Interaction pattern features and driving forces of intersectoral CO2 emissions in China: A network motif analysis

Resources, Conservation & Recycling 149 (2019) 391–412

Contents lists available at ScienceDirect

Resources, Conservation & Recycling journal homepage: www.elsevier.com/locate/resconrec

Full length article

Interaction pattern features and driving forces of intersectoral CO2 emissions in China: A network motif analysis

T

Ning Maa,b,c, Huajiao Lia,b,c, , Yuhai Wangd, Sida Fenga,b,c, Jianglan Shia,b,c, Kai Wange ⁎

a

School of Economics and Management, China University of Geosciences, Beijing 100083, China Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, China c Key Laboratory of Strategic Studies, Ministry of Natural Resources, Beijing 100812, China d Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China e Department of Finance, China Mobile Communications Corporation Government and Enterprise Service Company, Beijing 100053, China b

ARTICLE INFO

ABSTRACT

Keywords: Network motif Input-output Complex network Sector interaction pattern CO2 emissions China

To identify the interaction pattern features and driving forces of intersectoral CO2 emissions, this paper studies the interaction pattern types, interaction linkage strengths and key interaction patterns of 28-sector CO2 emissions flow networks in China from 1997 to 2015 via network motif analysis. Network motif analysis is a useful tool for measuring interactions between agents and their function in networks. This paper extends network motif analysis by investigating the weighted edges of motifs to observe the function of motifs. The results show that in 1997–2002 and 2008–2015, the strongest carbon intensity sectors, including electricity production, metal smelting and gas production, were the greatest contributors to the increase in intersectoral CO2 emissions. From 2003 to 2007, the strongest energy intensity sectors, including electricity production and fuel processing, were the greatest contributors. In contrast, the strongest export intensity sectors, including other electronic equipment, leather manufacturing, instrumentation, and textile, were the largest contributors to the decrease in intersectoral CO2 emissions. Second, the strongest interaction linkages were between construction and nonmetallic products and metal smelting, machinery and construction and metal smelting, construction and nonmetallic products, construction and metal smelting. Third, the key interaction patterns for the increase in intersectoral CO2 emissions were patterns such as infrastructure-export, manufacturing intensive, energy consumption, and energy tracing, while the export-intensive pattern and high export pattern were the key interaction patterns for the decrease in intersectoral CO2 emissions. This paper is beneficial to the establishment of a coordinated emissions reduction mechanism across sectors.

1. Introduction China is in a stage of rapid industrialization and urbanization. In recent decades, China has witnessed a high economic growth rate of approximately 10% and a high urbanization rate which increased from 35.9% in 2000 to 56.8% in 2016 (Ahmad and Zhao, 2018). Accompanied by this rapid economic development, China became the world’s largest energy consumer and CO2 emitter in 2007 (IEA, 2009). In 2017, China’s consumption of primary energy amounted to 3132 million tons

oil equivalent, and its CO2 emissions amounted to 9232.6 million tons (BP, 2018). Severe climate problems have introduced a series of challenges to China’s sustainable development and must be controlled (Lin and Moubarak, 2013). In the process of industrialization and urbanization, all sectors in China account for 70% of the country’s energies and 80% of its CO2 emissions in the production process (Chen, 2009). Thus, all sectors are the major source of CO2 emissions and must be given close attention. The CO2 emissions generated by sectors in the production process are

Abbreviations: EI, energy intensity; EG, economic growth; ECI, carbon intensity; EI, export intensity; U, industry upstreamness degree; ICEFNs, intersectoral CO2 emissions flow networks ⁎ Corresponding author. E-mail address: [email protected] (H. Li). https://doi.org/10.1016/j.resconrec.2019.03.006 Received 3 January 2019; Received in revised form 5 March 2019; Accepted 7 March 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

embodied in their products. In the production chain, the CO2 emissions flow across sectors in the form of intermediate products (WBCSD and WRI, 2009). Due to the significant differences in CO2 emissions levels and the increasingly close relationships between sectors, complex interaction patterns in intersectoral CO2 emissions have been formed. For decision-makers, it is necessary to identify the interaction patterns between sectors to construct a coordinated emissions reduction mechanism across sectors. It has been demonstrated that CO2 emissions in China are highly associated with energy intensity, economic growth and carbon intensity (Ouyang and Lin, 2015). Identifying the driving factors that affect intersectoral CO2 emissions is helpful for targeting attention to key areas in order to develop intensive emissions reduction policies and improve the effectiveness of the policies. Based on the above analysis, it is necessary to systematically analyze the interaction pattern features and the driving forces of intersectoral CO2 emissions in pursuit of China’s low carbonization development. Most studies of interactions patterns of CO2 emissions have examined spatial correlations at the regional level (Wang et al., 2014, 2018a,b; Wu et al., 2017; Xie et al., 2017), and several papers have generally explored differences in sector emissions and their influencing factors (Chen and Chen, 2010; Yang and Chen, 2014; Zhang et al., 2015; Zhang, 2013; Zhao et al., 2017). To examine the interaction patterns of intersectoral CO2 emissions, Liu et al. (2016a,b) tested the emission-coefficient elasticities of larger CO2 emissions sectors. The study found that the greatest linkages are between five groups of sectors, including construction and nonmetallic products. Wang et al. (2018a,b) investigated the critical sectors and CO2 transmission paths in emissions mitigation in the Jing-Jin-Ji region and found that the critical paths are between energy and raw material producers and consumers. In general, although previous studies have explored the interaction linkages between different sectors, they have failed to reveal the function of the interactions patterns in the intersectoral CO2 emissions flow. If the government focuses on one or several sectors for emission mitigation, decision-makers must understand their comoving sectors and the function of the interaction between the sectors. Otherwise, the effectiveness of policies may be offset. Table 1 summarizes the contributors to the changes in CO2 emissions in China in the previous literature. Most scholars have demonstrated that there is a long-term cointegration relationship between energy intensity, economic growth, and carbon intensity (Jalil and Mahmud, 2009; Li et al., 2011; Zhang and Cheng, 2009). Thus, energy intensity, economic growth, and carbon intensity are included in the driving factors analysis of this paper. In addition, the export of goods is one of the main reasons for China's high CO2 emissions (Guan et al., 2009; Lin and Sun, 2010; Liu et al., 2015; Su and Thomson, 2016), and

