Reviewing methodologies and deriving recommendations

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Resources, Conservation and Recycling 73 (2013) 211– 228

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Review

Critical aspects in the life cycle assessment (LCA) of bio-based materials –Reviewing methodologies and deriving recommendations

P. Pawelzika, M. Carusb, J. Hotchkissc, R. Narayanc, S. Selkec, M. Wellischd, M. Weisse,B. Wickea, M.K. Patela,∗

a Copernicus Institute of Sustainable Development, Faculty of Geosciences, Utrecht University, 3584 CD Utrecht, The Netherlandsb Nova-Institut, Industriestraße, 50354 Hürth, Germanyc School of Packaging and Center for Packaging Innovation and Sustainability, Michigan State University, East Lansing, MI 48824, USAd Agriculture and Agri-Food Canada, Innovation and Growth Policy Division, 1341 Baseline Road, Ottawa, Ontario K1A 0C5, Canadae European Commission – DG Joint Research Centre, Institute for Energy and Transport, Sustainable Transport Unit, via Enrico Fermi 2749, TP 230, 21010 Ispra, Italy

a r t i c l e i n f o

Article history:Received 7 August 2012Received in revised form 2 February 2013Accepted 4 February 2013

Keywords:Life cycle assessmentBio-based materialsEnvironmental impactsBiogenic carbon storageIndirect land use changeSoil carbon

a b s t r a c t

Concerns over non-renewable fossil fuel supply and climate change have been driving the Renaissance ofbio-based materials. To substantiate environmental claims, the impacts of bio-based materials are typi-cally quantified by applying life cycle assessment (LCA). The internationally agreed LCA standards providegeneric recommendations on how to evaluate the environmental impacts of products and services but donot address details that are specifically relevant for the life cycles of bio-based materials. Here, we providean overview of key issues and methodologies explicitly pertinent to the LCA of bio-based materials. Weargue that the treatment of biogenic carbon storage is critical for quantifying the greenhouse gas emis-sions of bio-based materials in comparison with petrochemical materials. We acknowledge that biogeniccarbon storage remains controversial but recommend accounting for it, depending on product-specificlife cycles and the likely time duration of carbon storage. If carbon storage is considered, co-product allo-cation is nontrivial and should be chosen with care in order to: (i) ensure that carbon storage is assigned tothe main product and the co-product(s) in the intended manner and (ii) avoid double counting of storedcarbon in the main product and once more in the co-product(s). Land-use change, soil degradation, wateruse, and impacts on soil carbon stocks and biodiversity are important aspects that have recently receivedattention. We explain various approaches to account for these and conclude that substantial methodo-logical progress is necessary, which is however hampered by the complex and often case- and site-specificnature of impacts. With the exception of soil degradation, we recommend preliminary approaches forincluding these impacts in the LCA of bio-based materials. The use of attributional versus consequen-tial LCA approaches is particularly relevant in the context of bio-based materials. We conclude that it ismore challenging to prepare accurate consequential LCA studies, especially because these should accountfor future developments and secondary impacts around bio-based materials which are often difficult toanticipate and quantify. Although hampered by complexity and limited data availability, the applicationof the proposed approaches to the extent possible would allow obtaining a more comprehensive insightinto the environmental impacts of the production, use, and disposal of bio-based materials.

© 2013 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2122. Carbon storage in bio-based products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

2.1. ADEME’s methodology for bio-based materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2122.2. The European Commission’s Lead Market Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2132.3. GHG Protocol Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2132.4. ISO 14067 – carbon footprint of products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2142.5. The ILCD Handbook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

∗ Corresponding author. Tel.: +31 030 253 7634; fax: +31 030 253 7601.E-mail address: [email protected] (M.K. Patel).

0921-3449/$ – see front matter © 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.resconrec.2013.02.006


212 P. Pawelzik et al. / Resources, Conservation and Recycling 73 (2013) 211– 228

2.6. PAS 2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2142.7. Process/material carbon footprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2152.8. Discussion and recommendations on the accounting of biogenic carbon storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

3. Land use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2173.1. Land use change and efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2183.2. Carbon stocks in soil and standing biomass. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2193.3. Early-stage impact assessment methods for bio-based materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

3.3.1. Water use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2203.3.2. Soil degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2213.3.3. Biodiversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221

4. Allocation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2225. Use of attributional versus consequential LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2236. Assessment frameworks for bio-based materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2247. Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226Appendix A. Example for the case specificity of land use efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226A.1. Comparison with PE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226A.2. Comparison with aluminum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226

1. Introduction

Bio-based plastics have a history of over 150 years. Key mile-stones include the invention of cellulose nitrate (celluloid) in the1860s, man-made cellulose (1890s), cellulose-hydrate films (cel-lophane) in 1912, casein protein (milk fiber) in the 1930s andsoy-based plastics in the 1930s (Monopolies, 1968; Ralston andOsswald, 2008; Shen et al., 2009). These materials lost their impor-tance with the rise of the petrochemical industry in the 1950s. Sincethe 1980s there has been increasing interest in the developmentof biodegradable plastics such as polylactic acid (PLA), polyhydrox-yalkanoates (PHAs) and various types of thermoplastic starch madefrom bio-based feedstock (Shen et al., 2009). The renaissance of bio-based plastics and bio-based materials in general has been fueledby progress in biotechnology, high fossil fuel prices as well as envi-ronmental concerns (Patel et al., 2005). Bio-based materials havethe potential to reduce non-renewable energy use as comparedto conventional materials but may come at the cost of additionalland use and related environmental impacts. To quantify the envi-ronmental impacts of bio-based materials, standardized Life CycleAssessment (LCA) methodology (ISO, 2006a,b) has been applied tobio-based materials in numerous studies (see, e.g., Shen and Patel,2010; Groot and Borén, 2010; Weiss et al., 2012).

The current ISO (2006a,b) standards for conducting an LCAprovide principal methodological guidance but no detailed instruc-tions on how to address critical issues that typically occur whenconducting an LCA for bio-based materials. Issues such as theaccounting for bio-based carbon storage or the impacts of land usechanges associated with biomass production significantly affect theassessment results and require standardized approaches for evalu-ation. The present shortcomings have led to the need for a broadlyshared, comprehensive, and yet sufficiently detailed methodologyto assess the environmental impacts of bio-based products (e.g.,OECD, 2010; Nowicki et al., 2008). To establish such a method-ology, it is important to first clarify the critical issues in the lifecycle assessment of bio-based materials in a comprehensive man-ner and to review the various approaches that have been developedto address the issues.

So far, such a review has not been published. This paper aimsto close this gap, beginning with a discussion of the approaches toaccount for carbon storage in Section 2. Next, we describe method-ologies to assess the various impacts of land use (Section 3). InSections 4 and 5, we focus on more general methodological issuessuch as allocation and a comparison of attributional and conse-quential LCA. Section 6 provides a short discussion on assessment

frameworks. The article ends with a discussion and conclusionson how to address the key issues presented here in the life cycleassessments of bio-based materials (Section 7).

2. Carbon storage in bio-based products

The carbon contained in bio-based materials is fully or partly ofbiogenic origin. When accounting for the biogenic carbon in bio-based materials (see Fig. 1 and related explanations in Box 1) twoprincipal approaches can be taken:

• biogenic carbon is considered to be CO2 neutral and excludedfrom the inventory analysis,

• biogenic carbon is accounted for as carbon storage, thus takinginto account that CO2 is captured from the atmosphere duringphotosynthesis and retained within the bio-based material for anumber of years.

Whether or not to account for biogenic carbon storage, that isgenerally temporary, is the subject of ongoing debates (Levasseuret al., 2012). On the one hand, biogenic carbon storage could beexcluded from the inventory analysis because it is in the majority ofcases reversible and inevitably adds carbon emissions in the future.On the other hand, biogenic carbon storage could be accounted forbecause it delays radiative forcing and can offset current anthro-pogenic carbon emissions. The achievable benefits from accountingfor biogenic carbon storage depend on the time horizon over whichthe global warming potential of emissions is considered as wellas external factors such as the future levels of anthropogenic car-bon emissions and atmospheric CO2 concentrations. Although theglobal warming potential is commonly considered over a time hori-zon of 100 years, the choice is intrinsically subjective (Levasseuret al., 2012).

This article identifies seven approaches on how to deal withbiogenic carbon storage (Table 1). The next sections introduce andexplain these approaches individually.

2.1. ADEME’s methodology for bio-based materials

The French Environment and Energy Management Agency(ADEME) suggests that the biogenic carbon contained in bio-basedmaterials should be considered CO2 neutral (BIS, 2009). They arguethat the lifespan of bio-products does not typically exceed 10–20years, making it reasonable to assume that the delay in radiativeforcing due to biogenic carbon storage is negligible. The approach


P. Pawelzik et al. / Resources, Conservation and Recycling 73 (2013) 211– 228 213

Box 1: Accounting for bio-based carbon storage in thesystem ‘‘cradle-to-factory gate’’The carbon that is sequestered from the atmosphere throughphotosynthesis during plant growth typically does not all endup in the bio-based material. Through the manufacturing pro-cess of bio-based materials, a portion of the bio-based carbonis lost as waste or emissions (carbon stream C2 in Fig. 1).When calculating total carbon emissions (Cem) for the sys-tem “cradle-to-factory gate”, the following components needto be quantified: (i) the carbon stored in the raw biomass(−C0; negative sign for carbon storage), (ii) the amount ofthe bio-based carbon released during the production process(including the emissions from direct land-use change, C2) and(iii) the release of fossil carbon (C3) related to process energyrequirements (e.g., heat and electricity). We exclude here thepotential effects of indirect land-use change and assume thatthe carbon stocked in above and below ground organic matteris maintained during the cultivation of biomass (sustainablecarbon management in agriculture/forestry).The calculation of Cem can be performed according totwo approaches which lead to the same result: (i) the“Biofeedstock-storage approach” and (ii) the “Biomaterial-storage approach”.When using the Biofeedstock-storage approach (Eq. (1)) all pro-cess emissions need to be quantified and Cem is calculated asfollows:

Cem = −C0 + C2 + C3 (1)

If the mass balance for bio-based carbon is considered, i.e.,

C0 = C2 + C4 (2)

with C4 being the bio-based carbon equivalents embodied inthe bio-based product, one can re-write Eq. (1) to:

Cem = C3 − C4 (3)

We refer to Eq. (3) as the second approach for calculating Cem,i.e., the Biomaterial-storage approach. In this approach Cem iscalculated as function of the bio-based carbon sequestered inthe final product and fossil CO2 emissions (which are equiva-lent to the fossil fuel used C1 = C3, see Fig. 1).Based on our experience, we recommend applying Eq. (3), i.e.,the Biomaterial-storage approach for calculation carbon emis-sions. This approach typically leads to more reliable resultsthan the Biofeedstock-storage approach, which should only beused if there is certainty about the completeness and accuracyof the biogenic carbon emissions (C2). Ideally, however, bothEqs. (3) and (1) are applied, which, in combination, offer themost comprehensive understanding.

of carbon neutrality is commonly applied for bioenergy (prod-ucts with no appreciable carbon storage). We argue here that theapplication to bio-based materials can be justified for short-livedproducts that are disposed of by incineration. For longer life prod-ucts that are recycled or landfilled carbon neutrality presents aconservative approach that disregards any short-term and mid-term benefits. It should be noted that the ADEME’s methodologyrequires methane emissions to be accounted for, even if they orig-inate from biogenic carbon.

