Regional assessment of water-energy nexus in China’s industrial sector: An interactive meta-frontier DEA approach

Journal of Cleaner Production 244 (2020) 118797

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Regional assessment of water-energy nexus in China’s industrial sector: An interactive meta-frontier DEA approach Tao Ding a, Huaqing Wu a, Junjun Jia a, Yuqi Wei b, *, Liang Liang b a b

School of Economics, Hefei University of Technology, 193 Tunxi Road, Hefei, 230009, Anhui, PR China School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, 230009, Anhui, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 June 2019 Received in revised form 21 August 2019 Accepted 7 October 2019 Available online 9 October 2019

Water and energy resource are inextricably linked. Rapid industrialization has led to severe water and energy shortage as well as environmental issues in China. In industrial sector, industrial production and wastewater treatment are two interdependent subsystems. Accordingly, understanding the internal operation performance of the two subsystems as well as assessing their coordination level are of great importance for sustainable water and energy usage. From the perspective of production efficiency, this paper aims to explore industrial water-energy nexus in China by proposing an interactive meta-frontier network DEA approach. The proposed approach is applied to assess the industrial water and energy utilization efficiency in the processes of industrial production and wastewater treatment from 2011 to 2015 in China. The results show that although eastern and central regions have high industrial production efficiency, most regions have relatively low wastewater treatment efficiency. In addition, the nexus degree of eastern region maintains a relatively high level and shows a stable improvement, while the rest two regions perform relatively worse. Comparing the central and western region, the industrial production technology of central region is better yet the wastewater treatment efficiencies of western region are higher, which results in higher nexus degree of western region. Based on the empirical results, policy implications are given in terms of national level, regional level and provincial level, respectively. © 2019 Elsevier Ltd. All rights reserved.

Handling editor: Xin Tong Keywords: Water-energy nexus Meta-frontier Industrial production Wastewater treatment Nexus degree

1. Introduction In the past decades, China’s industrial level has experienced a rapid development, which has been accompanied by an increasing demand for water and energy resources (Lin and Tian, 2016). According to statistics from 2000 to 2014, the energy consumption in China grew at an annual rate of 6.6% along with the demand for water resources increasing at an annual rate of 0.65% (NBS, 2015). Thus, it will exert enormous pressure upon existing water and energy system because the supply falls short of demand in most regions of China. In addition, environmental crisis caused by unsustainable water and energy consumption is becoming the biggest global hazard in recent years as reported by the World Economic Forum (Waughray, 2011). More importantly, water and energy are inevitably correlative (Gleick, 2003). On the one hand, in the energy sector, water is

essential for energy production, conversion transportation, and utilization. For example, thermal power generation is the second largest water consumption sector in China (Feng et al., 2014). On the other hand, the water sector is a major consumer of energy for uses such as water extraction, purification, transportation and desalination (Gu et al., 2016). Correspondingly, electric input accounts for about one third of the cost of water supply (Kahrl and Roland-Holst, 2008). Due to this interactive relation between water and energy sectors, increasing energy efficiency can reduce pressure on water resources, and saving water resource can also reduce the consumption of energy (Shang et al., 2018). Tracing back to the World Economic Forum in 2008, the global challenges with respect to economic growth were identified from the water-energy nexus viewpoint. Since then, the concept has gradually received great attention. There is a popular trend to study the water-energy nexus, especially in China (Kahrl and Roland-

* Corresponding author. E-mail addresses: [email protected] (T. Ding), [email protected] (H. Wu), [email protected] (J. Jia), [email protected] (Y. Wei), [email protected]. cn (L. Liang). https://doi.org/10.1016/j.jclepro.2019.118797 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

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T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

Holst, 2008; Hamiche et al., 2016; Fang and Chen, 2018; Hu et al., 2018). The geographical scales of case studies in terms of water and energy nexus vary from macro level to micro level, such as national level, regional level and city level (Fan et al., 2018; Chu et al., 2019; Feng et al., 2019). The research gap of existing studies related to water and energy nexus assessment primarily describes as follows. First, previous studies mainly focused on estimating water consumption for energy production and energy consumption for water utilization (Zhang et al., 2018). Few studies have evaluated the production efficiency on the whole process of water and energy consumption. Second, water and energy nexus assessment may have several interactive subsystems that are needed to be modeled in a unified framework (Xing et al., 2019), while previous studies dealt with them separately. Third, many studies were at the “understanding” stage only with an emphasis on the importance of water-energy nexus, while the coordination level of the subsystems are not quantitatively analyzed (Dai et al., 2018). From the perspective of production efficiency, this paper aims to establish a unified modeling framework to measure the waterenergy nexus of the industrial sector in China. Inspired by the life cycle analysis (LCA) theory, the industrial process is composed by two subsystems, i.e., the industrial production subsystem and the wastewater treatment subsystem (Foley et al., 2010). In the industrial production subsystem, water and energy resources accompanied by other inputs are consumed to produce desirable outputs as well as undesirable wastewater. In the wastewater treatment subsystem, wastewater is purified into recycled water. Luckily, the recycled water can also be seen as input in the industrial production system. Through this process, these two subsystems are interactive owning to the mutual flow of the means of production. Besides, technology heterogeneity exists in different regions because of multiple levels of industrialization in China (Ding et al., 2018; Yang et al., 2017). For example, compared with central and western regions in China, the eastern region is more attractive to attract advanced technology and talents, and thus more industrialized. By considering the factor of technology heterogeneity, this paper first develops interactive meta-frontier network DEA models to assess the industrial production (IP) subsystem and the wastewater treatment (WT) subsystem efficiency for provinces in China. Second, we also make technology gap analysis to find the source of inefficiency. Third, to assess the coordination of the two coupling subsystems, nexus degree index is defined in this paper. This paper makes the following contributions to current literature. First, while interactive network DEA models have been proposed by An et al. (2017) in multiplier form, this paper develops envelopment form of interactive network DEA models meanwhile considering both desirable and undesirable outputs, which contains more economic meaning. Second, by constructing meta-group frontiers, the technology heterogeneity caused by geographic differences is analyzed and the inefficiency sources are identified. Third, to quantify the coordination interaction between the coupling subsystems, nexus degree indexes are given. Finally, the proposed interactive meta-frontier network DEA approach is applied to the industrial production and wastewater treatment process in China from 2011 to 2015. From the perspective of multiple scales, i.e., national level, regional level and provincial level, policy implications are given based on the empirical results. The rest of this paper is designed as follows. The next section is literature review related to our study. The interactive metafrontier DEA approach is proposed in section 3. Section 4 presents a case study of provincial industrial water-energy nexus evaluation in China. Conclusion and policy implications are given in section 5.

