main categories of those factors critical to both site selection and biofuel production upscaling, are land use, climatic and economic factors which are strongly spatially dependent. This case study incorporated the unique variables of each of these criteria. The variables listed in Figure (2.3) are a summary of criteria used in this application. Those factors were identified from related literatures and large-scale research conducted at an experimental microalgae farm (Algae Energy Farm) of the University of Queensland in Pinjarra Hills, South East Queensland. Each one of these factors requires significant spatial information for allocating a biofuel farm.

Economic land value Transportation cost Land use Climatic Labour costs Ownership Land Use/Land Cover Temperature Urban Sunshine Agriculture Rainfall Wasteland Evaporation Forest Industrial Slope Algae Suitable Site Cultural value Spatial scale >50ha Flat land Warm all year Recyclable water & nutrient

Nearby source of co2 Close to infrastractures

Figure 2.3: Essential factors for identifying optimal sites

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2.2.3.1 Climatic Variables

Like other biofuel crops, suitable climatic conditions have a direct effect on microalgae productivity and operation costs (Maxwell et al., 1985). Critical climate parameters used in this study were temperature, solar radiation, the number of daily sunshine hours, precipitation, evaporation and wind speed which are hugely geographically dependent. To investigate a finer scale of climatically suitable sites, the climate criteria were narrowed to: annual average daily temperature between 15°C - 35°C, minimum winter and night time temperature ≥7°C, annual average cumulative sun hours ≥ 2,800 and annual average frost-free days ≥ 200 (Batten et al., 2011; Farrell and Sarisky-Reed, 2010; Wigmosta et al., 2011). The Bureau of Meteorology climate spatial maps data, based on 30 years of daily records of precipitation, temperature, evaporation, wind, and solar radiation in Queensland, were used for eco-climatic modelling.

2.2.3.2 Land Use Variables

To access feasible areas for microalgae production, land use and land cover are major constraints. Avoiding any conflict with other land use interest, environmentally and politically sensitive areas should be excluded from consideration. These include certain areas, such as urban, agriculture, waste disposal land, and national parks, industrial and cultural lands. The size of land for algal farm is another economic consideration. With small facility size, the profitability of business is quite low due to the high capital costs for establishment and low revenue stream (Stephens et al., 2010).

In some studies the minimum required land size to establish an open pond facility for biofuel production is considered at least 400 hectares (Campbell et al., 2009; Quinn et al., 2012; Wigmosta et al., 2011), but in the longer term, smaller size farms (e.g. 50 hectares) could be considered (Prasad et al., 2014). In this study, the minimum commercial farm size for the production of microalgae biomass for fuel was evaluated at the 50 hectare threshold.

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Since sitting an open pond needs relatively flat land, the other restriction for algae development is topography. Techno-economical studies consider suitable slopes at 0–2%. Although the areas with slopes 2–5% are feasible, those areas are less economical. Areas with slopes greater than 5% are uneconomical due to increased capital costs for preparation and levelling for site development (Farrell and Sarisky- Reed, 2010; Quinn et al., 2012; Stephens et al., 2010). Hence, in this study, land availability in the Stage 1 suitability analysis of algal biofuels potential was limited to the areas with slopes <2%.

2.2.3.3 Economic Variables

As previously discussed, the production cost of biofuel needs to be competitive with fossil fuel. Capital costs begin with land purchase and all subsequent production and processing steps add costs to the algae-to-biofuel supply chain and need to be considered precisely.

Land value mainly depends on where the land is geographically located, it’s to- date land use and land ownership (Lundquist et al., 2010). For example, some studies suggested marginal lands near coastlines as suitable sites, but considering other interests, such as tourism on those lands make them non-affordable for algae production. Land value in location decision is clearly a major constrain in the economic side of scaling up biofuel farms.

For biofuel production, operating costs include charges associated with transportation, labour and maintenance (Sun et al., 2011) that have to be considered in economic evaluations. Proximity to infrastructure is an obstacle in locating biofuel industry. Generally, transportation distances for water, nutrient, pond maintenance and energy supply to market need to be minimised when determining the economically best locations. Poor road assess is a major cost factor in rural areas for algae farm operations.

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Another input for economic analysis is labour cost, which can also vary by geographical location. In rural areas it might be higher as there is lower population and less interest in working in remote areas. To fill the gap between the wages considered for labour cost in a laboratory in an urban area and a farm in rural area, the amount was multiplied by the government labour coefficient in rural areas to adjust the estimated actual labour-cost.

2.2.4 Data Sources

Table 2.1 summarises the publically available Queensland government and national database sources and types of data used as the essential variable inputs in the suitability modelling of this study. Noting that, the data which I accessed has the minimum mapping unit 90m2 specification.

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Table 2.1: Variables, data sources and type, ideal condition of criteria used in study

Variable Data type Data source Ideal condition Solar radiation Raster, cell size 250m, ANU2011 Maximize Wm-2 /day monthly

Temperature Minimum, Maximum, ANU2011 15°C - 35°C daily and Raster, cell size 250m, ≥7°C winter and nightly monthly.

Precipitation Minimum, Maximum, ANU2011 Maximize mm/day Raster, cell size 250m, monthly. Evaporation & Minimum, Maximum, ANU2011 Minimize mm/day, Humidity Raster, cell size 250m, Minimize % monthly.

Land use Raster, primary land use ABARES2010 Exclude: urban, codes, size250m. national parks, wasteland, industrial and cultural lands Land value Raster, primary land use ABARES2010 Minimize $/ha codes, size250m.

