Life-cycle fossil energy consumption and greenhouse gas emission intensity of dominant secondary energy pathways of China in 2010

Life-cycle fossil energy consumption and greenhouse gas emission intensity of dominant secondary energy pathways of China in 2010

Energy 50 (2013) 15e23 Contents lists available at SciVerse ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy Life-cycle fossil...

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Energy 50 (2013) 15e23

Contents lists available at SciVerse ScienceDirect

Energy journal homepage: www.elsevier.com/locate/energy

Life-cycle fossil energy consumption and greenhouse gas emission intensity of dominant secondary energy pathways of China in 2010 Xin Li a, Xunmin Ou a, b, *, Xu Zhang a, b, Qian Zhang a, b, Xiliang Zhang a, b a b

Institute of Energy, Environment and Economy (3E), Tsinghua University, Beijing 100084, PR China China Automotive Energy Research Center (CAERC), Tsinghua University, Beijing 100084, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 February 2012 Received in revised form 16 November 2012 Accepted 20 December 2012 Available online 17 January 2013

Life-cycle fossil primary energy consumption (FPEC) and greenhouse gas (GHG) emission intensity of nine types of dominant secondary energy (SE) pathways for China in 2010 are calculated with iterative methods, using the TLCAM (Tsinghua Life-cycle Analysis Model). Three major types of GHG (CO2, CH4 and N2O) are considered for GHG emission intensity, and non-combustion CH4 leakage during the feedstock production sub-stage is included. We found the following. (1) Life-cycle FPEC intensities in units of per MJ SE are obtained and used, in order of magnitude, for: raw coal (recovered only); raw natural gas (NG, recovered and processed only); raw oil (recovered and processed); final coal (finally transported to end-user); final NG (finally transported to end-user); diesel; gasoline; residual oil and electricity. (2) Although their upstream GHG emission intensities are small, their life-cycle intensities are 103.5, 68.3, 81.6, 99.3, 70.0, 101.6, 91.7, 93.5 and 226.4 g CO2,e/MJ SE, respectively, when direct GHG emissions are included. (3) Life-cycle intensities of both FPEC and GHG emissions for SE in China are higher than those in some other countries, because of the relatively low overall efficiency and high percentage of coal in the national energy mix.  2012 Elsevier Ltd. All rights reserved.

Keywords: Life-cycle analysis Energy consumption Greenhouse gas Secondary energy China

1. Introduction

1.2. Life-cycle analysis (LCA) for energy pathways and/or systems

1.1. Importance of life-cycle energy use and greenhouse gas (GHG) emission analysis for Chinese energy strategy decisions

Numerous researchers have done LCA on energy pathways and systems, using: 1) A process-based LCA method [5e14] that has a simplified system boundary but less complete upstream system boundary; 2) an economic inputeoutput LCA method [15e17] that has a complete system but relatively complicated computation, based on economic interactions within a country and/or in the world; 3) a hybrid LCA method [18e21] that simultaneously combines process-based and economic inputeoutput and has a complete system and dependable application system. As stated in Suh and Huppes [21], the goal is not to choose a specific method that is superior to others, but to identify the most relevant tool based on the research objective and scope, plus available resources and time. For certain processes of an energy product or system, their own output is used directly and indirectly. Thus, it is neither accurate nor feasible to simply sum the energy requirements of each process to attain the final life-cycle energy consumption. Nevertheless, some iterative methods can be used to tackle this problem [21]. A good example is the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model, which was developed at the US Argonne National Laboratory over 10 years ago.

The world is facing a growing challenge from global climate change, mainly associated with GHG emissions from energy use. A strategic transition to low-carbon energy is considered essential for China, the second-largest energy consumer and the largest CO2 emitter, to tackle climate change alongside other countries [1]. It is therefore an important and fundamental task to investigate lifecycle fossil primary energy consumption (FPEC) and GHG emission intensities for various types of secondary energy (SE) used widely in industrial, residential and other sectors [2e5]. It is also important to find the key sectors or stages in which overall energy efficiency can be improved and total GHG emissions reduced in China, from a life-cycle perspective.