2. Literature review 2.1. Water-energy nexus applications in China From the perspective of study scales, the related literature is classified to three types, i.e., national level, regional level and city level. On the national level, Gu et al. (2016) explored the nexus between water saving and energy conservation in industry sector during 12th five-year plan period. These results indicated that by achieving the energy saving targets in the end of the 12th Five-Year Plan, progress will also be made toward to achieve the water conserving targets. Chu et al. (2019) conducted a quantitative analysis to estimate water for energy and energy for water in each province in China using a bottom-up approach. They suggested that water availability is becoming a growing concern for China’s energy sustainability. From the perspective of policy making, Fan et al. (2018) explored the water and energy nexus in China by proposing a recursive dynamic computable general equilibrium model. The results show that water fee can contribute to industrial water conservation, whereas its effect is limited under current water fee level. On the regional level, Li et al. (2019) studied the city-level waterenergy nexus in Beijing-Tianjin-Hebei region in 2012 from both production and consumption perspectives, where input-output analysis based on city-level input-output tables were applied to conduct consumption-based statistics. Feng et al. (2019) built an accounting framework to assess the synergy of energy conservation on both water quantity and quality on regional levels in China. They found that 27 regions achieved energy-related water saving during 2007e2012. Sun et al. (2018) explored the potential of energy structure adjustment and technological advancement in easing baseline water stress and promoting sustainable development in the Beijing-Tianjin-Hebei region. The result shows that the BeijingeTianjineHebei region is under high water stress, which will be aggravated under global climate change. On the city level, Chen and Chen (2016) proposed a systembased framework to synthesize the interwoven connections between energy consumption and water use in Beijing, China. The result shows that services consume the most energy directly in all areas, while manufacturing takes up the largest proportion in the households trigged electricity consumption. Wang et al. (2017) proposed a modified inputeoutput analysis to provide a unified framework to balance urban energy and water use by using the case of China’s capital cityeBeijing. The result shows that higher energy intensity can be found in mining and services sectors in perspective of energy consumption, and agriculture, services, and manufacture in perspective of water consumption. Yuan et al. (2018) proposed an environmental impact minimization model, which considers the water, energy and food nexus under four climate change scenarios to optimize the spatial distribution of three energy crops (rice, corn, and sugarcane). The major findings of LCA in this study indicate that electricity generation using bio-coal produced from rice straw is significantly beneficial to the environment. 2.2. Related water-energy nexus assessment method Input-output (IO) analysis, proposed by Leontief in the late 1930’s, is dominant in water-energy nexus assessment studies. It serves the purpose of quantifying the links between water and energy by calculating the physical flows and monetary flows through the process of resources consumption from an economic structure perspective (Wang et al., 2017). Nowadays, there are two widely used IO analysis approaches, the basic IO analysis and the multi-regional input-output, i.e., MRIO analysis. The former one approach is adopted to the city level, and the latter one is applied to

T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

the region level. The main shortage of IOA is the use of capital values and the lack of sector discrimination caused by sector aggregation. It makes the results sensitive to price changes and homogeneities in different sectors. In addition to input-output analysis approach, data envelopment analysis (DEA), introduced by Charnes et al. (1978), is a popular method to measure the efficiency of production systems with multiple inputs and outputs. It has also been applied to water and energy nexus assessment problems recently. By treating the production process as a “black box”, Li et al. (2016) calculated the input-output efficiency of water-energy nexus in 30 provinces across China, from 2005 to 2014. By opening the “black box”, Shao et al. (2019) assessed the eco-efficiency of China’s industrial sectors between 2007 and 2015 by using the directional distance function (DDF) of network data envelopment analysis (DEA), in which the production, wastewater and waste gas treatment processes are linked. To comprehensively evaluate water use and wastewater treatment in the Minjiang River Basin, Hu et al. (2018) integrated bi-level programming (BLP) and DEA with a feedback variable. Considering the water-energy-food nexus and the external environment, Zheng et al. (2019) applied a three-stage DEA evaluation approach to assess the agricultural production efficiency (APE) of seven provinces in the middle and lower reaches of the Yangtze River basin during 1996e2015. Although the above studies considered water-energy nexus problems in terms of production efficiency, they assumed the evaluated DMUs are homogeneous which is not always possible. By considering the factor of technology heterogeneity, O’Donnell et al., 2008 introduced the concept of meta-frontier to DEA framework, which can distinguish the technical differences of companies belonging to different groups. Since then, a large amount of meta-frontier DEA studies related to energy and environment efficiency have been published. For example, Chiu et al. (2012) integrated the directional distance function and metafrontier analysis to measure the effects of technology heterogeneities and undesirable output on environmental efficiency. Zhang et al. (2013) developed a meta-frontier approach to assess energy and CO2 emission efficiency in electricity generation system. Lin and Zhao (2016) investigated the regional differences of China’s textile industry in energy utilization by using the technology gap ratio index. Feng and Wang (2017) analyzed the total-factor energy efficiency and energy savings potential in China’s provincial industrial sectors in the period of 2000e2014. Tian and Lin (2018)

n



3

production and energy consumption for water utilization, while the production efficiency of the whole system is ignored. In addition, the research concentrates on single geographical scale case studies such as national, regional and provincial scales, thus may provide partial empirical results and policy implications. Although some DEA based literature have studied the water-energy nexus issue, more or less research gaps are existed. First, they have treated the production process as a black box, while the water-energy nexus problems usually consisted by more than one coupling subsystems. Second, they impliedly assumed that the production technology of all DMUs are at the same level, which is not always valid. Third, further analysis of the nexus degree between such water and energy coupling systems are lacking. Therefore, it is necessary to evaluate the production efficiency as well as quantify the nexus relations of such network structure by taking the actual technology heterogeneity into account.

3. Methodology 3.1. Interactive DEA models under group frontier and meta frontier Considering there are N regions under evaluated, which are denoted by DMUn (n ¼ 1,…,N) as shown in Fig. 1. The industrial sector of each region is formed by two sub-systems: the industrial production sub-system and the wastewater treatment subsystems. In the industrial production (IP) sub-system, industrial capital (XC), energy consumption (XE), industrial labor force (XL) and industrial water consumption (XW) are used to produce gross industrial output value (YG) as well as industrial wastewater (WW). And in the wastewater treatment (WT) sub-system, industrial wastewater (WW) and total investment in WT (XI) are consumed as inputs to generate total quantity of wastewater recycled and reused (RW) and product output value by disposing of and utilizing waste water (YO). It should be pointed out that output RW in WT system is recycled resource, which is also an input in the IP sub-system. In our study, we assume that the N DMUs are belonging to K different groups in terms of different production technologies. The P number of DMUs in group k is expressed by Nk such that Kk¼1 Nk ¼ N. In general, DMUs in the same group have the same or similar level of production technologies. Let X1 denotes the vector of ðXC; XE; XL; XWÞ, then according to Shao et al. (2019), the production possibility set of group Nk can be expressed as follows:



PðNk Þ ¼ ðYG; WW; YO; RWÞjwhere X1 ; RW can produce ðYG; WWÞ; and ðXI; WWÞ can produce ðRW; YOÞg

(1)

introduced meta-frontier approach to study technology difference of energy utilization in China’s light industry system. 2.3. The summary of literature The literature review above demonstrates that most studies mainly focus on estimating water consumption for energy

Then, considering the particularity of production structure in this study, i.e., WW is an undesirable intermediate output of IP subsystem and RW is a recycled resource form WT sub-system to IP sub-system, we have the simulated group production possibility set based on observed data as Equation (2):

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T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

PðNk Þ ¼ f

Nk X

lkn X1j  X1 ;

n¼1 Nk X

mkn XIj

lkn RWj ¼ RW;

n¼1

 XI;

n¼1

Nk X

Nk X

mkn WWj

lkn YGj  YG;

n¼1

¼ WW;

n¼1

Nk X

Nk X

mkn YOj

n¼1

Nk X

lkn WWj ¼ WW;

n¼1

 YO;

Nk X

mkn RWj

¼ RW

n¼1

Here ako and bko respectively denote the intensity variables according to the IP sub-system and the WT sub-system. The conPNk k PNk k straints RW, and n¼1 ln RWj ¼ n¼1 ln WWj ¼ WW PNk k PNk k m m WW ¼ WW, RW ¼ RW indicate that undesirable j j n¼1 n n¼1 n intermediate product WW satisfy the weak disposability, and the amounts of intermediate product WW and recycled resource RW in two sub-systems are conserved, respectively. Then, for each given DMUo belonging to group k, under the group production possibility set, the inefficiency measurements of the IP sub-system and WT sub-system denoted by ako and bko , are calculated by the following linear model (3).

