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Journal of Cleaner Production 28 (2012) 120e126

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Journal of Cleaner Production

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Calculating land use change in carbon footprints of agricultural productsas an impact of current land use

T.C. Ponsioen*, T.J. BlonkBlonk Milieu Advies (Blonk Environmental Consultants), Gravin Beatrixlaan 34, 2805 PJ Gouda, The Netherlands

a r t i c l e i n f o

Article history:Received 15 April 2011Received in revised form26 September 2011Accepted 11 October 2011Available online 21 October 2011

Keywords:Lifecycle assessmentCarbon footprintGlobal warmingLand use changeFoodAgriculture

* Corresponding author. Tel.: þ31 182 579970; fax:E-mail addresses: tommie@blonkmilieuadvies.n

blonkmilieuadvies.nl (T.J. Blonk).

0959-6526/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.jclepro.2011.10.014

a b s t r a c t

Land use change causes large amounts of greenhouse gas emissions, but there is no generally agreedmethod yet for attributing those emissions to food products. The so called direct land use change methodhas serious weaknesses (for example, the way of amortization). Impact modelling, often referred to asindirect land use change, where land use change is calculated as a function of land use, is complex andsubject to precarious assumptions. We present a simple impact model that is based on statistical trendsin land use developments within countries. Estimated global warming potential of annual burning anddecay of natural aboveground biomass for agricultural expansion in a country is divided between timberharvest and agriculture, based on revenue estimates from selling timber and agricultural land usereturns. Estimated global warming potential of soil organic carbon decay due to land use change is allallocated to agriculture. The total global warming potential of land use change is then allocated toagricultural activities that show a trend of area expansion. Although this method has some points ofdiscussion, it works with publicly available data and it can be improved when more detailed informationbecomes available. In our opinion, it also gives more sensible results than the currently used direct landuse change method(s). Furthermore, it can provide better grounds for motivating producers andconsumers to improve their behaviour in relation to global warming.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Recently, there is a rapidly increasing interest in attributingglobal warming potential to products in carbon footprints to giveproducers and consumers insight in their contribution to globalwarming and help them identify possible mitigation options. Theglobalwarmingpotential that is related to agricultural activities (e.g.nitrous oxide emission from the soil and manure storage, methaneemissions from manure storage and enteric fermentation) is about13% of the annual global warming potential that is related to allhuman activities (Olivier et al., 2005). Greenhouse gas emissionsfrom converting natural habitats into permanent agriculturecontribute about 7% to the global warming potential of anthropo-genic greenhouse gas emissions, and soil organic carbon decay andpeat oxidation contribute about 10% (based on IPCC, 2000, 2007;FAO, 2001; Olivier et al., 2005). The question of how emissionsfrom burning and decay of aboveground natural biomass and soilorganic carbon decay should be attributed to agricultural

þ31 182 579971.l (T.C. Ponsioen), hans@

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production in lifecycle assessment studies is therefore of highinterest and has occupied many lifecycle assessment practitioners(see, for example: Börjesson and Tufvesson, 2011; Nguyen et al.,2010; and their references).

When an activity in the product’s lifecycle that results ingreenhouse gas emissions serves more than one product, theresulting global warming potential is generally allocated to theproducts based on a logical characteristic or function according tothe international lifecycle assessment standards ISO 14040/44 (ISO,2006). Physical characteristics should be preferred before nonphysical, but in case of agricultural food products, the revenue ofproducts is often applied (Guinée, 2001). However, allocating theglobal warming potential of deforestation that leads to timber/fuelwood and a large number of agricultural products is problematic,mainly because the deforested land can be used for an indefinitetime period. The objective of this paper is to analyse existingsolutions to this problem and describe an alternative approach.