Slope Raster, GEODATA 3Sec Geoscience Australia, 1-2% DEM GEODATA 3Sec DEM

Proximity to the Raster, size 90×90 Department of Natural Maximum distance 2km infrastructure Resources and Mines

Labour cost by Average, Statistics ABS Average region in Qld

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2.3 Methodology

 2.3.1 Suitability analysis

 2.3.1.1 Overview of multi-criteria analysis

GIS-based multi-criteria analysis (Joerin et al., 2001; Malczewski, 2004) was used as it has been widely used for renewable energy analysis problems, involving economic, technical and environmental criteria (Carver, 1991; Charabi and Gastli, 2011; van Haaren and Fthenakis, 2011; Wang et al., 2009).

Multi-criteria analysis (MCA) requires criteria to be standardized and transformed to the same unit of measurement in order to be compared and integrated. Therefore, each criteria layer is reclassified within the range of 1-10, where the feature class with the most favourable is assigned the value of 10 and the feature class with the lowest potential favourable is assigned the value of 1. Then each input raster is weighted according to its contribution to the project purpose or its percent influence. The weight is a relative percentage, and the sum of the percentage influence weights must equal 100. By running the weighted overlay tool, the cell values of each input raster are multiplied by the raster's weight (or percent influence). The resulting cell values are added to produce the final output raster (ArcGIS 10.3.1 help; Jankowski, 1995; Kaliraj et al., 2015; Malczewski, 2006).

Suitability analysis using multi-criteria evaluation technique, performs within below steps:

Step 1: Selection criteria

In this case study, the particular criteria were developed based on literature detailing microalgae biophysical growth requirements, economic constraints in scale up production and socio-economic and land use factors. Related spatial data were obtained from government and national database sources such as ANU, ABARE, ABS and BOM (Table 2.1) for the state of Queensland.

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Step 2: Data preparation

Data preparation involved two stages. In first stage, land uses considered unsuitable for algae farms were identified and used to narrow the area of interest for further analyses. Land uses for urban, parklands, agriculture, wasteland, forest, industrial and other restricted uses were combined into an excluded data layer. Cultural land uses such as aboriginal land were also excluded as this was considered different to develop, but may be considered in the future. A slope layer was also developed to screen data for land with an average slope less than 2%.

The second stage developed economic land consideration. Land valuation was only available at a parcel scale from government sources (ABARES), but these data have many gaps in locations without a land use and inconsistencies in reliable evaluation of land values. To obtain a continuous spatial layer for land value we spatially interpolated between parcels where we had reliable data. Interpolation using a Nearest Neighborhood method in GIS (ArcGIS Help 10.3) was required to ‘fill in’ these missing land values. In this method, the value of the output cell is determined by the nearest cell value on the input grid which is specified in the neighbourhood. The nearest neighbourhood method assigns the value from the nearest observation to a certain grid cell. Road data was buffered to determine land within 2 km distance to roads. The spatial data included vector feature data for land uses and raster data for slopes and climatic variables. All data was converted to a raster with a geographical cell resolution of 3 seconds (approximately 90 sq. meters) for further analyses. For distance analysis, the Distance tools in Spatial Analysis toolset, allow us to perform distance analysis. Euclidean Distance in Distance tools gives the distance from each cell in the raster to the closest source. In this study, this method was used to find the most suitable location close to roads.

Step 3: weighting data

In this step to find out the weighting of each criteria, we used meta-analysis. Meta- analysis is the systematic review of a body of evidence. The idea is to draw together all

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of the appropriate studies that have addressed the same question, and calculate an overall effect and an overall measure of uncertainly for that effect (Crawley, 2012). Then, the effect size and a variance for each criteria in related studies calculated. The idea is to calculate an effect size and a variance for each criteria in related studies. The summary of the meta-analysis is then just a weighted average of these effect sizes.

For this research, several literatures related to biofuel production were reviewed and from them a list of most to less important factors in algae cultivation was obtained. Their citation frequencies for criteria were used to derive importance rankings and weights; the calculated results are presented in Table (2.2).

Table 2.2: Frequencies, importance ranking and weight criteria calculated using Meta - Analysis

Factors Sunlight Temperature Precipitation Evaporation Slope Land-Use Land Value Proximity to Road Labour Cost Maxwell et al., 1985 * * * * * * - - - USDOE, 2010 * * * * * * * * * Lundquist et al., 2010 * * * * * - - - Wigmosta et al., 2011 * * * * * * * - - Klise et al., 2011 * - - - * * - - - Quinn et al., 2012 * * * * - - * Borowitzka et al. 2012 * * * * * * - * - Karabee Das and P. Abdul * * * * * * * - - Salam, 2014 Milbrandt and Jarvis, 2010 * * * * * * - - - Frequency 9 8 6 7 9 9 3 2 2 Ranking 1st 2nd 5th 4th 2nd 3rd 6th 7th 7th Num (total 35) 7 6 3 4 6 5 2 1 1 Weight 0.20 0.17 0.09 0.11 0.17 0.14 0.06 0.03 0.03 *: Criteria included in study, - : Criteria hasn’t included in study

2.3.2 Reclassification and suitability modelling

The reclassification uses for quickly and easily reclassify data which will be used in spatial analysis. The reclassification tool enable the user to reclassify raster data and the values in the input raster can be replace with new values considering preference, sensitivity, priority of the new situation. It can be done in a table which its format allows the mapping of individual values, ranges of values, strings, or NoData to another value,

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or NoData. Based on the criteria’s influence on suitability modelling, all criteria map layers were reclassified.

