Triple bottom line study of a lignocellulosic biofuel industry

Transcription

1 GCB Bioenergy (2015), doi: /gcbb Triple bottom line study of a lignocellulosic biofuel industry ARUNIMA MALIK, MANFRED LENZEN and ARNE GESCHKE ISA, School of Physics A28, The University of Sydney, Sydney, NSW 2006, Australia Abstract Growing concerns about energy security and climate change have prompted interest in Australia and worldwide to look for alternatives of fossil fuels. Among the renewable fuel sources, biofuels are one such alternative that have received unprecedented attention in the past decade. Cellulosic biofuels, derived from agricultural and wood biomass, could potentially increase Australia s oil self-sufficiency. In this study, we carry out a hybrid lifecycle assessment (LCA) of a future cellulose-refining industry located in the Green Triangle region of South Australia. We assess both the upstream and downstream refining stages, and consider as well the life-cycle effects occurring in conventional industries displaced by the proposed biofuel supply chains. We improve on conventional LCA method by utilising multi-region input output (IO) analysis that allows a comprehensive appraisal of the industry s supply chains. Using IO-based hybrid LCA, we evaluate the social, economic and environmental impacts of lignocellulosic biofuel production. In particular, we evaluate the employment, economic stimulus, energy consumption and greenhouse gas impacts of the biofuel supply chain and also quantify the loss in economic activity and employment in the paper, pulp and paperboard industry resulting from the diversion of forestry biomass to biofuel production. Our results reveal that the loss in economic activity and employment will only account for 10% of the new jobs and additional stimulus generated in the economy. Lignocellulosic biofuel production will create significant new jobs and enhance productivity and economic growth by initiating the growth of new industries in the economy. The energy return on investment for cellulosic biofuel production lies between 2.7 and 5.2, depending on the type of forestry feedstock and the travel distance between the feedstock industry and the cellulose refinery. Furthermore, the biofuel industry will be a net carbon sequester. Keywords: biofuel, cellulose refining, energy return on investment, feedstock, forestry, hybrid life-cycle assessment, lignocellulose, triple bottom line Received 20 March 2014; accepted 26 October 2014 Introduction Research into alternative fuel sources is gaining worldwide attention due to growing concerns about environmental degradation and resource depletion. Our dependence on oil is not only contributing to an increase in greenhouse gas emissions (IPCC, 2013) but also the depletion of oil reserves around the world (Ndong et al., 2009). Australia has seen a dramatic rise in the demand for oil (Batten & O Connell, 2007; De Vries et al., 2007), an increase in oil prices (O Connell et al., 2007) and a rapid decline in the country s oil selfsufficiency. Australia is a net importer of crude oil, importing nearly half of what its population consumes. These imports are projected to increase to 76% of the total consumption in 2030 (Geoscience Australia & ABARE, 2010). The growing concern about greenhouse gas emissions and the need for fuel security has Correspondence: Arunima Malik, tel , fax , amal9110@uni.sydney.edu.au prompted interest in Australia and worldwide to look for renewable sources of fuels. A report by the Australian Academy of Technological Sciences and Engineering (ATSE) states: The key finding [...] is that biofuels [...] have useful roles to play as Australian transport fuels and can contribute to greenhouse gas mitigation and energy security (ATSE, 2008). Biofuels have the potential to become an alternative to oil. Currently, only 0.5% of Australia s transport fuels are biofuels (Geoscience Australia & ABARE, 2010). This proportion needs to increase for Australia to reduce its dependence on oil imports for transportation, to increase oil self-sufficiency, and to reduce greenhouse gas emissions. At present, no doubt, Australia is in the early days of establishing a domestic biofuel industry. Such an industry is expected to offer many benefits such as improved fuel security thus offering significant savings on the amount of money spent on imports (Odeh & Tran, 2007; Farine et al., 2011), opportunities for the development of rural and regional Australia (Odeh & Tran, 2007), and health benefits owing to reduced 2014 John Wiley & Sons Ltd 1