The enormous increase in land use for bio-fuel productioninitiated awidespread debate among policymakers and researchersonhow to take landuse change into account in lifecycle assessmentsof agricultural products. As a consequence, most research papers onthe land use change issue are related to bio-fuels (see for example:Börjesson and Tufvesson, 2011; Kim and Dale, 2009; Searchinger

T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126 121

et al., 2008; Fargione et al., 2008; Gallagher, 2008). However, it isrelevant for all agricultural products, regardless of their application.Especially in case of animal products, land use change can be animportant issue (for example: Nguyen et al., 2010; Steinfeld et al.,2006). The British Standards Institute (BSI, 2008) and the Euro-pean Parliament (EP, 2009) dictate in their protocols to allocate one-twentieth of the global warming potential from burning and decayof aboveground natural biomass and soil organic carbon decay toeach year of agricultural production in the first twenty years afterthe event. This approach is based on the question of who is to blamefor the land conversion that took place in the past. Proponentsappear to consider converted land as a type of historical investmentfor agricultural production: the emissions from burning and decayof aboveground natural biomass can be amortized over a productionperiod, like a capital good. Besides that, soil organic carbon isassumed to decay at a linear rate during the same period, as sug-gested by the IPCC (1996, 2006). Attributing the sum of the annuallyamortized burning and decay of aboveground natural biomass andannual soil organic carbon decay to each year of agriculturalproduction is referred to as direct land use change.

There are three important concerns for applying the direct landuse change method. The most pressing concern is that any amorti-zation period is arbitrary (Croezen and Kampman, 2008; Nguyenet al., 2010). The twenty years of the BSI and the EP was chosenbased on IPCC (1996), who use this period to enable the assumptionof a linear rate of soil organic carbon stock change (rather than a non-linear rate,whichwould complicate calculations considerably). It canbe discussed if this makes sense for soil organic carbon, but e in ouropinione it is not sensible to apply it to the globalwarming potentialfrom burning and decay of aboveground biomass, unless it is certainthat the land will be abandoned after (exactly) twenty years. Thesecond is a more practical concern. Detailed information is needed,such as theexact locationwhere the agricultural product under studywas produced and when the land was converted. When such infor-mation is lacking, worst case situations are prescribed (BSI, 2008) orprocedures are suggested to make estimates of land use change ina certain area (WRI, 2010). Using the method could easily lead tolarge differences between products that belong to the category ofwithin theamortizationperiodandproducts forwhich canbeproventhey do not belong to that category. Moreover, the consequence isthat the sumof the carbon footprintsof all landuseactivitieswith thedirect land use changemethod is not in linewith the IPCC compliantcalculations on country or global scale (for example: Olivier et al.,2005). A third concern is that the direct land use change methoddoesnot take anyactual displacementeffects into account (BörjessonandTufvesson, 2011). For example, crop growing areamayextend onexisting fertile grasslands, pushing livestock to recently deforestedmarginal land.

An alternative approach, often referred to as indirect land usechange, is future orientated and focuses on the mechanism of landuse change as a function of land use in a situation of globallyincreasing demand of agricultural products (see, for example: Kimand Dale, 2009; Searchinger et al., 2008; Fargione et al., 2008;Gallagher, 2008). The use of land puts pressure on the availabilityof land and therefore contributes to future land use change(regardless of whether it takes place on recently deforested land orelsewhere). This can be considered as a type of impact modelling,comparable with other types of impact modelling within lifecycleassessment. For example, the global warming potentials of green-house gases are derived against a background of current and futureatmospheric concentrations of gasses. Another example is thedepletion of mineral resources, which is calculated against thebackdrop of developments in current and future demand andreserves in several impact models (Guinée et al., 2002; Goedkoopet al., 2009). The land use change impact models start with

current land use and calculate the potential land use change againstthe backdrop of the trends in growing demand for crop products,changes in productivity, and clearing of natural areas.

The models are ideally able to determine how a certain amountof land use and crop consumption will influence future clearing ofnatural habitats and related greenhouse gas emissions. Especiallythe paper by Searchinger et al. (2008) triggered a debate on theindirect effects of increasing land use for crop based fuel productionon land use change in the world. The most important issue in thisdebate is that scenario studies such as the Searchinger et al. studyincludemany assumptions based on slender scientific evidence. Forexample, the relation between the soybean price and deforestationrate in the Amazon region e a crucial factor in the complexeconometric calculation model e is based on only four data pointsfrom Morton et al. (2006).

Nevertheless, such studies provide a useful indication on whatthe impact could bewhen policy makers promote the production ofbio-fuels. Some examples of governmental bodies that make use ofthis type of studies are the Californian Air Resource Board (CARB),the U.S. Environmental Protection Agency (EPA), the U.K. RenewableTransport Fuel Obligation (RTFO) program, and the EuropeanParliament. Some of those governmental bodies are also involved inthe current process of defining land use change impact factors fromthe complex scenario studies to be used in carbon footprintassessments of bio-fuel products. However, we think that as long asscenario studies do not result in more unambiguous conclusions,this way of calculating the global warming potential of land usechange is not readyyet for inclusion in carbon footprints of products.

