Analysis of China's regional thermal electricity generation and CO2 emissions: Decomposition based on the generalized Divisia index

Science of the Total Environment 682 (2019) 737–755

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Analysis of China's regional thermal electricity generation and CO2 emissions: Decomposition based on the generalized Divisia index Qingyou Yan a, Yaxian Wang a,b,⁎, Tomas Baležentis c, Dalia Streimikiene c a b c

School of Economics and Management, North China Electric Power University, Beijing 102206, PR China Electrical and Computer Engineering Department, Illinois Institute of Technology, Chicago, United States Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The GDIM is employed to quantize eight affecting factors of CO2 emissions. • Carbon intensity of GDP and technology effect decreased most emissions. • Northeast and East regions made contributions to reduce CO2 emissions, whereas North and Northwest regions exerted a drag. • The decreasing effect of technology tended to diminish since 2013. • The policy of “developing large units and suppressing small ones”should be adjusted.

a r t i c l e

i n f o

Article history: Received 28 March 2019 Received in revised form 10 May 2019 Accepted 11 May 2019 Available online 17 May 2019 Editor: Huu Hao Ngo Keywords: Carbon dioxide emissions Index Decomposition Analysis Thermal electricity generation Generalized Divisia Index Model

a b s t r a c t Even though Chinese government has been promoting the development of renewable energy, coal-fired thermal electricity generation still accounts for nearly 70% of the total electricity generation, proving to be the largest carbon dioxide (CO2) emitter in China. Uncovering the driving forces of CO2 emissions, thus, is of great significance to provide appropriate mitigation policies for the sustainable development of China's thermal electricity generation. In this regard, this study aims to fill a research gap by applying Index Decomposition Analysis (IDA) approach via the Generalized Divisia Index Model (GDIM) to examine the driving factors behind the CO2 emission changes in China's thermal electricity generation during 2000–2016. The decomposition results indicate that the factors contributing to the growth in CO2 emission can be ranked as follows: economic activity (52.0%), electricity demand (45.8%) and energy use (36.2%), whereas factors suppressing the growth in the mission are carbon intensity change (−17.7%), technology (−11.3%), energy mix (−2.4%), energy efficiency (−1.7%) and electricity efficiency (−0.9%). Noteworthy, the promoting effect of the economic activity varied little with time, whereas that of electricity demand and energy use exhibited a downward trend in general. Besides, though the progress in technology contributed a lot to the decrease of CO2 emission, its decreasing effects tended to diminish since 2013. Northeast and East regions appeared as those contributing to the mitigation of the CO2

Abbreviations: AAGR, Average Annual Growth Rate; CO2, carbon dioxide; CSY, China Statistical Yearbook; CESY, China Energy Statistical Yearbook; GDIM, Generalized Divisia Index Method; GDP, Gross Domestic Product; I-O, Input-Output; IDA, Index Decomposition Analysis; IPCC, Intergovernmental Panel on Climate Change; kg, kilogram; KP, Kyoto Protocol; kW, kilowatt; kWh, kilowatt hour; LNG, Liquefied natural gas; LPG, Liquefied petroleum gas; LMDI, Log Mean Divisia Index; m3, cubic meter; MJ, Million joule; Mt, Million ton; MW, Megawatt; Mtce, Million tonne coal equivalent; tc, tonne carbon; t, tonne; TJ, Terajoule; UNFCCC, United Nations Framework Convention on Climate Change. ⁎ Corresponding author at: School of Economics and Management, North China Electric Power University, Beijing 102206, PR China. E-mail address: [email protected] (Y. Wang).

https://doi.org/10.1016/j.scitotenv.2019.05.143 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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emissions from China's thermal electricity generation, whereas the North and Northwest regions exerted a lag to the abatement of CO2 emission. © 2019 Elsevier B.V. All rights reserved.

Nomenclature i type of fuel j region t time period Etij consumption of fuel i in region j in year t Fi carbon emission factor for fuel i Oi carbon oxidation factor for fuel i Etj energy consumption in region j in year t Ctj CO2 emission in region j in year t Gtj GDP of region j in year t Ptj electricity output of region j in year t 1 t Rj = Ctj /Gtj CO2 emission per unit of GDP 2 t Rj = Ctj /Etj CO2 emission per unit of energy use 3 t Rj = Ctj /Ptj CO2 emission per unit of electricity 4 t Rj = Ptj /Gtj electricity output per unit of GDP 5 t Rj = Etj /Gtj energy use per unit of GDP

1. Introduction In the context of addressing the global warming, low carbon economy featuring a win-win relationship between low carbon emission and economic development has become a direction to follow around the world (Kameyama et al., 2016; Liu et al., 2019). Following the United Nations Framework Convention on Climate Change (UNFCCC)1 and the Kyoto Protocol (KP),2 Paris Agreement is the third landmark international treaty which represents global determination and efforts to balance economic growth and climate change. In December 2015, negotiators from more than 190 countries3 adopted the Paris Agreement on climate change and low carbon development aiming to achieve 2 °C global temperature control target before the year of 2100. CO2 is supposed to be the major greenhouse gas and China's emission reduction targets are also focused on CO2. As one of the largest CO2 emitters and economies of the world (Mi et al., 2017), China submitted the document of Intended Nationally Determined Contributions including the commitment that China's CO2 emission would peak around 2030 and the carbon intensity would be reduced by 60–65% in 2030 compared to 2005. Furthermore, the more stringent targets of energy use and CO2 emission were put forward in China's National 13th Five-year Plan: the CO2 emission intensity and energy intensity (measured per unit of GDP) would be reduced by 18% and 15%, respectively, between 2015 and 2020. The National carbon emission trading market construction plan (power generation industry) adopted in 2017 instituted carbon trading system in China and electricity generation was included in the scheme. Along with China stepping into the mid-later stage of industrialization, the demand of electricity is likely to maintain stable rate of growth

1 The UNFCCC put forward in 1992 is the first international convention to deal with the adverse effect of global warming on human economic society as well as a basic framework for international cooperation on combating global climate change. 2 The KP raised in 1997 stipulated that developed countries were committed to reduce CO2 emissions from 2005 onwards whereas developing countries are obliged to cut their CO2 emissions from 2012 onwards. 3 Up to now, the only two countries that refused to join the Paris Agreement are Syria and the United States.

in the perspective (Song et al., 2018b; Zhang et al., 2019). In 2017, the total electricity consumption reached 6.3 trillion kWh in China with the annual per capita electricity consumption of 4538 kWh. Meanwhile, China's total installed capacity has reached 1.78 billion kW by the end of 2017.4 Noteworthy, electric generation accounted for approximately 50% of the coal consumption in China meaning that more than 40% of China's total CO2 emissions originated from electricity generation, especially coal-fired thermal electricity generation (Zhao et al., 2013; Yuan et al., 2018). According to World Energy Outlook 2018 issued by International Energy Agency, China will remain the largest coal consumer and account for 40% of the global coal demand by 2040. This may be due to the fact that China's coal-based resource endowment determines the coal-dominated energy-mix. Thermal electricity generation allows for stable supply, peaking regulation, central heating services and lower electricity generation costs. Although Chinese government has stimulated the development of clean energy (e.g. hydroelectricity power, nuclear power, solar power and wind power), China's power structure is still dominated by thermal power generation (see Fig. 1) with corresponding level of CO2 emissions (Xie et al., 2012; Du and Mao, 2015; Xie et al., 2019). On the other hand, nearly all the CO2 emissions from electricity sector are caused by thermal electricity, because the production process of clean energy does not generate CO2 directly. China's thermal electricity generation, thus, faces intense pressure of undertaking CO2 emission mitigation obligations (He et al., 2017; Pu et al., 2019; Wu et al., 2019). To reduce the CO2 emission from thermal electricity generation and to fulfill China's emission reduction commitment, comprehensive analysis is needed to explore the main drivers behind the CO2 emission and deliver sound policy implications. The mitigation of CO2 emission, representing the environmental pressures, should nevertheless allow for economic growth (Chen et al., 2018; Qi and Li, 2017; Yuan and Zhang, 2017 Wu et al., 2017; Mardani et al., 2019). Accordingly, there have been evidence based frameworks proposed by Song et al. (2018a) and Feng et al. (2019) to comprehensively analyze performance of the economies in terms of desirable and undesirable outputs. The transition towards renewables has been considered as an appealing pathway for reducing the environmental degradation along with economic growth (Popescu et al., 2019; Simionescu et al., 2017; Zhao et al., 2017a). The balance between environmental considerations and economic growth is particularly important to China, the largest developing country in the world. Following the concept of low carbon economy, the energy-related CO2 emission caused by China's thermal electricity generation should be studied along with dynamics in economic activity (Mi et al., 2015; Springer et al., 2019). In this regard, decomposition analysis can be employed to factorize the changes in the aggregate variable (e.g. CO2 emission) with respect to different factors (Yan et al., 2017; Wang et al., 2019b). Index Decomposition Analysis (IDA) is an important strand of decomposition analysis (Ang and Choi, 1997; Xu and Ang, 2013; Huang et al., 2019). The major advantages of the IDA are diverse decomposition identities and relatively low data intensity (Hoekstra and Van Den Bergh, 2002). IDA was applied for analysis of CO2 emissions at different levels of aggregation, e.g. international (Román-Collado and Morales-Carrión, 2018; Wang and Ang, 2018; Lopez et al., 2018), national (Mahony, 2013; Zhang and Da, 4 See http://shoudian.bjx.com.cn/news/20180201/878406-2.shtml Analysis and Forecast Report of National Power Supply and Demand during 2017–2018 China Electricity Council.