the export intensity is also included in this paper. Finally, considering that the basic relationships between sectors are based on the production value chain, the industry upstreamness degree is included in this paper. Antràs et al. (2012) proposed the concept of industry upstreamness degree and a measurement method to analyze the relative position of an industry in a global or a country value chain. In summary, indicators of driving factors of intersectoral CO2 emissions in this study include energy intensity (namely, EI), economic growth (namely, EG), carbon intensity (namely, ECI), export intensity (namely, EI), and the industry upstreamness degree (namely, U). In the studies on interaction patterns of CO2 emissions, input-output (I-O) analyses (Liao et al., 2017; Wang et al., 2018a, 2018b; Yang, 2015; Zhu et al., 2018) and complex network analyses (Du et al., 2018; Liu et al., 2016a,b; Zhao and Yan, 2017) are two widely applied methods. Both methods can fully reflect the production relationship between the various sectors of the national economy and have advantages in conducting impact analyses and ripple effect analyses. Meanwhile, structural decomposition analyses (SDA) and index decomposition analysis (IDA) are the two most common methods in the existing research analyzing the driving factors of CO2 emissions. However, the above methods for interaction patterns and driving forces investigating are not well combined, causing related studies are separately. To explore both the interaction patterns features and driving factors of intersectoral CO2 emissions, a network motif analysis method is used in this study. It has been proven that most topological and dynamic properties of various networks arise from complex interactions between agents in networks (Barabasi and Oltvai, 2004). A motif is defined as a recurring and statistically significant subgraph in complex networks (Milo, 2002) and is widely used to measure interactions between agents and their function in directed networks (Bergmann et al., 2013; Grant et al., 2011; Guan et al., 2017; Ohnishi et al., 2010; Sarajlic et al., 2016). Each motif captures a specific pattern of interconnections that characterize a given network at the local level (Barabasi and Oltvai, 2004) and may reflect a framework in which particular functions are achieved efficiently (Kashani et al., 2009). Even a set of typical motifs may be the building blocks of an entire network (Ohnishi et al., 2010). Some scholars have applied network motif analyses to international trade (Squartini and Garlaschelli, 2012), traffic (Liu et al., 2013), scientific research cooperation (Miu et al., 2012), and biological research (Geard et al., 2011). Therefore, network motif analyses have gathered considerable attention as a useful tool to reveal the global structure of complex networks from local network interaction patterns (MasoudiNejad et al., 2012). Complex intermediate trade between sectors establishes an economic network. Studies have revealed particular correlations between

Table 1 Studies of contributors to the changes in CO2 emissions in China. Reference

Sector

Contributors to the change in CO2 emissions

Zhang et al. (2009) Zha et al. (2010) Jalil and Mahmud (2009) Guan et al. (2009) Liu et al. (2015) Liu et al. (2007) Ouyang and Lin (2015) Lin and Moubarak (2013) Tian et al. (2013) Wang et al. (2013)

National National National National National Industrial Sector Industrial Sector Textile industry Iron and steel industry Cement industry

Energy intensity, Industrial activity Energy intensity, Income Energy consumption, Economic growth, Export Export Export, Energy consumption Energy intensity, Final fuel shift Energy intensity, Industrial activity Carbon intensity, Industrial activity, Industrial scale, Energy mix Energy intensity, Product scale, Emission factor change Energy intensity, Cement production activity, Clinker production activity

392

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

certain sectors in the flow of intersectoral CO2 emissions (Liao et al., 2017; Liu et al., 2016a,b; Wang et al., 2018a,b; Shi et al., 2019). As a result, the way that sectors are informationally connected and the function of these interactions must be further explored. Considering that network motif analysis is helpful for understanding the structure and function of the complex interactions between sectors that contribute to the structural properties of the intersectoral CO2 emission network, this study adopts a network motif method to analyze the interaction pattern features of internal CO2 emissions in China. To the best of our knowledge, this study is the first attempt to apply the network motif analysis method to research on intersectoral CO2 emissions. Network motif analysis explores interaction patterns by classified nodes and edges in a network (Ohnishi et al., 2010).In the intersectoral CO2 emissions flow network, a small set of sector interaction patterns load massive CO2 emissions. Those sector interaction patterns are driving forces in the network. In this study, five driving factors of intersectoral CO2 emissions are taken as the classification criteria in order to identify which type of sector interaction pattern is the greatest contributor. Thus, the driving forces of intersectoral CO2 emissions are obtained. Frequency is the most common solo indicator used in most of network motif analyses. To measure the extent of the function of each type of sector interaction pattern, we comprehensively investigate the load capacity of CO2 emissions of each sector interaction pattern. Furthermore, we propose a motif load index, namely LI, to measure both the frequency and the CO2 emissions load capacity of each sector interaction pattern. The three major contributions of our study are summarized as follows. First, this study provides a reference for the future establishment of a cooperative emissions reduction mechanism among sectors by discovering the interaction pattern features and driving forces in intersectoral CO2 emissions. This reference also supplements the existing literature. Second, this paper is the first to apply the network motif method to analyze the interaction patterns and the driving forces of intersectoral CO2 emissions. In addition to the SDA analysis, IDA analysis and I-O sensitive analysis, the network motif analysis provides a new perspective on the interaction effects across sectors. Third, because the motifs in social networks do not have a clear specific function, this paper presents an indicator named the motif load capacity index to measure the function of each motif. In previous network motif analyses, the importance of motifs is usually determined only by the frequency, but the weighted edges of the motif are also vital. The motif load capacity index examines both the weighted edges and frequency of motifs, which is helpful for better understanding the function of each motif. The purpose of this paper is to analyze the interaction pattern features and driving forces of intersectoral CO2 emissions by using the network motif analysis method. The interaction pattern features analysis includes three parts: a sector interaction pattern type analysis, a sector interaction strength analysis and a key interaction pattern analysis. The remainder of this paper is structured as follows: Section 2

presents the methodologies and data source. Section 3 describes the results and analysis, and Section 4 presents the study’s conclusions and policies. 2. Data and methodologies 2.1. Methodology 2.1.1. Intersectoral CO2 emissions network 2.1.1.1. Measurement of intersectoral CO2 emissions. For the measurement of the intersectoral CO2 emissions, we followed the methods of Bullard and Herendeen (1975), Brown and Herendeen (1996), and Chen and Chen (2013). The principle of the I-O balance for sector i in terms of the intersectoral CO2 emissions is shown in Fig. 1. For sector i, the CO2 emissions indirectly embodied in the usage of intermediate input from other sectors, plus the CO2 emissions directly emitted in that sector, are passed on as part of sector i’s output. The physical balance for sector i can be formulated as: n

DCi +

ECIj × xij = ECIi × Xi

(1)

j=1

DCi =

(Ei × EFk )

(2)

where ECIi denotes the complete carbon intensity of sector i; x ij denotes the intermediate input j to produce product i; and Xi denotes the total n output of sector i. j = 1 ECIj × x ij denotes intersectoral CO2 emissions from intermediate inputs; DCi denotes direct carbon emissions of sector i; Eik denotes the energy κ input of sector i; and EFk denotes the CO2 emissions factor of energy κ from the IPCC emissions factors. Original sectoral energy data are derived from the China Energy Statistical Yearbook, including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, natural gas, and electricity. Because electricity is the secondary energy and thermal power generation is the primary proportion of China, CO2 emissions will be calculated twice from coal consumption. Therefore, only the former eight energies are calculated in this paper. The Eq. (1) can be expressed in the following matrix equation:

ECI = DCI × (I

A)

(3)

1

where ECI=[ECI1, ECI2 , , ECIn ], as ECI denotes the complete carbon intensity of sectors in matrix form. DCI =[DCI1, DCI2 , , DCIn ], as DCI DC denotes the direct carbon intensity of sectors in matrix form; DCIi = X i , i a11 a1n a21 a2n as DCIi is direct carbon intensity of sector i. A= , as A is xij

an1

ann

direct consumption coefficient, aij = X . i Foreign trade accounts for a large proportion of China’s economy, and many intermediate products come from imports. CO2 emissions will be overestimated if intermediate products from imports are included. Assuming that there is no difference in imported goods between intermediate and final products, ECI can be modified as:

ECI * = DCI × I × [I

µ=

µ11 0 0 µ 22 0

where

Fig. 1. The principle of I-O balance of intersectoral CO2 emissions.

