Bioresource Technology 162 (2014) 1–7
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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Determining the life cycle energy efficiency of six biofuel systems in China: A Data Envelopment Analysis Jingzheng Ren a,b,d, Shiyu Tan b,c, Lichun Dong b,c,⇑, Anna Mazzi a, Antonio Scipioni a, Benjamin K. Sovacool d a
CESQA (Quality and Environmental Research Centre), Department of Industrial Engineering, University of Padova, Via Marzolo 9, 35131 Padova, Italy School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China Key Laboratory of Low-grade Energy Utilization Technologies & Systems of the Ministry of Education, Chongqing University, Chongqing 400044, China d Center for Energy Technologies, AU-Herning, Aarhus University, Birk Centerpark 15, DK-7400 Herning, Denmark b c
h i g h l i g h t s A model for life cycle energy efficiency analysis of biofuel is developed. DEA is used to identify the wasteful use energy in biofuel production. Recommendations are proposed for improving energy efficiency. Life cycle perspective is incorporated in the analysis process.
a r t i c l e
i n f o
Article history: Received 6 February 2014 Received in revised form 19 March 2014 Accepted 21 March 2014 Available online 29 March 2014 Keywords: Life cycle Energy efficiency Biofuel Data Envelopment Analysis
a b s t r a c t This aim of this study was to use Data Envelopment Analysis (DEA) to assess the life cycle energy efficiency of six biofuels in China. DEA can differentiate efficient and non-efficient scenarios, and it can identify wasteful energy losses in biofuel production. More specifically, the study has examined the efficiency of six approaches for bioethanol production involving a sample of wheat, corn, cassava, and sweet potatoes as feedstocks and ‘‘old,’’ ‘‘new,’’ ‘‘wet,’’ and ‘‘dry’’ processes. For each of these six bioethanol production pathways, the users can determine energy inputs such as the embodied energy for seed, machinery, fertilizer, diesel, chemicals and primary energy utilized for manufacturing, and outputs such as the energy content of the bioethanol and byproducts. The results indicate that DEA is a novel and feasible method for finding efficient bioethanol production scenarios and suggest that sweet potatoes may be the most energy-efficient form of ethanol production for China. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction With a booming and prosperous economy, China has become the largest energy consumer in the world (BP, 2012) and also the largest CO2 emitter (Gregg et al., 2008). A transition to renewable sources of energy such as biodiesel and bioethanol to substitute the traditional fossil fuels (Ren et al., 2013a) has therefore emerged as a promising pathway to mitigate emissions and substitute for fossil fuels in the transportation sector (Liang et al., 2013). Accordingly, it is vital that Chinese planners and investors select only those biofuels that are truly more sustainable and efficient than the dirtier and less optimal fuels they displace (Shie et al., 2011). ⇑ Corresponding author at: School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China. Tel./fax: +86 23 65106051. E-mail address: [email protected] (L. Dong). http://dx.doi.org/10.1016/j.biortech.2014.03.105 0960-8524/Ó 2014 Elsevier Ltd. All rights reserved.
Determining the life cycle energy efficiency of different bioethanol production pathways becomes of paramount importance in this selection process. To be sure, much research has been conducted investigating the energy efficiency of biofuel production from a life cycle perspective. Dong et al. (2008) investigated the total life cycle energy consumption of bioethanol production from wheat in Henan Province of China. Khoo et al. (2011) used life cycle energy analysis to study the microalgae-to-biodiesel process. Papong and Malakul (2010) studied the life cycle energy cost of bioethanol production from cassava in Thailand. Hu et al. (2008) assessed the life cycle energy consumption of soybean-based biodiesel as an alternative automotive fuel in China. Leng et al. (2008) analyzed the life cycle energy consumption of cassava-based fuel ethanol in China. Hu et al. (2004) calculated the net energy availability of cassava-based ethanol in China. Janulis (2004) explored the life cycle energy
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balance of rapeseed-based biodiesel. Malca and Freire (2006) studied the life cycle energy efficiency of sugar beet and wheat-based bioethanol. Wang et al. (2013) investigated the life cycle energy efficiency of bioethanol production from sweet potatoes. All of these studies, however, have one primary shortcoming: they focus on the net energy gain (NEG) or net energy ratio (NER) of different biofuel production processes. Although such methods are certainly valid – the energy consumption status of biofuel production can be determined by these methods and stakeholders can determine suitable scenarios based on their findings – it is difficult to determine how truly energy efficient these scenarios are. Moreover, it is difficult to improve the energy efficiency of some biofuels pathways because non-renewable energy sources are involved in each stage of the life cycle of that biofuel. Thus, a more rigorous and holistic methodology is needed which can help stakeholders measure the energy use efficiency in biofuel production and improve its overall energy efficiency. NEG and NER are often considered ‘‘parametric’’ methods for measuring end-use efficiency (Cristobal, 2011). Data Envelopment Analysis (DEA), by contrast, is a novel non-parametric method (Charnes et al., 1978). Mousavi-Avval et al. (2011) have shown how DEA can provide more complete estimates of the energy efficiencies of soybean producers, helping to rank efficient and inefficient farmers, and enabling the identification of optimal energy requirement and wasteful use of energy. Sarica and Or (2007) similarly employed DEA to analyze and compare the performance of electricity generation plants in Turkey. Mohammadi et al. (2011) used DEA to evaluate the energy efficiency of farmers, to find efficient and inefficient scenarios and to provide implements to improve the energy efficiency in kiwifruit production. Nassiri and Singh (2009) studied the energy use efficiency for paddy crop based on DEA. All these studies concerning energy use efficiency relied on DEA, and one implication is that DEA is an excessively feasible tool of judging (1) whether or not a given energy system is efficient, (2) which scenarios among many are the most efficient, and (3) which required energy inputs and outputs can minimize waste and result in optimal production processes. Despite its explanatory power, to the best of the authors’ knowledge no scholarship has yet utilized DEA to study the energy efficiencies of biofuel production. Accordingly, the main purpose of this paper is to use DEA as a mathematical tool to measure the energy use efficiencies of various approaches for biofuel production (bioethanol as an example) in China. This paper seeks to identify efficient and non-DEA efficient scenarios and proposes useful recommendations to minimize wasteful energy use in the Chinese biofuels sector. The study is structured as follows: Section 2 presents the DEA model. Section 3 utilizes this model for calculating the energy consumption and efficiency of six bioethanol production processes in China. Section 4 presents the paper’s conclusions. 2. Methods For those unfamiliar with the term, Data Envelopment Analysis (DEA) is a linear programming-based technique that is able to measure the relative performance of decision making units (DMUs) which are characterized by a multiple objectives and/or multiple inputs (Sozen et al., 2012; Basso and Funari, 2001). A DMU could be defined as a tangible or intangible system which can transform a set of inputs into outputs, as presented in Fig. 1. The main advantage of DEA is that its users do not need to know the underlying functional relationships between the inputs and the outputs in DMUs (Seiford and Thrall, 1990). Thus, the specific mechanism of the studied entity does not need to be specified and modeled in the calculation. In contrast, its main weak point is that the efficiencies of these entities are relative ones and only attain meaning in comparison to other elements. In other words,
u1 u2
x1j
y1j
x2j
y2j
v1 v2
DMUj ypj
xmj
um
vp
Fig. 1. Structure of DMU.
