Triple bottom line study of a lignocellulosic biofuel
industry
A R U N I M A M A L I K , M A N F R E D L E N Z E N and A R N E G E S C H K E
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 life-cycle assessment (LCA) of a future cellulose-refining industry located in the Green Triangle region of South Aus-tralia. 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
conven-tional 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 eco-nomic activity and employment in the paper, pulp and paperboard industry resulting from the diversion of for-estry 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 world-wide attention due to growing concerns about environ-mental 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 self-sufficiency. 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
prompted interest in Australia and worldwide to look for renewable sources of fuels. A report by the Austra-lian Academy of Technological Sciences and Engineer-ing (ATSE) states: ‘The key findEngineer-ing [. . .] 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 sav-ings on the amount of money spent on imports (Odeh & Tran, 2007; Farineet al., 2011), opportunities for the development of rural and regional Australia (Odeh & Tran, 2007), and health benefits owing to reduced Correspondence: Arunima Malik, tel. +61 2 9351 5451, fax
particulate matter, thus cleaner air in the cities (Batten & O’Connell, 2007; Mathews, 2007). Of particular rele-vance for this work is the expectation that future biofuel supply chains will create significant regional employ-ment, net positive energy production and economic stimulus (Mathews, 2007). Apart from the benefits bestowed by biofuels expansion in Australia, the green-house 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 comprehen-sive way using life-cycle assessment (LCA; Suh & Na-kamura, 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 (Suhet al., 2004; Suh & Nakamura, 2007), which is able to provide a complete supply chain cover-age (Foranet 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 1973. This technique uses data that describe economic structure in terms of inter-industry monetary transac-tions (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 (Lenzenet al., 2014). The ability of the IELab to handle large data sets and connect regionally dis-persed 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 under-take 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; Suhet al., 2004), as for example carried out in studies on greenhouse gas emissions (Wiedmann
et al., 2011; Liuet 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) includ-ing additional indicators describinclud-ing 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 indus-tries. 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 ligno-cellulose 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, Victo-ria, have abundant biomass for the production of elec-tricity (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 hard-wood and softhard-wood 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 For-estry, 2004). The two main towns in the region, Mount Gambier in South Australia and Warracknabeal in Vic-toria, 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 under-taken. As the forestry resource in the Green Triangle region is currently used for the production of pulp, paper and woodchips, adding new demand for for-estry products for producing biofuels will likely crowd out existing economic activities in the region. There-fore, 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,
1998). 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 (Foranet 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 consid-ered, 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 pany to all upstream suppliers. But, to attain system com-pleteness, 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 sto-chastic aggregation errors (Lenzen, 2000). A ‘best-of-both-worlds’ 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 pro-cess 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 (Lenzenet al., 2014; Fig. S1, Supporting information) and bottom-up pro-cess data for cellulose refining (Section Propro-cess data) to undertake a hybrid LCA. MRIO tables consist of every region’s IO table (diagonal matrices in Fig. S1) and off-diag-onal 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
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 (106m3)
(URS Forestry, 2004)
Density (t/m3)
(Greaves & May, 2012) Mass (104t)
Hardwood plantations Pulp logs 151 000 3 0.5 150
Softwood plantations Pulp logs 55 333 1 0.45 45
Sawlogs and Sawmill residues
110 667 2 0.45 90
price sheet to obtain values in purchaser’s price. Basic prices are factory-gate prices. Markups are margins (trade, trans-port, 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, eco-nomic 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 Cli-mate Change and Energy Efficiency (DCCEE, 2012), respec-tively. We create the economic stimulus satellite vector (Wiedmannet 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; whereN 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;Kis 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;Eis the row dimension of the margin and taxes sheets compressed into 1 row each; andFis the row dimension of satellite block with 1 row each for employment, economic stim-ulus, energy use and greenhouse gas emissions data. The entire MRIO table measures 13 532913 187 sectors. In the following paragraph, we give a brief overview of the steps for calculat-ing the TBL impacts of a cellulose-refincalculat-ing industry uscalculat-ing hybrid LCA, which will be explained further in the subsequent sections.