Eco-climatic and land use data reclassified in order to be used in MCA modelling. For instance the areas with the highest solar radiation, lowest slope percent and land value weighted 10, and the areas with lowest solar radiation, high slope percent and land value were weighted 1.

Subsequently, in the weighted overlay tool, each map layer was ranked by influenced percentage according to its relative importance (Table2.2), and then combined to create a weighted raster layer. The output map was classified into high, moderate and low suitability for algal production.

2.4 Results

2.4.1 Eco-climatic map

The quantity and quality of algae production is highly governed by climatic conditions. A potential site for a microalgae commercial farm for fuel as its primary product, is where all the major parameters affecting algal growth, such as maximum and minimum temperature, solar radiation, evaporation, humidity, precipitation, and slope coincide, are maximised.

Firstly, to show the influence of each above criteria in MCA modelling of eco- climatic evaluation of the study, using variables and their Influence weighting presented in Table 2.3, radar plot was produced and shown in figure 2.4, which indicated solar radiation and slope has the most influence in modelling eco-climatic map. The table below shows the effective variables, influence weighting and their ranking. As mentioned in the literature, among climatic parameters, sunlight and temperature are the most important factors which algae need for growing. Radar plot was used to compare multiple quantitative variables and it is also useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.

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Table 2.3: Variables and Influence weighting use in Radar plot of Eco-Climate suitability map Variable Influence Weighting Ranking Sunlight 0.19 1 Temperature 0.17 2 Slope 0.17 3 Evaporation 0.11 4 Precipitation 0.08 5 Humidity 0.03 6

Figure 2.4: Radar plot of criteria used in eco-climatic MCA modelling.

Secondly, according to eco-climatic and land use criteria (maximum and minimum temperature, solar radiation, evaporation, humidity, precipitation, and slope) the Eco- climatic Suitability map was generated. As it’s shown in Figure 2.5, the eco-climatic map has three suitability classes; good, moderate and poor (Figure 2.5).

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Figure 2.5: suitability map according to eco-climatic and land use criteria.

According to the multicriteria GIS algae model, algae farms should be located in north-western toward central Queensland (Figure 2.5). These areas are mostly marginal lands with ideal/suitable climate characteristics without existing conflict with other development interests. South East of Queensland is defined as poor suitability class which cannot be considered for growing algae and biofuel industry.

2.4.2 Algae production suitability map

This paper identifies the potential suitable sites for commercial algae production in the entire state of Queensland. An analysis was undertaken and the optimal sites for algae production were obtained by overlaying all the thematic maps in terms of weighted overlay methods using the spatial analysis tool in ArcGIS 10.3. This included economically suitable sites for production of algae, considering land value, proximity to

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roads and reclassified remoteness index (labour availability) map layers combined with eco_climatic parameters in suitability model.

Also, using variables and their influence weighting (Table 2.4), figure 2.6, radar plot of criteria used in algae site selection MCA modelling produced. As shown, land value, slope and solar radiation weight chose higher in the modelling for site selection. The table below shows the effective variables, Influence weighting and their ranking which were used in this study for allocating biofuel sites. As mentioned in the text, each criteria has individual effects on site selection with different weight and ranking. Radar plot of algae site selection shows all those variable values at one go. The table below shows the effective variables, Influence weighting and their ranking which were used in this study for allocating biofuel sites. As mentioned in the text, each criteria has individual effects on site selection with different weight and ranking. Radar plot of algae site selection shows all those variable values at one go.

Table 2.4: Radar plot of algae site selection MCA modelling Variable Influence Weighting Ranking Sunlight 0.19 1 Temperature 0.17 2 Slope 0.17 2 Land-Use 0.14 3 Evaporation 0.11 4 Precipitation 0.08 5 Land Value 0.06 6 Proximity to Road 0.03 7 Labour cost by region in Qld 0.03 7 Humidity 0.03 7

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Figure 2.6: Radar plot of criteria used in algae site selection MCA modelling

Based on land value, and eco_climatic parameters, proximity to roads and reclassified remoteness index, the suitability map result released and classified areas of Queensland in good, moderate and poor for scaling up algae production (Figure 2.7). The suitability map shows that a large portion of the state is good or moderate for microalgae cultivation facilities located mostly in the centre and west of Queensland which is ideal for biofuel commercial sites as these parts of the state are considered rural areas with suitable climatic condition with no conflict with agricultural or other development interests.

The areas as defined good and moderate mostly are marginal lands which has relatively poor natural condition or is not used for agricultural production with economically land value. Suitable eco-climatic conditions and inexpensive land in these marginal lands increase the feasibility for long-term profitable biofuel industries and development of energy plants at large scale. However economically industrial algae biofuel production in marginal lands across the study area could be achieved when

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advanced transportation system build in those areas to have the less transportation cost.

Figure2.7: Spatial distribution of microalgae production suitability levels

As it’s shown in map, South east of Queensland is classified as poor condition for growing algae. It’s because, this part of state has high land value as the result of tourism and agriculture interests in the area, along with unsuitable climatic condition for cultivation algae.

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2.5 Discussion

By incorporating the spatial and non-spatial data, the GIS model in this study provides the first accurate map results of potential sites for commercial microalgae production with fuel as their primary commodity. However, the data may also be used when considering the production of microalgae for other purposes, such as feed, food or higher value products, including the use of microalgae farms as biorefineries. Other factors should be considered (e.g. transport costs, closeness to market, etc.) under these scenarios.