2 2 A. MALIK et al. particulate matter, thus cleaner air in the cities (Batten & O Connell, 2007; Mathews, 2007). Of particular relevance for this work is the expectation that future biofuel supply chains will create significant regional employment, net positive energy production and economic stimulus (Mathews, 2007). Apart from the benefits bestowed by biofuels expansion in Australia, the greenhouse gas impacts of biofuel production and the impacts of displaced industries (such as petroleum products) on the society and the economy, along with other sustainability concerns (Stucley, 2010) need to be addressed. Such issues can be assessed in a comprehensive way using life-cycle assessment (LCA; Suh & Nakamura, 2007). Previous LCA studies on biofuel production (Sandilands et al., 2009; Katers et al., 2012) can be improved using input output analysis (IOA), in a hybrid LCA (Suh et al., 2004; Suh & Nakamura, 2007), which is able to provide a complete supply chain coverage (Foran et al., 2005a). IOA is a top-down economic technique developed in the 1930s by Wassily Leontief (Leontief, 1936), who received a Nobel Prize for his pioneering research in This technique uses data that describe economic structure in terms of inter-industry monetary transactions (Leontief, 1986; Miller & Blair, 2009). IO tables for different regions can be combined into a single database called a multi-regional input output (MRIO) table, which shows the interconnections among industries located in different regions. The Australian Industrial Ecology Virtual Laboratory (IELab) provides a unique platform for the compilation of such MRIO tables for Australia (Lenzen et al., 2014). The ability of the IELab to handle large data sets and connect regionally dispersed researchers makes it ideal for addressing specific research questions at a detailed regional level. Indeed, the IELab offers the highest resolution Australian MRIO database to date. Our study is the first ever to undertake a hybrid LCA in the IELab. IO tables can be used for hybrid LCA, combining monetary IO accounts with physical flow data (Heijungs & Suh, 2002; Suh et al., 2004), as for example carried out in studies on greenhouse gas emissions (Wiedmann et al., 2011; Liu et al., 2012). In this work, we improve on prior biofuel LCAs by (a) using IO-based hybrid LCA to cover impacts across complete supply chains, (b) including additional indicators describing the social, economic and environmental impacts, commonly known as the triple bottom line (TBL) and (c) taking into account effects in competing and displaced conventional industries. We apply our improved approach to the case of a cellulose-refining industry in the Green Triangle region of South Australia. In the following section, we explain our case study in detail. In Section Materials and methods, we present the methodology for quantifying the TBL impacts of lignocellulose biofuel production. We present our findings in Section Results and conclude in Section Discussion. Case study The sustainability of a region for biofuel production relies on a number of factors such as the area of land used for feedstock production, type of feedstock, yield of feedstock and a region s accessibility by transport. There are many regions in Australia that are capable of producing either biofuels for transportation or biomass for cofiring in coal-fuelled power plants. For example, Central West New South Wales and Gippsland, Victoria, have abundant biomass for the production of electricity (Rodriguez et al., 2012). In this work, however, we concentrate on the Green Triangle region. The Green Triangle region spans 6 million hectares in south-eastern South Australia and south-western Victoria (Fig. 1). This region consists of extensive hardwood and softwood plantations (Table 1) and has a well-established softwood processing industry. The major industries in the region include pulp and paper manufacturing, wood panels and sawmilling (URS Forestry, 2004). The two main towns in the region, Mount Gambier in South Australia and Warracknabeal in Victoria, have abundant agricultural and forestry biomass available for harvesting. The Green Triangle region is readily accessible by road and rail. Availability of abundant forestry biomass and extensive transport infrastructure makes Green Triangle an ideal region for cellulosic biofuel production in Australia. However, to date, a comprehensive assessment of the TBL impacts of cellulosic biofuel production has not been undertaken. As the forestry resource in the Green Triangle region is currently used for the production of pulp, paper and woodchips, adding new demand for forestry products for producing biofuels will likely crowd out existing economic activities in the region. Therefore, the loss of economic activity and employment resulting from the diversion of forest biomass resources to energy production needs to be quantified. Furthermore, production of biofuels is expected to result in socio-economic impacts in the displaced oil industries around the region. These obvious research gaps in the biofuel literature form the main aim of our study. Materials and methods TBL assessment using hybrid LCA A TBL assessment involves reporting on the three spheres of sustainability: social, economic and environmental (Elkington,

3 TBL STUDY OF A BIOFUEL INDUSTRY 3 Fig. 1 Map of Green Triangle region. (Redrawn from: Table 1 Key characteristics of forestry biomass in the Green Triangle region Type of biomass Type of feedstock Total area (ha) (Lambert & Quill, 2006) Volume (10 6 m 3 ) (URS Forestry, 2004) Density (t/m 3 ) (Greaves & May, 2012) Mass (10 4 t) Hardwood plantations Pulp logs Softwood plantations Pulp logs Sawlogs and Sawmill residues ). Until the late 1970s, companies only reported on their economic bottom line and disregarded the remaining two spheres. However, in recent times, companies have started analysing their impacts in a more thorough way, for example, by assessing their carbon footprint and their impact on regional employment (Savitz, 2006). The more comprehensive corporate TBL analyses to date employ LCA (Foran et al., 2005b). In turn, LCA practitioners have recently moved towards using a hybrid approach, combining process analysis (PA) and IOA (Bullard et al., 1978). PA involves collecting industry-specific data to provide a detailed representation of the impacts occurring on-site. IOA, in contrast, considers the entire supply chain and examines both the direct (on-site) and the total (direct plus indirect) impacts (Leontief & Ford, 1970). Both PA and IOA have strengths and weaknesses. PA offers a greater level of detail and specificity, but it lacks completeness because it does not take the entire supply chain into account; a finite boundary is drawn and all impacts falling within the boundary are considered, whereas the rest are deemed negligible (Lenzen, 2000). IOA resolves this boundary issue because all impacts in the supply chain are counted, starting from the producing company to all upstream suppliers. But, to attain system completeness, IOA compromises on the level of detail and it is not as specific as PA. Generally, many similar companies are aggregated together into one industry sector, leading to stochastic aggregation errors (Lenzen, 2000). A best-of-bothworlds approach called hybrid LCA involves combining PA s detailed bottom-up process data with IOA s complete system (Suh & Nakamura, 2007). This combination of bottom-up process data and top-down IO data bestows both completeness and specificity. IO database Because hybrid LCA incorporates IO methodology, an input output table (IOT) is needed. In our regionally explicit study, we combine a MRIO table of Australia (Lenzen et al., 2014; Fig. S1, Supporting information) and bottom-up process data for cellulose refining (Section Process data) to undertake a hybrid LCA. MRIO tables consist of every region s IO table (diagonal matrices in Fig. S1) and off-diagonal matrices that reveal the trade patterns between different regions. The MRIO table we use in our study follows the standard supply use structure (EEC, 2008). The table consists of use (U), supply (V), value added (v) and final demand matrices (y) for 19 Australian regions, a rest-of-world (RoW) exports vector (ξ), imports matrix (l), and margin and taxes sheets (M) compressed into 1 row each. Margin and taxes sheets contain markups, which are added on top of basic