* Corresponding author. Institute of Energy, Environment and Economy (3E), Tsinghua University, Beijing 100084, PR China. Tel.: þ86 10 6279 7376; fax: þ86 10 6279 6166. E-mail address: [email protected] (X. Ou). 0360-5442/$ e see front matter  2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2012.12.020

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Nomenclature CC carbon content factor of secondary energy (g/MJ) CH4,direct direct CH4 emissions (g/MJ) CH4 emission intensity (g/MJ) CH4,LC CH4,noncomb CH4 indirect emissions from non-combustion sources (g/MJ) CH4,resource indirect CH4 emissions from non-combustion sources for 1 MJ resource extracted (g/MJ) upstream CH4 emissions (g/MJ) CH4,up CO2,direct direct CO2 emissions (g/MJ) CO2 emission intensity (g/MJ) CO2,LC upstream CO2 emissions (g/MJ) CO2,up life-cycle primary fossil energy intensity for secondary EFLC energy (MJ/MJ) EI total primary fossil energy input (MJ/MJ) FOR fuel oxidation rate of secondary energy (e) GHGLC life-cycle emission intensity of (g CO2,e/MJ) N2Odirect direct N2O emissions (g/MJ) life-cycle N2O emission intensity (g/MJ) N2OLC upstream N2O emissions (g/MJ) N2Oup SH share of SE (e) RA ratio of specific pathway of electricity (e) Greek symbol h conversion energy efficiency factor x conversion factor of feedstock to resource in feedstock production sub-stage

It has been improved continuously since 1999 to include more than 100 transportation-fuel production pathways from various energy feedstocks [22]. The GREET model consists of about 30 Microsoft Excel spreadsheets. The data are interlinked for both input and output sides through iterative methods aimed at scientific analysis results from both whole-life and full-cycle perspectives, using a professional computing tool. The GREET and similar LCA models for energy and fuels (e.g., life-cycle emissions model, LEM [23]), based on US and European contexts, have been widely used by institutions and researchers since the 1990s to perform LCA on a variety of transportation fuels for different regions [24,25]. 1.3. LCA studies for energy pathways and model development in China Limited LCA studies specific to fuel pathways in China can be mainly classified into two types: 1) Comparisons between different fuel pathways (primarily for vehicles), using GREET as the basic model with some parameters adapted to China [4,7,8,26,27]; and 2) studying specific pathways using self-developed models [28,29]. There are potential problems obstructing comprehensive studies or comparisons of multiple fuel pathways in China based on a scientific approach, from a review of the aforementioned research: 1) There are unavoidable errors from localization for models with foreign-structured and foreign default databases; 2) System boundaries are inconsistent among the self-developed models; and 3) There are likely underestimations in model results based on process analysis by simple calculation, without interlinking or iterative methods [30e33]. LCA modeling works for a unified, but a comprehensive and systematic computing platform for different pathways in China is in

Subscripts i primary fossil fuel type j, x, z secondary energy type m life-cycle sub-stage number n electricity pathway type Abbreviations CAERC China Automotive Energy Research Center, Tsinghua University CC crude coal as primary fossil energy CN crude NG as primary fossil energy CO crude oil as primary fossil energy CO2 equivalents CO2,e FPEC fossil primary energy consumption FC final coal, stands for coal recovered, processed and transported as SE finally FN final NG, stands for NG recovered, processed and transported as SE finally GHG greenhouse gas LCA life-cycle analysis NG natural gas PE primary fossil energy RC raw coal, stands for coal only recovered as SE directly RN raw NG, stands for NG only recovered and processed as SE directly RO raw oil, stands for oil only recovered and processed as SE directly SE secondary energy TLCAM Tsinghua Life-cycle Analysis Model TSD transportation, storage and distribution

a poor situation for the following reasons: 1) A poor database regarding availability and credibility; 2) weak cooperation between related research institutes; and 3) insufficient funding for longterm research [34]. In summary, current research in the energy-use LCA field of China must make trade-offs between model scale (pathways covered, parameters set, stages distinguished) and model result (degree of precision, error analysis) [34]. 1.4. Objective and content of this study The objectives of this study were to set up a computer model for calculating a life-cycle FPEC and GHG emission intensity inventory of nine types of dominant SE in the real situation of China, and to pave the way for two types of applications for that situation. Specifically, this encompasses: 1) Providing the multipliers for specific LCA research by multiplying their corresponding process fuel uses to obtain final results; and 2) establishing a platform for sector lifecycle energy use and GHG emission analysis. In other words, life-cycle FPEC and GHG emission intensities (in 2010) are evaluated for nine dominant SE types in China, including raw coal (RC), raw NG (RN), raw oil (RO), final coal (FC), final NG (FN), diesel, gasoline, residual oil and electricity. We demonstrate the distinguishing features of the TLCAM (Tsinghua Life-cycle Analysis Model) [34] and overcome weaknesses described in the sub-section above: 1) Most types of dominant SE in China are covered, including both types used directly after extraction and/or transportation only; these types require further processing; 2) Interlinks among different energy pathways and consequent impacts for their final life-cycle results are captured using

