Embodied CO2 emissions and efficiency of the service sector: Evidence from China

Journal of Cleaner Production xxx (xxxx) xxx

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Embodied CO2 emissions and efficiency of the service sector: Evidence from China Ruizhi Wang a, Jin-Xing Hao a, **, Chunan Wang a, *, Xu Tang b, Xingzhi Yuan b a b

School of Economics and Management, Beihang University, 37 Xueyuan Road, Haidian, Beijing 100191, China School of Economics and Management, China University of Petroleum-Beijing, 18 Fuxue Road, Haidian, Beijing, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 December 2018 Received in revised form 29 October 2019 Accepted 30 October 2019 Available online xxx

Numerous studies discuss energy consumption and carbon emissions in industrial sectors. However, studies of environmental problems caused indirectly by the development of the service sector have, until now, been rather limited. Using the input-output analysis and the three-stage data envelopment analysis (DEA) model, we propose an improved method for estimation of embodied CO2 emissions efficiency in the service sector, and apply it to provincial data in China. We observe that first, the service sector emitted a significant amount of embodied CO2; second, significant regional differences in CO2 emissions efficiency exist that, nevertheless, are consistent with the levels of regional economic development. Finally, we propose several policy implications for emissions reduction. © 2019 Elsevier Ltd. All rights reserved.

^ as de Handling editor: Cecilia Maria Villas Bo Almeida Keywords: Embodied CO2 emissions The service sector Efficiency measurement Three-stage DEA

1. Introduction The service sector is generally regarded as environment-friendly because their processes that result in provision of services are intangible, and direct pollution generated in the service sector is minimal compared with that of industrial sectors (Piaggio et al., 2015). However, the service sector often involves the support of a wide range of material goods and natural resources that create additional demand for outputs of upstream sectors. For example, the hotel and catering sector provides its customers with services using goods produced by the agricultural and industrial sectors while operating in buildings created by the construction sector. Thus, we may conclude that the backward linkages of the service sector may cause substantial environmental issues in upstream non-service sectors indirectly (Fourcroy et al., 2012; Zhang et al., 2015). In recent years, the service sector has grown, and hence, the overall economic development will depend more on their

* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (R. Wang), [email protected] (J.-X. Hao), [email protected] (C. Wang), [email protected] (X. Tang), [email protected] (X. Yuan).

performance. Gadrey (2010) shows that countries where the service sector accounts for a large share of economy consume more energy and emit more pollutants than those that are less developed. Numerous studies discuss the subject of carbon emissions reduction. A number of researchers have explored the subject of energy and CO2 emissions in various economies to identify the synergy among energy consumption, CO2 emissions and economic development. The energy and carbon emissions efficiency in a number of fast-developing and newly industrialized economies in the Asia-Pacific Economic Cooperation (APEC) have attracted attention worldwide (Suzuki and Nijkamp, 2016; Wang et al., 2016; Wu et al., 2018). Additionally, energy conservation and environmental issues have also been of significant concern to countries with a common energy regulation (Duman and Kasman, 2018;  mez-Calvet et al., 2014). Cheng et al. (2018) explore the driving Go factors of CO2 emissions intensity in the Organization for Economic Co-operation and Development (OECD). Lu et al. (2013) and Woo et al. (2015) observe geographical differences in environmental efficiency across the OECD. Furthermore, Iftikhar et al. (2018) study energy and CO2 emissions efficiency in terms of economic and distributive efficiency simultaneously through network data envelopment analysis (DEA). In addition, Zhou et al. (2010) use a Malmquist CO2 emissions performance index to evaluate the total-

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factor carbon emissions efficiency of the world’s top 18 CO2 emitters during 1997e2004. Several studies discuss CO2 emissions from a national perspective. As China is the world’s largest emitter, its regional pollutant emissions efficiency has drawn widespread attention. Due to the country’s rapid development, the contradiction between environmental protection and economic development is difficult to resolve (Wang et al., 2012). Considering regional differences in emissions efficiency and economic development (Cheng et al., 2018; Meng et al., 2018; Wang et al., 2019), many researchers have tried to identify a strategy that can promote environment-friendly economy in the western regions by prioritizing development in the eastern regions (Wang and Wei, 2014; Yu et al., 2019). At a more micro level, industrial and construction sectors account for a large proportion of the economy in China, with very large energy consumption and pollutant emissions. Thus, these sectors are usually the primary subjects of existing studies (Wu et al., 2012; Zhang et al., 2016). The cited studies focus on a large number of industrial sectors, including cement, iron and steel industries (Wei et al., 2007; Zhang et al., 2014) and electricity generation (Hasanbeigi et al., 2013; Zhou et al., 2012). Zhang and Choi (2013) use the Malmquist CO2 emissions performance index to measure the dynamic change in fossil fuel power plants. Wei et al. (2015) use the data of Zhejiang province to assess the abatement of energy consumption and associated CO2 emissions of China’s coalfired power plants. Some studies in recent years have discussed the energy consumption and carbon emissions in the service sector and observed that the development of the service sector has increased the overall CO2 emissions by necessity, although it had positive effects on the decrease in CO2 emissions intensity (Krackeler et al., 1998; Nansai et al., 2009; Suh, 2006). However, most of these studies still focused on energy-intensive service sectors, such as the transportation and real estate sectors (Duan et al., 2015; Wang and He, 2017), by mainly using the bottom-up method based on the floor area (Xing et al., 2018) and the input-output analysis (Gui et al., 2014). For example, Ge and Lei (2014) use the input-output analysis to calculate CO2 emissions of the service sector in Beijing, observing that the transportation, storage, mail, and telecommunications subsectors account for a high level of direct emissions, while other subsectors contribute significantly to indirect emissions. To summarize, there are three major limitations of the previous research: first, even though the environmental effects of the industry-oriented economy are discussed in-depth, little attention is given to the service sector, which can stimulate energy consumption in non-service sector strongly through supply chain; second, most studies focus on the quantities of CO2 emissions but ignore its efficiency correlation to economic outputs, and the differences of CO2 emissions among regional development; and third, the assumption of constant returns to scale (CRS), which is widely used in DEA models in existing studies, is too absolute to reflect the actual production processes and is unable to characterize practical production processes (Wang et al., 2017). Through our literature review, we find that the input-output analysis and the three-stage DEA model are widely used for embodied CO2 accounting and its efficiency evaluation respectively. Therefore, we use the inputoutput analysis to measure CO2 emissions of the service sector, considering both direct and indirect effect on non-service sectors. Then, we take the result of input-output analysis as one of the output data for the three-stage DEA, which can well eliminate the effects of the external environment and random errors among different provinces. Our study entails important theoretical and practical contributions. First, we introduce the embodied CO2

emissions in the production function of the DEA model and select six driving factors of CO2 emissions in the service sector as environmental variables to eliminate the influences of the external environment and statistical noise. In this way, we can obtain a more accurately approximate emissions efficiency of each decisionmaking unit (DMU). Second, considering the practical production processes, we evaluate the CO2 emissions efficiency of 30 provinces in China from the perspective of variable returns to scale; as a result, we can analyse the regional differences in terms of the pure technology efficiency and efficiency of scale and provide a framework for similar analyses. The remainder of this paper is organized as follows. Section 2 describes the research methodology together with the variables and data sources, while Section 3 discusses the results of the inputoutput analysis and the three-stage DEA. Section 4 concludes the paper and proposes several policy implications.

