Drivers of CO2 emissions from power generation in China based on modified structural decomposition analysis

Journal of Cleaner Production 220 (2019) 1143e1155

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Drivers of CO2 emissions from power generation in China based on modified structural decomposition analysis Saige Wang a, Xiaojie Zhu b, Dan Song c, d, *, Zongguo Wen e, Bin Chen a, **, Kuishuang Feng f a

State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, PR China Ningbo Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Zhejiang, 315000, PR China Resource and Environmental Branch, China National Institute of Standardization, Beijing, 100191, PR China d School of Environment, Tsinghua University, Beijing, 100084, PR China e State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, PR China f Department of Geographical Sciences, University of Maryland, College Park, MD, USA b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 April 2018 Received in revised form 14 February 2019 Accepted 18 February 2019 Available online 22 February 2019

Currently, over 20% of global electricity and 30% of global CO2 emissions from fuel combustion are generated in China. Understanding the driving forces of CO2 emissions from power generation is critical for both decarbonizing the power sector and achieving national carbon reduction targets. The objectives of this study were to identify critical driving forces behind changes in CO2 emissions from the power sector and to propose appropriate decarbonization pathways. First, the generation and demand structures of the power sector were introduced into a CO2 emission accounting model and decomposition analysis. Instead of traditional inputeoutput analysis, structural decomposition analysis modified using a power transmission table was used to investigate the impacts of five driving factors of CO2 emissions from China’s power generation. The five driving factors comprised the proportion of thermal power, power generation technology, power generation structure, power demand structure and power demand, whereby the latter was divided further into nine detailed parts. Considering five regional power grids in China, the contributions of these factors were analyzed at both national and regional level. The results showed that the majority of the increase in CO2 emissions during 2007e2012 could be attributed to electricity generation (96.2%) driven by changes in power demand, which should be the key to power sector decarbonization. By contrast, 30.7% of emissions were offset by changes in the proportion of thermal power and technology, demonstrating the obvious effects of China’s policy on clean energy transition. Additionally, all power grids exhibited an increase in CO2 emissions from electricity generation, with the east and central grids accounting for 64% of the national increase. Power transmission structure had only a small impact on CO2 emissions from power generation. By using the electricity transmission table, we modified SDA to overcome the time lag issues and eliminate the reliance on IO data, and continuous annual data rather than aggregated five-yearly data can be used to capture the structural effects, thus providing more precise results for the driving forces of emission changes. Our case study shows that there is huge potential in extending the IO-based SDA method to other trade-related studies. © 2019 Elsevier Ltd. All rights reserved.

Keywords: CO2 emission Decomposition analysis Power transmission Power sector

1. Introduction

* Corresponding author.Resource and Environmental Branch, China National Institute of Standardization, Beijing, 100191, PR China. ** Corresponding author. E-mail addresses: [email protected] (D. Song), [email protected] (B. Chen). https://doi.org/10.1016/j.jclepro.2019.02.199 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

Fossil fuel combustion has been the main driver of the increase in CO2 emissions over the previous several decades (Lindner et al., 2013). In 2016, around 42% of global CO2 emissions were from electricity and heat generation, and 30.69% of these emissions occurred in China (IEA, 2018; Mi et al., 2017a). The soaring electricity demand and rapidly developing power transmission are

1144

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

considered the major driving forces of carbon emissions in the electric power sector (Zhang et al., 2013; Chen et al., 2018). In China, most power sources are situated in the northern, northwestern and northeastern regions, while the consumption centers are clustered mainly in eastern and southern areas (Xu and Ang, 2014; Chen et al., 2010a, 2010b). The geographical imbalance between power supply and demand means an interregional electricity transmission grid is important when considering various regional long-term benefits in China (Li et al., 2016). However, most investment has been focused on power supply. During 2001e2009, even though investment in China’s power grid increased to 45% of the total for the power sector, it was still lower than the international average of 50%e60% (Liu and Zhang, 2013). By 2012, the total electricity transmission capacity in China was 47.40 GW, which was less than 5% of the national installed generation capacity (Guo et al., 2016a). Meanwhile, rapid development of renewable energy in North China has placed additional stress on the construction of the power transmission grid. The maturity of ultrahigh voltage (UHV) power transmission technology has also made large-capacity long-distance power transmission more feasible (Zhang et al., 2018). Recently, greater emphasis has been put on the construction of interregional transmission lines, especially UHV lines (Yi et al., 2016). During 2007e2012, five UHV lines had been constructed to relieve environmental issues and to promote renewable energy consumption in China. However, increasing electricity demand and the development of the grid transmission system have consequences regarding carbon emissions that require further investigation. Regulatory policies instruments have been designed and implemented on both the supply side and demand side. For supply side, to improve the electricity generation efficiency and include more renewable power, various reforms over the last decade have aimed at changing the dispatch system, including changing dispatch formulas, as well as introducing generation rights trading, where each generator is allocated a certain share of total demand and can sell the right to generate instead of actually producing electric power (Lin and Purra, 2019). Also, feed-in tariffs have been introduced to accelerate investment in renewable energy capacity more attractive by adding a premium to the average wholesale prices of coal-fired electricity since 2006(Couture et al., 2010). Other changes intended to reduce prices are the biggest reform in the electricity system in 2009 that the central government has allowed some local governments to start up pilot projects for direct power purchases by large consumers (Zhang, 2015). However, electricity prices for industries have been kept low, and thus there is little incentive for industrial firms to improve energy efficiency. For demand side, to control industrial electricity consumption, the central government has introduced price discrimination to encourage high electricity efficient industries’ development in 2009 (Yu, 2012). In 2010, the highest level nationwide energy policy-making and coordination organization in China, National Energy Commission is established, which is helpful in integrating demand side of management (such as energy conservation and efficiency) into the current electricity system reform. Another important factor influencing the carbon emissions of the power sector is the remarkable difference in the power generation mix, i.e., the power generation structure among different regions (Xu and Lin, 2016; Wang and Chen, 2017, 2018). The spatial distribution of different types of power generating capacity is highly uneven. For example, considering the spatial distribution of non-fossil fuel capacity (including hydro, nuclear, wind, solar, and other renewable power sources), central and southern China account for the highest proportions (45.3% and 43.1%, respectively), whereas the northern region accounts for only 7.3% (Chen et al., 2014; Wang et al., 2019). Because of the difference in carbon

