Life cycle carbon emission flow analysis for electricity supply system: A case study of China

Energy Policy 61 (2013) 1276–1284

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Life cycle carbon emission flow analysis for electricity supply system: A case study of China G. Chen a, B. Chen b, H. Zhou a,n, P. Dai a a b

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China School of Environment, Beijing Normal University, Beijing 100875, China

H I G H L I G H T S


Hybrid model of LCA and carbon emission flow analysis is established.
Power supply system of China is abstracted as topological network.
Half of the carbon emission flow is carried by fuel transportation system.

art ic l e i nf o

a b s t r a c t

Article history: Received 25 November 2012 Accepted 29 May 2013 Available online 11 July 2013

The carbon emission embodied in trade is fundamental for allocation of responsibility between producers and consumers. This paper quantitatively analyzes embodied carbon emissions along the life cycle of electricity supply, based on network theory. A modified carbon emission flow model is established, based on life cycle assessment considering power losses. There is also a case study of China's interregional electricity supply system in 2010, focusing on two carbon emission carriers, electricity coal transportation and electricity transmission. Results show that the total carbon emission flow reached 169.355 MtCO2eq, i.e., 4.67% of the life cycle carbon emission. Of this, 61.1% was carried by electricity coal transportation before power generation and transmission, owing to an uneven distribution of coal resources. The eastern and southern regions are the major net sinks of carbon emission flows, representing 52.9% and 27.8% of the total, respectively, because of their enormous energy imports. In contrast, the Sanxi region and central China are major net sources of carbon emission flow. The proposed model may help allocate environmental responsibility among different regions, to guarantee balanced trans-regional development. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Life cycle Carbon emission flow Electricity supply chain

1. Introduction Carbon emissions embodied in trade have been attracting greater research interest, owing to their relevance to allocation of responsibility between producers and consumers (Munksgaard and Pedersen, 2001; Peters and Hertwich, 2007; Lenzen et al., 2007; Atkinson et al., 2011). The flow of embodied carbon emission transferred along trading paths can also be termed virtual carbon emission flow (Gavrilova et al., 2010; Atkinson et al., 2011), which is different from actual carbon flow studies, e.g., Orthofer et al. (2000) and Uihlein et al. (2006). The input–output (IO) framework has been widely used to account carbon emissions embodied in international trade, including the single-region input–output (SRIO) model (e.g., Li and

n

Corresponding author. Tel: +86 13 06775 7788. E-mail address: [email protected] (H. Zhou).

0301-4215/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2013.05.123

Hewitt, 2008; Guan et al., 2008; Alcántara and Padilla, 2009) and multi-regional input–output (MRIO) model (e.g., Liang et al., 2007; Tukker et al., 2009; Davis et al., 2011; Atkinson et al., 2011; Guo et al., 2012). Various IO-based methods to allocate responsibility of carbon emission along the supply chain have been developed (e.g., Rodrigues et al., 2006; Lenzen et al., 2007; Andrew and Forgie, 2008; Peters, 2008; Cadarso et al., 2012). In addition, the Product Land Use Matrix (PLUM) was used in several studies (Moran et al., 2009; Wiedmann, 2009a). Like international trade, interregional electricity transmission plays a critical role in carbon emission analysis and policy making, and there have been several studies in this area. Marriott and Matthews (2005) and Jiusto (2006) demonstrated clear effects of interstate electricity trading in the USA on carbon emission accounting and on designing production and/or consumption-side reduction policies. Soimakallio and Saikku (2012) evaluated the productionbased and consumption-based CO2 emission intensities of electricity for Organisation for Economic Co-operation and Development