393

0

ECI *

(I

0 0 µnn

µ ) A]

, µjj =

1

= [ECI1*, ECI2*,

, ECIn*]

(4)

IMj Xj + IMj

(5)

denotes the modified complete carbon emissions intensity

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table 2 Definition of variables. Variable

Determinant

EIi EGi

Ei/Xi (Xit Xit 1)/ Xit See Eq. (1)–(6)

ECIi* EPIi

EXi/(Xi

1

EXi + IMi)

See Eq. (8)–(10)

Ui

Description

Unit

Item

Energy intensity Economic growth

tce/RMB RMB/RMB

Ei : energy consumption of sector i t: period t

Carbon intensity

RMB/RMB

Export intensity

RMB/RMB

Industrial upstreamness index

RMB/RMB

ECIi* : total CO2 emissions of sector i EXi : export trade volume of sector i IMi : import trade volume of sector i Ui : a sector’s distance from final use

matrix; ECI *j denotes the modified complete carbon emissions intensity of sector j; µjj denotes the proportion of intermediate products imported; and IMj denotes the amount of the imports of sector j.

upstreamness degree of a sector is the average distance between a sector and the final use. The higher the upstreamness degree of a sector is, the closer the sector is to the upper end of the value chain. Conversely, a lower upstreamness degree means the sector is closer to the final user. If the upstreamness degree equals 1, the products of the sector are directly consumed. In an open economy, the upsteamness degree can be measured as follows:

2.1.1.2. Intersectoral CO2 emissions network. In this paper, each sector is considered as a node, and the flow of the intersectoral CO2 emissions from one sector to another is set as an edge. Then a directed and weighted complex network is built, as shown in Eq. (6): G= (N, E)

(6)

Ui = 1 ×

where G represents a complex network; N denotes the set of nodes in the network; and E represents the set of edges in the network, which is the quantity of intersectoral CO2 emissions. The matrix of the intersectoral CO2 emissions flow between sectors can be expressed in Eq. (7):

E=

e11

e1n

en1

enn

, eij = ECI * × x ij

N a *F j = 1 ij j

Fi +2× Xi

+4×

N j=1

Xi N k=1

+3×

N a *a * a * F l = 1 il lk kj j

Xi

N j=1

N a * a* F k = 1 ik kj j

Xi +…

(8)

where Fi and Fj represent the final use portion of sector i and output j, respectively. aij* denotes the modified direct consumption coefficient taking into account the change in imports and exports and inventories, which is formulated as:

(7)

aij* = aij

Based on the above equations, we establish five directed and weighted intersectoral CO2 emissions flow networks (namely, ICEFNs), including 1997, 2002, 2007, 2012, and 2015, respectively (as shown in Appendix A Fig. A1a-e).

Xi Xi

IMi

(9)

EXi + NIi

where NIi denotes the quantity of inventories of sector i. According to the I-O quantity model, Eq. (8) can be written as:

Ui =

2.1.2. Network motif analysis According to the definition proposed by Milo (2002), motifs are the basic subgraphs that repeat themselves in real networks, and their frequency is considerably higher than that in random networks with the same number of nodes and linkages. However, motifs that are functionally important but not statistically significant are missed by this approach. To improve it, an extended definition from Onnela et al. (2005) is adopted in this study, and motifs represent the subgraphs with the independence of their statistical significance. This paper explores 3-motifs, which are common motifs in directed networks; 3-motifs are the natural generalization of directed clustering coefficients and the starting point for understanding complex network communities (Squartini and Garlaschelli, 2012). We use the fanmod tool written by Wernicke and Rasche (2006) to identify motifs. The software uses a Rand-ESU algorithm to detect motifs and is much faster than any other available motif detection tool. It is suitable for detecting motifs of various scale networks.

A* ) 1X Xi

(I

(10)

where represents the modified direct consumption coefficient matrix. X denotes the vector of the total output matrix. (I A* ) 1 denotes the modified complete consumption coefficient matrix. The range of the five driving factors of all sectors is [ min(EIi, EGi , ECIi*, EPIi , Ui ) , max(EIi, EGi , ECIi*, EPIi , Ui )]. We average and classify whole sectors into three groups: strong 1 min(EIi, EGi, ECIi*, EPIi , Ui ), min(EIi, EGi , ECIi*, EPIi, Ui ) + 3 ,

A*

(

medium min(EIi, EGi, ECIi*, EPIi, Ui) + 13 , min(EIi, EGi, ECIi*, EPIi, Ui) + 23 , and weak 2 min(EIi, EGi , ECIi*, EPIi , Ui ) + 3 , max(EIi, EGi , ECIi*, EPIi, Ui )].

(

= max(EIi, EGi, ECIi*, EPIi , Ui ) min(EIi, EGi, ECIi*, EPIi , Ui ) . For detailed sector classification please refer to Table B1 in Appendix B.

2.1.2.2. Classification of sector interaction linkages in the intersectoral CO2 emissions network. The essence of classifying the sector interaction linkages in the ICEFNs is to classify the edges in the network. The range of edges in the ICEFNs is in [ min (E ), max (E )]. We average and classify the whole edges into three groups: strong 1 1 2 min (E ), min (E ) + 3 , medium min (E ) + 3 , min (E ) + 3 , and

2.1.2.1. Classification of sectors in the intersectoral CO2 emissions network. In this study, five driving factors of intersectoral CO2 emissions are taken as the standards for sector classification. Table 2 summarizes the definition of the five driving variables. The

(

394

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

(

its increasing function is. For example, if the frequency is 1% and the weighted edges proportion is 5%, then motif i can play an increasing role 5 times its own occurrence. Such motifs are regarded as increasing function motifs. In contrast, if the motif load capacity index LIi is less than 1, then the motif i can play a decreasing role in the network. The smaller the LIi is, the stronger its decreasing function is. This kind of motif is regarded as a decreasing function motif.

2

weak min (E ) + 3 , max (E )] three groups. = max (E ) min(E ) . As shown in Fig. C1a–c in Appendix C, the red edge indicates that the CO2 emissions flow between sectors is in the strong group; thus, the interaction linkage between sectors is strong, and the motifs with strong linkages are regarded as strong carbon-transfer motifs. The blue edge indicates that the CO2 emissions flow between sectors is in the medium group; thus, the linkage between sectors is medium, and motifs with medium linkages are considered medium carbon-transfer motifs. The black edge indicates that the CO2 emissions flow between sectors is in the weak group; therefore, the linkage between sectors is weak, and motifs with only weak linkages are regarded as weak carbon-transfer motifs.

2.2. Data The China National Bureau of Statistics issues an I-O table every 5 years, and the latest one is the 2012 version. To maintain the timeliness of the data, the latest 2015 I-O extension table is used. Thus, the 1997 version, the 2002 version, the 2007 version, the 2012 version, and the 2015 version are included in this paper (http://www.stats.gov.cn/ english/Statisticaldata/AnnualData/). The energy data are derived from the China Energy Statistical Yearbook from 1997 to 2016 (National Bureau of Statistics, 2001, 2017). The CO2 emissions factor is derived from the “2006 IPCC Guidelines for National Greenhouse Gas Inventories” of the IPCC (2006). Because the sector classifications of basic I-O tables are different from those of energy balance sheets, we have unified them into a 28sector table for the sake of data analysis (see Table E1 in Appendix E for details). Each sector classification is regarded as a scenario. Thus, in each ICEFN, there are five scenarios: EI, EG, ECI, EPI, and U.