First, we augment the Australian MRIO table with addi-tional rows and columns, and populate these rows and col-umns with data on different feedstock types and cellulose refining (Section Augmentation of the MRIO table with pro-cess data), as shown in Fig. 2. Data collection and propro-cessing 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 Sec-tion 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 cellulose-refining 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 cellulose-refining 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 Aus-tralian 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 feed-stock scenarios (see Table S1 for a list of feedfeed-stocks) and remaining 19 rows and columns with process data on cellu-lose-refining scenarios (see Table 2). The cellucellu-lose-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 hybridi-sation 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 feed-stocks correspond to the type of biomass available for harvest-ing (either hardwood or softwood plantations) and the transport distance between the feedstock industry and the cel-lulose refinery.
We populate the augmented columns (Fig. 2) with produc-tion 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 break-down 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) catego-ries for transporting biomass over a certain distance. As our bottom-up process data lack information on some input catego-ries, 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 feed-stock industry and subsequently transported to the cellulose refinery.
Additionally, we prepare production recipes for the cellu-lose-refining scenarios. To this end, we obtain detailed mone-tary 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 cel-lulose-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 cate-gories 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 feed-stocks (Table S1) as follows: (a) employment required for each feedstock by multiplying the full-time equivalent (FTE)/
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 CO2
sequestered by the forestry feedstocks. To this end, we obtain CO2 sequestration data for different wood types (Tucker
et al., 2009) and use density factors (Table 1) to calculate the total amount of CO2 sequestered per tonne of biomass
produced.
Fig. 2 Schematic diagram showing the Australian supply use MRIO table augmented with data on different feedstock and cellulose-refining scenarios. For illustration, only two feedstock and cellulose-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 Aus-tralian 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;
xi, total output from industries;xp, total output from commodities;zi, total input into industries;zp, total input into commodities;Q, satellite accounts; SA, South Australia; VIC, Victoria; RoW, rest-of-world;. . ., other categories/sectors;. . .,other regions.
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 mea-sure 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 employ-ment indicator as an example. We calculate the economic stim-ulus, energy use and carbon dioxide impacts in the same way.
LetQbe a satellite account containing the employmentQiof industry i. Then, the vector q¼Q^x1 describes the employ-ment intensityqiof industryias the employment per unit of total output x=(IA)1y, where A and y are the direct requirements and final demand matrices, respectively.
Then, m=qL is the employment multiplier, where
L=(IA)1contains all the supply chain repercussions ofq.
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 inverseL=(IA)1, and
rewrit-ing (IA)1asI+A+A2+A3+. . .+An(Waugh, 1950), we can decompose total TBL impacts as a consequence of final demandy*for the feedstocks as
Q¼qðIþAþA2þA3þ. . .þAnÞy ð1Þ ¼qyþqAyþqA2yþqA3yþ. . .þqAny; ð2Þ
wherey* is the final demand vector restricted to particular cel-lulose-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), they* vec-tor contains only one nonzero element, which is almost 500 m$ (see Appendix S3 for explanation). In Eq. 2, elementqy* repre-sents direct impacts in the cellulose-refining sector, qAy* impacts in suppliers of the cellulose-refining sector,qA2y* in suppliers of suppliers and so on. To break down these layer-wise TBL requirements into contributions from industries, we enumerate
Q¼q#Ly¼q#yþq#Ayþq#A2yþq#A3yþ...þq#Any; 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 pro-duction. 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 operat-ing inputs purchased by the cellulose refinery. This is com-monly known as a commodity breakdown (Foranet al., 2005a).
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 1 50 197 634 17.50 118.91 2.30 160.45
2 100 915 445 81.08 559.10 10.65 743.19 3 150 242 117 21.44 149.92 2.82 196.56 4 200 20 961 1.86 13.17 0.24 17.02 Forest residues 5 50 17 780 1.57 10.42 0.21 14.43 6 100 82 392 7.30 48.94 0.96 66.89 7 150 21 793 1.93 13.12 0.25 17.69 8 200 1878 0.17 1.15 0.02 1.52
Softwood plantations Pulplogs 9 50 280 916 24.88 165.83 3.27 228.06
10 100 91 881 8.14 55.23 1.07 74.56 11 150 12 907 1.14 7.90 0.15 10.48 12 200 10 000 0.89 6.23 0.12 8.12 Harvest residues 13 50 46 880 4.15 27.19 0.55 38.06 14 100 15 226 1.35 8.97 0.18 12.36 15 150 2190 0.19 1.31 0.03 1.78 16 200 10 000 0.89 6.07 0.12 8.12
Sawmill residues Chips 17 10 362 0.03 0.21 0.004 0.29
Bark 18 10 55 0.005 0.03 0.001 0.04
Green Sawdust 19 10 80 0.01 0.05 0.001 0.06
The number of tonnes for feedstocks 12 and 16 was zero in the original data, but are considered 10 000 here for the sake of completeness.