Thus, the research question that we put forward is: can wedevelop a simple method for calculating and allocating the globalwarming potential from land conversion to agricultural products,which gives a sensible picture and, at the same time, can bea starting point for designing improvement options? In this paper,we describe a method and present results for some cases,continued by a discussion.

2. Methods, data and results

2.1. General outline of a method for calculating the GWP of land usechange

The method described here aims to calculate product GWPvalues of land use change that are in line with the global assess-ments of the IPCC (IPCC, 2000, 2007; Olivier et al., 2005). Thismethod still has some arbitrary elements and causality remains anissue (see Discussion). Therefore, we propose to present the GWPresults of land use change separately from the other GWP results,which is also recommended in the draft ISO standard for carbonfootprints (ISO, 2010). The method includes greenhouse gas emis-sions from aboveground biomass as a one-time event where thecarbon stock drops from its natural values to the value in theagricultural system (Fig. 1). The greenhouse gas emissions from soilorganic carbon stock change is related to crop management and,therefore, included separately.

Equation (1) is used for calculating land use change GWP valueof aboveground carbon stock change that is attributed to a crop ina country per hectare of land use. The resulting values are based onhistoric and current data.

GWP� LUCAði; cÞ ¼ GWP� ACCðcÞ*f � agr:ðcÞ*f�nat: landðcÞ*exp: rateði; cÞ=areaði; cÞ

(1)

where GWP-LUCA is the attributed land use change GWP value ofaboveground carbon stock change for crop i in country c [tonnes

Ab

ove

gro

un

d

Be

lo

wg

ro

un

d

bio

ma

ss

Time

Fig. 1. Aboveground and belowground biomass in a natural habitat and changes overtime after a land conversion event.

T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126122

CO2eq per ha year]; GWP-ACC is theweighted average GWP value ofaboveground carbon stock change in country c [tonnes CO2eq perha]; f-agr. is the allocation fraction of agriculture, where one minusallocation is the allocation fraction of timber [-]; f-nat. land is thefraction of crop area expansion that is at the cost of natural land,where one minus the f-nat. land is at the cost of other crop area [-];exp. rate is the annual increase in area for crop i in country c [ha peryear]; area is the actual area of crop i in country c [ha].

In short, the equation is related to: a) country weighted averageGWP values of aboveground carbon stock change per hectare, b)allocation between timber and agriculture based on timber revenueand net present value of deforested land, c) a fraction of areaexpansion for a crop in a country that is at the cost of forest, and d)a relative area expansion rate of a crop in a country (annualexpansion rate divided by actual crop area). In the followingparagraphs, the data sources and methods for deriving theparameters in the equation are explained.

Equation (2) is used for calculating land use change GWP valueof belowground carbon stock change that is attributed to a crop ina country per hectare of land use.

GWP� LUCBði; cÞ ¼ GWP� BCCðcÞ*f � nat: landðcÞ*T� eq:*exp: rateði; cÞ=areaði; cÞ (2)

where GWP-LUCB is the attributed land use change GWP value ofbelowground carbon stock change for crop i in country c [tonnes

Table 1Shares of different types of forests, average aboveground biomass values and weighted(sources: FAO Global Forest Resources Assessment 2000 and 2006 IPCC guidelines and o

Forest type Indonesia Malaysia Arge

[Share of forest type]

Tropical rainforest 88% 94% 4%Tropical moist 2% e 22%Tropical dry e e 61%Tropical shrub 1% e e

Tropical mountain 9% 6% 5%Subtropical humid e e 3%Subtropical steppe e e 1%Subtropical mountain e e 1%Temperate oceanic e e 2%Temperate steppe e e 1%Temperate mountain e e 2%Average [tonne/ha] 333 341 211

CO2eq per ha year]; GWP-BCC is theweighted average GWP value ofbelowground carbon stock change in country c [tonnes CO2eq perha year]; T-eq. is the time to reach a new equilibrium level of soilorganic carbon.

