Applied Energy 86 (2009) S197–S208

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Applied Energy

journal homepage: www.elsevier .com/locate /apenergy

Energy consumption and GHG emissions of six biofuel pathways by LCAin (the) People’s Republic of China

Ou Xunmin a,b,c, Zhang Xiliang b,c,*, Chang Shiyan b,c, Guo Qingfang b,c

a School of Public Policy and Management (SPPM), Tsinghua University, Beijing 100084, (the) People’s Republic of Chinab China Automotive Energy Research Center (CAERC), Tsinghua University, Beijing 100084, (the) People’s Republic of Chinac Institute of Energy, Environment and Economy (3E), Tsinghua University, Beijing 100084, (the) People’s Republic of China

a r t i c l e i n f o a b s t r a c t

Article history:Received 13 January 2009Received in revised form 22 April 2009Accepted 23 April 2009Available online 7 June 2009

This article is sponsored by the AsianDevelopment Bank as part of theSupplement ‘‘Biofuels in Asia.

Keywords:(the) People’s Republic of ChinaGreenhouse gasEnergy consumptionBiofuelBio-ethanolBio-diesel

0306-2619/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.apenergy.2009.04.045

* Corresponding author. Tel.: +86 10 6277 2754; faE-mail address: zhang_xl@tsinghua.edu.cn (X. Zha

This paper presents life-cycle-analysis (LCA) energy consumption (EC) and greenhouse gas (GHG) emis-sions of China’s current six biofuel pathways, which are: corn-derived ethanol (CE); cassava-derived eth-anol (KE); sweet sorghum-derived ethanol (SE); soybean-derived bio-diesel (SB); jatropha fruit-derivedbio-diesel (JB); and used cooking oil (UCO)-derived bio-diesel (UB). The tool utilized here is the WTW(Well-to-Wheels) module of Tsinghua-CA3EM model covering the entire lifecycle including: raw materi-als cultivation (or feedstock collection); fuel production; transportation and distribution; and applicationin automobile engines, compared with Conventional Petroleum-based gasoline and diesel Pathways(CPP). The results indicate: (1) the fossil energy inputs are about 1.0–1.5 times the energy contained inthe fuel for the CE, SE and SB pathways, but 0.5–0.9 times for the KE, UB and JB pathways; (2) comparedwith CPP, the JB, KE and UB pathways can reduce both fossil fuel consumption and GHG emissions; the CEand SB pathways can only reduce fossil fuel consumption, but increase GHG emission; the SE pathwayincreases not only fossil fuel consumption but also GHG emission; and (3) the main factors inducing highEC and GHG emission levels include: high EC levels during the fuel production stage and high fertilizerapplication rates during the planting of raw feedstocks. Conclusions are that of the aforementioned bio-fuel pathways in (the) People’s Republic of China: (1) only the JB, KE and UB pathways have energy-sav-ing merits as indicated by the LCA energy inputs and outputs; (2) compared with CPP, all but the SEpathway reduces fossil fuel consumption. However, the SB and CE pathway increase GHG emission;(3) all six displace petroleum by utilizing more coal; and (4) feedstock productivity levels must beincreased, and there must be a reduction in fertilizer utilization and EC consumption during the cultiva-tion and transportation stages in order to achieve the goals of energy balance and GHG emissionreduction.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Over the past few decades, driven by the goals of promotingagricultural development, guaranteeing energy security, copingwith the climate change issue and protecting ecological environ-ment, many countries have promoted large-scale development ofthe bio-liquid fuel industry as the most near term alternative forconventional petroleum-based gasoline and diesel fuel throughdesigning active strategies and robust policies [1–4]. In 2007, glo-bal production of bio-ethanol (EtOH) and bio-diesel (BD) were39,570,000 and 8,820,000 tons, respectively, and 1,290,000 and100,000 tons, respectively, for (the) PRC [5]. Currently, about 80%of (the) PRC’s EtOH uses corn as its feedstock and many bio-refin-eries have turned to using newly or genetically produced corn to

ll rights reserved.

x: +86 10 6279 2166.ng).

displace low quality and old stocks of corn. Other feedstock cropsin use for EtOH, but on a much smaller scale, include rapeseed, cas-sava, sweet potato, sugarcane, sugarbeet, forestry waste, etc. [6–8].Meanwhile, BD has increasingly been produced from used cookingoil (UCO) or plant oil residuals [8]. However, to what degree therole of reducing GHG emissions and saving energy plays, especiallyin displacing petroleum fuel, by these biofuel pathways has be-come a focus for discussion in recent years [9–11].

Since the 1990s, researchers and institutions have began tobuild lifecycle analysis (LCA) models to model energy consumption(EC) and greenhouse gas (GHG) emissions. The result is a modelthat comprehends GHG, Regulated Emission and Energy consump-tion of Transportation fuel (GREET), and a lifecycle emissions mod-el (LEM) [12–17]. These models are able to evaluate alternativeliquid fuel LCA [18–22]. Numerous research reports have beenpublished for North-America, Europe and other countries, withlocalized conclusions based on these models [4,23,24]. The conclu-sions are very geographically dependent and therefore cannot nec-

Nomenclature

BD bio-dieselCE corn-derived ethanolCPP conventional petrol-based pathwaysEF emission factorGHG greenhouse gasKE cassava-derived ethanolNER net energy rateNG natural gasNGRV net GHG reduction valuePF process fuelSB soybean-derived bio-dieselT&S transportation and storageUB UCO-derived bio-dieselWTP Well-to-Pump

CD conventional dieselCG conventional gasolineEC energy consumptionEtOH bio-ethanolJB jatropha fruit-derived bio-dieselLCA life-cycle-analysisNEV net energy valueNGRR net GHG reduction ratePE primary energyPTW Pump-to-WheelsSE sorghum-derived ethanolTSD transportation, storage and distributionUCO used cooking oilWTW Well-to-Wheels

S198 X. Ou et al. / Applied Energy 86 (2009) S197–S208

essarily be applied to other places [10,25]. (the) PRC’s biofuel LCAstudies are mainly based on unique single pathways. Therefore, itis not possible to compare it to other platforms [26–42].

From an entire life cycle viewpoint, every biofuel pathway is acomplex system as shown in Fig. 1. The illustration indicates thatthe system: cuts across each of the three major sectors fromagriculture, industry to services sector; covers all of the stagesincluding raw materials cultivation/collection, fuel production,transportation, to fuel storage and distribution; uses all sorts of en-ergy including coal, electricity, petroleum products, natural gas(NG), hydropower and other renewable energy, as well as chemicalfertilizer and pesticides, etc.; and its EC and GHG emissions calcu-lations are comprehended under a national-level energy balanceframework.