2.2. The European Commission’s Lead Market Initiative

The European Commission’s Lead Market Initiative proposesthat the biogenic carbon contained in bio-based materials shall bededucted when calculating the total carbon emissions caused bya product for the “cradle-to-factory gate” system (EC, 2009a). Noguidance is given on whether to account for the time period of theuse phase (and hence of temporary carbon storage) and if so, howto account for it.

In the past few years, the method has been applied in numerousLCA studies, in particular for new bio-based polymers in primaryform, i.e., for granules as opposed to final products (e.g., Kim andDale, 2005; Patel et al., 2006; Vink et al., 2007). The method isdepicted on the right hand side of Fig. 1 and it is explained in moredetail in Box 1. The box also explains two variants of applying themethod (i.e., the Biofeedstock-storage approach and the Biomaterial-storage approach).

2.3. GHG Protocol Initiative

The GHG Protocol Initiative of the World Resources Instituteand the World Business Council for Sustainable Development havedeveloped a standardized method for the inventory of greenhousegas (GHG) emissions (GHGP, 2011). For the system “cradle-to-factory gate”, GHGP (2011) gives credit for biogenic carbon storage,similar to the European Commission’s Lead Market Initiative (EC,2009a). For the system “cradle-to-grave”, the amount of carbonreleased throughout the use and disposal of the product needs to beaccounted for, excluding the embedded carbon that is not releasedinto the atmosphere (e.g., the biogenic carbon that is containedwithin the ashes of a disposed wood product will not be releasedinto the atmosphere under anaerobic conditions in a landfill). Forintermediate bio-based materials that are used as inputs for otherprocesses, the biogenic carbon stored in the product needs to bereported. The GHGP (2011) does not prescribe weighting factors

C2, Biogenic CO2 fromproduction processes

C0, CO2 sequestered by treeC0 = C2 + C 4

Tree

C1,

Factory

Oil in ground

C3, Fossil CO2 from

Carbon equivalents from system

production processes

Carbon Storage

CO2 in air (C 0)

= C2 + C3 - C 0= C3 - C 4 (safer method)

CO2 in fossil fuelswith C1 = C 3

C4, CO2 embeddedin Biobasedproduct

Tree

C1,CO2 in fossil fuelswith C1 = C3

Factory

Oil in ground

C3, Fossil CO2 from

Carbon equivalents from system= C3 = C1

production processes

Carbon Neutral

Fig. 1. Two alternative methods for accounting for bio-based carbon when assessing the contribution of a fully bio-based product to global warming for the system “cradle-to-factory gate” (the two alternative methods are “carbon neutrality” on the left hand side and “carbon storage” on the right).



Table 1Key characteristics of initiatives/approaches developed to account for bio-based carbon storage.

Approach Key characteristics

Embeddedbio-basedcarbon

Time dependency of emissions Weightingfactor for timedependencydeveloped

Life cycle stage

ADEME’s LCA Methodologyfor Bioproductsa

Carbon neutral Not accounted for No Cradle-to-grave

Lead Market Initiativeb Carbon storage Not addressed No Cradle-to-factory gateGHG Protocol Initiativec Carbon storage Not addressed No Cradle-to-factory gate & cradle-to-graveISO 14067d Carbon storage Optional No Cradle-to-factory gate & cradle-to-graveILCD Handbooke Carbon storage Accounted for Yes Cradle-to-factory gate & cradle-to-gravePAS 2050f Carbon storage Optional Yes Cradle-to-factory gate & cradle-to-graveProcess/Material Carbon

FootprintgCarbon storage Not accounted for; but penalty

for petrochemical products.No Cradle-to-factory gate & cradle-to-grave

References within table:a BIO Intelligence Service (2009).b EC (2009a).c GHGP (2011).d ISO (14067).e EC (2010a).f PAS 2050 (2011).g Narayan (2011).

for delayed, offset, or avoided emissions due to biogenic carbonstorage (GHGP, 2011).

2.4. ISO 14067 – carbon footprint of products

The draft standard for the carbon footprint of products devel-oped by ISO (2012) states that in general when calculating thecarbon footprint for a product’s entire life cycle all the emissionsand removals (biogenic and fossil) must be taken into account,without considering the time period (see Section 6.3.8 of ISO14067). This means that biogenic carbon storage in bio-based prod-ucts should be accounted for as a removal from the atmospherewithout considering time. If the use stage or end-of-life treatmentlead to emissions or removals within 10 years these are supposedto be treated as if they had occurred at the beginning of the assess-ment period. In addition to the standard calculations, for instancewhere the emissions and removals from the use phase and end-of-life product disposal occur more than 10 years after the inceptionof the product, the effect of time for these emissions may be calcu-lated and reported separately. If the effects of the time componentare calculated, the final report must also include the GHG emis-sions calculated without considering the time dimension and themethod chosen for these calculations1 and the reason for choosinga method. No specific approach is indicated for taking into accountthe time period for bio-based products.

To summarize, based on ISO 14067, for the “cradle-to-factorygate” system, credit is given for biogenic carbon storage in materi-als. For the “factory gate-to-grave” system, the default is to assignno credit to temporal biogenic carbon storage; however, a separatecalculation that does take temporary carbon storage into accountcan be reported.

2.5. The ILCD Handbook

The method put forward by the International Reference LifeCycle Data System (ILCD) Handbook (EC, 2010a) accounts for timewhen assessing the effect of biogenic carbon on global warm-ing. In line with the timeframe chosen by IPCC (2007), the ILCD

1 We refer the reader to Brandão et al. (2013) for a review of methods to accountfor the potential climate impacts of temporary biogenic carbon storage.

distinguishes between carbon that is released within a 100 yearperiod and carbon that is released more than 100 years after thebio-based product was produced. For carbon that is released withinthe first 100 years, the credit or corrective flow value calculated forcarbon storage is given by multiplying the mass [kg] of embod-ied carbon (C4 in Fig. 1, expressed in kg CO2 equivalents) by thenumber of years of carbon storage, divided by 100 to represent thetimeframe of 100 years; this approach is equivalent to a weightingfactor of 1% per year.

Carbon that is released after 100 years is not taken into accountin the general LCA results, i.e., it is treated as permanently storedbut should be calculated and reported separately (as a memo item).EC (2010a) thereby aims to ensure that the (undesired) release ofcarbon beyond the 100 year timeframe is not totally ignored.

2.6. PAS 2050

The British Standards Institution (BSI, 2011) developed the PAS2050 (Publicly Available Specification No. 2050) which adopts theconcept of biogenic carbon storage. PAS 2050 considers the dimen-sion of time in line with IPCC (2007) and EC (2010a) by applyinga timeframe of 100 years. In line with ISO (14067), all emissionsand removals (fossil and biogenic) that occur within the 100 yearperiod are quantified and treated as if they occurred at the begin-ning of the time period. The effects of the delay in emissions may betaken into account, however already after one year from productinception (as opposed to 10 years in ISO (14067)). To account forthe delay in emissions, the same approach is applied as in the ILCDHandbook (EC, 2010a), with the exception of a special case, whenall the emissions are released in a single event between the 2ndand 25th year.

In calculating the GHG emissions for this special case, a mul-tiplicative factor m is incorporated within the weighting factorequation. This factor is based on the removal rate of CO2 fromthe atmosphere, which was derived to be 0.76 (Clift and Brandão,2008). The reason for the removal of CO2 from the atmosphere is itsabsorption in oceans and its incorporation in terrestrial and aquaticbiomass. For every metric ton of CO2 only 0.76 metric tons thus needto be considered, leading to the following weighting factors whichare applied to determine the global warming impact of emissionsover the assessment period:



• If all the carbon emissions occur within the first year, they aretreated as a single emissions event at the beginning of the 100year assessment period; in this case the weighting factor is 1.

• If all the carbon emissions occur as a single emissions eventbetween 2 and 25 years after the manufacture of the product,the weighting factor is calculated by multiplying 0.76 with thenumber of years of full carbon storage and divided by 100 years,which represents the assessment period.

• In all other situations, the weighting factor is in principle the sameas proposed by ILCD Handbook, i.e., the fraction of the number ofyears of biogenic carbon storage and the 100 years of assessmentperiod.

The presented approach leads to discontinuity in the way theweighting factor is calculated. For a single release, the multiplierm within the weighting factor formula differs (up to one year afterproduction and before production: m = 1; between year two and 25:m = 0.76; and beyond year 25: m = 1). Contrary to single release, forgradual release, the general weighting factor equation is used (i.e.,m = 1). This also holds if the gradual release occurs within the first25 years. This inconsistency further compounds the discontinuityjust described.

By including the CO2 decay rate within the weighting factor for-mula, PAS 2050 aimed to more accurately reflect global warmingpotential of CO2 released at different times. However, the appli-cation of the weighting factor is an optional step that should beconsidered cautiously, because it entails the risk of inconsistencies.

2.7. Process/material carbon footprint

Narayan (2006, 2011) distinguishes between the process car-bon footprint and the material carbon footprint. The process carbonfootprint represents the carbon emissions resulting from energyuse and manufacturing processes of the system “cradle-to-factory”gate. The material carbon footprint represents the carbon emissionsresulting from carbon stored in materials. The process carbon foot-print is generally non-zero and process-specific. The material carbonfootprint is zero for fully bio-based materials, while it representsthe embodied (stored) fossil carbon for petrochemical materials.The material carbon footprint as described here implicitly assumesa “cradle-to-grave” system, with full oxidation (e.g., via incinera-tion) of the materials at the end of their life. The material carbonfootprint can be accurately measured by quantifying the biogeniccarbon content based on the signature of radioactive isotope Car-bon 14 (C-14): since the half-life of radioactive C-14 is around 5730years, the petrochemical feedstocks formed over millions of yearswill have no C-14 signature. The quantity of bio-based carbon con-tent in a test material can be readily determined by combusting itand analyzing the CO2 gas emitted to provide a measure of its C-14/C-12 content ratio relative to the modern carbon-based oxalicacid radiocarbon standard reference material (SRM) 4990c, referredto as HOxII by the National Institute for Standards and Technology(NIST). This methodology to determine bio-based carbon contenthas an accuracy of ±3% and has been codified into a standard in theUSA (ASTM, 2010).