2.2. Global warming potential of aboveground carbon stock change

The annual land conversion rate (from natural land to agricul-ture) is assumed here equal to the annual agricultural expansionrate in a country. Because it is not known howmuch of each type ofnatural land (natural grassland, steppe, shrub-land, and differenttypes of forest land) is converted to agriculture, shares of existingforest types (including steppe and shrub-land) are used (Table 1).The weighted average aboveground biomass is then calculated byusing IPCC default values for aboveground biomass of each foresttype per continent (Table 1). The share of land conversion fromnatural grassland to agriculture is difficult to determine, becausepart of the natural grassland may be converted to cultivatedgrassland and part to cropland. As we are focussing on cropland(and not on cultivated grassland), we assume that a contraction oftotal grassland in a country is equal to conversion from naturalgrassland to cropland (providing that total cropland is expanding inthe country).

The belowground carbon stock change was calculated by takingthe default reference soil organic carbon stock from IPCC (2006)based on the predominant climate zone and soil type ina country and assuming all converted land is managed with fulltillage and medium inputs. This means that the carbon stockchange depends only on the shares of land use change to paddy rice(10% increase in soil organic carbon), perennial crops (no change)and long-term cultivation (decrease, depending on climate zone).

Based on IPCC publications (IPCC, 2006; Andreae and Merlet,2001), we calculated that the global warming potential is about1.81, 1.76, and 1.73 kg CO2 equivalents per kg biomass from burningtropical forest, extra tropical forest and savannah/grassland,respectively. This is almost equal to the amount of CO2 that wouldbe released from the oxidation of carbon in biomass (assuming 50%of the biomass is carbon, 1.83 kg CO2 per kg biomass is released).We used a rounded value of 1.8 and applied the factor for biomassdecay as well, assuming that methane emission from biomassdecay does not significantly contribute to the total global warmingpotential. For example, the average natural biomass in Brazilianforests is about 280 tonnes per ha (based on the values in Table 1).The global warming potential of burning and decay of abovegroundnatural biomass is therefore about 500 tonnes CO2 equivalents perha: the result of 280 tonnes per ha multiplied by 1.8 kg CO2eq perkg biomass.

averages of five important countries for oil palm and soybean and land conversionwn calculations).

ntina Brazil Southeast Asia South America

[tonne biomass/ha]

76% 350 30014% 290 2208% 160 210

e 70 801% 205 1452% 290 220

e 70 80e 205 145e 120 180e 0 0e 130 130281 e e

0

5

10

15

20

25

1985 1990 1995 2000 2005 2010

Area (m

illio

n h

ectares)

SoybeansMaizeOther crops (contracting)Other crops (expanding)Sugar cane

Fig. 2. Area development of important crops in Brazil (source: FAO, 2011).

T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126 123

2.3. The relative expansion rate per crop and country

The global warming potential is only allocated to agriculturalland use activities that increase in area within a country. However,the use of annual increases could result in very high fluctuations inthe allocation fractions fromyear to year. Therefore, we propose theuse of expected increases from a trend analysis. The allocationfractions are then equal to the area expansion trends in proportionto the sum of those expansion trends.

Using FAO (2011) statistics, the trend analysis of area expansionresulted in 0.76 million ha per year for soybean, 0.19 for sugar caneand 0.17 for other crops in Brazil with expanding area between1990 and 2009. This period was chosen as most representative forthe expected trend based on OECD-FAO Agricultural Outlookreports (OECD, 2010). We realize that the choice for this period issubject to debate (see Discussion section). Fig. 2 shows the areadevelopment of important crops and pastures of the past twentyyears in Brazil as an example. The area of meadows and pasturesexpanded during that period in Brazil, but as there is a clear trendtowards stabilisation (Fig. 3). Therefore, no expansion is expected inthemeadowand pasture area in Brazil. This results in a relative areaexpansion for soybean in Brazil of 0.68 (0.76/[0.76þ 0.19þ 0.17]).

The total net expected agricultural area expansion in Brazil is 0.90million ha per year. This means that part of the area expansion forsoybean, sugar cane andother cropswith expected expanding area isdue to the contraction of the area under other crops, such as rice,beans, cotton, wheat and coffee. This part is equal to 0.22 million haper year divided by 0.90 million ha per year, which yields 0.19 (so,1�0.19¼ 0.81of theexpandedarea is at theexpenseofnatural land).