Similar to this study, to identify the best locations for constructing commercial-scale algae-to-biofuel production facilities in Western Australia (WA), almost the same criteria were used in another study (Borowitzka, et al, 2012). They limited production facilities based on environmental characteristics such as topography, climate and availability of

CO2 but also incorporated construction considerations such as soil workability. However, they did not perform an economic analysis in their study.

These maps from the present study are capable of providing precise locations according to climatic, economic and environmental factors. It provides comprehensive map-information for better management strategies in sustainable land use planning and also support for decision making towards a sustainable bio-economy. Although it is recommended that the investment is made for large scale commercial farms, it is also necessary to incorporate the stakeholders and locals knowledge and decisions in the final decision-making process for ultimate optimal site selection.

The main limitation of this work is probably the proximity to the resources and this should be addressed in follow-up studies. Nevertheless, this study developed an accurate location model to be easily used in biofuel investment projects across Queensland and allows flexibility towards weighting and incorporation of other parameters required for decision making.

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2.6 Conclusion

This study identified suitable sites for microalgae cultivation in Queensland, in two stages i) suitability modelling and (ii) economic evaluation, adopting a GIS approach and MCA techniques. A combination of climatic, economic and land-use criteria, supported by the literature to date, were transformed into a weighted spatial model. This technique was used to demonstrate site suitability identification for microalgae biofuel farms in Queensland with the greatest potential for long term economic and environmental sustainability. The resulted maps presented the information which help to identify suitable locations for commercial development of an algal farm.

Queensland has indeed very favourable conditions for investment in the microalgae biofuel industry. Flat terrain, sufficient solar radiation, sunshine hours, and warm temperatures along with refineries, mines, agriculture activities and animal organic waste scattered throughout the state as sources of low-quality water and nutrients, are the main characteristics of the state as a potential location for algae commercial production. This study has shown that highly suitable locations for algae production at commercial scales, are in the North West and along the East of Queensland. Those areas have the advantages of low land value, non-agricultural land use coupled with proper climatic condition. Our study provides a robust approach for further analysis through the incorporation of land value as an economic factor, which can affect directly the capital cost of development an algae farm. It is important to note that this study did not include nutrient resources (including

CO2) and water. Therefore, further research is required on the feasibility developing farms adjacent to the nutrient sources. Also investigation is needed into water availability, focusing on the location of agricultural runoff collection sites, evaporative ponds used by the oil/gas production and mining industry, and other wastewater treatment facilities. I believe that, this study is a base for further investigation in allocation biofuel farm in Australia to assist policymakers and industry developers in many ways.

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Chapter 3: Comparison of potential sites for microalgae and sugarcane as biofuel crops

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3.1 Introduction

In the third part of this thesis, addressed the last objective of the study by answering these research questions: I. Where are the most suitable/potential locations for sugarcane production in Qld? II. Comparing with suitable algae production sites, where is the best locations for both, sugarcane and algae?

Existing and potential sugarcane production areas were compared with algae production suitable sites. For the first time, this research provided map detailed allocations which are suitable for both algae and sugarcane productions. The aim is to assist government and investors have a better details for site selection. ArcGIS application was used to develop map layer represents the same location suitable for both crop production. Overlay analysis was applied to generate map presentation, considering constraint criteria.

Queensland sugarcane is the largest intensive agriculture industry and has a major contribution to Queensland in economy, social and culture for decades. To have a better understanding of sugarcane industry importance in Qld, brief industry information has been included in this chapter.

3.1.1 Sugarcane industry in Qld

Australia is among the major sugarcane producer and exporter countries in the world, as it is shown in Table3.1 and Figure 3.1. The main sugarcane production regions in Australia are located in Queensland north-eastern tropical catchments along the coast and small part of NSW.

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Table 3.1: Sugarcane production in major countries (by area harvest in 2008), according to FAO estimates (Food and Agricultural Organization of United Nations (FAO), 2013)

Figure 3.1: Sugarcane worldwide distribution. Source (Food and Agriculture Organization of United Nations (FAO), 2007 apud FAO, 2013).

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Sugarcane is currently grown on 565162 hectares or 0.3 per cent of Queensland, which existing infrastructure such as mills, cane tramways, sugar export terminals and water supply schemes for irrigation support the production of sugarcane and economic growth of the region (Figure 3.2) (Audit 2013).

Figure 3.2: Queensland Sugarcane production regions and gross value (Audit 2013).

As its shown in figure 3.3, A high proportion of sugarcane land are located in northern Queensland regions (Mackay, Burdekin and Far North Queensland) and southern regions of Queensland, Bundaberg and South Queensland and Wide Bay Burnett, have the least sugarcane land in Queensland state (Audit, 2013).

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Figure 3.3: Percentage of current sugarcane land in each region (Audit 2013)

3.1.2 Yes or no to continue sugarcane production

One of the key problem in sugarcane production is uncertainty of climate. Qld often experiences harsh climate, like drought. Shifts in rainfall and flood pattern and temperature are forecast as the results of climate change, exacerbating the uncertainty in sugarcane production sustainability and profitability.

As it is shown in figure 3.4, harvested cane area has been declining in Queensland between 2000 and 2012, due to seasonal conditions (Department of Agriculture, Fisheries and Forestry Queensland Government, 2014).