LCA studies do not usually distinguish between the processcarbon footprint and the material carbon footprint. The rationalefor differentiating the material carbon footprint from the processcarbon footprint is to clearly articulate and communicate the ori-gin of the carbon emissions related to bio-based products. It alsoclearly shows the process carbon footprint implications in com-parison to current processes and the possible need to reduce it.The differentiation shows where further improvement is required,while presentation of the combined value can hinder innovation,especially if the total carbon footprint of the bio-based productis larger compared to the petrochemical product. Given the early

development stage of the bio-based materials, it would be inade-quate according to Narayan (2011) to focus only on the combinedcarbon footprint.

2.8. Discussion and recommendations on the accounting ofbiogenic carbon storage

Each method discussed above has strengths and weaknesses;any choice regarding the accounting of biogenic carbon storagereflects to a certain extent subjective value judgment. Among theapproaches described, only ADEME (BIS, 2009; Section 2.1) does notconsider biogenic carbon storage. This makes the approach sim-ple but it may be criticized for neglecting the delayed radiativeforcing that results from the temporary storage of carbon. The Euro-pean Commission’s Lead Market Initiative (EC, 2009a; Section 2.2)and the GHG Protocol Initiative (GHGP, 2011; Section 2.3) proposesimple methods to account for biogenic carbon storage but do notconsider the time period of carbon storage in bio-based materials.ISO 14067 (Section 2.4) provides the option of incorporating time.PAS 2050 (Section 2.6) provides a similar approach to ISO 14067.However, the optional method proposed by PAS 2050 for taking intoaccount the time dimension of carbon storage may be criticized. Inparticular, there are concerns about the validity of using a weight-ing factor of 0.76 for emissions released between the 2nd and the25th year after manufacture of a product while using a differentweighting factor for all other time periods of carbon storage. Finally,the ILCD Handbook (EC, 2010a; Section 2.5) takes into account thelifespan of materials and gives credit to biogenic carbon storageover a 100 year time period for the “cradle-to-grave” system.

To evaluate these approaches, the first question is whether ornot to account for bio-based carbon storage. This question is onlycontroversial for the system “cradle-to-factory gate” because thefinal application and the type of disposal are unknown and there-fore the ultimate fate of the biogenic carbon is unclear. Ideally,the method chosen for the system “cradle-to-factory gate” shouldresult in the same ranking of materials as the outcome for the sys-tem “cradle-to-grave”.

To obtain deeper insight, we discuss the carbon emissions ofthree hypothetical cases, i.e., a bio-based material with carbonstorage (abbreviated as “S”), the same bio-based material withoutcarbon storage (abbreviated as “N”), and their petrochemical coun-terpart (abbreviated as “P”). We distinguish the system boundaries“cradle-to-factory gate” (abbreviated as “1”) and “cradle-to-grave”with three alternative types of waste management, i.e., incineration(abbreviated as “2”), landfilling with degradation of the bio-basedproduct (“3a”) and landfilling without degradation (“3b”).Thesecases cover the full spectrum ranging from complete carbon storageto complete carbon release. The combination of the product sys-tems with the system boundaries (including waste managementoptions) leads to a total of twelve cases (abbreviated as “1N”, “1S”etc., see Fig. 2).

For the comparison we choose as starting point a material thatcan either be produced from bio-based or from petrochemical feed-stock, e.g., polyethylene. The use phase is uniformly excluded sincewe disregard the time period of carbon storage.

Fig. 2 (top part) shows a stylized comparison for the fullybio-based material with high process energy requirements in com-parison with its petrochemical counterpart. Without accountingfor carbon storage, the GHG emissions of the bio-based materialfor the system “cradle-to-factory gate” are larger than those of thepetrochemical product (1N > 1P); only if biogenic carbon storage inthe material is deducted, the bio-based material shows lower GHGemissions than its petrochemical counterpart (1S < 1P). The chosencase is hence the most critical one because the treatment of carbonstorage is decisive for the overall conclusions. Using this case, weaim to answer the question whether the “cradle-to-factory” gate



Fig. 2. Stylized comparison of greenhouse gas emissions for a fully bio-based material (first and second column from left) with its fully petrochemical counterpart for thesystems “cradle-to-factory gate” (top, No. 1) and “cradle-to-grave”.

results with or without carbon storage reflect the results for the“cradle-to-grave” system more accurately. To answer this question,we discuss three systems (Fig. 2):

• “Cradle-to-grave” results with incineration (System 2, see middlepart of Fig. 2) are determined by adding the carbon equivalentsof the embodied fossil carbon (i.e., the difference between case1N and 1S) to the “cradle-to-factory gate” results: (i) for thebio-based material with carbon storage (hence resulting in case2S) and (ii) for the petrochemical material (hence resulting incase 2P). The bio-based materials (identical results for 2N and 2Sfor this case) then show lower emissions than the petrochem-ical material (2P). The result for case 2N (“cradle-to-grave”, nobiogenic carbon storage) is identical with the case 1N (“cradle-to-factory gate”, no biogenic carbon storage) because only the

fossil carbon emissions are accounted for up to the factory gateand because waste incineration does not add any (net) carbonemissions for the fully bio-based material. The conclusion forthis case is that the bio-based material (2N and 2S) shows lowerGHG emissions than the petrochemical material (2P) regardlessof whether carbon storage is considered or not; this system ishence insensitive to the treatment of carbon storage.

• “Cradle-to-grave” results with landfilling and permanent carbonstorage (System 3a, see lower part of Fig. 2) can be approximatedby “cradle-to-factory gate” data (System 1) because emissionsrelated to transportation to the landfill are typically negligible.Zero carbon oxidation implies that the carbon embodied in thematerial remains stored in its original state forever. This assump-tion must be equally applied for the embodied carbon of fossiland of biogenic origin. This means that, for this system boundary,



carbon storage is the adequate approach for the bio-based mate-rial (i.e., case 1S should be chosen), while case 1N would leadto a false conclusion. This system is hence highly sensitive tothe approach, clearly calling for accounting of biogenic carbonstorage.

• “Cradle-to-grave” results with landfilling and complete car-bon oxidation (System 3b) are comparable to “cradle-to-grave”results with incineration (System 2). Also compositing or diges-tion with full oxidation of biogenic carbon would fall under thiscategory. As discussed above for incineration, the results for thebio-based material are identical (2N = 2S) regardless of whethercarbon storage is considered or not.

To summarize, the results are insensitive to the accounting ofbiogenic carbon storage for the Systems 2 and 3b where the bio-based material is found to be favorable over the petrochemicalmaterial. For System 3a, accounting for biogenic carbon storageis a necessity because otherwise the assumption of no oxidationwould be violated. The results from the three systems, which coverthe full spectrum of end-of-life treatment (2, 3a and 3b), hence sup-port the accounting of biogenic carbon storage while leading to theconclusion that the bio-based material results in lower GHG emis-sions than the petrochemical material. We now revert to System 1(“cradle-to-factory gate”), where the inclusion or exclusion of bio-genic carbon storage decides whether the bio-based material incurshigher or lower carbon emissions as compared to the petrochem-ical material. In order to ensure that the ranking determined forthe system “cradle-to-factory gate” coincides with the “cradle-to-grave” system, it is necessary to account for biogenic carbon storagein any “cradle-to-factory gate” assessment of bio-based materials.

Wherever possible, the “cradle-to-grave” system should bestudied and the analysis should not be limited to the system“cradle-to-factory gate”. However, a pragmatic solution is requiredfor polymers and chemical intermediates because it is mostly notpossible to foresee the application (or the portfolio of all applica-tions) which may also change over time. Most of the approachesdiscussed above in Sections 2.1–2.7 coincide in deducting biogeniccarbon storage. While this leads to lower carbon emission valuescompared to incineration (size of bars in System 1 as opposed toSystem 2 in Fig. 2), it is essential to realize that the difference incarbon emissions between the petrochemical and the bio-basedproduct, expressed in kg CO2 eq. per kg, is identical in the twosystems.2

As explained above, the reasoning is based on a chemicallyidentical material, produced from bio-based as opposed to petro-chemical feedstock. This choice was made for convenience, in orderto avoid complicating the comparison by differences in embodiedcarbon. However, the argumentation is equally valid also whennon-identical organic materials are compared. When comparingbio-based materials with petrochemical materials the reasoningabove is valid because both materials contain embodied carbon thatis released in the case of carbon oxidation (symmetry). While thistype of comparison is most common, bio-based materials may alsobe compared to inorganic materials, e.g., glass or cement. In thiscase, the assumed bio-based carbon storage leads to a bias in favorof the bio-based material (asymmetry) because it is quite likely thatthe bio-based carbon will be released at one stage while no carbonis released due to oxidation of the inorganic materials. When com-paring bio-based materials to inorganic materials the LCA shouldtherefore be extended to a “cradle-to-grave” system that includesthe use phase and end-of-life waste management.

2 In contrast, the relative savings in % differ in the two systems (compare Fig. 2);this should be considered when drawing conclusions.

With the exception of the latter case, we advocate the methodol-ogy proposed by the European Commission’s Lead Market Initiative(EC, 2009a; see Section 2.2) if the carbon footprint of bothintermediate and final bio-based materials is assessed on a “cradle-to-factory gate” basis.

When performing an LCA for the system “cradle-to-grave”, werecommend using the method proposed by the ILCD Handbook(EC, 2010a) that assumes that biogenic carbon storage temporarilydecreases the carbon content of the atmosphere. The results fromthe “cradle-to-factory gate” analysis are then combined with theassessment of the use phase – thereby accounting for biogenic car-bon storage – and finally with end-of-life treatment. Moreover, westrongly endorse reporting separately (as a memo item) biogeniccarbon storage next to the energy and process related emissions offossil carbon. This ensures transparency and it provides guidancewhere further process improvements can be made, as suggested byNarayan (Section 2.7).