4 4 A. MALIK et al. price sheet to obtain values in purchaser s price. Basic prices are factory-gate prices. Markups are margins (trade, transport, wholesale etc.), taxes on products and subsidies on products. For performing a TBL analysis using MRIO tables, we also require physical accounts (satellite) data (Q) on economic, social and environmental indicators such as employment, economic stimulus, energy use and greenhouse gas emissions. The employment, energy use and greenhouse gas satellites used in this study are based on data published by the Australian Bureau of Statistics (ABS, 2012b), Bureau of Resources and Energy Economics (BREE, 2013) and the Department of Climate Change and Energy Efficiency (DCCEE, 2012), respectively. We create the economic stimulus satellite vector (Wiedmann et al., 2009) by calculating the sum of intermediate use of goods and services by all industries in the economy (ABS, 2011, 2012a). The dimensions of the MRIO table shown in Fig. S1 are N = 344, M = 6, K = 5, R = 1, S = 344, E = 17, F = 4; where N is the column dimension for both use and supply matrix for each region; M is the column dimension of final demand matrix for each region; K is the row dimension of each region s value-added block; R is the column dimension of the RoW exports vector; S is the row dimension of the RoW imports block; E is the row dimension of the margin and taxes sheets compressed into 1 row each; and F is the row dimension of satellite block with 1 row each for employment, economic stimulus, energy use and greenhouse gas emissions data. The entire MRIO table measures sectors. In the following paragraph, we give a brief overview of the steps for calculating the TBL impacts of a cellulose-refining industry using hybrid LCA, which will be explained further in the subsequent sections. First, we augment the Australian MRIO table with additional rows and columns, and populate these rows and columns with data on different feedstock types and cellulose refining (Section Augmentation of the MRIO table with process data), as shown in Fig. 2. Data collection and processing steps are explained in Section Process data. Then, we use the augmented table to calculate the direct and indirect social (employment), economic (stimulus) and environmental (energy use & carbon dioxide emissions) impacts of cellulose refining (Section Measuring the TBL impacts). Following, in Section Breakdown of TBL impacts into producing industries, we apportion the total impacts into various upstream layers of producing industries. We also breakdown the TBL impacts corresponding to operating inputs purchased by the celluloserefining industry (Section Breakdown of TBL impacts into operating inputs). Augmentation of the MRIO table with process data The Australian MRIO table (Fig. S1) does not include any detail on the Green Triangle forestry operations and the celluloserefining operations. We integrate this detail using a hybrid LCA approach. In this study, we model 19 different cellulose-refining scenarios based on 19 different forestry feedstock inputs (Section Process data). To integrate this detail into the Australian MRIO table (Fig. S1), we augment the table with 38 new rows and columns. We populate 19 rows and columns with process data (Section Process data) for 19 forestry feedstock scenarios (see Table S1 for a list of feedstocks) and remaining 19 rows and columns with process data on cellulose-refining scenarios (see Table 2). The cellulose-refining scenarios are based on the 19 forestry feedstock scenarios. For example, forestry feedstock scenario 1 provides input into the cellulose-refining scenario 1 (Fig. 2). We choose the South Australian (SA) region of the MRIO table for hybridisation because majority of the forestry biomass is located in the SA part of the Green Triangle region. Further detail on the augmentation process can be found in Appendix S3, where we: (a) show a schematic of a section (IO table of SA) of the augmented MRIO table to demonstrate the process of hybridisation and (b) offer a step-wise explanation of the insertion process. Process data We analyse 19 different forestry feedstocks (see Table S1) for biofuel production in the Green Triangle region. These feedstocks correspond to the type of biomass available for harvesting (either hardwood or softwood plantations) and the transport distance between the feedstock industry and the cellulose refinery. We populate the augmented columns (Fig. 2) with production recipes that are operating inputs needed for carrying out the forestry operations. For preparing the production recipes, we first obtain monetary data on the total cost of transportation of the various feedstocks (Rodriguez et al., 2011). We breakdown the total costs into different operating inputs using a transport model (Lambert & Quill, 2006) that incorporates both fixed cost (e.g. registration) and variable cost (e.g. fuel) categories for transporting biomass over a certain distance. As our bottom-up process data lack information on some input categories, we use the IO data for the forestry sector (ABS, 2011) to fill the gaps in the process data. We scale the data so that they reflect the number of tonnes of feedstock harvested by the feedstock industry and subsequently transported to the cellulose refinery. Additionally, we prepare production recipes for the cellulose-refining scenarios. To this end, we obtain detailed monetary capital and operating cost data on the conversion of lignocellulose biomass to ethanol from the National Renewable Energy Laboratory (NREL, 2011). We prepare 19 different cellulose-refining scenarios based on the number of tonnes for each of the 19 feedstock scenarios described above. We use the IO data for the petrol and diesel sector only for certain categories such as food, communication, trade, business services and personal services to fill the gaps in the cellulose-refining process data (ABS, 2011). A detailed explanation of all data preparation steps is provided in Supporting information Appendix S4. We also prepare data for the TBL indicators for the feedstocks (Table S1) as follows: (a) employment required for each feedstock by multiplying the full-time equivalent (FTE)/

5 TBL STUDY OF A BIOFUEL INDUSTRY 5 Fig. 2 Schematic diagram showing the Australian supply use MRIO table augmented with data on different feedstock and celluloserefining scenarios. For illustration, only two feedstock and cellulose-refining scenarios are shown in the diagram. However, in reality, we insert 19 rows and columns for feedstock scenarios, and 19 rows and columns for cellulose-refining scenarios into the South Australian region of the MRIO table. The entire MRIO table after augmentation measures. V, Supply; U, Use; y, final demand; v, value-added; ξ, exports; l, imports; M, margins and taxes; Ind, Industries; Com, Commodities; x i, total output from industries; x p, total output from commodities; z i, total input into industries; z p, total input into commodities; Q, satellite accounts; SA, South Australia; VIC, Victoria; RoW, rest-of-world;..., other categories/sectors;..., other regions. tonne value for year 2013 (Lambert & Quill, 2006) with the number of tonnes available for harvesting; (b) energy use using the energy content (DRET, 2011) and current year (2013) prices for different fuel types used for carrying out the forestry operations; and (c) carbon dioxide emissions using carbon content factors that convert energy units into carbon dioxide equivalent values (DCCEE, 2012). As with the monetary table, we augment the satellite accounts matrix (Q) in the MRIO table with 19 additional columns and populate these columns with employment, energy and greenhouse gas data for the feedstocks. We construct the economic stimulus satellite using the augmented table (Fig. 2), and it therefore contains the stimulus data for the feedstocks. We prepare the TBL data for cellulose-refining scenarios using the same approach as the feedstock scenarios described above, except that we obtain the employment FTE/tonne value from the NREL report (NREL, 2011). We also add the TBL data for cellulose refining into the satellite accounts matrix (Q). Furthermore, we calculate the total carbon dioxide CO 2 sequestered by the forestry feedstocks. To this end, we obtain CO 2 sequestration data for different wood types (Tucker et al., 2009) and use density factors (Table 1) to calculate the total amount of CO 2 sequestered per tonne of biomass produced.