G. Chen et al. / Energy Policy 61 (2013) 1276–1284

(OECD) countries, finding significant differences between the two types of CO2 emission intensities in some European OECD countries. Recently, Lindner et al. (2013) calculated large differences of production-based and consumption-based CO2 emissions for different Chinese provinces, and discussed the implications for mitigation policy making. Compared to ordinary commodity trade systems, it is more complicated to analyze embodied carbon emission flow in an electric power system, since electricity from different sources becomes mixed when transmitted in one electrical bus, making it impossible to identify sources and their contribution to the electricity that one demand has consumed. From the perspective of consumption, Kang et al. (2012) applied network theory to spatial distribution evaluation of carbon emission flows embodied in China's electric power system. One benefit of this application is that all types of connecting systems between producers and consumers, including electric power networks, roads, railways, shipping lines, pipelines, and others, can be abstracted as topological networks that transfer environmental responsibilities from producers to consumers; thus, quantitative evaluation results can be obtained from the network model in the context of graphic theory. There is carbon emission flow not only in the electricity transmission stage, but also in other life stages of electricity supply. For example, fuels like coal, petroleum and natural gas are often produced far from power plants, resulting in large-scale fuel transportation. Carbon emission flow is therefore induced concurrent with fuel flow. To make carbon emission analysis more integrated, all life cycle carbon emissions generated along the production and distribution chain of electricity should be covered (Wiedmann, 2009b). The main purpose of this paper is to extend the scope of carbon emission flow analysis in view of life cycle assessment (LCA), so as to picture the migration map of carbon emission responsibility along the electricity supply chain more integrally, on both temporal and spatial scales. We also perform a case study to quantify life cycle carbon emission flows concurrent with electricity coal flows and electricity flows in China. In this case study, the indicator “carbon emission” measures the global warming potential (GWP) by the mass of the combined CO2equivalent emissions. The considered emissions include CO2, N2O, CH4, SF6, HFCs and PFCs, which are the six types of greenhouse gas targeted by the Kyoto Protocol. The remainder of the paper is organized as follows. Section 2 introduces the proposed model and calculation method. The background of China's electricity supply system is summarized briefly in Section 3. Section 4 lists data sources. Results of the case study are presented and discussed in Section 5, and conclusions are given in Section 6.

2. Method The proposed method, namely life cycle carbon emission flow analysis, quantitatively evaluates spatial carbon emission flows in various life stages in the electricity supply chain. The analysis is consumption-based, i.e., all responsibility of life cycle carbon emissions is transferred to consumers, following the supply chain (Fig. 1). The approach may be regarded as a modified LCA method, in which interregional trading activities are individually analyzed to reveal connections of different regions for carbon emission. Production-based analysis can be carried out without considering interregional transmissions, and analysis based on shared responsibility can be developed by referring to methods of previous studies (e.g., Munksgaard and Pedersen, 2001; Lenzen et al., 2007). As an example, only carbon emission flows concurrent with fuel transportation and electricity transmission are addressed here, given their overwhelming proportions of the total amount and large spatial distributions. Both fuel transportation and

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Fig. 1. Schematic representation of electricity life cycle.

Fig. 2. Abstraction of flow network of concern.

electricity transmission systems can be abstracted as topological networks composed of nodes and branches (Fig. 2). Each node in this figure represents a region. In practical analysis, a node can also be a state, province or bus in the electric power system. The smaller the representation of the node, the more detailed the evaluation can be. A calculation method of carbon emission flow in the electric power network is developed as follows, based on the method of Kang et al. (2012). Carbon emission flows embodied in fuel transportation and other independent commodity flows can also be analyzed by the proposed method or MRIO model. Suppose there is an electric power system with K power plants, J substations and L loads that can be abstracted as a topological network composed of N nodes and M branches. From the perspective of electricity supply, there are three different roles that one node can play, solely or together. These are supplier (electricity generation), hub (electricity transfer), and consumer (electricity consumption). In carbon emission flow analysis, a node can be regarded solely or together as a source of carbon emission flow that supplies energy to other nodes and simultaneously generates carbon emission, or a sink that receives energy from other nodes and generates carbon emission. A branch that connects different nodes is an energy and carbon emission flow transmitter and, if emission is produced, also a source. In a carbon emission source, the life cycle carbon emission generated from a power plant or substation is set for injection into the node where it is connected, and that from a transmission line is set for injection into the node from which energy is transmitted (Figs. 3 and 4, respectively).

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It is assumed that the life cycle carbon emission of a power plant consists of two parts, one directly related to the amount of electricity and the other nearly unrelated. Carbon emission flow injected by power plant k (k¼ 1, 2,…, K) when it generates EG,k electricity within ΔT time is

using ζBI,m and ζBO,m to represent the in and out carbon emission flow intensity of branch m, and assuming that all carbon emissions are driven by consumers, the emission flow in a branch is constant even with power loss. Then we have

R0 ΔT υG;k ¼ γ G;k EG;k þ G;k T G;k

As shown in Fig. 4, carbon emission flux and energy flux are used to calculate carbon flow. The carbon emission flux through node p (p ¼ 1, 2,…, N) is defined as the total amount of carbon emission flow injected into it:

ð1Þ

where γG,k is carbon emission intensity of power plant k, meaning the equivalent carbon emission amount when generating 1 unit of electricity; R0G;k is life cycle carbon emission of power plant k unrelated to the amount of electricity generation; TG,k is the lifetime of power plant k. Thus, the carbon emission intensity ζG,k of power plant k can be represented as ζ G;k ¼

R0 ΔT υG;k ¼ G;k þ γ G;k EG;k T G;k EG;k

ð2Þ

Life cycle carbon emissions of transmission lines and substations are nearly unrelated to the amount of transmitted electricity. Carbon emission flows injected by transmission line m (m¼1, 2, …, M) and substation j (j¼ 1, 2,…, J) are calculated as RB;m ΔT υB;m ¼ T B;m RS;j ΔT υS;j ¼ T S;j