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Figure 3.4: Harvested sugar cane area and tonnage (DAFF Queensland)

Historically sugarcane production is the major profitable industry in Queensland. Sugarcane is grown in 26 major river catchments in Queensland, most in environmentally sensitive areas(Rayment, 2003). Along with the expansion of the industry, the negative environmental consequences of sugar production are concerned. These concerns extend to the sea, where discharges of nutrients, sediments and toxicants above natural levels are unwelcome, particularly when they drain to the Great Barrier Reef World Heritage Area and other coastal waters of Queensland (Rayment, 2003).

Despite the economic benefits of the sugarcane industry, there is a concern about the environmental and natural resources issues on sugarcane production (from planting to harvest) in community such as: i) Habitat loss, cumulative impacts and impacts on biodiversity, ii) Excessive water consumption in cultivation, iii) Soil erosion, declining soil health and fertility, iv) Agrochemical use, vii) Water pollution, viii) Sugarcane processing, viii) Farming marginal lands, ix) release of ashes and greenhouse gases during the burning prior to harvesting To mitigate these issues one of the options is to change the land use with another type of crop with less harm to environment and water resources(Andreae, 1991)

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(Christofoletti et al., 2013; Martinelli and Filoso, 2008). The ability of algae to grow in most places, with any source of water (Pittman et al., 2011); offers it as an alternative plant for mitigating the negative environmental effects of sugarcane production along with water consumption reduction.

Accordingly, this part of the study compares site suitability of sugarcane and algae for bioethanol production according to economic efficiency and land use concerns. Results are presented as a detailed map analysis between algae and sugarcane production allocation in Queensland. The multi criteria evaluation is` used to produce algae suitable allocation considering land use, climate and economic criteria. Suitable sugarcane production sites are allocated by the same method using ArcGIS.

This study advanced the information of biofuel production from both sugarcane and algae. The highlights of this research is providing map results for multiple- use or dominant- use land managements in Queensland for producing biofuel and bioproducts for the first time. The outcomes of this study advance the techniques for biofuel site assessment and provide comprehensive results which can support the microalgae-based biofuel industry development in Queensland.

3.2 Method

Suitable production locations for sugarcane were drawn from the Queensland Agricultural Land Audit (the Audit) and compared with algae suitability map generated from earlier work in this study. ArcGIS overlay application was used to present those areas in map detail. The Audit was conducted during 2012-13 to identify land important to current and future agricultural production across Queensland.

The approach used was based on the FAO method (FAO 1976). The main conceptual steps in land-evaluation in FAO method consisted of i) Initial consultation on the objectives, ii) Determination of the requirements of relevant land-use options, iii) Mapping land qualities, iv) Interim matching of land-use requirements with actual land qualities, iiv) Final matching.

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The Audit considers all land in Queensland other than land that is alienated from use for agriculture in the long-term. Land suitability classification in Queensland is the evaluation of soil and land attributes based on the requirements of a specified land use using current technology and management and Socio-economic factors are considered in general terms only. Other limitations for assessing agricultural land suitability in Queensland are listed in Table (3.2).

Table 3.2: Limitation criteria used for assessing agricultural land suitability in Queensland (Guidelines for Agricultural Land Evaluation in Queensland, Second edition) Land use requirements

Limitations Climate, Drainage Water, Wind erosion Water erosion, Subsoil erosion hazard, Flooding, Water infiltration, Soil water availability, Soil physical factors, Salinity, Topography, Nutrients, Vegetation, Pests and diseases

Land excluded from consideration in the Audit includes land permanently inundated, land gazetted as national parks, defence and other commonwealth purposes, established mines, existing urban areas and other intensive non-agricultural land uses. Different criteria were used by the DAFF Qld Agricultural Land Audit (2013) to map potential sugarcane production areas according to data from the Queensland Land Use Mapping Program (QLUMP). DAFF mapping is considered an information source for policy and planning decision-making at a regional level and includes agricultural land class A and class B with slope less than 5 per cent and fewer than 55 days per year with a minimum temperature of 9°C or less and excludes: land that is urban, under intensive use (such as mining), national parks, state forests, land managed by the Department of Defence or permanently under water (Figure 3.5)(Department of Agriculture Fisheries and Forestry Queensland Governmenrt, 2013) .

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Figure 3.5: Queensland Sugarcane potential production sites. (Department of Agriculture Fisheries and Forestry Queensland Governmenrt, 2013) .

Both the algae production land suitability map and sugarcane potential map were

produced according to climatic, environmental and land use considerations. Both crops

requirements in terms of land qualities were previously reviewed and used in the

evaluation process outlined in Chapter 2 and in the Audit, respectively. The major

difference between these maps is economic evaluation, which was considered in the

algae production mapping in this study but was not considered in sugarcane map

assessment by the Audit.

As mentioned in chapter two, the algae map suitability map takes to account land value, labour cost and proximity to roads as economic factors. These factors are relevant to both algae and sugarcane production, hence, overlaying those two maps deliver a product which accounts for economic assessment and other criteria relevant to the location of multi-use lands.

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ArcGIS offers accurate geographical map results which can assist decision makers in determining the spatial suitability of a specific land use in an area. To achieve the aim of this study, an overlay technique was performed and both maps combined into one to find the locations suitable for identifying the particular lands proper for algae and sugarcane or combination of both crops production (Figure 3.6).

The overlay techniques allow the evaluation criterion map layers (input maps) to be combined in order to determine the composite map layer (output map). This approach is often used to find locations that are suitable for a particular use (Malczewski, 2004). In general, there are two methods for performing overlay analysis—feature overlay (overlaying points, lines, or polygons) and raster overlay. Overlay analysis to find locations meeting certain criteria is often best done using raster overlay (although you can do it with feature data). In raster overlay, which were used for this part of study, each cell of each layer references the same geographic location. That makes it well suited to combining characteristics for numerous layers into a single layer (ArcGIS help Desktop 10.1).