2.1.2.3. Motif statistical significance analysis 2.1.2.3.1. Z-score. Z-score is widely used to measure the statistical significance of motifs. The larger the Z-score is, the more important the motif is. When the Z-score is less than or equal to 0, the motif structure fails. The formula can be written as:

Zi =

Nreali

Nrandi randi

(11)

where Nreali denotes the number of occurrences of motif i in the real network. Nrandi denotes the number of occurrence of motif i in the randomized network. Nrandi denotes the average value of Nrandi . randi denotes the standard deviation of Nrandi . 2.1.2.3.2. Motif load capacity index. In contrast to biological networks, motifs in social networks do not have a clear specific function. As the basic unit of the complex network, motifs carry the weighted edges in the real network. The more weighted edges a motif carries, the greater the function of the motif is. Some functionally important motifs do not have statistical significance (Milo, 2002). In this paper, a motif load capacity index is proposed to investigate the function of motifs in both weighted edges and frequency. For motif i, the motif load capacity index is defined as the ratio of the weighted edges in proportion to the frequency. The formula can be written as:

fi =

Li =

LIi =

ni N

3. Results and analysis 3.1. Sector interaction pattern type analysis 3.1.1. Basic sector interaction pattern type As shown in Fig. 2, there were ten basic types of motifs in the ICEFNs during 1997–2015.The vast majority of motifs in the ICEFNs were from motifs M238, M174, and M46, with averages of 83.07%, 12.41%, and 3.86%, respectively. The other seven types of motifs accounted for less. Some types of motifs disappeared over time. For example, motifs M78, M164, M14, and M36 disappeared after 1997, and motif M102 disappeared after 2007. After disappearing in 2002, motif M166 reappeared in 2012. As shown in Fig. 3, the average CO2 emissions load capacity of each type of motif in the ICEFNs varied significantly over time. Motif M166 had the largest average CO2 emissions capacity in 1997, reaching 67.03 million tons. There were two specific sector interaction patterns in motif M166 in1997: the agriculture-food-water production pattern and the agriculture-food-electrical equipment pattern. However, the average CO2 emissions load capacity of motif M166 decreased sharply after 1997 and its function decreased accordingly. Even from 2002 to 2012, the average CO2 emissions load capacity of motif M166 was close to 0. This is because there was only one specific sector interaction pattern in motif M166, the metal mining-agriculture-other electronic equipment pattern, which consisted of low-carbon sectors and energy production sector requiring secondary processing. As a result, this motif only loaded 0.12 million tons of CO2 emissions. Second, motifs M238 and M174, which had a larger number of motifs, also had a larger average CO2 emissions load capacity. The average CO2 emissions load capacity of these two types of motifs kept a steady increasing trend from 1997 to 2015. In addition, the growth trends were considerably

(12) n sum (Ej ) j=1 N sum (Eq ) q= 1

Li fi

(13) (14)

where fi denotes the frequency of motif i; ni denotes the number of occurrences of motif i; N denotes the number of occurrences of all subgraphs in node m. Li denotes the weighted edges proportion of motif i; Ej denotes the weighted edges of subgraphs j in motif i; sum (Ej ) denotes the sum of the weighted edges of subgraph j in motif i, also n called the load capacity of subgraph j; j = 1 sum (Ej ) denotes the sum of the weighted edges of ni subgraphs in motif i, also called the load capacity of motif i; Eq denotes the weighted edges of subgraphs q in the real network; sum (Eq) denotes the sum of the weighted edges of N subgraph q in the real network; q = 1 sum (Eq) denotes the sum of the weighted edges of N subgraphs in the real network, and LIi denotes the motif load capacity index of motif i. The node positions in the 3-motifs are shown in Fig. D1 in Appendix D. If the motif load capacity index LIi is greater than 1, then motif i can play an increasing role in the network. The larger the LIi is, the stronger

395

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Fig. 2. Distribution of all basic types of 3-motifs in ICEFNs from 1997 to 2015. Note: The node denotes the sector in ICEFNs. The edge denotes the flow of the intersectoral CO2 emissions from one sector to another. If there is a linkage between node i and j, the direction of the edge is the intermediate trade from sector i to sector j. The motif name is the standard motif ID number from the software fanmod tool. The nodes and edges of basic type of motif are not classifed by variables. The color of both the node and the edge are in green. This note applies to Fig. 3.

steeper since 2002. Furthermore, the growth trends of M238 and M174 were consistent with the trend of total intersectoral CO2 emissions (as shown in Fig. 4). This finding indicated that motifs M238 and M174 were major contributors to the increase in the ICEFNs, while motifs M46 and M102had the opposite trend, and their average CO2 emissions load capacity decreased gradually since 2002. The average CO2 emissions load capacity of motif M38 had a relatively stable trend between 1997 and 2015. The CO2 emissions load capacities of other types of motifs

were very small and appeared only in 1997. The evolution of each type of motif is independent and not necessarily associated with the existing network motif structure. Conant and Wagner (2003) pointed out that the emergence of motifs is not only due to simple gene replication but also due to some mechanism of natural selection, which has independent evolutionary characteristics. The sector interaction patterns in the ICEFNs are mainly generated in the development of intermediate trade, which is primarily affected by

Fig. 3. Average CO2 emissions load capacity of all types of motifs from 1997 to 2015.

396

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Fig. 4. Trends in carbon emissions from 1997 to 2015.

social division and technological progress. Accompanied by changes in social division and technological progress, the original sector interaction patterns will weaken, disappear or become new ones. As a result, under the influence of these two factors, the types and quantity of motifs in the ICEFNs change dynamically over the years.

policy was promulgated, which greatly promoted a boom in construction. As a result, the strong ECI group, including melt smelting, which was a major indirect energy supplier of construction, was the greatest contributor to the increase in intersectoral CO2 emissions. The strong EPI group, mostly from low-carbon sectors such as other electronic equipment, leather manufacturing, instrumentation, and textile, was the largest contributor to the decrease in intersectoral CO2 emissions. It is worth noting that both the highest LIi and the smallest LIi maintained an increasing trend over the year. It could be concluded that the increasing function of strong ECI and strong EI sectors were enhanced, while the decreasing function of strong EPI sectors was weakened. To allocate the resources to key areas, future emissions reduction policies should focus on the low carbonization of strong EPI and strong EI sectors, including electricity production, metal smelting, gas production, and fuel processing.

3.1.2. Driving force analysis in the intersectoral CO2 emissions network As shown in Table 3, in the EI and EPI scenarios, the scales of three groups of sectors were relatively stable from 1997 to 2015. The strong EI and EPI groups were relatively small, while most sectors belong to medium groups. The scales in the ECI scenario remained relatively stable from 1997 to 2012, while the scale of the medium ECI group increased significantly in 2015. Of the five scenarios, scenario U was the only one in which the scales of strong and medium groups accounted for the majority. This means that most of the sectors in China were in the upper part of the production value chain. Obviously, the scales of three groups in the EG scenario fluctuated widely. As shown in Table 4, in 1997–2002 and 2008–2015, the LIi of the strong ECI group was the highest. In 2003–2007, the LIi of the strong EI group was the highest, while the LIi of the strong EPI group was the smallest from 1997 to2015. As the Asian financial crisis broke out in 1997, China’s GDP growth which was highly driven by exports, slowed down accordingly. Zhang and Cheng (2009) identify unidirectional Granger causality from GDP to energy consumption. Thus, in the period of 1997–2002, the driving forces from EG, EI, EPI were weakened. In addition, most of the strongest U group comprised sectors from primary energy producers that needed secondary processing and could not directly or widely affect the intersectoral CO2 emissions. As a result, in the period of 1997–2002 and 2008–2015, the strong ECI group, including electricity production, metal smelting and gas production, was the greatest contributor to the increase in intersectoral CO2 emissions. From 2003 to 2007, China’s yearly GDP growth rate exceeded 10% and the energy consumption growth rate was even faster than that of GDP. Therefore, from 2003to 2007, the strong EI group, including electricity production and fuel processing, was the greatest contributor. In 2006, the Chinese government committed to reducing energy intensity by 20% in 2010 compared to the year 2005. Therefore, the driving force from EI was weakened accordingly since 2006 (Ouyang and Lin, 2015). To cope with the economic recession caused by the US subprime mortgage crisis in 2008, a 4-trillion-yuan infrastructure investment