Max ako þ s:t: Nk X

bko





lkn X1n  1  ako X10 ;

(3.1)

lkn RWn ¼ RWo ;

(3.2)

lkn YGn





1 þ ako



YGo ;

(3.3)

lkn WWn ¼ WWo ;

(3.4)

n¼1 Nk X



(3.6)

n¼1 Nk X





mkn YOn  1 þ bko YOo ;

(3.7)

mkn RWn ¼ RWo ;

(3.8)

lkn ; mkn  0; n ¼ 1; :::; Nk

(3.9)

n¼1 Nk X n¼1

1  bmeta of evaluated DMUo; constraint (3.2) denotes the linear o combination of RW for all Nk DMUs should be equal to RW of evaluated DMUo; (3.3) denotes the linear combination of YG for all Nk DMUs should be great than or equal to qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi meta meta ¼ ð1  ameta Þð1  bo Þ of evaluated DMUo; (3.4) denotes Do o the linear combination of WW for all Nk DMUs should be less than or equal to WW0 of evaluated DMUo; Constraints (3.5)e(3.8) are similar to the (3.1)e(3.4), and not be repeated here. Constraint (3.9) indicate

n¼1 Nk X

mkn WWn ¼ WWo ;

for all Nk DMUs should be less than or equal to MEEWT ¼ 1  ameta o o

n¼1 Nk X

Nk X

Here constraint (3.1) denotes the linear combination of MEEIP o ¼

n¼1 Nk X

(2)

)



mkn XIn  1  bko XIo ;

(3.5)

n¼1

Industrial capital Energy consumption

TGR

and

TGRIP o ¼

Input

Output

¼

1ameta o 1ako

are

non-negative

multipliers. By solving the above model, the degrees of inefficiency of the IP sub-sub-system and WT sub-system are represented as ako and bko , respectively. Consequently, the group efficiencies of the two subWT k systems of DMUo are expressed as GEEIP ¼ o ¼ 1  ao and GEEo k 1  bo , respectively. Obviously, the efficiencies of both sub-systems are between 0 and 1. If the efficiency value is 1, it means that DMUo is efficient under the group-frontier technology. Then, the meta-production possibility set is obtained as an envelopment of the group production possibility set. Usually, there are two expressions possible for meta-production possibility set:

IP system

WT system Wastewater

Investment in WT

Industrial labor force Industrial water consumption

MEEIP o GEEIP o

Wastewater recycled Industrial output value

Intermediate Input or recycled resource

Fig. 1. Schematic of IP and WT system.

Output value of WT system

T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

the convex (e.g., Shao et al., 2019) and the non-convex envelopment (e.g., O’Donnell et al., 2008), where convex meta-production possibility set can be obtained by make the additional assumption of convexity of the envelopment to the non-convex one (Walheer, 2018). Here, considering the simplicity of processing, we adopt the convex meta-production possibility set hypothesis in this paper. The simulated meta-production possibility set based on observed data of N is shown as following Equation (3):

PðNk Þ ¼ f

Nk K X X

lkn X1j  X1 ;

k¼1 n¼1 Nk K X X

mkn XIj

 XI;

k¼1 n¼1

Nk K X X

lkn RWj ¼ RW;

k¼1 n¼1 Nk K X X

mkn WWj

k¼1 n¼1

¼ WW;

Nk K X X

lkn YGj  YG;

k¼1 n¼1

mkn YOj

 YO;

Nk K X X

meta





(5.1)

k¼1 n¼1 Nk K X X

lkn RWn ¼ RWo ;

(5.2)

k¼1 n¼1 Nk K X X

  lkn YGn  1 þ ameta YGo ; o

(5.3)

lkn WWn ¼ WWo ;

(5.4)

k¼1 n¼1 Nk K X X k¼1 n¼1 Nk K X X





Definition 1. For each given DMUo, the nexus degree of the IP system and WT system in specific kth group frontier is expressed as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dko ¼ ð1  ako Þð1  bko Þ. Definition 2. For each given DMUo, the nexus degree of the IP ¼ system and WT system in meta frontier is expressed as Dmeta o qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi meta Þð1  bo Þ. ð1  ameta o

3.2. Technology gap analysis of sub-system efficiency and nexus degree To evaluate the gap between the group frontier and metafrontier, the technology gap ratio (TGR) of environment efficiency for DMUo is constructed as follows (O’Donnell et al., 2008).

(5.6)

TGRIP o ¼

(5.7)

TGRWT o ¼

   1 þ bmeta YOo ; o

k¼1 n¼1 Nk K X X

from the physical discipline, is a situation that multiple systems influence each other by interactive factors. Recently, this concept has been widely applied to economic and environmental areas. For example, the water-energy nexus, the water-energy-food nexus, water-energy-environment nexus are getting more and more attention. This paper views the nexus degree of the IP subsystem and WT subsystem as a dynamic process in which the fully coupled status is achieved when both the two subsystems are efficient. A higher index implies a more balanced and efficient system. In this manner, the definition of nexus degree is given as follows (Dong et al., 2018).

mkn WWn ¼ WWo ;

mkn YOn





mkn RWn ¼ 1 þ bmeta RWo ; o

(5.8)

k¼1 n¼1

lkn ; mkn  0; n ¼ 1; :::; Nk

(4)

)

¼ RW

(5.5)

k¼1 n¼1 Nk K X X

mkn RWj

mkn XIn  1  bmeta XIo ; o

k¼1 n¼1 Nk K X X

lkn WWj ¼ WW;

k¼1 n¼1

Max ameta þ bo o s:t:

lkn X1n  1  ameta X10 ; o

Nk K X X k¼1 n¼1

Under the meta frontier technology, the inefficiency of DMUo is determined by the following program (5).

Nk K X X

meta frontier technology, the efficiencies of the two sub-systems of meta and MEEWT ¼ 1  bmeta , DMUo are expressed as MEEIP o o ¼ 1  ao o respectively. In addition, it is obvious that we have MEE  GEE for the reason that each optimal solution of model (3) is also a feasible solution of model (5). As we have the efficiencies of the two sub-systems under the group or meta production possibility set, we can start measuring the nexus degrees of the two sub-system. Nexus, which derives

k¼1 n¼1 Nk K X X

5

(5.9)

The explanation of the constraints of model (5) is similar with that of model (3), thus we do not repeat it here. Then, under the

MEEIP o GEEIP o

¼

MEEWT o GEEWT o

1  ameta o 1  ako ¼

(6)

1  bmeta o k

1  bo

(7)

Because the meta-frontier is enveloped in the K group frontiers, MEE  GEE always holds. Therefore, the value of technology gap ratio is between 0 and 1. The closer the TGR is to 1, the smaller is the technology heterogeneity of the production system, which means that the environment efficiency of the group frontier is closer to that of the meta-frontier. On the contrary, when TGR is closer to 0, the technology heterogeneity is bigger, which means the environment efficiency between the group frontier and meta-frontier are quite different.

6

T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797 Table 1 The division of different regions. Region

Provinces

Eastern Group Region Central Group Region Western Group Region

Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, Zhejiang Anhui, Guangxi, Henan, Heilongjiang, Hubei, Hunan, Jilin, Jiangxi, Inner Mongolia, Shanxi Gansu, Guizhou, Ningxia, Qinghai, Shanxi, Sichuan, Xinjiang, Yunnan, Chongqing

Table 2 Statistical description of the data. Indicator

Unit

Eastern Group

Central Group

Western Group

All

XC XE XL XW YG WW XI YO RW

Billion yuan Million tons of coal equivalent Thousand people 108m3 Billion yuan Million ton Million yuan Million yuan Million ton

959.21 179.52 4255.08 57.69 1477.14 3353.85 2265.21 2871.23 264.52

447.89 139.15 2304.47 56.04 917.69 2224.10 1936.67 1957.56 45.91

286.09 95.67 1390.16 21.91 489.72 1181.47 1154.72 1375.91 66.57

586.83 140.91 2745.40 46.41 994.43 2325.55 1822.55 2118.08 132.27

Although the technology heterogeneity between the metafrontier and specific group frontier of DMUo can be distinguished by TGR, the source of the meta-inefficiency cannot be identified. Hence, according to Chiu et al. (2012), the inefficiency of DMUo under the meta-frontier is decomposed into technology gap inefficiency (TGI) and managerial inefficiency of group frontiers (GMI), as depicted in Equations. (8) and (9), respectively.