X. Li et al. / Energy 50 (2013) 15e23

a computerized iterative method based on the TLCAM used in Excel; 3) Upstream or indirect energy use and GHG emission impacts are analyzed and shown in detail, to describe embodied energy and emissions for SE pathways in China. Results of this study (life-cycle intensity inventory of dominant SE pathways) will provide comprehensive source data for both specific micro-level energy pathway LCA and macro-level sectoral life-cycle (LC) calculation, as follows. 1) The results can serve as multipliers for application to the corresponding process fuel use during each stage; we thereby obtain the final LCA and indirect energy use and GHG emission results that are useful for specific LCA research. For example, the photovoltaic power generation pathway covers material extraction and production, equipment manufacturing and packaging, and equipment installation and operation. 2) The results can serve as a unified platform for sectoral life-cycle energy use and GHG emission analysis. From an energy user perspective, the agricultural, construction, industrial, transportation and residential sectors all consume several kinds of SE, and their life-cycle energy use and GHG impacts can be calculated on the basis of the inventory data obtained from this study. We foresee that future studies based on this research will be more accurate and feasible because of the Chinese-specific structure and data, system boundary consistency, and calculation completeness derived here. Specifically: 1) For future micro-level, lifecycle energy use and GHG emissions in a wide range of energypathway LCAs, there will be obvious improvements in the accuracy of specific results and their comparative conclusions; and 2) in future macro-level sector life-cycle energy use and GHG emission calculations, the calculated results based on this unified platform

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Table 1 Interpretation of i, j, m and n.

1 2 3 4 5 6 7 8 9 a b c d e

i (PE)

j, x or z (SE)

m (Stage name)

n (Electricity pathway)

Crude coal (CC) Crude NG (CN) Crude oil (CO)

Raw coal (RC)a Raw NG (RN)b Raw oil (RO)c Final coal (FC)d Final NG (FN)e Diesel Gasoline Residual oil Electricity

Feedstock production Feedstock transportation Fuel production Fuel transportation

Coal-based NG-based Oil-based Others

Raw coal (recovered only) served as SE. Raw NG served as SE after recovery and processing. Raw oil served as SE after recovery and processing. Final coal served as SE after recovery, processing and transport. Final NG served as SE after recovery, processing and transport.

(i stands for PE type)dand nine types of SE (represented by j, x or z). For each type of SE, its LCA includes m stages. For electricity, four pathways are considered: coal-, NG- and oil-based, and others (n stands for the electricity pathway). 2.2. Calculation of fossil energy intensity EFLC,j (life-cycle FPEC intensity of SE j) is calculated as the sum of all the EFLC,j,i (life-cycle PE i intensity of SE j): 3 X

EFLC;j ¼

EFLC;j;i

ðj ¼ 1; 2; .; 9Þ:

(1)

i¼1

EFLC,j,i is calculated using EIm,j(total PE input during sub-stage m when 1 MJ of SE j is finally obtained), SHm,j,z (the share of SE z in total energy use during sub-stage m for 1 MJ of SE j obtained), and EFLC,z,i (life-cycle PE i intensity of SE z):

! 4 9   P P EIm;j EFLC;j;i ¼ SHm;j;z EFLC;z;i þ di;j z¼1  m¼1 1 when ði; jÞ˛fð1; 1Þ; ð1; 4Þ; ð2; 2Þ; ð2; 5Þ; ð3; 3Þ; ð3; 6Þ; ð3; 7Þ; ð3; 8Þg di;j ¼ : 0 otherwise

will be of greater use in discovering the key sectors or stages in which energy efficiency can be improved and GHG emissions reduced. Following the introduction above, Section 2 describes the methods used, including a basic definition and calculation, and Section 3 describes the data and assumptions. Results are shown in Section 4, which also includes discussions on comparative studies, results application, and future research. 2. Methodology 2.1. Basic definitions The fossil energy intensity (EFLC, MJ/MJ) and GHG emission intensity (GHGLC, g CO2,e/MJ) of a specific type of SE are defined as the sum of all FPEC and GHG emissions, respectively, during the entire fuel life-cycle for 1 MJ final fuel obtained and utilized. As Table 1 shows, there are three types of fossil primary energy (PE) considereddcrude coal (CC), crude NG (CN), and crude oil (CO)

(2)

Therefore, EFLC,j can be calculated as

EFLC;j ¼ 

gj ¼

3 4 P P i¼1 m¼1

EIm;j

0

for j ¼ 9

1

otherwise

9  P z¼1

SHm;j;z EFLC;z;i



! þ gj (3)

:

For non-electricity SE (j ¼ 1,2,.,8), energy input (EI) can be derived from hm,j(energy transformation efficiency factor during sub-stage m when 1 MJ of SE j is obtained) and the conversion factor of fuel to feedstock during the sub-stage of fuel production for SE j (xj, MJ/MJ):

EI1;j ¼

EI2;j ¼



. 1=h1;j  1 xj

ðj ¼ 1; 2; .; 8Þ;