emissions intensity between non-fossil and fossil fuel power generation, the carbon emissions intensity differs considerably between regions. In 2012, the highest and lowest intensities were 884.3 g/kWh (Kilowatt hour) (northeastern region) and 525.7 g/ kWh (central region), respectively, which makes adjustment of the energy structure for decarbonization of the power sector highly complex. Therefore, further investigation of the driving forces of the regional energy structure in China is required (Yi et al., 2016; National Climate Strategy Center, 2015). Decomposition analysis has been used widely to investigate the mechanisms that influence energy consumption (Guan et al., 2008; Ang et al., 2004; Xu and Ang, 2014; Wang et al., 2014) and the environmental side effects of such consumption (Guan et al., 2009; Wang et al., 2015; Ang and Zhang, 2000), including energy-related CO2 emissions (Ang et al., 2009; Su and Ang, 2012; Yan et al., 2017; Cai et al., 2019). Two decomposition techniques are commonly used to investigate energy-related CO2 emissions, i.e., structural decomposition analysis (SDA) and index decomposition analysis (IDA) (Hoekstra and van den Bergh, 2003). Based on index theory, IDA is often employed to understand both the drivers of energy use and the related emissions in a specific economic sector. It has the advantages of simplicity, transparency and low data requirements but the disadvantage of producing a less detailed decomposition of the economic production structure. Furthermore, IDA cannot analyze the interdependency of different economic sectors, nor can it quantify the indirect impacts of change in final consumption because it does not distinguish between intermediate and final demand. The logarithmic mean divisia index (LMDI), which is one of the most popular IDA approaches, has been used widely to investigate CO2 emissions in relation to a specific sector or industry, including the electric power industry (Sun et al., 2011; Ang, 2015; Zhang et al., 2011; Ang and Xu, 2013). For instance, Malla et al. (2009) used LMDI to explore the evolution of CO2 emissions related to electricity generation in seven countries. Recently, Zhang et al. (2013) applied LMDI to analyze the driving factors underlying CO2 emissions from electricity generated in China between 1991 and 2009, which revealed several interesting results regarding China’s power generation technology, efficiency and fuel mix. Based on the inputeoutput (IO) framework (Rose and Casler, 1996) SDA has been used to investigate CO2 emissions related to multiple sectors (Feng et al., 2012; Peters et al., 2007; Xu et al., 2011; Su and Ang, 2012, 2015; Baiocchi and Minx, 2010; Su et al., 2013). One of its advantages is that it overcomes the static flaws of IO models, enabling dynamic evaluation of the economic structure, final demand components and categories (Kone et al., 2015). SDA has been used widely to calculate the contribution of different factors to the overall change in both energy consumption and carbon emissions at different scales, including city, regional, national and global levels (Wei et al., 2017; Feng et al., 2017; Xu et al., 2011; Brizga et al., 2014). For example, Mi et al. (2016) combined SDA with environmental IO analysis to estimate the determinants of China’s carbon emission changes during 2005e2012. Chang et al. (2008) analyzed the key factors causing emission changes in Taiwan by decomposing the CO2 emission changes through SDA. Recently, SDA had been used to analyze the final demand through more refined decompositions (Feng et al., 2015). For example, from a final demand perspective, Wang et al. (2013) investigated the increased CO2 emissions in Beijing during 1997e2010 using SDA to provide targeted demand management in Beijing. Because of its advantages in quantifying the final demand and in analyzing dynamic evaluation, we selected SDA for the decomposition of CO2 emissions in this study through the introduction of an electricity transmission table based on an IO accounting model. To investigate the power transmission effect, we modified the SDA by constructing power generation structure and power

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

demand structure tables based on China’s electricity data between 2007 and 2012. Accordingly, we proposed an electricity CO2 emission decomposition model comprising the following five factors: 1) proportion of thermal power, 2) power generation technology, 3) power generation structure, 4) power demand structure and 5) power demand. Here, power generation technology refers to the technology that determines the thermal CO2 emission coefficient. Power generation structure is defined as the matrix representing the volume of regional electricity mix transferred out per unit power generation. Power demand structure is defined as the matrix representing the amount of regional electricity transferred in to meet the per unit regional power demand. The power transmission structure is defined as the sum of the contribution of the (external) power generation structure change and the contribution of the (external) power demand structure change. The contributions of these factors were analyzed at national and regional scales using the additive decomposition method proposed by Dietzenbacher and Los (1998). In addition, the final demand perspective could be investigated because the proposed model was similar in form to the IO framework. In the electricity power system, the final demand is equivalent to the power demand, which can be divided further into nine detailed categorizes in China, as shown in Table 1. By analyzing the decomposition results, a new perspective for controlling and mitigating CO2 emissions from electricity generation can be obtained.

Table 1 Nine categories of power demand. Demand Demand Demand Demand Demand Demand Demand Demand Demand

Wout

::: ::: ::: :::

(4)

and the electricity inflow of r regions Win can be represented as:

Win

For an electricity power system comprising r regions, the electricity power transmission table T can be defined as follows:

T12 T22 ::: Tr2

1 D1 B D2 C C ¼ A,B @ ::: A Dr 0

1 G1 B G2 C C ¼ BT ,B @ ::: A Gr 0

2.1. Modified structural decomposition analysis model

T11 B T21 B T ¼@ ::: Tr1

Farming, forestry, husbandry and fishing Industry Construction Transportation, warehousing, postal service Information transmission, computer services and software Commercial, accommodation and catering industry The financial, real estate, business services and residents Public utilities and management organizations Urban and rural residents

1 2 3 4 5 6 7 8 9

demand structure in the j-th region. Similarly, the power generation structure in the i-th region can be expressed by element BTij of matrix B, which is the amount of power transferred out of the i-th region per power generation in the i-th region. Thus, matrix A can be considered the power demand structure table and matrix B can be considered the power generation structure table. Therefore, the electricity outflow of r regions Wout can be represented as:

2. Methodology and data source

0

1145

1 T1r T2r C C ::: A Trr

(1)

(5)

Thus, the power generation can be calculated as:

0 1 1 1 1 0 0 D1 G1 D1 G1 B D2 C B G2 C C B D2 C B T B G2 C B B C C C B @ ::: A ¼ Wout  Win þ @ ::: A ¼ ðA þ IÞ,@ ::: A  B ,@ ::: A Gr Dr Dr Gr 0

(6) where Tij is the electricity flux flowing from the i-th region to the jth region ði; j ¼ 1; 2; 3:::rÞ. Combining regional power generation and power demand, the electricity power transmission table can be developed further, as follows:



A ¼ ATij





; where ATij ¼ Tij Dj

(2)

   B ¼ BTij rr ; where BTij ¼ Tij Gi

(3)

rr

where Gi is the amount of power generation in the i-th region ði ¼ 1; 2; 3:::rÞ and Di is the power demand in the i-th region ði ¼ 1; 2; 3:::rÞ. Element ATij of matrix A is the amount of electricity transferred into the j-th region per power demand that is consumed in the j-th region. Thus, element ATij of matrix A can be regarded as the power

0

Ctot ¼ ð P1

P2

:::

C1 B 0 B Pr Þ,@ 0 0

0 C2 0 0

0 0 ::: 0

From Eq. (6), the power generation can be expressed as:

0

1 1 0 G1 D1 1 B G2 C  B D2 C T B C C ,ðI þ AÞ,B @ ::: A ¼ I þ B @ ::: A Gr Dr

(7)

where I is the identity matrix. Thermal power is the main emitter of CO2, whereas other types of electricity generation have relatively lower CO2 emissions. Here, we consider only the CO2 emissions associated with thermal power. Combining the thermal CO2 emission coefficient and the proportion of thermal power, the CO2 emissions from electricity generation can be calculated as follows:

1 1 0 D1 0   1 B D2 C 0C b C, I þ BT C ,ðI þ AÞ,B @ ::: A ¼ P, C ,EG ,ED ,D 0A Dr Cr

(8)

1146

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

BTij

Tij / Gi

Tij / D j

Transmission line

Generator Load Region i

ATij

Load Generator

Transmission line

AT ji

BT ji

T ji / Di

T ji / G j

Region j

Fig. 1. Schematic of an electric power system with two regions.

where Pi is the proportion of thermal power in the i-th region and Ci is the thermal CO2 emission coefficient in the i-th region (i.e., the CO2 emissions per kWh of thermal power in the i-th region) ði ¼ 1; 2; 3:::rÞ. Matrix P can be regarded as the proportion of thermal power, b can be regarded as the technology factor and the diagonal matrix C matrix D can be regarded as the power demand factor. To understand the roles of matrices EG and ED , it is necessary to understand matrices A and B, which directly generate matrices EG and ED . For convenience, this description is based on an electricity power system with two regions, i and j (Fig. 1), although additional regions could be analyzed using the same method. Two methods can be used to calculate the power transmission in each transmission line: one uses matrices B and G, while the other uses matrices A and D. Equation (4) shows how the electricity outflow for one region is calculated using the first method, and the electricity inflow is calculated using the second method. In other words, matrices A and B represent the power generation structure in one region and the power demand structure in other regions. However, the power generation and power demand structures determine the local power transmission. For example, in Fig. 1, the electricity inflows and outflows of region i are determined by ATij and BTji , which represent the power demand structure and power generation structure in region j, respectively. Matrices EG and EG can be considered as the national-scale power generation structure and power demand structure factors, respectively. However, at the regional scale, matrix EG should be considered to represent external power generation structure factors. Similarly, matrix ED should be regarded as external power demand structure factors, representing the effect of the power demand structure in other regions on regional electricity inflow. This model first introduces the power generation structure and power demand structure to reveal the effects of the power transmission on CO2 emission change. The form of this model is similar to the IO accounting model. Here, the electricity transmission table is used rather than the IO table; thus, the data requirement of the