  IP IP ¼ ameta TGIIP  ako o o ¼ GEE o 1  TGRo   1  TGRWT ¼ bmeta TGIWT ¼ GEEWT  bko o o o o

IP GMI IP o ¼ 1  GEEo ¼ WT GMI o ¼ 1  GEEWT o

ako ¼ bko

TGRDo ¼

Dko

¼

nk X n¼1

TGRIP n

.

(12)

TGRDn =nk

(13)

n¼1

TGRDk ¼

nk X n¼1

3.3. Data source

(9)

(10)

For the reasons that MEE  GEE, TGRDko  1 always holds. In addition, to observe the group level performance, the average indexes of the kth group are calculated as:

TGRIP k

. nk TGRWT n

(8)

Definition 3. To evaluate the gap between the group frontier and meta-frontier, the technology gap ratio of nexus degree (TGRDko ) for DMUo is constructed as follows.

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi    meta 1  ameta 1  bo o qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ¼   1  ako 1  bko

nk X

in which denotes the number of DMUs in the kth group.

Specifically, the TGI expresses the technical gap between the meta-frontier and the specific group frontiers caused by technology differences. And the GMI expresses the inefficiency source from desirable outputs shortage and inputs as well as undesirable outputs excess, which is caused by managerial failure. Reasonably, the DMUs in the same group are considered to have the same or similar production technology. Hence, the source of environment inefficiency in the same group is attributed to managerial inefficiency.

Dmeta o

TGRWT k ¼

By applying the proposed interactive meta-frontier DEA approach, this paper takes a sample of 30 provincial data during 2011e2015 to explore the water-energy nexus in China. The data regarding the XC, XE, XL, XW and YG are collected from the “China Statistical Yearbook” and the data regarding the WW, XI, RW and YO are derived from the “Environmental Statistical Database of China”. It should be pointed out that the data of Tibet, Macao, Hong Kong and Taiwan are excluded because of data availability. In order to analysis the technology heterogeneity in terms of geographical locations, the 30 provinces in our study are divided into three groups. According to the statistical caliber of the National Bureau of Statistics, the detailed division is shown in Table 1 (see Chen and Song, 2008). And Table 2 lists the average values of input and output variables during 2011e2015 for the three groups. From Table 2, it can be found that the volumes of China’s IP-WT system in three groups are significantly different. The eastern group equips with the largest inputs and outputs of IP-WT system, followed by the central group. And the western group has the lowest volume. It is apparently consistent with the volume of industrial development in these three regions. Besides, compared with the central group, the eastern group consumes almost the same amount of water and one and half amount of energy to produce nearly twice of gross industrial output value (YG). 4. Case study 4.1. Efficiency analysis

nk

(11)

By solving the Models (1) and (2), the degrees of inefficiency of the each DMU0’s IP sub-system in group k or meta-frontier technology can be obtained as ako and ameta , respectively. Then, the IP o

T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

1.000 0.980 0.960 0.940 0.920 0.900 0.880 0.860 0.840 0.820 0.800

7

1.000 0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000

Average of Eastern

Average of Central

Average of Eastern

Average of Western

Fig. 2. The average IP efficiencies of different regions of China.

energy efficiency of DMU0 based on group frontier k and metak frontier can be calculated according to formula GEEIP o ¼ 1 ao and meta . Similarly, the values of WT energy efficiency of MEEIP ¼ 1  a o o DMU0 based on group frontier and meta-frontier can also be obtained. The results of the IP and WT energy efficiencies of 30 provinces of China in 2011e2015 are shown in Tables 3 and 4 in the Appendix, respectively, and here we plot the average MEE and GEE efficiency of the three regions in 2011e2015 in Fig. 2. From Fig. 2, we can find that there is a clear gap between different regions of China in MEEIP average performance, where the eastern group performs better than central group and western group. This result illustrates the imbalance between industrial development in different regions of China: the eastern region is more developed, which is consistent with the cross-section result in the year of 2010 investigated by Bian et al. (2014). In addition, this technology heterogeneity between different regions also illustrates the purpose of our evaluation of provinces respectively under the meta frontier and the group frontier, indicating it is unfair to compare undeveloped provinces with developed provinces. Take Ningxia, a western province in the year of 2011, as an example. According to Table 3, the IP GEE score of Ningxia is 1, which means Ningxia is efficient in the western group technology. However, considering that the overall IP MEE level of the western region is lower, the effective province in the western region may be ineffective in the meta technology. And the IP MEE score of Ningxia is 0.975. However, western region also consists of effective provinces under the meta frontier, where Chongqing display 1.000 IP efficiency in both meta and group frontiers, which makes it one of the best-practice of IP energy efficiency within the western region and nationwide. In Fig. 3, we list the average MEE and GEE efficiency of WT system of three groups of China. According to Fig. 3, it can be found that a large number of provinces are not performing well in wastewater treatment. It is consistent with the result of Zhou et al. (2018) that the wastewater treatment efficiencies are often lower than the wastewater use efficiencies in the period of 2006e2015. In our study, from the perspective of MEE, the average efficiency of each region in 2011e2015 is less than 0.8. In addition, according to Fig. 3 and Table 4 in Appendix, the waste water treatment level in the central region is considerably lower than the other regions. Take Hubei province as an example. Hubei got 1.000 GEE score in WT system in the year of 2011, but its MEE score is 0.529, which shows the huge gap between the group technology (central region) and the overall technology. Considering that the undeveloped provinces in the western region perform better than the central provinces, and the average ratio between its

Average of Central

Average of Western

Fig. 3. The average WT efficiencies of different regions of China.

RW and WW is 0.021 while the same ratio in eastern and western group is 0.117 and 0.056, respectively. We can believe that the inefficiency in the central region is due to the lack of awareness of wastewater treatment. In general, most provinces in China are not efficient in waste water treatment. Fortunately, the Chinese government has deeply recognized this problem and proposed corresponding measures, and our research results can be used to conduct different provinces’ strategy under different technologies (group or meta) following the guidelines below: 1) If one province is ineffective under regional technology, then it should improve WT system to get closer to its group benchmark. 2) If one province is effective under regional technology but ineffective under meta technology, it should improve WT system to get closer to its overall benchmark.

4.2. Technology gap analysis The technology gap ratio of efficiencies the group frontier and meta-frontier in IP and WT subsystems are then calculated by Eqs. (6) and (7) (Table 5). The changing trends of the average TGR of two subsystems in the regions of eastern group, central group and western group are depicted in Figures (4) and (5), respectively. Figure (4) shows that the TGR of IP efficiency in the eastern group is distinctly higher than that in the central and western groups. During the sample period, the TGR value in the eastern group remains above 0.99, which leads the Chinese technology level in the IP subsystem. Refer to O’Donnell et al. (2008), it is apparent that the IP technology of eastern group is close to the meta technology. In terms of the 1.000 0.980 0.960 0.940 0.920 0.900 0.880 0.860 2011

2012

Average of Eastern

2013 Average of Central

2014

2015

Average of Western

Fig. 4. Trend of TGR in IP subsystem for the eastern, central and western groups.