(4)



. 1=h2;j  1 xj

ðj ¼ 1; 2; .; 8Þ;

(5)

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X. Li et al. / Energy 50 (2013) 15e23

EI3;j ¼ 1=h3;j  1

ðj ¼ 1; 2; .; 8Þ;

(6)

EI4;j ¼ 1=h4;j  1

ðj ¼ 1; 2; .; 8Þ:

(7)

For electricity (j ¼ 9), the national-grid mix is considered and the life-cycle results are computed directly from sub-stage 3:

( EIm;9 ¼

  4 X RAn =h3;9;n =h4;9;n

for m ¼ 3

0

otherwise;

n¼1

(8)

2.3.1. General description GHGLC,j(life-cycle GHG emission intensity of SE j) consists of the three key types of GHG emissions (CO2, CH4 and N2O). These GHG types are converted into CO2 equivalents (CO2,e) according to their global warming potential (GWP) [35]:

(9)

where CO2,LC,j, CH4,LC,j and N2OLC,j are the life-cycle CO2, CH4 and N2O emission intensities for SE j, respectively. Similar to EFLC,j,i, the calculation of GHGLC,j is also performed by iterative methods (see equations (12), (15) and (18)). 2.3.2. CO2 emissions CO2,LC,j consists of two parts: the upstream part (CO2,up,j) and direct emission during combustion (CO2,direct).

CO2;j;direct ¼

44 CC FORj ; 12 j

(10)

(11)

where CO2,up,j represents upstream CO2 emissions (g/MJ), CO2,direct reflects direct CO2 emissions (g/MJ), CCj is the carbon content factor of SE j (g/MJ), FORj is the fuel oxidation rate of SE j, and 44/12 is the mass conversion rate from C to CO2. The upstream CO2 emissions (CO2,up,j) result from the direct CO2 emission of SE x (CO2,direct,x, g/MJ):

CO2;up;j

4 9  X X   ¼ EIm;j SHm;j;x CO2;direct;x þ CO2;up;x :

(12)

m¼1 x¼1

CO2,direct,x can be calculated by the following carbon balance equation:

CO2;direct;x ¼

44 CCx FORx ; 12

(13)

where CCx is the carbon content factor of SE x (g/MJ), FORx is the fuel oxidation rate of SE x, and 44/12 is the mass conversion rate from C to CO2.

(14)

4 9  X X   EIm;j SHm;j;x CH4;direct;m;x þ CH4;up;x m¼1 x¼1

þ CH4;j;noncomb ;

2.3. Calculation of GHG emission intensities

CO2;LC;j ¼ CO2;up;j þ CO2;direct ;

CH4;LC;j ¼ CH4;up;j þ CH4;direct ;

CH4;up;j ¼

where RAn is the ratio of the nth electricity pathway to total electricity generation; h3,9,n and h4,9,n are the energy transformation efficiency factor of electricity generation and electricity transmission and distribution sub-stages for the nth electricity pathway, respectively. Based on equations (1)e(8), hm,j, xj and SHm,j,z are the data required for calculation of EFLC,j,i(j ¼ 1,2,.,8), and RAn, h3,9,n and h4,9,n are required for EFLC,9,i. Considering that all SE pathways are interlinked during their life-cycle stages, equations (2)e(7) can only be solved with iterative methods using a computer.

GHGLC;j ¼ CO2;LC;j þ 23CH4;LC;j þ 296N2 OLC;j ;

2.3.3. CH4 emissions Similarly, CH4,LC,j comprises two parts: the upstream part (CH4,up,j) and direct emission during combustion (CH4,direct).

CH4;j;noncomb ¼ CH4;j;resource =xj ;

ð15Þ (16)

where CH4,direct,m,x represents the direct CH4 emissions for SE x during sub-stage m (g/MJ); CH4,j,noncomb corresponds to indirect CH4 emissions from non-combustion sources, including spills and losses during the feedstock extraction stage (g/MJ SE j obtained); and CH4,resource symbolizes the aforementioned indirect CH4 emissions during the resource extraction stage (g/MJ resource obtained). 2.3.4. N2O emissions Similarly, N2OLC,j consists of two parts: the upstream part (N2Oup,j) and direct emission during combustion (N2Odirect).