it is impossible to describe the relationships between the inputs and the outputs in DMUs. In this study, it is assumed that there are m inputs xrj ðr ¼ 1; 2; ; mÞ in the jth DMU (j = 1,2,. . .,t), the weight of the input is denoted by lr ðr ¼ 1; 2; ; mÞ, and there are also p outputs yij ði ¼ 1; 2; ; pÞ in the jth DMU (j = 1,2,. . .,t), the weight of the output is denoted by v i ði ¼ 1; 2; ; pÞ. The technical efficiency of a DMU is defined as the ratio between the outputs and inputs (Leal et al., 2012), as formulated in Eq. (1):
TE ¼
Outputs Inputs
ð1Þ
The technical efficiency of a DMU is defined as the ratio between the sum of the weighted inputs and the sum of the weighted outputs, taking DMUj as an example, and the technical efficiency of DMUj could be obtained, as presented in Eq. (2):
Pp
TEj ¼ Pmi¼1 r¼1
v i yij
ð2Þ
lr xrj
Eqs. (3) and (4) present the non-Archimedean infinitesimal CCR model (the abbreviation of the initial inventors’ name, namely Charnes, Cooper and Rhodes). It is notable that Eq. (3) is to measure the efficiency of the DMU j0, and Eq. (4) is to limit the upper bound of the efficiency of other DMUs, because the maximal technical efficiency is 1:
Pp
max hj0 ¼ Pmi¼1
v i yij0
ð3Þ
r¼1 lr xrj0
Subject to:
Pp vy Pmi¼1 i ij 1 ðj ¼ 1; 2; ; tÞ l
x r¼1 r rj
ð4Þ
ur e r ¼ 1; 2; ; m
vi e
i ¼ 1; 2; ; p
where hj0 represents the technical efficiency of DMU j0, and e is a non-Archimedean construct. After the Charnes–Cooper transformation and dual transformation, the procedures are specified in (Leal et al., 2012; Ren et al., 2013b). Then, the non-Archimedean infinitesimal CCR model could be transformed into the following form, as presented in (5) and (6):
min hj0 e
p m X X sþr þ si r¼1
!
ð5Þ
i¼1
Subject to: t X xrj kj þ sr hxrj0 ¼ 0 j¼1 t X yij kj sþi yij0 ¼ 0 j¼1
ð6Þ
kj 0 ðj ¼ 1; 2; ; tÞ sr 0 ðr ¼ 1; 2; ; mÞ sþi 0 ði ¼ 1; 2; ; pÞ þ where s r and si represent slack and surplus variable, respectively.
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Generally, two definitions can determine whether a DMU is DEA efficient or not. These are as follows: Definition 1. If the optimal value h ¼ 1, then the decision making unit can be identified as weak DEA effective, and vice versa. Definition 2. If the optimal value h ¼ 1, and the solution satisfies s ðr ¼ 1; 2; ; mÞ, sþ ði ¼ 1; 2; ; pÞ then the decision r ¼ 0 i ¼ 0 making unit can be identified as DEA effective, and vice versa. A projection improvement analysis methodology (Bian and Yang, 2010) can further improve a non-DEA-efficient DMU into a DEA-efficient one. For a non-DEA-efficient DMU j, then the projection of the inputs and outputs on the relative efficient surface can be calculated by Eqs. (7) and (8), respectively:
_rj ¼ hj0 xrj x
srj
ð7Þ
_ij ¼ yij þ sþij
ð8Þ
y
where _rj and _ij are the projection inputs and outputs, x y respectively. 3. Results and discussion This paper assessed the life cycle energy efficiency of wheatbased, corn-based, cassava-based and sweet potato-based bioethanol. The authors selected the lower heating value of bioethanol as
26.77 MJ/kg based on (Xia et al., 2012). The authors selected a function unit (FU) to be 1000 MJ of bioethanol, and the life cycle boundary of bioethanol consists of four main stages including (1) cropping, (2) transportation to the production facility, (3) production, and (4) transportation to distribution centers. Fig. 2 presents an overview of these four stages. Then, the consumption of various sources in each stage has been presented as follows: The energy of various sources at the stage (1) of cropping (crop production) can be determined by Eq. (9). The left part FNC j BLHVr i Y i
represents the amount of the jth resource consumed for
crop production:
Ej ¼
FN C j BLHV ri Y i
EIj
ð9Þ
where Ej is the consumed energy corresponding to the jth feedstock, FN represents the function unit, BLHV represents the lower heating value of biofuel, r i represents the conversion ratio of the ith crop to biofuel, Y i represents the yield of the ith crop per unit land, C j represents the consumption of the jth feedstock per unit land, and EIj represents the energy intensity of the jth feedstock. The energy of fuels for the second stage of (2) transportation to the production facility can be calculated by Eq. (10). Similarly, the m L1 left part FNC represents the amount of the mth fuel consumed BLHVr i for crop transportation. It is notable that the assumed distance from the crop distribution center to the factory for bioethanol production is
Cropping Seed
Field Preparation
Diesel
N fertilizers
P fertilizers Sowing
Electricity
K 2O fertilizer Irrigation
Herbicide
Fertilization Pest control
Human labor
Pesticide
Weeding
Machinery
Harvesting
Diesel
Crop transport
Steam Bioethanol production Electricity
Diesel
Bioethanol transport
Bioethanol Fig. 2. Life cycle boundary of bioethanol.