Some of the results from the LCA studies for (the) PRC’s biofuels,are indicating problems with accuracy due to: (1) the incompletedata from foreign models or from the process simulation softwaredirectly [30,43]; and (2) the lack of full comprehension of the com-plexity of the energy system requirements, especially for biofuelLCA, which is deeply affected by the nation’s general energy mix,the fertilizer production and utilization situation, and electricitygeneration and consumption status [10,30,31]. So, based on (the)PRC’s actual conditions, carrying out a comparison of the EC andGHG emissions of various biofuel pathways is a fundamental andessential requirement to enable (the) PRC to develop strategies

Fig. 1. Complex energy consumption system of

and policies promoting large-scale development of the biofuelindustry.

2. Methods

2.1. Model utilized (Tsinghua-CA3EM)

In this study we use the Well-to-Wheels (WTW) analysis mod-ule of the Tsinghua-CA3EM (China Automotive Energy, Environ-ment and Economy Model) model, which is an integratedcomputerized model module for China’s automotive energy supplyand demand balance calculation and analysis. The model is basedon China’s national conditions with the integration of the widelyknown transportation energy micro-level computing GREET model[22]. Part of the GREET model structure has been adjusted to Chi-nese specific situations, such as the dominance of coal utilization.Therefore, a majority of the parameters have been modified withlocal Chinese data [44].

2.2. System boundary

Well-to-Pump (WTP) and Pump-to-Wheels (PTW) are the twostages included in this WTW EC and GHG analysis: WTP studiesthe upstream production stage, including the exploitation of rawresources/feedstock plantation, feedstock transportation, fuel pro-

biofuels pathways. Adapted from Ref. [30].

Fig. 2. EtOH EC and GHG emissions LCA diagram (KE pathway case).

Table 1Well-to-Wheels research framework for biofuel pathways.

Pathway no. Exploitation of raw resources/feedstock plantation

Feedstock transportation (WTP) Fuel production Fuel TSD Vehicle operation (PTW)

CG/baseline1 Oil exploitation Oil transportation Gasoline and oxygenatesproduction, blend

Oxygenated gasolineTSD

Oxygenated gasolinecombustion

CE Corn plantation Corn transportation EtOH production EtOH TSD EtOH combustionKE Cassava plantation Cassava transportation EtOH production EtOH TSD EtOH combustionSE Sweet-sorghum plantation Sweet-sorghum transportation EtOH production EtOH TSD EtOH combustion

CD/baseline2 Oil exploitation Oil transportation Diesel production Diesel TSD Diesel combustionSB Soybean plantation Soybean transportation BD production BD TSD BD combustionJB Jatropha plantation Jatropha fruit transportation BD production BD TSD BD combustionUB UCO collection UCO transportation BD production BD TSD BD combustion

Note: CG: conventional gasoline; CE: corn-derived ethanol; KE: cassava-derived ethanol; SE: sweet sorghum-derived ethanol; CD: conventional diesel; SB: soybean-derivedbio-diesel; JB: jatropha fruit-derived bio-diesel; UB: UCO-derived bio-diesel.

X. Ou et al. / Applied Energy 86 (2009) S197–S208 S199

duction, fuel transportation, storage and distribution (TSD) andPTW studies the downstream fuel combustion process in the vehi-cle’s engine, as described in Fig. 2 and Table 1.

2.3. LCA primary energy consumption calculation

When 1 MJ biofuel is supplied to the vehicle, we can calculatethe LCA primary energy (PE) consumption based on; (1) the path-way’s total process fuel (PF) consumption; and (2) the PFs’ LCA PEinput using the following equations and Table 2.

ENp;j ¼ ENp � RAp;j ð1Þ

ENj ¼X4

p¼1

ENp;j ð2Þ

ELCA;p ¼X9

j¼1

ELCA;p;j ð3Þ

ELCA;i ¼X4

p¼1

ELCA;p;i ð4Þ

ELCA ¼X4

p¼1

ELCA;p ¼X3

i¼1

ELCA;i ð5Þ

Here, ENp,j is the PF j consumption during the unit p; ENp isthe total PF consumption during the unit of p; ENj is the totalPF consumption during all the units; RAp,j is the PF j consump-tion share during the unit p; ELCA,p is the LCA PE input of theunit of p; ELCA,i is the LCA PE i input of all the units; ELCA isthe LCA PE input of all the units; i indicates the name of PE; jindicates the name of PF; p indicates the name of unit withinthe studied pathway; and the details behind i, j and p can beseen in Table 2.

Just as indicated in the Appendix and [44], the PF’s LCA PE inputfactors can be calculated following these methods and when 1 MJPF j is achieved, its LCA total PE input (EFLCA,j) results is the sumof each total PE LCA input (EFLCA,j,i):

EFLCA;j ¼X3

i¼1

EFLCA;j;i ð6Þ

Table 2The interpretation of i, j and p.

i (PE) j (PF) p (Unit of the pathways)

1 Coal Crudecoal

Feedstock production (including the related fertilizer andherbicide production)

2 NG Crude NG Feedstock transportation3 Petrol Crude oil Fuel production4 Coal Fuel transportation5 NG6 Diesel7 Gasoline8 Residual

oil9 Electricity

S200 X. Ou et al. / Applied Energy 86 (2009) S197–S208

So combined, Eqs. (1)–(5) result in:

ELCA;p ¼X9

j¼1

X3

i¼1

ðENp;j � EFLCA;j;iÞ ð7Þ

ELCA;i ¼X4

p¼1

X9

j¼1

ðENp;j � EFLCA;j;iÞ ð8Þ

ELCA ¼X4

p¼1

X9

j¼1

X3

i¼1

ðENp;j � EFLCA;j;iÞ ð9Þ

Here each EFLCA,j,i is calculated in the CA3EM model and can bereferred from [44], as the Appendix shows.