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Thermal

Hydro

Nuclear

Wind

Solar

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The remainder of the paper is organized as follows. Section 2 presents a comparison of approaches to decomposition analysis. Section 3 describes the techniques employed for the analysis and data sources. Section 4 focuses on the trends in the CO2 emissions and brings forward the results of GDIM for China's thermal electricity sector at regional level. Section 5 draws conclusions and policy implications.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

2. Comparison of decomposition methods

2010

2011

2012

2013

2014

2015

2016

2017

Fig. 1. The installed capacity of different power generations in China, 2010–2017. Data source: the website of National Bureau of Statistics of the People's Republic of China.

2015; Moutinho et al., 2018), provincial (Zhang et al., 2016; Yan et al., 2018) or a single sector (Karmellos et al., 2016; Wang et al., 2018a; Wang et al., 2018b). IDA has also been applied to analyze the CO2 emissions from China's electricity generation sector. For instance, He et al. (2013) decomposed China's Aggregate Electricity Intensity change into structure effect and intensity effect. Zhang et al. (2013) analyzed the factors for CO2 emissions in China's electricity generation sector during 1991–2009. Wang et al. (2018b) analyzed the factors of Aggregate Carbon Intensity in China's electricity sector during 1995–2014 and accounted for spatial effects. Indeed, most of the applications of the IDA to China's electricity generation sector did not consider different types of energy, although different types of electricity generation are present in China. This can be partly explained by the fact that the structure of Chinese electricity generation sector had been rather homogenous prior to the expansion of the renewables. Given the dominant roles of coal-fired thermal electricity generation in China's CO2 emission reduction, Zhou et al. (2014) and Yan et al. (2016) have done special research emphasizing the decomposition analysis on CO2 emissions in thermal electricity generation. Zhou et al. (2014) analyzed the main drivers of CO2 emissions from China's seven regional thermal electricity generation grids during 2004–2010. Yan et al. (2016) examined the effects of regional economic development on CO2 emissions from this sector. However, the use of different techniques for operationalizing the IDA is needed to ensure the robustness of the results. In addition, the IDA identities can be expanded to account for diverse factors governing the CO2 emission in China's thermal electricity generation. Even though Log Mean Divisia Index (LMDI) has been extensively applied for analysis of CO2 emission across different sectors (Lee and Oh, 2006; Li et al., 2019; Shen et al., 2018), Vaninsky (2014) outlined certain limitations associated with LMDI approach. First, results of decomposition mainly depend on the factor interdependence. Second, the effects of absolute and relative indicators on the resulting indicators cannot be included simultaneously. Vaninsky (2014) put forward Generalized Divisia Index Method (GDIM) incorporating more complicated interrelationships among underlying factors defining the change in the aggregate variable and, thus, allows for a finer decomposition analysis. Shao et al. (2016) employed GDIM to identify the main driving forces of energy-related CO2 emissions from China's mining sector and its five sub-sectors. Yan et al. (2017) applied GDIM to decompose the CO2 emission changes in European countries' agriculture sector over the period of 1995–2012. For the sake of comprehensive and non-linear identity, this study will apply GDIM to measure the key forces (including effects of technology, electricity efficiency, carbon intensity, energy mix, energy efficiency, economic activity, electricity demand and energy use) of CO2 emissions from China's thermal electricity generation.

Decomposition analysis can be applied to factorize the change in CO2 emission into several predefined factors, such as economic development, energy structure, carbon factor, energy intensity among others. The decomposition allows quantifying the factors driving CO2 emission and, thus, providing policy implications for meeting the carbon emission targets. Myers and Nakamura (1978) and Bossanyi (1979) can be given as the earliest examples of decomposition applied in energy studies. Since then, decomposition analysis found numerous applications and extensions in CO2 emission and other domains. Index Decomposition Analysis (IDA) and Structural Decomposition analysis (SDA) are two main decomposition techniques widely utilized to study the driving forces of CO2 emission changes over time (Hoekstra and Jeroen, 2003, Su and Ang, 2012; Wang et al., 2017). Su and Ang (2012) and Wang et al. (2017) conducted literature reviews on these two techniques (i.e. IDA and SDA) in energy and carbon emission studies. There are obvious differences between IDA and SDA in terms of data requirements, application scope and time span: • IDA requires data on the factors entering the IDA identity whereas SDA rests on the Input-Output (I-O) table. • IDA is more elastic in modeling and easier to actualize. By contrast, SDA is contracted on I-O model which restrict its application scope. • Compared with IDA, SDA analyses are usually characterized by relative short time periods because I-O tables are not always issued annually.

Despite the I-O information and inter-linkages among the sectors of the economy included in SDA decomposition analysis, the data acquisition of the I-O tables is the restraining factor for employing SDA. The increasing spread of the applications of IDA on CO2 emission analysis has been documented by Su and Ang (2012). The applications of IDA are available for different regions and sectors (Karmellos et al., 2016; Zhao et al., 2017b; Xu and Tao, 2018). Various types of indices can be applied to establish models for IDA. Among these, LMDI proposed by Ang and Choi (1997) is regarded as the most widespread, practical, and precise index applied in IDA due to its theoretic basis, flexibility, and result interpretation (Ang, 2004; Xu et al., 2014; Wang et al., 2017; Wang et al., 2019a). However, recently, Vaninsky (2014) pointed that two deficiencies are discovered in the conventional IDA approaches including LMDI. One is that the present IDA methods always consider only one absolute indicator as a quantitative indicator failing to take account of the effects of the other absolute indicators. Another shortcoming is that different effects results may appear as the IDA identity is altered which is counterintuitive and may cause paradoxes. In this context, Vaninsky (2014) proposed General Divisia Index Method (GDIM), an index decomposition technique, which can avoid these two deficiencies. GDIM allows for quantification of the effects of all absolute and relative factors underlying a certain IDA identity. Let Xi(i = 1, 2, 3) denote three absolute indicators involved in the CO2 emission identity. Then assume CO2 X 1 X 2    X 3 . Followthat CO2 emission can be calculated as CO2 ¼ X1 X2 X3 ing Vaninsky (2014), the main differences between LMDI and GDIM in decomposition analysis of CO2 emission changes are shown in Fig. 2. In comparison with traditional method, appropriate programs based on different identity matrices and Jacobian matrices are necessary for

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Fig. 2. The major differences between LMDI and GDIM in the decomposition analysis of CO2 emission changes.

GDIM which makes its solution process relatively complex. However, GDIM has prominent advantages in terms of the decomposition analysis of CO2 emission, as the example exhibited in Fig. 2. First, GDIM can analyze more significant and practical factors compared with traditional decomposition method (such as LMDI) when given the identical data by quantizing the effects of both absolute and relative factors. Second, the relative indicators of GDIM are not affected by linear relations and can be freely combined with absolute indicators through changing Jacobian matrix. For instance, X2/X3 in LMDI represents the inverse of electricity intensity, yet we can measure electricity intensity, i.e. electricity efficiency effect, directly when applying GDIM. 3. Methodology and data

4 t Rj

¼

5 t Rj

¼

Gtj

 Gtj ¼

C tj Etj

 Etj ¼

C tj P tj

 P tj :

ð1Þ

Furthermore, these equations are integrated into a factor model: t

t

Etj Gtj

¼

¼

t

C tj ¼ 1 R j  Gtj ¼ 2 R j  Etj ¼ 3 R j  P tj ;

ð2Þ

t

t

t

t

 Gtj ¼ 2 R j  Etj ; 1 R j  Gtj ¼ 3 R j  P tj ; 4 R j ¼

C tj =P tj C tj =Gtj C tj =Etj

ΦX ¼

1 t Rj B B 1 Rt B j B 4 t B− R j @ t −5 R j

P tj

Etj 5 t ; R ¼ ; j Gtj Gtj

ð3Þ

¼

1 t Rj 3 Rt j

;

ð4Þ

¼

1 t Rj 2 Rt j

:

ð5Þ

t

Gtj

−2 R j

−Etj

0

Gtj

0

0

0

0

0

1

0

0

−3 R j

−P tj

0

0

1

0

−Gtj

0

0

0

0

t

0

1T

C 0C C C : 0C A Gtj

ð6Þ

The Jacobian matrix consists of all first order partial derivatives. In our case, it represents the marginal impacts of the factors of the CO2 emission. Let L denote a time span; upper index “+” represent the generalized inverse matrix; I represent the identity matrix; ∇C denote (1Rtj , Gtj , 0,0,0,0, 0,0)T. In addition, assume that the columns of the matrix −1 T T ΦX are linearly independent, then we can get Φ+ ΦX. AcX = (ΦXΦX) counting for factor interconnections, the changes in CO2 emissions caused by different factors can be quantified as follows. Z ΔC ½X jΦ ¼

1 t Rj

C tj =Gtj

0

The application of GDIM allows modeling multiple interrelationships among the variables included into the IDA identity. Therefore, the possible linkages need to be enumerated. Following Vaninsky (2014), the CO2 emissions arising from the j-th regional thermal electricity generation system in the t-th year can be calculated as C tj

Gtj

where Gtj , Etj and Ptj are three absolute factors whereas 1Rtj , 2Rtj , 3Rtj , 4Rtj and 5 t Rj are five relative factors. The details regarding the eight factors and their effects are shown in Nomenclature and Table 1. For easier presentation, let Gtj , Etj , Ptj , 1Rtj , 2Rtj , 3Rtj , 4Rtj and 5Rtj be represented by X. Let C(X) represent the gradient of the function; ΦX represent the Jacobian matrix. Thus ΦX can be shown as follows.