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T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

4.3. Inefficiency source analysis and efficiency improvement direction

1.000 0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 2011

2012

Average of Eastern

2013 Average of Central

2014

2015

Average of Western

Fig. 5. Trend of TGR in WT subsystem for the eastern, central and western groups.

central group, the TGR of IP efficiency always holds above 0.96, superior to that in the western group which holds less than 0.95. In addition, a noteworthy phenomenon is that the technology gap between the central group and western group are widened from 2011 to 2015. In this manner, the western group is suggested to improve its IP technology by importing production technology from other regions. In the WT subsystem, as shown in Figure (5), the eastern group maintains the best-performing on the TGR value except 2012. As the TGR of WT efficiencies of eastern group improved steadily from 2012 to 2015, the gap between the WT technology of the eastern group and meta technology is narrowing. In contrast, the performance of the other two regions needs to be strengthened. The TGR of WT efficiency of western group is steady around 0.78, while that of central group fluctuates widely between 0.38 and 0.75. From these results we can draw the following inferences: 1). The eastern provinces have attracted importance to environmental protection and achieved pleasing results. 2). The central and western groups need to improve their WT efficiencies for narrowing the gap with the meta technology. 3). Considering that the central group has achieved similar level of technology as other groups (in 2011 and 2013), the inefficiency of central group may be not entirely due to lag in wastewater treatment technology level, but may also due to the lack of concept of sustainable development.

Two parts of energy inefficiency, TGI and GMI, caused by the technology gap and managerial inefficiency, are calculated by Eqs. (8) and (9), respectively (Tables 6 and 7). In the IP subsystem, it can be found that the inefficiency degree of eastern group is the lowest among the three group regions from 2011 to 2015 (Fig. 6.). It indicates that the eastern group equips with the highest IP level the whole time. For the rest group regions, the inefficiency degree of western group region is higher than that of central group region. Especially, in the central group region, the TGI values are greater than the GMI values (2013e2015). It means that the loss of IP efficiency is mainly due to technology gap. While in the western group region, both the TGI and GMI values are relatively high, which means that both technology gap and management are important sources of inefficiency in the western region. In terms of the WT subsystem, Fig. 7 shows that the inefficiency degrees of all three groups are relatively high. It is consistent with the efficiency analysis results that the IP subsystem performs better than the WT subsystem. Hence, there is great room for efficiency improvement in WT subsystem. Especially, in the eastern group, the average GMI and TGI respectively are 0.263 and 0.035 in 2015, respectively. It means that the loss of efficiency is primarily caused by management. In the central and western groups, both the average GMI and TGI are relatively high. It means that the loss of efficiency is caused by both the technology and management factors, which is consistent with the inference 3) in section 4.2. Overall, the priority of improving the WT subsystem efficiency is superior than the IP subsystem. It is consistent with the goal of high-quality development designed by Chinese government. For the IP subsystem related to economic growth, eastern regions need to maintain their advantages, central regions should develop technology and western regions should improve both technology and management in industry production. For the WT subsystem related to environmental protection, eastern regions should focus on managerial improvement such as optimizing allocation of resources and inspiring efficient management patterns, while central and western regions should improve both wastewater treatment technologies and pollution control modes.

4.4. Nexus degree analysis In addition, in the next subsection 4.3 of the paper, the fluctuation in case of central group are also demonstrated by the decomposition of inefficiency source such as the technology gap inefficiency (TGI) and managerial inefficiency of group frontiers (GMI).

To explore the coordination level further between the IP subsystem and the WT subsystem, this subsection calculates the nexus degree for each province during the period from 2011 to 2015 (Table 8).

0.700

0.140

0.600

0.120

0.500

0.100 0.080

0.400

0.060

0.300

0.040

0.200

0.020

0.100

0.000

0.000

IP Subsystem TGI

IP Subsystem GMI

Fig. 6. Decomposition of IP subsystem inefficiency in the east, central and west.

WT Subsystem TGI

WT Subsystem GMI

Fig. 7. Decomposition of WT subsystem inefficiency in the east, central and west.

T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

9

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

2011

2012

2013

2014

2015

Fig. 8. The nexus degree in eastern group.

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

2011

2012

2013

2014

2015

Fig. 9. The nexus degree in central group.

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

2011

2012

2013

2014

2015

Fig. 10. The nexus degree in western group.

Figures 8e10 respectively depict the results in eastern, central and western group. In the eastern group, the nexus degree values of most provinces are higher than 0.7 during the sample period, which indicates high level of coupling relations. For example, the nexus degree of Beijing always maintains unity during the sample years. It means that Beijing has the best coupling degree between the IP subsystem and WT subsystem all the time. In the central group, there are clear discriminationd of nexus degree between the ten provinces or five years. Especially, during the sample period, the nexus degree in Inner Mongolia is always above 0.95, while the value is around of 0.60 in Hubei. In addition, the nexus degree in Heilongjiang for 2011 is 0.838, while the value for 2012 is 0.371. In terms of the western group, the values of nexus degree between the nine provinces almost have little difference, which is similar to that of the eastern group. Correspondingly, the trends of the nexus degree of three region groups are shown in Fig. 11, thus reflecting how the IP sub-system

Nexus degree 1.000 0.900 0.800 0.700 0.600 0.500 0.400 2011

2012 Average of Eastern

2013

2014 Average of Central

Average of Western Fig. 11. Trends of the nexus degree in three groups.

2015

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T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

and WT sub-system mutually effect. Obviously, the trends of these three groups are quite different. The nexus degree of the eastern group shows a stable improvement from 0.899 to 0.968 during the period of 2011e2015. However, in the central group, the values fluctuate widely in the interval of [0.577, 0.855], which can be due to the fluctuation of their performance of WT sub-system. In terms of the western group, the nexus degree keeps almost unchanged about the value of 0.84. In general, the level of nexus degree in the eastern region performs the best, western region performs the second, and the central region performs the worst. 5. Conclusion and policy implications In this study, an interactive meta-frontier network DEA framework is developed to evaluate the water-energy nexus performance in industrial sector of China. Both undesirable output and the recycled desirable output are considered in the model. The proposed approach focuses on exploring the internal relations between the industrial production subsystem and wastewater treatment subsystem by combining the efficiency measurement with technology gap analysis and nexus degree index. Accordingly, the dynamic water-energy nexus performance of 30 provinces in China from 2011 to 2015 is analyzed from national level, regional level as well as provincial level. With respect to the efficiency measurement and technology gap as well as the nexus degree of two subsystems, significant differences are found between three regions, i.e. eastern group, central group and western group. Especially, the efficiencies in the eastern group and central group are higher than that in the western group for the IP subsystem. While in the WT subsystem, the efficiencies in three groups are all in low level compared with the IP efficiency. From the technology gap analysis, in IP subsystem, the technology level of eastern group is the highest, following with the central group and the western group. In WT subsystem, the technology level of eastern group is also the highest, yet the western group is the second and the central group is the third. In addition, during the sample period, the nexus degree of eastern group maintains a relatively high level and shows a stable improvement. In the rest two groups, the nexus degree of western group performs better than central group. Based on the empirical results, policy implications related to water-energy nexus of China’s industrial sector are as follows: From the national perspective, although China’s industrial capacity utilization is achieving a high level, the water pollution control is still at poor level. Therefore, China’s industrial sector needs to shift from high-speed growth to high-quality growth by considering the environmental factors in special wastewater pollution. According to our study, to achieve a high-quality growth mode in industrial sector, there are two directions of efforts, i.e. reducing the wastewater pollution associated with the industrial production subsystem and improving the efficiency in the wastewater treatment subsystem. For the former direction, the government should release relevant laws to punish highly polluting manufacturing enterprises and to stimulate clean production enterprises. For the latter direction, innovation of technology and optimization of process control for advanced industrial wastewater treatment are needed. From the regional perspective, relatively speaking, eastern region performs well in both IP and WT subsystems, central