2.4. Allocation to timber and agriculture

The calculated annual global warming potential from burningand decay of aboveground biomass in a country is allocated todifferent economic activities. First, the emissions are allocated to

180

185

190

195

200

1990 1995 2000 2005 2010

Pe

rm

an

en

t m

ea

do

ws

an

d

pa

stu

re

s a

re

a (

Mio

h

a)

Fig. 3. Area development of permanent meadows and pastures in Brazil (source: FAO,2011).

timber harvest, based on the economic value of timber and ofcleared land for agricultural purposes. For timber, prices can beused. Prices of cleared land, on the other hand, do not necessarilyrepresent the economic value, because of a lacking or underde-veloped land market (no clear land ownership and few docu-mented transactions). Therefore, we suggest the use of agriculturalreturns converted to net present value.

The average volume of timber that is extracted from defores-tation areas in Brazil is about 20 m3 per hectare and its stumpagevalue is about US$ 13 per m3. The average income from timberextraction is therefore about US$ 250 per deforested hectare. Ananalysis by Grieg-Gran (2008) gives a realistic indication of thevalue of cleared land based on agricultural land use returns fromdeforested areas, converted to net present value in the year 2007with a discount rate of 10% and a time horizon of thirty years. Thechoice of this time horizon is common for this type of analysis,because the results do not change significantly when increasing thetime horizon. This amounts to about US$ 460 per hectare (Table 2).The allocation fraction for timber is therefore calculated to be 0.35(251 US$/ha/[251 US$/haþ 462 US$/ha]), and so the allocationfraction for agricultural land use activities is 0.65. For Argentina, weassumed the same values as for Brazil. For Malaysia, we assumedthe same values as for Indonesia.

2.5. Calculating GWP land use change values

The land use change GWP of aboveground biomass per hectareof soybean in Brazil is calculated as follows:

� 500 tonnes CO2eq/ha year (GWP-ACC),� multiplied by 0.65 (the allocation to agricultural land useactivities),

� multiplied by 0.81 for expansion from forest (where theremaining fraction if from contraction of other crops’ area),

� multiplied by 0.76 million ha per year, and� divided by the actual soybean area (23.2 million ha in 2010according to the trend),

� which gives 8.5 tonnes CO2 equivalents per ha (Table 3;Equation (1)).

The land use change GWP of belowground biomass per hectareof soybean in Brazil is calculated as follows:

� 3.6 tonnes CO2eq/ha year (GWP-BCC),� Multiplied by 20 years (time to reach new soil organic carbonequilibrium)

� multiplied by 0.81 for expansion from forest (where theremaining fraction is from contraction of other crops’ area),

� multiplied by 0.76 million ha per year, and� divided by the actual soybean area (23.2 million ha in 2010according to the trend),

� which gives 2.6 tonnes CO2 equivalents per ha (Table 3;Equation (2)).

In Table 4, the calculated land use change GWP values for themost important crop products in 2010 in Brazil, Argentina,

Table 2Deforested land use returns in Brazil and Indonesia (source: Grieg-Gran, 2008).

Land use Brazil Indonesia

Total agriculture returns (US$/ha) 462 909One-time timber harvesting (US$/ha) 251 1099Total returns (US$/ha) 713 2008Allocation fraction agriculture (e) 0.65 0.45

Table 3Example parameter values for soybean and oil palm in Brazil, Argentina, Indonesia and Malaysia and stepwise calculations of the GWP land conversion default values for theproduction year 2010 (see Equations (1) and (2)).

Parameter Units SoybeanBrazil

SoybeanArgentina

Oil palmMalaysia

Oil palmIndonesia

GWP-ACC (aboveground) tonnes CO2eq/ha year 500 380 600 600GWP-BCC (belowground) tonnes CO2eq/ha year 3.6 6.1 0.2 1.7Time to soil carbon equilibrium (T-eq.) Years 20 20 20 20Allocation fraction to agriculture (f-agr.) e 0.65 0.65 0.45 0.45Fraction expansion (f-nat. land) ha/ha 0.81 0.86 0.60 0.83Expected expansion (exp. rate) 106 ha/year 0.76 0.73 0.13 0.24Expected area in 2010 (area) 106 ha 23.2 17.3 4.2 4.9GWP-LUCA (aboveground e Eq. (1)) tonnes CO2eq/ha 8.5 8.9 4.9 11.0GWP-LUCB (belowground e Eq. (2)) tonnes CO2eq/ha 2.6 3.3 0.0 0.1

T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126124

Indonesia and Malaysia are shown. The values of other crops andcountries are available on request or can be produced by followingthe above described method.