3.1. The Generalized Divisia Index Model

C tj ¼

P tj

L

  ∇C T I−ΦX Φþ X dX:

ð7Þ

This decomposition factorizes the changes in CO2 emissions into three absolute indicators (i.e. Gtj , Etj and Ptj ) as well as five relative factors

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4. Results and discussion

Table 1 The definitions of eight variables and their effects in decomposition analysis.

4.1. The dynamics in absolute indicators

Variable

Definition

Effect

Gtj Etj

GDP, i.e. economic activity Energy use

Economic activity effect Energy use effect Electricity demand effect

Ptj

Electricity output

1 t Rj

= Ctj /Gtj

Carbon intensity of GDP

2 t Rj

=

3 t Rj 4 t Rj 5 t Rj

= Ctj /Ptj = Ptj /Gtj = Etj /Gtj

Ctj /Etj

Carbon intensity of energy, i.e. carbon factor Carbon intensity of electricity Electricity intensity Energy intensity

Carbon intensity effect Energy mix effect Technology effect Electricity efficiency Energy efficiency

(i.e. 1Rtj , 2Rtj , 3Rtj , 4Rtj and 5Rtj ) thus allowing for a more comprehensive discussion. The R-language computer program of the GMDI model is offered in Appendix B. 3.2. Data used Following the principles outlined by Intergovernmental Panel on Climate Change (IPCC), the CO2 emissions Ctj resulting from thermal electricity generation of region j in year t can be calculated via C tj ¼

X

Etij  F Ci  Oi  44=12;

741

ð8Þ

where 44/12 denotes the ratio of molecular weight ratio of CO2 to the atomic weight of carbon. As the research period is relatively short we assume that the CO2 emission factors are constant. The research period spans over the years 2000–2016. Initial data on energy consumption (in Mtce)5 and electricity output (in billion kWh) are retrieved from China Energy Statistical Yearbook (CESY) (Department of Energy Statistics, 2001–2017), whereas regional GDP (in billion CNY, base year 2000) are obtained from the China Statistical Yearbook (CSY) (National Bureau of Statistics, 2001–2017). The values of FCi and Oi are based on the IPCC methodology (Eggleston et al., 2006; Yan et al., 2016).6 Following the data from CESY (Department of Energy Statistics, 2001–2017) and the data presented in Table A2, we derive Etij. This study focuses on regional thermal electricity generation in China. As shown in Fig. 3, the six major state-owned companies control the regions of China's power grid7: • North China Power Grid Company (i.e. North): Beijing, Tianjin, Hebei, Shanxi, Shandong and Inner Mongolia. • Northeast China Power Grid Company (i.e. Northeast): Liaoning, Jilin and Heilongjiang. • East China Power Grid Company (i.e. East): Shanghai, Jiangsu, Zhejiang, Anhui and Fujian. • Central China Power Grid Company (i.e. Central): Jiangxi, Henan, Hubei, Hunan, Chongqing and Sichuan. • Northwest China Power Grid Company (i.e. Northwest): Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. • South China Power Grid Company (i.e. South): Guangdong, Guangxi, Guizhou, Yunnan and Hainan.

5 The detailed information of the fuel types consumed in thermal electricity generation and the related conversion factors used to transfer to coal equivalents are present in Table A1. 6 FCi and Oi for each fuel type i are summarized in Table A2. 7 In 2002, the vertical structure of the State Power Corporation of China was unbundled into power generation companies, power grid companies, and engineering and service companies. This measure aims to break the monopoly, introduce the competition and build competitive markets.

In this subsection, we look at the dynamics of CO2 emissions and three absolute indicators for China's six regional power grids during 2000–2016. Fig. 4 depicts the CO2 emissions and their Average Annual Growth Rates (AAGRs) in China's thermal electricity sector and its six regional thermal electricity power grids during 2000–2016. In general, the trends in Fig. 4 are upward (with exceptions for certain years), which suggests the CO2 emission from China's thermal electricity sector had doubled for the Northeast region (increasing least) and increased five-fold for the Northwest region (increasing most). The nation-wide CO2 emission from thermal electricity generation increased by some 207.4% with AAGR of 7.3% during 2000–2016. Given the underlying rates of growth in the emission, the regional power grids can be grouped into high-, medium- and low- growth power grids. Highgrowth power grids are Northwest (11.8%) and North (9.3%) regions. Medium-growth power grids are South (6.1%) and Central (6.4%) ones. Low-growth group includes East (5.4%) and Northeast (4.4%) ones. Fig. 5 indicates the spatial distribution of CO2 emissions, energy use, electricity output and GDP across the six power grids. As is shown in Fig. 5a, the shares of CO2 emissions arising from Northeast and East in the whole sample presented a downward trend and shrunk to 7.9% and 21.8% in 2016, respectively. Thus, these two regions contributed to the goals of emission mitigation. By contrast, North and Northwest followed an upward trend reaching 33.7% and 13.1% in 2016, respectively. Furthermore, the shares of Central (from 14.8% in 2000 to 13.0% in 2016) and South (from 12.6% in 2000 to 10.5% in 2016) did not show very significant decrease during the study period. Fig. 5b and c presents the shares of energy use and electricity output for different regions. The trends in the energy use and electricity output correspond to those in the CO2 emission in general. However, certain differences were observed due to the changes in carbon factor (i.e. adoption of different technologies). For instance, the share of electricity output in South varied little (from 24.5% in 2000 to 24.0% in 2016), but the shares of energy use and CO2 emission exhibited conspicuous reduction trends across 2000–2016 (from 28.9% to 21.8% for CO2 emission and from 28.9% to 21.9% for energy use). Besides, the share of energy use in Northwest increased from 6.8% in 2000 to 12.5% in2016, whereas the share of CO2 emission added from 6.8% in 2000 to 13.1% in 2016. Therefore, one needs to isolate multiple factors governing the changes in the CO2 emission through the IDA. The trends in Fig. 5d indicate that the shares of GDP remained stable on the whole across the six regions during 2000–2016. Howbeit, even though the share of GDP in North is not relatively high, the region emitted the highest share of CO2 (see Fig. 5a). In contrast, Central region with the largest GDP did not show the highest share of CO2 emission. The comparison of GDP and CO2 emission in North and Central regions suggests that economic growth and emission can be decoupled in China's thermal electricity sector. Of course, this conclusion is based on the fact that China's regional power grids are independent in thermal electricity production and the transmissions of thermal electricity are mainly within regions.8 Therefore, chain-linked and period-wise decomposition analyses are required to account for changes in both absolute and relative factors driving the changes in CO2 emission across the power grid regions in China. 8 In order to address the problem of unbalanced distribution of energy resources and electricity demand, China has been implementing the “West-East Electricity Transmission Project” since 2001. The project mainly includes three major corridors, i.e. southern, central and northern corridors. The central corridor only transports hydropower and the thermal electricity transportations of southern and northern corridors are intraregional. Specifically, the southern corridor transports thermal electricity from Guizhou and Yunnan to Guangdong, and the northern corridor transports thermal electricity from Shanxi and Inner Mongolia to Beijing, Tianjin and Hebei.

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Fig. 3. The spatial distribution of China's six regional power grids. (Tibet, Hong Kong SAR, Macao SAR and Taiwan are not considered in this study because of insufficient energy data.)