ð3Þ

Φp ¼

∑

ð5Þ

Ψ p ¼ ∑ EG;k − k∈p

∑

∀n-spn ¼ −1

spn υn þ ∑ υG;k þ υS;p ;

ð7Þ

k∈p

∑

∀n-spn ¼ −1

spn EBO;n

ð8Þ

The carbon emission flux and energy flux flowing in and out of node p should be, respectively, balanced as follows:

∑

∀m-spm ¼ 1

spm υm þ ∑ υD;l ¼ Φp

ð9Þ

l∈p

spm EBI;m þ ∑ ED;l ¼ Ψ p

ð10Þ

l∈p

Nodal carbon intensity can be calculated as the ratio of carbon emission flux to energy flux: ep ¼

Φp Ψp

ð11Þ

Electricity becomes mixed when converging on one node, so the carbon emission flow intensities of branches and loads flowing out of the node would be the same as ζ D;l ¼ ζ BI;m ; ∀m; l∈p;

ð12Þ

where ζ D;l represents carbon emission flow intensity for load l connected to node p, ζ D;l ¼ υD;l =ED;l . From Eqs. (9)–(12), we obtain ζ D;l ¼ ζ BI;m ¼

Fig. 3. Schematic diagram of carbon emission flow through branch m. υB,m and υm represent carbon emission flows injected and transmitted by branch m, respectively.

ð6Þ

where k∈p indicates that power plant k is connected with node p and ∀m-spm ¼1 represents the set {spm|spm ¼ 1, m ¼1, 2, …,M}. spm is an element of incidence matrix S between nodes and branches. If branch m connects node p, spm ¼1/−1 when energy flows from/to node p; else, spm ¼0. The energy flux through node p (p ¼1, 2,…, N) is defined as the total energy flow injected into it:

∑

ð4Þ

spm υB;m −

∀m-spm ¼ 1

∀m-spm ¼ 1

where RB,m and TB,m are life cycle carbon emission and lifetime of branch m, respectively; RS,j and TS,j are life cycle carbon emission and lifetime of substation j, respectively. Carbon emission flow intensity of branch m can be defined as the ratio of carbon emission flow to energy flow in it. Usually there is a power loss ΔEB,m when branch m transmits electric power. Letting EBI,m and EBO,m represent the energy flow flowing into (before loss) and out of (after loss) branch k, respectively, which can be attained by load flow calculation (Kundur, 1993), one obtains EBO;m ¼ EBI;m −ΔEB;m

ζ BO;m EBO;m ¼ ζ BI;m EBI;m ¼ υm

Φp ¼ ep ; ∀m; l∈p Ψp

ð13Þ

Eq. (13) shows that carbon emission flow intensities of branches and loads flowing out of one node are equal to its nodal carbon intensity. The carbon emission flow of branch m can be calculated as υm ¼ EBI;m ζ BI;m ¼ EBI;m ep ; m∈p

ð14Þ

Eqs. (11) and (14) can be rewritten in matrix form as Eqs. (15) and (16): −1

^ ðYυ þ Q υG þ XυB þ RυS Þ e¼Ψ

ð15Þ

υ ¼ E^ BI XT e;

ð16Þ

where (\widehat) represents the diagonalization of a vector; Q and R are incidence matrices of node–to- power plant and node-tosubstation, respectively. X ¼{xij}N  M, and if sij ¼1, xij ¼1; else xij ¼ 0. Y¼{yij}N  M, and if sij ¼–1, yij ¼1; else yij ¼0. Consequently, the nodal carbon intensity is −1

Fig. 4. Schematic diagram of active power flux and carbon emission flux. Note: υD,l and ED,l represent carbon emission flow and energy absorbed by load l (l¼ 1, 2,…, L).

−1

^ Y E^ BI XT Þ−1 Ψ ^ ðQ υG þ XυB þ RυS Þ e ¼ ðI−Ψ

ð17Þ

Then, the carbon emission flow vector υ can be calculated by Eq. (16).