Coal
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J. Ren et al. / Bioresource Technology 162 (2014) 1–7
500 km, and that diesel is used as the fuel for transport with the transportation intensity of 0.05 L t1km1, its energy intensity is 44.13 MJ L1 (Xia et al., 2012; Chen and Chen, 2011):
Em ¼
FN C m L1 EIm BLHV r i
ð10Þ
where Em is the consumed energy corresponding to the mth fuel, C m represents the consumption of the mth fuel for transport per unit crop for per mileage, EIm represents the energy intensity of the mth fuel, and L1 represents the distance from the crop distribution center to the factory for bioethanol production. The energy of various resources at the stage (3) of biofuel n production can be determined by Eq. (11). Similarly, FNC BLHV represents the amount of the nth resource consumed for biofuel production:
En ¼
FN C n EIn BLHV
ð11Þ
C n represents the consumption of the nth feedstock for per unit biofuel, EIn represents the energy intensity of the mth fuel. The energy of fuel for the state of (4) transportation to distribum L2 tion centers can be determined by Eq. (12), FNC which repreBLHV sents the amount of the mth fuel consumed for biofuel transportation. It is also assumed that the distance from the factory for biofuel production to biofuel distribution center is 500 km:
Em ¼
FN C m L2 EIm BLHV
ð12Þ
where Em is the consumed energy corresponding to the mth fuel, C m represents the consumption of the mth fuel for transport per unit crop for per mileage, EIm represents the energy intensity of the mth fuel, and L2 represents the distance from the factory for biofuel production to biofuel distribution center. The proposed DEA methodology for determining the various energy efficiencies of bioethanol production has investigated wheat, corn, cassava, and sweet potato as feedstocks. More specifically, it has been presumed that two alternative processes (old process P1 and new process P2) exist for wheat-based bioethanol production and two alternative processes (dry process P3 and wet process P4) exist for corn-based bioethanol production. Thus, the proposed DEA ultimately has been used to assess six DMUs including wheat-based bioethanol production using P1 (DMU1),
wheat-based bioethanol production using P2 (DMU2), corn-based bioethanol production using P3 (DMU3), wheat-based bioethanol production using P4 (DMU4), cassava-based bioethanol production (DMU5) and sweet potato-based bioethanol production (DMU6). This analysis presumed that the yields of wheat are 4301 kg/ hectare; yields of corn 7500 kg/hectare; yields of cassava 17,520 kg/hectare; and yields of sweet potato 45,000 kg/hectare. As such, corresponding conversion ratios of these crops to bioethanol are 0.306 for wheat, 0.313 for corn, 0.121 for cassava, and 0.125 for sweet potato. Data for the calculation of the energy consumption of bioethanol is based derived from (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). According to Eqs. (9)–(12), Tables 1–4 present the energy consumption of 1000 MJ bioethanol based on different feedstocks and production pathways. The consumed energy for seed, machinery, fertilizer, diesel, chemicals and the primary energy (the sum of the consumed coal, electricity and steam) are used as the inputs in the DMUs, and the energy of the bioethanol and byproduct are employed as the outputs, as shown in Table 5. Table 6 presents the life cycle energy efficiency of the six alternative bioethanol scenarios as determined by the DEA model. It is apparent that there are two scenarios including wheat-based bioethanol production using P2 (DMU2) and sweet potato-based bioethanol production (DMU6) that are regarded as DEA efficient (i.e., ‘‘good’’), and there are also two scenarios including corn-based bioethanol production using P3 (DMU3) and corn-based bioethanol production using P4 (DMU4) that are considered as weak-DEA efficient (i.e., ‘‘average’’). However, there are two other scenarios including wheat-based bioethanol production using P1 (DMU1) and cassava-based bioethanol production (DMU5) that are regarded as non-DEA efficient (i.e., ‘‘bad’’). Fig. 3 presents the results of the projection improvement analysis methodology. This analysis suggests that the efficiencies of the weak-DEA efficient systems (corn-based ethanol using P3 and P4) and non-DEA efficient systems (wheat ethanol using P1 and cassava) can be improved in a variety of ways. First, decrease the energy intensity of inputs. The energy intensity of seeds for the production 1000 MJ bioethanol would need to be reduced from 130.07 MJ to 25.60 MJ; of machinery from 5.07 MJ to 2.46 MJ; of fertilizer from 431.78 MJ to 188.49 MJ; of diesel from 265.22 MJ to 217.5 MJ; of chemicals from 24.9 MJ to 16.03 MJ; and of primary energy used in DMU1 (wheat-based bioethanol production using P1) from
Table 1 Various energy consumption for 1000 MJ wheat-based bioethanol using the old and new processes (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Item
Unit
Amount
Energy intensity (MJ unit1)
Energy (MJ)
Seed Machinery N fertilizer
kg kg kg
2.56 0.25 6.39
50.81 20.29 57.46
130.07 5.07 367.17
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
2.34 7.03 2.03 8.52 0.05 0.04
7.03 6.85 44.13 11.91 284.82 266.56
16.45 48.16 89.58 101.47 14.24 10.66
Crop transport
Diesel
L
3.05
44.13
134.60
P1
P2
P1
P2
Steam Coal Electricity Diesel
t t kWh L
0.138 0.017 11.790 0.93
0.131 0.014 9.106
3830 26,790 8.93 44.13
528.54 455.43 105.285 41.04
501.73 375.06 81.32
P1
P2
Bioethanol Byproduct
MJ MJ
1000 387.37
1000 579.75
1 1
1000 387.37
1000 579.75
Stage Inputs
Bioethanol production Bioethanol transport Outputs
5
J. Ren et al. / Bioresource Technology 162 (2014) 1–7 Table 2 Various energy consumption for 1000 MJ corn-based bioethanol using the dry and wet processes (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Item
Unit
Amount
Energy intensity (MJ unit1)
Energy (MJ)
Seed Machinery N fertilizer
kg kg kg
0.48 0.12 2.99
53.36 20.29 57.46
25.61 2.43 171.80
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
1.20 1.20 1.01 3.59 0.016 0.043
7.03 6.85 44.13 11.91 284.82 266.56
8.44 8.22 44.57 42.76 4.56 11.46
Crop transport
Diesel
L
Stage Inputs
Bioethanol production Bioethanol transport
2.99
44.13
P3
P4
131.95
Steam Coal Electricity Diesel
t t kWh L
0.12 0.015 9.41 0.93
0.11 0.014 8.85
3830 26,790 8.93 44.13
Bioethanol Byproduct
MJ MJ
1000 266.34
1000 392.60
1 1
Outputs
P3
P4
459.60 401.85 84.03 41.04
421.30 375.06 79.03
P3
P4
1000 266.34
1000 392.60
Table 3 Various energy consumption for 1000 MJ cassava-based bioethanol (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Stage
Item
Unit
Seed Machinery N fertilizer
kg kg kg
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
Crop transport
Diesel Steam
Bioethanol production Bioethanol transport
Amount
Energy intensity (MJ unit1)
Energy (MJ)
Inputs 15.85 0.18 1.77
15.67 20.29 57.46
248.41 3.70 101.58
0.88 3.54 1.31 4.22 0.019 0.011
7.03 6.85 44.13 11.91 284.82 266.56
6.21 24.22 57.88 50.26 5.68 3.04
L t
7.72 0.12
44.13 3830
340.99 459.6
Coal Electricity
t kWh
0.013 0.88
26,790 8.93
348.27 7.86
Diesel
L
0.93
44.13
41.04
Bioethanol Byproduct
MJ MJ
Outputs 1000 201.34
1 1
1000 201.34
Table 4 Various energy consumption for 1000 MJ sweet potato-based bioethanol (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Stage
Amount
Energy intensity (MJ unit1)
Item
Unit
Energy (MJ)
Seed Machinery N fertilizer
kg kg kg
7.97 0.068 1.05
15.42 20.29 57.46
122.90 1.38 60.33
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
0.54 1.64 0.50 1.59 0.003 0.003
7.03 6.85 44.13 11.91 284.82 266.56
3.80 11.23 22.07 18.94 0.85 0.80
Crop transport
Diesel Steam
L t
7.47 0.12
44.13 3830
329.65 459.60
Bioethanol production
Coal Electricity
t kWh
0.008 0
26,790 8.93
214.32 0
Bioethanol transport
Diesel
L
0.93
44.13
41.04
Bioethanol Byproduct
MJ MJ
Inputs
Outputs 1000 252.52
1 1
1000 252.52
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J. Ren et al. / Bioresource Technology 162 (2014) 1–7
Table 5 The inputs and outputs used in DEA model. Item
DMU1
DMU2
DMU3
DMU4
DMU5
DMU6
Inputs Seed Machinery Fertilizer Diesel Chemicals Primary energy
x11 x21 x31 x41 x51 x61
x12 x22 x32 x42 x52 x62
x13 x23 x33 x43 x53 x63
x14 x24 x34 x44 x54 x64
x15 x25 x35 x45 x55 x65
x16 x26 x36 x46 x56 x66
Outputs Bioethanol Byproduct
y11 y21
y12 y22
y13 y23
y14 y24
y15 y25
y16 y26
Table 6 The calculated results: effective values, slack value and surplus value. Slack or surplus value
hj0
s 1
s 2
s 3
s 4
s 5
s 6
sþ 1
sþ 2
0.8203 1 1 1 0.8367 1
81.1 0 0 0 98.7 0
1.7 0 0 0 1.6 0
165.7 0 0 0 19.2 0
0 0 0 0 0 0
4.4 0 0 0 3.6 0
58.6 0 70.2 70.2 0 0
0 0 0 0 0 0
5.2 0 126.3 0 70.9 0
1190.725 MJ to 918.15 MJ. In tandem, the yield of the byproduct should also be improved from 387.37 MJ to 392.57 MJ. Therefore, the energy recovery of waste biomass is highly recommended as an effective way to improve the life cycle energy efficiency. Brazil has utilized a similar strategy with harnessing bagasse from the cultivation of sugarcane ethanol (Brown and Sovacool, 2011). For DMU5 (cassava-based bioethanol production), the recommendations are similar with DMU1. The inputs should be decreased by 43.94% (seeds), 40.54% (machinery), 69.12% (fertilizer), 83.67% (diesel), 42.43% (chemicals), and 83.67% (primary energy). Analogously, the recovery of the byproducts should be increased by 135.26%. For the two weak-DEA efficient options including DMU3 (cornbased bioethanol production using P3) and DMU4 (corn-based bioethanol production using P4), the recommendations are more nuanced. Planners and operators need only reduce primary energy from 988.24 MJ to 918.04 MJ for DMU3, and from 918.15 MJ to 847.95 MJ for DMU4. The outputs of DMU4 do not need to be
4. Conclusion A life cycle DEA analysis of biofuel production scenarios is of vital importance for stakeholders and decision-makers in China and beyond so that they can select the most efficient approaches and improve the energy productivity of ethanol compared to fossil fuels. This study has examined the life cycle efficiencies of six approaches for bioethanol production in China, utilizing Data Envelopment Analysis to identify efficient, weakly efficient and non-efficient scenarios. It could be concluded that only wheatbased bioethanol and sweet-potato based bioethanol production are DEA efficient. Chinese planners may need to rethink the biofuel policies accordingly.