4.2. The dynamics in relative indicators The five relative indicators (as defined in Section 3.1) can further shed light on the changes of CO2 emission in China's thermal electricity generation. Fig. 6 presents the dynamics in the relative indicators associated with thermal electricity generation at the national level. Though the trends of five relative indicators were not pronounced during 2000–2005, they all had varying degrees of reduction on the whole since 2005. This shift could be attributed to the shutdown of small power units with high energy consumption and heavy pollution during

North

Northeast

East

Central

China's 11th Five-Year Plan (2006–2010). The small thermal power units with a total capacity approximately 7600 MW were closed in China during 2006–2010 suggesting conspicuous emission reduction and energy saving in thermal electricity generation. Consequently, carbon intensity of GDP (−61.1%), energy intensity (−59.0%) and electricity intensity (−48.8%) all decreased more than 45% in 2016 in comparison with the year of 2000. Howbeit, the decrease of electricity intensity was not evident if contrasted to carbon intensity of GDP and energy intensity. Furthermore, the carbon intensity of electricity presents negative trends (decreasing −24.0% in 2016 compared with

Northwest

South

Total

700% 600%

AAGR=11.8%

500% 400%

AAGR=9.3%

300%

AAGR=7.3% AAGR=6.4% AAGR=6.1% AAGR=5.4% AAGR=4.4%

200% 100% 0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Fig. 4. Indices of CO2 emission for China's thermal electricity sector and its six regional power grids, 2000–2016.

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Fig. 5. The spatial structure of carbon dioxide emissions and absolute indicators for the six grid regions of China, 2000–2016.

2000) stemming from the technical improvements for emission reduction. The change in the carbon factor (−5.1%) was rather subdued as we focus on the thermal generation only. In order to identify the similarities and differences in the dynamics of the relative factors across the six regions, Fig. 7 presents the

Carbon intensity of GDP Carbon intensity of electricity

corresponding trends. For the high-growth group (see Fig. 7a–b), the decreasing rates of relevant indicators are irregular and relatively small. This phenomenon is especially evident in Northwest region where the changes of carbon factor (−0.20%) and electricity intensity (−9.38%) during 2000–2016 were very minor if contrasted to the

Carbon factor Electricity intensity

Energy intensity

120%

Index (2000=100%)

100% 80% 60% 40% 20% 0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Fig. 6. Dynamics in the relative indicators at the national level, 2000–2016.

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other regions. For North region, even though carbon intensity of GDP, energy intensity and electricity intensity featured slight decline during study period, the carbon intensity of electricity showed few signs of subsiding indicating poor performance in emission reduction techniques. Turning to the medium-growth group (see Fig. 7e–d), the tendencies of relative indicators are comparable to those of whole sample since 2005. Still, the decreases were obvious in both regions of medium-growth group in comparison with the changes at national level. Specifically, the changes of carbon intensity of GDP (−69.1% for Central and −67.6% for South), carbon factor (−8.2% for Central and −6.3% for South), energy intensity (−66.3% for Central and −65.4% for South), carbon intensity of electricity (−30.5% for Central and −24.0% for South) and electricity intensity (−55.5% for Central and −57.4% for South) all proved this point. This finding suggests that the emission reduction techniques and energy efficiencies in South and Central regional power grids were above average for six regions. With regards to the low-growth group (see Fig. 7e–f), the decrease trends of carbon intensity of GDP (−70.6% for East and −62.9% for Northeast), carbon factor (−5.6% for East and −5.4% for Northeast) and energy intensity (−68.9% for East and −60.7% for Northeast) had not much difference with those of medium-growth group across 2000–2016. However, the carbon intensity of electricity in East region declined significantly from 2000 to 2016 (−41.5%) in contrast to the other regions indicating that East China Power Grid had taken lead in enhancing emission reduction technology of thermal electricity generation. For Northwest region, its electricity intensity descended evidently during 2000–2016 (decreasing −59.6% in 2016) in comparison with other regions. The policy of upgrading energy structure in Northeast region9 may cause the decrease of thermal electricity demand, thus, rendering the decrease of electricity intensity. 4.3. The results of GDIM

Technology could decrease the CO2 emission across 2000–2016 except for 2003–2004 and 2007–2009. Although carbon intensity played a suppressive role for the CO2 emission in Northwest region (2000–2001, 2004–2005, 2007–2009), it has contributed to growth in the emission since 2010 with exception of 2015–2016 implying the necessity to curb the growth in carbon intensity for Northwest China Power Grid. Even though such factors as energy efficiency, energy mix and electricity efficiency contributed to reduction in the CO2 emission with the high-growth group, their effects appeared to be minimal (the same pattern can also be observed in medium- and low-growth groups). Within the medium-growth group, the decline in CO2 emission was observed in both South (see Fig. 9c) and Central (see Fig. 9d) regions during 2007–2008, 2011–2012 and 2013–2015 due to the effects of carbon intensity, technology and energy use. Carbon intensity appeared as a major factor behind the declining CO2 emission in South region (2007–2008, 2011–2016) and Central region (2007–2009, 2011–2012, 2013–2016). However, there has been no long-term trend established for the factor of carbon intensity and its role in mitigation of the CO2 emission in South and Central regions remains instable. In contrast, technology could arouse the abatement of CO2 emission since 2005 in both South and Central. Besides, the decreasing effect of technology was tending steady since 2012 indicating the benignant emission reduction caused by technical progress in emission reduction. As regards the low-growth group, carbon intensity contributed to an increase in the CO2 emission before 2005 in East region (see Fig. 9e), yet its contribution rates decreased year by year. Over the period of 2005–2016 (with the exception of 2010–2011), the carbon intensity became a factor pushing the emission down in East region. Technology was also an imperative factor to govern CO2 emission in East and its mitigation affect turned stabilized since 2012. In Northeast region (see Fig. 9f), the carbon intensity appeared as the major factor decreasing the CO2 emission during 2000–2016 though this was not the case for 2002–2004, 2010–2011 and 2015–2016. The region saw a decrease in its CO2 emission after 2011 with a minor increase of 13.8 Mt in 2013–2014.

4.3.1. Chain-linked analysis GDIM is employed to quantify the contributions of the eight factors to the change in CO2 emission from China's thermal electricity generation. Results of the chain-linked decomposition at the national level are presented in Fig. 8. In particular, contribution rates (in %) indicate the relative effects of the eight factors, whereas the CO2 emission change (in Mt) shows the absolute magnitude of dynamics in CO2 emission. For presentation purposes, the decomposition results of the exact magnitude of CO2 emission change and average contribution rates are presented in Table A3. The more detailed decomposition is carried out by considering the six regions separately (see Fig. 9). For the sake of clear presentation, the decomposition results of the rates and exact magnitude of CO2 emission changes are provided in Tables A4–A9. Obviously, the main drivers of the increase in the CO2 emission are similar across the six regions (i.e. economic activity, electricity demand and energy use), yet those of minor importance appeared as both inducing increase and decrease in the emission. We further look into the patterns of the factors behind the change in the CO2 emission across the three groups (as classified in Section 4.1) of regions. For North region (see Fig. 9a) belonging to the high-growth group, the period of 2000–2016 marked an increase in CO2 emission with a minor decrease of 37.4 Mt in 2012–2014. Before 2005, there had been virtually no factors that possessed a substantive impact on the reduction in the CO2 emission in North region. Since 2006, technology and carbon intensity started to induce the mitigation of CO2 emission with the opposite trend for 2009–2011. Northwest region (see Fig. 9b) showed an increase in the CO2 emission during the whole period covered.

4.3.2. Period-wise analysis China has clear “Five-Year Plan” to schedule and propel the economic development.10 To investigate the changes of driving factors at specific development stages, the research period can be divided into the three sub-periods, namely 2000–2005, 2005–2010, and 2010–2015. In addition, the analysis can be carried out by considering the two endpoints for the period of 2000–2016. Thus, four timeperiods are to be compared in the sequel. The analysis is carried out in a period-wise manner, e.g. for the sub-period of 2000–2005, we regard 2000 as the base year and 2005 as the target year when decomposing the change in CO2 emission. Decomposition at the national level for China's thermal electricity generation considering the different sub-stages is presented Fig. 10. Looking at the entire period of 2000–2016 reveals that economic activity, energy use and electricity demand were the major factors that contributed to the increase in CO2 emission by 1040.1 Mt, 1053.9 Mt and 1033.1 Mt, respectively. The five factors associated with decrease in the emission are carbon intensity (−370.1 Mt), technology (−196.6 Mt), energy mix (−53.3 Mt), energy efficiency (−48.1 Mt) and electricity efficiency (−26.7 Mt). Apparently, carbon intensity and technology played the most important role in reduction of CO2 emission from China's thermal electricity generation during 2000–2016. The effects included in the IDA played different roles during the subperiods considered in the analysis. During the sub-period of 2000–2005, the largest contributors to the increase in CO2 emission was due to

9 Along with the national strategy of Vitalizing the Northeastern Traditional Industrial Base, the energy structure of Northeast region upgraded much and there had been a reduction in the proportion of the secondary industry and an increase in the proportion of third industry.

10 Correspondingly, Chinese government also released the Five-Year development plan for electricity at certain phases, such as the “The 10th Five-Year Plan for the Electricity Industry (2001–2005)” issued in 2001 and “The 13th Five-Year Plan for Electricity Development (2016–2020)” issued in 2016.