G. Chen et al. / Energy Policy 61 (2013) 1276–1284

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3. China's electricity supply system Expansion of the electric power industry has contributed greatly to rapid growth of the Chinese economy since the reform and opening-up policy in 1978. From 1980 to 2009, Chinese electricity consumption increased by approximately 1127%, ranking first in the world in 2010 (Lin et al., 2012; Zhang et al., 2012). In the next two decades, the economy is expected to maintain a high growth rate (Guan et al., 2008; IEA, 2009), requiring substantial expansions in electric power generation capacity. Given characteristics of primary energy structure and historical reasons, electric power generation in the country has been dependent on coal for many years. Currently, nearly 80% of electricity is generated from coal, and such dependency may persist for many years before sufficient cleaner power plants are constructed (Li, 2010). Most electricity demand is met by intraregional power generation. However, coal as the main primary energy resource for electricity generation is mainly in western Inner Mongolia, Shanxi, Shaanxi, east Ningxia (also known as the Sanxi region) and Guizhou. A considerable number of coal-fired power plants are in the eastern and southern coastal regions near demand centers, but far from coal mining areas. For instance, Liaoning, Hebei, Beijing, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian and Guangdong represent about 47% of total coal-fired generation capacity, but possess only 8% of all coal resources (Liu, 2012). Consequently, massive amounts of electricity coal must be continuously transported to load centers of coastal provinces in the southeast over long distances, forming strong coal flows across the country (Mou and Li, 2012). According to statistical data of the National Bureau of Statistics of China (NBSC, 2011a), the average distance of coal transport from coalmines to locations of combustion was 642 km. In 2010, nearly 1.2 billion tons of coal were transported out of Sanxi, while nearly 0.6 billion tons was transported into North, Northeast and Central China, about half of which was consumed to generate electricity (Liu, 2012). There are three principal means of transport, highway, railway and shipping. Among these, railways are dominant, with greater than 60% of total transport. Shipping is an important means of coal transport for coastal provinces in the south and east of China, including Jiangsu, Shanghai, Zhejiang, Fujian and Guangdong. Highways represent an auxiliary path for railway and shipping, transporting electricity coal mostly over short distances. Largescale coal flows in the country have supported rapid social and economic development mainly in the east and south, and these flows will persist years in the future. The detailed transport map of electricity coal contains several major and many minor paths. Here, we evaluate life cycle carbon emission flows concurrent with the principal electricity coal flows (Fig. 5). Because of transportation capacity limits and geographic and climatic constraints, there is frequent electricity coal shortage in the central and east regions, especially with much greater electric power demands in summer and winter. As another means to deliver coal energy, electricity transmission has received increased attention in recent years. This is because of frequent power shortages and progress in long-distance and large-capacity transmission technologies, such as AC and DC extra- and ultra-highvoltage transmission. There are presently several connecting transmission lines between the six main regions in China, which are important for balancing regional power supply and demand. The major interregional electricity flows are shown in Fig. 6. The interregional connections have made the national electric power system one of the most complex power networks in the world. However, the interconnections are relatively weak. Energy transmitted by interregional transmission lines in the form of electricity is much less than that by railway and shipping in the form of coal, because of transmission capacity limits. In 2009, the

Fig. 5. Schematic diagram of coal flows in China.

Fig. 6. Schematic diagram of electric power flows in China.

ratio of coal power transmitted in the form of coal to that of electricity was 20:1, indicating that great capacity expansion of interregional electricity transmission is urgent in the coming years (Liu, 2012).

4. Data sources and equivalent models In this case study, the indicator “carbon emission” measures global warming potential (GWP) by the mass of combined CO2equivalent emissions. The considered emissions include CO2, N2O, CH4, SF6, HFCs and PFCs, which are the six types of greenhouse gases targeted by the Kyoto Protocol. Their corresponding equivalent contribution factors relative to CO2 are from IPCC (2006; 2007). The system framework and boundary to evaluate the life cycle carbon emission of electricity mixes suggested by Frischknecht et al. (2007) and Dones et al. (2007) are used as reference here. For convenience, the electricity supply chain is divided into two parts, and some processes are omitted because of their small contributions to total carbon emission. The first part covers the processes of electricity coal mining and transport, and forms the carbon emission flow concurrent with coal transport. The second part covers other processes related to power generation, transmission and distribution, including the mining and production of raw materials such as limestone, crude oil, natural gas and others. Most calculation is based on emission inventories of unit processes provided by China life cycle and ecoinvent databases. In the power generation process, four types of generation are considered, thermal, hydro, nuclear and wind. Other types of generation, e.g.,

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geothermal, tidal and solar power, are omitted due to their small quantities (Table 1). Only centrally dispatched electricity is considered and international transactions are excluded. Emission data of thermal power generation in various regions are calculated according to fuel consumption data (Table 2). Emissions from construction of thermal power plants and electric networks are included, but the disposal process is omitted. Also, in-station power consumption of power generation and loss in power transmission and distribution are considered (Table 3). SF6 emission from gas-insulated switchgears in electric networks is estimated using emission data from the ecoinvent database. Emission factors data of processes in the electricity supply chain are listed in Table 4. Most of the inventory data are from the Chinese life cycle database (CLCD) (IKE, 2012). Major electricity coal flows and electricity flows across China are depicted in Fig. 7. The interregional power flows changed their directions several times in 2010. The power flow volumes shown in the figure are net flows, i.e., algebraic sums. Average distances of various coal transportation paths are calculated according to statistical data and listed in Table 5. It should be noted that electricity coal transported in the coal flows is only part of all coal used in thermal power generation. Carbon emission from the transportation process of this part is calculated according to the volume and distance data in Fig. 7 and Table 5. Carbon emission caused by transporting the remaining electricity coal is calculated according to the average transportation distance provided in the CLCD. The power loss rate on interregional connecting transmission lines is assumed to be 2% according to historical record, and life cycle carbon emissions of these lines are omitted owing to their small quantities.