2. Method and data 2.1. Input-output analysis With the progress of economic globalization, intersectoral linkages have become increasingly complex. The development of the service sector creates additional demand for the output of upstream sectors through backward linkages which, in turn, increase CO2 emissions and cause substantial environmental issues indirectly. This ‘pull effect of services’ should also be considered in the ntara and green development assessment of the service sector (Alca Padilla, 2009). Leontief (1974) proposes to use the input-output analysis method in order to tackle the relationship of inputs and outputs among the intricate economic and technological networks. This method captures both direct and indirect influences based on the inter-sectoral linkages and has been widely applied in accountings for ecological endowments, such as energy, water and carbon emissions (Liu and Wang, 2017; Wu and Chen, 2017; Zhang et al., 2016). As formulated in Eqs. (1)e(7), this paper calculates the embodied CO2 emissions of the service sector in various provinces by using input-output analysis (Dai et al., 2015), where the inputoutput system is decomposed into n non-service sectors and p service sectors. Both subsystems use m kinds of energy that emit CO2.

fi ¼

m X

qk  wki ;

(1)

k¼1

ei ¼

Ei ; Xi

(2)

in which fi is the comprehensive CO2 emissions factor of service sector i, qk is the CO2 emissions factor of energy type k, wki is the proportion of energy type k in the total energy consumption by service sectors, and ei is the energy consumption (Ei) per unit of GDP in service sector i (Xi). The direct CO2 emissions coefficient can be formulated in Eq. (3) as follows:

ced ¼ fi  ei :

(3)

The indirect CO2 emissions coefficient can be formulated according to Eq. (4):

Please cite this article as: Wang, R et al., Embodied CO2 emissions and efficiency of the service sector: Evidence from China, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.119116

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0 ceind ¼ fi  @

n X

1

possibility set is defined by Eq. (8):

ej  sji A;

(4)

j¼1

in which ej is the energy intensity of the non-service sectors, and sji is the complete consumption coefficient caused by the intermediate products’ transfer from sector j to sector i, which can be calculated using the procedures in Appendix A. The total carbon emissions coefficient (cei) includes both direct and indirect CO2 emissions coefficients, as shown in Eq. (5):

0 cei ¼ ced þ ceind ¼ fi  ei þ fi  @

n X

1

0

ej  sji A ¼ fi  @

j¼1

n X

1 ej  Cji A;

j¼1

(5) in which Cij denotes the complete demand coefficient of a service sector from the input-output system:

 Cji ¼

sji

1 þ sji

isj ; i¼j :

(6)

Therefore, the embodied CO2 emissions in the service sector can be obtained by

CO2 ¼ cei  Xi :

3

(7)

2.2. Three-stage DEA Efficiency evaluation approaches can be roughly divided into two types: parametric and nonparametric. Parametric approaches require a definite production function, which is a limitation in research. However, nonparametric approaches, such as the DEA model, can find the optimal weights through linear programming without estimating the production function. In addition, this approach may simplify calculations and conversion process since it is not affected by the dimension of input and output variables (Kao, 2014; Wei and Zhang, 2013). The DEA model, as a mathematical programming technique, assumes that each decision-making unit (DMU) is independent and closed under the same environment. However, in actual economic activities, exchanges of resources connect different regions closely. The efficiency index of DMUs in a traditional DEA model would be influenced by the external environment (measured by the impacts of uncontrollable variables), management inefficiencies and statistical noise (Fried et al., 2002). Thus, in this study, we use the three-stage DEA model to eliminate the effects of uncontrollable variables and statistical noise so that we can compare different regions’ efficiencies. 2.2.1. Stage 1: DEA model A typical DEA model contains two forms: CCR (Charnes A, Cooper W W, Rhodes E) and BCC (Banker R D, Charnes A, Cooper W W). CCR is the earliest type of DEA presented by Charnes et al. (1978), which implies constant returns to scale (CRS). However, in actual production activities, the return to scale is always variable. Pn Therefore, Banker et al. (1984) adds a constraint j¼1 lj ¼ 1 to obtain a new type of model based on the CCR model. The modified model properly represented the aspect of variable returns to scale (VRS). Suppose that K DMUs use n inputs (x ¼ ðx1 ; x2 ; :::; xN Þ) to produce M desirable outputs (y ¼ ðy1 ; y2 ; :::; yM Þ2Rþ M ) and emit J undesirable outputs (b ¼ ðb1 ; b2 ; :::; bJ Þ2Rþ ). The production J

PðxÞ ¼ fðy; bÞ : ðx; y; bÞ 2 Tg; x2Rþ N:

(8)

P(x) is a closed and bounded set in which desirable and undesirable outputs are null-joint, and the undesirable outputs are characterized by weak disposability (Meng et al., 2016a). Based on the above equation and assumptions, we can incorporate environmental pollution as an undesirable output of the production process into the reference technology T. We consider the correlation between CO2 and GDP as being similar to the input-output interaction in practical activities and aim to avoid errors caused by function selection in data transform. We use CO2 as an input variable in the three-stage DEA model (Chen, 2010; Dong et al., 2017; Liu and Hu, 2015). The formula is derived in Appendix B.

min q ¼ h0 n 8X > lj Xj þ s ¼ qx0 ; > > > > j¼1 > > > > n > X > > < lj Yj  sþ ¼ Y0 ; s:t: j¼1 > > > n >X > > > lj ¼ 1; > > > > > : j¼1 l  1; j ¼ 1; 2:::n; s  0; sþ  0;

(9)

in which Xj and Yj indicate the input and output variables, respectively, of each DMU, X0 and Y0 are the corresponding variables of the jth 0 DMU, n is the number of DMUs, l is the dimensional weight vector of DMUs, q illustrates the comprehensive provincial embodied CO2 emissions efficiency of each DMU, and s- and sþ are the slack and surplus variables, respectively. If q ¼ 1, s- ¼ sþ ¼ 0, the DMU is DEA-efficient. If q ¼ 1, s-ssþs0, the DMU is weakly DEAefficient. If q < 1, the DMU is DEA-inefficient. 2.2.2. Stage 2: Stochastic frontier analysis We can obtain the comprehensive provincial embodied CO2 emissions efficiencies from Stage 1. We can also calculate the slack values of input variables, which show the gaps between actual and targeted input values. These slacks are influenced by the environment, managerial inefficiencies and statistical noise. Therefore, this paper uses stochastic frontier analysis (SFA) models to decompose the inputs’ slacks into three different effects. Given that there are n DMUs with m inputs and k external environmental variables, the SFA model can be constructed as in Eq. (10):

  sij ¼ f i zj ; bi þ vij þ uij ;

(10)

in which sij is the ith slack of input in the jth DMU (i ¼ 1,2 … m;j ¼ 1,2 … n), zj¼(z1j, z2j … zkj) are the independent external environmental variables, bi is a vector of the environmental factor parameters to be estimated, f i(zj;bi) is the deterministic feasible slack frontier, and vij þ uij is the error term, including the statistical noise (vij, vij ~ N(0,s2vi)) and the management inefficiencies (uij, uij ~ Nþ(mi,s2ui)). s2 In addition, we use g ¼ s2 þuis2 as a criterion to determine ui vi whether the SFA regression is necessary (Coelli, 1996). As g approaches zero, statistical noise becomes the dominant factor. In contrast, the inefficient management will play a more important role, and the results of Stage 2 are meaningful. The estimator of uij can be obtained by using Eq. (11) (Chen et al.,