3.2. Sector interaction strength analysis As shown in Fig. 5a, the most frequent sector interaction linkages in the ICEFNs were weak linkages, accounting for roughly more than 90% since 1997. The frequency of the medium linkage increased from 1997 to 2012 and decreased slightly from 2012 to 2015. The frequency of the strong linkages increased sharply from 1997 to 2002 then declined gradually. The frequency of motifs was not consistent with the function of motifs in the ICEFNs. As shown in Fig. 5b, the LIi of the weak carbontransfer motifs, which accounted for the largest proportion in the ICEFNs, were less than 1. Although the frequency of strong carbontransfer and medium carbon-transfer motifs was lower, the LIi were much larger than 1. For example, the LIi of the medium carbon-transfer motifs were approximately 4 (see Table 5). Even the LIi of the strong carbon-transfer motifs were more prominent, reaching 8.97 and 14.59 in 1997 and 2015, respectively. This result means that the strong carbon-transfer motifs contributed more than others. As shown in Table 5, more strong or medium linkages contained in the motifs caused a higher LIi. From 1997 to 2015, four strong carbontransfer motifs had a LIi greater than 10: motif M238 contained a machinery-construction-metal smelting pattern in 2007, motif M238 contained a construction-nonmetallic products-metal smelting pattern in 2012 and 2015, and motifs contained strong linkages of construction-

397

Resources, Conservation & Recycling 149 (2019) 391–412

Note: EI, EG, ECI, EPI, and U are the five driving factor classification scenarios. EI denotes the energy intensity classification scenario, EG denotes economic growth classification scenario, ECI denotes the carbon intensity classification scenario, EI denotes the export intensity classification scenario, and U denotes the industry upstreamness degree classification scenario. This note applies to Tables 4, 6, B1 in Appendix B, Tables F1–F5 in Appendix F, and Tables G1–G5 in Appendix G.

39.29 50.00 10.71 39.29 46.43 14.29 35.71 53.57 10.71 75.00 21.43 3.57 10.71 57.14 32.14 7.14 25.00 67.86 14.29 10.71 75.00 3.57 10.71 85.71 Strong Medium Weak

3.57 10.71 85.71

7.14 7.14 85.71

7.14 0.00 92.86

7.14 0.00 92.86

53.57 39.29 7.14

10.71 35.71 53.57

67.86 25.00 7.14

7.14 60.71 32.14

3.57 17.86 78.57

7.14 10.71 82.14

3.57 17.86 78.57

3.57 17.86 78.57

3.57 14.29 82.14

7.14 53.57 39.29

10.71 14.29 75.00

7.14 10.71 82.14

7.14 14.29 78.57

1997 2007 2002 1997 1997 2002 1997 1997 Type

2002

2007

2012

2015

EG EI Sector

Table 3 Percentage of each type of sectors from 1997 to 2015 (Unit: %).

2007

2012

2015

ECI

2002

2007

2012

2015

EPI

2012

2015

U

2002

2007

2012

2015

N. Ma, et al.

nonmetallic products or construction-metal smelting in 2015. In general, the above strong carbon-transfer motifs were based in construction. That matched with the economic development characterized by investment in infrastructure in China from 1997 to 2015. In the future, the production technology of construction should be changed to a low carbonization direction and the application of low-carbon materials should be increased. Thus, the demand for high-carbon sectors can be reduced from the source. Mazurie et al. (2005) and Sole and Valverde (2006) pointed out that abundant network motifs are not the true adaptive units of the network, but should be regarded as the byproducts during network construction. The emergence of these structures can be explained as the pressure of a prior decision. Common motifs might be a side effect of inevitable rules of growth and the change in agents in the network. Thus, if priority emission reduction policies are placed on a few key sectors, the total emissions of the whole economy can be greatly reduced. Accordingly, the strong interaction linkages of machinery-construction-metal smelting, construction-nonmetallic products-metal smelting, construction-nonmetallic, and construction-metal smelting should receive close attention in the future. 3.3. Key sector interaction patterns In each ICEFN from 1997 to 2015, there were around 71.08 motifs per each driving force classification. Considering that Guan et al. (2009) choose the top five export production sectors from 42 sectors and Cheng et al. (2018) choose the three industries with the greatest net CEs- PT from 40 sectors as key sectors, we categorize the motifs with the top seven Z-scores as key sector interaction patterns, which accounted for approximately 10% of total average yearly motifs. Table F1-F5 in Appendix F showed the motifs with the top seven Zscores from 1997 to 2015. Obviously, the motifs with the top seven Zscores were the functionally important motifs but not the most recurring ones. The top seven Z-score motifs could be divided into two kinds: the increasing function motifs with an LIi of greater than 1, and the decreasing function motifs with an LIi of less than 1. The increasing function motifs accounted for the majority of the top seven Z-score motifs, which indicated that the ICEFNs were in an increasing trend in general. It is worth noting that the increasing function motifs also existed in weak carbon-transfer motifs (as shown in Appendix F in red bold font). In contrast, as shown in Appendix F in blue bold font, there were the decreasing function motifs in the top seven Z-score motifs. In most situations, these motifs were found in the EPI scenario. In the top seven Z-score motifs, there were more increasing function motifs in the EPI scenario in 1997, 2002 and 2012, which carried emissions accounting for 3.33%, 3.16%, and 3.78%, respectively. As for the ICEFNs in 2007 and 2015, the emissions load capacity of motifs in the ECI scenario were the largest, accounting for 2.51% and 7.71%. As shown in Tables G1–G5 in Appendix G, in the top seven Z-score motifs, detailed sector interaction patterns were further explored in the EPI and ECI scenarios. As Table 6 summarized, in the EPI scenario, the increasing function motifs included the following patterns: the first pattern was the infrastructure-export motifs based in construction, such as construction-nonmetallic products-strong/medium export sectors, and construction-metal smelting-strong/medium export sectors. The second pattern was the manufacturing intensive motifs based in metal smelting, which matched with China’s industrial structure oriented by secondary industry. The third pattern was the energy consumption motifs based in types of energies, including electricity production, fuel processing, and metal smelting. Conversely, the decreasing function

398

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Fig. 5. Distributions of motifs with strong/medium/weak linkages from 1997 to 2015. Note: If a motif contains both strong and medium linkages, then both strong and medium linkages are counted once. As a result, the percentage of all types of motifs in each network is greater than 100%.

motifs were export intensive motifs cored in strong or medium export sectors, which included leather manufacturing, textile, and instrumentation. In the ECI scenario, the sector interaction pattern was the energy tracing motif from the energy consumption sectors to the energy supply sectors, for example, electrical equipment-metal smelting-metal mining, chemical-fuel processing- P&G extraction. The interactions between the sectors in the same motif are stronger than those between sectors outside the motif. Thus, if an emission reduction policy were implemented on one sector in the motif, the other sectors would be affected considerably more than those outside the

Table 5 LIi of motifs with all types of linkages from 1997 to 2015. Type

1997

2002

2007

2012

2015

Strong linkage -Linkage-1 -Linkage-2 -Linkage-3 Medium linkage -Linkage-1 -Linkage-2 -Linkage-3 Weak linkage

8.97 8.97

5.27 5.20 9.67

3.29 3.14 10.30

7.35 7.23 13.24

14.59 14.60 14.14

4.69 4.53 7.91

3.58 3.44 6.22

4.20 3.98 7.05

4.88 4.64 8.07

0.79

0.64

4.08 3.88 6.50 7.98 0.71

0.61

0.59

Note: Linkage-1/2/3 represents the number of strong or medium linkages contained in a motif. The bold value denotes the LIi are greater than 10.