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Fig. 7. Dynamics in the relative indicators for regional power grids, 2000–2016.

electricity demand (404.8 Mt) and energy use (379.5 Mt). The rapid development of thermal electricity construction projects during 10th FiveYear plan (2000–2005) may be associated with the increasing effects of above two factors. Across 2001–2005, more than 450 thermal electricity projects with an installed capacity of approximately 350,000 MW were approved by China's State Environmental Protection Administration implying copious electricity output and energy consumption. However, economic activity appeared as the major driver behind the growth in CO2 emission throughout 2005–2010 and 2010–2015 with contributions of 485.1 Mt and 609.6 Mt, respectively. This might be due the

fact that China's economy grew rapidly with an Average Annual Growth Rate of 11.2% (far higher than the world average of 3.5%) during the 11th Five-Year Plan period (2005–2010). At the end of the 11th FiveYear Plan period, China's GDP surpassed Japan's for the first time and China became the world's second-largest economy since 2011. Although the rate of China's economic development slowed down during the 12th Five-Year Plan period (2011–2015), the average annual rate of 8.0% was still maintained. On the other hand, the contribution of electricity demand (from 404.8 Mt in 2000–2005 to 258.0 Mt in 2010–2015) and energy use (from 379.5 Mt in 2000–2005 to 167.8 Mt

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Economic activity Energy mix Electricity efficiency

Carbon intensity Electricity demand Energy efficiency

Energy use Technology Carbon emission change

100%

600

80%

500

Contribution rate

60%

400

40%

300

20% 200 0% 100

-20%

0

-40%

-100

-60% -80%

Carbon emission change (Mt)

746

-200 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Fig. 8. Relative contribution of eight factors and CO2 emission change in China's thermal electricity generation, 2000–2016 (target years are provided in the figure).

2010–2015) tended to diminish as a consequence of the slow development of thermal power generation after the 10th Five-Year plan. In the meantime, the factors cutting down CO2 emissions also varied at different periods. Carbon intensity contributed to the growth in the CO2 emission during 2000–2005 by 123.7 Mt. As for the other two sub-periods, the carbon intensity was the most significant factor causing decline in the emission (−165.4 Mt in 2005–2010 and −370.3 Mt in 2010–2015) owing to the upgrading of industrial structure and the development of renewable energy. Technology ranked behind only carbon intensity in reducing CO2 emissions and its mitigating effects were notable in 2005–2010 and 2010–2015 by −146.2 Mt and −132.2 Mt respectively. This result could be attributed to the technical progress of emission reduction in China's thermal electricity generation. In addition, the CO2 emission diminished by energy efficiency and energy mix increased gradually at distinct stages (from −13.0 Mt to −50.3 and from −3.9 Mt to −36.2, respectively), which suggests that energy conservation and energy structure adjustment (e.g. replace coal with renewable energy such as natural gas) exerted more and more valuable effects on the abatement of CO2 emission. The regional perspective is taken in Fig. 11 which presents the decomposition of CO2 emission from China's thermal electricity sector for the six regional power grids and the three sub-periods. The detailed results of the decomposition at both national and regional level are provided in Tables A10–A16. For the high-growth group (see Fig. 11a–b), there was no factor exerting a sensible role in the reduction of CO2 emissions during 2000–2005 uncovering the low technological level and poor efficiency at initial stage. The increase in CO2 emission rendered by electricity demand had no downward trend in North region and even showed a rising trend in Northwest region. Moreover, the increase in CO2 emission rendered by energy use in Northwest region also demonstrated upward trend (from 27.4 Mt in 2000–2005 up to 45.6 Mt in 2005–2010 and 77.1 Mt in 2010–2016). This finding implies that almost there has been little success in decoupling energy use and CO2 emission in Northwest region during 2000–2016. This may be attributed to the fact that the Northwest region is an area concentrated of thermal power installation in China based on its resource advantages. Taking Xinjiang's thermal electricity generation as an example, its thermal electricity output increased by approximately 13 times through 2000–2016 from 15.1 billion kWh to 221.9 billion kWh implying a mass of energy consumption and CO2 emission. However, for the other two groups of regions, CO2 emission

added by electricity demand and energy use tapered off, especially for energy use. In the case of the medium-growth group (see Fig. 11c–d), the periods of 2000–2016, 2005–2010 and 2010–2015 marked the decrease in the CO2 emission mainly due to both carbon intensity and technology (this was also the case for the national level decomposition of CO2 emission). Moreover, though electricity demand and energy use induced massive CO2 emission during the periods of 2000–2005 and 2005–2010, their contributions dropped heavily across 2010–2015 in both South and Central (energy use effect even reduced CO2 emission in South by −10.1 Mt during 2010–2015). The new energy developed rapidly in South and Central, thus, causing low growth of thermal electricity generation and energy consumption. However, economic activity has always been the primary factor compelling CO2 emission to increase and the emission caused by economic activity showed an upward trend in both South and Central (reaching 76.7 Mt and 97.3 Mt in 2010–2015, respectively). For the low-growth group (see Fig. 11e–f), electricity demand and energy use damped down their promoting effects on CO2 emission, whereas economic activity had been a significant role in the increase of CO2 emission all along, especially in East region. Moreover, Northeast region is the only one where carbon intensity contributed to the mitigation of CO2 emission in all the sub-periods, and the increase in the emission from thermal electricity stood at just 18.3 Mt during 2010–2015. For East region, carbon intensity induced reduction in the CO2 emission for the sub-periods of 2005–2010 and 2010–2015. The latter period was associated with particularly steep reduction of 92.9 Mt due to the carbon intensity. 5. Conclusions and policy implications 5.1. Conclusions As the largest energy consumer and CO2 emitter sector in China, the CO2 emission mitigation of thermal electricity generation is a critical step for China to cope with the challenge of climate change and low carbon economy. In this research, we attempted to look into energyrelated CO2 emissions in China's thermal electricity generation of six regional power grids and obtain the reduction direction of CO2 emission in this sector. This study, thus, adopts GDIM to decompose the CO2 emission changes in China's thermal electricity generation. The

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Fig. 9. Contribution rates of eight factors and CO2 emission change for thermal electricity generation in China's six regional power grids, 2000–2016 (target years are provided in the figure).

decomposition considers all the absolute indicators and relative ones simultaneously, which could not be carried out by applying other IDA methods, including LDMI. The main conclusions drawn from this study can be summarized as follows. At the national level, CO2 emission from China's thermal power sector grew by 7.3% on average during 2000–2016 implying that the level of the emission increased two-fold. The chain-linked GDIM

decomposition indicates that economic activity, electricity demand and energy use have appeared as the factors inducing increase in the CO2 emission from China's thermal electricity generation, whereas carbon intensity, technology, energy mix, electricity efficiency and energy efficiency have suppressed growth in the emission. Generally, economic activity contributes most to the increase of CO2 emission and carbon intensity is the major factor pushing the emission down at the national

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Carbon emission change (Mt)

Economic activity Energy mix 4000

Carbon intensity Eelectricity demand

Energy use Technology

3000 2000 1000 0 -1000 2000-2016

2000-2005

2005-2010

2010-2015

Fig. 10. Decomposition of change in CO2 emission at the national level considering the three sub-periods.

level. Noteworthy, the promoting effect of the economic activity varied little with time, whereas the effects of electricity demand and energy use exhibited a downward trend in general and energy use even induced the decrease of CO2 emission during 2013–2015. Besides, though the technology was the second major factor behind the decrease of CO2 emission, its reduction effects tended to diminish since 2013. Therefore, though the introduction of cleaner technologies used to be the effective means for reducing the CO2 emission, new actions, such as upgrade energy structure, must be taken to further expand the emission reduction space for CO2 emission generating from China's thermal electricity generation. The region-wise analysis considered the differences in the growth rates of the CO2 emission. The share of the high-growth regions in the national CO2 emission increased from 31.6% (24.8% for North region and 6.8% for Northwest region) in 2000 up to 46.8% (33.7% for North region and 12.5% for Northwest region) in 2016. Looking at the trends for the relative indicators suggests that carbon intensity, energy intensity and electricity intensity have been relatively high in high-growth group during 2000–2016, especially in Northwest region. Following the decomposition results for high-growth group, carbon intensity appeared as a factor mitigating the CO2 emission in North region during 2010–2016, whereas carbon intensity kept increasing the CO2 emission in Northwest region even during the sub-periods of 2000–2005, 2005–2010. This indicates the need for CO2 emission mitigation strategies in these particular regions with particular focus on the reduction of the carbon intensity. Turning to medium-growth group, its share in the national CO2 emission from the thermal electricity did not decrease significantly during 2000–2016 (from 12.6% in 2000 to 10.5% in 2016 for South region and from 14.8% in 2000 to 13.0% in 2016 for Central region). The CO2 emission rendered by electricity demand and energy use were large during the periods of 2000–2005 and 2005–2010, but their contributions declined a lot across 2010–2015 in both South and Central. Despite the indefinite trend at beginning of the research period, carbon intensity and technology appeared as the strong effect behind reduction in the CO2 emission within medium-growth group. The share of low-growth group drooped in the national emission. Specifically, the shares of East and Northeast regions shrunk from 28.9% and 12.2% in 2000 down to 21.8% and 7.9% in 2016, respectively. This implies that both North and Northwest regions have successfully contributed to the abatement of CO2 emission from China's thermal electricity sector. In comparison with other regions, the electricity intensity declined significantly in East region and the electricity efficiency reduced much emission in Northwest region throughout the period of 2000–2016. Carbon intensity appeared as the major contributors of CO2 emissions for both South and Central. Besides, tough technology appeared as the most important factor behind the reduction of emission from East during the sub-period of 2005–2010 by −85.8 Mt, the decline of emission caused by technology was only −12.7 Mt during

2010–2015 indicating smaller emission mitigation space based on technology.