N2 OLC;j ¼ N2 Oup;j þ N2 Odirect

N2 Oup;j ¼

4 X 9  X

  EIm;j SHm;j;x N2 Odirect;m;x þ N2 Oup;x ;

(17)

(18)

m¼1 x¼1

where N2Odirect,m,x indicates direct N2O emissions for SE x during stage m (g/MJ). 2.3.5. Data required Based on equations (9)e(18), data for each type of SE, including CC, FOR, CH4,resource and CH4,direct,m and N2Odirect,m (m ¼ 1,2,3,4), are required for calculation of GHGLC,j. 3. Data and assumptions Based on the method, two data components are required for each type of SE: (1) The energy conversion efficiency factors and share of SE directly used (h and SH) during each sub-stage, the conversion factor of resource to fuel (x), and the ratio of each pathway for electricity generation (RAn); and (2) data related to direct and indirect GHG emission factors (CC, FOR, CH4,direct, N2Odirect, and CH4,resource). Intermediate data (EI) can be calculated based on h and x, and used for the calculation of GHGLC,j. 3.1. h, SH, x and RAn Table 2 presents China-specific data for coal-, NG- and oil-based fuels and electricity. Included are efficiencies and process fuel mix in the feedstock extraction and processing and fuel production stages, plus fuel mix and average transport distances in the transportation, storage and distribution (TSD) stage. Most data are taken from energy/transport statistical yearbooks and an electricity industry annual report [36e38]. Some data are taken directly from reference [34], which is a comprehensive and detailed LCA study of the Chinese transportation sector by authors from the China Automotive Energy Research Center (CAERC) at Tsinghua University. CAERC is a university-based research center exploring sustainable transportation energy pathways for China. It is jointly supported by Tsinghua University, General Motors Corporation and

X. Li et al. / Energy 50 (2013) 15e23

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Table 2 Input data for calculation of h and SH. Item

Description

Source

1) Coal extraction and processing Coal extraction efficiency Coal processing efficiency The SE mix for coal extraction and processing

97% 97% RC (81%), electricity (15%), diesel (3%) and FN (1%)

[37] [34] [37]

2) Coal transportation Railway: 49% (642 km); waterway: 26% (650 km); road (long distance): 30% (310 km) and road (short distance): 100% (50 km) 3) NG extraction and processing Extraction efficiency SE mix for NG extraction NG processing efficiency SE mix for NG processing

96% RN (44%), RO (28%), electricity (14%), diesel (9%), FC (4%) residual oil (1%) and gasoline (1%) 94% RN (99%) and electricity (1%)

[37] [37]

Based on energy balance sheet for oil and NG extraction industry.

[34] [37]

[34]

93% RN (44%), RO (28%), electricity (14%), diesel (9%), FC (3%) residual oil (1%) and gasoline (1%)

[34] [37]

6) Oil transportation mode Sea tanker: 50% (11,000 km); rail: 30% (942 km); pipeline: 78% (440 km) and waterway: 10% (250 km)

[36]

7) Oil refinery SE mix for oil refinery

[37]

Gasoline production efficiency Diesel production efficiency Residual oil production efficiency

Based on energy balance sheet for coal industry.

[36]

4) NG transportation mode Pipeline: 100% (1500 km) 5) Oil extraction Extraction efficiency SE mix for oil extraction

Note

RO (69%), refinery still gas (10%), FC (9%), electricity (6%), RN (4%) and residual oil (2%) 89.1% 89.7% 94.0%

Based on energy balance sheet for oil and NG extraction industry.

Based on energy balance sheet for oil refinery industry.

[34] [34] [34]

8) Gasoline and diesel TSD mode Railway: 50% (900 km); pipeline: 15% (160 km); waterway: 10% (1200 km) and road (short distance): 10% (50 km)

[36]

9) Residual oil TSD mode Sea tanker: 52% (7000 km); railway: 50% (900 km); pipeline: 15% (160 km); waterway: 10% (1200 km) and road (short distance): 10% (50 km)

[34]

10) Electricity supply mix FC (73.9%), FN (5.6%), residual oil (0.8%) and many other sources (19.7%)

[38]

11) Loss ratio during transmission and distribution In 2010 6.53%

[38]

12) Power supply efficiencies Coal-based (36.4%), oil-based (32.0%), NG-based (45.9%)

[38]

Transportation mode: % share (average distance); percentages of transportation modes may add to over 100% because two or more modes are used jointly. While refinery still gas is produced onsite from crude oil inputs, no additional PE inputs are necessary for production of this byproduct, which is characterized by a CO2 emission factor of 65 g/MJ fuel and has a production energy efficiency of 100%.

the Shanghai Automotive Industry Company. CAERC has launched onsite investigations, held expert panel meetings, and conducted extensive literature reviews for creating a full and detailed picture of Chinese automotive energy issues, including LCA. Table 3 shows data on transportation energy intensity (kJ/ton km) and the fuel mix for each mode. These data, together with the lower heating values (MJ/kg) of different transportation fuels [34,36,37], are used to calculate the amount of SE directly used (EI) during feedstock transportation and fuel TSD sub-stages. All energy conversion efficiency factors are calculated and listed in Table 4, and all SE shares are listed in Tables 5 and 6. For some types of SE that do not go through certain stages during their whole-cycle production, energy conversion efficiency factors during skipped stages are treated as 100% in the calculation method.