399

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table 6 Sector interaction patterns with the top 7 Z-score from 1997 to 2015. Motif type

EPI

ECI

The increasing function motifs

Infrastructure Motifs Manufacturing intensive Motifs Energy consumption Motifs Export intensive Motifs

Energy tracing Motifs

The decreasing function motifs

motif. As a result, a cooperation mechanism should be considered to ensure the efficiency of polices.

High export Motifs

equipment, leather manufacturing, instrumentation, and textile, were the largest contributors to the decrease in intersectoral CO2 emissions. Thus, if priority emissions reduction policies are implemented for a few key sectors, the total emissions of the whole economy can be greatly reduced. In this way, the government may allocate resources to the strong ECI and EI sectors, including electricity production, metal smelting, gas production, and fuel processing, to improve energy efficiency.

4. Discussion Previous studies have investigated the sectoral correlation in intersectoral CO2 emissions flow in China. In a study of the correlations among large CO2 emission industries, Liu et al. (2016a,b) point out that the greatest emission-coefficient elasticities are between construction and nonmetallic products, gross fixed capital formation and construction, agriculture and food, and electricity production and coal. In an analysis of critical sectors and paths in emissions mitigation in the JingJin-Ji region, Wang et al. (2018a,b) conclude that the critical paths are between energy and raw material providers and their consumers. These results are similar to the infrastructure motifs and energy consumption motif in our study. Since the motifs in our study are in 3-motifs structure, the correlation in this paper covers three sectors, which is a further extension for a pairwise correlation analysis. Furthermore, our study analyzed the function of each kind of sector interaction pattern in intersectoral CO2 emissions. This supplements existing literature. Regarding the driving force analysis, the increase driving force in this study, the strong ECI and EI sectors, are similar to findings in most existing studies (Lin and Moubarak, 2013; Liu et al., 2007; Zha et al., 2010). However, the decreased driving force, the strong EPI sectors, is different from existing literature (Guan et al., 2009; Liu et al., 2015). The reason may be rooted in the research objective. Our study focuses on the intersectoral CO2 emissions embodied in the intermediate system, while the national CO2 emissions in the mentioned studies are based on final users. This study is beneficial to the establishment of a coordinated emissions reduction mechanism across sectors and focuses attention on the key areas to improve the effectiveness of the policies. However, because the latest 2017 I-O table has not been released to date, our study cannot reflect the latest situation in China’s economy. Additionally, the reason that different sector classification scenarios lead to different statistical significance of motifs is still unknown. Both of these limitations could be addressed in future research.

In the 13th Five-year Plan issued in 2016, CO2 emissions quantity control and energy intensity control were promulgated for future emissions mitigation. Considering emissions quantity control, specific energy saving measures should be considered for ECI and EI sectors in the short term. For example, increasing the share of clean energy power generation is an efficient solution for electricity production. For melt smelting, the phase-out of low efficient production capacity is helpful for reducing energy consumption. For fuel processing, policies should be directed to end users to promote energy saving, which includes the optimization of traffic roads and the development of electronic public transport systems. Considering energy intensity control, increasing investments in energy utilization technologies is suggested in the long run. Given the high risk and external benefits of technology investment, it is suggested for the government to provide subsidies or tax benefits. (2) In the ICEFNs from 1997 to 2015, the strongest interaction linkages were between construction and nonmetallic products and metal smelting, machinery and construction and metal smelting, construction and nonmetallic products, construction and metal smelting. For the strongest interaction linkages above based in construction, the production technology of construction should be changed to a low carbonization direction and the application of low-carbon materials should be increased. Thus, the demand for high-carbon sectors can be reduced from the source. (3) In the ICEFNs from 1997 to 2015, the key interaction patterns for the increase in intersectoral CO2 emissions were such patterns as infrastructure-export, manufacturing intensive, energy consumption, and energy tracing, while the export-intensive pattern and high export pattern were the key interaction patterns for the decrease in intersectoral CO2 emissions. The interactions between the sectors in the same pattern are stronger than those outside. Therefore, a cooperation mechanism across key sectors should be established to improve policy effectiveness.

5. Conclusions and policies Using a network motif analysis method, this paper explored the interaction pattern types, interaction strength and key interaction patterns of 28-sector CO2 emissions flow networks in China from 1997 to 2015. Based on the results and analysis, the main conclusions are as follows:

Acknowledgments

(1) In 1997–2002 and 2008–2015, the strong ECI sectors, including electricity production, metal smelting and gas production, were the greatest contributors to the increase in intersectoral CO2 emissions. From 2003 to 2007, the strong EI sectors, including electricity production and fuel processing, were the greatest contributors. In contrast, the strong EPI sectors, including other electronic

This research is supported by grants from the National Natural Science Foundation of China (Grant No. 41701121 and No. 41871202), the Beijing Youth Talents Funds (2017000020124G190), the Fundamental Research Funds for the Central Universities (Grant No. 29-2017-041) and the Graduate teaching reform program of China University of Geosciences, Beijing (Grant No.YJG2019002).

400

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Appendix A

Fig. A1. Intersectoral CO2 emissions flow networks during. 1997-2015. Note: The node represents the sector. The node's size denotes the accepter degree; the larger the accepter degree is, the larger the node is. The color's depth denotes the emitter degree; the larger the emitter degree is, the deeper the color is. The edge's thickness denotes the indirect carbon emissions flow's weight; the thicker the line is, the greater the indirect carbon emissions flow is.