5.2. Policy implications As regards the entire thermal electricity sector in China, policy recommendations for CO2 emission abatement can be proposed at four perspectives. First, despite the CO2 emission reduction policies devised by Chinese government during 12th and 13th Five-Year plan periods, situation remained unbalanced across the six regional power grids. Therefore, target-setting should rely on the analysis of the factors affecting change in the CO2 emission within particular region. Identification of low-performing regional power grids (in our case, North and Northwest) allows developing the tailored strategies for CO2 emission management. Second, the decision making process should rely on theoretically sound methodologies. In this paper, application of the GDIM revealed that the relative indicators (energy mix, energy efficiency and electricity efficiency) showed weak effects on the change in the CO2 emission failing to play their due roles. The effects of energy efficiency and energy-mix can be strengthened by introducing tax incentives, cultivating professional talents and eliminating backward production capacity. Moreover, government should vigorously develop the technologies of energy storage and long-distance energy transportation to store and transmit renewable energy, thus increasing the proportion of renewable energy supply and reducing thermal electricity intensity. Third, although the policy of “developing large units and suppressing small ones”11 had exerted a huge role in decreasing CO2 emission and improving energy efficiency since issued in 2007, it was supposed to be adjusted in the details to further promote the decrease of CO2 emission in future. For instance, the policy of “developing gas-fired units and suppressing small coal-fired ones” can be implemented where possible and cleaner energy sources (e.g. natural gas)12 used for the generation, thus mitigating the CO2 emission by energy structure upgrade. Fourth, generation efficiency can be promoted by means of the market-based mechanisms besides the command-and-control regulation. It is essential to accelerate the establishment of carbon trading market system concentrating on CO2 emissions from thermal power generation, thereby introducing market mechanism to adjust emission reduction quota. Regions with high energy efficiency and favorable economic development can purchase more carbon emission quotas, while regions featuring poor performance can improve their energy efficiency and

11 When constructing units with large capacity, high parameters, low consumption and low emission, the company must shut down some small thermal power units simultaneously. 12 The case of Beijing can be mentioned in this context as the closure of coal-fired units in Huaneng Beijing Thermal Power Plant in 2017 marked the beginning of “Coal Free Era” there.

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Fig. 11. Decomposition of the change in CO2 emission across the regions and sub-periods.

technology through the profits gained by selling carbon emission quotas. Given the differences existing among the six regional power grids, specific CO2 emission mitigation strategies can be developed as follows. For North and Northwest regions, the decreasing effect of technology

was not evident, especially for Northwest region during the subperiods of 2000–2005 and 2000–2010, though the effect of technology was proved to be the efficient mitigation factors in the other regions. Thus, policies and measures aimed at enhancing emission reduction techniques should be focused on these two regional power grids. As

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regards South and Central regions, technologies for improving the stability of energy structure and efficiency in the thermal power industry should be implemented, as the direction of carbon intensity effect varied with time in both South and Central regions. Turning to East and Northeast regions, one can note that carbon intensity and technology have exerted a significant role in reducing CO2 emission there, yet other mitigation factor (i.e. energy mix, energy efficiency) showed little effect. Identifying the ways to strengthen the effects of energy mix, energy efficiency and electricity efficiency in low-growth group may further provide references and insights for the reduction of CO2 emission from thermal electricity generation in China. In general, our research suggests that the application of the generalized Divisia index can provide valuable insights into the factors of the changes in CO2 emission related to thermal electricity generation. The proposed framework, therefore, can be applied in different regions in order to identify effective policy measures for mitigating the climate change. The inter-regional differences in the results of decomposition (i.e. contributions of the driving factors) existing among the six power grids in China indicate that the decomposition analysis should be carried out at sub-national level in case substantial differences exist in energy supply and/or demand. The energy policies should be adjusted with respect to those differences. The study features certain limitations. The aggregate data used for quantifying the economic activity and CO2 emissions contain a certain degree of uncertainty due to the measurement errors. In addition, the

data used might have been affected by short-term fluctuations. Therefore, further studies could focus on accounting for various types of random errors in the index decomposition analysis. Besides, it is necessary to identify the targets for reductions in energy use and CO2 emissions in China's thermal electricity generation. Decomposition analysis of carbon intensity of electricity (as shown in Wang et al., 2018b) can be employed in this regard by the means of GDIM. CO2 emission allocation models can be utilized to identify which regional power grids should face specific CO2 emission mitigation targets in China's thermal electricity generation. Finally, scenario analysis can be applied to estimate the potential CO2 emission mitigation pathways in China's thermal electricity generation under certain policy and theoretical assumptions. Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.05.143. Declaration of Competing Interest The authors declare that there is no conflict of interests regarding the publication of this paper. Acknowledgements This work is supported by the Fundamental Research Funds for the Central Universities (2019QN069) and the 111 Project (Grant No. B18021).

Appendix A Table A1 Conversion factors for different fuel types. Fuel type Coal

Raw coal Cleaned coal Other washed coal Briquettes Gangue Coke Other cooking products Crude oil Gasoline Diesel oil Fuel oil Petroleum coke Other petroleum Coke oven gas Blast furnace gas Converter gas Other gas Liquefied petroleum gas (LPG) Refinery gas Natural gas Liquefied petroleum gas (LNG) Other energy Heat

Oil

Gas

Others

Unit

Conversion factor

104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 104 tn 108 m3 108 m3 108 m3 108 m3 104 tn 104 tn 108 m3 104 tn 104 tce 1010 kJ

0.714 kg ce/kg 0.900 kg ce/kg 0.286 kg ce/kg 0.700 kg ce/kg 0.179 kg ce/kg 0.971 kg ce/kg 1.500 kg ce/kg 1.429 kg ce/kg 1.471 kg ce/kg 1.457 kg ce/kg 1.429 kg ce/kg 1.092 kg ce/kg 1.400 kg ce/kg 0.614 kg ce/m3 1.286 kg ce/104 m3 2.714 kg ce/104 m3 0.657 kg ce/m3 1.714 kg ce/kg 1.571 kg ce/kg 1.330 kg ce/m3 1.757 kg ce/kg 1.000 kg ce/kg 0.034 kg ce/106 J

Table A2 caloric value 2 and fuel caloric value Vfuel . The carbon emission factor FCi , carbon oxidation factor Oi, CO2 emission factor FCO t i Fuel

Unit

Raw coal Cleaned coal Other washed coal Briquettes Gangue Coke Other cooking Crude oil Gasoline Diesel oil

104 ton 104 ton 104 ton 104 ton 104 ton 104 ton 104 ton 104 ton 104 ton 104 ton

FCi (tc/TJ)

Oi (%)

2 = FCi × Oi × 44/12 × 103 FCO i (kg CO2/TJ)

caloric value Vfuel i (MJ/ton or MJ/m3)

25.8 25.8 25.8 26.6 25.8 29.2 25.8 20.0 18.9 20.2

100 100 100 100 100 100 100 100 100 100

94,600 94,600 94,600 97,500 94,600 107,100 94,600 73,300 69,300 74,100

20,908 26,344 8363 20,908 8372 28,435 28,435 41,816 43,070 42,652

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Table A2 (continued) Fuel

Unit

Fuel oil Petroleum coke Other petroleum Coke oven gas Blast furnace gas Converter gas Other gas LPG Refinery gas Natural gas LNG

104 ton 104 ton 104 ton 108 m3 108 m3 108 m3 108 m3 104 ton 104 ton 104 ton 108 m3

FCi (tc/TJ)

Oi (%)

2 = FCi × Oi × 44/12 × 103 FCO i (kg CO2/TJ)

caloric value Vfuel i (MJ/ton or MJ/m3)

21.1 26.6 20.0 12.1 70.8 49.6 12.1 17.2 15.7 15.3 15.3

100 100 100 100 100 100 100 100 100 100 100

77,400 97,500 73,300 44,400 259,600 181,900 44,400 63,100 57,600 56,100 56,100

41,816 31,980 41,816 16,726 3767 7953 5227 50,179 46,055 38,931 51,486

Table A3 The decomposition results at the national level for 2000–2016 (in Mt).