6 6 A. MALIK et al. Table 2 Data on triple bottom line indicators for 19 different cellulose-refining scenarios based on the amount of tonnes and the travel distance between the forestry feedstock industry and the cellulose refinery Type of biomass Type of feedstock Scenario Distance (km) Mass (tonnes) Employment (FTE) Stimulus (million $) Energy (TJ) GHG emissions (tonnes) Hardwood plantations Pulplogs Forest residues Softwood plantations Pulplogs Harvest residues Sawmill residues Chips Bark Green Sawdust The number of tonnes for feedstocks 12 and 16 was zero in the original data, but are considered here for the sake of completeness. Measuring the TBL impacts We mentioned in Section TBL assessment using hybrid LCA that TBL analysis using PA alone results in incompleteness, as it does not take the entire supply chain into account. To measure the direct (on-site) and total (direct and indirect) TBL impacts, we apply the basic input output methodology. In the following, we explain the methodology by taking the employment indicator as an example. We calculate the economic stimulus, energy use and carbon dioxide impacts in the same way. Let Q be a satellite account containing the employment Q i of industry i. Then, the vector q ¼ Q^x 1 describes the employment intensity q i of industry i as the employment per unit of total output x = (I A) 1 y, where A and y are the direct requirements and final demand matrices, respectively. Then, m = ql is the employment multiplier, where L = (I A) 1 contains all the supply chain repercussions of q. Breakdown of TBL impacts into producing industries Impacts can originate (a) on-site, (b) from immediate suppliers of the cellulose-refining industry (1st order), (c) suppliers of suppliers (2nd order) and so on. The various sets of producing and supplying entities are called production layers (Foran et al., 2005a). We decompose the supply chain into production layers of increasing order to investigate which industry sectors in the cellulose refinery s supply chain are responsible for the greatest proportion of TBL impacts. Recalling that the Leontief inverse L = (I A) 1, and rewriting (I A) 1 as I + A + A 2 + A A n (Waugh, 1950), we can decompose total TBL impacts as a consequence of final demand y* for the feedstocks as Q ¼ qði þ A þ A 2 þ A 3 þ...þ A n Þy ¼ qy þ qay þ qa 2 y þ qa 3 y þ...þ qa n y ; where y* is the final demand vector restricted to particular cellulose-refining sectors, that is the y* vector contains only one nonzero element. For example, to decompose the TBL impacts of refining hardwood pulplogs (Scenario 2, Table 2), the y* vector contains only one nonzero element, which is almost 500 m$ (see Appendix S3 for explanation). In Eq. 2, element qy* represents direct impacts in the cellulose-refining sector, qay* impacts in suppliers of the cellulose-refining sector, qa 2 y*in suppliers of suppliers and so on. To break down these layerwise TBL requirements into contributions from industries, we enumerate Q ¼ q#ly ¼q#y þq#ay þq#a 2 y þq#a 3 y þ...þq#a n y ; ð3þ where # is element-wise multiplication. We also calculate the losses in the pulp, paper and paperboard industry as a result of diverting forestry biomass to biofuel production. To this end, we allocate the nonzero element as the total output of the forestry biomass supplied to the cellulose refinery. Using the total output of the biomass as the demand shock, we enumerate the loss in employment and economic stimulus in the pulp, paper and paperboard sector of the SA region. Breakdown of TBL impacts into operating inputs TBL impacts can also be broken down according to the operating inputs purchased by the cellulose refinery. This is commonly known as a commodity breakdown (Foran et al., 2005a). ð1þ ð2þ