Appendix B

401

402

23, 11, 2

14, 20, 13, 12, 3, 26, 10, 5, 4, 6, 7, 16, 21, 17, 9, 1, 28, 15, 27, 24, 18, 8, 25, 19

Weak

22

Medium

Strong

2, 14, 26, 13, 3, 12, 10, 5, 7, 23, 21, 4, 1, 6, 27, 28, 24, 9, 16, 15, 25, 18, 8, 17, 20, 19

2, 14, 26, 13, 23, 12, 3, 5, 10, 21, 4, 7, 1, 6, 24, 27, 16, 28, 9, 17, 15, 25, 8, 18, 20, 19

14, 13, 26, 12, 3, 10, 5, 7, 6, 4, 1, 27, 16, 28, 21, 9, 24, 17, 15, 25, 8, 18, 20, 19

13, 14, 12, 26, 5, 3, 24, 10, 4, 1, 6, 7, 21, 17, 16, 15, 9, 27, 28, 8, 18, 25, 20, 19

27, 23

N/A

4, 19, 9, 24, 20, 8, 18, 15, 13, 6, 17, 25, 11, 22, 21 12, 2, 28, 5, 1, 3, 26, 10, 14, 7, 16

11, 22

N/A

22, 11

2, 23

22, 11

2015

11, 23, 2

22

2012

1997

2007

1997

2002

EG

EI

25, 10, 16, 24, 12, 18, 4, 15, 1, 8, 6, 7, 5, 21, 13

26, 20, 22, 3, 14, 11, 27, 17, 2, 9

19, 23, 28

2002

27, 1

5, 2, 25, 26, 10, 28, 24

4, 22, 14, 13, 18, 21, 11, 17, 19, 23, 16, 15, 3, 20, 6, 12, 7, 9, 8

2007

2, 25, 27, 6, 13, 4, 10, 17, 12, 26, 11, 16, 18, 1, 15, 14, 9 5, 8, 19, 22, 7, 24, 3, 20, 21

23, 28

2012

Table B1 Sector classification and sector code of EI, EG, ECI, EPI, and U from 1997 to 2015.

5, 13, 9, 8, 10, 15, 26, 6, 12, 18, 17, 19, 21, 22, 1, 7, 16, 4, 14, 2, 11, 3

24, 28, 25, 27, 20

23

2015

23, 11, 2, 13, 14

24, 12, 15, 4, 5, 26, 25, 18, 16, 17, 10, 7, 9, 3, 20, 21, 6, 8, 19, 1, 27, 28

13, 24, 15, 12, 20, 4, 18, 16, 25, 17, 5, 26, 10, 3, 9, 21, 19, 7, 28, 6, 27, 8, 1

22

2002

11, 2, 14

22, 23

1997

ECI

15, 13, 12, 24, 18, 4, 11, 25, 2, 5, 16, 21, 3, 23, 10 26, 17, 9, 20, 7, 19, 8, 6, 28, 1, 27

12, 15, 26, 24, 5, 4, 18, 25, 16, 10, 23, 17, 3, 9, 7, 20, 21, 8, 19, 6, 1, 28, 27 12, 4, 15, 24, 26, 25, 5, 18, 16, 10, 3, 17, 7, 9, 20, 8, 19, 21, 6, 28, 27, 1

22, 14

2015

11, 14, 2, 13

22

2012

11, 2, 14, 13, 23

22

2007

15, 9, 21, 12, 27, 26, 16, 14, 11, 17, 6, 5, 28, 13, 2, 1, 4, 22, 25, 23, 24

7, 3, 18, 10

20, 19, 8

1997

EPI

18, 20, 9, 27, 7, 10, 16

15, 17, 12, 26, 21, 13, 14, 6, 11, 28, 5, 1, 3, 25, 4, 22, 2, 24, 23 7, 15, 16, 27, 26, 17, 12, 13, 21, 14, 6, 11, 28, 5, 1, 3, 25, 2, 4, 22, 24, 23, 27 10, 16, 26, 21, 27, 12, 17, 14, 13, 6, 28, 5, 11, 2, 1, 3, 4, 25, 22, 24, 23 15, 9, 21, 10, 27, 26, 12, 5, 16, 13, 17, 6, 11, 28, 2, 3, 14, 1, 4, 22, 25, 24, 23

19, 8

2015

20, 18, 9, 10

8, 19

2012

18, 9, 15

19, 20, 7, 8

2007

19, 7, 18

20, 8

2002

11, 22, 12, 5, 10, 21, 20, 7, 26, 24, 19, 16, 15, 18, 27 13, 17, 9, 23, 1, 28, 6, 8, 25

4, 3, 14, 2

1997

U

25

3, 4, 2, 11, 12, 22, 14, 10, 7, 24, 26, 5, 15, 21, 20, 9, 18, 19, 27, 13, 17 16, 23, 1, 6, 28, 8

2002

15, 19, 21, 24, 20, 23, 26, 9, 18, 1, 16, 13, 27, 17, 6 8, 28, 25

3, 2, 4, 22, 7, 11, 12, 14, 10, 5

2007

28, 8, 17, 25

10, 20, 26, 15, 9, 1, 18, 13, 27, 23, 24, 6, 16

2, 3, 4, 22, 21, 5, 11, 12, 7, 14, 19

2012

28

6,

9,

1,

17, 8, 25

10, 20, 26, 15, 18, 27, 23, 16, 13, 24,

3, 2, 4, 22, 11, 21, 5, 7, 12, 19, 14

2015

N. Ma, et al.

Resources, Conservation & Recycling 149 (2019) 391–412

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Appendix C

Fig. C1. All types of linkages and carbon-transfer motifs.

Appendix D

Fig. D1. Node positions of 3-motifs.

Appendix E

Table E1 Sectors classification, sectors abbreviations and sector code. Final classification (sector code)

Sector Abbreviations

Sub-sectors (Sector code)

Agriculture (1)

Agriculture

Coal Mining (2)

Coal Mining

Petroleum and Gas Extraction (3) Metal Ores Mining (4)

P&G Extraction Metal Mining

Nonmetal Ores and Other Mining (5)

Nonmetal Mining

Food Manufacturing (6)

Food

Farming (1-01) Forestry (1-02) Animal Husbandry (1-03) Fishery (1-04) Water Conservancy (1-05) Mining and Washing of Coal (2-01) Petroleum and Gas Extraction of Petroleum and Natural Gas (3-01) Mining and Processing of Ferrous Metal Ores (4-01) Mining and Processing of Non-Ferrous Metal Ores(4-02) Mining and Processing of Nonmetal Ores (5-01) Mining of Other Resources (5-02) Processing of Food from Agricultural Products (6-01) Manufacture of Foods (6-02) Manufacture of Beverages (6-03)

(continued on next page) 403

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table E1 (continued) Final classification (sector code)

Sector Abbreviations

Textile Manufacturing (7) Textile Wearing and Leather Manufacturing(8)

Textile Leather Manufacturing

Timber Processing and Furniture Manufacturing (9)

Furniture Manufacturing

Manufacture of Paper and Education-related products (10)

Education-related Products

Petroleum Processing, Coking, and Nuclear Fuel (11) Chemical Industry (12)

Fuel Processing Chemical Industry

Manufacture of Nonmetallic Mineral Products (13) Smelting and Pressing of Metals (14)

Nonmetallic Products Metal Smelting

Manufacture of Metal Products (15) Manufacture of General and Special Purpose Machinery (16)

Metal Products Machinery

Manufacture of Transport Equipment (17) Manufacture of Electrical Machinery and Equipment (18) Manufacture of Communication Equipment,Computers and Other Electronic Equipment (19) Manufacture of Instrumentation and Machinery (20)

Transport Equipment Electrical Equipment Other Electronic Equipment Instrumentation

Other Manufacturing(21)

Other Manufacturing

Production and Distribution of Electricity and Thermal (22) Production and Distribution of Gas(23) Production and Distribution of Water(24) Construction (25) Transport, Storage and Post (26)

Electricity Production Gas Production Water Production Construction Transport

Wholesale, Retail Trade and Hotel, Restaurants (27) Services (28)