The recommendations above reflect the latest stakeholder andexpert discussions. We finalize this section with an aspect whichhas not been addressed in the context of bio-based materials. Bio-genic carbon storage in materials may only be a suitable strategy tomitigate climate change if carbon remains stored over long timeperiods and moreover if the stored carbon is emitted at timeswhen anthropogenic carbon emissions and atmospheric carbondioxide concentrations are lower than today (see also Brandão andLevasseur, 2011). However, the IPCC (2001) has projected that theconcentration of atmospheric CO2 will be higher in the next 50–100years. Under such a scenario, the release of previously stored bio-based carbon may have a greater climate impact in the future thanit would have today. In order to establish whether this is the casethe contribution of bio-based materials to deceleration (in the shortterm) and acceleration (in the longer term) of CO2 concentrationsshould be known. This would require a good understanding of thefuture market volumes, the use-phase applications and the wastemanagement systems. It would, moreover, be necessary to havesolid insight into the impacts of the concentration gradient overtime (e.g., 10 ppm in 2 versus 3 years) and at different starting lev-els (e.g., 10 ppm increase starting from 400 ppm versus 500 ppm).Further research would be required to establish these relationshipsand to combine them with strategies for favorable timing of emis-sions released from stored bio-based carbon. While most of theapproaches discussed above advocate the storage of biogenic car-bon for bio-based products, there is still no consensus for otherproducts or systems on how to account for biogenic carbon stor-age despite considerable efforts to develop robust methodologies(Brandão and Levasseur, 2011). In any case, the climate effects ofbiogenic carbon storage in materials will remain negligible in theshort and mid-term due to the small quantities of bio-based mate-rials (e.g., bio-based plastics) produced. However, aspects of carbonstorage should be considered in more detail once the commercial-ization of bio-based materials in bulk quantities (tens of millions oftons) is foreseeable.

3. Land use

Land is required for the production of terrestrial biomass for bio-fuels and bio-based materials as well as for the production of foodand feed. Land use and changes in land use can lead to unintendedenvironmental impacts, such as biogenic carbon emissions, carbonloss from soils, soil erosion, nutrient depletion, water consump-tion, and loss of biodiversity. This section provides an overview ofthe prominent land use impacts. We first focus in more generalterms on the accounting of direct and indirect land use change andland use efficiency (Section 3.1). Afterward, we specifically addresscarbon loss from soils and standing biomass (Section 3.2) and the



Fig. 3. Schematic representation of the calculation method for the indicator “land use efficiency”.Data on PLA from Bos et al. (2012); data on PET from Chen and Patel (2012).

assessment of impacts such as general soil degradation, water use,and biodiversity that is currently still in an early stage (Section 3.3).

3.1. Land use change and efficiency

In LCA, Direct Land Use Change (DLUC) and Indirect Land UseChange (ILUC) are typically differentiated. DLUC is the intentionaltransition from the current use of land (e.g., forest) to the cultiva-tion of biomass as feedstock for bio-based materials (or for food,feed or bioenergy). The environmental impacts can be calculatedby applying the methodology presented below in Section 3.2. Theimpacts of direct land use change must be taken into account in thelife cycle assessment of bio-based materials according to the PAS2050 methodology, ISO 14067, ILCD, the European Commission’sLead Market Initiative (EC, 2009a) and the GHG Protocol Initiative(GHGP, 2011).

ILUC, by contrast, is the unintentional land use change thatoccurs outside the bio-based material’s feedstock production area,but that is induced by producing biomass feedstock. ILUC occurswhen land being used for food and feed production becomesrededicated and used for biomass production for material pur-poses (likewise for bioenergy). This, in turn, can lead to a situationwhere additional land is being taken into agricultural productionelsewhere for food and feed production. Other secondary effectsinclude the intensification of existing agriculture or a reductionin actual food production (Searchinger et al., 2008; Tipper et al.,2009; Hertel et al., 2010). Several approaches have been developedto quantify LUC-induced GHG emissions;3 those based on market-equilibrium models can capture complex market feedbacks andendogenize agricultural intensification. However, the main limi-tation of such models is that large uncertainties exist primarilyin the underlying data (e.g., yields on newly converted land, rateof agricultural intensification, and price-yield elasticity) as well asprojections on the location and type of land use changes, resultingproduction and trade patterns of biomass, price effects and related-price elasticity, and the accounting for co-products (Wicke et al.,2012; IEA, 2009; van Dam et al., 2010; Plevin et al., 2010).

In addition to the uncertainties of the market-equilibrium mod-eling approaches, the concept of ILUC is being challenged by thepossibility of double counting of emissions. That is, emissions from

3 Market equilibrium models cannot distinguish between direct and indirect LUCbut instead present total induced LUC.

ILUC caused by bio-based materials represent at the same time theemissions from DLUC of the agricultural crop that has been dis-placed. Adding up the emissions of the bio-based materials andthe displaced crop hence leads to double counting, i.e., the sameemissions are counted twice and therefore do not add up to theactual total impact (IEA, 2009). Double counting commonly occursin LCA whenever system expansion is applied (in the context ofconsequential analysis and allocation). Double counting also occurswhen the impacts of intermediate and final products are quantified.In such cases, the impacts are counted once in the life-cycle assess-ment of the intermediate product and once again in the assessmentof the down-stream final product. Double counting is thus not anargument to dismiss the inclusion of ILUC emissions in the life-cycleassessment of bio-based materials.

Another approach for accounting for land use changes is to quan-tify land use efficiency. Land use efficiency for bio-based productsis typically calculated by determining the ratio of (i) the differ-ence between the environmental impacts of the production of apetrochemical material and its bio-based counterpart and (ii) thedifference between land used for the production of the bio-basedmaterial and its respective petrochemical counterpart (here at theexample of Non-Renewable Energy Use, NREU; see Fig. 3 for illus-trated example):

Land Use Efficiency = (NREUPCHEM − NREUBIO-BASED)(LANDBIO-BASED − LANDPCHEM)

(4)

Land use efficiency is a measure of the avoided environmentalimpact per unit of land use or vice versa as the ratio of acceptedadditional environmental impact per unit of avoided land use.Applying this concept, several authors (e.g., Dornburg et al., 2004;Weiss et al., 2007; Würdinger et al., 2002; Bos et al., 2012) havelooked at energy savings and GHG emission reductions for bio-based polymers and bioenergy on a per hectare basis. So far, theconcept of land use efficiency has typically been applied to directland use, but in theory it is also possible to include the impacts ofindirect land use change (ILUC).

We recommend including the GHG emissions of indirect landuse change in a sensitivity analysis of the results of life cycleassessments, thereby working with the latest and the most cred-ible ILUC factors; the impacts of direct land use change should inany case be included in any LCA of bio-based materials. The con-cept of land use efficiency can offer valuable additional insightinto resource efficiency (see for example Bos et al., 2012) andis therefore also recommended for LCA studies on bio-based



Cradle -to-fac tory gat e CO2 emi�ed fr om ace� c ac id produc� on, 0.5 t CO2

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0.2 t petrochem icalace�c aci d

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Biomas s2.0 t CO2from ai r

System expansion (here: modelled as cred it; see te xt)

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Fig. 4. Allocation by system expansion for the production of dissolving pulp with acetic acid as co-product (graph depicts approach 1, see text; data are fictitious and havebeen chosen such that they underpin the methodological discussion).

materials. The land-use efficiency calculated for bio-based materi-als depends on the conventional material chosen as reference (seeAppendix A). Depending on the reference material, also the rankingamong a given set of bio-based materials regarding their land-useefficiency can change. It is therefore important to note that any con-clusion regarding the land-use efficiency of bio-based materials iscase-specific, i.e., that a suitable conventional material is chosenas reference when assessing a bio-based material. Special atten-tion is therefore required when applying the concept of land useefficiency.

3.2. Carbon stocks in soil and standing biomass

Biomass and soils act as both sinks and sources of atmosphericcarbon. Various approaches have been developed to calculate thechanges in carbon stocks related to the use of biomass. The IPCC(2006) as well as EC (2009b, 2010b) have published guidelines forcalculating the carbon stocks for agriculture, forestry, and otherland use. The IPCC (2006) guidelines allow calculating changes inthe stock of five carbon pools:

• Above ground biomass, which contains all biomass of living vege-tation.

• Below ground biomass, which contains all biomass of living rootsabove 2 mm in diameter

• Dead wood, which contains non-living biomass not included inlitter, standing or lying on ground, or in soil.

• Litter, which contains non-living biomass of size less than deadwood and greater than soil organic matter.

• Soil, which contains living and dead fine roots and dead organicmatter that is less than 2 mm in diameter; this fifth pool includesthe soil organic carbon (SOC).

IPCC (2003) proposes different tiers for calculating changesin the carbon stock within each carbon pool. The method pro-vides specific equations for calculating biomass loss (e.g., dueto removal, fuel wood collection, disturbance, mortality, slash),biomass increase (e.g., due to growth), carbon loss from drainedorganic soils, and emissions from fire. For tier 1, default data aregiven; however, in most cases additional detailed data are needed.Such data are site-specific and often unavailable when preparingan LCA study for a specific bio-based material.

The guidelines proposed by EC (2009b) are based on the IPCC(2003) guidelines mentioned above. The main difference betweenthe two methods is that EC (2009b) proposes a simplified methodfor estimating the combined carbon stock composed of the first four

carbon pools (which represent the total of above and below groundbiomass carbon). For calculating carbon change in the fifth carbonpool (soil organic carbon) the EC (2009a) adopts the IPCC (2003)method.

In spite of its simplicity, the method proposed by the EC (2009b,2010a,b) allows calculating changes in total carbon stocks for a setof soil types, land cover (e.g., crop types or natural ecosystems), cli-mate conditions for different land use, and management practices.Default values are used for above and below ground vegetationcarbon stock for calculating the carbon stock on a per-unit areaassociated with reference land use (CSR) and actual land use (CSA).Reference land use is defined as land use in January 2008 or 20years before the raw material was harvested, whichever is the laterstate. In case the carbon stock accumulates for more than one year,the value representing the actual land use is the estimated carbonstock per area after 20 years or when the crop reaches maturity,whichever is earlier. For calculating carbon stock (CS) the generalequation used is

CSi = (CVEG,i + SOCi) × Ai (5)

where i denotes either reference land use (i = R) or actual land use(i = A), CVEG is above and below ground vegetation carbon stock, SOCis soil organic carbon (as calculated by Eq. (6), see below), A is landarea in hectares. Default values for CVEG for different vegetationtypes, are given in Tables 9–18 in EC (2010b).

As mentioned above, IPCC (2003) and EC (2009b) use the samemethod to calculate soil organic carbon, namely:

SOC = SOCNAT × FLU × FMG × FI (6)

where SOCNAT is the carbon stock of the land in its natural state(Table 3.3.3 in IPCC, 2003)4 and the other multipliers are so-calledstock change factors (Table 3.3.4 in IPCC, 2003), namely:

• Base Factor (FLU): associated with the conversion of land in its nat-ural state to agricultural land; only a limited number of differenttypes of agricultural land are distinguished (e.g., long-term cul-tivated in either temperate and/or tropical regions in either dryand/or wet climate)

• Tillage Factor (FMG): offering a distinction between full till,reduced till, or no-till management

4 In the IPCC methodology this parameter is referred to as SOCREF, i.e. the carbonstock of the “reference”. We avoid the term “reference” here since it has alreadybeen used in order to describe the carbon stock at the beginning of the greenhousegas inventory period, which was defined above as reference land use (CSR).