region performs well in IP subsystem but badly in WT subsystem and western region performs well in WT subsystem but badly in IP subsystem. As a result, the water-energy nexus degree of the eastern region is the highest, following by the western region and the central region. Based on this study, central region has the largest potential and responsibility to shift the industrial development mode because of the lowest water-energy nexus degree. Thus, compared with other regions, central region governments should introduce higher intensity of relevant environmental protect policies. Besides, to improve the industrial production technology, the western region should expand the level of opening to other regions and even other developed Countries. From the provincial perspective, the performance level of each province is distinct. For example, Beijing always maintains the best performance in both IP and WT subsystem. However, some provinces have low level of IP performance such as Gansu and Guizhou, and some provinces have a low level of WT performance such as Guangxi and Jiangxi. For those provinces, it is impractical to achieve the meta-frontier performance the same as Beijing in a short time, but it is feasible to pursuit the group-frontier performance in which the provinces have similar geographical conditions. Hence, in the short run, the local government should encourage industrial enterprises to improve their management capabilities in the whole process of IP and WT. For the long run, the industrial enterprises ought to evolve their technology level in virtue of emerging information technology such as artificial intelligence and big data analytics. This study proposes an interactive network meta-frontier DEA approach to evaluate the regional water-energy nexus in Chinese provinces. However, the complexity and heterogeneity embodied in the water-energy nexus assessment convince us that no single method should be interpreted as the most standard approach. Our approach, which takes into account of the technology heterogeneity of different regions, which may ignore the technical progress over time. Moreover, we only investigate the water-energy nexus issue in industry sector, which may provide insufficient insights for the balance between economic development and water and energy sustainable utilization. To that end, we suggest that further improvements need to focus on the following directions: (1). methodological integration of the dynamic network DEA models and the meta-frontier framework and (2). case study of crosssector water and energy nexus issues, such as the agriculture and tourist sectors. Acknowledgments The authors thank the Editor and three anonymous reviewers for their helpful comments. This research is supported by the National Natural Science Foundation of China under Grant (Nos. 71871081 and 71801068), the Fundamental Research Funds for the Central Universities in China (No. JZ2019HGTB0096). Appendix

T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

11

Table 3 The efficiencies of 30 provinces in IP sub-system during 2011e2015. Province

Eastern Group Beijing Fujian Guangdong Hainan Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Central Group Anhui Guangxi Henan Heilongjiang Hubei Hunan Jilin Jiangxi Inner Mongolia Shanxi Eastern Group Gansu Guizhou Ningxia Qinghai Shanxi Sichuan Xinjiang Yunnan Chongqing Aver-age

2011

Eastern Central Western

2012

2013

2014

2015

MEE

GEE

MEE

GEE

MEE

GEE

MEE

GEE

MEE

GEE

1.000 1.000 1.000 1.000 1.000 1.000 0.968 1.000 0.873 1.000 1.000

1.000 1.000 1.000 1.000 1.000 1.000 0.975 1.000 0.876 1.000 1.000

1.000 1.000 1.000 1.000 1.000 1.000 0.982 1.000 0.839 1.000 1.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.841 1.000 1.000

1.000 1.000 1.000 1.000 1.000 0.999 0.966 1.000 0.885 1.000 0.981

1.000 1.000 1.000 1.000 1.000 0.999 0.981 1.000 0.968 1.000 0.981

1.000 1.000 1.000 1.000 1.000 1.000 0.987 1.000 0.896 1.000 0.989

1.000 1.000 1.000 1.000 1.000 1.000 0.996 1.000 0.954 1.000 0.989

1.000 1.000 1.000 1.000 1.000 0.997 0.988 1.000 0.888 1.000 0.981

1.000 1.000 1.000 1.000 1.000 0.997 1.000 1.000 0.950 1.000 0.981

0.994 1.000 1.000 0.966 0.891 1.000 1.000 1.000 1.000 0.940

1.000 1.000 1.000 0.980 0.907 1.000 1.000 1.000 1.000 1.000

1.000 1.000 1.000 0.944 0.903 1.000 1.000 1.000 1.000 0.904

1.000 1.000 1.000 0.962 0.911 1.000 1.000 1.000 1.000 1.000

1.000 1.000 0.935 0.982 0.932 1.000 1.000 1.000 1.000 0.869

1.000 1.000 1.000 1.000 0.934 1.000 1.000 1.000 1.000 1.000

1.000 1.000 0.920 0.936 0.920 0.998 1.000 1.000 1.000 0.814

1.000 1.000 1.000 1.000 0.926 0.998 1.000 1.000 1.000 1.000

1.000 1.000 0.899 0.954 0.953 1.000 1.000 1.000 1.000 0.817

1.000 1.000 1.000 1.000 0.969 1.000 1.000 1.000 1.000 1.000

0.776 0.743 0.975 1.000 1.000 1.000 0.829 0.980 1.000 0.986 0.979 0.923

0.883 0.863 1.000 1.000 1.000 1.000 0.969 1.000 1.000 0.986 0.989 0.968

0.753 0.761 0.910 1.000 1.000 1.000 0.868 0.928 1.000 0.984 0.975 0.913

0.880 0.893 1.000 1.000 1.000 1.000 0.975 1.000 1.000 0.986 0.987 0.972

0.751 0.777 0.929 0.972 0.973 0.987 0.880 0.990 1.000 0.985 0.972 0.918

0.838 0.910 1.000 1.000 1.000 1.000 0.996 1.000 1.000 0.994 0.993 0.972

0.729 0.842 0.865 0.997 0.956 1.000 0.844 0.884 1.000 0.988 0.959 0.902

0.798 1.000 1.000 1.000 1.000 1.000 0.978 1.000 1.000 0.994 0.992 0.975

0.720 0.799 0.872 0.976 0.936 0.976 0.813 0.876 1.000 0.987 0.962 0.885

0.779 0.971 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.993 0.997 0.972

Table 4 The efficiencies of 30 provinces in WT sub-system during 2011e2015. Province

Eastern Group Beijing Fujian Guangdong Hainan Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Anhui Central Group Guangxi Henan Heilongjiang Hubei Hunan Jilin Jiangxi Inner Mongolia

2011

2012

2013

2014

2015

MEE

GEE

MEE

GEE

MEE

GEE

MEE

GEE

MEE

GEE

1.000 0.374 0.558 0.427 0.486 0.261 0.336 0.657 1.000 0.842 0.492 0.346

1.000 0.499 0.558 0.654 0.656 0.378 0.459 0.882 1.000 1.000 0.616 0.536

1.000 0.382 0.326 0.548 0.470 0.342 0.162 0.688 1.000 0.953 0.345 0.249

1.000 0.808 0.351 0.994 0.815 0.660 0.331 1.000 1.000 1.000 0.599 0.649

1.000 0.456 0.904 0.353 0.797 0.422 0.592 0.630 1.000 1.000 0.505 0.340

1.000 0.820 0.904 0.469 1.000 0.551 0.748 1.000 1.000 1.000 0.710 0.460

1.000 0.165 0.782 0.526 0.867 0.490 1.000 0.773 0.984 1.000 0.221 0.183

1.000 0.217 0.787 0.587 1.000 0.490 1.000 0.981 0.984 1.000 0.326 0.543

1.000 0.374 0.557 0.355 1.000 0.393 1.000 0.870 1.000 0.864 0.316 0.275

1.000 0.452 0.557 0.355 1.000 0.419 1.000 0.961 1.000 1.000 0.368 0.620

0.331 0.760 0.471 0.529 0.399 0.420 0.247 1.000

0.458 1.000 0.661 1.000 0.570 0.577 0.374 1.000

0.198 0.205 0.087 0.234 0.280 0.259 0.059 0.908

0.809 1.000 0.621 1.000 0.837 0.864 0.567 1.000

0.444 0.586 0.479 0.286 0.234 0.698 0.296 1.000

0.623 0.882 0.745 0.963 0.347 1.000 0.439 1.000

0.253 0.383 0.372 0.328 0.169 0.254 0.174 0.970

0.753 1.000 1.000 1.000 0.502 0.736 0.518 1.000

0.464 0.490 0.395 0.251 0.203 0.219 0.273 1.000

0.916 1.000 0.884 1.000 0.473 0.504 0.616 1.000

(continued on next page)