In this procedure, overlay analysis performed on algae suitability map from MCA suitability modelling and sugarcane potential sites map from Audit and the map result of is presented in following chapter (figure 3.6).

Algae suitability map (MCA suitabilty modeling) Algae and sugarcane suitablity production sites or combination of both crops. Sugarcane potential sites (Audit )

Figure 3.6: The process of the final map production

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3.3 Results

The algae and sugarcane potential production site map (Fig 3.7) demonstrates the enormous capacity for development of both crops across the state. This map was strongly influenced by limitation criteria such as climatic, biophysical, scio-economic. Hence it provides valuable regional information for commercial interests in algae/sugarcane production.

Figure 3.7: Queensland Algae and Sugarcane suitable production sites.

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According to the map, in north of Queensland there are some potential locations which can be used for algae production combined with sugarcane growing. Other parts of Queensland with hot seasons, steep slope, low humidity and precipitation with long distance from infrastructures, limit the production of either sugarcane or algae. Consequently, those areas are outside of any biofuel investment plan consideration and have been excluded in this study. Land value is one of the important component of the capital cost for development of biofuel production facilities. Considering land value criteria in suitability analysis leads to identification of economically viable biofuel production sites. Figure 3.7 indicates the overlap locations of algae and sugarcane, are the lands economically suitable for both crops investment.

However another important factor in sugarcane economic production is proximity

to the mills. In next stage, CO2 resources map and sugarcane mills location map added to the figure 3.7 to have a better According to the figure 3.8, the best location for sugarcane and algae facilities development are the places which are near to the mills

and CO2 resources with economically land value. These areas are located mostly in north east and east of Queensland. In Table 3.3, the selected areas were compered to

each other according to their land value and proximity to mills, CO2 resources and roads. However some areas are categorised in moderate condition in value analysis

but they could considered economic as their proximity to the mills facilities and CO2 resources (area 4 and area3). In some areas with poor access to the mills and other resources, investment for commercial production of algae or sugarcane would be more costly which needs to be considered.

As it is shown in Figure 3.8, this particular area was ranked highly for algae cultivation. It means it has the suitable climatic, land use and more importantly cheap land value values. These parameters outweighed the distance to roads criterion, but clearly this region requires further infrastructure developments. Hence, this location is chosen as a proper site.

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Table 3.3: Selected area suitable for sugarcane and algae farm

Selected Area Proximity to Proximity to CO2 Proximity to roads Land value mills resources Area 1 Poor Poor Poor Good

Area 2 Good Moderate-poor Good Good

Area 3 Good Good Good Moderate-Good

Area 4 Good Good Good Poor-Moderate

Figure 3.8: Queensland Algae and Sugarcane suitable production sites close to the mills with economically land value.

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3.4 Discussion

To mitigate the sugar production environmental and water resources management issues, two practical land-use management options and justifications are suggested below:

1. The alteration of the crop production from sugarcane to algae in future land use management change: The main reasons for this are:

I. Water resources usage: A disadvantage of sugarcane, compared with the other crops, is its potentially high use of fresh water resources. High water use is often required to achieve the high sugar yields required for economically viable production. Water use will be an important consideration, particularly in countries such as Australia(Renouf and Wegener, 2007). In contrast, algae can grow in in fresh, brackish, waste water or even saline water(Prasad et al., 2014; Quinn et al.,

2012). The algae growing ability in seawater or saline groundwater rather than freshwater reduce the competition for a valuable limited water resource(Borowitzka and Moheimani, 2013).

II. Greenhouse gas emissions: One of the main issues in production and oil extracting of sugarcane is contribution to greenhouse gas emission. Regardless of how effective sugarcane is for producing ethanol, its benefits quickly diminish if carbon-rich tropical forests are being razed to make the sugarcane fields, thereby causing vast greenhouse- gas emission increases (Scharlemann and Laurance, 2008; Timothy Searchinger1, 2008).

Renouf and Wegener studied the environmental life cycle assessment (LCA) of sugarcane production and processing in Australia. Their study showed the aspects of raw sugar production that contribute to greenhouse gas emissions in Figure (3.9). Based on the average results, nitrous oxide (N2O) emissions from

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soil nitrification/denitrification processes are the dominant source (59%). The other significant sources are electricity for irrigation (20%), transport/machinery emissions (9%), fertiliser and pesticide production (5%) and bagasse combustion which releases some methane and N2O (5%)(Renouf and Wegener, 2007).

Figure 3.9: Greenhouse gas emissions for cane sugar, showing contributing activities in Queensland. (Proceedings of the Australian Society of Sugar Cane Technologists, 29, 2007).

One of the key advantages from algae is the capacity to capture GGE and reduce their emissions(Campbell et al., 2009; Elbehri et al., 2013). It has the

ability to fix CO2 efficiently from sources like the atmosphere, exhaust gases from industries and amounts of carbonate salts(Das and Salam, 2014). So a major advantages of microalgae biomass production is its significant global contribution to the objectives of renewable and sustainable biofuels and feeds, as well as greenhouse gas reduction(Klein‐Marcuschamer et al., 2013a).