• Input Factor (FI): characterizes the intensity of nutrient input bydistinguishing between low, medium, and high (with or withoutmanure) inputs.

This methodology allows a rough first-order approximation ofthe change in soil organic carbon. If the land remains cropland butthe type of crop is changed, then the values for the SOCNAT and theFLU factors remain the same, but the tillage (FMG) and input factor(FI) may change. If a (managed) forest is converted to cropland thenthe natural land is the same (natural forest) for both land types;therefore the value for SOCNAT is identical, only the stock changefactors (FLU, FMG, FI) would change (for land in its natural state offorest or native grassland, the values for the stock change factorsare 1).

The methods discussed are broadly accepted for preparingnational greenhouse gas inventories. Both the methods, IPCC (2003)and EC (2009b), are subject to uncertainty. In the case of soil organiccarbon, uncertainty is caused by the multiplicity of factors thatinfluence the accumulation of carbon in the soil such as tempera-ture, precipitation, pH value, orographic conditions (Murphy et al.,2010; Bot and Benites, 2005; Parliament of Victoria, 2010) as wellas human activity (García-Oliva and Masera, 2004).

The potential role of soil organic carbon in greenhouse gas abate-ment strategies is controversial. Lal (2004) proposes to dramaticallyincrease the amount of carbon sequestered in soil. Converting fromtillage to no-till practices has substantially increased soil carbon inCanada’s agricultural soils (Environment Canada, 2011). However,Schlesinger and Lichter (2001) argue that increasing the estimatesfor carbon sequestration in soils is not realistic in the case of forestsoils. Schlesinger and Lichter (2001) found that in a forest exposedto high levels of atmospheric CO2, there is little accumulation ofcarbon in the humic soil layer where carbon is stored perma-nently.

Some attempts have been made to incorporate SOC data forspecific crops into an LCA (Brandão et al., 2011). However, theseare limited in scope (they do not look at all material flows asso-ciated with calculating SOC) and they are specific to a few crops.Milà i Canals et al. (2007) propose to quantify soil organic matter(SOM) by adding up the effects of cultivation (they refer to thisphase as “occupation”) and of a subsequent natural recovery pro-cess (which they call “relaxation”). The ILCD Handbook (EC, 2011)recommends this approach for application with caution (Level IIIrecommendation). Data on soil organic carbon are required as inputto the approach. Milà i Canals et al. (2007) report three possible datasources, i.e., direct measurements, model calculations or literaturevalues. In order to exemplify their approach and demonstrate theimpact of different soil management options, they apply the RothCmodel for soil organic carbon that was developed by Coleman andJenkinson (1996). The lack of tabled data and the typically largedata requirements for soil models limit the applicability of Milài Canals’ approach for LCA practitioners. This is also the case forsoil models such as DayCent (Del Grosso et al., 2009), DNDC (Smithet al., 2008) and NCGAVS (McConkey et al., 2008). These modelsdescribe soil carbon as a function of the type of cultivation andlocal biotic and abiotic factors (e.g., air temperature, precipitation,soil type, vegetation, crop specific data, and land use data). Thesemodels are highly complex and require detailed, hard to acquiredata and calibration to provide accurate estimates (Grace et al.,2006).

The inherent complexity of soil science, the high degree ofsite variability and the challenges of linking biomass feedstocksupply to specific soils collectively explain why the effects ofbiomass cultivation on the soil carbon stock have largely beenignored in LCA studies of bio-based materials (Larson, 2006). It alsoexplains why many of the proposed carbon accounting methods(BIS, PAS 2050, ILCD, and The European Commission’s Lead Market

Initiative) do not include emissions and storage arising fromchanges in soil carbon stocks.

We conclude here that changes in soil carbon stocks dependon a variety of interrelated factors that are challenging to quantifyin detail, making it difficult to properly account for these in theenvironmental assessments of bio-based materials. In the absenceof site specific information, we recommend using the methodologyproposed by EC (2009b, 2010b) to quantify effects of changes in soilcarbon due to management practice or land use change.

3.3. Early-stage impact assessment methods for bio-basedmaterials

After having discussed carbon stocks and the potential car-bon losses from soils and standing biomass, we now proceed tochallenges in the assessment of additional environmental impacts,largely related to land use. Multiple endpoint assessment meth-ods are available (Frischknecht et al., 2007), including CML 2001(Guinée, 2001), TRACI (Bare et al., 2002) and ReCiPe (Goedkoopet al., 2009). However, these methods have not been specificallydeveloped for assessing the complete range of land-use impacts ofbio-based materials and typically only cover impact categories forwhich the methodology is in a rather mature stage. In this sec-tion we will present and discuss approaches to assess land-useimpacts of bio-based materials that are still in a rather early stageof development, namely water use, soil degradation and impactson biodiversity. These impact categories are often excluded fromthe life cycle impact assessment of bio-based materials due to per-sisting methodological problems and limited data availability.

3.3.1. Water useWater use related to bio-based materials, specifically for

biomass cultivation but also to a lesser extent for process indus-tries, is a potential concern. With increased agricultural biomassproduction, the amount of water use is likely to rise (Gosling, 2005;Dornburg et al., 2010; Dalla Marta et al., 2011). The additionalwater demand could substantially increase the overall environ-mental impact of bio-based materials, specifically in areas that arealready water stressed (Berndes, 2002). Various methods have beendeveloped for assessing water usage at different scales.

The Water Footprint (Hoekstra and Chapagain, 2006) is usedto calculate the total yearly freshwater consumption needed by anation’s population to supply goods and services. The Water Foot-print is calculated as the total of green water (rainfall), blue water(fresh water stored in lakes, rivers, and aquifers) and gray water(water needed to dilute aquatic pollutants). The method has beenadopted to assess the water use of, e.g., bioenergy crops (Gerbens-Leenes et al., 2009).

Owens (2002) accounts for the impacts of water quality andquantity by distinguishing the following indicators: in-streamwater use, off-stream water withdrawal, surface water, ground-water, water release or returned, water use, water consumption orconsumptive use and water depletion.

Commonly applied LCA methods (e.g., CML 2001; Eco-indicator99; IMPACT 2002+; ReCiPe 2008) limit the assessment to impactson water quality by quantifying, e.g., ecotoxicity, eutrophication,and acidification (Koehler, 2008). The ILCD method (EC, 2010a; seeSection 2.5) distinguishes the different sources of water (surfacefreshwater, renewable groundwater, fossil/deep groundwater, seawater), emissions (liquid water loss, vapor loss), and differenti-ates between internally recycled water (e.g., cooling water) and theactual net extraction of water from the environment (EC, 2010a).

The methods discussed so far treat water as an abiotic resource,either quantifying the volume of water used or its contamination,without taking into account the impacts of water use and pollu-tion on the environment and human health. Milà i Canals et al.



(2009) proposed a methodology to assess the impacts caused bythe evaporative use of freshwater. The authors identified threeimpact pathways: (i) change in freshwater availability affectinghuman health and ecosystem quality, (ii) depletion of groundwa-ter from extraction, and (iii) land use affecting the water cycle. Aset of equations are provided for characterization factors to quan-tify impacts on freshwater depletion and ecosystems. Bayart et al.(2010) proposed a conceptual framework to assess the degrada-tive and consumptive use of water from lakes, rivers, and aquifers.Three impact pathways are proposed, including the availability offreshwater for contemporary human activities, existing ecosys-tems, and future generations. These pathways are linked toendpoint indicators such as human health, biodiversity, biotic pro-ductivity, and abiotic resources.

Pfister et al. (2009) propose to assess the impacts from freshwa-ter consumption by considering the cause and effect relationshipbetween water consumption and impacts to human health, ecosys-tem quality, and damage of resources. Water consumption isdefined similarly to Owens (2002) as freshwater withdrawals (off-stream use) which includes evaporative losses (e.g., from reservoirs,and irrigation), transfers to other watersheds, and water incorpo-rated into products. The method applies a spatially differentiatedwater stress index (WSI) as a weighting factor, thereby relatingfreshwater use to water availability. The index indirectly addresseswater quality because it accounts for used water other than con-sumed water and for the fact that polluted water deprives others ofusable freshwater. The WSI is derived by determining the weightedaverage (to account for variations in annual and monthly pre-cipitation) of the ratio of total annual freshwater withdrawalsto hydrological availability. The development of the impact fac-tors (human health, ecosystem quality, and damage to resources)is primarily based on the Eco-Indicator 99 methodology. To cal-culate the damage to human health, two related water impactfactors are determined, i.e., “lack of freshwater for hygiene andingestion, resulting in the spread of communicable diseases” and“water shortages for irrigation, resulting in malnutrition”. Cause-effect relationships from water consumption and lack of waterfor hygiene and ingestion cannot generally be derived, as theseproblems are mainly related to infrastructure and water treatmentoptions. Therefore, the method only considers the pathway of lackof water for agriculture, based on socioeconomic parameters for theregion covered in the analysis and the WSI. To calculate ecosystemhealth, freshwater consumption is multiplied by ecosystem dam-age in land use equivalents (ratio of net primary production that islimited by water availability over the theoretical area-time equiv-alent that would be needed to recover the amount of consumedwater by natural precipitation). To calculate damage to resources,the energy required for seawater desalination (backup technology)is multiplied by the fraction of non-renewable freshwater con-sumption (that contributes to depletion) and consumptive wateruse. Pfister et al. (2009) thereby propose a fully functional frame-work. The choices made to model the impacts at the endpoint levelcan be criticized for not being sufficiently comprehensive, scientif-ically proven and accepted. In comparison, the approach proposedby Pfister et al. (2009) for midpoint assessment is clearly morerobust and, compared to other approaches, it has the advantage ofproviding the necessary background data.5 We therefore favor themidpoint method developed by Pfister et al. (2009) as a first orderapproximation of the impacts of water use. This recommendationis in line with the conclusions of the ILCD Handbook (EC, 2011) for

5 See the footnote in Section 3.3.3 for an explanation of the principles of midpointassessment methods and endpoint assessment methods.

midpoint assessments, where no methods are recommended forassessing the impacts of water consumption at endpoint level.