Table 3 Transportation mode and fuel mix. Energy Source Fuel mix and percentage intensity (kJ/ton km) Sea tanker Rail Pipeline: oil

23 68 300

[34] [36] [34]

Pipeline: NG 372 Waterway 148 Road: short 1362 distance Road: long 1200 distance

Source

[34] [36] [34]

[34] [34] [34]

Residual oil (100%) Diesel (41%) and electricity (59%) Residual oil (50%) and electricity (50%) FN (99%) and electricity (1%) Residual oil (100%) Diesel (72%) and gasoline (28%)

[34]

Diesel (72%) and gasoline (28%)

[37]

[34] [34] [37]

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X. Li et al. / Energy 50 (2013) 15e23

Table 4 Results of h, x and RAn (%).

Table 6 Share of SE during power generation sub-stage.

Sub-stage

1

2

3

4

e

Pathway no.

1

2

3

4

Mixed

Item

h

h

h

x

h

RA

RC RN RO FC FN Diesel Gasoline Residual oil Coal-based electricity NG-based electricity Oil-based electricity Other pathway electricity

94.6 90.2 91.3 94.6 90.2 91.3 91.3 91.3 e e e e

100.0 e e 98.9 99.6 99.0 99.0 99.0 e e e e

100.0 e e e e 91.5 90.8 94.0 36.4 45.9 32.0 e

100.0 e e e e 95.0 95.0 97.0 36.4 45.9 32.0 e

100.0 e e e e 99.5 99.5 99.5 93.5 93.5 93.5 e

e e e e e e e e 73.9 5.6 0.8 19.7

RC RN RO FC FN Diesel Gasoline Residual oil Electricity

0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.92 0.07 0.00 0.00 0.01 0.00

Table 7 Data related to direct and indirect GHG emission.a

Note: “e” indicates that the corresponding stage is not applicable for the pathway.

3.2. Data related to GHG emissions Data related to direct (CC, FOR, CH4,direct, and N2Odirect) and indirect GHG emissions (CH4,noncomb) are shown in Table 7. The non-combustion CH4 emission value (CH4,resource) was found to be 0.009, 0.072 and 0.406 g for each MJ of crude oil, crude NG and crude coal obtained in China, respectively [39]. The CH4,noncomb values for SE are then calculated, based on equation (15).

SE

CCj

FORj

CH4,direct

N2Odirect

CH4,noncomb

Unit

g-C/MJ

e

g/MJ

g/MJ

g/MJ

RC RN RO FC FN Diesel Gasoline Resid. oil Electricity

26.35 15.30 20.00 24.74 15.70 20.20 18.90 21.10 e

0.90 0.99 0.98 0.90 0.99 0.98 0.98 0.98 e

0.001 0.001 0.002 0.001 0.001 0.004 0.080 0.002 e

0.001 0.001 0.000 0.001 0.001 0.002/0.028a 0.002 0.000 e

0.406 0.072 0.009 0.406 0.072 0.009 0.009 0.009 0.980

a

For vehicles, the utilization value is 0.002, but for others it is 0.028.

4. Results and discussion 4.1. Life-cycle results for secondary energy pathways in China Results of fossil energy intensity (EFLC and the specific PE intensities) and GHG emission intensity (GHGLC as well as the upstream GHG intensities) of the nine types of SE are listed in Table 8. Fig. 1 shows life-cycle fossil energy and GHG emission intensity for the nine types of SE, ordered from low to high in terms of energy intensity. Electricity ranks the highest because it is a high-quality SE; energy intensity of coal-based SE (including RC and FC) is lower than those of NG- and oil-based SE, but GHG intensity is higher; NG-based SE has lower energy and GHG emission intensity

than oil-based SE. If ranked by GHG intensity, the order from low to high is RN, FN, RO, gasoline, crude coal, diesel, residual oil, FC and electricity. The life-cycle intensities of these SE pathways are much larger in comparison to other fuels, such as bio-energy and renewable electricity in China [2,4,31], which have very low intensities. For these fossil fuel-based energy pathways, they consume this unit of fossil energy and emit corresponding GHGs when finally burned, and are also allocated some energy consumption and GHG emission from their upstream. However, the renewable energy pathways have some advantages, including lifetime carbon neutral bio-energy, and zero fossil energy input of renewable electricity.

Table 5 Share of SE during each sub-stage for non-electricity SE. Fuel

RC

RN

RO

Sub-stage no.