EðDPÞ ¼

proposed accounting model is much lower than that of the IO accounting model. The modified model can provide a decomposition of the final demand that is more refined. Our modified SDA is based on the complete additive decomposition method, proposed by Dietzenbacher and Los (1998), which has the advantages of having no residuals and being able to handle the matrix problem (Su and Ang, 2012). Equation (9) is used as an example to illustrate the use of the decomposition method between two periods, i.e., (t) and (tþ1):

DCtotðtÞ ¼ DCtotðtþ1Þ  DCtotðtÞ

  b þ EðDE Þ þ EðDE Þ þ EðDDÞ ¼ EðDPÞ þ E D C D G

(9)

where EðDPÞ is the contribution of the proportion change of therb Þ is the contribution of the power generation mal power, EðD C technology change, EðDEG Þ is the contribution of the (external) power generation structure change, EðDED Þ is the contribution of the (external) power demand structure change and EðDDÞ is the contribution of the power demand change. The sum of EðDEG Þ and EðDED Þ can be used to represent the contributions of the power transmission structure change. In the SDA method, each of the five determinant variables should be weighted using Laspeyres or Paasche weights to obtain a complete decomposition form. However, when there are n determinant variables, there will be n! different complete decomposition forms (Feng et al., 2012; Xu et al., 2011; Wang et al., 2013). To the best of our knowledge, these n! different forms represent n! ways to allocate the residuals, and the average of the residuals for these different forms means that the residual is divided equally among the n determinant variables, which corresponds to the ideas of Sun (1998). The same results were also observed in the work of Hoekstra and van den Bergh (2003). Here, we use the averages of these different forms to allocate the residuals. The decomposition formulation for the proposed model between the two periods ðtÞ and ðt þ 1Þ can be expressed as follows:

  1 1 DðPÞ Cb t EGt EDt Dt þ DðPÞ Cb tþ1 EGtþ1 EDtþ1 Dtþ1 þ ðDðPÞ Cb tþ1 EGt EDt Dt 5 20

btE b b b þDðPÞ C Gtþ1 EDt Dt þ DðPÞ C t EGt EDtþ1 Dt þ DðPÞ C t EGt EDt Dtþ1 þ DðPÞ C t EGtþ1 EDtþ1 Dtþ1  b E E b E b E þDðPÞ C D þ D ðPÞ C E D þ D ðPÞ C E D tþ1 Gt Dtþ1 tþ1 tþ1 Gtþ1 Dt tþ1 tþ1 Gtþ1 Dtþ1 t 1 b E b b þ ðDðPÞ C tþ1 Gtþ1 EDt Dt þ DðPÞ C tþ1 EGt EDtþ1 Dt þ DðPÞ C tþ1 EGt EDt Dtþ1 30  btE b b þDðPÞ C Gtþ1 EDtþ1 Dt þ DðPÞ C t EGtþ1 EDt Dtþ1 þ DðPÞ C t EGt EDtþ1 Dtþ1

(10)

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

1147

          1 b ¼ 1 Pt D C b E E Dt þ P b b E E Dt ðPtþ1 D C E DC Gt Dt tþ1 D C EGtþ1 EDtþ1 Dtþ1 þ Gt Dt 5 20         b E b b b þPt D C Gtþ1 EDt Dt þ Pt D C EGt EDtþ1 Dt þ Pt D C EGt EDt Dtþ1 þ Pt D C EGtþ1 EDtþ1 Dtþ1        b E E b E b E þPtþ1 D C D þ P D E D þ P D E D C C Gt Dtþ1 tþ1 tþ1 Gtþ1 Dt tþ1 tþ1 Gtþ1 Dtþ1 t

(11)

      1 b E b b E D þ P D E D þ P þ ðPtþ1 D C C E Gtþ1 Dt t tþ1 Gt Dtþ1 t tþ1 D C EGt EDt Dtþ1 30        b E b b þPt D C Gtþ1 EDtþ1 Dt þ Pt D C EGtþ1 EDt Dtþ1 þ Pt D C EGt EDtþ1 Dtþ1

EðDEG Þ ¼

  1 1 b t ðDE ÞE Dt þ P C b b t ðDE ÞE Dt Pt C ðP C G Dt tþ1 tþ1 ðDEG ÞEDtþ1 Dtþ1 þ G Dt 5 20 tþ1

b ðDE ÞE Dt þ Pt C b t ðDE ÞE b b þPt C tþ1 G Dt G Dtþ1 Dt þ Pt C t ðDEG ÞEDt Dtþ1 þ Pt C tþ1 ðDEG ÞEDtþ1 Dtþ1  b t ðDE ÞE b b þPtþ1 C G Dtþ1 Dtþ1 þ Ptþ1 C tþ1 ðDEG ÞEDt Dtþ1 þ Ptþ1 C tþ1 ðDEG ÞEDtþ1 Dt

(12)

b ðDE ÞE Dt þ P C b b þð1=30Þ,ðPtþ1 C tþ1 G Dt tþ1 t ðDEG ÞEDtþ1 Dt þ Ptþ1 C t ðDEG ÞEDt Dtþ1  b ðDE ÞE b b þPt C tþ1 G Dtþ1 Dt þ Pt C tþ1 ðDEG ÞEDt Dtþ1 þ Pt C t ðDEG ÞEDtþ1 Dtþ1

EðDED Þ ¼

  1 1 b t E ðDE ÞDt þ P C b E b t E ðDE ÞDt Pt C ðP C ð D E ÞD D D D Gt tþ1 tþ1 Gtþ1 tþ1 þ Gt 5 20 tþ1

b E ðDE ÞDt þ Pt C btE b b þPt C D tþ1 Gt Gtþ1 ðDED ÞDt þ Pt C t EGt ðDED ÞDtþ1 þ Pt C tþ1 EGtþ1 ðDED ÞDtþ1  btE b b þPtþ1 C Gtþ1 ðDED ÞDtþ1 þ Ptþ1 C tþ1 EGt ðDED ÞDtþ1 þ Ptþ1 C tþ1 EGtþ1 ðDED ÞDt 1 b E ðDE ÞDt þ P C b b þ ðPtþ1 C D tþ1 Gt tþ1 t EGtþ1 ðDED ÞDt þ Ptþ1 C t EGt ðDED ÞDtþ1 30  b E b E ðDE ÞD btE þPt C ð D E ÞD þ P þ P ð D E ÞD C C t t t D D D tþ1 Gtþ1 tþ1 Gt tþ1 Gtþ1 tþ1

(13)

1148

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

1 1 b t E E ðDDÞ þ P C b b t E E ðDDÞ ðPt C ðP C Gt Dt tþ1 tþ1 EGtþ1 EDtþ1 ðDDÞ þ Gt Dt 5 20 tþ1 b E E ðDDÞ þ Pt C btE b b þPt C tþ1 Gt Dt Gtþ1 EDt ðDDÞ þ Pt C t EGt EDtþ1 ðDDÞ þ Pt C tþ1 EGtþ1 EDtþ1 ðDDÞ EðDDÞ ¼

btE b b þPtþ1 C Gtþ1 EDtþ1 ðDDÞ þ Ptþ1 C tþ1 EGt EDtþ1 ðDDÞ þ Ptþ1 C tþ1 EGtþ1 EDt ðDDÞ