3.3.2. Soil degradationSoil degradation comprises any undesirable change in soil char-

acteristics including the loss of soil productivity caused by wind andwater erosion, chemical degradation (loss of nutrients, salinization,acidification, or contamination), and physical degradation (com-paction, crusting, sealing, or waterlogging). Among these causes,erosion by water (affecting approx. 110 million ha) and erosionby wind (affecting approx. 550 million ha) are most important(Oldeman, 1992). We therefore discuss primarily soil erosion inthis section (partly discussed also in EC, 2011) and only brieflyaddress other forms of soil degradation. All types of soil degradationdecrease the productivity of land, resulting in higher requirementsof, e.g., fertilizer inputs, which in turn can enhance acidificationand aquatic eutrophication. The currently used LCI methodologies(e.g., CML 2001; Eco-indicator 99; IMPACT 2002+; ReCiPe 2008;Frischknecht et al., 2007) do not incorporate soil erosion. Basedon water erosion only, Nùnez et al. (2010) have developed a soilerosion indicator that can be applied in life cycle assessments.The indicator is composed of three intensity categories based onthe universal soil loss equation.6 Saad et al. (2011) developedthe Erosion Regulation Potential (ERP) to quantify the ability ofecosystems to stabilize soil and to prevent sediment accumulationdownstream, which is calculated also by using the universal soilloss equation. Cowell and Clift (2000) do not address soil erosiondirectly but propose to assess soil quantity and quality by using themass of soil, mass of nutrients, weeds and weed seeds, pathogens,soil pH, salts, organic matter, and soil texture and structure asindicators. Despite these attempts, a broadly accepted method-ology for assessing soil erosion in the life cycle assessment ofbio-based materials is still unavailable to date. The prospects ofsuch a methodology may remain limited due to complex inter-actions of site-specific factors determining soil erosion potentialsin the cultivation of biomass. As explained by EC (2011), a suit-able method would need to be developed to quantify enhanced (orreduced) erosion relative to natural erosion as a consequence ofhuman activity. EC (2011) briefly discusses also other forms of soildegradation, namely salination and dessication. Both parametersare related to water use and land use, and could be addressed byfuture research.

3.3.3. BiodiversityBiodiversity can be defined as the variety of life that encom-

passes the diversity of ecosystems and living organisms includinganimals, plants, their genes and habitats (IUCN, 2012). The loss ofbiodiversity is widely acknowledged as a key challenge of sustain-able development (Rockström et al., 2009). The potentially largeincrease in biomass cultivation for food, fiber, bioenergy and bio-based materials entails the risk of accelerated biodiversity loss(Koh, 2007; Koh and Ghazoul, 2008). According to MEA (2009)terrestrial and aquatic habitat change, invasive species, overex-ploitation of wild populations, pollution, and climate change arethe most important causes for biodiversity loss.

Among the more widely established life cycle impact assess-ment methods, biodiversity loss caused by the following stressorsis taken into account: ecotoxicity, acidification, eutrophication,climate change, ionizing radiation, and land use. These so-calledend-point methods7 were evaluated in the ILCD Handbook (EC,

6 The universal soil loss equation can be found in Roose (1976).7 The first four of these six environmental impact categories are typically included

in full-fledged LCA studies, however by means of the respective midpoint assess-ment methods. These methods provide indicators for comparison of emissions and



2011) according to seven criteria, among them completeness ofscope, scientific robustness & certainty and applicability. EC (2011)concluded that none of the endpoint methods describing the impacton biodiversity can be recommended because they are too imma-ture or have not been sufficiently validated. However, some proxiesfor assessing the loss of biodiversity are recommended as interimsolutions:

• Ecotoxicity: Of all methods to quantify ecotoxicity at midpointand endpoint level (7 and 3 methods, respectively), USEtox is themidpoint method preferred by the EC (2011) because it is con-sidered scientifically sound (except for metals; Rosenbaum et al.,2008). We therefore propose to apply USEtox as first indication ofthe impacts on biodiversity, combined with the caveat that thismethod excludes marine and terrestrial ecotoxicity. The scien-tific rigor of the endpoint methods ReCiPe (Goedkoop et al., 2009)and Impact 2000+ (Jolliet et al., 2003) is limited but they have theadvantage that they can be applied relatively easily due to theavailability of extensive databases (with >2000 and >400 com-pounds, respectively; EC, 2011). We therefore recommend that allthree methods (USEtox, ReCiPe/Endpoint and Impact 2000+/End-point) should be applied and critically discussed, if time allows.

• Acidification: The impact of acidification on biodiversity dependson the types of species and the buffer capacity of the soil. Itis therefore important to conduct spatially-specific analyses.Among all endpoint methods studied by EC (2011), the methoddeveloped by Van Zelm et al. (2007), as implemented in ReCiPe,is found to be most convincing. This method is based on 240plant species and has been extensively reviewed. While notbeing fully endorsed, EC (2011) recommends it as interim solu-tion. The main weaknesses of ReCiPe (Goedkoop et al., 2009)are that it is based exclusively on forest ecosystems, that it islimited to Europe, and that it focuses on terrestrial acidifica-tion while aquatic acidification (both freshwater and marine) isexcluded.

• Eutrophication: EC (2011) recommends no endpoint methodfor terrestrial, aquatic, and marine eutrophication. ReCiPe(Goedkoop et al., 2009) can serve as interim endpoint method foraquatic eutrophication (EC, 2011). For terrestrial eutrophication,only the midpoint method proposed by Seppälä et al. (2006) andPosch et al. (2008) is endorsed; however, EC (2011) considers themodels and data to be difficult to be understood without expertknowledge. No midpoint method is recommended for marineeutrophication. For aquatic eutrophication, the spatial variationwithin Europe and the USA was found to be less than one orderof magnitude (EC, 2011). This may be seen as justification forapplying the method as a first proxy also to other world regions.

• Climate change: For the three endpoints methods (EPS2000,Ecoindicator 99, and LIME), EC (2011) identified substantial dif-ferences in key model parameters determining the impacts ofclimate change on biodiversity; examples are the speed of speciesmigration and species adaptation. As a consequence, the meth-ods differ in the magnitude of the identified impact (see, e.g.,De Schryver and Goedkoop, 2009). EC (2011) recommends theReCiPe endpoint method as interim solution because it is thescientifically most robust method.

• Ionizing radiation: Regarding the impact of ionizing radiationon ecosystems, EC (2011) assessed only the method developed

resource consumption at a point of the cause-effect chain, for which the scien-tific basis is still robust (e.g., global warming potential of individual greenhousegasses or depletion of abiotic resources). In contrast, endpoint assessment methodsare indicators for the ultimate damage (e.g., of ecosystems, human health, resourceavailability). Endpoint assessment methods are subject to larger uncertainty thanmidpoint assessment methods.

by Garnier-Laplace et al. (2008, 2009); it considers exclusivelyimpacts in the freshwater environments, while the impacts in themarine and terrestrial environments are excluded. The methodis evaluated as scientifically sound and is therefore proposed asinterim midpoint method (EC, 2011). There is so far no endpointmethod.

• Land use: Nearly all endpoint characterization factors representthe loss of biodiversity. The ReCiPe method is recommended asinterim solution (EC, 2011). The method distinguishes 12 dif-ferent land-use types, and three levels of land use intensity. Itis based on the most recent British data and inventory data byKöllner (2001).

Curran et al. (2011) stress that existing endpoint indicatorsfor biodiversity loss are deficient in data availability and prob-lematic with respect to their underlying concepts. Conceptuallimitations include the assumption that impacts at different scales(e.g., regional versus global) can be directly compared and aggre-gated, the assumption of a (mostly) linear relation between areaand damage, and the disregard of invasive species and the overex-ploitation of habitats as drivers of biodiversity loss.

While these aspects need to be addressed by future research, itcan be concluded that the ReCiPe method (Goedkoop et al., 2009)currently captures best the measurable impacts on biodiversity,with the following exceptions (based on ILCD):

(i) For ecotoxicity, the additional use of USEtox (midpoint level;Rosenbaum et al., 2008) and of the endpoint method of Impact2000+ (Jolliet et al., 2003) are recommended next to ReCiPe(endpoint).

(ii) For eutrophication, the assessment method by Seppälä et al.(2006) and Posch et al. (2008) is recommended.

4. Allocation

In the LCA of bio-based materials, the allocation dilemma occurswhen the production process generates multiple products, of whichonly one or a few are relevant for the product scenario analyzed.This is the case for biorefinery configurations that are designed tomaximize the value of a given feedstock by producing a number ofmarketable bio-based products. Here the inputs to and the environ-mental impacts from the production process need to be allocatedacross the various products. This section addresses allocation firstin general before proceeding to the specific challenges related tobio-based materials.

ISO 14040 (ISO, 2006a) recommends avoiding allocation wher-ever possible through the expansion of the product system, i.e.,through the inclusion of a related product systems or by dividingthe unit process into two or more sub-processes. Both proceduresare subject to shortcomings: system expansion may fail to quan-tify the environmental impacts of a specific product with sufficientaccuracy (the uncertainties of the added system can be overriding),while subdivision of the unit process may only shift the prob-lem of partitioning to smaller subsystems (which can, however,be expected to improve accuracy compared to partitioning of thelarger system). For partitioning, the most common methods areallocation based on physical parameters such as mass or area, mon-etary value, or energy content (Mortimer et al., 2007). The choiceregarding either of these parameters may depend on the specificgoals of an LCA and on the characteristics of the product systemstudied.

Partitioning of the environmental impacts based on mass cangenerally be considered as appropriate when the economic valueof the product and co-product is similar. Partitioning based oneconomic value is generally preferred when there is a substantial



difference in the price between products and co-products.8 Energypartitioning may be done if the energy content of both product andco-products are critical for the goal of the LCA.

When calculating greenhouse gas emissions of biofuels, EC(2009b) recommends, contrary to ISO (2006a), to apply allocationbased on energy content of products and co-products. Accordingto the EC (2009b), system expansion (referred to as “substitutionmethod”) is appropriate only “for the purposes of policy analy-sis, but not for the regulation of individual economic operators”.The choice of partitioning based on energy content (calorific value)was made in order to reduce the risk of litigation because sys-tem expansion offers a much larger freedom of choice. Similarly,RFA (2011) has also used the “energy allocation method” as partof the Renewable Transport Fuel Obligation (RTFO) regulations ofthe United Kingdom. An overview of allocation choices made in sus-tainability certification schemes for bioenergy was prepared by vanDam et al. (2010). While in the case of biofuels there is a valid ratio-nale for allocation by energy content (BIS, 2009), using this methodis not necessarily reasonable for bio-based materials since these areproduced for their use as material and not for their calorific value.

For bio-based materials, system expansion is an option whenthe co-product from the bio-based process can also be produced bymeans of other processes, typically using petrochemical feedstock(BIS, 2009). For example, acetic acid can be produced by two alter-native pathways: (i) the pulping of wood (Shen and Patel, 2010)and (ii) the carbonylation of petrochemical methanol, followed byliquid-phase oxidation of n-butane, naphtha, or acetaldehyde asalternative routes (UEIC, 2007). To account for the co-productionof bio-based acetic acid, the system can be expanded by the produc-tion of equivalent amounts of petrochemical acetic acid; a variationof the system expansion approach is to provide a credit for theavoided production of petrochemical acetic acid. System expansionis also applicable if combustible co-products are used to generatesteam or power, which replace steam or power from fossil sources.In addition to acetic acid, pulping also yields xylose, furfural, andthick liquor. Pulping of wood is the only commercially viable pro-cess for making xylose and there is no other production methodthat generates this product. In such cases, system expansion isirrelevant, and partitioning is the only feasible method of alloca-tion. System expansion would, however, be an option if anotherindustrially produced compound was used for the same purpose,thereby ensuring functional equivalence in spite of the differencein chemical composition.