2.6. Carbon footprint of meat products

For illustration of possible results in a carbon footprint study, wecalculated the carbon footprints of several meat products as sold inDutch supermarkets: Irish beef, Dutch beef from dairy cows, veal,lamb, pork and chicken meat. The calculation method and useddata (excluding land use and land use change) are described indetail by Blonk et al. (2011). The method is in line with the ISO14040/44 standards (ISO, 2006) and largely with PAS2050 (BSI,2008), where the main difference with the latter concerns landuse and land use change. The reference flow is a kg of average freshmeat at the gate of the supermarket (excluding package weight).The consumer phase is excluded, but waste processing of packagingis included within the system boundaries.

In Table 5, the carbon footprint results for several meat productsas sold by Dutch supermarkets are shown for illustration of possibleoutcomes of a carbon footprint study including land use change

Table 4Land use change GWP values for the most important crop products in 2010 in Brazil, Ar

Crop name Brazil(abovea)

Brazil(belowb)

Argentina(abovea)

A(

[tonnes CO2eq per hectare]

Bananas e e 0.2 0Barley 3.0 0.7 7.2 3Beans, dry e e 1.9 0Cashew nuts, with shell 1.9 0.4 e e

Cassava e e 6.8 3Cocoa beans e e e e

Coconuts 3.6 0.8 e e

Coffee, green e e e e

Fruit, tropical freshness 1.0 0.2 e e

Grapes 4.1 0.9 0.7 0Groundnuts, with shell 3.8 0.8 2.4 1Maize 0.8 0.2 2.3 1Natural rubber 7.6 1.7 e e

Oil palm fruit 9.3 2.1 e e

Oilseeds, Nes e e e e

Oranges e e 2.2 1Rice, paddy e e 2.3 1Seed cotton e e e e

Sorghum 12.6 2.8 e e

Soybeans 8.5 1.9 8.9 4Sugar cane 6.8 1.5 2.8 1Sunflower seed 12.6 2.8 e e

Vegetables freshness 2.4 0.5 e e

Wheat 3.3 0.7 0.1 0

a Above refers to the land use change GWP of aboveground carbon stock change.b Below refers to land use change GWP of belowground carbon stock change.

GWP values. The main findings from these results are that a) typesof meat with relatively small carbon footprints, pork and chickenmeat, have high land use change scores when using the presentedmethod, and b) land use change scores are roughly half of thecarbon footprint in case of pork and chicken meat and much lowerin case of the other meat types in this analysis.

3. Discussion

We have presented a simple method for calculating and allo-cating the global warming potential from land conversion to agri-cultural products, but the question remains: does it give sensibleresults and can it be a starting point for designing improvementoptions? In the Introduction we argued that the so called directland use changemethod does not give sensible results and so calledindirect land use change impact models still result in ambiguousconclusions, which makes them difficult to use for designingimprovement options.

We believe that our method gives more sensible results than thedirect land use change method, which is based on the amortizationconcept. Our results are less sensitive to arbitrary choices and

gentina, Indonesia and Malaysia.

rgentinabelowb)

Indonesia(abovea)

Indonesia(belowb)

Malaysia(abovea)

Malaysia(belowb)

.1 2.9 0.4 e e

.6 e e e e

.9 e e e e

7.1 0.9 3.4 0.1.4 e e e e

10.2 1.3 e 0.7e e

4.4 0.6 6.6 0.1e e e e

.4 e e e e

.2 0.2 0.0 e 1.6

.1 1.8 0.2 e e

4.5 0.6 e e

11.0 1.4 4.9 0.1e e 3.5 0.1

.1 e e 0.0 0.0

.1 1.7 0.2 e e

1.2 0.2 e e

e e e e

.4 e e

.4 0.1 0.0 e e

e e e e

e e 0.3 0.0.1 e e e e

Table 5Carbon footprint results for several meat products as sold by Dutch supermarkets.