(14)

1 b E E ðDDÞ þ P C b b þ ðPtþ1 C tþ1 Gt Dt tþ1 t EGtþ1 EDt ðDDÞ þ Ptþ1 C t EGt EDtþ1 ðDDÞ 30  b E b b E ð D DÞ þ P E E ð D DÞ þ P E E ð D DÞ þPt C C C t tþ1 Gt Dtþ1 t t Gtþ1 Dtþ1 tþ1 Gtþ1 Dt

It can be seen that a disadvantage of the complete additive decomposition method is the huge amount of calculation work needed, which increases exponentially with the increase of driving factor numbers. We used five factors in our accounting model, for which there were 120 decomposition forms. Therefore, although Eqs. (10)e(14) are simplified, they remain complicated. 2.2. Study area China’s electricity power system has developed rapidly during the previous several decades. Chinese electricity consumption increased by more than 1998% during 1980e2017 (China Electricity Council, 2018). China’s CO2 emissions increased dramatically from 3659.3 million tons in 2000 to 10,291.9 million tons in 2014. In 2007, China became the largest emitter of CO2 worldwide, contributing 21% of the total global CO2 emissions. A large proportion of the increase in CO2 emissions is attributable to electricity generation (IEA, 2016). Consequently, China has faced increasing pressure to curb its CO2 emissions, which has resulted in an urgent need to understand the changes in CO2 emissions, especially in relation to electricity generation. In China, most primary energy sources (i.e., coal and hydro) for power generation are located far from power consumption centers. Coal-based generation is located mainly in Northwest China, while hydroelectric power generation is located mainly in Southwest China; Wang et al., 2017). In 2008, more than 46% of the nation’s total coal output was derived from three coal-rich inland regions: Inner Mongolia, Shanxi and Shaanxi. However, nearly 34% of the total electric power generated was consumed in the four coastal regions undergoing the most rapid economic development: Shandong, Zhejiang, Guangdong and Henan (Wang and Chen, 2010). Although large amounts of coal are transported by train or ship to power plants near power consumption centers, large amounts of electricity are still generated near coal production areas (Chen et al., 2013b). Consequently, demand for power transmission in China is increasing rapidly, which has inspired calls for transmission projects such as the “West to East power transmission project” (Ming et al., 2013). The Chinese electricity network comprises six regional grids with limited interconnection that are operated by three grid companies: the State Grid Corporation of China (SGCC), China Southern Power Grid and Inner Mongolia Grid Company. Over recent years, the SGCC has been in charge of more than 80% of China’s electricity grid (China Electric Power Yearbook, 2014). Because of data availability, only five grids are considered here: the north grid, northeast grid, northwest grid, central grid and east grid. The pattern of electricity power transmission among these five grids is shown in Fig. 2. Tibet and Taiwan were not included in this study because

their generation capacity and electricity consumption are small and isolated from other regions (China Electric Power Yearbook, 2011). 2.3. Data sources Data of electricity power transmission among the five grids were derived from the SGCC Power Exchange Annual Report (State Grid Corporation of China Power Exchange Annual Report, 2007e2012). To validate the intergrid electricity transmission data, we also collected interprovincial electricity transmission data from the China Electricity Council, i.e., from the delivering side. These data have been used widely in previous studies (Zhang et al., 2017; Qu et al., 2017; Zhu et al., 2015; Guo et al., 2016b). Provinciallevel electricity generation and consumption data were collected from the China Electric Power Year Book (China Electric Power Year Book, 2008e2013). Thermal power plants with generation capacity larger than 6 kW were considered because they account for more than 99% of the total thermal power capacity. Moreover, CO2 emission coefficients were calculated based on data from standard coal, in which coefficient to conversion to standard coal equivalent was also obtained from the China Electric Power Yearbook (2011). And, CO2 emission coefficients and carbon oxygenation efficiency are collected from IPCC (2006). The state grid electric power trading center has been in operation since September 2006. Our results are based on available data of electricity power transmission for the period 2007e2012. During this period, considerable changes in the electricity system have occurred, which could provide useful examples and insights for the decarbonization of China’s power sector. The recent rapid economic development of China has led to GDP doubling from 27.02 trillion

Fig. 2. Schematic of electric power transmission pattern among the five grids in China.

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

Yuan in 2007 to 54.04 trillion Yuan in 2012. This has led to an increase in electricity generation from 2.71 trillion kWh in 2007 to 4.15 trillion kWh in 2012 and to a similar scale of growth in CO2 emissions from the power sector (China Electric Power Yearbook, 2008e2013; National Bureau of Statistics of China, 2018e2013). Moreover, since 2009, UHV electricity transmission in China has been developing rapidly in relation to the transmission of alternating current (AC) and direct current (DC) electricity over long distances. In 2009, both the 1000-kV Jindongnan NanyangeJingmen UHV AC Pilot Project (length: 640 km) and the ±800-kV UHV DC YunnaneGuangdong transmission project (length: 1373 km) commenced operation (Hu et al., 2011; Zhou et al., 2013). In 2010, the XiangjiabaeShanghai ±800-kV UHV DC transmission project (length: 1907 km) was put into operation, which was followed in 2012 by the JinpingeSouthern Jiangsu €m ±800-kV UHV DC transmission project (length: 2059 km) (Åstro et al., 2010; Zhou et al., 2018). During 2007e2012, the amount of electricity power transmission in China increased by 273%. Thus, investigation of this period might help us understand the driving forces of China’s power sector under conditions of rapid economic growth and power transmission development, which would be critical for providing targeted management toward achieving decarbonization of the power sector in China. 3. Results and discussion 3.1. Decomposition analysis at national level Decomposition results at the national level are shown in Fig. 3. The contributions of power demand changes increased between 2007 and 2010, peaked in 2010, and decreased to a negative value in 2012. A similar inverse “U”-shaped tendency can be observed when considering the total CO2 emission changes resulting from electricity generation, with maximum CO2 emissions occurring in 2011. It is believed that CO2 emissions in China exhibit a Kuznets curve (Yin et al., 2015). Zheng and Zhu (2012) proved the existence of a carbon Kuznets curve (Jalil and Mahmud, 2009) in China using the panel error correction model, noting that the inflection point was 29,847 Yuan per capita GDP. The GDP per capita in 2010 reached 29,992 Yuan, i.e., close to the determined inflection point.

1200 1000

Million tons of CO2

800

1149

By combining the CO2 emissions during 2013e2016, we can see that the inverse “U”-shaped tendency for CO2 emissions in China had reached the inflection point (Mi et al., 2017b). Changes in the power demand and the power demand structure were the main drivers for the increase in national CO2 emissions between 2007 and 2012 (Fig, 3). Specifically, changes in power demand contributed most (96%) to the increase in CO2 emissions. During the period of the 11th Five-Year Plan (2006e2010), because of the rapid industrialization and high proportion of heavy industry within the economic structure, there was rapid growth in electricity consumption, which led to similar growth in CO2 emissions of the power sector. Decreases in CO2 emissions from power generation were driven mainly by changes in power generation technology, the proportion of thermal power and the power generation structure. However, these three factors only offset 34.15% of the increase, which highlights the vital role of power demand management in reducing CO2 emissions. Furthermore, in comparison with other factors, it can be seen that the contributions from changes in both the power generation structure and the power demand structure were small. There are two reasons for the small scale of these contributions. First, although the amount of electricity power transmission increased by 273% from 2007 to 2012, electricity flow through power transmission accounted for only 4.1% of the total electricity generation. Thus, the effect of power transmission in reallocating electricity resources remains limited. Second, there is no direct relationship between power generation change and power generation structure change (or power demand structure change). The proportion of thermal power was the main driver of declining CO2 emissions from the generation of electricity in 2012, accounting for 69.3% (120.7 million tons) of the decrease in CO2 emissions from electricity generation. In 2012, the total power generation increased by 5.41%, whereas thermal power generation increased by only 0.65% (China Electric Power Yearbook, 2011). Consequently, the proportion of thermal power decreased during this period. The disproportional increase in total power relative to thermal power might reflect the use of hydropower, which decreased by 2.7% in 2011 and then increased by 28% in 2012. This growth in hydropower greatly reduced the use of thermal power, although approximately 52 GW of thermal power was added in

Thermal power proportion Technology Power generation structure Power demand structure Power demand

600 400 200 0 -200 -400 -600 2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

2007-2012

Fig. 3. Contribution of five factors to CO2 emission change from electricity generation from 2007 to 2012.