1400 1200
Seed Machinery
Energy (MJ)
1000 800
Fertilizer Diesel
600
Chemicals
400
Primary energy Bioethanol
200
Byproduct
5
4
D M va U5 lu es fo rD M U
ec tio n
D M va U4 lu es fo rD M U n
Pr oj
ec tio
D M va U3 lu es fo rD M U n
ec tio
D M va U1 lu es fo rD M U Pr oj
Pr oj
ec tio
n
1
3
0
Pr oj
DMU1 DMU2 DMU3 DMU4 DMU5 DMU6
Effective value
improved, but the recovery of the byproducts in DMU3 should be increased by 147.42% compared to the original value. Six systems for producing first generation biofuel have been examined according to a DEA in this study, but this does not means that the proposed model can only be used for these fuels. Further research could build on this work to examine second or even third generation biofuels such as cellulosic ethanol and biodiesel from algae. Moreover, given that the inputs and outputs in the DMUs of the proposed methodology are flexible, users can change these variables to match local conditions. However, it is likely that recycling and minimizing waste will hold immense promise independent of location; that is, a significant amount of energy could be obtained by the recovery of the waste biomass in the production of biofuels in many different situations. This study has significant policy implications for China. Wheatbased bioethanol production using the new process and sweet potato-based bioethanol production process have been regarded as the most promising, efficient, and productive pathways. Accordingly, policymakers, stakeholders, and administrators in China may want to consider drafting regulations that incentivize wheat- and potato-based fuels and disincentivize those from cassava and corn. Wheat and corn, et al. have the further drawback of being staple foods in China meaning large-scale biofuel production dependent on them could in some situations lead to conflicts over land and deteriorating food security. Potatoes, by contrast, are is quite suitable for biofuel production in large scales, given that they have a high starch content, are generally cheaper, and have good adaptability to the different planting environments of China (Tao et al., 2011). The potato resources are also abundant: China is the largest producer of potatoes accounting for 80–85% of the global total (Wang et al., 2013).
Fig. 3. The projection values of the inputs and outputs of the weak-DEA efficient and non-DEA efficient scenarios.
J. Ren et al. / Bioresource Technology 162 (2014) 1–7
Acknowledgement This work is financially supported by the National Science Foundation of China (21176270, 51272296). References Basso, A., Funari, S., 2001. A data envelopment analysis approach to measure the mutual fund performance. Eur. J. Oper. Res. 135, 477–492. Bian, Y., Yang, F., 2010. Resource and environment efficiency analysis of provinces in China: a DEA approach based on Shannon’s entropy. Energy Policy 38, 1909– 1917. BP, 2012. BP Statistical Review of World Energy June 2012. BP. Brown, M.A., Sovacool, B.K., 2011. Climate Change and Global Energy Security: Technology and Policy Options. MIT Press, Cambridge. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444. Chen, H., Chen, G.Q., 2011. Energy cost of rapeseed-based biodiesel as alternative energy in China. Renew. Energy 36, 1374–1378. Cristobal, J.R.S., 2011. A multi criteria data envelopment analysis model to evaluate the efficiency of the renewable energy technologies. Renew. Energy 36, 2742– 2746. Dong, X., Ulgiati, S., Yan, M., Zhang, X., Gao, W., 2008. Energy and eMergy evaluation of bioethanol production from wheat in Henan Province, China. Energy Policy 36, 3882–3892. Gregg, J.S., Andres, R.J., Marland, G., 2008. China: emissions pattern of the world leader in CO2 emissions from fossil fuel consumption and cement production. Geophys. Res. Lett. 35 (8), L08806. Hu, Z., Fang, F., Ben, D., Pu, G., 2004. Net energy, CO2 emission, and life-cycle cost assessment of cassava-based ethanol as an alternative automotive fuel in China. Appl. Energy 78, 247–256. Hu, Z., Tan, P., Yan, X., Lou, D., 2008. Life cycle energy, environment and economic assessment of soybean-based biodiesel as an alternative automotive fuel in China. Energy 33, 1654–1658. Janulis, P., 2004. Reduction of energy consumption in biodiesel fuel in life cycle. Renew. Energy 29, 861–871. Khoo, H.H., Sharratt, P.N., Das, P., Balasubramanian, R.K., Naraharisetti, P.K., Shaik, S., 2011. Life cycle energy and CO2 analysis of microalgae-to-biodiesel: preliminary results and comparisions. Bioresour. Technol. 102, 5800–5807. Leal Jr., I.C., de Almada Garcia, P.A., de Almeida D’Agosto, M., 2012. A data envelopment analysis approach to choose transport modes based on ecoefficiency. Environ. Dev. Sustain. 14, 767–781.