7 TBL STUDY OF A BIOFUEL INDUSTRY 7 We derive the commodity breakdown equation from Eq. 1: Q ¼ qði þ A þ A 2 þ A 3 þ...þ A n Þy ¼ qy þ qði þ A þ A 2 þ A 3 þ...þ A n ÞAy where (I + A + A 2 + A A n ) is Leontief inverse L. Rewriting Eq. 4, we compute: Q ¼ q#y þ ql#ay ; where # is element-wise multiplication, q#y* and ql#ay* represent direct and indirect impacts, respectively. Results Heat-map of South Australia s augmented IO table We successfully constructed a sub-national MRIO table for Australia and augmented the part of the table pertaining to South Australia with additional rows and columns that contain process data for 19 different forestry feedstock scenarios and 19 different celluloserefining scenarios (Fig. 3, extracted from the MRIO table in Fig. 2). The vertical (I) and horizontal (II) rectangles in the use matrix U (Fig. 3) hold the production recipes and sales structures of the scenarios, respectively. The square (III) in the supply matrix V contains the total monetary worth of all feedstocks harvested and transported to the cellulose refinery and the total monetary worth of lignocellulose biomass converted to ethanol. A schematic of Fig. 3 and step-wise explanation of the insertion procedure is given in Appendix S3. The fact that we utilise a sub-national MRIO table makes our study a world-first in three aspects: (a) the pre-augmentation MRIO table distinguishes 19 Australian states and sub-state regions, represented by 344 industry sectors each (Lenzen et al., 2014); (b) our study represents the first instance of an augmented MRIO table (Fig. 2) to be used in a hybrid LCA of a cellulose refinery; and (c) it is the first sub-national hybrid LCA to report on all three spheres of TBL: social, economic and environmental. We evaluate the direct and total impacts of different cellulose-refining scenarios (Section TBL impacts), carry out a production layer decomposition (PLD) analysis to demonstrate that IOA eliminates truncation error and offers a complete assessment of the TBL impacts occurring throughout the supply chains of the cellulose refinery, and to analyse the loss in economic activity and employment owing to the diversion of forest biomass to ethanol production (Section Production layer decomposition), and perform a commodity breakdown analysis to appraise the TBL impacts due to the purchase of commodities as operating inputs by the cellulose refinery (Section Commodity breakdown). ð4þ ð5þ TBL impacts Our comprehensive TBL impact analysis (Section Measuring the TBL impacts) of forestry feedstock and cellulose-refining supply chains (Table S2 and Table 3, respectively) yields four main insights, about (a) the substantial contribution of the forestry feedstock and cellulose-refining supply chains (Section Direct and total impacts) to total life-cycle impacts, (b) economies of scale (Section Economies of scale) for 19 forestry feedstock operations, (c) lack of variation in economic stimulus (Section Economic stimulus) for both the forestry feedstock operations and cellulose-refining scenarios and (d) significant variation in job creation (Section Job creation) and energy consumption (Section Energy and greenhouse gas impacts) across all forestry feedstock scenarios. We also study the impacts of crude oil displacement by comparing the TBL multipliers of cellulose-refining industry with those of crude oil refining (Table 3). Direct and total impacts. The total TBL impacts m of all forestry feedstock operations (Table S2) and cellulose refining (Table 3) are significantly greater than the direct (on-site) impacts q, because IOA captures all the direct and indirect effects occurring throughout the industry s supply chains in addition to impacts occurring within the operating premises of the industries. Our results, therefore, demonstrate the added value of including IOA into an LCA in that IOA eliminates truncation errors, by counting impacts starting from the industry to all upstream suppliers. At the same time, the inclusion of detailed bottom-up process data into the hybrid LCA confers accurate assessment of the industry s direct impacts. Including the losses in the pulp, paper and paperboard sector (Fig. 5), there is a 10% effect on the multipliers for cellulose refinery (Table 3). All cellulose-refining scenarios include the losses, and therefore, the conclusions can be read by keeping this in mind. Economies of scale. The feedstock industry s direct impacts are determined by: (a) the on-site forestry operations such as growing, harvesting, handling, loading and transporting the feedstock to the cellulose refinery; and (b) the fixed cost (i.e. repairs and maintenance, insurance, registration and salaries) and variable cost (i.e. fuel, oil and lubricants and tyres) operating inputs purchased by the industry. Variable cost depends on the type of truck (B-double or semi-trailer) used for transporting the feedstock and the travel distance between the feedstock industry and the cellulose refinery.

8 8 A. MALIK et al. Fig. 3 Heat map of the augmented supply use MRIO table (top-view, schematic Fig. 2) and the South Australian section of the MRIO table (zoomed up bottom-view, schematic Fig. S2). x- and y-axes show sector numbers. The complete MRIO table measures , whereas the South Australian section of the table has [1*( ) ] 9 [1* ( ) ] = sectors. Grey shades represent the log 10 of transaction values expressed in 000 Australian Dollars. U, use matrix; V, supply matrix; v, value added; y, domestic final demand; l, imports matrix; ξ, exports vector; p, bottom-up process data for 19 feedstock and 19 cellulose-refining scenarios; M, margins and taxes; Q, satellite accounts matrix. We observe that the economic and employment impacts decrease as the travel distance between the industry and the cellulose refinery increases (Table S2), which indicates economies of scale, that is the fixed costs remain the same, irrespective of the travel distance between the feedstock industry and the cellulose refinery. Unlike employment and economic stimulus, the economies of scale are not evident for the environmental indicators (energy use and carbon emissions), because these depend on the variable cost inputs bought by the feedstock industry. As the travel distance between the industry and the cellulose refinery increases, the variable input requirements increase as well. For example, more fuel is needed for transporting

9 TBL STUDY OF A BIOFUEL INDUSTRY 9 Table 3 Direct and total triple bottom line (TBL) impacts of 19 different cellulose refining scenarios Cellulose refining of different feedstock types Distance (km) Hardwood plantations Pulplogs Forest residues Softwood plantations Pulplogs Harvest Residues Chips Bark Green Sawdust Crude oil refining (Petrol and diesel sector) Economic stimulus q $ per $ m $ per $ Employment q FTE per million $ m FTE per million $ Energy use q TJ per million $ m TJ per million $ Carbon dioxide emissions q Tonnes per million $ m Tonnes per million $ The scenarios are based on the travel distance between the forestry feedstock industry and the cellulose refinery. The TBL impacts of crude oil refining (Petrol and diesel sector) are also shown. q, direct intensity; m, total intensity; $, Australian dollar; FTE, Full-time equivalent; TJ, Tera joules.