We derive the commodity breakdown equation from Eq. 1:
Q¼qðIþAþA2þA3þ. . .þAnÞy
¼qyþqðIþAþA2þA3þ. . .þAnÞAy ð4Þ
where (I+A+A2+A3+. . .+An) is Leontief inverse L. Rewriting Eq. 4, we compute:
Q¼q#yþqL#Ay; ð5Þ
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 per-taining to South Australia with additional rows and columns that contain process data for 19 different for-estry feedstock scenarios and 19 different cellulose-refining scenarios (Fig. 3, extracted from the MRIO table in Fig. 2). The vertical (I) and horizontal (II) rect-angles in the use matrixU(Fig. 3) hold the production recipes and sales structures of the scenarios, respec-tively. The square (III) in the supply matrix Vcontains 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 Austra-lian 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 ana-lyse 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 com-modities as operating inputs by the cellulose refinery (Section Commodity breakdown).
TBL impacts
Our comprehensive TBL impact analysis (Section Mea-suring the TBL impacts) of forestry feedstock and cellu-lose-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 for-estry feedstock operations and cellulose-refining scenar-ios and (d) significant variation in job creation (Section Job creation) and energy consumption (Sec-tion Energy and greenhouse gas impacts) across all for-estry feedstock scenarios. We also study the impacts of crude oil displacement by comparing the TBL multipli-ers 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 indus-tries. Our results, therefore, demonstrate the added value of including IOA into an LCA in that IOA elimi-nates 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 oper-ations 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.
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 refin-ery. Unlike employment and economic stimulus, the
economies of scale are not evident for the environmen-tal 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
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 13 608913 263, whereas the South Australian section of the table has [1*(344+344+5)+19+19+19+19+344+17+4]9[1* (344+344+6)+19+19+19+19+1]=11349771 sectors. Grey shades represent the log10of 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.
Table 3 Direct and total triple bottom line (TBL) impacts of 19 different cellulose refining scenarios
Distance (km)
Cellulose refining of different feedstock types
Crude oil refining (Petrol and diesel sector) Hardwood
plantations Softwood plantations
Pulplogs Forest
residues Pulplogs
Harvest
Residues Chips Bark
Green Sawdust Economic stimulus q $ per $ 10 0.89 0.89 0.89 0.64 50 0.89 0.89 0.89 0.89 100 0.89 0.89 0.89 0.89 150 0.89 0.89 0.89 0.89 200 0.89 0.89 0.89 0.89 m $ per $ 10 1.69 1.66 1.66 0.78 50 1.71 1.69 1.70 1.68 100 1.72 1.70 1.71 1.69 150 1.73 1.71 1.72 1.70 200 1.74 1.72 1.74 1.71 Employment q
FTE per million $
10 0.15 0.16 0.16 0.34 50 0.15 0.15 0.15 0.15 100 0.14 0.15 0.15 0.15 150 0.14 0.15 0.14 0.15 200 0.14 0.14 0.14 0.15 m
FTE per million $
10 5.53 5.65 5.54 1.37 50 5.53 5.48 5.51 5.47 100 5.50 5.45 5.47 5.43 150 5.47 5.42 5.43 5.40 200 5.44 5.38 5.41 5.37 Energy use q TJ per million $ 10 0.020 0.021 0.020 1.279 50 0.019 0.020 0.020 0.020 100 0.019 0.019 0.019 0.020 150 0.019 0.019 0.019 0.019 200 0.018 0.019 0.019 0.019 m TJ per million $ 10 1.29 1.26 1.28 4.26 50 1.47 1.61 1.52 1.65 100 1.65 1.81 1.75 1.86 150 1.83 1.99 1.97 2.06 200 2.00 2.18 2.22 2.26
Carbon dioxide emissions
q
Tonnes per million $
10 1.38 1.43 1.42 87.54 50 1.34 1.38 1.37 1.39 100 1.32 1.36 1.34 1.37 150 1.30 1.34 1.32 1.35 200 1.28 1.32 1.29 1.33 m
Tonnes per million $
10 92.2 90.0 91.5 305.7
50 104.6 114.7 108.2 117.0
100 117.4 128.2 124.4 131.8
150 129.9 140.9 140.0 145.7
200 141.9 153.0 156.7 159.6
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.
the feedstock over 200 km, as opposed to a distance of 50 km.