The electric power system is modeled in Fig. 8, in which every region is regarded as one composed of a power source node that produces electric power, a hub node that receives and sends power, a demand node that represents intraregional terminal consumers, and a transmission and distribution system that delivers electric power from the hub to the demand.

5. Results and discussion Life cycle carbon emission and flow analysis results are depicted in Table 6 and Fig. 9. It is seen that thermal power generates the overwhelming majority of carbon emission. Carbon emission caused by other power generation and power networks only comprises 0.57% of the total emission. Most of the injected life cycle carbon emission flow of electricity goes to intraregional loads rather than flows out to other regions, since most of the electricity is consumed locally. Nevertheless, 4.67% of the entire carbon emission forms interregional carbon emission flows. The total interregional carbon emission flow embodied in electricity flows is 65.958 MtCO2eq, and that in electricity coal flows is 103.397 MtCO2eq. This means that 61.1% of carbon emission flow exits prior to the electricity generation and transmission stage in the electricity supply chain. Sanxi and the central regions are the main net sources of interregional carbon emission flow, with exports of 79.061 MtCO2eq and 31.863 MtCO2eq, respectively. As different types of energy exporters, Sanxi injects carbon emission flow mainly by exporting electricity coal, while Central China does so by exporting electricity. On the contrary, the east and south regions are the main net sinks of carbon emission flow, because of their large energy imports.

Table 1 Electric power generation data 108 kW h (CEC, 2011). Type

North

Northeast

East

Central

Northwest

South

Total

Thermal Hydro Nuclear Wind Others Total

10396 70 0 266.1 0.04 10732.24

2494 182 0 113.3 0 2789.30

8646 729 414 41.9 0.46 9831.36

5127 3089 0 4.1 0.01 8220.11

2769 822 0 43.9 0.03 3634.93

4733 1961 334 16.5 0 7044.50

34165 6853 748 485.8 0.54 42252.44

Table 2 Data of the principal fuels consumed in thermal power generation (NBSC, 2011b). Fuel

North

Northeast

East

Central

Northwest

South

Total

Raw coal (Mt) Washed coal (Mt) Briquette (Mt) Coal gangue (Mt) Coke oven gas (Gm3) Blast furnace gas (Gm3) Converter gas (Gm3) Refinery gas (Gm3) Diesel oil (Mt) Fuel oil (Mt) LNG (Mt) Natural gas (Gm3)

489.03 14.93 0.44 38.72 5.57 62.15 0.86 0.59 0.06 0.04 0.00 2.34

128.83 9.26 0.01 3.98 1.09 10.43 0.16 0.73 0.02 0.09 0.00 0.40

328.89 15.34 0.00 2.93 1.72 31.16 1.90 4.43 0.07 0.23 0.03 6.85

235.77 7.99 0.02 6.58 0.54 29.21 0.61 0.38 0.35 0.05 0.00 2.59

135.17 0.62 0.00 5.59 0.36 2.76 0.03 1.22 0.02 0.01 0.00 1.22

207.83 0.52 1.79 3.91 0.81 10.05 0.64 0.06 0.08 0.83 1.65 4.17

1525.53 48.66 2.26 61.72 10.08 145.75 4.19 7.40 0.60 1.26 1.68 17.56

Table 3 In-station power consumption rate and power loss rate, % (CEPYEC, 2011). Item

North

Northeast

East

Central

Northwest

South

In-station consumption rate of thermal power In-station consumption rate of hydropower Loss rate of power transmission and distribution

7.28 0.56 5.61

7.21 0.85 7.02

5.26 0.31 6.42

5.96 0.28 7.24

5.89 0.70 5.65

6.37 0.42 6.20

G. Chen et al. / Energy Policy 61 (2013) 1276–1284

Table 4 Emission factors of key processes.

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Table 5 Average distances of electricity coal transportation.