If system expansion is implemented by means of a credit, theimpact of the main product is calculated by deducting the credit forthe co-product from the impact of the process as a whole. In casethe quantity of the main product is small (say, a few drops of oil), thequantity of co-products (e.g., stalks or straw) can be large. If the low-value co-product now replaces, e.g., fossil fuels, then the credit forthe avoided fossil fuel use can be larger than the direct fossil fuel useof the main process, resulting in negative values for the net fossil fueluse caused by the main product. Such negative results cannot beobtained, however, by partitioning. This difference between systemexpansion and partitioning is particularly relevant for bio-basedmaterials.

In the case of bio-based materials, system expansion basedon the fossil fuel derived counterpart requires special attentionbecause the chosen procedure can influence the accounting of car-bon storage in bio-based materials. To exemplify the situation, weassume that system expansion is applied to biomass feedstock andthat the co-product is accounted for in the form of a credit for theavoided carbon emissions related to the fossil-based counterpart

8 Combining the reasoning in the last two sentences, one can argue that economicallocation should always be used.

Box 2: Allocation by System Expansion: pitfall for car-bon calculations performed for bio-based materialsIf carbon storage (see Fig. 1, right-hand side) is considered andif the production process leads to co-products, then carbonallocation is not a trivial task. In the process of making dis-solving pulp for products like viscose, one co-product is aceticacid (see Fig. 4; Shen and Patel, 2010). We apply Eq. (1) fromBox 1 (Biofeedstock-storage approach) because this allows usto demonstrate the pitfalls of system expansion. When calcu-lating the carbon emissions associated with the production ofdissolving pulp only, we deduct from the total carbon emis-sions of the multi-product process the impacts of the avoidedpetrochemical acetic acid production (Fig. 4). If 0.2 metric tonsof bio-based acetic acid are produced when making dissolvingpulp, the most obvious choice is to deduct the avoided “cradle-to-factory gate” impacts of petrochemical acetic acid. We willcall this “Approach 1”. The carbon emissions of petrochem-ical acetic acid for the system “cradle-to-factory gate” havebeen set in this example at 2.5 t CO2-equivalents/t acetic acid,which translates into 0.2 × 2.5 = 0.5 t CO2-equivalents for theco-produced acetic acid; this value represents the emissionsreleased by the series of processes leading to acetic acid butit excludes carbon from the feedstock which would only bereleased if acetic acid was oxidized. It is easily overlooked thatthis approach assigns all the credits of carbon uptake relatedto photosynthesis (except for the bio-based carbon returnedto air, 0.7 t CO2-equivalents) to the dissolving pulp (this is thepitfall). This can be understood from Fig. 4, where the lefthorizontal arrow is accompanied with a large credit due to car-bon storage, which is nearly completely assigned to dissolvingpulp. Since in Approach 1 the co-produced acetic acid is cred-ited with its avoided impact of petrochemical production, itmust not be sold as a bio-based product. Following Approach1, the dissolving pulp company must hence report the “cradle-to-factory gate” carbon emissions of petrochemical acetic acidnext to the emissions for dissolving pulp.It would not be valid to additionally claim the benefits of car-bon storage related to bio-based acetic acid because this wouldlead to double counting. If the company decides to sell boththe dissolving pulp and the acetic acid as bio-based, the cal-culation must be adapted (Approach 2). In Approach 2, thecarbon footprint of dissolving pulp must be higher in orderto justify the lower carbon footprint of “bio-based” acetic acid.One way of doing this is to start from the result of Approach1 and then adding the fossil carbon content of acetic acid tothe original result for dissolving pulp; the related burden isthereby shifted, leading to a product basket that consists of (i)dissolving pulp with a somewhat higher carbon footprint (ascompared to Approach 1) and (ii) an acetic acid “grade” whichcan be sold as bio-based (no fossil carbon content).Although the allocation of biogenic carbon uptake is a businessdecision, we recommend here Approach 2 as default because itis closer to (physical) reality by regarding not only viscose butalso acetic acid from the dissolving pulp process as bio-based.

(Box 2). However, using the “cradle-to-factory gate” carbon emis-sions from the fossil fuel-based counterpart in determining thecarbon credit of the main product represents a potential pitfall.It implies that the carbon emissions will not only account for theembodied bio-based carbon of the product itself, but also of the co-product. This is not usually the intended outcome and should beavoided by adapting the calculation (Box 2).

5. Use of attributional versus consequential LCA

Over the past few years there has been a controversial discus-sion about where and when to use attributional LCA (ALCA) versus



consequential LCA (CLCA). The two LCA types are summarized asfollows:

• ALCA, also referred to as cause-oriented, descriptive, or retro-spective LCA, accounting LCA, and bookkeeping LCA, analyzesthe environmental impacts of a product or service based on thecurrent (or recent) average state of the technology and infra-structure (Ekvall and Tillman, 1997; Guinée, 2001; Tillman, 2000;Erlandsson and Almemark, 2009). ALCA uses average data (aver-age across technologies) to quantify environmental impacts ata given point in time (Brander et al., 2009). ALCA determinesthe impacts that are directly associated with the production,consumption and disposal of products. It is characterized byrelatively confined system boundaries and inherently higher cer-tainty than CLCA.

• CLCA analyzes the environmental impacts based on the conse-quences that occur as a result of production, use, and disposal ofproducts. Consequential LCA is also referred to as effect oriented,change oriented, or prospective LCA (Ekvall and Tillman, 1997;Guinée, 2001; Tillman, 2000).

The differences between attributional LCA and consequentialLCA may be best explained with the example of a bio-based materialwith waste biomass being generated in one or several conver-sion steps. This waste biomass could be combusted for generatingpower. The attributional LCA assumes that average grid power isreplaced, while the consequential LCA would assume the replace-ment of marginal grid power, i.e., CLCA asks the question whichpower plants decrease their output as a consequence of the newsource of power. Depending on daytime and power generationinfrastructure, the source of marginal grid power may change. Inthe case of biogas (which is typically generated around the clock)the substitution of base-load power may be most plausible, whilemore storable bio-based energy carriers such as wood pellets fromwaste products could be used to offset peak demand.

The application of CLCA typically implies system expansion todeal with co-product associated changes in production (Weidema,2003). In general, CLCA is considered to be more suitable for policymakers than ALCA because it provides causal information basedon different scenarios of the life cycle of the product (see, e.g.,Brander et al., 2009). Key challenges related to the application ofCLCA include:

• CLCA uses marginal data, which are often not available (in con-trast to average data used for ALCA).

• CLCA requires a good understanding of the interdependenciesof the background systems (e.g., base load versus peak load forpower generation).

• Since the background systems change over time, CLCA is bestcombined with scenario analysis (e.g., for future power gen-eration); however this increases the uncertainty of the results(Brander et al., 2009; see also below) especially when consideringlong time periods and in combination with economic forecast-ing, e.g., by use of Computable General Equilibrium models,Multi-Market models or Multi-Region Partial Equilibrium Models(Earles and Halog, 2011).

• CLCA requires the proper definition of system boundaries, i.e.,a decision whether the assessment is limited to direct conse-quences or whether also secondary or higher-order consequencesare considered (see discussion on carbon accounting and indi-rect land use change). For example, the transition to a bio-basedeconomy will require higher yields, which may imply the use ofmore fertilizers, pesticides and herbicides, introduction of genet-ically modified organisms, expanded irrigation, etc.; increasedbiodiversity loss, accelerated fertility loss of soils (e.g., dueto compaction and salinization) and larger N2O emissions are

among the possible consequences. One may argue that, for exam-ple, the production of bio-based polyethylene (PE) will triggersuch a development. Important questions to be answered arewhich level of pressure will be reached and how much of it canbe assigned to bio-based PE (e.g., as opposed to biofuels), whichagain requires a scenario approach. Comparable questions arisefor the avoided conventional processes (e.g., the use of savedpetrochemical feedstocks).

• Scale effects in the production of bio-based materials may leadto higher or lower net environmental impacts, but the preciseeffects are hardly foreseeable. Moreover, if the bio-based materi-als studied become cheaper than their conventional counterparts,this could lead to rebound effects (Earles and Halog, 2011).

To conclude, it is challenging to prepare an accurate CLCA(Ekvall, 2002). While this can, in principle, be achieved by meansof analyzing alternative LCA scenarios (in comparison to the ref-erence), the problem lies in the fact that there are too manypossible and plausible development trajectories, especially if longtime periods are considered. The body of consequential LCA stud-ies so far therefore only accounts for the (secondary) consequencesthat are considered most important and most obvious; for biomassuse these are indirect land use change (see Section 3.1) and theconsequences of the co-production of power and heat.

6. Assessment frameworks for bio-based materials

When comparing bio-based materials with their petrochemi-cal counterparts or when comparing different feedstock optionsfor bio-based materials, tradeoffs across the various environmentalimpacts (at midpoint level) often occur (Weiss et al., 2012). Evalu-ating these trade-offs requires a robust assessment framework.

Several methods have been developed to interpret the results oflife cycle assessment studies and provide a way of comparing thevarious environmental impacts of different products or services.We distinguish here four methods for deriving weighting factors forenvironmental impacts; these are: (i) science-based assessmentsof the relative damage caused in the various impact categories(globally or in a region), subsequently translated to a point sys-tem or to monetary units, (ii) expert judgment on the relativeimportance of environmental/health impacts (panel methods), (iii)avoidance costs, i.e., the costs incurred to avoid a given impact, and(iv) distance-to-target methods based on existing policy targets.

Option (i) would be the preferred approach due to its scientificrigor but it does not exist in pure form because our understandingof the scientific interrelations is still incomplete. Option (i) there-fore often contains some elements of expert judgment. Option (ii)may be applied with a distinction by world views/perspectives (asimplemented in the various ReCiPe endpoint methods, i.e., the Hier-archist, Individualist and Egalitarian world view), thereby cateringfor the incomplete scientific understanding and the ongoing debate.In principle, this is also valid for options (iii) and (iv) but the vari-ous perspectives are typically not applied there (e.g., EPS 2000 andExternE methods).