2.4. NEV and NER

We define the net energy value (NEV) and net energy rate (NER)to assess the biofuel pathways’ energy saving effect: NEV is the re-sult of the energy contained in the fuel minus its LCA PE consump-tion; NER is the ratio of the energy contained in the fuel to the LCAfossil fuel consumption. Therefore, when 1 MJ biofuel is supplied tothe vehicle, NEV and NER are calculated to be:

NEV ¼ 1� ELCA ð10ÞNER ¼ 1=ELCA ð11Þ

2.5. LCA GHG emissions calculation

2.5.1. General descriptionFor each unit, when 1 MJ biofuel is supplied to the vehicle, we

first calculate both direct and indirect emissions of each of threekey types of GHG emissions (CO2, CH4 and N2O) and convert allof them to their CO2 equivalents (CO2,e) according to their globalwarming potential (GWP) value [45].

CO2;LCA ¼ CO2;direct þ CO2;indirect ð12ÞCH4;LCA ¼ CH4;direct þ CH4;indrect ð13ÞN2OLCA ¼ N2Odirect þ N2Oindirect ð14ÞGHGLCA ¼ CO2;LCA þ 23 � CH4;LCA þ 296 �N2OLCA ð15Þ

Here, CO2,LCA is the LCA CO2 emission; CO2,direct is the LCA CO2

direct emission; CO2,indirect is the LCA CO2 indirect emission; CH4,LCA

is the LCA CH4 emission; CH4,direct is the LCA CH4 direct emission;CH4,indrect is the LCA CH4 indirect emission; N2OLCA is the LCA N2Oemission; N2Odirect is the LCA N2O direct emission; N2Oindirect isthe LCA N2O indirect emission.

2.5.2. CO2 emissions calculationCO2,direct is calculated according to each PF’s Carbon Content

Factor and Oxidation Rate [46,47].

CO2;direct ¼X9

j¼1

CO2;direct;j ð16Þ

CO2;direct;j ¼ ERCO2;j � ENj ð17ÞERCO2;j ¼ CCj � FORj ð18Þ

Here, CO2,direct,j is the LCA CO2 direct emission for PF j utilized; ER-CO2,j is the CO2 emission rate for PF j; ENj is the EC of PF j; CCj is theCarbon Content Factor of PF j; FORj is the Oxidation Rate of PF j .

CO2,indirect is calculated based on the carbon balance functions[23,44] and each PF’s indirect LCA CO2 emission rate (TCO2,j) isshown in the Appendix and [44]:

CO2;indirect ¼X9

j¼1

CO2;indirect;j ð19Þ

CO2;indirect;j ¼ TCO2;j � ENj ð20Þ

2.5.3. CH4 emissions calculationCH4,direct is calculated based on each PF’s CH4 emission rate

(ERCH4,j):

CH4;direct ¼X9

j¼1

CH4;direct;j ð21Þ

CH4;direct;j ¼ ERCH4;j � ENj ð22Þ

CH4,indrect is calculated based on each PF’s indirect LCA CH4

emission rate (TCH4,j) which is shown in the Appendix and [44]:

CH4;indirect ¼X9

j¼1

CH4;indirect;j ð23Þ

CH4;indirect;j ¼ TCH4;j � ENj ð24Þ

2.5.4. N2O emissions calculationN2Odirect is calculated in two parts: one is the EC induced and the

other is N2O soil emissions from N-fertilizers applied in biofuelfeedstock farming (N2Odirect,fertilizer):

N2Odirect ¼X9

j¼1

N2Odirect;j þ N2Odirect;fertilizer ð25Þ

N2Odirect;j ¼ ERN2Oj � ENj ð26ÞN2Odirect;fertilizer ¼ ERNF � AMOUNTNF ð27Þ

Here N2Odirect,j is the LCA N2O emission of PF j; ERN2Oj is the N2Oemission rate for PF j; ERNF is the N2O emission rate of N-fertilizersapplied; AMOUNTNF is the application amount of the N-fertilizers.

N2Oindirect is calculated based on each PF’s indirect LCA N2Oemission rate (TN2Oj) as shown in the Appendix:

N2Oindirect ¼X9

j¼1

N2Oindirect;j ð28Þ

N2Oindirect;j ¼ TN2Oj � ENj ð29Þ

2.5.5. Resource for these EFsAll the TCO2,j, TCH4,j and TN2Oj are analyzed in the Appendix and

can be found in [44].

2.6. NGRV and NGRR

We define the net GHG reduction value (NGRV) and net GHGreduction rate (NGRR) to assess the biofuel pathways’ GHG emis-sion reduction effect, where NGRV is result of the LCA GHG emis-sion of the responding baseline pathway minus the studiedbiofuel pathway’s LCA GHG emission; NGRR is the ratio of the NGRVof the studied biofuel pathway to the LCA GHG emission of the

X. Ou et al. / Applied Energy 86 (2009) S197–S208 S201

responding baseline pathway. Therefore, when 1 MJ of biofuel, CGor CD, is supplied to the vehicle, NGRV and NGRR can be calculated,as shown, in Eqs. (30) and (31).

NGRV ¼ GHGLCA;baseline � GHGLCA;biofuel ð30ÞNGRR ¼ NGRV=GHGLCA;baseline ð31Þ

3. Basic data

3.1. Basic parameters for biofuel pathways

The basic parameters of (the) PRC’s current six biofuel pathwaysincluding: CE, KE, SE, SB, JB and UB are shown in Tables 3 and 4.

3.2. Data within the LCA calculations

For each PF, the related LCA PE consumption rates (sorted bycoal, NG and petrol), LCA GHG indirect emission rates (sorted byCO2, CH4 and N2O), direct emission rates (sorted by CH4 and

Table 3Basic parameters of EtOH biofuel pathways.

Pathway CE Data s

Production (tons/ha) 6.5 Jilin dPlanting energy (MJ/ha) 4047a [24]N fertilizer inputs (kg/ha) 162 [31]P fertilizer inputs (kg/ha) 13.3 [31]K fertilizer inputs (kg/ha) 131 [31]Pesticide inputs (kg/ha) 8 [31]Collection radius (km) 125 The avConversion rate (tons of feedstock/tons of fuel) 3.2 [24]Energy for extraction (GJ/ton) 25 [30]Distance transmission and distribution (km) 520 [30]Sharing ratio of the by-product (%) 30.90 [22]

a The energy mix is gasoline (7.16%), diesel (86.62%) and electricity (6.02%).b The values of [30] and [24] are 100 and 150 km, respectively.c According to [27], diesel fuel and electricity are 44 l ha�1 and 60,923 kW h yr�1(200

density of diesel are 42.7 MJ kg�1 and 0.837 kg l�1.d Including 200 km in lorry mode and 50 km in truck mode.e In [27] the energy consumption per liter EtOH is 11.898 MJ and near 100% of it is cof Including 350 km in lorry mode and 100 km in truck mode.g According to its average results.h The data of SE are based on the field visit to Inner Mongolia of China.i The energy mix is gasoline (10%), diesel (80%) and electricity (10%).j The energy mix is coal (90%) and electricity (10%), and the energy efficiency from co

Table 4Basic parameters of BD biofuel pathways.