III. General environmental benefits This option offers a solution for improving water quality entering the Great Barrier Reef Lagoon and reducing the water quality impact of agricultural landscapes. The other positive impacts are particularly noticeable in the air quality improvement of metropolitan areas but also in rural areas

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where mechanized harvesting of green cane is being introduced, eliminating the burning of sugarcane (Goldemberg et al., 2008).

Hence, positioning the suitable sites for both crops would led to better management of altering the type of crop according to local government and stakeholders’ preference. Despite of environmental, land use and water quality benefits of the diversion of sugarcane to algae, competing biofuel with food production would be concern in this option which needs to be addressed.

3.4.1 Cost effectiveness comparison of producing biofuel from algae and sugarcane Biofuel production on a commercial scale requires producing oil which is economically competitive with fossil fuel. Traditionally fossil energy is used to produce biofuel. Chisti (2008) in his review paper discussed the economics and quality constraints of biodiesel from microalgae and suggested that, to have a compatible price with traditional energy sources, the cost of growing microalgae for biofuel production must be reduced. For example, more energy must be recovered in the fuel compared to the fossil energy used in its production. In short, the energy ratio of the fuel must substantially exceed unity. Preferably, the energy ratio should be 8, or more, as is possible to achieve for bioethanol derived from sugarcane. Estimates suggest an energy ratio of <1 for algal fuels in many cases (Chisti, 2008). Reducing the energy consumption required for algal fuels may lead to improve energy ratio (Chisti, 2008). Other sources of energy, such as solar, offer an exciting opportunity towards a zero ratio in energy consumption for algae production. This form of production is already being tested at the University of Queensland algae farm.

The other aspect of algae biofuel project capital costs are expenses for land infrastructure establishment, bioreactors and labour. The production costs may include expenses for cultivation (expenses for nutrients); harvesting and dewatering; and extraction and separation. Besides these, costs include maintenance, components replacement, transportation and overhead expenses (Parmar et al., 2011; Singh and Gu, 2010). In producing biofuel from sugar cane, lower energy content, high solubility in water

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and high vapor pressure impact on its cost and employability and raises the total operating costs. The technology is not sustainable without subsidies and requires more land and water for biofuel production which leads to significant increasing price of biofuel production (Hassan et al., 2015).

In 2012, the University of Queensland investigated three process models for the production of aviation-fuel from microalgae, Pongamia pinnata seeds and sugarcane molasses. This analysis indicated that the biorefineries processing the microalgae, Pongamia seeds, and sugarcane feedstocks would be competitive with crude oil at $1343, $374, and $301/bbl, respectively (all currencies used in the models are based on 2011 US dollars). This economic analysis considered total Capital Investment ($M), annual operating cost ($M), facility costs, raw materials, utilities, labour cost and consumables (Klein-Marcuschamer et al., 2013).

However, in another study in University of Queensland new, low-cost technology has been developed by Peer Schenk and his team and it was aim to producing a cheap protein source in the form of microalgae to supplement cattle in northern Australia during the dry season. Major technological advances throughout the project included: (1) the selection and adaptation of fast-growing, protein-rich, easy-to-harvest, saline- and heat- tolerant microalgae collected from cattle farms in the NT, (2) a new hydrodynamic pond design that cuts the cost of mixing cultures by half, (3) a new airlift design for efficient

culture mixing and CO2 supply to ensure rapid growth of healthy cultures, (4) a new, low- cost harvesting process that uses gravity for induced settling instead of costly centrifugation, (5) a low-cost solar dryer. The techno-economic analysis has been performed based on data collected at the Pinjarra Hills farm that was applied to a 10 ha farm with 8 ha pond surface area (annual production capacity: > 400 tons DM pa).

In their study, related economic factors such as cost of construction, cultivation,

dewatering, drying and CO2 transfer along with cost of water, electricity, maintenance, engineer wage, labourer wage, lifetime of project and interest rate were considered in analysis. In their techno-economic model, oil with feedstock production with and without

utilising purchased CO2 and with and without the use of solar panels for electricity generation were analysed shown figure (3.10) and figure (3.11). The results indicated that growing can be economical by using solar energy and advanced technologies described in related paper(Schenk, 2016).

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OPEX ($/kg) Solar OPEX ($/kg) CAPEX (total $) energy (kWh/day) cultivation 0.11 0.10 1005500.00 22.40 Dewatering 0.15 0.13 80000.00 85.50 Drying 0.01 171300.00 67.00 Oil extraction 0.06 0.05 40000.00 55.42 CO2 1.45 1.45 171800.00 0.00 Solar 190397.87 Labour 0.42 0.42 Total energy Maintenance 0.21 0.24 230.32 total 2.42 2.39 Electricity total1700150.00 Amortisation of CAPEX 0.29 0.32 Solar total 1890547.87 Total with amortisation 2.70 2.71 Oil cost 9.01 9.03 Relative oil cost 0.50 0.50

Figure 3.10: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm with utilising purchased CO2 and with (right OPEX column) or without (left OPEX column) the use of solar panels for electricity generation (Schenk, 2016).

OPEX ($/kg) Solar OPEX ($/kg) CAPEX (total $) energy (kWh/day) cultivation 0.36 0.10 3351666.67 67.20 Dewatering 0.48 0.42 266666.67 256.50 Drying 0.01 171300.00 67.00 Oil extraction 0.06 0.05 40000.00 55.42 CO2 Solar 368792.53 Labour 0.58 0.58 Total energy Maintenance 0.51 0.56 446.12 total 2.01 1.71 Electricity total4068616.67 Amortisation of CAPEX 0.68 0.75 Solar total 4437409.20 Total with amortisation 2.69 2.46 Oil cost 8.98 8.19 Relative oil cost 0.63 0.56 Figure 3.11: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm without utilising purchased CO2 and with (right OPEX column) or without (left OPEX column) the use of solar panels for electricity generation (Schenk, 2016).