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2014; Jondrow et al., 1982; Luo, 2012):

" ε j l  # f s    εl sl b   þ j ; i ¼ 1; 2::::n; j ¼ 1; 2::::::m; E uij uij þ vij ¼ 2 s 1 þ l 4 εsj l (11) in which εj ¼ uij þ vij , l ¼ ssuv , s2 ¼ s2u þ s2v , and f and 4 are the probability density and distribution functions of the standard normal distribution, respectively. Next, the estimator of vij can be calculated by Eq. (12):

i h   i b b  bE uij uij þ vij ; i ¼ 1; 2::::n; j ¼ 1; 2::::m; E vij juij þ vij ¼ sij  zj b (12) Finally, the new input variables based on the most effective DMU are equal to

n io h ii

  A xij ¼ xij þ maxj zj b b  zj bb þ maxj bv ij  bv ij ; i ¼ 1; 2::::n; j ¼ 1; 2::::m; (13) i i Term ½maxj fzj b b g zj bb  in Eq. (13) demonstrates the adjustment assigning all DMUs to the most unfavourable condition. Term ½maxj fb v ij g b v ij  is an adjustment assigning all DMUs to the same A most unfavourable environment. Variables xij are the adjusted input variables of each DMU in the same external environment.

2.2.3. Stage 3: adjusted DEA model Finally, in Stage 3, we use the adjusted inputs’ values obtained in Stage 2 to replace the original inputs and recalculate Eq. (9). The results can exclude the effects of environmental and statistical noise and reflect the adjusted embodied CO2 emissions efficiency. 2.3. Variables and data sources Previous studies of energy and emissions efficiency are mainly concentrated in Asian countries, especially in China. As China is the largest developing country in the world, the contradiction between the country’s economic growth and environmental management has drawn attention worldwide. The imbalance of regional development also poses significant challenges to the implementation of energy conservation and emissions reduction. More importantly, the proportion of the service sector in China’s GDP has increased significantly, stimulating a large demand for energy. Therefore, we consider China as an example to calculate the provincial embodied CO2 emissions efficiencies of the service sector. 2.3.1. Input and output variables To measure the efficiency of embodied CO2 emissions, we first establish 30 DMUs to represent the 30 provinces in China (excluding Tibet, Hong Kong, Macao and Taiwan) and then choose capital stock, labor force and energy consumption as three inputs, with the value added and CO2 emissions of the service sector being desirable and undesirable outputs, respectively. The capital stock, defined as the investment in fixed assets in 2012, the labor force that refers to the number of employees and the gross service values are all collected from China Statistical Yearbooks. The energy consumption and embodied CO2 emissions are calculated from the Provincial Input-Output Table of 42 sectors in 2012. All these input-output tables have been aggregated into a unified classification referred to as the Energy Balance Sheet. Table 1

summarizes the numbers and sector information. We choose 9 representative types of energy resources. Energy sources used for generating electric power and thermal power are reallocated into the electricity sector; subsequently, consumption of electric and thermal power in each sector is removed to avoid double-counting. CO2 emissions factors are adopted from the Intergovernmental Panel on Climate Change (IPCC, 2006) and the study of (Yuan et al., 2017). Table 2 shows the CO2 emissions factors of various energy sources. Based on Formula (5), the complete consumption coefficients are key to compute the embodied CO2 emissions of the service sector. These coefficients can reflect the quantitative relationships between sectors in the input-output analysis according to Miller et al. (2014). We take the complete consumption coefficients of Jiangsu, Zhejiang, and Shanghai as an example to analyse the correlation between the service sector and non-service sectors. These three provinces are located in Eastern China, which has developed to be one of the world’s most active economies. Among them, Shanghai has already been the leader of China’s economic development. In 2012, the output value of the service sector in Shanghai reached 12199.150 billion yuan, accounting for about 60.447% of its economy. In Table 3a, the complete consumption coefficient of 1.031 represents both direct and indirect demands of products on the industrial sector (Sector 2), when the transport, storage and post service sector (Sector 4) produces one unit final product in Jiangsu province. The greater the complete consumption coefficient is, the closer interdependence between the two sectors will be (Minx et al., 2009; Su et al., 2013). As shown in Table 3b, in Zhejiang province, the coefficients between service sub-sectors (Sectors 4, 5 and 6) and the industrial sector (Sector 2) are 0.967, 0.556 and 0.760 respectively, which are larger than the values between service sub-sectors and other sectors. This indicates that the service sector has the most abundant demand for products from the industrial sector. At the same time, the transport, storage and post sector has closer inter-relationship with the industrial sector compared with other service subsectors. These findings can also be found in Tables 3a and 3c As Table 3c shows, due to the fast development and large proportion of the service sector, Shanghai has the largest coefficients (i.e., 2.007, 0.811, 1.125) between service sub-sectors (Sectors 4, 5 and 6) and the industrial sector (Sector 2) among these three provinces. The ‘pull effect’ from the service sector will be more significant in Shanghai than Jiangsu and Zhejiang.

2.3.2. Uncontrollable environmental variables The embodied CO2 emissions efficiency in the service sector is influenced by not only input and output variables but also external environmental factors. However, due to the objective conditions and the limitation of the model itself, it is difficult to consider all factors. Thus, we follow the principle that these environmental variables have effects on CO2 emissions but cannot be controlled by observations. Finally, according to previous studies, we select industrial structure, energy intensity, economic development, government influence, urbanization process, and education as external environmental factors. (1) Industrial structure. The ratio of added value in the service sector to GDP is used to represent the industrial structure. The service sector has significant advantages in energy consumption and CO2 emissions efficiency. Since there are significant differences in regional economic development in China, the changes in industrial structure may significantly affect CO2 emissions in different provinces (Li and Lin, 2016; Mairet and Decellas, 2009; Mulder et al., 2014).

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Table 1 Adjusted classification of sectors. No.

Sector

No.

Sector

1 2 3

Agriculture, Forestry, Animal Husbandry, and Fishery Industry Construction

4 5 6

Transport, Storage and Post Wholesale and Retail Trade, Hotels and Catering Services Others

Table 2 CO2 emissions factors of various energy sources. Fuel

Net calorific value (KJ/ Potential carbon content kg) (kg/GJ)

Oxidation rate (%)

CO2 emissions factor (kgCO2/kg)

CO2 emissions factors (104 tons CO2/104 tons SCEc)

Coal Coke Crude oil Gasoline Kerosene Diesel fuel oil Fuel oil Liquefied petroleum gases Natural gas

20908 28435 41816 43070 43070 42652 41816 50179

25.8 29.2 20 18.9 19.6 20.2 21.1 17.2

0.94 0.93 0.98 0.98 0.98 0.98 0.98 0.98

1.86 2.83 3.01 2.93 3.03 3.10 3.17 3.10

2.60 2.91 2.10 1.99 2.06 2.12 2.22 1.81

38931a

15.3

0.99

2.16b

1.63

a

The unit is KJ/m3. The unit is kg CO2/m3. Standard Coal Equivalent.

b c

Table 3 The complete consumption coefficients of Jiangsu, Zhejiang and Shanghai. a. The complete consumption coefficients of Jiangsu Sector Number