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Table 4 (continued ) Province

Shanxi Western Group Gansu Guizhou Ningxia Qinghai Shanxi Sichuan Xinjiang Yunnan Chongqing Aver-age

2011

Eastern Central Western

2012

2013

2014

2015

MEE

GEE

MEE

GEE

MEE

GEE

MEE

GEE

MEE

GEE

1.000

1.000

1.000

1.000

0.972

1.000

0.453

1.000

0.299

0.796

0.677 1.000 0.494 0.396 0.723 0.503 0.773 0.353 0.309 0.585 0.550 0.581

0.931 1.000 0.560 0.527 1.000 0.713 1.000 0.395 0.493 0.700 0.718 0.735

1.000 0.873 1.000 0.686 0.861 0.277 0.431 0.709 0.119 0.565 0.348 0.662

1.000 1.000 1.000 1.000 0.891 0.840 0.509 0.923 0.512 0.778 0.835 0.853

0.690 0.699 1.000 0.378 0.699 0.374 0.598 0.780 0.557 0.696 0.533 0.642

0.736 1.000 1.000 0.471 0.738 0.708 0.700 1.000 1.000 0.837 0.746 0.817

0.398 0.406 1.000 0.310 0.289 0.137 0.693 0.720 1.000 0.710 0.354 0.550

0.492 1.000 1.000 0.356 0.388 0.215 0.820 1.000 1.000 0.761 0.805 0.697

0.217 0.498 1.000 0.337 0.465 0.290 0.639 0.744 0.806 0.703 0.387 0.555

0.365 1.000 1.000 0.346 0.588 0.412 0.772 1.000 1.000 0.737 0.781 0.720

Table 5 The TGR values of 30 provinces in IP and WT sub-systems during 2011e2015. Province

Eastern Group Beijing Fujian Guangdong Hainan Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Central Group Anhui Guangxi Henan Heilongjiang Hubei Hunan Jilin Jiangxi Inner Mongolia Shanxi Western Group Gansu Guizhou Ningxia Qinghai Shanxi Sichuan Xinjiang Yunnan Chongqing Average Eastern Central Western

2011

2012

2013

2014

2015

IP

WT

IP

WT

IP

WT

IP

WT

IP

WT

1.000 1.000 1.000 1.000 1.000 1.000 0.993 1.000 0.997 1.000 1.000

1.000 0.750 1.000 0.652 0.741 0.691 0.732 0.745 1.000 0.842 0.799

1.000 1.000 1.000 1.000 1.000 1.000 0.982 1.000 0.998 1.000 1.000

1.000 0.473 0.926 0.551 0.576 0.518 0.490 0.688 1.000 0.953 0.576

1.000 1.000 1.000 1.000 1.000 1.000 0.985 1.000 0.914 1.000 1.000

1.000 0.556 1.000 0.754 0.797 0.765 0.791 0.630 1.000 1.000 0.711

1.000 1.000 1.000 1.000 1.000 1.000 0.991 1.000 0.940 1.000 1.000

1.000 0.760 0.993 0.896 0.867 1.000 1.000 0.788 1.000 1.000 0.679

1.000 1.000 1.000 1.000 1.000 1.000 0.988 1.000 0.934 1.000 1.000

1.000 0.828 1.000 1.000 1.000 0.936 1.000 0.905 1.000 0.864 0.861

0.994 1.000 1.000 0.986 0.983 1.000 1.000 1.000 1.000 0.940

0.646 0.723 0.760 0.712 0.529 0.699 0.728 0.660 1.000 1.000

1.000 1.000 1.000 0.981 0.991 1.000 1.000 1.000 1.000 0.904

0.384 0.245 0.205 0.140 0.234 0.334 0.300 0.105 0.908 1.000

1.000 1.000 0.935 0.982 0.998 1.000 1.000 1.000 1.000 0.869

0.739 0.714 0.664 0.642 0.297 0.673 0.698 0.674 1.000 0.972

1.000 1.000 0.920 0.936 0.993 1.000 1.000 1.000 1.000 0.814

0.337 0.337 0.383 0.372 0.328 0.336 0.345 0.336 0.970 0.453

1.000 1.000 0.899 0.954 0.984 1.000 1.000 1.000 1.000 0.817

0.443 0.507 0.490 0.447 0.251 0.429 0.436 0.443 1.000 0.376

0.879 0.862 0.975 1.000 1.000 1.000 0.856 0.980 1.000 0.999 0.990 0.950

0.728 1.000 0.882 0.752 0.723 0.705 0.773 0.893 0.627 0.814 0.746 0.787

0.856 0.851 0.910 1.000 1.000 1.000 0.890 0.928 1.000 0.998 0.988 0.937

1.000 0.873 1.000 0.686 0.966 0.330 0.847 0.768 0.232 0.705 0.386 0.745

0.896 0.854 0.929 0.972 0.973 0.987 0.883 0.990 1.000 0.991 0.978 0.943

0.937 0.699 1.000 0.803 0.947 0.528 0.854 0.780 0.557 0.819 0.707 0.790

0.914 0.842 0.865 0.997 0.956 1.000 0.863 0.884 1.000 0.994 0.966 0.924

0.810 0.406 1.000 0.871 0.744 0.636 0.846 0.720 1.000 0.908 0.420 0.782

0.924 0.823 0.872 0.976 0.936 0.976 0.813 0.876 1.000 0.993 0.965 0.911

0.596 0.498 1.000 0.975 0.791 0.703 0.829 0.744 0.806 0.945 0.482 0.771

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Table 6 The TGI of 30 provinces in IP and WT sub-systems during 2011e2015 Province

Eastern Group Beijing Fujian Guangdong Hainan Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Central Group Anhui Guangxi Henan Heilongjiang Hubei Hunan Jilin Jiangxi Inner Mongolia Shanxi Western Group Gansu Guizhou Ningxia Qinghai Shanxi Sichuan Xinjiang Yunnan Chongqing Average Eastern Central Western