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Leng, R., Wang, C., Zhang, C., Dai, D., Pu, G., 2008. Life cycle inventory and energy analysis of cassava-based fuel ethanol in China. J. Clean. Prod. 16, 374–384. Liang, S., Xu, M., Zhang, T., 2013. Life cycle assessment of biodiesel production in China. Bioresour. Technol. 129, 72–77. Malca, J., Freire, F., 2006. Renewability and life-cycle energy efficiency of bioethanol and bio-ethyl tertiary butyl ether (bioETBE): assessing the implications of allocation. Energy 31, 3362–3380. Mohammadi, A., Rafiee, S., Mohtasebi, S.S., Avval, S.H.M., Rafiee, H., 2011. Energy efficiency improvement and input cost saving in kiwifruit production using data envelopment analysis approach. Renew. Energy 36, 2573–2579. Mousavi-Avval, S.H., Rafiee, S., Jafari, A., Mohammadi, A., 2011. Optimization of energy consumption for soybean production using data envelopment analysis (DEA) approach. Appl. Energy 88, 3765–3772. Nassiri, S.M., Singh, S., 2009. Study on energy use efficiency for paddy crop using data envelopment analysis (DEA) technique. Appl. Energy 86, 1320–1325. Papong, S., Malakul, P., 2010. Life-cycle energy and environmental analysis of bioethanol production from cassava in Thailand. Bioresour. Technol. 101, S112– S118. Ren, J., Manzardo, A., Toniolo, S., Scipioni, A., Tan, S., Dong, L., Gao, S., 2013a. Design and modeling of sustainable bioethanol supply chain by minimizing the total ecological footprint in life cycle perspective. Bioresour. Technol. 146, 771–774. Ren, J., Manzardo, A., Mazzi, A., Fedele, A., Scipioni, A., 2013b. Emergy analysis and sustainability efficiency analysis of different crop-based biodiesel in life cycle perspective. Sci. World J. 2013, 1–12. Sarica, K., Or, I., 2007. Efficiency assessment of Turkish power plants using data envelopment analysis. Energy 32, 1484–1499. Seiford, L.M., Thrall, R.M., 1990. Recent developments in DEA: the mathematical programming approach to frontier analysis. J. Econometrics 46, 7–38. Shie, J.L., Chang, C.Y., Chen, C.S., Shaw, D.G., Chen, Y.H., Kuan, W.H., Ma, H.K., 2011. Energy life cycle assessment of rice straw bio-energy derived from potential gasification technologies. Bioresour. Technol. 102, 6735–6741. Sozen, A., Alp, I., Kilinc, C., 2012. Efficiency assessment of the hydro-power plants in Turkey by using data envelopment analysis. Renew. Energy 46, 192–202. Tao, J., Yu, S., Wu, T., 2011. Review of China’s bioethanol development and a case study of fuel supply, demand and distribution of bioethanol expansion by national application of E10. Biomass Bioenergy 35 (9), 3810–3829. Wang, M., Shi, Y., Xia, X., Li, D., Chen, Q., 2013. Life-cycle energy efficiency and environmental impacts of bioethanol production from sweet potato. Bioresour. Technol. 133, 285–292. Xia, X., Zhang, J., Xi, B., 2012. Study on Fuel Bioethanol Assessment and Policy Based on Life Cycle Thinking, first ed. China Environmental Science Press, Beijing, China (in Chinese). Yang, H., Chen, L., Yan, Z., Wang, H., 2011. Emergy analysis of cassava-based fuel ethanol in China. Biomass Bioenergy 35, 581–589.
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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Determining the life cycle energy efficiency of six biofuel systems in China: A Data Envelopment Analysis Jingzheng Ren a,b,d, Shiyu Tan b,c, Lichun Dong b,c,⇑, Anna Mazzi a, Antonio Scipioni a, Benjamin K. Sovacool d a
CESQA (Quality and Environmental Research Centre), Department of Industrial Engineering, University of Padova, Via Marzolo 9, 35131 Padova, Italy School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China Key Laboratory of Low-grade Energy Utilization Technologies & Systems of the Ministry of Education, Chongqing University, Chongqing 400044, China d Center for Energy Technologies, AU-Herning, Aarhus University, Birk Centerpark 15, DK-7400 Herning, Denmark b c
h i g h l i g h t s A model for life cycle energy efficiency analysis of biofuel is developed. DEA is used to identify the wasteful use energy in biofuel production. Recommendations are proposed for improving energy efficiency. Life cycle perspective is incorporated in the analysis process.
a r t i c l e
i n f o
Article history: Received 6 February 2014 Received in revised form 19 March 2014 Accepted 21 March 2014 Available online 29 March 2014 Keywords: Life cycle Energy efficiency Biofuel Data Envelopment Analysis
a b s t r a c t This aim of this study was to use Data Envelopment Analysis (DEA) to assess the life cycle energy efficiency of six biofuels in China. DEA can differentiate efficient and non-efficient scenarios, and it can identify wasteful energy losses in biofuel production. More specifically, the study has examined the efficiency of six approaches for bioethanol production involving a sample of wheat, corn, cassava, and sweet potatoes as feedstocks and ‘‘old,’’ ‘‘new,’’ ‘‘wet,’’ and ‘‘dry’’ processes. For each of these six bioethanol production pathways, the users can determine energy inputs such as the embodied energy for seed, machinery, fertilizer, diesel, chemicals and primary energy utilized for manufacturing, and outputs such as the energy content of the bioethanol and byproducts. The results indicate that DEA is a novel and feasible method for finding efficient bioethanol production scenarios and suggest that sweet potatoes may be the most energy-efficient form of ethanol production for China. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction With a booming and prosperous economy, China has become the largest energy consumer in the world (BP, 2012) and also the largest CO2 emitter (Gregg et al., 2008). A transition to renewable sources of energy such as biodiesel and bioethanol to substitute the traditional fossil fuels (Ren et al., 2013a) has therefore emerged as a promising pathway to mitigate emissions and substitute for fossil fuels in the transportation sector (Liang et al., 2013). Accordingly, it is vital that Chinese planners and investors select only those biofuels that are truly more sustainable and efficient than the dirtier and less optimal fuels they displace (Shie et al., 2011). ⇑ Corresponding author at: School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China. Tel./fax: +86 23 65106051. E-mail address: [email protected] (L. Dong). http://dx.doi.org/10.1016/j.biortech.2014.03.105 0960-8524/Ó 2014 Elsevier Ltd. All rights reserved.