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 Average CO2 change Average contribution rate Trend

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

Total

32.2 39.0 48.4 58.0 76.1 94.8 117.8 88.9 87.6 103.2 100.9 92.9 91.3 93.0 55.0 85.2 79.0 52.0% 2.79

2.0 13.9 38.1 49.4 6.5 −8.7 −26.1 −55.5 −51.8 −42.3 69.1 −99.5 −57.2 −113.9 −87.2 −66.4 −26.9 −17.7% −7.89

33.7 55.0 87.5 113.1 84.1 89.2 92.8 31.5 35.6 60.1 165.7 −0.9 43.6 −18.4 −12.9 21.6 55.1 36.2% −4.65

0.5 −1.3 0.3 −3.1 −1.1 −2.9 −2.2 −2.2 −2.4 −0.5 6.1 −11.4 −11.6 −9.9 −9.3 −8.2 −3.7 −2.4% −0.66

52.6 36.0 81.4 70.6 92.3 118.2 128.1 24.2 61.3 134.3 150.3 21.6 83.5 6.0 16.5 36.4 69.6 45.8% −2.19

−17.0 17.8 6.3 38.0 −9.4 −30.5 −36.3 4.8 −27.7 −72.9 21.5 −34.6 −51.2 −35.0 −25.3 −22.9 −17.2 −11.3% −2.89

−1.2 −0.7 −1.2 −1.0 −0.4 −0.4 −0.3 −2.1 −0.6 −1.1 −2.0 −2.1 −0.7 −3.6 −1.7 −2.2 −1.3 −0.9% −0.11

−0.2 −0.6 −1.6 −2.8 −1.1 −0.1 −0.7 −2.4 −1.4 −10.6 −1.8 −4.1 −2.4 −5.6 −3.6 −2.5 −2.6 −1.7% −0.25

102.6 159.0 259.3 322.0 246.9 259.6 273.1 87.4 100.5 170.1 509.7 −38.1 95.4 −87.5 −68.4 41.1 152.0 100.0% −15.84

Note: Trend represents the coefficient of the linear time trend for Gtj, 1Rtj, Etj , 2Rtj, Ptj, 3Rtj , 4Rtj and 5Rtj. Table A4 The decomposition results in North China Power Grid for 2000–2016 (in %). Gtj

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 Trend

2.7 3.1 3.4 3.4 3.8 4.2 4.7 3.2 3.0 3.6 3.2 2.5 2.5 2.0 1.4 2.4 −0.10

1 t Rj

1.4 2.7 2.5 3.4 2.1 −0.4 −0.5 −1.0 −2.3 1.8 1.9 −2.0 −3.3 −2.0 −0.5 −1.6 −0.31

Etj

4.1 6.2 5.9 7.6 6.1 4.1 4.5 2.2 0.8 4.8 5.1 0.8 −0.5 0.1 1.1 0.9 −0.42

2 t Rj

0.1 −0.2 0.1 −0.6 −0.1 −0.3 −0.3 0.0 −0.2 0.7 0.2 −0.3 −0.4 −0.2 −0.1 −0.2 0.00

Ptj

3 t Rj

7.5 −0.2 4.7 6.2 5.3 5.1 5.5 1.3 1.8 4.2 3.3 1.5 1.8 0.9 2.0 1.5 −0.27

−3.0 6.2 1.2 0.7 0.6 −1.3 −1.2 0.9 −1.2 1.2 1.8 −1.0 −2.7 −1.0 −1.0 −0.8 −0.16

4 t Rj

5 t Rj

−0.3 −0.2 0.0 −0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.01

0.0 −0.2 −0.1 −0.2 −0.1 0.0 0.0 0.0 −0.1 0.0 0.0 0.0 −0.1 0.0 0.0 0.0 0.00

4 t Rj

5 t Rj

Dynamics in CO2 Growth rate

Change (in Mt)

12.6 17.6 17.8 20.3 17.8 11.4 12.7 6.6 1.8 16.3 15.5 1.5 −2.8 −0.3 2.8 2.2 −1.24

36.6 57.7 68.6 92.3 97.1 73.5 91.4 53.2 15.5 142.9 157.7 17.5 −33.7 −3.7 31.9 26.0 −3.59

Table A5 The decomposition results in Northeast China Power Grid for 2000–2016 (in %). Gtj

2000–2001 2001–2002

2.7 3.0

1 t Rj

−2.0 −0.5

Etj

0.6 2.6

2 t Rj

0.0 −0.1

Ptj

2.0 3.2

3 t Rj

−1.4 −0.7

0.0 0.0

−0.1 0.0

Dynamics in CO2 Growth rate

Growth rate

2.0 7.6

2.8 11.0 (continued on next page)

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Table A5 (continued)

2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 Trend

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

3.4 3.4 3.7 4.2 4.6 3.2 3.0 3.5 3.2 2.5 2.5 1.7 0.2 −3.1 −0.25

0.2 1.3 −2.2 −0.7 −3.3 −1.2 −3.4 −0.4 1.2 −3.1 −3.4 −0.1 −1.7 3.3 0.04

3.6 4.9 1.2 4.0 1.0 2.0 −0.3 3.0 4.4 −0.9 −1.0 2.0 −1.0 0.4 −0.19

0.0 −0.2 0.2 −0.5 0.1 0.0 −0.2 0.2 0.0 0.2 −0.1 −0.4 −0.5 −0.5 −0.02

1.9 3.1 1.8 3.5 3.2 1.2 0.0 2.9 2.7 0.4 −0.6 1.2 −0.4 1.0 −0.18

1.7 1.6 −0.4 −0.1 −2.1 0.7 −0.6 0.2 1.6 −1.2 −0.6 0.5 −1.1 −1.1 −0.03

0.0 0.0 0.0 0.0 0.0 0.0 −0.1 0.0 0.0 0.0 −0.1 0.0 0.0 −0.2 0.00

0.0 0.0 −0.1 0.0 −0.2 0.0 −0.1 0.0 0.0 −0.1 −0.1 0.0 0.0 −0.2 0.00

Ptj

3 t Rj

Dynamics in CO2 Growth rate

Growth rate

10.6 14.0 4.3 10.4 3.4 5.8 −1.7 9.4 13.1 −2.3 −3.4 4.8 −4.5 −0.5 −0.64

16.6 24.3 8.5 21.4 7.7 13.6 −4.2 23.0 35.0 −6.9 −10.0 13.8 −13.5 −1.4 −1.08

Table A6 The decomposition results in East China Power Grid for 2000–2016 (in %). Gtj

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 Trend

2.8 3.0 3.4 3.4 3.8 4.2 4.7 3.2 3.1 3.2 3.2 2.6 2.5 2.7 2.3 3.5 −0.04

1 t Rj

0.0 0.1 3.0 1.0 0.4 −0.8 −1.4 −0.8 −1.1 −8.9 2.4 −1.9 −1.2 −4.0 −3.2 −2.6 −0.30

Etj

2.8 3.1 6.5 4.4 4.2 3.3 3.3 2.3 2.0 −5.5 5.3 0.7 2.0 −1.6 −0.6 0.9 −0.34

2 t Rj

0.0 0.0 0.0 0.0 0.0 0.1 −0.1 0.0 −0.1 −0.6 0.3 −0.1 −0.7 0.0 −0.5 −0.1 −0.02

2.6 3.9 6.1 4.9 5.2 5.5 4.5 1.7 2.2 4.1 5.3 0.2 2.0 −0.7 −0.3 1.0 −0.32

0.1 −0.8 0.3 −0.5 −0.9 −2.0 −1.3 0.6 −0.2 −9.9 0.4 0.3 −0.6 −0.9 −0.7 −0.3 −0.04

4 t Rj

0.0 0.0 −0.1 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 0.0 −0.1 0.0 −0.1 −0.1 −0.1 0.00

5 t Rj

0.0 0.0 −0.1 0.0 0.0 0.0 0.0 0.0 0.0 −1.2 0.0 0.0 0.0 −0.2 −0.1 −0.1 −0.01

Dynamics in CO2 Growth rate

Growth rate

8.3 9.4 19.2 13.2 12.6 10.2 9.6 6.9 5.8 −18.9 16.8 1.7 4.0 −4.9 −3.2 2.3 −1.07

28.0 34.3 76.9 63.1 68.0 62.0 64.4 51.1 45.5 −157.2 113.6 13.2 32.2 −40.5 −25.3 17.4 −4.85

Table A7 The decomposition results in Central China Power Grid for 2000–2016 (in %).