Pathway SB

Production (tons/ha) 1.8Planting energy (MJ/ha) 4494a

N fertilizer inputs (kg/ha) 88P fertilizer inputs (kg/ha) 33K fertilizer inputs (kg/ha) 27Pesticide inputs (kg/ha) 4Collection radius (km) 200Conversion rate (tons of feedstock/tons of fuel) 5.9Energy for extraction (GJ/ton) 12.9b

Distance transmission and distribution (km) 200c

Sharing ratio of the by-product (%) 27.50

a The energy mix is gasoline (7.33%), diesel (88.87%) and electricity (3.80%).b The energy mix is coal (90%) and electricity (10%), and the energy efficiency from coc In current (the) PRC, most of the BD is being used in some agricultural machines and The data of JB are based on the field visit to Hainan of (the) PRC.e The data are confirmed by [24].f The data are confirmed by [24].g The data of UB are based on the field visit to Beijing of China and [24].h Collection energy for UCO is 30 MJ/ton (to collection points) and 135 MJ/ton (transp

N2O), and Carbon Content Factor and Oxidation Rate are shownin Table 5.

We assume that 2% of N in N-fertilizers is released to form N2O[22,30,31], so the N2O emission rate of N-fertilizers applied (ERNF)is 31.5 g/kg N-fertilizers (31.5 = 2% � 44/28 � 1000).

4. Results analysis

4.1. WTP EC

All types of PE consumption (including the heat value of the ob-tained fuel) of these pathways, when 1 MJ fuel is obtained, areshown in Table 6.

As Fig. 3 shows, like both CG and CD baselines, the biofuel path-ways of CE, SE and SB have negative NEV; but the pathways of KE,JB and UB have positive NEV’s. Ranked by their low to high NER effi-ciencies, the pathways are SE, CG, CD, CE, SB, UB, KE and JB.

The fossil EC of these biofuel pathways, excluding the SEpathway, is decreased from that of conventional petroleum-based

ource KE Data Source SEh

ata in Ref. [48] 13.3 [27] 64.51572 [27]c 2800i

100 [27] 600100 [27] 150200 [27] 00 [33] 0

erage of [30,24]b 250 [27]d 503.0 [27] 18.813.9 [27]e 20j

450 [27]f 30018.06 [27]g 20

,000 ha), so the total planting energy can be determined based on the LHV and the

al.

al to steam is 80%.

Data source JBd UBg

Heilongjiang data in [48] 5.0 –[24] 800 –[31] 97 –[31] 27 –[31] 18 –[31] 0 –[30] 250 35h

[24] 3.3e 20.0[31] 10f 7.5Field visit 300 100[31] 40 0

al to steam is 80%.d fishing boats because it is forbidden for vehicle fuel.

orted to processing plants).

Table 5LCA PE consumption rates, direct and indirect GHG emission rates for PFs.a

PF EFLCA,j,Coal

(MJ/MJ)EFLCA,j,NG

(MJ/MJ)EFLCA,j,Petrol

(MJ/MJ)TCO2,j

(g/MJ)TCH4,j

(g/MJ)TN2Oj

(10�3 g/MJ)ERCH4,j

(g/MJ)ERN2Oj

(g/MJ)CCj

(g-C/MJ)FORj

Crude coalb 1.053 0.000 0.002 4.259 0.422 0.062 0.001 0.001 26.35 0.90Crude NGb 0.080 1.011 0.064 11.909 0.072 0.154 0.001 0.001 15.30 0.99Crude oilb 0.097 0.023 1.047 15.998 0.054 0.265 0.002 0.000 20.00 0.98Coal 1.061 0.001 0.110 5.733 0.425 0.172 0.001 0.001 24.74 0.90NG 0.081 1.015 0.065 13.544 0.110 0.161 0.001 0.001 24.70 0.99Diesel 0.156 0.027 1.119 28.287 0.078 0.441 0.004 0.002/0.028c 20.20 0.98Gasoline 0.164 0.049 1.130 30.506 0.086 0.472 0.080 0.002 18.90 0.98Resid. oil 0.139 0.026 1.055 25.323 0.071 0.409 0.002 0.000 21.10 0.98Electricity 2.506 0.015 0.115 265.218 1.010 3.917 – – – –

a Details to [44].b These fuel are just mined and transported and not processed for secondary energy.c For these vehicles, the utilization value is 0.002 but for others the value is 0.028.

Table 6WTP EC of biofuel pathways (1 MJ fuel obtained).

Pathway no. CG CE KE SE CD SB JB UB

NEV �0.344 �0.129 0.347 �0.463 �0.303 �0.020 0.501 0.127NER 0.744 0.886 1.531 0.684 0.767 0.981 2.004 1.461Fossil EC (MJ) 1.34 1.13 0.65 1.46 1.30 1.02 0.50 0.87Reduction rate (%) – 16.00 51.41 �8.85 – 21.74 61.70 33.02Coal EC (MJ) 0.16 0.90 0.57 1.20 0.16 0.61 0.34 0.41Increase rate (%) – 450.00 245.73 627.44 – 290.48 113.38 161.83NG EC (MJ) 0.05 0.06 0.01 0.13 0.03 0.12 0.08 0.10Petrol EC (MJ) 1.13 0.17 0.07 0.12 1.12 0.29 0.09 0.36Reduction rate (%) – 84.97 93.55 89.39 – 92.23 74.51 68.00

(a) NEV (MJ/MJ) (b) NER

Fig. 3. NEV and NER of the pathways.

(a) Petrol energy input (MJ/MJ) (b) Coal energy input (MJ/MJ)

Fig. 4. Petrol energy and coal energy input of the pathways.

S202 X. Ou et al. / Applied Energy 86 (2009) S197–S208

gasoline and diesel pathways (CPP) fuels. What’s more, all of thealternative fuel pathways, according to their fossil energy inputs,

increase coal consumption and reduce petroleum consumption,as Fig. 4 shows. This is compared with CPP where the coal increase

X. Ou et al. / Applied Energy 86 (2009) S197–S208 S203

rates from 113.38% to 627.44% and the associated petroleumreduction rates are from 68.00% to 93.55%.