Determining the life cycle energy efficiency of different bioethanol production pathways becomes of paramount importance in this selection process. To be sure, much research has been conducted investigating the energy efficiency of biofuel production from a life cycle perspective. Dong et al. (2008) investigated the total life cycle energy consumption of bioethanol production from wheat in Henan Province of China. Khoo et al. (2011) used life cycle energy analysis to study the microalgae-to-biodiesel process. Papong and Malakul (2010) studied the life cycle energy cost of bioethanol production from cassava in Thailand. Hu et al. (2008) assessed the life cycle energy consumption of soybean-based biodiesel as an alternative automotive fuel in China. Leng et al. (2008) analyzed the life cycle energy consumption of cassava-based fuel ethanol in China. Hu et al. (2004) calculated the net energy availability of cassava-based ethanol in China. Janulis (2004) explored the life cycle energy
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J. Ren et al. / Bioresource Technology 162 (2014) 1–7
balance of rapeseed-based biodiesel. Malca and Freire (2006) studied the life cycle energy efficiency of sugar beet and wheat-based bioethanol. Wang et al. (2013) investigated the life cycle energy efficiency of bioethanol production from sweet potatoes. All of these studies, however, have one primary shortcoming: they focus on the net energy gain (NEG) or net energy ratio (NER) of different biofuel production processes. Although such methods are certainly valid – the energy consumption status of biofuel production can be determined by these methods and stakeholders can determine suitable scenarios based on their findings – it is difficult to determine how truly energy efficient these scenarios are. Moreover, it is difficult to improve the energy efficiency of some biofuels pathways because non-renewable energy sources are involved in each stage of the life cycle of that biofuel. Thus, a more rigorous and holistic methodology is needed which can help stakeholders measure the energy use efficiency in biofuel production and improve its overall energy efficiency. NEG and NER are often considered ‘‘parametric’’ methods for measuring end-use efficiency (Cristobal, 2011). Data Envelopment Analysis (DEA), by contrast, is a novel non-parametric method (Charnes et al., 1978). Mousavi-Avval et al. (2011) have shown how DEA can provide more complete estimates of the energy efficiencies of soybean producers, helping to rank efficient and inefficient farmers, and enabling the identification of optimal energy requirement and wasteful use of energy. Sarica and Or (2007) similarly employed DEA to analyze and compare the performance of electricity generation plants in Turkey. Mohammadi et al. (2011) used DEA to evaluate the energy efficiency of farmers, to find efficient and inefficient scenarios and to provide implements to improve the energy efficiency in kiwifruit production. Nassiri and Singh (2009) studied the energy use efficiency for paddy crop based on DEA. All these studies concerning energy use efficiency relied on DEA, and one implication is that DEA is an excessively feasible tool of judging (1) whether or not a given energy system is efficient, (2) which scenarios among many are the most efficient, and (3) which required energy inputs and outputs can minimize waste and result in optimal production processes. Despite its explanatory power, to the best of the authors’ knowledge no scholarship has yet utilized DEA to study the energy efficiencies of biofuel production. Accordingly, the main purpose of this paper is to use DEA as a mathematical tool to measure the energy use efficiencies of various approaches for biofuel production (bioethanol as an example) in China. This paper seeks to identify efficient and non-DEA efficient scenarios and proposes useful recommendations to minimize wasteful energy use in the Chinese biofuels sector. The study is structured as follows: Section 2 presents the DEA model. Section 3 utilizes this model for calculating the energy consumption and efficiency of six bioethanol production processes in China. Section 4 presents the paper’s conclusions. 2. Methods For those unfamiliar with the term, Data Envelopment Analysis (DEA) is a linear programming-based technique that is able to measure the relative performance of decision making units (DMUs) which are characterized by a multiple objectives and/or multiple inputs (Sozen et al., 2012; Basso and Funari, 2001). A DMU could be defined as a tangible or intangible system which can transform a set of inputs into outputs, as presented in Fig. 1. The main advantage of DEA is that its users do not need to know the underlying functional relationships between the inputs and the outputs in DMUs (Seiford and Thrall, 1990). Thus, the specific mechanism of the studied entity does not need to be specified and modeled in the calculation. In contrast, its main weak point is that the efficiencies of these entities are relative ones and only attain meaning in comparison to other elements. In other words,
u1 u2
x1j
y1j
x2j
y2j
v1 v2
DMUj ypj
xmj
um
vp
Fig. 1. Structure of DMU.
it is impossible to describe the relationships between the inputs and the outputs in DMUs. In this study, it is assumed that there are m inputs xrj ðr ¼ 1; 2; ; mÞ in the jth DMU (j = 1,2,. . .,t), the weight of the input is denoted by lr ðr ¼ 1; 2; ; mÞ, and there are also p outputs yij ði ¼ 1; 2; ; pÞ in the jth DMU (j = 1,2,. . .,t), the weight of the output is denoted by v i ði ¼ 1; 2; ; pÞ. The technical efficiency of a DMU is defined as the ratio between the outputs and inputs (Leal et al., 2012), as formulated in Eq. (1):
TE ¼
Outputs Inputs
ð1Þ
The technical efficiency of a DMU is defined as the ratio between the sum of the weighted inputs and the sum of the weighted outputs, taking DMUj as an example, and the technical efficiency of DMUj could be obtained, as presented in Eq. (2):
Pp
TEj ¼ Pmi¼1 r¼1
v i yij
ð2Þ
lr xrj
Eqs. (3) and (4) present the non-Archimedean infinitesimal CCR model (the abbreviation of the initial inventors’ name, namely Charnes, Cooper and Rhodes). It is notable that Eq. (3) is to measure the efficiency of the DMU j0, and Eq. (4) is to limit the upper bound of the efficiency of other DMUs, because the maximal technical efficiency is 1:
Pp
max hj0 ¼ Pmi¼1
v i yij0
ð3Þ
r¼1 lr xrj0
Subject to:
Pp vy Pmi¼1 i ij 1 ðj ¼ 1; 2; ; tÞ l
x r¼1 r rj
ð4Þ
ur e r ¼ 1; 2; ; m
vi e
i ¼ 1; 2; ; p
where hj0 represents the technical efficiency of DMU j0, and e is a non-Archimedean construct. After the Charnes–Cooper transformation and dual transformation, the procedures are specified in (Leal et al., 2012; Ren et al., 2013b). Then, the non-Archimedean infinitesimal CCR model could be transformed into the following form, as presented in (5) and (6):
min hj0 e
p m X X sþr þ si r¼1
!
ð5Þ
i¼1
Subject to: t X xrj kj þ sr hxrj0 ¼ 0 j¼1 t X yij kj sþi yij0 ¼ 0 j¼1
ð6Þ
kj 0 ðj ¼ 1; 2; ; tÞ sr 0 ðr ¼ 1; 2; ; mÞ sþi 0 ði ¼ 1; 2; ; pÞ þ where s r and si represent slack and surplus variable, respectively.
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J. Ren et al. / Bioresource Technology 162 (2014) 1–7
Generally, two definitions can determine whether a DMU is DEA efficient or not. These are as follows: Definition 1. If the optimal value h ¼ 1, then the decision making unit can be identified as weak DEA effective, and vice versa. Definition 2. If the optimal value h ¼ 1, and the solution satisfies s ðr ¼ 1; 2; ; mÞ, sþ ði ¼ 1; 2; ; pÞ then the decision r ¼ 0 i ¼ 0 making unit can be identified as DEA effective, and vice versa. A projection improvement analysis methodology (Bian and Yang, 2010) can further improve a non-DEA-efficient DMU into a DEA-efficient one. For a non-DEA-efficient DMU j, then the projection of the inputs and outputs on the relative efficient surface can be calculated by Eqs. (7) and (8), respectively:
_rj ¼ hj0 xrj x
srj
ð7Þ
_ij ¼ yij þ sþij
ð8Þ
y
where _rj and _ij are the projection inputs and outputs, x y respectively. 3. Results and discussion This paper assessed the life cycle energy efficiency of wheatbased, corn-based, cassava-based and sweet potato-based bioethanol. The authors selected the lower heating value of bioethanol as
26.77 MJ/kg based on (Xia et al., 2012). The authors selected a function unit (FU) to be 1000 MJ of bioethanol, and the life cycle boundary of bioethanol consists of four main stages including (1) cropping, (2) transportation to the production facility, (3) production, and (4) transportation to distribution centers. Fig. 2 presents an overview of these four stages. Then, the consumption of various sources in each stage has been presented as follows: The energy of various sources at the stage (1) of cropping (crop production) can be determined by Eq. (9). The left part FNC j BLHVr i Y i
represents the amount of the jth resource consumed for
crop production:
Ej ¼
FN C j BLHV ri Y i
EIj
ð9Þ
where Ej is the consumed energy corresponding to the jth feedstock, FN represents the function unit, BLHV represents the lower heating value of biofuel, r i represents the conversion ratio of the ith crop to biofuel, Y i represents the yield of the ith crop per unit land, C j represents the consumption of the jth feedstock per unit land, and EIj represents the energy intensity of the jth feedstock. The energy of fuels for the second stage of (2) transportation to the production facility can be calculated by Eq. (10). Similarly, the m L1 left part FNC represents the amount of the mth fuel consumed BLHVr i for crop transportation. It is notable that the assumed distance from the crop distribution center to the factory for bioethanol production is
Cropping Seed
Field Preparation
Diesel
N fertilizers
P fertilizers Sowing
Electricity
K 2O fertilizer Irrigation
Herbicide
Fertilization Pest control
Human labor
Pesticide
Weeding
Machinery
Harvesting
Diesel
Crop transport
Steam Bioethanol production Electricity
Diesel
Bioethanol transport
Bioethanol Fig. 2. Life cycle boundary of bioethanol.