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

RC RN RO FC FN Diesel Gasoline Residual oil Electricity

0.73 0.00 0.00 0.07 0.01 0.03 0.00 0.00 0.15

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.04 0.43 0.28 0.00 0.00 0.09 0.01 0.01 0.14

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.04 0.43 0.28 0.00 0.00 0.09 0.01 0.01 0.14

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.73 0.00 0.00 0.07 0.01 0.03 0.00 0.00 0.15

0.00 0.00 0.00 0.00 0.00 0.78 0.08 0.08 0.06

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Fuel

FN

Sub-stage no.

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

RC RN RO FC FN Diesel Gasoline Residual oil Electricity

0.04 0.43 0.28 0.00 0.00 0.09 0.01 0.01 0.14

0.00 0.00 0.00 0.00 0.99 0.00 0.00 0.00 0.01

0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.50

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.04 0.43 0.28 0.00 0.00 0.09 0.01 0.01 0.14

0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.72 0.25

0.06 0.00 0.79 0.03 0.04 0.00 0.00 0.02 0.06

0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.73 0.17

0.04 0.43 0.28 0.00 0.00 0.09 0.01 0.01 0.14

0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.72 0.25

0.06 0.00 0.79 0.03 0.04 0.00 0.00 0.02 0.06

0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.73 0.17

0.04 0.43 0.28 0.00 0.00 0.09 0.01 0.01 0.14

0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.72 0.25

0.06 0.00 0.79 0.03 0.04 0.00 0.00 0.02 0.06

0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.83 0.10

Diesel

FC

Gasoline

Residual oil

X. Li et al. / Energy 50 (2013) 15e23

21

Table 8 Fossil energy intensity and GHG emission intensity results. Item

EFLC

EFLC,Coal

EFLC,NG

EFLC,Petrol

GHGLC

CO2,up

CH4,up

N2Oup

Unit

MJ/MJ

MJ/MJ

MJ/MJ

MJ/MJ

gCO2,e/MJ

G/MJ

G/MJ

Mg/MJ

RC RN RO FC FN Diesel Gasoline Residual oil Electricity

1.073 1.146 1.128 1.089 1.151 1.271 1.282 1.233 2.548

1.069 0.044 0.038 1.071 0.044 0.074 0.076 0.064 2.303

0.002 1.054 0.047 0.003 1.058 0.059 0.060 0.055 0.180

0.003 0.049 1.043 0.015 0.049 1.139 1.146 1.113 0.065

103.5 68.3 81.6 99.3 70.0 101.6 91.7 93.5 226.4

6.155 10.12 8.876 7.250 10.40 19.43 20.18 16.59 203.6

0.434 0.094 0.029 0.435 0.095 0.045 0.046 0.041 0.951

0.129 0.413 0.362 0.394 0.418 0.481 0.487 0.445 3.228

energy intensities in the Chinese transportation sector, which are higher than corresponding U.S. values [22]; and (4) China’s coal-dominated energy mix [46] and high CH4 emissions associated with coal mining, crude oil and NG exploration stages also produce higher GHG emissions. As Table 10 shows, GHG intensity for electricity generation in China is also the worst in the world [5,11,13]. The reason is that coal-based electricity dominates electricity generation and supply in China, with a larger contribution (greater than 70%) [37,38] than others (0e33.6%) [11,13]. Relatively lowcarbon pathways contribute only about 20%, including hydropower (about 16%) and nuclear (about 2%) [37,38]. 4.3. Explanations for our relatively high results Fig. 1. Fossil energy intensity and GHG emission intensity of SE in China.

4.2. Comparative studies: oil-based fuel and electricity pathways The intensity results here are very different from other studies, especially for oil-based fuel and electricity pathways. As Table 9 shows, the FPEC intensities for oil-based fuels in China [6,40e42] are close to the worst levels worldwide [43e45]. Among studies specific to China, the current work reports the most pessimistic GHG emission figures. Differences between results for China and other countries are largely attributed to: (1) Low efficiencies of feedstock extraction/ processing. For example, crude oil extraction efficiency in China is only 93.0%, whereas this value is 98% in the US as used in the GREET model [22]; (2) Relatively high energy consumption in the process of fuel production in China [37,41], particularly for widely used steam boilers largely fueled by coal, which have an efficiency level of 80% compared with the global average of 90% [34]; (3) High

We found that life-cycle energy use and GHG intensities of dominant SE in China are mostly higher than those in the developed world (even the world average). The primary reasons include relatively low energy efficiency and high CH4 leakage levels for feedstock extraction. As an example, the GHG intensity of electricity generation in China is the worst globally, because coal-based electricity dominates national electricity generation and supply, with a large contribution (above 70%). 4.4. Further utilization of our results The present results of life-cycle FPEC and GHG emission intensities of SE pathways in China lay a solid foundation for related research in the future. For example, the inventory of intensities may be used as multipliers to calculate upstream and life-cycle fossil energy consumption and GHG emission when the specific SE use mix is known. The life-cycle calculation result is the sum of multiplying the intensity data by corresponding specific SE use.