4.2. WTW GHG emissions

As shown in Table 7, when 1 MJ of fuel is obtained and utilized,SE and CE pathways lead to significant increases in GHG emissionsup to 26.43% and 39.91%, respectively, but KE, JB and UB indicatesignificant GHG emission reductions of 33.96%, 49.34% and21.54%, respectively.

4.3. Factors in the decomposition of fossil energy consumption

As shown in Table 8, when 1 MJ fuel is obtained, all pathways’fossil energy consumption amounts are large in each of the two di-vided stages – the feedstock stage and the fuel stage, especiallyduring the latter one: EC amounts are from 0.33 to 0.75 MJ, andthe proportion in total amount are from 41% to 70%, which is an

Table 7WTW GHG emission of biofuel pathways (1 MJ fuel obtained and utilized).

Pathway No. CG CE KE

Emission amount (g CO2,e) 103.920 131.384 68.630Increase amount (g CO2,e) – 27.464 �35.290Increase rate (%) – 26.43 �33.96

Note: 1. Emission amount is the GHG emissions amount when 1 MJ of fuel is obtained andof pathways CE, KE, SE, SB, and JB are planted biomass and absorb CO2 from the air, theretherefore be offset by these sinks or credits.

Table 8Fossil energy consumption factor decomposition of biofuel pathways (1 MJ fuel obtained

Pathway no. (unit) CE (MJ) CE (%) KE (MJ) KE (%) SE (M

Total 1.13 100 0.65 100 1.461. Feedstock stage 0.43 38 0.20 30 0.721.1. Plantation energy 0.07 6 0.02 3 0.041.2. Fertilizer input 0.25 22 0.10 16 0.631.2.1. N input 0.23 20 0.09 13 0.61.2.2. P input 0.02 2 0.01 1 0.031.2.3.K input 0 0 0.01 1 01.3. Pesticide input 0.06 5 0 0 0.011.4. Feedstock transportation 0.04 4 0.08 12 0.042. Fuel stage 0.70 62 0.46 70 0.752.1. Fuel production 0.68 61 0.44 68 0.722.2. Fuel transportation 0.02 1 0.02 2 0.02

Note: When 1 MJ fuel is obtained and utilized, the EC (corresponding proportions) of the(55%/45%) for the diesel pathway, respectively.

Table 9GHG emission factor decomposition of biofuel pathways (g CO2,e/MJ fuel obtained and uti

Pathway no. (unit) CE (g) CE (%) KE (g) KE (%) SE (g)

Total 131.38 100 68.63 100 145.31. Feedstock stage 61.74 47 20.85 30 59.61.1. Plantation energy 6.41 5 2.36 3 1.71.2. Fertilizer input 46.62 35 13.40 20 55.41.2.1. N input 40.56 31 12.73 19 54.31.2.1.1. N2O effect 23.92 18 6.06 9 22.51.2.2. P input 2.80 2 0.40 1 1.11.2.3. K input 3.26 2 0.27 0 01.3. Pesticide input 5.25 4 0 0 0.51.4. Feedstock transportation 3.46 3 5.09 7 1.82. Fuel stage 69.62 53 47.74 70 85.72.1. Fuel production 68.30 52 46.50 68 83.72.2. Fuel transportation 1.32 1 1.24 2 2.0

Note: 1. When 1 MJ fuel is obtained and utilized, the GHG emissions (corresponding prop56.84/45.76 g (55%/45%) for the diesel pathway, respectively. 2. N2O effect means N2O e

important factor leading to these pathways high EC. For the path-ways based on biomass plantations, high fertilizer input is themain reason for high EC during the feedstock stage resulting inan increased total stage. The energy inputs derived from fertilizerinput take the proportion of total EC from 16% to 43%. The N fertil-izer input is the most important sub-factor. The current (the) PRCinputs are higher than the value of 2.8% estimated in [9], whichrepresents the US CE pathway. Plantation energy, pesticide inputand energy for feedstock transportation are also important factorsfor a high EC feedstock stage. During the fuel stage, the EC for fuelproduction is the dominant factor and the EC for fuel transporta-tion is a minor factor.

4.4. Factors in the decomposition of GHG emissions

As shown in Table 9, when 1 MJ fuel is obtained and utilized, allpathways’ GHG emission amounts are large in two divided stages –the feedstock stage and the fuel stage, especially during the first

SE CD SB JB UB

145.391 102.592 110.500 51.971 74.74641.471 – 7.908 �50.621 �22.10339.91 – 7.71 �49.34 �21.54

utilized (that is, use phase emissions of the vehicle are included); 2. The feedstocksfore acting as a carbon sink. Consequently, use phase emissions from the vehicle can

and utilized).

J) SE (%) SB (MJ) SB (%) JB (MJ) JB (%) UB (MJ) UB (%)

100 1.02 100 0.5 100 0.87 10049 0.60 59 0.17 35 0.40 46

3 0.14 13 0.02 343 0.24 24 0.09 1841 0.21 21 0.09 17

2 0.02 2 0 10 0.01 1 0 00 0.07 7 0 03 0.16 15 0.06 13 0.40 46

51 0.42 41 0.33 66 0.47 5449 0.42 41 0.32 65 0.47 53

2 0 0 0.01 1 0 1

two stages are 0.66/0.69 MJ (49%/51%) for the gasoline pathway, and 0.72/0.58 MJ

lized).

SE (%) SB (g) SB (%) JB (g) JB (%) UB (g) UB (%)

9 100 110.50 100 51.97 100 74.75 1001 41 73.89 67 24.61 47 34.40 463 1 16.91 15 1.57 35 38 42.27 38 18.06 354 37 38.88 35 17.51 341 15 20.16 18 9.01 171 1 2.11 2 0.36 1

0 1.27 1 0.18 08 0 5.85 5 0 05 1 8.87 8 5.03 10 34.40 468 59 36.39 33 27.36 53 40.39 542 58 36.14 33 27.11 52 39.94 536 1 0.25 0 0.25 0 0.44 1

ortions) of the two stages are 50.82/53.10 g (49%/51%) for the gasoline pathway, andmissions from nitrogen nitrification and denitrification.