10 10 A. MALIK et al. the feedstock over 200 km, as opposed to a distance of 50 km. Economic stimulus. Economic stimulus is the total monetary worth of input required directly and indirectly by an industry for carrying out its operations. The purchase of operating inputs creates production opportunities further up the supply chain and therefore stimulates economic activity. For example, to harvest and transport a dollar worth of hardwood pulplogs to the cellulose refinery located 50 km away, the industry buys $0.89 of direct operating inputs (Table S2), stimulating for example the industrial machinery and equipment industries in the supply chain. Overall, $1.49 of economic stimulus is generated, $0.6 of which is indirect. Similarly, cellulose refining of hardwood pulplogs delivered to the refinery situated 50 km from the forestry feedstock industry generates $0.89 worth of direct stimulus and $0.82 worth of indirect stimulus (Table 3). Interestingly, the amount of economic stimulus generated is similar across all feedstock scenarios (Table S2), which implies that overall the feedstock industry spends the same amount of money on intermediate operating inputs for all feedstock operations, regardless of the differences in the harvesting methods or the type of truck used for transporting the feedstocks to the cellulose refinery. For example, hardwood and softwood residues (branches, foliage and stumps) and sawmill residues (chips, bark and green sawdust) are transported using semitrailers, whereas pulplogs are transported using B-double trucks. Semitrailers carry 32% less load than B-double trucks (Lambert & Quill, 2006), whereas B-double trucks use more fuel and oil. Therefore, the amount of money spent on tyres and fuel for transporting pulplogs levels out the money spent on buying more semitrailers for transporting residues. Similarly, the amount of economic stimulus for all celluloserefining scenarios is quite similar (Table 3). Direct economic stimulus for the cellulose-refining scenarios includes the machinery bought for converting lignocellulose biomass to ethanol. All feedstocks are subjected to the same method of lignocellulosic conversion; therefore, not much variation in economic stimulus is observed for the cellulose-refining scenarios. A comparison of the direct and indirect stimulus generation for cellulose refining with that of crude oil refining reveals that a future cellulose-refining industry would stimulate the economy much more than a conventional crude oil refinery (Table 3). Job creation. Our results indicate that forestry feedstock operations for softwoods are more labour intensive than those of hardwoods (Table S2), because softwoods are more demanding in terms of skills and equipment needed for harvesting (Lambert & Quill, 2006). Furthermore, softwood plantations undergo thinning regimes (Rodriguez et al., 2011), which are undertaken by labourers and contractors as part of silvicultural management (NWFIG, 2002). A comparison of pulplogs and residues reveals that the direct employment impacts q of the latter are more pronounced than those of the former (Table S2). This holds true for the direct employment impacts of cellulose-refining scenarios as well (Table 3). Both softwood and hardwood residues are nonuniform, hence require extra loading time (Rodriguez et al., 2011). Therefore, additional people are required onsite for handling and loading the residues at the forestry feedstock industry, and extra personnel onsite cellulose refinery for unloading and processing the residues. Interestingly, the total employment impacts m of pulplogs are greater than those of residues. As mentioned above, B-double trucks are used for transporting pulplogs to the cellulose refinery. B-double truck drivers undergo extensive training, which is coordinated by well-established training centres (Lambert & Quill, 2006) that in turn employ trainers, administrative assistants and managers. So, the indirect supply chain of pulplogs is more human resource intensive than the supply chain of residues. Out of all feedstocks, the employment impacts of sawmill residues are substantial. Sawmill residues are the by-products of softwood processing. There are three main types: chips, bark and green sawdust, which are produced at different processing stages in a sawmill (Kehbila, 2010). Bark is the most labour intensive of all because it is first removed from the logs using a debarker, and then undergoes additional processing step to break it into smaller pieces before transporting it to the cellulose refinery. In contrast, chips and sawdust do not require additional processing steps (Kehbila, 2010). Small sawmill facilities do not have the capital to invest in automatic feedstock handling equipment. They instead employ people for handling the feedstock (Kehbila, 2010). Also, sawmill residues are transported using a semitrailer, which is the least efficient transport system (Lambert & Quill, 2006). In other words, additional people for feedstock loading and unloading, and more semitrailer units are needed to transport the residues to the cellulose refinery. A crude oil refinery is one of the major employers of the energy sector (Commonwealth of Australia, 2013). Therefore, a comparison of the direct impacts of cellulose refining with those of crude oil refining indicates that more jobs are created on-site a crude oil refinery than a cellulose refinery. However, the opposite is true for the total employment impacts (Table 3). Overall, the supply chain of the cellulose refinery is more socially sustainable than the crude oil refinery supply chain.

11 TBL STUDY OF A BIOFUEL INDUSTRY 11 Energy and greenhouse gas impacts. A comparison of the direct (q) and total (m) energy intensities reveals that energy is mainly consumed indirectly in the supply chain of both the forestry industry (Table S2) and the cellulose refinery (Table 3). This is because the operating inputs bought by the feedstock industry and the cellulose refinery are energy intensive. For example, the feedstock industry buys gas oil and fuel oil for transporting the feedstock to the cellulose refinery. The gas oil and fuel oil sector, in turn, buys energy-intensive inputs such as crude oil and coal. As energy use is mostly indirect, the energy multiplier m has significantly higher values than the direct energy intensity q. The direct energy and greenhouse gas impacts of the residues are markedly higher than those of pulplogs. As mentioned in Section Job creation, both softwood and hardwood residues are nonuniform, and therefore, in addition to extra loading time, more energy is required for collecting, loading and transporting the residues to the cellulose refinery. Interestingly, direct energy and greenhouse gas emissions are quite similar across all 19 cellulose-refining scenarios (Table 3) because all feedstocks undergo the same conversion process on-site cellulose refinery. Indirect supply chain, however, includes the harvesting practices and impacts of transportation. Therefore, the indirect energy and GHG impacts increase as the transportation distance between the forestry feedstock industry and the cellulose refinery increases. Both the energy and GHG impacts of cellulose refining are lower than those of crude oil refining indicating that the supply chain of cellulose refining is more sustainable than that of crude oil refining. Hereafter, we only elaborate on the TBL impacts of refining hardwood pulplogs (Scenario 2, Table 2). The TBL impacts of all other cellulose-refining scenarios follow the same pattern. Production layer decomposition A PLD analysis provides a breakdown of the total impacts m into different supply chain tiers. Direct (onsite) impacts are represented by layer 1, impacts of 1st order suppliers layer 2, suppliers suppliers layer 3 and so on (Fig. 4). As IOA considers impacts occurring throughout the supply chain, it removes truncation errors that are prevalent in process LCAs. This feature of hybrid LCA is well demonstrated in this section. We provide two sets of PLD results: (a) the economic, social and environmental impacts of cellulose biofuel production (Fig. 4), and (b) the offsets and losses in economic activity and employment in the pulp, paper and paperboard sector because of wood-to-fuel diversion (Fig. 5). Suppose the cellulose industry has 100 suppliers, then there are 100 layer 2 paths. Assume that each of these in turn has 100 suppliers; hence, there are = layer 3 paths; 1 million layer 4 paths and so on. Generally, a process analyst is unlikely to have the time and resources to follow upon a large number of individual paths occurring throughout the supply chain. Therefore, a finite boundary is often chosen and only the impacts falling within such a boundary are counted. In our study, considering the boundary to be layer 3 would inadvertently lead to ignoring 8% of economic stimulus, 21% of employment, 31% of energy and 27% of greenhouse gas impacts of the celluloserefining industry (Fig. 4). Truncating the system at layer 2 would result in 26% of economic stimulus, 57% of employment, 56% of energy and 57% of greenhouse gas impacts being overlooked. IOA solves this problem by converting the infinite series of supply chains into a single inverse matrix L (Section Breakdown of TBL impacts into producing industries). Our results indicate that the suppliers in the first five layers of production are responsible for more than 95% of the employment, economic, energy use and greenhouse gas impacts (Figs 4 and 5). As the graphs tend to converge after production layer 5, the suppliers in production layers 6 and beyond only play a minor role in the industry s total impacts. The area graphs also illustrate which sectors in the supply chain contribute to the total TBL impacts, for example, the forestry sector supplies feedstock to the cellulose refinery (Fig. 4). This sector is responsible for generating employment and economic stimulus and also plays a major role in the cellulose refinery s total energy and greenhouse gas impacts. Apart from the emissions generated during the production of biofuels, the use of forestry biomass as feedstock for biofuel production provides opportunities for the sequestration of carbon dioxide from the atmosphere. Using the CO 2 sequestration data (Section Process data), we find that 358 kt of CO 2 are sequestered by a tree for producing biomass for the cellulose refinery. Comparing this with the total CO 2 emissions for the production of biofuels (Fig. 4d), the net CO 2 sequestered (total CO 2 sequestered minus the total CO 2 emissions) is almost 298 kt. Therefore, the cellulose-refining industry is a net carbon sequester. Diverting forest resources to biofuel production is expected to impact the existing conventional industries, such as the pulp, paper and paperboard industry, in the Green Triangle region. More than a third of total jobs and half of economic stimulus will be lost on-site the pulp, paper and paperboard industry. There will be a loss in economic activity and employment in the supply chain of the industry as well mainly in the Trade, Business services, Transport services and wood & paper sectors (Fig. 5). Nonetheless, a comparison of the PLD results for the paper, pulp and paperboard industry