1150

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

2012. Unlike technology and power demand, which both had stable effects during the studied period (the values were all positive or negative), the proportion of thermal power fluctuated every year. This unstable trend might result from multiple factors (e.g., climate, mutability of load and operating plans of units). By introducing the electricity transmission table, the effects of regional CO2 emissions can be investigated by considering the electricity flows across regions. The decomposition results from a regional perspective are shown in Fig. 4. The CO2 emissions from the power sector increased in all regions between 2007 and 2012. The east and north grids had the largest increases in CO2 emissions from electricity generation, accounting for 31.8% and 32.3% of the national CO2 emissions increase, respectively. By contrast, the northeast grid accounted for only 3.4% of the CO2 emissions from electricity generation. Although the east and north grids are two of the largest power producers, their effects on national CO2 change are limited. Thus, West-to-East Electricity Transmission Project is supposed to play an increasingly important role in reducing the CO2 emissions of the east grid in future. Between 2011 and 2012, the CO2 emissions of four of the grids decreased. The central grid was the largest contributor to the emissions decrease, accounting for more than 76% of the decrease in national CO2 emissions. Although the north grid had increased CO2 emissions, the contribution of this increase was still smaller than that during 2010e2011. In addition, during the same period, UHV electricity transmission developed rapidly in China, transmitting both AC and DC electricity over long distances. These projects have helped optimize the allocation of power resources between the two major power grids, i.e., the north and central grids. In addition, it is noteworthy that the contribution of the central grid to national emissions changed considerably during the studied period, with marked fluctuation. For example, although it was the largest contributor to emissions decrease during 2011e2012, the central grid contributed 19.6% (62.8 million tons) to the national CO2 emissions increase during 2010e2011. Thus, it is important to investigate further the reasons behind such huge fluctuation.

generation. In SDA, each of the final demand categories can be investigated separately within the IO framework (Sun et al., 2011). Power demand can also be examined in detail using the proposed model by dividing it into nine categories, as shown in Table 1. Table 2 shows the contributions of the nine categories of power demand to the total change in CO2 emissions between 2007 and 2012 for the five studied grids. Overall, industry played the most important role in the change of CO2 emissions in all five grids, particularly in the northwest, where more than 81.8% of the increase in CO2 emissions was from the industrial sector. Power demand related to construction contributed only slightly to the increase in CO2 emissions (<3%), except in the northeast, where it accounted for 6.3% of the increase in CO2 emissions. Similarly, the contributions of commercial, accommodation and catering sectors to power demand were higher in the northeast grid than in the other grids. Moreover, there were huge differences among the grids for power demand from urban and rural residents (i.e., 16% for the central grid, 13% for the north and east grids and 7% for the northeast and northwest grids), reflecting the uneven regional development in China (Zhang and Anadon, 2014). Economically developed regions such as the north, east and central grids with large populations had higher CO2 emissions from urban and rural residents. Moreover, the CO2 emissions of the farming, forestry, husbandry and fishing sectors in the north and central grids decreased (1.2% for the north grid and 1.4% for the central grid).

Table 2 Allocation of the change in CO2 emissions from electricity generation from 1997 to 2012 by nine power demand categories and five grids.

3.2. Power demand analysis The above analyses demonstrate that changes in power demand are the main drivers increasing CO2 emissions from electricity

Demand Demand Demand Demand Demand Demand Demand Demand Demand Sum

1 2 3 4 5 6 7 8 9

Northeast (%)

Northwest (%)

North (%)

East (%)

Central (%)

0.20 70.15 6.37 0.64 2.24 7.62 2.46 2.35 7.96 100

1.90 81.81 1.65 1.64 0.47 1.93 1.82 1.51 7.26 100

1.20 70.02 2.20 3.40 1.20 3.83 4.43 2.93 13.20 100

0.39 67.12 1.71 2.19 1.30 4.50 4.97 3.97 13.85 100

1.40 67.79 2.72 1.94 1.02 4.90 3.59 2.70 16.75 100

800 700

Million tons of CO2

600

North Northwest Northeast East Central Total

500 400 300 200 100 0 -100 2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

2007-2012

Fig. 4. Contribution of five grids to CO2 emission change from electricity generation from 2007 to 2012.

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

3.3. Decomposition analysis at regional level The analyses at the national level raised three questions. 1) What drives the regional decrease in CO2 emissions during 2011e2012? 2) What is the reason for the fluctuation of CO2 emissions in the central grid? 3) What impact does power transmission have on regional CO2 emissions? In this section, decomposition analysis at the regional level is conducted to investigate further the drivers of regional changes in CO2 emissions. 3.3.1. Decomposition analysis for 2011e2012 The decomposition results at the regional level for 2011e2012 are shown in Fig. 5. During this period, decreases in CO2 emissions from electricity generation resulted primarily from changes in the proportion of thermal power generation. In the northwest and central grids, the proportion of thermal power accounted for 80.1% (33.4 million tons) and 82.5% (47.6 million tons) of the decrease in CO2 emissions from electricity generation, respectively. However, in the north grid, a decrease in CO2 emissions of only 43.4% was caused by changes in the proportion of thermal power. Table 3 shows the contributions of the changes in the proportion of thermal power to the decrease in CO2 emissions from electricity generation and the proportion of non-thermal power in the five grids in 2012. The northwest and central grids have the largest proportions of non-thermal power, the majority of which is hydropower. By contrast, 95% of the power in the north grid is thermal power. In relation to electricity generation in 2012, Table 3 indicates that grids with the greatest proportion of thermal power exhibited the largest decreases in CO2 emissions because of changes in the proportion of thermal power (except for the east and northeast grids). Regression analysis was conducted to analyze the relationships between the proportions of non-thermal power and the

Million tons of CO2

60

1151

decreases in CO2 emissions from electricity generation caused by the changes in the proportion of thermal power (Fig. 6). It was found that a logarithmic function with an R2 value of 0.9436 could characterize the relationship, which proved the strong positive correlation relationship between them. Increases of non-thermal power generation could improve the contributions of changes in the proportion of thermal power. However, the enhancement effect decreases as the proportion of non-thermal power increases, reflecting a saturation characteristic. Changes in power generation technology also contribute notably to emissions decreases across the five grids, especially the north grid, where more than 56% of the decrease in CO2 emissions was from power generation technology change. In addition, the contributions from the external power generation structure and the external power demand structure vary considerably across grids. Regarding the contributions of the external power generation structure, three grids (i.e., the northwest, east and central grids) contributed to emissions decreases. However, in the north grid, the contribution of the external power generation structure contributed to emissions increase. Except for the east grid, the contributions of the external power generation structure were reasonably small. In the east grid, the external power demand structure contributed to emissions decrease, whereas it drove increases in emissions in the other four grids. During 2011e2012, changes in the proportions of thermal power and technology were the main reasons for regional decreases in CO2 emissions from electricity generation. Except for the northeast and north grids, a substantial amount of the regional decrease in CO2 emissions was from external power generation structures in the other three grids, particularly the east grid.