S204 X. Ou et al. / Applied Energy 86 (2009) S197–S208

stage: GHG emission amounts are from 20.85 g to 73.89 g, and theproportion in total amounts are from 30% to 67%, which is animportant factor leading to the pathways’ GHG emission increasecompared to that of CPPs; for the pathways based on biomass plan-tations, high fertilizer input is the main reason for high GHG emis-sions during both the feedstock stage and the total stage. Theproportions of GHG emission derived from fertilizer input are from19% to 37% and N fertilizer input is the most important sub-factordue to the N2O effect. High energy intensity during the sub stagesof plantation, pesticide input and feedstock transportation are alsoimportant factors in the high GHG emission level of the feedstockstage. During the fuel stage, the high energy intensity for fuel pro-duction is the dominant factor, while fuel transportation is a minorfactor.

4.5. By-product credits of EC and GHG emissions

During their process, there are some by-products for the CE, KE,SE, SB and JB pathways [22,24,27,30,31,33]. Consequently, when1 MJ fuel is obtained and utilized, certain amounts of EC andGHG emissions have been assigned to their by-products and thecredit rates are different for each pathway, as shown in Table 10.

5. Discussions

5.1. Sensitivity analysis

The purpose of a sensitivity analysis is to estimate the data ef-fects on the outcome of this study. The influence of increasing and

Table 10By-products’ credit of EC and GHG emissions of biofuel pathways (1 MJ fuel obtainedand utilized).

Pathways CE KE SE SB JB

By-product credit rates (%) 30.90 18.06 20.00 27.50 40.00Fossil energy shared (MJ) 0.505 0.144 0.366 0.387 0.333GHG shared (g CO2,e) 58.752 15.167 36.348 41.914 34.647

Table 11Fossil EC change of each pathway due to the energy input value variation of 10% (MJ/MJ).

Pathway no. CE KE SE SB JB

Origin value 1.13 0.65 1.46 1.02 0.50Plantation energy (+10%) 1.14 0.66 1.47 1.03 0.50Plantation energy (�10%) 1.12 0.65 1.46 1.01 0.50Fertilizer input (+10%) 1.15 0.66 1.53 1.04 0.51Fertilizer input (�10%) 1.10 0.64 1.40 1.00 0.49N input (+10%) 1.15 0.66 1.52 1.04 0.51N input (�10%) 1.11 0.64 1.40 1.00 0.49Fuel production energy (+10%) 1.20 0.70 1.54 1.06 0.53Fuel production energy (�10%) 1.06 0.61 1.39 0.98 0.47

Table 12GHG emission increasing rate change of each pathway due to the energy input value vari

Pathway no. CE KE

Origin value 26.43% �33.96%Plantation energy (+10%) 27.02% �33.77%Plantation energy (�10%) 25.78% �34.23%Fertilizer input (+10%) 30.89% �32.71%Fertilizer input (�10%) 21.91% �35.29%N input (+10%) 30.30% �32.78%N input (�10%) 22.50% �35.22%Fuel production energy (+10%) 32.97% �29.53%Fuel production energy (�10%) 19.83% �38.47%

decreasing input factors by 10%, one at a time, was studied. Thesensitivity analysis results are shown in Tables 11 and 12.

5.2. Comparison of the results to other studies

As Table 13 indicates, the EC results of different studies, includ-ing (the) PRC and other countries, are compared. For CE and SBpathways, this study has a relatively pessimistic result where theNER values are only 88.57% and 98.04%, but the NER values of otherstudies for (the) PRC are all greater than 100% [26,28,41,51]; for KEand JB pathways, all studies for (the) PRC (including this study)have similar results [27,28,33,43,51]; (the) PRC’s KE pathway isless optimistic than that of Thailand [25].

As Table 14 indicates, the GHG emission results of differentstudies, including (the) PRC and other countries, are compared.For the SB pathway, this study has a relatively pessimistic results,where the NGRR is �7.71%; for the CE pathway, this study gener-ates an even more pessimistic, negative NGRR, which is oppositeto the results from the studies done in the US and Sweden[10,22]; for the KE pathway, all studies for (the) PRC (including thisstudy) have similar positive results to that of Thailand [26,49,50].

The following reasons explain the differences between the (the)PRC studies and other countries studies: (1) China’s coal-dominantenergy mix [52]; (2) China farmers utilize high fertilizer applica-tion rates for their agricultural practices [30,31]; and (3) (the)PRC’s relatively higher energy consumption in the industries pro-cesses producing EtOH and BD [53].

The following reasons explain the differences between thisstudy and other (the) PRC studies [44]: (1) on-site monitoring ofChina’s nitrogen fertilizer issues including feedstock source, trans-portation modes, and process energy consumption; (2) full consid-eration of the N2O emissions in N-fertilizer applications. Forexample, this factor is 15% of the total LCA GHG emission for theSE pathway; and (3) comprehension of the CO2 and CH4 emissionsassociated with China’s coal mining, crude oil and NG explorationstages [30,31,37].

5.3. Changes of a variety of factors can influence each pathway NEVand GHG emissions levels

For these pathways, if their feedstock productivity (productionamount/ha) have increased, accompanied by stable fertilizer inputs(input amount/ha), then the total WTP fossil energy per MJ fuel ob-tained will decrease because of the corresponding reductions infertilizer input, and EC for transport (due to the distance beingshorter). Other ways of reducing EC are during cultivation, andduring the application of fertilizer. EC for refining processes andtransportation can in fact reduce the WTP fossil energy consump-tion and GHG emissions.

As shown in Table 15, these factors have different changingrates in order to achieve the goal of Positive NEV (the calorific valuecontained in the fuel obtained is more than the WTP fossil energyinput).

ation of 10%.

SE SB JB

39.91% 7.71% �49.34%40.07% 9.15% �49.15%39.73% 5.85% �49.45%45.24% 11.62% �47.54%34.56% 3.38% �51.06%45.13% 11.29% �47.59%34.67% 3.71% �51.01%39.90% 11.02% �46.66%39.90% 3.98% �51.94%

Table 13Biofuel pathways’ LCA EC results compared in different studies.