Economic stimulus. Economic stimulus is the total mone-tary worth of input required directly and indirectly by an industry for carrying out its operations. The pur-chase of operating inputs creates production opportuni-ties 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), stimu-lating for example the industrial machinery and equip-ment industries in the supply chain. Overall, $1.49 of economic stimulus is generated, $0.6 of which is indi-rect. Similarly, cellulose refining of hardwood pulplogs delivered to the refinery situated 50 km from the for-estry feedstock industry generates $0.89 worth of direct stimulus and $0.82 worth of indirect stimulus (Table 3).
Interestingly, the amount of economic stimulus gener-ated 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 cel-lulose refinery. For example, hardwood and softwood residues (branches, foliage and stumps) and sawmill residues (chips, bark and green sawdust) are ported using semitrailers, whereas pulplogs are trans-ported 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. There-fore, the amount of money spent on tyres and fuel for transporting pulplogs levels out the money spent on buying more semitrailers for transporting residues. Sim-ilarly, the amount of economic stimulus for all cellulose-refining scenarios is quite similar (Table 3). Direct eco-nomic stimulus for the cellulose-refining scenarios includes the machinery bought for converting lignocel-lulose biomass to ethanol. All feedstocks are subjected to the same method of lignocellulosic conversion; there-fore, not much variation in economic stimulus is observed for the cellulose-refining scenarios. A compari-son 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). Further-more, softwood plantations undergo thinning regimes (Rodriguez et al., 2011), which are undertaken by labourers and contractors as part of silvicultural man-agement (NWFIG, 2002).
A comparison of pulplogs and residues reveals that the direct employment impactsqof the latter are more pronounced than those of the former (Table S2). This holds true for the direct employment impacts of cellu-lose-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 unload-ing and processunload-ing the residues. Interestunload-ingly, 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 refin-ery. B-double truck drivers undergo extensive training, which is coordinated by well-established training cen-tres (Lambert & Quill, 2006) that in turn employ train-ers, 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 debar-ker, 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 (Keh-bila, 2010). Also, sawmill residues are transported using a semitrailer, which is the least efficient transport sys-tem (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 cellu-lose 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.
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 operat-ing inputs bought by the feedstock industry and the cel-lulose refinery are energy intensive. For example, the feedstock industry buys gas oil and fuel oil for trans-porting 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 signifi-cantly higher values than the direct energy intensityq.
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 feed-stocks undergo the same conversion process on-site cel-lulose 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 for-estry feedstock industry and the cellulose refinery increases. Both the energy and GHG impacts of cellu-lose 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 fol-low the same pattern.
Production layer decomposition
A PLD analysis provides a breakdown of the total impactsminto different supply chain tiers. Direct (on-site) 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 produc-tion (Fig. 4), and (b) the offsets and losses in economic activity and employment in the pulp, paper and paper-board 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 1009100=10 000 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 cho-sen 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 cellulose-refining 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 sin-gle 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 green-house gas impacts (Figs 4 and 5). As the graphs tend to converge after production layer 5, the suppliers in pro-duction layers 6 and beyond only play a minor role in the industry’s total impacts. The area graphs also illus-trate which sectors in the supply chain contribute to the total TBL impacts, for example, the forestry sector sup-plies feedstock to the cellulose refinery (Fig. 4). This sec-tor 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 atmo-sphere. Using the CO2 sequestration data (Section Pro-cess data), we find that 358 kt of CO2 are sequestered by a tree for producing biomass for the cellulose refin-ery. Comparing this with the total CO2 emissions for the production of biofuels (Fig. 4d), the net CO2 seques-tered (total CO2sequestered minus the total CO2 emis-sions) 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
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 +2500 jobs. Likewise, a total of 65 million$ will be lost in economic activity as a consequence of biofuel expan-sion. This will be followed by an 880 million$ gain in economic stimulus, resulting in a net economic gain of +815 million$.