2011

2012

2013

2014

2015

IP

WT

IP

WT

IP

WT

IP

WT

IP

WT

0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.000 0.003 0.000 0.000

0.000 0.125 0.000 0.228 0.170 0.117 0.123 0.225 0.000 0.158 0.124

0.000 0.000 0.000 0.000 0.000 0.000 0.018 0.000 0.002 0.000 0.000

0.000 0.426 0.026 0.446 0.345 0.318 0.169 0.312 0.000 0.047 0.254

0.000 0.000 0.000 0.000 0.000 0.000 0.015 0.000 0.083 0.000 0.000

0.000 0.364 0.000 0.115 0.203 0.129 0.156 0.370 0.000 0.000 0.205

0.000 0.000 0.000 0.000 0.000 0.000 0.009 0.000 0.058 0.000 0.000

0.000 0.052 0.006 0.061 0.133 0.000 0.000 0.208 0.000 0.000 0.105

0.000 0.000 0.000 0.000 0.000 0.000 0.012 0.000 0.063 0.000 0.000

0.000 0.078 0.000 0.000 0.000 0.027 0.000 0.091 0.000 0.136 0.051

0.006 0.000 0.000 0.013 0.016 0.000 0.000 0.000 0.000 0.060

0.190 0.127 0.240 0.190 0.471 0.172 0.157 0.127 0.000 0.000

0.000 0.000 0.000 0.018 0.008 0.000 0.000 0.000 0.000 0.096

0.400 0.611 0.795 0.533 0.766 0.557 0.605 0.508 0.092 0.000

0.000 0.000 0.065 0.018 0.002 0.000 0.000 0.000 0.000 0.131

0.120 0.178 0.296 0.267 0.677 0.113 0.302 0.143 0.000 0.028

0.000 0.000 0.080 0.064 0.006 0.000 0.000 0.000 0.000 0.186

0.360 0.499 0.617 0.628 0.672 0.333 0.482 0.344 0.030 0.547

0.000 0.000 0.101 0.046 0.016 0.000 0.000 0.000 0.000 0.183

0.345 0.452 0.510 0.489 0.749 0.270 0.284 0.343 0.000 0.496

0.106 0.119 0.025 0.000 0.000 0.000 0.140 0.020 0.000 0.001 0.009 0.046

0.254 0.000 0.066 0.131 0.277 0.210 0.227 0.042 0.184 0.115 0.167 0.155

0.126 0.133 0.090 0.000 0.000 0.000 0.107 0.072 0.000 0.002 0.012 0.059

0.000 0.127 0.000 0.314 0.030 0.563 0.078 0.214 0.393 0.213 0.487 0.191

0.087 0.133 0.071 0.028 0.027 0.013 0.116 0.010 0.000 0.009 0.022 0.054

0.046 0.301 0.000 0.093 0.039 0.334 0.102 0.220 0.443 0.140 0.212 0.175

0.069 0.158 0.135 0.003 0.044 0.000 0.134 0.116 0.000 0.006 0.034 0.073

0.093 0.594 0.000 0.046 0.099 0.078 0.126 0.280 0.000 0.051 0.451 0.146

0.059 0.171 0.128 0.024 0.064 0.024 0.187 0.124 0.000 0.007 0.035 0.087

0.147 0.502 0.000 0.009 0.123 0.122 0.132 0.256 0.194 0.035 0.394 0.165

Table 7 The GMI of 30 provinces in IP and WT sub-systems during 2011e2015 Province

Eastern Group Beijing Fujian Guangdong Hainan Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Central Group Anhui Guangxi Henan Heilongjiang Hubei Hunan Jilin Jiangxi Inner Mongolia Shanxi Western Group Gansu Guizhou Ningxia

2011

2012

2013

2014

2015

IP

WT

IP

WT

IP

WT

IP

WT

IP

WT

0.000 0.000 0.000 0.000 0.000 0.000 0.025 0.000 0.124 0.000 0.000

0.000 0.501 0.442 0.346 0.344 0.622 0.541 0.118 0.000 0.000 0.384

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.159 0.000 0.000

0.000 0.192 0.649 0.006 0.185 0.340 0.669 0.000 0.000 0.000 0.401

0.000 0.000 0.000 0.000 0.000 0.001 0.019 0.000 0.032 0.000 0.019

0.000 0.180 0.096 0.531 0.000 0.449 0.252 0.000 0.000 0.000 0.290

0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.046 0.000 0.011

0.000 0.783 0.213 0.413 0.000 0.510 0.000 0.019 0.016 0.000 0.674

0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.050 0.000 0.019

0.000 0.548 0.443 0.645 0.000 0.581 0.000 0.039 0.000 0.000 0.632

0.000 0.000 0.000 0.020 0.093 0.000 0.000 0.000 0.000 0.000

0.464 0.542 0.000 0.339 0.000 0.430 0.423 0.626 0.000 0.000

0.000 0.000 0.000 0.038 0.089 0.000 0.000 0.000 0.000 0.000

0.351 0.191 0.000 0.379 0.000 0.163 0.136 0.433 0.000 0.000

0.000 0.000 0.000 0.000 0.066 0.000 0.000 0.000 0.000 0.000

0.540 0.377 0.118 0.255 0.037 0.653 0.000 0.561 0.000 0.000

0.000 0.000 0.000 0.000 0.074 0.002 0.000 0.000 0.000 0.000

0.457 0.247 0.000 0.000 0.000 0.498 0.264 0.482 0.000 0.000

0.000 0.000 0.000 0.000 0.031 0.000 0.000 0.000 0.000 0.000

0.380 0.084 0.000 0.116 0.000 0.527 0.496 0.384 0.000 0.204

0.117 0.137 0.000

0.069 0.000 0.440

0.120 0.107 0.000

0.000 0.000 0.000

0.162 0.090 0.000

0.264 0.000 0.000

0.202 0.000 0.000

0.508 0.000 0.000

0.221 0.029 0.000

0.635 0.000 0.000

(continued on next page)

14

T. Ding et al. / Journal of Cleaner Production 244 (2020) 118797

Table 7 (continued ) Province

Qinghai Shanxi Sichuan Xinjiang Yunnan Chongqing Average

2011

Eastern Central Western

2012

2013

2014

2015

IP

WT

IP

WT

IP

WT

IP

WT

IP

WT

0.000 0.000 0.000 0.031 0.000 0.000 0.014 0.011 0.032

0.473 0.000 0.287 0.000 0.605 0.507 0.300 0.282 0.265

0.000 0.000 0.000 0.025 0.000 0.000 0.014 0.013 0.028

0.000 0.109 0.160 0.491 0.077 0.488 0.222 0.165 0.147

0.000 0.000 0.000 0.004 0.000 0.000 0.006 0.007 0.028

0.529 0.262 0.292 0.300 0.000 0.000 0.163 0.254 0.183

0.000 0.000 0.000 0.022 0.000 0.000 0.006 0.008 0.025

0.644 0.612 0.785 0.180 0.000 0.000 0.239 0.195 0.303

0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.003 0.028

0.654 0.412 0.588 0.228 0.000 0.000 0.263 0.219 0.280

Table 8 The values of nexus degree between IP and WT subsystems during 2011e2015 Province Eastern Group Beijing Fujian Guangdong Hainan Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Central Group Anhui Guangxi Henan Heilongjiang Hubei Hunan Jilin Jiangxi Inner Mongolia Shanxi Western Group Gansu Guizhou Ningxia Qinghai Shanxi Sichuan Xinjiang Yunnan Chongqing Average

Eastern Central Western

2011

2012

2013

2014

2015

1.000 0.866 1.000 0.808 0.861 0.831 0.852 0.863 0.998 0.918 0.894

1.000 0.688 0.962 0.743 0.759 0.720 0.694 0.830 0.999 0.976 0.759

1.000 0.746 1.000 0.868 0.893 0.875 0.883 0.794 0.956 1.000 0.843

1.000 0.872 0.996 0.946 0.931 1.000 0.995 0.888 0.969 1.000 0.824

1.000 0.910 1.000 1.000 1.000 0.968 0.994 0.952 0.966 0.930 0.928

0.801 0.850 0.871 0.838 0.721 0.836 0.853 0.812 1.000 0.970

0.619 0.495 0.453 0.371 0.481 0.578 0.548 0.323 0.953 0.951

0.860 0.845 0.788 0.794 0.544 0.820 0.835 0.821 1.000 0.919

0.580 0.580 0.594 0.590 0.571 0.580 0.588 0.580 0.985 0.607

0.666 0.712 0.664 0.653 0.497 0.655 0.660 0.666 1.000 0.554

0.800 0.928 0.927 0.867 0.850 0.840 0.813 0.936 0.792 0.899 0.855 0.861

0.925 0.862 0.954 0.828 0.983 0.574 0.868 0.844 0.482 0.830 0.577 0.813

0.916 0.773 0.964 0.883 0.960 0.722 0.868 0.879 0.747 0.896 0.823 0.857

0.860 0.585 0.930 0.932 0.844 0.798 0.854 0.798 1.000 0.948 0.625 0.845

0.742 0.640 0.934 0.975 0.861 0.828 0.821 0.807 0.898 0.968 0.673 0.834

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