Relying on recent study by Peer Schenk lab team, with outstanding economical results and having the suitability map result for algae/sugarcane production, developing commercial biofuel industry would be reality for Qld. Developing strategies and cost estimates for commercial-scale production of biofuel from algae or sugarcane also depends on government agencies and private company investment in further investigation of advancing production technologies and other effective cost-factors like land value.

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3.5 Conclusion

This research is the first attempt to identify the possibilities of growing algae in sugarcane cultivation sites for land use change determination in the future or cultivation algae in waste water and nutrition release of sugarcane farms in order to reduce the environmental and water resources issues of sugarcane production. My study provides map information which includes the suitable locations for biofuel production with two main plants, sugarcane and algae. In locating the proper sites, land value and proximity to the

infrastructures and CO2 resources were considered as the base of analysis. According to GIS analysis and the resulting map, the best potential locations for both sugarcane and algae cultivation are the north east and north west of Qld. Along the north west to south west of Queensland there is a lack of suitable sites of cultivation of both plants. The reasons are, those areas are already under other land use along with the fact that the land value is very high and it is not economic.

The findings of this study indicated that there is enormous opportunity for investment in multi-crop production. This study advances the potential of biofuel production in Queensland. The potential users of the study’s results include policy/decision makers and consultants of regional environmental, land use and natural resource management policy area, local governments and investors. In addition it has been noted that land use planning is dynamic and complex and worthy of more comprehensive investigation as part of biofuel production feasibility assessment.

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CHAPTER 4. Synthesis and Conclusion

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4.1 Overview

This thesis commenced with a brief summary of the rising demand for energy and environmental concerns about climate change and GGE emission increment in last decade. Protecting environment from further pollution and mitigating global GGE are the most critical priority in Australia parallel with other countries. In rapid pace of searching a clean energy as an alternative for petroleum oil, biofuel draw the most interest among other source of energy in recent years. Australia with a large land mass and suitable climatic conditions is consider as a major country for bioenergy production.

In this thesis, I investigated the potential sites for growing algae in Queensland considering potential constrains such as climatic, environmental and socio-economic factors (chapter 2). The third chapter consisted of comparison between algae and sugarcane production potential sites. The aim was mitigating the environmental and resources management issues of sugarcane production in Qld. Proposed solutions include changing the land use from sugarcane to algae or growing algae at the same

sugarcane production sites to consume the Co2, P, N and waste water produced. Those chapters covered the detailed response to the objectives of the study. This final chapter presents a synthesis of the main findings of the research and the contribution it has for the study area of Queensland. Also, this chapter presents the main limitations and further study needs to be addressed in future, followed by a short conclusion.

4.2 The contribution of biofuel production

This thesis makes an important contribution to building biofuel production and its advantages for Queensland. At present, biofuel production is in early development stages and this research will assist the upgrade biofuel production from laboratory to commercial production. This thesis makes an important contribution to national or international investors by providing accurate geographic land suitability location maps. Furthermore it has provided valuable information for local communities and governments in order to consider crop combination or land use change as an alternative for eliminating environmental issues.

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4.3 Limitations and future research

The main limitation of the study was the lack of historical, economic and land use data. To overcome these restriction, I adopted ArcGIS to interpolate the missing data locations. The main benefit of this approach is that it enabled analysis but it is not a substitute for accurate long-term data. Water resources location and nutrient source data were another limitation for this study. More precise researches needs to be done with those updated data for locating accurate locations for algae commercial farm. Future work needs to focus on environmental and socio-economic effects of algae production at commercial scale, as those were out of this study scope. To assess the practicalities of such production in specific areas, further learnings from research in smaller scale and details will be necessary. These studies would be included more economical factors from crop cultivation to producing oil.

4.4 Conclusion

To optimise the benefits and constraints of particular land uses in a certain area, a planner needs geographically detailed mapping of specific characteristics of related to the purpose of land use as inputs to the planning process. In this study, the criteria specifically related to algae production were firstly investigated and analysed. Then using ArcGIS applications, locations in Queensland suitable for algae were categorised as poor, moderate and good locations for biofuel production as the first objective of the study. The map result of this part of study shows that Queensland has very favourable condition and land for algae production at commercial scales. Highly suitable sites are mostly located in North West and along East of Queensland. Those areas have the advantages of low land value, non-agricultural land use coupled with proper climatic condition.

Combing this map with the existing studies of potential sugarcane production from

Audit, CO2 resource map and road map showed the locations which are suitable for combination crop production, which are located in four areas. The first area is chosen according to its low land value and suitable eco-climatic and land use situation. The

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other areas are chosen based on proximity to mills, CO2 resources and infrastructure.

It’s important to note, there is a concern for water accessibility of water and nutrition of the suggested areas. Due to lack of data, some resources were not evaluated (particularly co-produced water and agricultural wastewater). So, it’s not assured if those areas are economically sustainable for cultivation algae in case of transport requirement.

Queensland is large state with lots of resources and proper climate. Therefore, future work could focus on a smaller geographic area to investigate the potential of algae cultivation precisely. The authors believe that, the information provided in this study will serve as a base for further studies of the algae biofuels potential in Queensland and assists policymakers, industry developers and decision makers.

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