Across the four options, option (ii) offers largest freedom toaccount for the specific circumstances of a sector, a country and/orother scopes. Option (ii) was implemented in the scheme “Buildingfor Environmental and Economic Sustainability” (BEES), which isthe first and – to our knowledge – so far the only comprehensiveassessment framework meant for bio-based materials (Lippiatt,2007). The development of BEES was commissioned by the U.S.government as a tool for selecting building products that are bothenvironmentally friendly and economically feasible based on theevaluation by the decision makers. The environmental assessmentwithin BEES encompasses life cycle inventory data and an impact



assessment tool, which makes use of a methodology called TRACI(Tool for Reduction and Assessment of Chemical and Other Environ-mental Impacts, developed by the U.S. Environmental ProtectionAgency’s Office of Research and Development; Lippiatt, 2007).

The 2002 and 2008 Farm Bill (USDA, 2008) authorized thecreation of the BioPreferred Program, which aims to increasepurchasing of bio-based products by government procurement offi-cers. To address the question of the environmental and economicperformance, the government mandated that all bio-based prod-ucts are evaluated through BEES. For the BioPreferred Program,the weighting factors for the environmental impact categories cal-culated with BEES have been pre-defined by USDA (USDA, 2008),allowing calculation of a single score.9 It is interesting to note,though, that ISO 14044 (2006b) considers single score analysis forLCA to have no scientific basis and therefore does not endorse it.In selecting the products that qualify to be used under the Bio-Preferred Program, the USDA identifies bio-based products in themarket and contacts manufacturers for voluntary submission ofproduct samples for testing of bio-based content and BEES anal-ysis. Based on the results of the analysis, the USDA makes thedecision whether to include a product in the BioPreferred pro-curement program (USDA, 2008). No formal rule, e.g., a minimumvalue of the single score result and/or a maximum price, has beenestablished for this purpose; neither has any minimum percentagebeen set for the bio-based content across all product categories.Instead it is acknowledged that the achievable bio-based contentdiffers depending on the application. Thus, materials with only afew percent bio-based content (e.g., insulating foam, carpet clean-ers, carpets) to as much as 90% bio-based content (e.g., diesel fueladditives, vegetable oil-based lubricants) can qualify for inclusionin the BioPreferred Program, depending on the application. Gener-ally, products with a high bio-based content are preferred, while theimpacts of partially or fully bio-based products in the various cat-egories seem to be of subordinate importance. This demonstratesthe early stage of assessment frameworks for bio-based products,calling for further research in order to account for the various envi-ronmental tradeoffs.

7. Discussion and conclusions

Many countries are pursuing the concept of a bio-basedeconomy (OECD, 2010), with key drivers being the expected envi-ronmental, economic, and social benefits (Jenkins, 2008). LCA isthe most widely used tool to assess the environmental impacts ofbio-based materials. This paper provides an overview of key meth-odological issues that are critical for bio-based materials but eithernot typically assessed or poorly evaluated in most LCA studies.

The lack of a standardized approach for accounting for thebiogenic carbon storage in bio-based materials presents a keychallenge to LCA practitioners. We recommend that a credit forcarbon storage should initially be given to bio-based materialsby: (i) accounting for bio-based carbon storage in products forthe system “cradle to factory gate” and (ii) applying the ILCDmethod (EC, 2010a) for systems that include the use and disposal ofbio-based materials. However, recently a clear convergence in favor

9 The weighting factors used for the evaluation of bio-based products in theBioPreferred Program are (USDA, 2008): Acidification (5%), Criteria Air Pollution(6%), Ecological Toxicity (6%), Ecological Toxicity (11%), Eutrophication (5%), Fos-sil Fuel Depletion (5%), Global Warming (16%), Habitat Alteration (16%), HumanHealth (11%), Indoor Air (11%), Ozone Depletion (5%), Smog (6%), and Water Intake(3%). These factors differ from the distance-to-target weighting factors identifiedfor Germany by Weiss et al. (2007). The observed differences highlight the effectsof regional characteristics and subjective value judgment on the establishment ofweighting factors. The weighting factors proposed by the BEES Stakeholder Panel(Lippiatt, 2007) were not used in the BioPreferred Program.

of assigning a credit for bio-based carbon storage can be observed(Sections 2.1–2.7). This is justified as our reasoning in Section 2.8shows: for the system “cradle-to-factory gate”, bio-based carbonstorage needs to be taken into account in order to ensure that theranking of the bio-based as opposed to the petrochemical mate-rial is in line with the results for the system “cradle-to-grave”.While the approach is justified when comparing bio-based to petro-chemical material, “cradle-to-factory gate” analyses with bio-basedcarbon storage should be avoided when making comparisons toinorganic materials; in such cases proper “cradle-to-grave” analy-ses including the use phase and end-of-life waste management arerequired. Moreover, we acknowledge that bio-based carbon storageremains a controversial topic and that product-specific life cyclesand the likely time duration of carbon storage should be considered(Brandão and Levasseur, 2011). Since the concentration of atmo-spheric CO2 in the next 50–100 years is likely to be higher thantoday, the benefit of contemporary carbon storage may be illusive:Delayed carbon emissions may disproportionately enhance globalwarming if atmospheric CO2 concentrations are higher in the futurethan they are today.

When quantifying the environmental impact of land use, it israther common to evaluate the GHG emissions associated withdirect land use change (DLUC). Impacts associated with indirectland use change (ILUC) are less frequently assessed. This is primarilyrelated to the multiplicity of drivers behind ILUC, the uncer-tainty related to their assessment, and the disagreement amongexperts about how to allocate the resulting impacts. With increasedunderstanding of the dynamics and with growing consensus aboutrepresentative assumptions, ILUC should be included in LCA stud-ies on bio-based materials, at least as part of the sensitivityanalysis.

Incorporating changes in soil organic carbon into LCA studiesis highly complicated and subject to substantial data uncertain-ties. Moreover, effects on the organic carbon stock in soils is sitedependent. Despite these limitations, we recommend using themethod put forward by EC (2009b), since it provides the most feasi-ble approach to incorporate changes in soil carbon stocks associatedwith growth of feedstocks for bio-based materials.

Allocation of emissions and resource use among products is acritical issue for life cycle assessments where several products areco-manufactured. For bio-based materials, we abstain from devis-ing a general recommendation. The approaches reviewed in Section4 appear to depend on the specific product mix, i.e., the types andamounts of (co-)products that are manufactured. We also point outthe pitfall of false (or unintended) accounting of carbon storagewhen applying system expansion.

Addressing the controversy around attributional and conse-quential LCA, we have made an attempt to assess the implications ofboth approaches for bio-based materials. We conclude that attri-butional LCA is relatively straightforward to implement, while atruly consequential assessment is likely to become complex andmay be subject to substantial uncertainties and double counting ofcarbon emissions. Good practice guidance rules would be requiredfor consequential LCA, which are, however, unavailable to date. Asprerequisite for the preparation of such guidelines, deeper insightwould be needed into consequential phenomena and their impor-tance on a case-by-case basis. Given the early stage of research, werecommend addressing consequential modeling in the context oflife cycle assessment (including indirect land use change) by meansof a sensitivity analyses.

Currently available impact assessment methods for water use,biodiversity, and soil degradation are not fully satisfactory. Forthe assessment of water use, we favor the midpoint assessmentmethod by Pfister et al. (2009) because it is fully operational andprovides data for calculating characterization factors. The availablemethods for assessing the impacts on biodiversity are generally



immature; however, as preliminary solution, the ReCiPe methodcan be used (in combination with USEtox, the endpoint ecotox-icity method of Impact 2000+ and the eutrophication assessmentmethod by Seppälä et al. (2006) and Posch et al. (2008)). For soildegradation, no operational methods are identified for use in LCAs.Similar to soil organic carbon, soil losses are site-specific.

Currently, BEES (Lippiatt, 2007) is the only tool that we haveidentified to be specifically designed for the comprehensive assess-ment of the environmental and economic impacts of bio-basedmaterials. The tool is a single score method that does, however,not take into account several impact categories (e.g., soil erosion,biodiversity, and water use). BEES can serve as basis for a discussionabout the potential environmental impacts of bio-based materialsand may therefore trigger material producers and product man-ufacturers to make better choices, next to facilitating effectivedecision making by authorities. Further work is recommended inthis area.

In conclusion, we find that work is underway in many areasto develop more comprehensive and robust assessment tools toevaluate the environmental impacts of bio-based materials. As thisis a highly complex area of assessment, it is expected to takeseveral more years before such tools are complete and compre-hensive assessments become mainstream. Also, further assessmentexperience can help making environmentally sound choices in theproduction, use, and disposal of bio-based materials.

Acknowledgements

This research was funded by the European Commission underthe 7th Framework Program on Environment; ENV.2008.3.3.2.1:PROSUITE – PROspective SUstaInability Assessment of TEchnolo-gies, grant agreement number 227078 and by the program BPM– Biobased Performance Materials, subproject FEASIBLE (Feasibil-ity of End use Applications: SustainaBiLity and techno-Economicaspects). The views expressed here are those of the authors and maynot be regarded as an official position of the European Commission.The authors would like to thank Graham Sinden and Roland Cliftfor providing valuable information on the PAS 2050 methodology.We thank Stephan Pfister for commenting on the impacts of wateruse.

Appendix A. Example for the case specificity of land useefficiency

Fictive Data, NREU:Bio-based product A: 3 GJ, 2 haBio-based product B: 1 GJ, 4 haPE: 4 GJ, 0 haAluminum: 10 GJ, 0 ha

A.1. Comparison with PE

(1a) Comparison of bio-based product A with PE:

(4 − 3) GJ(2 − 0) ha

= 12

GJha

(1b) Comparison of bio-based product B with PE:

(4 − 1) GJ(4 − 0) ha

= 34

GJha

⇒ Bio-based product B is better option (i).

A.2. Comparison with aluminum

(2a) Comparison of bio-based product A with aluminum:

(10 − 3) GJ(2 − 0) ha

= 72

GJha

= 144

GJha

(2b) Comparison of bio-based product B with aluminum:

(10 − 1) GJ(4 − 0) ha

= 94

GJha

⇒ Bio-based product A is better option (ii)

At first sight, the conclusions (i) and (ii) seem contradictory.However, the example here shows that choices regarding the land-use efficiency of alternative bio-based materials depend on theconventional reference product. If the conventional product hasa high energy consumption (as in the case of aluminum), a bio-based material with slightly higher energy requirements but lowdemands for land appears favorable (as it is the case for bio-basedmaterial A) in comparison with a bio-based material with lowenergy requirements but high demands for land (as it is the casefor bio-based material B). Conversely, if the conventional productrequires little energy for its production, the energy consumption ofthe bio-based material becomes increasingly important in the cal-culation of land-use efficiency while lower yields per hectare can beaccepted. Consequently, bio-based material B which requires lessenergy but more land appears to be favorable from the aspect ofland-use efficiency if the reference product is PE.

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Reviewing methodologies and deriving recommendations

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