Fuel pathways Region Source Press time EC (MJ/MJ) NEV (MJ/MJ) NER (%)

CG (the) PRC In this study from [44] 2008 1.344 74.40CD (the) PRC In this study from [44] 2008 1.303 76.75CE (the) PRC In this study 2009 1.129 88.57CE (the) PRC [41] 2006 0.962 0.038 104.00CE (the) PRC [51] 2007 0.782 0.279 127.90CE (the) PRC [28] 2009 0.700 0.300 142.86CE US [9] 2006 0.769 0.300 130.00KE (the) PRC In this study 2008 0.653 0.347 153.14KE (the) PRC [51] 2007 0.779 0.284 128.40KE (the) PRC [33] 2008 0.623 0.377 160.60KE (the) PRC [27] 2006 0.732 0.366 136.58KE (the) PRC [28] 2009 0.540 0.460 185.19KE (the) PRC [43] 2008 0.781 0.219 128.00KE Thailand [25] 2008 0.245 0.755 408.81SB (the) PRC In this study 2009 1.020 �0.020 98.04SB (the) PRC [51] 2007 0.307 0.693 326.00SB (the) PRC [26] 2008 0.306 0.694 326.80JB (the) PRC In this study 2008 0.499 0.501 200.40JB (the) PRC [34] 2006 0.129 0.871 777.00

Table 14Biofuel pathways’ LCA GHG emission results compared in different studies.

Fuel pathways Region Source Press time GHG (g CO2,e/MJ) GHG (g CO2,e/l) NGRR (%)

CG (the) PRC In this study from [44] 2008 103.92 3268.284CD (the) PRC In this study from [44] 2008 102.592 3679.770CE (the) PRC In this study 2009 131.384 2781.662 �26.43CE US [22] 2007 +48.40CE Sweden [10]: The forest chips as fuel situation 2009 48.000CE Sweden [10]: The NG as fuel situation 2009 76.000CE Sweden [10]: The coal as fuel situation 2009 103.000KE (the) PRC In this study 2009 68.63 2168.708 33.96KE (the) PRC [49] 2004 18.49KE Thailand [50] 2007 964.000 66.96SB (the) PRC In this study 2009 110.5 3491.800 �7.71SB (the) PRC [26] 2008 67.44

Table 15The factor’s changing rates for Positive NEV goal of the biofuel pathways (%).

Pathway no. CE SE SB

To increase productivity 36.25 68.45 4.31To reduce plantation energy N.E. N.E. 14.53To reduce fertilizer input 51.57 73.88 8.34To reduce process energy 18.86 63.97 4.82To reduce transportation energy N.E. N.E. 12.45

Note: N.E. stands for not effective.

X. Ou et al. / Applied Energy 86 (2009) S197–S208 S205

As shown in Table 16, these factors have different changingrates in order to achieve the goal of Positive NGRR (the GHG emis-sion level of the biofuel pathway is lower than that of its alterna-tive fuel: gasoline or diesel pathway).

Table 16The factor’s changing rates for Positive NGRR goal of the biofuel pathways (%).

Pathway no. CE SE SB

To increase productivity 49.64 71.66 13.44To reduce plantation energy N.E. N.E. 45.28To reduce fertilizer input 58.91 74.79 18.11Of which: N 67.71 76.32 19.69P N.E. N.E. N.E.K N.E. N.E. N.E.To reduce process energy 40.21 49.54 21.18To reduce transportation energy N.E. N.E. 83.93

Note: N.E. stands for not effective.

6. Concluding remarks

6.1. China’s current biofuel pathways are geographically unique: theybehave differently from LCA EC and GHG emission analysis andconclusions for the US, EU, Brazil, etc

As Fig. 5 shows, the six pathways located in three differentquadrants separated by the EC and GHG emission levels of CDand CG. JB, KE and UB pathways are in quadrant I, which indicatesthat both are lower in EC and GHG emission levels than CPP. SB andCE pathways are in quadrant II, which indicates that they are lower

Fig. 5. Fossil energy input and GHG emissions for biofuel pathways.

Table A. 1The interpretation of i, j and m.

i (PE) j (PF) m (stage name)

1 Coal Crude coal Feedstock production2 NG Crude NG Feedstock transportation3 Petrol Crude oil Fuel production4 Coal Fuel transportation5 NG6 Diesel7 Gasoline8 Residual oil9 Electricity

S206 X. Ou et al. / Applied Energy 86 (2009) S197–S208

in EC but higher in GHG emissions than CPP. Only the SE pathwayis in quadrant III, which indicates that it has higher EC and GHGemissions than CPP.

The dominant biofuel pathways of SB and CE indicate only en-ergy savings without any GHG reduction effect. However, thenon-food feedstock pathways like JB, KE and UB, are very sustain-able in the sense that they reduce both EC and GHG emissions. And,the results indicate that careful research should be conducted be-fore the pathway of SE is encouraged.

6.2. All current pathways are feasible in (the) PRC due to theirpetroleum substitution effect, though they have not obvious energy-saving or GHG reduction effect

Currently, for all of the biofuel pathways compared to CPPs,petroleum consumption can be reduced through the increase incoal consumption. So, they are all feasible in (the) PRC due to the‘rich coal and poor petroleum’ scenario described in [52].

6.3. Fertilizer inputs and energy for fuel production are the two majorfactors for the high EC and GHG emission levels

For feedstock-planting biomass liquid fuel pathways, the EC ishigh due to (the) PRC’s custom of utilizing high fertilizer applica-tion rates in the process of agricultural production, where half ofthe nitrogen fertilizer is made from coal-feedstock. Therefore, eachpathway’s GHG level is especially high because of the nitrogen fer-tilizer’s N2O emissions effect and the coal’s dominant power roleduring the process stage.

6.4. The current EC and GHG situation can be improved by productivityincreases, resource reductions, and by-product output optimization

Within the current pathways, the EC and GHG situation can beimproved by applying the following measurements: higher pro-ductivity through seed selection and gene engineering; reductionof EC for cultivation and fertilizer inputs through selecting appro-priate planting sites, resulting in reduced EC for irrigation and fer-tilizer usage; reduction in EC for transportation of feedstock andfuel, through rationalizing the fuel plant sites arrangement andlocalizing the fuel deployment; reduction in EC during the extrac-tion process, through promotion of high-performance low-ECrefining equipments; and higher energy efficiencies and creditsthrough optimizing by-product output.

Acknowledgements

The project is co-supported by the China National Natural Sci-ence Foundation (Grant No. 90410016) and the CAERC program(Tsinghua/GM/SAIC-China). The authors would like to thank thethree colleagues for their valuable reviewing comments and Dr.Kristin. B. Zimmerman of GM and Mr. Benny Zhang of GM-Chinafor their generous help.