12 12 A. MALIK et al. (a) (b) (c) (d) Fig. 4 Production layer decomposition of the total triple bottom line (TBL) impacts (Eq. 3) of converting lignocellulose biomass from hardwood pulplogs to ethanol. The results represent the TBL impacts of scenario 2 (Table 2). The diagrams represent the demand shock of almost 500 m$ (see Appendix S3). (a) (b) Fig. 5 Loss of economic activity and employment resulting from the diversion of hardwood pulplogs (Scenario 2, Table 2) to ethanol production. The diagrams represent the demand shock equal to the total output of the feedstock supplied to the cellulose refinery. with those of cellulose refining reveals that the losses (Fig. 5a,b) are much smaller than the gains (Fig. 4a,b). Almost 300 jobs will be lost in the supply chain of the pulp, paper and paperboard sector if the biomass is diverted for ethanol production. However, the diversion will create 2800 new jobs, resulting in a total job gain of jobs. Likewise, a total of 65 million$ will be lost in economic activity as a consequence of biofuel expansion. This will be followed by an 880 million$ gain in economic stimulus, resulting in a net economic gain of +815 million$. Commodity breakdown In the previous section, we apportioned the total TBL impacts according to different layers of production. Here, we carry out a commodity breakdown analysis to reveal the commodities purchased as operational inputs, in layer 2, that have the greatest contribution to the total TBL impacts of the cellulose refinery. We highlight five immediate suppliers of the refinery that play a substantial role in the total TBL impacts (Table 4). The property operator and developer services are required during the

13 TBL STUDY OF A BIOFUEL INDUSTRY 13 construction phase of the cellulose refinery. Once the industry is operational, the process of converting lignocellulose to ethanol is divided into many stages such as feed handling and drying, gasification, gas cleanup, alcohol synthesis and alcohol separation (NREL, 2011). The process starts with the delivery of hardwood pulplogs feedstock to the cellulose refinery. This step generates employment as workers are needed for harvesting, handling, loading and transporting the feedstock to the cellulose refinery; generates stimulus as machinery needs to be bought for undertaking forestry operations. Transport of feedstock to the cellulose refinery requires petrol and diesel that results in high energy use and greenhouse gas emissions. Once the feedstock arrives at the refinery, it goes through many stages that require specialised industrial machinery and equipment. For example, biomass gasifier vertical vessel is required for gasification, alcohol synthesis reactor is used for alcohol synthesis, and distillation columns & condensers are needed Table 4 Top five immediate suppliers of the cellulose refinery, and their percentage contribution towards the total impacts Triple bottom line indicator Employment Stimulus Energy use Carbon dioxide emissions Top five immediate suppliers of the cellulose refinery Industrial machinery 76.6 and equipment Hardwood pulplogs 13.9 Storage 1.1 Property operator and 1.0 developer services Pumps 0.9 Industrial machinery 71.0 and equipment Hardwood pulplogs 21.9 Property operator and 1.1 developer services Storage 0.9 Pumps 0.7 Industrial machinery 58.3 and equipment Hardwood pulplogs 35.1 Pumps 1.1 Property operator and 0.5 developer services Storage 0.5 Industrial machinery 58.5 and equipment Hardwood pulplogs 34.8 Pumps 1.1 Property operator and 0.5 developer services Storage 0.5 Per cent contribution to total impacts for alcohol separation. Throughout the conversion process, a variety of pumps such as the water recirculation pump (for cooling the condensers), diesel pump (for fuelling the machinery) and chemical pump (for feeding boiler chemicals) are used (NREL, 2011). A range of storage containers are needed, for example : (a) tanks for short-term storage of forestry feedstock, (b) diesel storage tank for storing diesel, (c) firewater storage tank in case of fire emergencies, (d) chemical storage tank for storing ammonia, sodium hydroxide, catalysts and other chemicals and (e) tanks for storing ethanol. Discussion Supply chain analysis An assessment of a cellulose refinery s supply chain is crucial for determining its sustainability performance. In this study, we have successfully demonstrated the power of combining multi-region input output analysis and process analysis for appraising the upstream supply chain of the industry. By carrying out PLD analysis, we have demonstrated that IO analysis eliminates truncation errors that are evident in many process analyses. Furthermore, we have proved that an accurate assessment of the industry s upstream supply chain can only be achieved if a finite boundary is not chosen and top-down & bottom-up assessments are carried out in unison (Creutzig et al., 2012). IO-based hybrid LCA is thus a useful technique for assessing the entire supply chain of an industry. It allows the enumeration of both the direct and indirect effects and provides a complete representation of the impacts (Plevin et al., 2014). TBL performance of the industry The TBL assessment of the cellulose refinery provides a snapshot of its performance in the three spheres of sustainability. The results provided in this study are comprehensive and robust in that they include all offsets and losses, including the economic and social impacts of diverting forest biomass for biofuel production, and the displacement of crude oil. Clearly, the gains in economic activity and employment outweigh the losses. The biofuel industry would increase productivity and economic growth, especially in rural and regional Australia. The existing or new rural industries will experience an increase in demand for skilled workers, which could also promote the migration of workers from different parts of the country. This would in turn have positive flow-on effects for the expansion of existing infrastructure in rural and regional Australia.