1

2

3

4

5

6

1 2 3 4 5 6

0.120 1.037 0.005 0.047 0.049 0.076

0.102 2.286 0.005 0.090 0.075 0.170

0.089 2.201 0.016 0.076 0.062 0.190

0.034 1.031 0.007 0.395 0.056 0.185

0.044 0.493 0.004 0.053 0.054 0.193

0.033 0.701 0.006 0.066 0.138 0.244

b. The complete consumption coefficients of Zhejiang Sector Number 1

2

3

4

5

6

1 2 3 4 5 6

0.078 2.299 0.002 0.090 0.134 0.248

0.067 2.121 0.022 0.093 0.145 0.274

0.026 0.967 0.003 0.448 0.069 0.284

0.049 0.556 0.002 0.075 0.074 0.376

0.031 0.760 0.003 0.053 0.075 0.293

c. The complete consumption coefficients of Shanghai Sector Number 1

2

3

4

5

6

1 2 3 4 5 6

0.023 2.575 0.006 0.140 0.196 0.363

0.017 2.456 0.130 0.165 0.218 0.429

0.013 2.007 0.008 0.733 0.148 0.524

0.012 0.811 0.007 0.175 0.201 0.588

0.010 1.125 0.011 0.181 0.121 0.591

0.077 0.863 0.001 0.031 0.057 0.168

0.147 1.677 0.003 0.107 0.183 0.243

Note: Section 1: Agriculture, Forestry, Animal Husbandry, and Fishery; Section 2: Industry; Section 3: Construction; Section 4: Transport, Storage and Post; Section 5: Wholesale and Retail Trade, Hotels and Catering Services; Section 6: Others.

(2) Energy intensity. We use the energy consumed per unit of GDP as the proxy of energy intensity (Kwon et al., 2017; Xie et al., 2017). It is well known that the very large consumption of energy is one of the major factors of environmental deterioration. Energy intensity can reflect the energy consumption performance of production processes and identify whether the CO2 emissions are efficient in provinces. (3) Economic development. GDP per capita is an indicator of the level of economic development (Meng et al., 2016b; Wang et al., 2013). On the one hand, economic development improves technological and organizational management, which

is conducive to reduction of each unit’s CO2 emissions. On the other hand, it can influence the final consumption and stimulate the production activities, which may cause more pollutant emissions. (4) Government influence is measured by the proportion of government expenditure. It can reflect the government’s ability to supervise and manage the market (Yang et al., 2009). In the Chinese economy, the supervision and management of the government can maintain the interests of market participants, ensure safety and an efficient operation of the market mechanism and strengthen the motivation for

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Table 4 Descriptive statistics of variables in the three-stage DEA.

Inputs CO2 emissions (104 tons) Energy consumption (104 tons SCE) Labor force (104 persons) Capital stock (108 yuan) Outputs Value added of the service sector (108 yuan) Environmental variables Industrial structure (%) Energy intensity (kg SCE/yuan) Economic development (yuan/person) Government influence (%) Urbanization process (%) Education (%)

Max

Min

Mean

CV

84596.348 4587.729 2141.100 15875.721

2213.653 233.145 120.500 921.672

24885.578 1467.859 920.287 6661.049

0.687 0.636 0.608 0.577

26519.688

624.290

7938.395

0.790

0.765 0.052 93173.000 0.444 0.893 0.374

0.309 0.007 19710.000 0.213 0.364 0.058

0.409 0.023 44068.433 0.298 0.545 0.117

0.220 0.435 0.447 0.215 0.242 0.556

Note: CV represents the coefficient of variation.

production and operation of enterprises. The portion of the government’s fiscal expenditure on public education and cultural development will also lead to improvement of technology and productivity. (5) Urbanization process refers to the ratio of urban population to total population. With the acceleration of this process, the lifestyle and consumption habits of residents may change, which affects socioeconomic development (Wang et al., 2013; Zhao et al., 2019). Furthermore, the differences between urban and rural energy utilization patterns may also alter carbon emissions efficiency. (6) Education refers to the proportion of the population with a college degree or above, in the population aged 6 and above. It is used to measure the education level of residents in a province. The overall education level may affect technological innovation and development, thus affecting productivity growth (Yuan et al., 2017; Zhang and Lin, 2018). At the same time, a higher level of education makes it easier for individuals to accept innovations and to promote the development of the service sector, including information transmission, financial intermediation, education, culture and entertainment. Table 4 summarizes the descriptive statistics of variables in this paper. Table 4 shows that indicators vary significantly among 30 provinces in China. The largest added value of the service sector is approximately 2651.969 billion yuan, while the lowest value is 62.429 billion yuan. There also exist significant differences in CO2 emissions and energy consumption of the service sector. Although the government provides various economic opportunities to regions, its attention seems to be apportioned equally. The average proportion of industrial sectors in the economy is nearly 50%, signifying the accelerated transformation and upgrade of China’s economy. 3. Results and discussion 3.1. Results 3.1.1. Embodied CO2 emissions As shown in Table 5, the average CO2 emissions of 30 provinces in 2012 is 248.855 million tons, and 10 provinces’ emissions are above average. The provinces ranked by emissions in the order from high to low are Shandong, Shanghai, Liaoning, Guangdong, Hebei, Beijing, Jiangsu, Inner Mongolia, Zhejiang, and Shaanxi. Excluding Shanxi and Inner Mongolia, the other 8 provinces are coastal provinces of China. Guangdong province has the largest gross

service product with a value of 2.652 trillion yuan. Jiangsu and Shandong provinces follow. Shandong, Guangdong and Inner Mongolia consume 45.877, 32.947 and 29.351 million tons of energy resources, respectively, and are the three provinces with the largest energy consumption. In addition, Qinghai emit only 98.252 million tons of CO2, representing the lowest amount in China. Fig. 1 illustrates the CO2 emissions intensity of each province. We observe that the intensity values in Hainan and Ningxia provinces are significantly higher than those in other provinces. Hunan has the lowest emissions intensity of approximately 0.88 million tons per 100 million yuan. The figures for Fujian, Jiangsu, Chongqing, Guangdong, and Zhejiang are slightly higher, with the CO2 intensity of 10,000 to 20,000 tons per billion yuan. After considering economic factors, Shandong’s CO2 intensity is greater than 4.23, which is higher than the 5th percentile of the national average. Among the top 10 provinces by CO2 emissions, only Jiangsu, Guangdong, Zhejiang, and Beijing have below-average CO2 intensity, and the remaining provinces are all above 40,000 tons/ 100 million. 3.1.2. Stage 1: Efficiency analysis This stage uses the DEAP2.1 software to measure the embodied CO2 emissions efficiency of the China’s service sector as of 2012, and obtains the comprehensive technical efficiency (CRSTE1), pure technical efficiency (VRSTE2) and scale efficiency (SCALE3). Table 6 shows that the mean CRSTE of provinces is 0.664, the mean VRSTE is 0.77, and the mean SCALE is 0.863. Without considering external environmental variables and statistical noise, the CRSTE values of Beijing, Shanghai, Jiangsu, Hunan, and Guangdong in 2012 are all 1.000, indicating that these provinces are most efficient under the optimal production scale. Among the remaining provinces, Tianjin’s value is the highest, followed by those of Zhejiang, Fujian, Chongqing, and Shaanxi. The CRSTE values of these 5 provinces are all above average. Yunnan’s value is the lowest at 0.414. VRSTE values of provinces differ significantly, with only 8 provinces (Beijing, Shanghai, Jiangsu, Hunan, Guangdong, Tianjin, Ningxia, and Qinghai) having an index of 1.000 and approximately 13 provinces being above average. This result means that these provinces have obtained possibly maximal outputs with given inputs under the

1 CRSTE is defined as CRSTE ¼ VRSTE  SCALE and represents the ability to obtain the possibly maximal outputs with given inputs under the optimal production scale. 2 VRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the actual production scale. 3 SCALE represents the gap between the actual production scale and the optimal production scale.