Appendix A. Calculation methods for LCA EC and GHG emission

A.1. Basic assumptions

There are three types of primary energy (PE) considered in thisstudy, coal, NG, and petrol. We set i to stand for them, respectively,there are nine types of process fuel and we set j to stand for them,respectively. For each type of process fuel (PF), its lifecycle analysis(LCA) includes m stages; all of them are shown in Table A. 1. Eachtype of PF has K kinds of technologies to be utilized in the stage ofm, and each of them has different emission factor (EF).

A.2. Calculation of LCA EC

In this section, calculation of the LCA energy consumption (EC) fora specific PF is based on the energy transformation efficiency, the ECmix and energy utilization technology of each of its LCA stages.

A.2.1. Relationship of energy input of each stageThere are the efficiency equations as followed:

EIm�1;j ¼ EIm;j=EFFm�1 ðj ¼ 1;2; . . . ;9; m ¼ 2;3;4Þ ðA:1Þ

Here, EIm,j is the total PE input during stage m, when 1 MJ of PF jis finally produced; EFFm is the energy transformation efficiencyfactor of stage m, when 1 MJ of PF j is produced.

The resulting equation is:

EI4;j ¼ 1=EFF4 ðj ¼ 1;2; . . . ;9Þ ðA:2Þ

A.2.2. LCA energy input factors calculationHere we define EFLCA,j,i as the LCA PE i input for 1 MJ of PF j ob-

tained. The types of PE and PF have the same content: coal, NG, andcrude oil, therefore, the following equations can be solved withiterative methods using a computer that combines Eqs. (A.1) and(A.2):

EFLCA;j;i ¼X4

m¼1

EIm;j �X9

z¼1

ðSHm;j;z � EFLCA;z;iÞ !

ði ¼ 1;2;3; j ¼ 1;2; . . . ;9Þ ðA:3Þ

Here SHm,j,z is the share of PF z for all EC in stage m for 1 MJ of PFj produced.

Knowing SHm,j,z and EFFm, and combining the equations from(A.1), (A.2), (A.3), results in EFLCA,j,i and EIm,j(i = 1, 2, 3; j = 1,2, . . . , 9; m = 1, 2, 3, 4).

In addition, we can get the LCA total PE input for 1 MJ of PF j ob-tained (EFLCA,j):

EFLCA;j ¼X3

i¼1

EFLCA;j;i ðj ¼ 1;2; . . . ;9Þ ðA:4Þ

Combined with Eq. (A.3), we get:

EFLCA;j ¼X3

i¼1

X4

m¼1

ðEIm;j �X9

z¼1

ðSHm;j;z � EFLCA;z;iÞÞ !

ðj ¼ 1;2; . . . ;9Þ ðA:5Þ

The details of the above calculation process and results can befound in [44].

A.3. Direct emissions of CO2, CH4, N2O of PF utilized

A.3.1. Direct emissions of CO2 of PF utilizedTo calculate the LCA EC for 1 MJ of PF, the results indicate that

the LCA stages utilize a lot of PF x. Therefore, the calculation ofthe emissions of such PF x utilized must be completed first.

X. Ou et al. / Applied Energy 86 (2009) S197–S208 S207

When 1 MJ of PF x is utilized, the direct emission of CO2 in thestage of m can be calculated by the following carbon balanceequations:

FCO2;m;x ¼XK

k¼1

ðFCO2;m;x;k � SHm;x;kÞ ðA:6Þ

FCO2;m;x;k ¼ ½DENx=LHVx � ROCx � 0:85 � HCm;x;k

� 0:43 � COm;x;k � 0:75 � CH4;m;x;k�=0:27 ðA:7Þ

where FCO2,m,x is the CO2 EF for PF x during the stage of m; FCO2,m,x,k

is the CO2 EF for PF x using technology k during the stage of m;SHm,x,k is the share of technology k in the PF x during the stage ofm; DENx is the density of the PF x; LHVx is the low heat value ofthe PF x; ROCx is the average carbon content for PF x; HCm,x,k isthe HC EF for PF x using technology k during the stage of m; 0.85is the average carbon content for HC emissions; COm,x,k is the COEF for PF x using technology k during the stage of m; 0.85 is theaverage carbon content for CO emissions; CH4,m,x,k is the CH4 emis-sions factor for PF x using technology k during the stage of m; 0.75 isthe carbon content for CH4 emissions; and 0.27 is the carbon con-tent for CO2 emissions.

The result is the LCA CO2 emissions of the stage of m (TCO2,m,x)and all stages (TCO2,x) when EIm,x of PF x is utilized:

TCO2;m;x ¼ EIm;x � FCO2;m;x ðA:8Þ

TCO2;x ¼X4

m¼1

TCO2;m;x ðA:9Þ

Finally, the resulting LCA CO2 emissions of all stages when 1 MJof PF j is obtained (TCO2) is:

TCO2 ¼X9

x¼1

TCO2;x ðA:10Þ

A.3.2. Direct emissions of CH4 of PF utilizedSimilarly, CH4 EF for PF x utilized during the stage of m is:

FCH4;m;x ¼XK

k¼1

FCH4;m;x;k ðA:11Þ

where FCH4,m,x,k is the CH4 EF for PF j using technology k during thestage of m.

The resulting LCA CH4 emissions of the stage of m (TCH4,m,x) andall stages (TCH4,x) when EIm,x of PF x is utilized, is:

TCH4;m;x ¼ EIm;x � FCH4;m;x ðA:12Þ

TCH4;x ¼X4

m¼1

TCH4;m;x ðA:13Þ

Finally, the resulting LCA CH4 emissions of all stages when 1 MJof PF j is obtained (TCH4) is:

CH4 ¼X9

x¼1

TCH4;x ðA:14Þ

A.3.3. Direct emissions of N2O of PF utilizedSimilarly, N2O EF for PF x during the stage of m is:

FN2Om;x ¼XK

k¼1

FN2Om;x;k ðA:15Þ

where FN2Om,x,k is the N2O EF for PF x using technology k during thestage of m.

The resulting LCA N2O emissions of the stage of m (TN2Om,x) andall stages (TN2Ox) when EIm,x of PF x is utilized is:

TN2Om;x ¼ EIm;x � FN2Om;x ðA:16Þ

TN2Ox ¼X4

m¼1

N2Om;x ðA:17Þ

Finally, the resulting LCA N2O emission of all stages when 1 MJof PF j is obtained (TN2O) is:

TN2O ¼X9

x¼1

TN2Ox ðA:18Þ

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