14 14 A. MALIK et al. Table 5 Energy return on investment (EROI) values for the conversion of lignocellulose biomass to ethanol Type of biomass Hardwood plantations Softwood plantations Sawmill residues Type of feedstock Scenario Distance (km) EROI Pulplogs Forest residues Pulplogs Harvest residues Chips Bark Green Sawdust On the environmental front, two indicators were analysed CO 2 emissions and energy consumption. The industry will be net carbon-negative as the amount of CO 2 emitted will be less than the amount sequestered for obtaining the wood biomass. The environmental sustainability of the cellulose-refining industry also depends on the energy content of ethanol in comparison with the energy expended in the production process. The ratio of energy in to energy out is commonly known as energy return on investment (EROI). This ratio is absolutely critical for evaluating the energy use impacts of an industry. To obtain a high EROI, the embodied energy in the cellulose refining and forestry operations should be less than the energy content of the ethanol. EROI values of all 19 cellulose-refining scenarios indicate a net gain in energy (Table 5). A review by Hammerschlag (2006) reports the biofuel EROIs of for cellulosic ethanol. The EROIs calculated in this study range from 2.7 to 5.2, depending on the type of feedstock and the travel distance between the feedstock industry and the cellulose refinery. Unsurprisingly, EROI values decrease with an increase in travel distance between the feedstock industry and the cellulose refinery. The amount of energy expended for transporting the feedstock at long distances is greater than the energy needed for short distance transportation. Therefore, a cellulose refinery located 50 km from the feedstock industry would be more energy efficient than a refinery located 200 km away. Management and planning The use of the IO model means that all TBL indicators are handled and analysed in a consistent framework featuring a common system boundary. Such an analysis is useful for studying the trade-offs between the indicators. Typically, high economic stimulus, high employment generation and low energy use are viewed positively by the stakeholders of an industry and the government. TBL analysis undertaken using hybrid LCA not only provides a comprehensive snapshot of the impacts occurring throughout the supply chain but also informs the industry about how to improve its overall sustainability performance. To make the cellulose refinery more sustainable, the amount of energy used in the supply chains of the industry would need to be reduced. Generally, it is easier to focus on the direct suppliers of the industry than the distant ones. If the energy used for undertaking forestry operations is reduced, this in turn would result in an increase in the EROI values making the industry more environmentally sustainable. Furthermore, results of the TBL assessment can assist the government and/or stakeholders in future planning and decision-making, such as, for implementing strategies to retain crude oil workers and to reduce job losses in the existing conventional wood-to-paper industry in the Green Triangle. Acknowledgements This work was financially supported by the Australian Research Council through its Discovery Projects DP and DP , and by the National eresearch Collaboration Tools and Resources project (NeCTAR) through its Industrial Ecology Virtual Laboratory. NeCTAR is an Australian Government project conducted as part of the Super Science initiative and financed by the Education Investment Fund. The authors thank Sebastian Juraszek for expertly managing the advanced computation requirements. We thank the reviewers for their insightful comments. References ABS (2011) Australian National Accounts: Input Output Tables (Product Details). Electronic Publication, , ABS Catalogue Number ABS (2012a) Australian National Accounts: Input Output Tables, ABS Catalogue Number ABS (2012b) Census of Population and Housing Australian Bureau of Statistics. Internet site ATSE (2008) Biofuels for Transport: A Roadmap for Development in Australia. Australian Academy of Technological Sciences and Engineering, Parkville, Victoria. Batten D, O Connell D (2007) Biofuels in Australia: Some Economic and Policy Considerations. Rural Industries Research and Development Corporation, Kingston, Australian Capital Territory. BREE (2013) Australian Energy Statistics. Bureau of Resources and Energy Economics, Canberra, Australia. Bullard CW, Penner PS, Pilati DA (1978) Net energy analysis: handbook for combining process and input output analysis. Resources and Energy, 1, Commonwealth of Australia (2013) Report on Australia s Oil Refinery Industry. The Parliament of the Commonwealth of Australia, Canberra, Australia.

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TBL results of forestry feedstock industry and References. Table S2. Direct and total TBL impacts of harvesting, handling, loading and transporting the feedstocks from the farm to the refinery, located 10, 50, 100 or 200 km away.