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Table 5 Overall embodied CO2 emissions of 30 provinces. Province

CO2

Eastern region Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Mean

Province

CO2

Western region 40519.373 15705.683 43106.501 48805.507 52504.840 38214.130 30599.066 12043.586 84596.348 46745.257 11623.144 24885.578

Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Guangxi Inner Mongolia

Province

CO2

Central region 7670.260 18273.420 19644.713 17799.108 20137.716 15571.962 2213.653 11666.917 19066.755 11202.877 33622.818

Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan

28050.265 16711.875 20856.648 18257.310 10980.409 22303.050 20493.954 7580.183

Note: The unit of CO2 emissions is 104 tons.

Fig. 1. CO2 intensity of each province. Note: The unit of CO2 emissions intensity is 100 g/yuan.

present production scale. There are 19 provinces with SCALE values above average. Ningxia and Qinghai are at bottom of the list, with the index values of 0.505 and 0.437, implying that there still exists a significant gap between the actual production scale and the optimal production scale. In Fig. 2, we use the average of different regions’ three efficiency measures to represent the regional efficiencies. It is observed that SCALE is higher than VRSTE among the three regions. Furthermore, the regional differences in CRSTE are significant. The eastern region has the highest efficiency values in China, with a very large gap compared to the other two regions. 3.1.3. Stage 2: Impact of the external environment In Stage 2, the SFA model is used to measure the environmental influence on input variables, and the result shows that for six environmental variables and input slacks, the likelihood ratio (LR) of the one-side error is greater than the critical value of the mixed chi-squared distribution test, at the 5% significance level, indicating the fair robustness of the overall model. The value of g is almost 1,

implying that inefficient management has a significant influence on the efficiency of each decision-making unit (DMU). In Table 7, a negative regression coefficient of environmental variables indicates that the increase in environmental factors will reduce the slacks of inputs, with less resource waste and higher relative efficiency. Conversely, a positive coefficient implies that the increase in environmental factors will lead to more wasted inputs or less outputs. The industrial structure has a negative coefficient with the slacks of energy consumption, labor force and capital stock. This indicates that a higher proportion of the service sector in the economy will improve the output efficiency of input variables. The effect of the industrial structure on the CO2 emissions is 26677.311 units, indicating that the total consumption of energy resources will be reduced by the industrial transition from energy-intensive to technology-intensive sectors. Although this change will increase CO2 emissions of the service sector, the indirect CO2 emissions caused by upstream sectors will be significantly decreased.

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Table 6 The service sector’s embodied CO2 emissions efficiencies of 30 provinces in China as of 2012. Province Eastern region Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Central region Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Western region Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Guangxi Inner Mongolia Mean

CRSTEa

VRSTEb

SCALEc

1.000 0.984 0.562 0.549 1.000 1.000 0.887 0.874 0.607 1.000 0.429

1.000 1.000 0.602 0.556 1.000 1.000 0.896 0.918 0.651 1.000 0.768

1.000 0.984 0.934 0.987 1.000 1.000 0.989 0.952 0.932 1.000 0.559

0.457 0.550 0.539 0.477 0.646 0.635 0.615 1.000

0.528 0.645 0.626 0.582 0.831 0.689 0.648 1.000

0.864 0.852 0.861 0.819 0.778 0.922 0.948 1.000

0.784 0.596 0.502 0.414 0.690 0.515 0.437 0.505 0.449 0.591 0.639 0.664

0.883 0.637 0.614 0.518 0.742 0.776 1.000 1.000 0.616 0.667 0.705 0.770

0.888 0.936 0.817 0.800 0.930 0.664 0.437 0.505 0.728 0.887 0.907 0.863

Note. a CRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the optimal production scale. b VRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the actual production scale. c SCALE represents the gap between the actual production scale and the optimal production scale.

Fig. 2. Comparison of regional efficiency values. Note: CRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the optimal production scale. VRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the actual production scale. SCALE represents the gap between the actual production scale and the optimal production scale.

For energy intensity, its coefficients with regard to the four inputs’ slacks are all positive and statistically significant at the 1% level, implying that improvements in technology increase the efficiency of energy consumption and enhance the utilization of production factors such as capital and the labor force. Resource usage and pollutant emissions are reduced for given production outputs. For economic development, the added value per capita in the service sector shows positive effects on four input variables. It implies that the economic development stimulates the industrial restructuring and enhances the demands of the service sector, which results in the growth of CO2 emissions and low efficiency of production in the short term. For government influence, the coefficients are all significant and negative for the total number of CO2 emissions, energy consumption, labor force, and capital stock. The government influence determines the government’s power of organization and supervision in the market. Increased government influence will optimize production processes and reduce waste of inputs. At the same time, government’s investments in infrastructure, education and innovation facilitate technological progress, which may stimulate the improvement of CO2 emissions efficiency. For the urbanization process, the result shows that the increase in the percentage of urban residents can reduce the demand for inputs variables and improve productivity in the service sector. Compared with the energy consumption pattern in rural areas, the energy structure in urban areas is more environment-friendly with less usage of fossil energy and greater usage of renewable energy. And the energy consumption is more efficient, requiring less energy input. The increase in the urban population also improves the quality of the labor force in cities, facilitating the decrease in capital and CO2 emissions. For education, the result is contrary to our expectation. We generally believe that the increase in education level will improve the quality of employment, which may promote technological innovation and improve productivity. However, the coefficients with four input variables are negative, indicating that education mainly pushes the growing demand of the service sector at the present stage of social development and that its contribution to productivity cannot offset its effect on demand. 3.1.4. Stage 3: adjusted efficiency analysis Table 8 presents the adjusted CO2 emissions efficiencies of the service sector (eliminating the impacts of the external environment and statistical noise) of 30 provinces in China. The CRSTE, VRSTE and SCALE of Beijing, Shanghai, Jiangsu, Hunan, and Guangdong still indicate a high efficiency. The mean CRSTE of provinces in China is 0.579, and there are 14 above-average provinces. Except for the five aforementioned provinces, the order of provinces from high to low CRSTE values is Zhejiang, Liaoning, Shandong, Fujian, Tianjin, Sichuan, Henan, Shaanxi, and Hubei. Although the rank of Liaoning is close to that of Zhejiang, CRSTE of the former is much lower than that of the latter. The CRSTE values of Ningxia and Qinghai are 0.136 and 0.110, respectively, and are significantly lower than the values of other provinces. Considering the VRSTE index, values for more than half of the provinces are higher than average (0.938), showing a good overall performance. Heilongjiang has the worst efficiency according to VRSTE, with a value of 0.748. SCALE is a primary factor causing significant differences among comprehensive provincial efficiency values, with a standard deviation of 0.271. The SCALE values of 15 provinces are higher than the average, and those of Shandong and Zhejiang are over 0.9, followed by the value of Tianjin (0.844). The indices of most provinces are from 0.45 to 0.75, while the values for Hainan, Ningxia and Qinghai are still at the bottom of the list.