Table 9 Oil-based SE energy intensity and carbon intensity results of different studies. SE

Data source

Region

Petroleum intensity (MJ/MJ)

Energy intensity (MJ/MJ)

Gasoline Gasoline Gasoline Gasoline Gasoline Gasoline Gasoline Diesel Diesel Diesel Diesel

This study Huang and Zhang [6] Hu et al. [40] Shen et al. [41] Hekkert et al. [44] Granovskii et al. [43] Campanari et al. [42] This study Hu et al. [6] Shen et al. [41] Hekkert et al. [44]

China China China China World Canada Italy China China China World

1.15

1.28 1.27 1.31 1.25 1.09/Best, 1.14/probable, 1.23/worst 1.18 1.25 1.27 1.26 1.22 1.04/Best, 1.05/probable, 1.12/worst

1.14

GHG intensity (g CO2,e/MJ) 92

83 102 95

22

X. Li et al. / Energy 50 (2013) 15e23

Table 10 Life-cycle CO2 emission results for electricity generation and supply in various countries. Country Intensity (g CO2,e/kWh) Source The year of intensity

China 815 This study 2010

National-grid electricity mix (%) Coal-based 73.9 Oil-based 0.8 NG-based 5.6 Others 19.7

Singapore 618 [11] 2007

0 21.9 75.8 2.3

Mexico 571 [13] 2006

14.0 21.1 42.6 22.3

UK 597 [13] 2004

33.6 1.1 42.1 23.2

Portugal 611 [13] 2004

33.0 12.7 26.0 28.3

Italy 634 [13] 2004

15.1 16.1 47.6 21.2

Acknowledgments The project was co-supported by the China National Natural Science Foundation (Grant Nos. 71041028, 71103109 and 71073095), China National Social Science Foundation (09&ZD029), MOE Project of Key Research Institute of Humanities and Social Sciences at Universities in China (2009JJD790029), and the CAERC program (Tsinghua/GM/SAIC-China). The authors would like to thank the reviewers, Dr. Michael Wang of the Argonne National Laboratory (Lemont, IL, USA), Dr. Hong Huo of Tsinghua University (Beijing, China), and Dr. Xiaoyu Yan of Cambridge University (Cambridge, UK) for their generous help.

Source: Tan et al. [11] and Santoyo-Castelazo et al. [13].

4.5. Future research plan As mentioned in the introduction section, a strategic transition to low-carbon energy is considered essential for China, and certain measures could be introduced to help key sectors improve their overall energy efficiency and reduce total GHG emissions from a life-cycle perspective. The model and results obtained here can be extended as a solid fundamental platform for overall energy systems in China, which serves to interlink energy pathways and avoid the disadvantage of the simple calculation of process-based LCA. In the future, different LCA approaches will be harmonized for key sectors, including building [16], transport [30], and agricultural, industrial and residential sectors. Analysis and assessment of Chinese energy supply and utilization can be done via real life-cycle analysis approaches that consider both full life stages and indirect consumption and emission implications owing to direct consumption and emission in the cycle methods. 5. Concluding remarks Using the computerized iterative-calculation model, we captured the interlinking relationship of energy pathways and attained a life-cycle FPEC and GHG intensity inventory of dominant SE pathways in China. Upstream or indirect energy use and GHG emission impacts were analyzed and presented in detail, to describe those embodied energy and emissions for SE pathways in China. The inventory derived here can fulfill dual applications in the real context of China. The data can be used as factors for specific LCA research by multiplying their corresponding process energy use amounts to arrive at final results, and as a unified platform for sectoral life-cycle energy use and GHG emission analysis. Comparisons showed that life-cycle energy use and GHG intensities of dominant SE in China are mostly greater than those in some other countries (even the world average). The main reasons for this include relatively low energy efficiency, high CH4 leakage levels in feedstock extraction, and the dominance of coal in the energy mix of China. The Chinese-specific structure and data, system boundary consistency, and calculation completeness in this study will benefit accurate and feasible micro- and macro-level LCA study in China, through application of the life-cycle intensities inventory derived here: 1) For future micro-level life-cycle energy use and GHG emissions across a wide range of energy-pathway LCA in China, there will be obvious improvement in accuracies of specific results and their comparisons; and 2) For future macro-level sector LC energy use and GHG emission calculation in China, results calculated based on a unified platform will be more meaningful and beneficial for discovering key sectors or stages in which overall energy efficiency can be improved and total GHG emissions reduced, from a life-cycle perspective.

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