Process

Carbon emission factor

Unit

Source

Mining of raw coal Coal washing Production of coke oven gas Production of blast furnace gas Production of converter gas Production of refinery gas Production of diesel oil Production of fuel oil Mining of natural gas Shipping transportation

0.164 0.299 0.323 0.060 3.197 0.465 0.889 0.744 0.564 0.429

CLCD CLCD CLCD CLCD CLCD CLCD CLCD CLCD CLCD CLCD

Railway transportation

0.011

Highway transportation

0.125

Construction of thermal power plant Construction of hydropower plant Construction of electric network Mining of limestone Hydropower generation

0.154

kgCO2eq/kg kgCO2eq/kg kgCO2eq/m3 kgCO2eq/m3 kgCO2eq/m3 kgCO2eq/m3 kgCO2eq/kg kgCO2eq/kg kgCO2eq/m3 kgCO2eq/ (t  km) kgCO2eq/ (t  km) kgCO2eq/ (t  km) MtCO2eq/ item kgCO2eq/ MW h kgCO2eq/ MW h kgCO2eq/t kgCO2eq/ MW h kgCO2eq/ MW h kgCO2eq/ kW h kgCO2eq/ MW h

3.994 1.452 4.116 7.150

Electricity from nuclear power 7.790 Electricity from wind power

0.011

SF6 emission of electric networks

1.719

Path

Average distance (km)

Type

Sanxi–North Sanxi–Northeast Sanxi–East Sanxi–Central North–East North–South Guizhou–South

695 995 1150 1000 1212 2424 1200

Railway Railway Railway Railway Shipping Shipping Railway

CLCD CLCD Ecoinvent CLCD CLCD CLCD CLCD Ecoinvent Ecoinvent

Fig. 8. Schematic of interregional electric power network of China.

Ecoinvent

intensity will increase if a region imports energy from other regions, and vice versa. But in this case, there are several exceptions. For example, the northeast region imports 0.251 EJ energy by importing 12 Mt electricity coal and exporting 8.816 TW h of electricity, but its carbon emission intensity slightly decreases. There is a similar result in the central region, where the net energy import is positive but carbon emission intensity declines. However, such phenomena are easy to understand because most of the life cycle carbon emission along the electricity supply chain is generated in the power generation stage, specifically the thermal power generation stage. Thus, the carbon emission flow concurrent with electricity is much more intensive than that with electricity coal. Importing 1 GJ in the form of electricity coal from the source produces about 8.4–13.3 kgCO2eq of carbon emission flow, whereas the amount reaches at least 198.39 kgCO2eq in the form of electricity. The present results for carbon emission flow in the electric power system support the findings of Meng et al. (2011) and Lindner et al. (2013) that interregional electricity transmission enables regions of coastal load centers to shift part of their carbon emissions to the northern and central regions. Also, as revealed by Lindner et al. (2013), carbon intensity embodied in an electricity flow is largely impacted by the type of electricity production mix in the exporting region, and such a mix depends on the abundance of local energy resources. For the east region, importing the same amount of electricity from the north region will generate more carbon emission than from the central region, because the proportion of thermal power in the north is much higher owing to its rich coal resources. To the contrary, the central region is rich in waterpower resources and generated nearly half the hydropower in China in 2010. It is estimated that total electricity consumption will increase to 8.6 PW h and 11.8 PW h in 2020 and 2030, respectively, most of which will concentrate in coastal and central regions (Liu, 2012). There is no doubt that the government would like to develop the hydropower industry as a priority, since that power is very low in cost and much cleaner than thermal power for carbon emission. Many hydropower stations with total capacity reaching 120 GW

Fig. 7. Major interregional electricity coal and electric power flows in China (NBSC, 2011b; YBHCTC, 2011; CEC, 2011).

About 52.9% of all the life cycle carbon emission flow goes to the east region and 27.8% to the south. As a hub of energy, the north region is also a hub of carbon emission flow in the entire network. The total life cycle carbon emission flow injected by the north is only 12.634 MtCO2eq, but the carbon emission flow that passes through it reaches 55.532 MtCO2eq. Fig. 10 shows differences of production-based (neglecting carbon emission flows) and consumption-based (considering those flows) life cycle carbon emission intensities allocated to electricity consumption. Generally, when considering the effect of interregional trade, it is believed that local carbon emission

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Table 6 Results of life cycle carbon emission flow analysis, unit: MtCO2eq. Carbon emission flow

North

Northeast

East

Central

Northwest

South

Total

Caused by thermal power Caused by hydropower Caused by nuclear power Caused by wind power Caused by network construction SF6 emission equivalent Imported from coal flow Imported from power flow Transferred to demand

1140.063 0.078 0.000 0.298 1.354 1.605 23.172 −10.538 1156.032

301.831 0.203 0.000 0.127 0.340 0.403 2.099 −10.495 294.508

742.210 0.812 0.323 0.047 1.349 1.599 40.708 48.902 835.949

549.717 3.441 0.000 0.005 0.998 1.183 7.350 −31.863 530.832

306.404 0.916 0.000 0.049 0.454 0.539 0.000 −13.063 295.299

464.109 2.185 0.260 0.018 0.948 1.124 30.068 17.057 515.768

3504.334 7.634 0.583 0.544 5.443 6.453 103.397 0.000 3628.388

Note: Carbon emission caused by thermal power covers all processes of its generation listed in Section 4, except mining and transport of the coal in coal flows. A negative value indicates that a region exports carbon emission flow.