Thermal power proportion Technology External power generation structure External power demand structure Power demand

40 20 0 -20 -40 -60 -80 Northeast

Northwest

North

East

Central

Fig. 5. Contribution of five factors to CO2 emission change from electricity generation in five grids from 2011 to 2012.

Table 3 Proportion of non-thermal power generation and contributions of changes in the proportion of thermal power in the five grids in 2012. Item

Northeast (%)

Northwest (%)

North (%)

East (%)

Central (%)

Thermal power proportion change contribution to the CO2 emission decrement Non-thermal power generation proportion

64.46 11.04

80.13 29.63

43.40 4.96

55.07 11.75

82.56 40.10

Contribution of thermal power proportion change (%)

1152

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

Northwest 80

Central

Northeast y=0.1925ln(x)+1.0161 R2=0.9433

60 East

North 40 20 0

0

5

10

15

20

25

30

35

40

Non-thermal power proportion (%)

Fig. 6. Relationship between the proportion of non-thermal power and decrease in CO2 emissions from electricity generation caused by the change in the proportion of thermal power in five grids.

3.3.2. Decomposition analysis of the central grid during 2007e2012 The factor decomposition results for the central grid for 2007e2012 are shown in Fig. 7. The contributions from changes in power demand and technology were stable, whereas the contributions from changes in both the proportion of thermal power and the transmission structure changed markedly between 2007 and 2012. It is noteworthy that the positive and negative contributions of changes in thermal power were the same as in the total change, whereas the positive and negative contributions of changes in transmission structure were the opposite of the total change. During 2007e2012, an increase in CO2 emissions of 2.91 million tons from electricity generation resulted from changes in the external power demand structure, which indicated an increase in electricity outflow from the central grid. Furthermore, a decrease in CO2 emissions of 9.27 million tons from electricity generation resulted from the external power generation structure changes, indicating an increase in electricity inflow to the central grid. However, it is difficult to determine the electricity flow increment that was largest based on their absolute value because the CO2 emission coefficient and the proportion of thermal power were both different across the grids. Thus, changes in the power transmission structure and the proportion of thermal power were likely the main drivers of the fluctuation in CO2 emissions in the central grid.

200

Million tons of CO2

150

3.3.3. Effects of power transmission on grids during 2007e2012 The contributions of changes in the external power demand structure in the five grids during 2007e2012 are shown in Fig. 8. The CO2 emissions of all grids increased as the external power demand structure changed, except for the east grid, where the external power demand structure resulted in a decrease in CO2 emissions of approximately 1.4 million tons during 2007e2012. In the northwest grid, the external power demand structure changes increased CO2 emissions by more than 33.2 million tons, which was much larger than in the other regions. The contributions of the external power generation structures in the five grids during 2007e2012 are shown in Fig. 9. In the northeast and east grids, changes in the external power generation structure increased CO2 emissions (0.78 million tons for the northeast grid and 0.79 million tons for the east grid). Conversely, changes in the external power generation structure of the other grids contributed to decrease in CO2 emissions during 2007e2012. In the north grid, CO2 emissions decreased by more than 28 million tons driven because of changes in the external power generation structure, which was much larger than in the other regions. The contributions of the power transmission structures in the five grids during 2007e2012 are shown in Fig. 10. Changes in the power transmission structure contributed most to the CO2 emission changes in the northwest and northeast grids. In the northwest grid, changes in the power transmission structure increased CO2 emissions by more than 31.2 million tons. However, in the north and northeast grids, changes in the transmission structure contributed to substantial decreases in CO2 emissions, particularly in the north grid, where changes in the transmission structure reduced CO2 emissions by more than 26.7 million tons. This finding could be attributed mainly to the “West to East power transmission project,” through which more electricity is transferred from the northwest grid to the north grid via the southern path of the electricity transmission system (Ming et al., 2013). For the east grid, the effects of the power transmission structure on CO2 emissions were small and stable. 4. Conclusions Traditionally, SDA is based on an IO table that is used to capture both direct and indirect effects. Because of time constraints, an IO table is normally issued every five years in China; therefore, an obvious time lag will exist between the date of publication of a study and the data it used. Moreover, the high reliance of SDA on the IO table limits its use for in-depth studies. In this study, SDA was modified using a power transmission table instead of an IO table,

Thermal power proportion Technology External power generation structure External power demand structure

100 50 0 -50 -100 2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

2007-2012

Fig. 7. Contribution of five factors to changes in CO2 emissions from electricity generation in the central grid from 2007 to 2012.

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

50 40

1153

Northeast Northwest North East Central

Million tons of CO2

30 20 10 0 -10 -20 2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

2007-2012

Fig. 8. Contribution of the external power demand structure to changes in CO2 emissions from electricity generation in the five grids from 2007 to 2012.

20

Million tons of CO2

10

0

-10

-20

-30

Northeast Northwest North East Central

-40 2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

2007-2012

Fig. 9. Contribution of the external power generation structure to changes in CO2 emissions from electricity generation in the five grids from 2007 to 2012.

40 30

Million tons of CO2

20

North Northwes t

10 0 -10 -20 -30 -40 2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

2007-2012

Fig. 10. Contribution of the power transmission structure to changes in CO2 emission from electricity generation in the five grids from 2007 to 2012.

1154

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155

which extended the application of SDA in terms of the trade decomposition analysis. The power transmission table among grids was used to build a framework of the intermediate flows among different sectors in the IO table, and the power demand of nine separate categories was used as the sectoral final demand. The modified SDA can overcome the time lag issues and eliminate the reliance on IO data. The main limitation of this study was that only one dataset of electricity power transmission and power generation during 2007e2012 was used. This method, which used continuous annual data rather than aggregated five-yearly data to capture the structural effects, could provide more precise results for the driving forces of emission changes. Moreover, the accessibility of the latest power transmission tables at the global level or for other countries provides the opportunity to analyze recent driving factors using the modified SDA method. Furthermore, there is huge potential in extending the IO-based SDA method to other trade-related studies. For example, we could apply the modified SDA method to the trading activities among regions (sectors) and regional (sectoral) virtual water use (or virtual land) to build a decomposition framework. The proportion of thermal power was found to be the main driver of decreases in CO2 emissions from electricity generation in China. In the previous decade, energy transition from coal and other fossil fuels to renewable energy has reduced the proportion of thermal power substantially. The implementation plan and technological roadmap for China’s 2050 renewable energy development are critical for China to achieve its CO2 emission goals. However, solar and wind power generation are concentrated mainly in the west and north regions of China, while the electricity load is mainly in the east and south regions. Thus, power transmission has an important role in securing a stable electricity supply. Even though electricity transmission has increased rapidly in recent years, the potential for improving energy use efficiency remains high, especially for renewable energy such as solar and wind (which are not stable or reliable) because of their time variant characteristic. Thus, more electricity transmission is needed to integrate renewable energy into the electricity grids. In addition, innovating market trading mechanisms and strengthening the coordination among grids are key to solving the serious problems of wind and solar power abandonment and to promoting the balance between their demand and supply. The north and east grids were the main sources of the increase in CO2 emissions from electricity generation during 2007e2012. More than 64.1% (443.72 million tons) of the increase in CO2 emissions from electricity generation occurred in the north and east grids, whereas the northeast grid accounted for only 3.2% (23.48 million tons) of the increase. The influence of the power transmission structure was also investigated, with CO2 emissions decreasing by 26.7 and 6.4 million tons in the north and central grids, respectively, because of changes in the power transmission structure. However, changes in the power transmission structure in the northwest and northeast grids promoted CO2 emissions by 31.2 and 4.8 million tons, respectively. Thus, specific emissions reduction strategies for each grid and province-specific and industrycentric policy recommendations should be designed by central government, given the large variations of power demand structure, energy transmission structure and energy generation technology across China. Acknowledgements This work was supported by the National Science Fund for Distinguished Young Scholars of China (71725005), National Natural Science Foundation of China (Nos. 71704163, 51721093), Distinguished Young Scholar of Beijing (2018), special fund of State