Product Land use change(aboveground)

Land use change(belowground)

Carbon footprint(excl. land use change)

kg CO2 eq per kg fresh meat

Beef, Irish 0.76 0.14 38.3Beef, Dutch

(dairy cows)0.29 0.05 9.2

Veal 0.30 0.10 7.8Lamb 0.49 0.09 15.5Pork 1.42 0.70 4.8Chicken meat 1.46 0.58 3.6

T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126 125

independent of data on the exact location of agricultural produc-tion and the history of a certain area of land. However, we alsointroduced several choices that may be subject to debate. Forexample, the trend analysis of crop area expansion is based ontwenty years of production data. A choice of ten or thirty yearsgives different results (not shown in this paper). On the other hand,the choice is based on an analysis of crop area development ina country and OECD-FAO outlook reports (OECD, 2010) and needs tobe reconsidered for each country and new insights.

Another point of contention in our method is the calculation ofallocation fractions to timber and agriculture. Data on revenue fromtimber harvest from natural forests and the value of deforested landare scarce and difficult to judge on reliability. The study by Grieg-Gran (2008) provides data for the most important countrieswhere large-scale land conversion takes place (for example Brazil,Indonesia and Malaysia). For other countries, additional analysesmay be required. On the other hand, it is also arbitrary to allocate100% of the deforestation GWP to agriculture and none to timber asis done in all other carbon footprint studies (which are known tous).

The concept of relating land use change to crop area expansionwas also introduced by Leip et al. (2010). However, they estimatedseveral scenarios to the cost of what fractions of each type of naturalvegetation is converted into crop area based on studies such asMorton et al. (2006). The problem is that such data are scarce anddifficult to judge on reliability and completeness. Our approach is touseweighted average aboveground biomass of natural vegetation ina country. This may overestimate the actual aboveground biomassthat is cleared for land conversion (and in some cases it mayunderestimate), but it results in reproducible and consistent results.

Contrary to indirect land use change impact models, whichrelate land use change to increasing demand for certain cropproducts anywhere in the world, our method identifies productionof specific crops in countries where large-scale deforestation fortimber harvest and land use change takes place. This may bedebatable, because in the global market agricultural activities allover the world are completely interrelated. Our arguments tochoose for country specific calculations are partly because thoserelations are too complex to model and partly because deforesta-tion can be stimulated or discouraged by governmental policies.Road construction and new settlements motivate people to starttimber businesses, while agricultural business is attracted toexploit the resulting cleared land in a later stage. Governmentpolicies have a large influence on such processes by infrastructuredevelopment and market interventions (Margulis, 2004). On theother hand, policies on knowledge development, nature conser-vation and law enforcement may prevent the exploitation of themost vulnerable natural habitats. In the end, governments need tochoose between stimulating economic development/securingenergy supply and implementing policies against deforestation.From an agricultural perspective, a combination is possible whenefforts are made to use the available land more efficiently andreduce pressure on land.

A risk, however, with our country specific method is that buyersof certain crops decide to switch from the same crop product froma country where large-scale land conversion takes place toa country where this problem does not occur. Unless the crop yieldis higher in the latter country, this does not lead to anymitigation ofgreenhouse gas emissions. So, reduced pressure on land byimproved yields and moving crop production to more productiveareas (in other countries) are the main options that result inimprovements. We must emphasize that assessing carbon foot-prints of products does not give complete answers to what is thebest strategy for mitigating greenhouse gas emissions. It merelygives insight into the greenhouse gas emissions that are related toexisting or hypothetical production chains. To fully evaluate thepositive and negative effects of mitigation strategies on greenhousegas emissions in production chains, consequential lifecycleassessments or scenario models that are specific to the case ofinterest are more appropriate.

4. Conclusion

In this paper, we presented a method for including globalwarming potentials of land use change into lifecycle assessment ofproducts. We suggest this method as a better basis for includingland use change in carbon footprints of products than the currentlyused direct land use change factor as used in several carbon foot-print protocols, which is based on an amortization period. Themethod gives an indication of the carbon footprint of land usechange in relation to crop demand and region of crop productionthat is in line with annual global warming potential on a globalscale. This GWP is allocated to timber and crops with expandingareas within countries. Although the method has some debatableissues, it largely works with publicly available data, it can beimproved when more detailed information is available. It also givesmore sensible and consistent results than currently used methodsfor calculating the GWP of land use change in carbon footprints. Itcould also provide better grounds for motivating producers andconsumers to improve their behaviour in relation to greenhousegas emissions.

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