Coal
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J. Ren et al. / Bioresource Technology 162 (2014) 1–7
500 km, and that diesel is used as the fuel for transport with the transportation intensity of 0.05 L t1km1, its energy intensity is 44.13 MJ L1 (Xia et al., 2012; Chen and Chen, 2011):
Em ¼
FN C m L1 EIm BLHV r i
ð10Þ
where Em is the consumed energy corresponding to the mth fuel, C m represents the consumption of the mth fuel for transport per unit crop for per mileage, EIm represents the energy intensity of the mth fuel, and L1 represents the distance from the crop distribution center to the factory for bioethanol production. The energy of various resources at the stage (3) of biofuel n production can be determined by Eq. (11). Similarly, FNC BLHV represents the amount of the nth resource consumed for biofuel production:
En ¼
FN C n EIn BLHV
ð11Þ
C n represents the consumption of the nth feedstock for per unit biofuel, EIn represents the energy intensity of the mth fuel. The energy of fuel for the state of (4) transportation to distribum L2 tion centers can be determined by Eq. (12), FNC which repreBLHV sents the amount of the mth fuel consumed for biofuel transportation. It is also assumed that the distance from the factory for biofuel production to biofuel distribution center is 500 km:
Em ¼
FN C m L2 EIm BLHV
ð12Þ
where Em is the consumed energy corresponding to the mth fuel, C m represents the consumption of the mth fuel for transport per unit crop for per mileage, EIm represents the energy intensity of the mth fuel, and L2 represents the distance from the factory for biofuel production to biofuel distribution center. The proposed DEA methodology for determining the various energy efficiencies of bioethanol production has investigated wheat, corn, cassava, and sweet potato as feedstocks. More specifically, it has been presumed that two alternative processes (old process P1 and new process P2) exist for wheat-based bioethanol production and two alternative processes (dry process P3 and wet process P4) exist for corn-based bioethanol production. Thus, the proposed DEA ultimately has been used to assess six DMUs including wheat-based bioethanol production using P1 (DMU1),
wheat-based bioethanol production using P2 (DMU2), corn-based bioethanol production using P3 (DMU3), wheat-based bioethanol production using P4 (DMU4), cassava-based bioethanol production (DMU5) and sweet potato-based bioethanol production (DMU6). This analysis presumed that the yields of wheat are 4301 kg/ hectare; yields of corn 7500 kg/hectare; yields of cassava 17,520 kg/hectare; and yields of sweet potato 45,000 kg/hectare. As such, corresponding conversion ratios of these crops to bioethanol are 0.306 for wheat, 0.313 for corn, 0.121 for cassava, and 0.125 for sweet potato. Data for the calculation of the energy consumption of bioethanol is based derived from (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). According to Eqs. (9)–(12), Tables 1–4 present the energy consumption of 1000 MJ bioethanol based on different feedstocks and production pathways. The consumed energy for seed, machinery, fertilizer, diesel, chemicals and the primary energy (the sum of the consumed coal, electricity and steam) are used as the inputs in the DMUs, and the energy of the bioethanol and byproduct are employed as the outputs, as shown in Table 5. Table 6 presents the life cycle energy efficiency of the six alternative bioethanol scenarios as determined by the DEA model. It is apparent that there are two scenarios including wheat-based bioethanol production using P2 (DMU2) and sweet potato-based bioethanol production (DMU6) that are regarded as DEA efficient (i.e., ‘‘good’’), and there are also two scenarios including corn-based bioethanol production using P3 (DMU3) and corn-based bioethanol production using P4 (DMU4) that are considered as weak-DEA efficient (i.e., ‘‘average’’). However, there are two other scenarios including wheat-based bioethanol production using P1 (DMU1) and cassava-based bioethanol production (DMU5) that are regarded as non-DEA efficient (i.e., ‘‘bad’’). Fig. 3 presents the results of the projection improvement analysis methodology. This analysis suggests that the efficiencies of the weak-DEA efficient systems (corn-based ethanol using P3 and P4) and non-DEA efficient systems (wheat ethanol using P1 and cassava) can be improved in a variety of ways. First, decrease the energy intensity of inputs. The energy intensity of seeds for the production 1000 MJ bioethanol would need to be reduced from 130.07 MJ to 25.60 MJ; of machinery from 5.07 MJ to 2.46 MJ; of fertilizer from 431.78 MJ to 188.49 MJ; of diesel from 265.22 MJ to 217.5 MJ; of chemicals from 24.9 MJ to 16.03 MJ; and of primary energy used in DMU1 (wheat-based bioethanol production using P1) from
Table 1 Various energy consumption for 1000 MJ wheat-based bioethanol using the old and new processes (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Item
Unit
Amount
Energy intensity (MJ unit1)
Energy (MJ)
Seed Machinery N fertilizer
kg kg kg
2.56 0.25 6.39
50.81 20.29 57.46
130.07 5.07 367.17
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
2.34 7.03 2.03 8.52 0.05 0.04
7.03 6.85 44.13 11.91 284.82 266.56
16.45 48.16 89.58 101.47 14.24 10.66
Crop transport
Diesel
L
3.05
44.13
134.60
P1
P2
P1
P2
Steam Coal Electricity Diesel
t t kWh L
0.138 0.017 11.790 0.93
0.131 0.014 9.106
3830 26,790 8.93 44.13
528.54 455.43 105.285 41.04
501.73 375.06 81.32
P1
P2
Bioethanol Byproduct
MJ MJ
1000 387.37
1000 579.75
1 1
1000 387.37
1000 579.75
Stage Inputs
Bioethanol production Bioethanol transport Outputs
5
J. Ren et al. / Bioresource Technology 162 (2014) 1–7 Table 2 Various energy consumption for 1000 MJ corn-based bioethanol using the dry and wet processes (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Item
Unit
Amount
Energy intensity (MJ unit1)
Energy (MJ)
Seed Machinery N fertilizer
kg kg kg
0.48 0.12 2.99
53.36 20.29 57.46
25.61 2.43 171.80
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
1.20 1.20 1.01 3.59 0.016 0.043
7.03 6.85 44.13 11.91 284.82 266.56
8.44 8.22 44.57 42.76 4.56 11.46
Crop transport
Diesel
L
Stage Inputs
Bioethanol production Bioethanol transport
2.99
44.13
P3
P4
131.95
Steam Coal Electricity Diesel
t t kWh L
0.12 0.015 9.41 0.93
0.11 0.014 8.85
3830 26,790 8.93 44.13
Bioethanol Byproduct
MJ MJ
1000 266.34
1000 392.60
1 1
Outputs
P3
P4
459.60 401.85 84.03 41.04
421.30 375.06 79.03
P3
P4
1000 266.34