Key Joint Laboratory of Environment Simulation and Pollution Control (18L03ESPC), and China Postdoctoral Science Foundation (2018M631418). References Ang, B.W., 2015. LMDI decomposition approach: a guide for implementation. Energy Policy 86, 233e238. Ang, B.W., Huang, H.C., Mu, A.R., 2009. Properties and linkages of some index decomposition analysis methods. Energy Policy 37, 4624e4632. Ang, B.W., Liu, F.L., Chung, H.S., 2004. A generalized Fisher index approach to energy decomposition analysis. Energy Econ. 26, 757e763. Ang, B.W., Xu, X.Y., 2013. Tracking industrial energy efficiency trends using index decomposition analysis. Energy Econ. 40, 1014e1021. Ang, B.W., Zhang, F.Q., 2000. A survey of index decomposition analysis in energy and environmental studies. Energy 25, 1149e1176. € m, U., Lescale, V.F., Menzies, D., Weimin, M., Zehong, L., 2010, October. The Åstro Xiangjiaba-Shanghai 800 kV UHVDC project, status and special aspects. In: Power System Technology (POWERCON), 2010 International Conference on. IEEE, pp. 1e6. Baiocchi, G., Minx, J.C., 2010. Understanding changes in the UK’s CO2 emissions: a global perspective. Environ. Sci. Technol. 44, 1177e1184. Brizga, J., Feng, K., Hubacek, K., 2014. Drivers of greenhouse gas emissions in the Baltic States: a structural decomposition analysis. Ecol. Econ. 98, 22e28. Cai, B., Zhang, W., Hubacek, K., Feng, K., Li, Z., Liu, Y., Liu, Y., 2019. Drivers of virtual water flows on regional water scarcity in China. J. Clean. Prod. 207, 1112e1122. CEP-Editorial Board of the China Electric Power Yearbook, 2011. China Electric Power Yearbook 2008-2013. China Electric Power Press, Beijing (in Chinese). CEP-Editorial Board of the China Electric Power Yearbook, 2014. China Electric Power Yearbook 2014. China Electric Power Press, Beijing (in Chinese). Couture, T.D., Cory, K., Kreycik, C., Williams, E., 2010. A policymaker’s guide to feedin tariff policy design. No. NREL/TP–6A2-44849, 1219187. https://doi.org/10. 2172/1219187. Chang, Y.F., Lewis, C., Lin, S.J., 2008. Comprehensive evaluation of industrial CO2 emission (1989e2004) in Taiwan by inputeoutput structural decomposition. Energy Policy 36, 2471e2480. Chen, G., Hou, F., Chang, K., Zhai, Y., Du, Y., 2018. Driving factors of electric carbon productivity change based on regional and sectoral dimensions in China. J. Clean. Prod. 205, 477e487. Chen, Q., Kang, C., Ming, H., Wang, Z., Xia, Q., Xu, G., 2014. Assessing the low-carbon effects of inter-regional energy delivery in China’s electricity sector. Renew. Sustain. Energy Rev. 32, 671e683. https://doi.org/10.1016/j.rser.2013.12.050. Chen, Z.M., Chen, G.Q., Chen, B., 2010a. Embodied carbon dioxide emissions of the world economy: a systems input-output simulation for 2004. Procedia Environmental Sciences 2, 1827e1840. Chen, W., Li, H., Wu, Z., 2010b. Western China energy development and West to east energy transfer: application of the western China sustainable energy development model. Energy Policy 38, 7106e7120. China Electricity Council, 2018. China Power Industry Annual Development Report 2018. China Market Press, Beijing (in Chinese). Dietzenbacher, E., Los, B., 1998. Structural decomposition techniques: sense and sensitivity. Econ. Syst. Res. 10, 307e324. Feng, K., Davis, S.J., Sun, L., Hubacek, K., 2015. Drivers of the US CO2 Emissions 19972013. Nat. Commun. vol. 6. https://doi.org/10.1038/ncomms8714. Feng, K., Siu, Y.L., Guan, D., Hubacek, K., 2012. Analyzing drivers of regional carbon dioxide emissions for China. Jouranl of Industrial Ecology 16, 600e611. Feng, L., Chen, B., Hayat, T., Alsaedi, A., Ahmad, B., 2017. The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. J. Clean. Prod. 163, S322eS328. Guan, D., Hubacek, K., Weber, C.L., Peters, G.P., Reiner, D.M., 2008. The drivers of Chinese CO2 emissions from 1980 to 2030. Glob. Environ. Chang. 18, 626e634. Guan, D., Peters, G.P., Weber, C.L., Hubacek, K., 2009. Journey to world top emitter: an analysis of the driving forces of China’s recent CO2 emissions surge. Geophys. Res. Lett. 36. Guo, R., Zhu, X., Chen, B., Yue, Y., 2016a. Ecological network analysis of the virtual water network within China’s electric power system during 2007-2012. Appl. Energy 168, 110e121. Guo, Z., Ma, L., Liu, P., Jones, I., Li, Z., 2016b. A multi-regional modelling and optimization approach to China’s power generation and transmission planning. Energy 116, 1348e1359. https://doi.org/10.1016/j.energy.2016.06.035. Hoekstra, R., van den Bergh, J.C., 2003. Comparing structural decomposition analysis and index. Energy Econ. 25, 39e64. Hu, X., Hao, Z., 2011. July). Simulation and analysis of induced voltage and induced current on overhead ground wire of Jindongnan-Nanyang-Jingmen 1000kV UHV AC transmission line. In: Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on. IEEE, pp. 622e625. International Energy Agency (IEA), 2016. CO2 emissions from fuel combustion 2016- Highlights. https://emis.vito.be/sites/emis.vito.be/files/articles/3331/ 2016/CO2EmissionsfromFuelCombustion_Highlights_2016.pdf. International Energy Agency (IEA), 2018. CO2 emissions from fuel combustion 2018- Highlights. https://www.iea.org/statistics/co2emissions/. IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories.