Fig. 9. Major life cycle carbon emission flows.

Fig. 10. Comparison of carbon emission intensities.

are being constructed as a part of the Twelfth Five-Year Plan. However, only expanding hydropower capacity cannot meet the rapid growth of power demand. Increases of other power sources, including thermal power, nuclear power, wind power and other renewable energies are necessary. A major obstacle to intensively exploiting hydro, wind, solar and other renewable energies is that most resource-rich areas are distant from load centers. For example, hydropower resources are mostly in the southwest, and wind and solar power resources are mostly in the northern, northwestern, and northeastern regions. These renewable energies must be transformed into electricity first and then transmitted to distant load centers. Nuclear power stations can be located flexibly in or near the load centers, but any increase of installed capacity is strongly limited by security concerns. Chen et al. (2011) showed that thermal power capacity will continue to

increase greatly with increased application of cleaner technologies, such as supercritical, ultra-supercritical, carbon capture and storage power generation systems. There are two options for the government to construct more thermal power stations in or near load centers or distant from them. It appears that the government favors the latter option. Many large-scale thermal power bases are planned for construction in the main coal-mining regions, so that the coal can be used more intensively for power generation. Medium and small thermal power stations in load centers that were urgently built to meet the excessively rapid growth of power demand will be shut down because of their low efficiencies and high emissions. In 2020, the transmission capacity of generated thermal power will rise to 286 GW after the more tightly connected AC electric power network is constructed. Zhu et al. (2005) pointed out that such interregional electricity transmission can bring obvious benefits to the environment and human health of coastal load centers, where the economy is more developed and the population more concentrated. Locally transforming coal to electricity will also increase the added value of the energy product and benefit economic development of these coal-rich areas, which are relatively backward. Power from large-scale wind power bases can be transmitted together through the same interregional power transmission channel. However, since thermal power generation causes much more carbon emission than coal mining, large increases of carbon emission in coal-rich areas are almost inevitable in coming years. Still, thermal power capacity in coastal regions must be expanded and coal transportation volume will continue increasing to feed this. Upgrades of the transportation system to achieve a 2–3-fold increase in transportation volume of electricity coal by 2020 (relative to 2010) are in progress. Consequently, the carbon emission flow originating from the coal-rich areas will increase drastically. This overall reverse spatial distribution of energy production and consumption will persist for a long time. In short, coastal regions are generally more developed and in need of energy than energy-rich regions, but they are already suffering from environmental pollution from thermal power generation. Energy-rich regions are distant from these coastal regions, but they have ample energy resources that coastal regions desperately need. It is believed that with rational responsibility allocation of carbon emission based on quantitative accounting and analysis, forthcoming carbon emission mitigation policies such as carbon cap-and-trade schemes and carbon tax will help incline both the energy-producing and consuming regions follow a coordinated development roadmap (Lindner et al., 2013). Clearly, the carbon emission intensity of thermal power bases will be much higher than other types of power bases. Differential treatment of regions is necessary in policy design. On the consumption side, it is necessary to make consumers take responsibility for carbon emission of the supply chain, to promote energy saving and