1000 392.60
Table 3 Various energy consumption for 1000 MJ cassava-based bioethanol (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Stage
Item
Unit
Seed Machinery N fertilizer
kg kg kg
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
Crop transport
Diesel Steam
Bioethanol production Bioethanol transport
Amount
Energy intensity (MJ unit1)
Energy (MJ)
Inputs 15.85 0.18 1.77
15.67 20.29 57.46
248.41 3.70 101.58
0.88 3.54 1.31 4.22 0.019 0.011
7.03 6.85 44.13 11.91 284.82 266.56
6.21 24.22 57.88 50.26 5.68 3.04
L t
7.72 0.12
44.13 3830
340.99 459.6
Coal Electricity
t kWh
0.013 0.88
26,790 8.93
348.27 7.86
Diesel
L
0.93
44.13
41.04
Bioethanol Byproduct
MJ MJ
Outputs 1000 201.34
1 1
1000 201.34
Table 4 Various energy consumption for 1000 MJ sweet potato-based bioethanol (Xia et al., 2012; Wang et al., 2013; Yang et al., 2011; Chen and Chen, 2011). Stage
Amount
Energy intensity (MJ unit1)
Item
Unit
Energy (MJ)
Seed Machinery N fertilizer
kg kg kg
7.97 0.068 1.05
15.42 20.29 57.46
122.90 1.38 60.33
Cropping
P fertilizer K2O fertilizer Diesel Electricity Pesticide Herbicide
kg kg L kWh kg kg
0.54 1.64 0.50 1.59 0.003 0.003
7.03 6.85 44.13 11.91 284.82 266.56
3.80 11.23 22.07 18.94 0.85 0.80
Crop transport
Diesel Steam
L t
7.47 0.12
44.13 3830
329.65 459.60
Bioethanol production
Coal Electricity
t kWh
0.008 0
26,790 8.93
214.32 0
Bioethanol transport
Diesel
L
0.93
44.13
41.04
Bioethanol Byproduct
MJ MJ
Inputs
Outputs 1000 252.52
1 1
1000 252.52
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J. Ren et al. / Bioresource Technology 162 (2014) 1–7
Table 5 The inputs and outputs used in DEA model. Item
DMU1
DMU2
DMU3
DMU4
DMU5
DMU6
Inputs Seed Machinery Fertilizer Diesel Chemicals Primary energy
x11 x21 x31 x41 x51 x61
x12 x22 x32 x42 x52 x62
x13 x23 x33 x43 x53 x63
x14 x24 x34 x44 x54 x64
x15 x25 x35 x45 x55 x65
x16 x26 x36 x46 x56 x66
Outputs Bioethanol Byproduct
y11 y21
y12 y22
y13 y23
y14 y24
y15 y25
y16 y26
Table 6 The calculated results: effective values, slack value and surplus value. Slack or surplus value
hj0
s 1
s 2
s 3
s 4
s 5
s 6
sþ 1
sþ 2
0.8203 1 1 1 0.8367 1
81.1 0 0 0 98.7 0
1.7 0 0 0 1.6 0
165.7 0 0 0 19.2 0
0 0 0 0 0 0
4.4 0 0 0 3.6 0
58.6 0 70.2 70.2 0 0
0 0 0 0 0 0
5.2 0 126.3 0 70.9 0
1190.725 MJ to 918.15 MJ. In tandem, the yield of the byproduct should also be improved from 387.37 MJ to 392.57 MJ. Therefore, the energy recovery of waste biomass is highly recommended as an effective way to improve the life cycle energy efficiency. Brazil has utilized a similar strategy with harnessing bagasse from the cultivation of sugarcane ethanol (Brown and Sovacool, 2011). For DMU5 (cassava-based bioethanol production), the recommendations are similar with DMU1. The inputs should be decreased by 43.94% (seeds), 40.54% (machinery), 69.12% (fertilizer), 83.67% (diesel), 42.43% (chemicals), and 83.67% (primary energy). Analogously, the recovery of the byproducts should be increased by 135.26%. For the two weak-DEA efficient options including DMU3 (cornbased bioethanol production using P3) and DMU4 (corn-based bioethanol production using P4), the recommendations are more nuanced. Planners and operators need only reduce primary energy from 988.24 MJ to 918.04 MJ for DMU3, and from 918.15 MJ to 847.95 MJ for DMU4. The outputs of DMU4 do not need to be
4. Conclusion A life cycle DEA analysis of biofuel production scenarios is of vital importance for stakeholders and decision-makers in China and beyond so that they can select the most efficient approaches and improve the energy productivity of ethanol compared to fossil fuels. This study has examined the life cycle efficiencies of six approaches for bioethanol production in China, utilizing Data Envelopment Analysis to identify efficient, weakly efficient and non-efficient scenarios. It could be concluded that only wheatbased bioethanol and sweet-potato based bioethanol production are DEA efficient. Chinese planners may need to rethink the biofuel policies accordingly.
1400 1200
Seed Machinery
Energy (MJ)
1000 800
Fertilizer Diesel
600
Chemicals
400
Primary energy Bioethanol
200
Byproduct
5
4
D M va U5 lu es fo rD M U
ec tio n
D M va U4 lu es fo rD M U n
Pr oj
ec tio
D M va U3 lu es fo rD M U n
ec tio
D M va U1 lu es fo rD M U Pr oj
Pr oj
ec tio
n
1
3
0
Pr oj
DMU1 DMU2 DMU3 DMU4 DMU5 DMU6
Effective value
improved, but the recovery of the byproducts in DMU3 should be increased by 147.42% compared to the original value. Six systems for producing first generation biofuel have been examined according to a DEA in this study, but this does not means that the proposed model can only be used for these fuels. Further research could build on this work to examine second or even third generation biofuels such as cellulosic ethanol and biodiesel from algae. Moreover, given that the inputs and outputs in the DMUs of the proposed methodology are flexible, users can change these variables to match local conditions. However, it is likely that recycling and minimizing waste will hold immense promise independent of location; that is, a significant amount of energy could be obtained by the recovery of the waste biomass in the production of biofuels in many different situations. This study has significant policy implications for China. Wheatbased bioethanol production using the new process and sweet potato-based bioethanol production process have been regarded as the most promising, efficient, and productive pathways. Accordingly, policymakers, stakeholders, and administrators in China may want to consider drafting regulations that incentivize wheat- and potato-based fuels and disincentivize those from cassava and corn. Wheat and corn, et al. have the further drawback of being staple foods in China meaning large-scale biofuel production dependent on them could in some situations lead to conflicts over land and deteriorating food security. Potatoes, by contrast, are is quite suitable for biofuel production in large scales, given that they have a high starch content, are generally cheaper, and have good adaptability to the different planting environments of China (Tao et al., 2011). The potato resources are also abundant: China is the largest producer of potatoes accounting for 80–85% of the global total (Wang et al., 2013).
Fig. 3. The projection values of the inputs and outputs of the weak-DEA efficient and non-DEA efficient scenarios.
J. Ren et al. / Bioresource Technology 162 (2014) 1–7
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