S. Wang et al. / Journal of Cleaner Production 220 (2019) 1143e1155 Intergovernmental Panel on Climate Change. NGGIP Publications, IGES, Japan. Jalil, A., Mahmud, S.F., 2009. Environment Kuznets curve for CO2 emissions: a cointegration analysis for China. Energy Policy 37, 5167e5172. Kone, A., Buke, T., 2015. A Decomposition Analysis of Energy-Related CO2 Emissions: the Top 10 Emitting Countries. Energy Systems and Management. Springer, Cham, pp. 65e77. Li, Y., Lukszo, Z., Weijnen, M., 2016. The impact of inter-regional transmission grid expansion on China’s power sector decarbonization. Appl. Energy 183, 853e873. Lin, K.-C., Purra, M.M., 2019. Transforming China’s electricity sector: politics of institutional change and regulation. Energy Policy 124, 401e410. https://doi. org/10.1016/j.enpol.2018.07.041. Lindner, S., Liu, Z., Guan, D., Geng, Y., Li, X., 2013. CO2 emissions from China’s power sector at the provincial level: consumption versus production perspectives. Renew. Sustain. Energy Rev. 19, 164e172. Liu, Z., Zhang, Q., 2013. Study on the development mode of national power grid of China. Proceedings of the CSEE 33, 1e10. Malla, S., 2009. CO2 emissions from electricity generation in seven Asia-Pacific and North American countries: a decomposition analysis. Energy Policy 37, 1e9. Mi, Z., Meng, J., Guan, D., Shan, Y., Liu, Z., Wang, Y., Feng, K., Wei, Y.-M., 2017a. Pattern changes in determinants of Chinese emissions. Environ. Res. Lett. 12, 074003. https://doi.org/10.1088/1748-9326/aa69cf. Mi, Z., Wei, Y.-M., Wang, B., Meng, J., Liu, Z., Shan, Y., Liu, J., Guan, D., 2017b. Socioeconomic impact assessment of China’s CO2 emissions peak prior to 2030. J. Clean. Prod. 142, 2227e2236. Mi, Z., Zhang, Y., Guan, D., Shan, Y., Liu, Z., Cong, R., Yuan, X.-C., Wei, Y.-M., 2016. Consumption-based emission accounting for Chinese cities. Appl. Energy 184, 1073e1081. https://doi.org/10.1016/j.apenergy.2016.06.094. Ming, Z., Honglin, L., Mingjuan, M., Na, L., Song, X., Liang, W., Lilin, P., 2013. Review on transaction status and relevant policies of southern route in China’s WesteEast Power Transmission. Renew. Energy 60, 454e461. National Climate Strategy Center, 2015. China’s Regional Grid Baseline Emission Factor. NBS-National Bureau of Statistics of China, 2008-2013. China Statistical Yearbook 2008-2013. China Statistics Press, Beijing (in Chinese). Peters, G.P., Weber, C.L., Guan, D., Hubacek, K., 2007. China’s growing CO2 emissions-A race between increasing consumption and efficiency gains. Environ. Sci. Technol. 41, 5939e5944. Qu, S., Liang, S., Xu, M., 2017. CO2 emissions embodied in interprovincial electricity transmissions in China. Environ. Sci. Technol. 51 (18), 10893e10902. Rose, A., Casler, S., 1996. Inputeoutput structural decomposition analysis: a critical appraisal. Econ. Syst. Res. 8, 33e62. State Grid Corporation of China Power Exchange Annual Report, 2007-2012. State Grid Corporation of China. Su, B., Ang, B.W., 2012. Structural decomposition analysis applied to energy and emissions: some methodological developments. Energy Econ. 34, 177e188. Su, B., Ang, B.W., 2015. Multiplicative decomposition of aggregate carbon intensity change using inputeoutput analysis. Appl. Energy 154, 13e20. Su, B., Ang, B.W., Low, M., 2013. Inputeoutput analysis of CO2 emissions embodied in trade and the driving forces: processing and normal exports. Ecol. Econ. 88, 119e125. Sun, J.W., 1998. Changes in energy consumption and energy intensity: a complete decomposition model. Energy Econ. 20, 85e100. Sun, W., Cai, J., Yu, H., Dai, L., 2011. Decomposition analysis of energy-related carbon dioxide emissions in the iron and steel industry in China. Front. Environ. Sci. Eng. 6, 265e270. Wang, C., Zhan, J., Li, Z., Zhang, F., Zhang, Y., 2019. Structural decomposition analysis of carbon emissions from residential consumption in the Beijing-Tianjin-Hebei region, China. J. Clean. Prod. 208, 1357e1364. Wang, Q., Chen, Y., 2010. Status and outlook of China’s free-carbon electricity. Renew. Sustain. Energy Rev. 14, 1014e1025. Wang, Q., Su, B., Sun, J., Zhou, P., Zhou, D., 2015. Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities. Appl.

1155

Energy 151, 85e92. Wang, S., Cao, T., Chen, B., 2017. Watereenergy nexus in China’s electric power system. Energy Procedia 105, 3972e3977. Wang, S., Chen, B., 2017. The societal costs of deep decarbonization in China. Energy Procedia 142, 3115e3121. https://doi.org/10.1016/j.egypro.2017.12.453. Wang, S., Chen, B., 2018. Three-Tier carbon accounting model for cities. Appl. Energy 229, 163e175. https://doi.org/10.1016/j.apenergy.2018.07.109. Wang, W., Liu, X., Zhang, M., Song, X., 2014. Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China’s energy consumption. Energy 67, 617e622. Wang, Y., Zhao, H., Li, L., Liu, Z., Liang, S., 2013. Carbon dioxide emission drivers for a typical metropolis using inputeoutput structural decomposition analysis. Energy Policy 58, 312e318. Wei, J., Huang, K., Yang, S., Li, Y., Hu, T., Zhang, Y., 2017. Driving forces analysis of energy-related carbon dioxide (CO2) emissions in Beijing: an inputeoutput structural decomposition analysis. J. Clean. Prod. 163, 58e68. Xu, B., Lin, B., 2016. Regional differences of pollution emissions in China: contributing factors and mitigation strategies. J. Clean. Prod. 1454e1463. Xu, M., Li, R., Crittenden, J.C., Chen, Y., 2011. CO2 emissions embodied in China’s exports from 2002 to 2008: a structural decomposition analysis. Energy Policy 39, 7381e7388. Xu, X.Y., Ang, B.W., 2014. Analyzing residential energy consumption using index decomposition analysis. Appl. Energy 113, 342e351. Yan, D., Lei, Y., Li, L., 2017. Driving factor Analysis of carbon emissions in China’s power sector for low-carbon economy. Math. Probl Eng. 2017, 1e10. https://doi. org/10.1155/2017/4954217. Yi, B.-W., Xu, J.-H., Fan, Y., 2016. Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: a multi-region bottom-up optimization model. Appl. Energy 184, 641e658. Yin, J., Zheng, M., Chen, J., 2015. The effects of environmental regulation and technical progress on CO2 Kuznets curve: an evidence from China. Energy Policy 77, 97e108. Yu, Y., 2012. How to fit demand side management (DSM) into current Chinese electricity system reform? Energy Econ. 34, 549e557. https://doi.org/10.1016/j. eneco.2011.08.005. Zhang, Y.-F., 2015. The regulatory framework and sustainable development of China’s electricity sector. China Q. 222, 475e498. https://doi.org/10.1017/ S0305741015000727. Zhang, C., Anadon, L.D., 2014. A multi-regional inputeoutput analysis of domestic virtual water trade and provincial water footprint in China. Ecol. Econ. 100, 159e172. Zhang, C., Zhong, L., Liang, S., Sanders, K.T., Wang, J., Xu, M., 2017. Virtual scarce water embodied in inter-provincial electricity transmission in China. Appl. Energy 187, 438e448. Zhang, M., Li, H., Zhou, M., Mu, H., 2011. Decomposition analysis of energy consumption in Chinese transportation sector. Appl. Energy 88, 2279e2285. Zhang, M., Liu, X., Wang, W., Zhou, M., 2013. Decomposition analysis of CO2 emissions from electricity generation in China. Energy Policy 52, 159e165. Zhang, Y., Yu, Y., Ma, T., 2018. System optimization of long-distance energy transportation in China using ultra-high-voltage power transmission. J. Renew. Sustain. Energy 10, 045503. https://doi.org/10.1063/1.5013177. Zheng, L.L., Zhu, Q.G., 2012. The existence research on Kuznets Curve of China’s carbon emission. Stat. Res 5, 58e65 (in Chinese). Zhou, B., Jin, X., Hong, C., Zhang, P., Zhigang, W., 2013. Influences of ± 800 kV Yunnan-Guangdong HVDC System on Security and Stability of China Southern Power Grid. Zhou, H., Qiu, W., Sun, K., Chen, J., Deng, X., Qian, F., et al. (Eds.), 2018. Ultra-high Voltage AC/DC Power Transmission. Springer Berlin Heidelberg. Zhu, X., Guo, R., Chen, B., Zhang, J., Hayat, T., Alsaedi, A., 2015. Embodiment of virtual water of power generation in the electric power system in China. Appl. Energy 151, 345e354.