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 Trend

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

2.7 3.0 3.4 3.6 3.7 4.2 4.7 3.1 3.0 3.5 3.2 2.5 2.5 3.0 2.2 3.3 −0.05

1.5 0.9 5.3 4.9 −2.3 −0.3 −0.8 −4.6 −2.5 1.1 2.2 −5.8 0.6 −5.1 −4.3 −3.4 −0.44

4.3 4.0 9.0 8.8 1.3 4.2 4.0 −1.7 0.4 5.3 5.1 −3.1 3.7 −2.2 −2.1 −0.1 −0.49

0.0 0.0 −0.1 0.0 0.0 −0.3 −0.1 −0.1 0.0 −0.6 0.4 −0.7 −0.4 −0.3 −0.2 −0.3 −0.02

6.5 4.0 6.7 0.0 3.3 5.6 5.8 0.0 3.3 4.6 5.7 −1.4 4.4 −1.6 −0.7 0.0 −0.36

−2.0 0.0 2.0 8.6 −1.9 −1.6 −1.9 −1.9 −2.9 0.1 −0.2 −2.4 −1.1 −0.9 −1.6 −0.4 −0.16

−0.2 0.0 −0.1 −0.2 0.0 0.0 0.0 −0.1 0.0 0.0 −0.1 −0.2 0.0 −0.2 −0.1 −0.1 0.00

0.0 0.0 −0.3 −0.4 −0.1 0.0 0.0 −0.3 −0.1 0.0 0.0 −0.4 0.0 −0.3 −0.2 −0.1 0.00

Dynamics in CO2 Growth rate

Growth rate

12.8 12.0 26.0 25.2 4.1 11.8 11.5 −5.5 1.2 14.0 16.3 −11.5 9.6 −7.6 −7.0 −1.1 −1.53

22.2 23.4 56.9 69.5 14.0 42.4 46.3 −24.6 5.2 60.2 79.9 −65.2 48.3 −42.2 −35.9 −5.1 −4.05

Q. Yan et al. / Science of the Total Environment 682 (2019) 737–755

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Table A8 The decomposition results in Northwest China Power Grid for 2000–2016 (in %).

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 Trend

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

2.7 3.0 3.4 3.5 3.7 4.2 4.7 3.2 3.1 3.6 3.2 2.6 2.6 3.0 0.5 2.1 −1.00

−2.3 0.8 4.4 4.5 −1.7 0.4 0.8 −0.1 −1.1 4.0 5.5 0.2 1.4 −0.1 0.6 −1.7 −0.04

0.3 4.0 7.8 8.1 2.1 4.9 5.9 3.1 1.8 7.0 8.8 2.9 4.3 2.8 1.2 0.4 −0.14

0.1 −0.1 0.1 0.1 −0.1 −0.2 −0.3 −0.1 0.1 0.7 0.0 −0.1 −0.3 0.0 0.0 0.0 0.00

4.4 4.6 8.4 4.2 3.7 5.3 6.9 2.8 1.0 8.3 10.1 3.3 4.7 3.8 1.4 1.3 −0.16

−3.8 −0.6 −0.5 3.7 −1.7 −0.6 −1.3 0.3 0.9 −0.5 −1.2 −0.4 −0.7 −0.9 −0.3 −0.9 0.02

−0.1 0.0 −0.3 0.0 0.0 0.0 −0.1 0.0 0.0 −0.2 −0.5 0.0 −0.1 0.0 0.0 0.0 0.00

−0.1 0.0 −0.2 −0.3 0.0 0.0 0.0 0.0 0.0 −0.1 −0.3 0.0 0.0 0.0 0.0 0.0 0.00

Dynamics in CO2 Growth rate

Growth rate

1.1 11.7 23.3 23.8 6.1 14.0 16.7 9.2 5.7 22.7 25.7 8.4 11.9 8.5 3.4 1.2 −0.42

0.9 9.4 20.9 26.3 8.3 20.3 27.6 17.7 12.0 50.5 70.3 28.8 44.5 35.6 15.4 5.5 1.37

Table A9 The decomposition results in South China Power Grid for 2000–2016 (in %).

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 Trend

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

2.8 3.1 3.3 3.5 3.8 4.2 4.7 3.1 3.1 3.5 3.2 2.5 2.5 2.9 2.5 3.4 −0.04

0.0 1.8 0.2 4.2 3.0 0.2 −1.1 −4.8 −0.5 0.9 0.9 −4.0 −1.5 −5.9 −5.3 −3.3 −0.43

2.7 5.3 3.4 7.8 7.4 4.2 3.2 −1.8 2.2 4.8 4.1 −1.1 0.5 −2.4 −2.9 0.4 −0.47

0.0 −0.3 0.1 0.1 −0.4 0.3 0.3 −0.2 0.3 −0.3 0.1 −0.7 0.6 −1.1 −0.3 −0.5 −0.03

3.0 3.7 7.4 4.5 6.1 5.5 4.7 −1.8 3.7 4.7 4.2 −0.5 1.9 −2.0 −1.2 0.5 −0.40

−0.2 1.2 −3.5 3.2 0.8 −1.0 −1.2 −0.2 −1.1 −0.2 −0.1 −1.2 −0.8 −1.6 −2.0 −0.7 −1.00

0.0 0.0 −0.2 0.0 0.0 0.0 0.0 −0.3 0.0 0.0 0.0 −0.1 0.0 −0.3 −0.1 −0.1 0.00

0.0 −0.1 0.0 −0.2 −0.1 0.0 0.0 −0.3 0.0 0.0 0.0 −0.2 −0.1 −0.3 −0.4 −0.1 −0.01

Dynamics in CO2 Growth rate

Growth rate

8.2 14.6 10.6 23.0 20.5 13.4 10.5 −6.3 7.5 13.4 12.4 −5.3 3.1 −10.7 −9.7 −0.3 −1.50

12.1 23.2 19.4 46.5 51.0 40.0 35.7 −23.6 26.5 50.7 53.2 −25.5 14.1 −50.5 −40.9 −1.3 −3.63

Table A10 The decomposition results in nation-wide thermal electricity generation for the four sub-periods (in Mt).

2000–2016 2000–2005 2005–2010 2010–2015

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

1040.1 240.2 485.1 609.6

−370.1 123.7 −165.4 −370.3

1053.9 379.5 306.1 167.8

−53.3 −3.9 −11.3 −36.2

1033.1 404.8 443.1 258.0

−196.6 −10.7 −146.2 −132.2

−26.7 −30.8 −1.7 −33.4

−48.1 −13.0 −19.1 −52.3

Total change 2432.3 1089.8 890.7 410.9

Table A11 The decomposition results in North China Power Grid for the four sub-periods (in Mt).

2000–2016 2000–2005 2005–2010 2010–2015

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

326.8 63.6 146.8 174.1

−89.2 55.3 −19.4 −99.0

362.4 126.0 125.8 64.9

−13.9 −2.4 −0.8 −10.7

363.4 98.2 137.4 100.0

−11.8 21.6 −12.7 −47.1

−6.7 −1.3 0.0 −3.2

−6.4 −8.7 −0.5 −9.3

Total change 924.5 352.3 376.5 169.7

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Q. Yan et al. / Science of the Total Environment 682 (2019) 737–755

Table A12 The decomposition results in Northeast China Power Grid for the four sub-periods (in Mt).

2000–2016 2000–2005 2005–2010 2010–2015

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

87.1 27.5 43.8 43.0

−38.5 −5.4 −19.1 −29.3

57.9 20.8 21.1 8.2

−4.7 0.0 −1.1 −2.6

59.6 19.7 24.3 8.2

−8.0 1.1 −4.6 −2.6

−5.7 −0.3 −1.1 −3.3

−6.2 −0.2 −1.7 −3.3

Total change 141.6 63.2 61.5 18.3

Table A13 The decomposition results in East China Power Grid for the four sub-periods (in Mt).

2000–2016 2000–2005 2005–2010 2010–2015

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

242.3 68.7 117.5 152.4

−113.2 20.0 −84.1 −92.9

223.6 92.3 26.8 36.4

−15.3 −0.5 −5.5 −7.2

226.9 99.8 110.0 41.4

−93.0 −7.2 −85.8 −12.7

−5.8 −1.9 0.0 −11.6

−19.2 −0.8 −13.1 −12.9

Total change 446.4 270.3 65.8 93.1

Table A14 The decomposition results in Central China Power Grid for the four sub-periods (in Mt).

2000–2016 2000–2005 2005–2010 2010–2015

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

136.6 36.8 76.3 97.3

−58.8 25.4 −30.1 −77.7

142.3 64.4 47.6 1.8

−11.9 −0.1 −5.0 −7.7

138.9 61.1 70.4 24.5

−39.2 2.7 −27.7 −32.6

−4.3 −1.7 0.0 −7.0

−8.3 −2.6 −2.1 −13.6

Total change 295.4 186.0 129.5 −15.0

Table A15 The decomposition results in Northwest China Power Grid for the four sub-periods (in Mt).

2000–2016 2000–2005 2005–2010 2010–2015

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

136.8 16.0 35.1 66.0

−23.2 5.5 7.1 −1.8

162.8 22.3 43.0 66.5

−0.2 0.1 0.3 −1.3

138.3 27.4 45.6 77.1

−19.5 −4.2 −2.2 −11.7

0.0 −1.2 −0.5 −0.4

−1.1 −0.2 −0.2 0.0

Total change 393.9 65.8 128.1 194.5

Table A16 The decomposition results in South China Power Grid for the four sub-periods (in Mt).

2000–2016 2000–2005 2005–2010 2010–2015

Gtj

1 t Rj

Etj

2 t Rj

Ptj

3 t Rj

4 t Rj

5 t Rj

110.6 27.6 65.6 76.7

−47.2 23.0 −19.8 −69.6

104.9 53.6 41.8 −10.1

−7.2 −1.0 1.0 −6.7

106.0 98.5 55.3 6.8

−25.3 −24.6 −13.0 −25.6

−4.3 −24.3 −0.1 −7.9

−6.9 −0.6 −1.5 −13.2

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