Commodity breakdown
In the previous section, we apportioned the total TBL impacts according to different layers of production. Here, we carry out a commodity breakdown analysis to reveal the commodities purchased as operational inputs, in layer 2, that have the greatest contribution to the total TBL impacts of the cellulose refinery. We highlight five immediate suppliers of the refinery that play a substan-tial role in the total TBL impacts (Table 4). Theproperty operator and developer services are required during the
(a) (b)
(c) (d)
Fig. 4 Production layer decomposition of the total triple bottom line (TBL) impacts (Eq. 3) of converting lignocellulose biomass from hardwood pulplogs to ethanol. The results represent the TBL impacts of scenario 2 (Table 2). The diagrams represent the demand shock of almost 500 m$ (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.
construction phase of the cellulose refinery. Once the industry is operational, the process of converting ligno-cellulose 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 ofhardwood pulplogs
feedstock to the cellulose refinery. This step generates employment as workers are needed for harvesting, han-dling, loading and transporting the feedstock to the cel-lulose refinery; generates stimulus as machinery needs to be bought for undertaking forestry operations. Trans-port of feedstock to the cellulose refinery requires petrol and diesel that results in high energy use and green-house gas emissions. Once the feedstock arrives at the refinery, it goes through many stages that require spec-ialised industrial machinery and equipment. For example, biomass gasifier vertical vessel is required for gasifica-tion, alcohol synthesis reactor is used for alcohol synthe-sis, and distillation columns & condensers are needed
for alcohol separation. Throughout the conversion pro-cess, a variety ofpumpssuch 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 storagecontainers are needed, for example : (a) tanks for short-term storage of forestry feedstock, (b) diesel stor-age tank for storing diesel, (c) firewater storstor-age 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 sup-ply chain of the industry. By carrying out PLD analy-sis, we have demonstrated that IO analysis eliminates truncation errors that are evident in many process analyses. Furthermore, we have proved that an accu-rate 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 enu-meration of both the direct and indirect effects and provides a complete representation of the impacts (Ple-vinet 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 off-sets and losses, including the economic and social impacts of diverting forest biomass for biofuel produc-tion, and the displacement of crude oil. Clearly, the gains in economic activity and employment outweigh the losses. The biofuel industry would increase pro-ductivity 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 regio-nal Australia.
Table 4 Top five immediate suppliers of the cellulose refin-ery, and their percentage contribution towards the total impacts
Triple bottom line indicator
Top five immediate suppliers of the cellulose refinery
Per cent contribution to total impacts Employment Industrial machinery
and equipment
76.6 Hardwood pulplogs 13.9
Storage 1.1
Property operator and developer services
1.0
Pumps 0.9
Stimulus Industrial machinery and equipment
71.0 Hardwood pulplogs 21.9 Property operator and developer services
1.1
Storage 0.9
Pumps 0.7
Energy use Industrial machinery and equipment
58.3 Hardwood pulplogs 35.1
Pumps 1.1
Property operator and developer services 0.5 Storage 0.5 Carbon dioxide emissions Industrial machinery and equipment 58.5 Hardwood pulplogs 34.8 Pumps 1.1
Property operator and developer services
0.5
On the environmental front, two indicators were anal-ysed – CO2 emissions and energy consumption. The industry will be net carbon-negative as the amount of CO2 emitted will be less than the amount sequestered for obtaining the wood biomass. The environmental sus-tainability 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 scenar-ios indicate a net gain in energy (Table 5). A review by Hammerschlag (2006) reports the biofuel EROIs of 4.4–6.6 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. Unsurpris-ingly, 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 transporta-tion. 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 analy-sis 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 snap-shot 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 undertak-ing forestry operations is reduced, this in turn would result in an increase in the EROI values – making the industry more environmentally sustainable. Further-more, results of the TBL assessment can assist the government and/or stakeholders in future planning and decision-making, such as, for implementing strate-gies 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 DP0985522 and DP130101293, and by the National eResearch Collaboration Tools and Resources project (NeCTAR) through its Industrial Ecology Virtual Laboratory. NeCTAR is an Australian Govern-ment 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.
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Supporting Information
Additional Supporting Information may be found in the online version of this article:
Appendix S1. Schematic of the Australian supply use
multi-region input–output (MRIO) table.
Figure S1.Schematic diagram showing the Australian sup-ply use MRIO table.
Appendix S2.Data on forestry feedstock scenarios.
Table S1.Amount of tonnes and triple bottom line data for 19 different forestry feedstock scenarios depending on the travel distances between the industry and a future bio-refinery.
Appendix S3.Augmentation of the MRIO table with pro-cess data.
Figure S2. Schematic diagram showing the South Austra-lian IO table, which is a section of the MRIO table.
Appendix S4.Preparation of process data.
Appendix S5. TBL results of forestry feedstock industry and References.
Table S2.Direct and total TBL impacts of harvesting, han-dling, loading and transporting the feedstocks from the farm to the refinery, located 10, 50, 100 or 200 km away.