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indirectly reduce emissions. Strict caps on carbon emission of thermal power in coastal regions are also needed to avoid its redundant increase. In contrast, carbon emission caps in regions of thermal power bases should be relatively flexible for development of power generation industry, but efficient policies are needed to encourage power plants to seek cleaner technologies. The electricity producing and consuming regions will be economically linked by the carbon trade scheme, to establish a mechanism for coastal regions to help backward regions in solving environmental problems caused by power generation. Strong pressure on high electricity-consumption industries might persuade them to seek cheaper power sources under the circumstances of additional costs that they must bear for environmental responsibility. For example, these industries may move out of the coastal regions toward the power bases, to avoid the added cost of power loss in electricity transmission and of expensive carbon emission charges. This is helpful to both the coastal regions for energy saving and the power bases for accommodating renewable energies and economic development. A smaller capacity of longdistance electricity transmission is also beneficial to stability of the large-scale power system. Another potential option for high electricity-consumption industries is to use more electricity from cleaner power sources in place of thermal power, which will aid development of renewable energies. This can be accomplished with the help of a flexible power market mechanism. For example, large consumers can buy cleaner electricity via direct powerpurchasing contracts. However, it is presently very difficult for consumers to obtain such contracts, because of the undeveloped power market. The present findings also indicate that policy design for carbon emission mitigation in China should not only consider power generation and transmission processes. As we have shown, the process of electricity coal transportation has substantial carbon emission flow. Although producing per unit coal energy generates much less carbon emission than per unit electricity energy from coal, the large capacity of interregional coal transport makes the carbon emission flow large. From the perspective of coal supply, coal-rich regions are the producers and areas of coal consumption for power generation are the consumers. Considering the forthcoming large increase of interregional coal transport, corresponding responsibility of carbon emission should be properly allocated to the producers and consumers. One benefit of this is that more pressure will be placed on thermal power stations in the coastal regions, helping to control their growth. The proposed method provides a feasible method for quantitative analysis of characteristics and connections of carbon emissions of different regions, along the life cycle of electricity supply. Following the method, a map of sources, sinks and migration paths of carbon emission flow along the electricity supply chain can be constructed to reflect the transfer mechanism of carbon emission responsibility. Although it is somewhat incomplete to design regional carbon emission mitigation policies based only on results of such analysis, and to ignore many other industries and economic sectors, this analysis at least provides useful quantitative reference data. Moreover, considering the importance of the power industry for both economic development and carbon emission, the characteristics of emission flow in this industry largely determines the direction of policy design. In addition, the proposed method is based on network theory, and it is suitable to do evaluation according to the physical architecture and characteristics of the power system. This means that the more detailed and accurate data from power companies can be easily used for analysis with high precision. Results of such analysis can be used to improve power system design and operation for the purpose of carbon emission mitigation. For example, in a power system, a transmission line that transmits more electricity but has less

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carbon emission flow should receive greater consideration in power grid planning; it is better to cut off a consumer with higher nodal carbon emission intensity if the transmission is interruptible. Moreover, price signals of carbon emission can be readily integrated into nodal electricity prices, which helps consumers adjust their production schedule to save money, while at the same time mitigating emissions. 6. Conclusions Network theory-based carbon emission flow analysis was extended to include LCA, to analyze carbon emission flows embodied in all life stages of electricity supply. The assumption that “all the carbon emissions are driven by the consumers” must be modified when responsibility is shared by both producers and consumers, as should corresponding formulas. How to integrate the proposed model into existing economy-energy-environment frameworks (such as an input–output framework) that address common goods is another question to be solved. This integration is necessary since comprehensive analysis of all types of trades, including electricity supply, is fundamental for multi-regional responsibility allocation and policy making. Further, the proposed method requires substantial data to perform detailed life cycle carbon emission flow analysis, which limits study sites to several major coal transportation and interregional electricity transmission paths in China. A major finding of the case study of China's 2010 electricity supply system is that there was a large interregional carbon emission flow in both electricity transmission and electricity coal transport, although minor coal flows were not included. This demonstrates the need to fully consider the life cycle of electricity supply, rather than just electricity generation and transmission, when evaluating environmental responsibility among regions. Up to the present, there have been large carbon emission flows concurrent with energy flows from energy bases such as Sanxi and the central regions to the east and south regions, which are load centers. In the coming years, the increase of interregional electricity coal transport and electricity transmission will create more and larger carbon emission flows all over the country. Consequently, emission responsibility must be properly allocated between energy supply and consumption regions, to prevent unbalanced development and unfairness. Acknowledgments This study was supported by the Key Program of the National Natural Science Foundation (Nos. 50939001, 41271543) and Program for New Century Excellent Talents in University (NCET-09– 0226). References Alcántara, V., Padilla, E., 2009. Input–output subsystems and pollution: an application to the service sector and CO2 emissions in Spain. Ecological Economics 68, 905–914. Andrew, R., Forgie, V., 2008. A three-perspective view of greenhouse gas emission responsibilities in New Zealand. Ecological Economics 68, 194–204. Atkinson, G., Hamilton, K., Ruta, G., van der Mensbrugghe, D., 2011. Trade in ‘virtual carbon’: Empirical results and implications for policy. Global Environmental Change 21, 563–574. Cadarso, M., López, L., Gómez, N., Tobarra, M., 2012. International trade and shared environmental responsibility by sector. An application to the Spanish economy. Ecological Economics 83, 221–235. Chen, Q., Kang, C., Xia, Q., Guan, D., 2011. Preliminary exploration on low-carbon technology roadmap of China's power sector. Energy 36, 1500–1512. China Electricity Council (CEC), 2011. Statistical Data Collection of Electric Power Industry of 2010. Department of statistics and information, China Electricity Council [in Chinese].

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