Please cite this article as: Wang, R et al., Embodied CO2 emissions and efficiency of the service sector: Evidence from China, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.119116

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Table 7 SFA estimation results. Independent variable

Dependent variable

Constant terms Industrial structure Energy intensity Economic development Government influence Urbanization process Education

sb ga Log likelihood function LRb

CO2 emissions

Energy consumption

Labor force

Capital stock

28854.023*** 26677.311*** 19453.825*** 0.384*** 39804.543*** 63450.352*** 51335.946*** 139354150.000 0.994 302.079 15.705***

1750.415*** 1510.565*** 2605.346*** 0.022*** 3123.299*** 3504.535*** 2413.877*** 359684.040 0.941 217.448 9.384***

1289.783*** 1633.900*** 66.563** 0.001** 1759.971*** 1130.318*** 4798.290*** 124594.240 0.990 199.583 10.104***

12661.848*** 15451.289*** 5417.122*** 0.026** 19325.941*** 11001.093*** 32160.677*** 3140981.600 0.872 252.996 3.877**

Note: *** and ** denote the significance levels of 1% and 10%, respectively. According to the SFA model results, we should adjust the input variables of each DMU to reevaluate the efficiency values. a g represents the proportion of management effect in the inefficiency decision-making unit. b The likelihood ratio (LR) of the one-side error is greater than the critical value of the mixed chi-squared distribution test at the 5% significance level.

Table 8 Adjusted the service sector’s embodied CO2 emissions efficiencies of 30 provinces in China as of 2012. Province Eastern region Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Central region Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Western region Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Guangxi Inner Mongolia Mean

CRSTEa

VRSTEb

SCALEc

1.000 0.698 0.543 0.785 1.000 1.000 0.924 0.699 0.748 1.000 0.168

1.000 1.000 0.823 0.930 1.000 1.000 1.000 0.947 0.764 1.000 0.916

1.000 0.698 0.660 0.844 1.000 1.000 0.924 0.738 0.980 1.000 0.184

0.431 0.416 0.363 0.477 0.438 0.635 0.611 1.000

0.928 0.940 0.748 0.948 0.981 0.924 0.894 1.000

0.465 0.443 0.486 0.504 0.447 0.687 0.684 1.000

0.457 0.696 0.374 0.355 0.627 0.287 0.110 0.136 0.354 0.494 0.540 0.579

0.880 0.917 0.932 0.891 1.000 0.964 1.000 0.963 0.964 0.947 0.924 0.938

0.519 0.758 0.401 0.398 0.627 0.297 0.110 0.141 0.367 0.521 0.585 0.616

Note. a CRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the optimal production scale. b VRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the actual production scale. c SCALE represents the gap between the actual production scale and the optimal production scale.

3.2. Discussion 3.2.1. Provincial comparison in CO2 emissions efficiency A comparison in Fig. 3 shows some changes in CO2 emissions efficiency after eliminating the impacts of the external

environment and statistical noise. Considering various provinces, the CRSTE values of Beijing, Shanghai, Jiangsu, Hunan, and Guangdong remain at 1, indicating that these five provinces have reached the optimal production scale with their technology far beyond others. And the external environment change may not affect their CO2 emissions efficiencies. The CRSTE values of Liaoning, Zhejiang, Shandong, and Sichuan become higher after adjustment. Among them, Liaoning has the greatest increase of 0.236, indicating that low efficiency before the adjustment is mainly due to the unfavourable external environment. Tianjin, Hebei, Shanxi and other provinces experience efficiency declines, and among these, Ningxia experiences the greatest drop. Qinghai and Chongqing perform better than does Ningxia, with a change of 0.327, indicating that the high efficiency of these provinces has a certain relationship with their favourable environments. The increase in VRSTE in each province demonstrates that except for management inefficiency, the bad external environment and opportunities can also lead to the low development of production technologies in China’s service sector, with the average value changing from 0.770 to 0.983. The average CRSTE value decreases from 0.664 to 0.579, and the SCALE value declines from 0.863 to 0.616, showing that the reduction of scale efficiency is the main reason for the decrease in comprehensive technical efficiency of some provinces after adjustment. According to the results above, we observe several characteristics of the development of China’s service sector: the production technology has become efficient, while the production activities are still in the stage of increasing returns to scale. The demand for inputs is high, but the effective outputs are relatively insufficient. Overall, the optimal production plan has not yet been achieved. Table 9 shows the provinces’ different ranks in the first and third stages. We observe that Beijing, Shanghai, Jiangsu, Hunan, and Guangdong achieve the highest ranks according to the values of CRSTE, with Zhejiang, Liaoning and Shandong following the above. The rankings of Hebei, Shanxi, Anhui, Henan, Hainan, Sichuan, Guizhou, Yunnan, and Xinjiang increase after the adjustment. Those of Hubei and Guangxi are unchanged, and the ranks of the remaining provinces decline after the adjustment.

3.2.2. Regional differences in CO2 emissions efficiency According to CRSTE values, we divide 30 provinces into 3 regions with the CO2 emissions efficiency ranging from low to high. As shown in Fig. 4, there are significant regional differences in CO2 emissions efficiency in the service sector. The high-efficiency regions include Liaoning, Beijing, Shandong, Jiangsu, Shanghai,

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R. Wang et al. / Journal of Cleaner Production xxx (xxxx) xxx Table 9 Provinces sorted in the order of comprehensive efficiency in the first and third stages. Province

After

Before

Province

After

Before

Beijing Shanghai Jiangsu Hunan Guangdong Zhejiang Liaoning Shandong Fujian Tianjin Sichuan Henan Shaanxi Hubei Hebei

1 1 1 1 1 2 3 4 5 6 7 8 9 10 11

1 1 1 1 1 3 16 11 4 2 12 9 6 10 14

Inner Mongolia Guangxi Anhui Chongqing Jiangxi Shanxi Jilin Guizhou Heilongjiang Yunnan Xinjiang Gansu Hainan Ningxia Qinghai

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

8 13 21 5 7 22 15 20 17 26 23 18 25 19 24

Fig. 4. Adjusted CO2 emissions efficiency of the service sector in China.

Fig. 3. Comparison of the provincial CRSTE values in China. Note: CRSTE represents the ability to obtain the possibly maximal outputs with given inputs under the optimal production scale.

Zhejiang, Fujian, Guangdong, and Hunan. The coastal areas of Eastern China are subject to an active opening-up policy, resulting in economies that are both well-developed and fast-growing. Beijing, Tianjin, Shanghai, and Guangdong have implemented economic transformation and upgrade earlier. In these provinces, their service sectors are well developed and have better production scales. Furthermore, the levels of urbanization and education are higher, and the capabilities of technological innovation and application are significantly better than in other provinces. Meanwhile, the financial and policy support by governments provides a sustainable development environment for technological innovation and rapid growth of their service sectors. In addition, the Hunan province has developed tourism as a strategic pillar for its industrial structure, which further promotes the booming development of the

economy. Compared with other provinces, the service sector of Hunan started to develop early and attained a high scale efficiency and strong competitive advantage. Next, Liaoning province has several coastal cities, and its urbanization process and the education level rank the first among the three provinces in Northeast China. Thus, it has a higher efficiency of inputs. The comprehensive efficiencies of Liaoning and Shandong provinces increase after adjustment, implying that the external environment of these two provinces has a significant potential for improvement, as does the CO2 emissions efficiency. The medium-efficiency regions include 9 provinces: Inner Mongolia, Hebei, Shaanxi, Henan, Anhui, Hubei, Chongqing, Sichuan, and Guangxi. These provinces have common features: their economies have entered a period of rapid development, and they have abundant natural resources and a solid industrial foundation. However, the dominant sectors in their industrial structure are the industrial and construction sectors. The CO2 emissions efficiency of each province in this region varies significantly in the first and third stages. Most of them are unrealistic efficient caused by the favourable external environment, and then appear to be lack of scale efficiency as soon as being adjusted into the same environments. Hebei has absorbed a large number of industrial sectors from Beijing and Tianjin, after the national strategy of the Beijing-

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Tianjin-Hebei urban agglomerations development was implemented. The production in Hebei relies heavily on the use of natural resources. Inner Mongolia has a significant amount of coal reserves, and the long-term industrial structure dominated by the coal mining industry hinders the structure’s transformation to cleaner modern industries. Finally, inefficient regions include Heilongjiang, Jilin, Shanxi, Jiangxi, Hainan, and the western areas of China (except Sichuan and Guangxi). Heilongjiang and Jilin are traditional industrial provinces in Northeast China. Their proportions of industrial sectors were 44.1% and 53.2%, respectively, in 2012, which were approximately 9% higher than those of the service sector. Shanxi’s pillar industries are the traditional industrial sectors, such as mining and energy generation and distribution. Its energy consumption structure is dominated by fossil energy. In 2012, Shanxi’s total coal consumption was up to 31.85 million tons. Hainan has developed into a tourism-oriented province, but its scale efficiency is only higher than that of Qinghai and Ningxia. This result indicates that Hainan has deficiencies in resource allocation and market management in the development of the service sector, resulting in wasted inputs and inefficient CO2 emissions. Finally, Ningxia, Qinghai, Gansu, Xinjiang, Yunnan, and Guizhou are rich in natural resources, bringing significant advantages in developing resource- or energyintensive industries. The industrial structures of the above provinces focus on agricultural, industrial and construction sectors that are often accompanied by energy-extensive consumption and a high level of emissions.

3.2.3. An example of Guangdong The Pearl River Delta urban agglomeration is in the centre of Guangdong province, which is one of the important economic centres in the eastern coastal region of China. Together with Beijing-Tianjin-Hebei and the Yangtze River Delta, these three urban agglomerations have become the main drivers of China’s economic development due to their advantageous geographic locations and political opportunities. The Pearl River Delta, with Guangzhou and Shenzhen as core cities, was the first to accomplish economic restructuring and upgrade. Although it consumes the largest amount of energy among the eastern regions, its CO2 emissions efficiency is still lower than that of the other two urban agglomerations. Because of the advanced production technology and a high degree of marketization in the Pearl River Delta, the gaps in CO2 emissions efficiency between it and other regions are increasing every year. In this section, we consider Guangdong province as an example to study the CO2 emissions of various sectors. Table 10 shows the CO2 emissions performance in 6 main subsectors of services in Guangdong. Compared with its 68.076 million tons of directly calculated CO2 emissions, the result of the input-output analysis is much higher. This result implies that the service sector created additional environmental pressure through their indirect effects on upstream sectors. The total CO2 emissions

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of Guangdong is 590.639 million tons and is mainly caused by industrial sectors. The transport, storage and post sector emits 56.667 million tons, ranking just below the industrial sectors. This performance is similar to the findings of the previous research (e.g., Duan et al., 2015). In the same year, Guangdong consumes approximately 291.440 million tons of standard coal, creating a value of 5706.792 billion yuan. Using the DEA model, we obtain the efficiency values of CO2 emissions in Guangdong. The values of CRSTE, VRSTE and SCALE are all equal to 1, indicating that Guangdong has already reached the optimal production frontier. From the perspective of economic development and carbon emissions performance of Guangdong province, economic integration has facilitated the overall development of the region. Shenzhen is the earliest open city in the Pearl River Delta. Through the policy of economic reform and opening up, Shenzhen not only has rapidly developed into a first-tier city in China but also has driven the development of surrounding cities, such as Zhuhai, Zhongshan, Dongguan, and Huizhou. At present, the Pearl River Delta has become one of the most economically vital urban agglomerations in China. The advanced production technology in Guangdong is improving rapidly, effectively facilitating the economic development and carbon emissions reduction of the surrounding provinces.

4. Conclusions and policy implications This paper combines the input-output analysis and the data envelopment analysis (DEA) model to propose an improved estimation method of embodied CO2 emissions efficiency in the service sector. To mitigate the influences of the external environment and statistical noise, we use the SFA model to equalize the environment of each decision-making unit (DMU) and re-evaluate the emissions efficiency according to regression results. Considering the evidence from China, we observe that the embodied CO2 emissions of the China’s service sector in 2012 are closely related to the regional economic development and the level of production technology. CO2 emissions of provinces with high output values and developed economies as well as of provinces with low levels of production technology and use of fossil energy as the main energy source are high. CO2 emissions of provinces with advanced production technology and high utilization of energy as well as of provinces with undeveloped economies are relatively low. According to the comprehensive efficiency index from Stage 3 of the DEA model, China can be divided into three areas with high, medium and low carbon emissions efficiency levels; this result is approximately consistent with the eastern, central and western geographic regions. Undoubtedly, the industrial sectors will still be the main driving force of the economic development of some provinces, and their proportions may not be substantially reduced in the short term. Therefore, it is necessary to accurately calculate the actual carbon emissions of the service sector and to determine the main factors

Table 10 CO2 emissions performance of Guangdong as of 2012. Sector

CO2 emissions

Percent

Agriculture, Forestry, Animal Husbandry, and Fishery Industry Construction Transport, Storage and Post Wholesale and Retail Trade, Hotels and Catering Services Others Total

442.334 51603.033 210.904 5666.654 956.314 184.628 59063.867

0.749% 87.368% 0.357% 9.594% 1.619% 0.313%

Note: The unit of CO2 emissions is 104 tons.

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that cause the differences in the regional carbon emissions efficiency. This requires China to adjust economic structure and implement energy conservation and emissions reduction strategies according to local conditions. Compared with previous studies, this paper improves the approach to evaluating carbon emissions efficiency. First, we replace CO2 emissions in a traditional DEA model by the embodied CO2 emissions without only focusing on the amount of CO2 emissions in the service sector (Iftikhar et al., 2018; Meng et al., 2016a; Zhao et al., 2019). Second, we select the main factors affecting CO2 emissions of the service sector as environmental variables to eliminate the influence of the external environment of each decision-making unit (DMU). Afterwards, we can calculate the actual CO2 emissions efficiency and ensure the comparability of efficiency values of all provinces. Third, we decompose the comprehensive technical efficiency into pure technical and scale efficiencies to discuss the differentiated influences of production technology and scale on the CO2 emissions efficiency of 30 provinces of China. We propose the following policy implications for governments. First, as the service sector emits a very large amount of embodied CO2 by increasing demand for production of upstream industries, governments should pay more attention to the development of the service sector. Second, because production technology is an important factor of CO2 emissions efficiency, governments should promote technological innovations and applications. Third, as CO2 emissions efficiency is significantly relevant to the level of economic development, governments should put more emphasis on the undeveloped regions in the future to balance the level of economic development among different regions and then improve the emissions efficiency. Finally, it is worth noting that because of the lack of data related to the energy consumption of the service sector, this paper only evaluates the overall embodied CO2 emissions efficiency in the service sector. The analysis and comparison of CO2 emissions performance of subsectors will be an interesting topic for future research. Additionally, the proportion of CO2 emissions and economic development contributed by the service sector in relation to other sectors of the economy may also be a potential avenue for further studies. Declaration of competing interest

Usually, we use letter A to denote the matrix composed by the direct consumption coefficients of each sector. The complete consumption coefficients of sector j include both direct and indirect consumption caused by the jth sector. These components also form a matrix, denoted by B, which can be calculated on the basis of the direct consumption coefficient matrix (matrix A):

B ¼ ðI  AÞ1  I:

(A.2)

In Eq. (A.2), I is a unit matrix.

Appendix B. Formulation change of the BCC model Suppose that there are n decision-making units (DMUs). Each DMU has m kinds of inputs (Xj ¼ ðx1j ; x2j ; :::; xmj ÞT , j ¼ 1,2, …,n) as well as s kinds of outputs (Yj ¼ ðy1j ; y2j ; :::; ysj ÞT , j ¼ 1,2, …,n). v ¼ ðv1 ; v2 ; :::; vm ÞT is the weight of m inputs, while u ¼ ðu1 ; u2 ; :::; us ÞT is the weight of s outputs. The efficiency of the jth 0 DMU is defined in Eq. (B.1):

hj0 ¼

uT Y0 ; vT X0

j ¼ 1; 2:::::n;

(B.1)

in which X0 ¼ Xj0 , and Y0 ¼ Yj0 . With the restriction in Eq. (B.2),

hj ¼

uT Yj  1; vT Xj

j ¼ 1; 2:::::::n;

(B.2)

the fractional expression of the CCR model is given by Eq. (B.3),

8 uT Y > > max T 0 > > > v X0 > > < T u Yj >  1; j ¼ 1; 2:::n; > > T > > v Xj > > : u  0; v  0;

(B.3)

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

which can be transformed into linear programming expressions given by Eq. (B.4) and Eq. (B.5),

Acknowledgements

uT Y0 ¼ mT Y0 ; vT X0

(B.4)

mT Yj uT Yj ¼  1; j ¼ 1; 2::::n; uT Xj vT Xj

(B.5)

The work described in this paper was partially supported by grants from the National Natural Science Foundation of China under Project Nos. 71974010, 71471011, 71531001. Appendix A. Consumption coefficients of input-output tables A direct consumption coefficient represents the value of goods directly consumed by each unit’s outputs in the jth sector from the ith sector, as given by Eq. (A.1):

aij ¼

xij ; Xj

i; j ¼ 1; 2:::n;

(A.1)

in which xij is the value of goods directly consumed by the jth sector from the ith sector. Xj is the total output of sector j.

in which t ¼ vT1X0 , u ¼ tv, and m ¼ tu. Given t ¼ vT1X , it should hold that uT X0 ¼ 1. The CCR model is 0 shown in Eq. (B.6):

8 max mT Y0 > > < T u Xj  mT Yj  0; > uT X0 ¼ 1; > : u  0; m  0;

j ¼ 1; 2:::n;

(B.6)

which has a dual problem shown in Eq. (B.7):

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8 min q > > n >X > > lj Xj  qX0 ; > > > < j¼1

n X > > > lj Yj  Y0 ; > > > > > : j¼1 lj  0; j ¼ 1; 2:::n:

(B.7)

Based on the above equation, we can add a constraint Pn j¼1 lj ¼ 1 to obtain the BCC model. The role of the slack and surplus variables is to change inequality into equality, which has been shown in Section 2.2. Appendix C. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.119116. References Alc antara, V., Padilla, E., 2009. Inputeoutput subsystems and pollution: an application to the service sector and CO2 emissions in Spain. Ecol. Econ. 68, 905e914. https://doi.org/10.1016/j.ecolecon.2008.07.010. Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30, 1078e1092. https://doi.org/10.1287/mnsc.30.9.1078. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429e444. https://doi.org/10.1016/03772217(78)90138-8. Chen, S., 2010. Energy-save and emission-abate activity with its impact on industrial win-win development in China: 2009d2049. Econ. Res. J. 45, 129e143. Chen, W., Zhang, L., Ma, T., Liu, Q., 2014. Research on three-stage DEA model. Syst. Eng. 32, 144e149. Cheng, Z., Li, L., Liu, J., Zhang, H., 2018. Total-factor carbon emission efficiency of China’s provincial industrial sector and its dynamic evolution. Renew. Sustain. Energy Rev. 94, 330e339. https://doi.org/10.1016/j.rser.2018.06.015. Coelli, T., 1996. A Guide to FRONTIER Version 4.1: a Computer Program for Stochastic Frontier Production and Cost Function Estimation (CEPA Working Papers). Dai, X., Qi, Y., Tang, H., 2015. Embodied CO2 emission calculation and influence factors decomposition in China’s agriculture sector. Resour. Sci. 37, 1668e1676. Dong, F., Long, R., Bian, Z., Xu, X., Yu, B., Wang, Y., 2017. Applying a Ruggiero threestage super-efficiency DEA model to gauge regional carbon emission efficiency: evidence from China. Nat. Hazards 87, 1453e1468. https://doi.org/10.1007/ s11069-017-2826-2. Duan, H., Hu, M., Zhang, Y., Wang, J., Jiang, W., Huang, Q., Li, J., 2015. Quantification of carbon emissions of the transport service sector in China by using streamlined life cycle assessment. J. Clean. Prod. 95, 109e116. https://doi.org/10.1016/ j.jclepro.2015.02.029. Duman, Y.S., Kasman, A., 2018. Environmental technical efficiency in EU member and candidate countries: a parametric hyperbolic distance function approach. Energy 147, 297e307. https://doi.org/10.1016/j.energy.2018.01.037. Fourcroy, C., Gallouj, F., Decellas, F., 2012. Energy consumption in service industries: challenging the myth of non-materiality. Ecol. Econ., Special Section: “Planetary Boundaries” and Global Environmental Governance 81, 155e164. https:// doi.org/10.1016/j.ecolecon.2012.07.003. Fried, H.O., Lovell, C.A.K., Schmidt, S.S., Yaisawarng, S., 2002. Accounting for environmental effects and statistical noise in data envelopment analysis. J. Prod. Anal. 17, 157e174. Gadrey, J., 2010. The environmental crisis and the economics of services: the need for revolution. In: Chapters. Edward Elgar Publishing. Ge, J., Lei, Y., 2014. Carbon emissions from the service sector: an input-output application to Beijing, China. Clim. Res. 60, 13e24. https://doi.org/10.3354/ cr01224.  mez-Calvet, R., Conesa, D., Go  mez-Calvet, A.R., Tortosa-Ausina, E., 2014. Energy Go efficiency in the European Union: what can be learned from the joint application of directional distance functions and slacks-based measures? Appl. Energy 132, 137e154. https://doi.org/10.1016/j.apenergy.2014.06.053. Gui, S., Mu, H., Li, N., 2014. Analysis of impact factors on China’s CO2 emissions from the view of supply chain paths. Energy 74, 405e416. https://doi.org/10.1016/ j.energy.2014.06.116. Hasanbeigi, A., Morrow, W., Sathaye, J., Masanet, E., Xu, T., 2013. A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry. Energy 50, 315e325. https:// doi.org/10.1016/j.energy.2012.10.062. Iftikhar, Y., Wang, Z., Zhang, B., Wang, B., 2018. Energy and CO2 emissions efficiency of major economies: a network DEA approach. Energy 147, 197e207. https:// doi.org/10.1016/j.energy.2018.01.012. Jondrow, J., Knox Lovell, C.A., Materov, I.S., Schmidt, P., 1982. On the estimation of

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