Wholesale Trade Services

Sub-sectors (Sector code) Manufacture of Tobacco (6-04) Manufacture of Textile (7-01) Manufacture of Textile Wearing Apparel, Footwear, and Caps (8-01) Manufacture of Leather, Fur, Feather and Related Products (8-02) Processing of Timber, Manufacture of Wood, Bamboo, Rattan, Palm, and Straw Products (9-01) Manufacture of Furniture (9-02) Manufacture of Paper and Paper Products (10-01) Printing, Reproduction of Recording Media (10-02) Manufacture of Articles For Culture, Education and Sports Activity (10-03) Processing of Petroleum, Coking, Processing of Nuclear Fuel (11-01) Manufacture of Raw Chemical Materials and Chemical Products (1201) Manufacture of Medicines (12-02) Manufacture of Chemical Fibers (12-03) Manufacture of Rubber (12-04) Manufacture of Plastics (12-05) Manufacture of Nonmetallic Mineral Products (13-01) Smelting and Pressing of Ferrous Metals (14-01) Smelting and Pressing of Non-ferrous Metals (14-02) Manufacture of Metal Products (15-01) Manufacture of General Purpose Machinery (16-01) Manufacture of Special Purpose Machinery (16-02) Manufacture of Transport Equipment (17-01) Manufacture of Electrical Machinery and Equipment (18-01) Manufacture of Communication Equipment, Computers and Other Electronic Equipment (19-01) Manufacture of Instrumentation and Machinery for Cultural Activity and Office Work(20-01) Manufacture of Artwork and Other Manufacturing(21-01) Recycling and Disposal of Waste (21-02) Production and Distribution of Electricity and Thermal (22-01) Production and Distribution of Gas (23-01) Production and Distribution of Water (24-01) Construction (25-01) Transport, Storage (26-01) Post (26-02) Wholesale, Retail Trade and Hotel, Restaurants (27-01) Information, Transmission, Computer Services &Software (28-01) Real Estate, Leasing and Business Services (28-02) Financial Intermediation (28-03) Other Services (28-04)

Appendix F

Table F1 Motifs with the top 7 Z-scores in 1997. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

404

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table F2 Motifs with the top 7 Z-scores in 2002. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

Table F3 Motifs with the top 7 Z-scores in 2007. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

Table F4 Motifs with the top 7 Z-scores in 2012. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

405

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table F5 Motifs with the top 7 Z-scores in 2015. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

Appendix G Table G1 Detail sectors of motifs with the top 7 Z-scores in 1997 under EPI classification.

Table G2 Detail sectors of motifs with the top 7 Z-scores in 2002 under EPI classification.

406

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table G3 Detail sectors of motifs with the top 7 Z-scores in 2007 under ECI classification.

Table G4 Detail sectors of motifs with the top 7 Z-scores in 2012 under EPI classification.

407

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table G5 Detail sectors of motifs with the top 7 Z-scores in 2015 under ECI classification.

408

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table 4 Associative type of sectors, fi(%), Li(%) and LIi under 5 scenarios. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

(continued on next page)

409

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table 4 (continued)

(continued on next page)

410

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al.

Table 4 (continued)

Note: (i) Homogenized type indicates that all sectors in one motif have the same classification. Heterogeneous type indicates that all sectors in one motif have different classification. (ii) Red nodes represent strong group sectors. Blue nodes represent medium group sectors. Green nodes represent weak group sectors. Strong (3) denotes and

, medium(3) denotes

. Mix denotes

and weak (3) denotes

. Strong (2) includes

and

. Medium (2) includes

and

. Weak (2) includes

. It should be noted that sector linkages in motifs can be arbitrary. (iii) Red bold font denotes the highest LIi, while blue bold font denotes

the smallest LIi. This note applies to Appendices C and D.

Kashani, Z.R.M., Ahrabian, H., Elahi, E., Nowzari-Dalini, A., Ansari, E.S., Asadi, S., Mohammadi, S., Schreiber, F., Masoudi-Nejad, A., 2009. Kavosh: a new algorithm for finding network motifs. BMC Bioinf. 10. Li, F., Dong, S.C., Li, X., Liang, Q.X., Yang, W.Z., 2011. Energy consumption-economic growth relationship and carbon dioxide emissions in China. Energy Policy 39 (2), 568–574. Liao, H., Andrade, C., Lumbreras, J., Tian, J., 2017. CO2 emissions in Beijing Sectoral linkages and demand drivers. J. Clean. Prod. 166, 395–407. Lin, B., Moubarak, M., 2013. Decomposition analysis: change of carbon dioxide emissions in the Chinese textile industry. Renew. Sustain. Energy Rev. 26, 389–396. Lin, B.Q., Sun, C.W., 2010. Evaluating carbon dioxide emissions in international trade of China. Energy Policy 38 (1), 613–621. Liu, L.C., Fan, Y., Wu, G., Wei, Y.M., 2007. Using LMDI method to analyzed the change of China’s industrial CO2 emissions from final fuel use: an empirical analysis. Energy Policy 35 (11), 5892–5900. Liu, L., Han, C., Xu, W., 2013. Motifs and superfamilies in critical infrastructure engineering network. Syst. Eng. - Theory Pract. 33 (05), 1335–1344. Liu, H., Liu, W., Fan, X., Zou, W., 2015. Carbon emissions embodied in demand–supply chains in China. Energy Econ. 50, 294–305. Liu, H., Liu, C., Chen, M., 2016a. The conduction network and collaborative reduction among different industries of carbon dioxide emission in China’s industries. China Popul. Resour. Environ. 26 (4), 90–99. Liu, L.C., Cao, D., Wei, Y.M., 2016b. What drives intersectoral CO2 emissions in China? J. Clean. Prod. 133, 1053–1061. Masoudi-Nejad, A., Schreiber, F., Kashani, Z.R.M., 2012. Building blocks of biological networks: a review on major network motif discovery algorithms. IET Syst. Biol. 6 (5), 164–174. Mazurie, A., Bottani, S., Vergassola, M., 2005. An evolutionary and functional assessment of regulatory network motifs. Genome Biol. 6 (4). Milo, R., 2002. Network motifs: simple building blocks of complex networks. Science 298 (5594), 824–827. Miu, L., Han, C., Liu, L., Cao, J., 2012. Using motif to characterize building block of scientific collaboration networks. Stud. Sci. Sci. 30 (10), 1468–1475. National Bureau of Statistics, P.R.C, 2001. China Energy Statistical Yearbook 1997–1999. China Statistics Press, Beijing. National Bureau of Statistics, P.R.C, 2017. China Energy Statistical Yearbook 2017. China Statistics Press, Beijing. Ohnishi, T., Takayasu, H., Takayasu, M., 2010. Network motifs in an inter-firm network. J. Econ. Interact. Coord. 5 (2), 171–180. Onnela, J.P., Saramaki, J., Kertesz, J., Kaski, K., 2005. Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 71 (6 Pt. 2), 065103. Ouyang, X., Lin, B., 2015. An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector. Renew. Sustain. Energy Rev. 45, 838–849. Sarajlic, A., Malod-Dognin, N., Yaveroglu, O.N., Przulj, N., 2016. Graphlet-based characterization of directed networks. Sci. Rep. 6. Shi, J., Li, H., An, H., Guan, J., Arif, A., 2019. Tracing carbon emissions embodied in 2012

References Ahmad, M., Zhao, Z.Y., 2018. Empirics on linkages among industrialization, urbanization, energy consumption, CO2 emissions and economic growth: a heterogeneous panel study of China. Environ. Sci. Pollut. Res. 25 (30), 30617–30632. Antràs, P., Chor, D., Fally, T., Hillberry, R., 2012. Measuring the upstreamness of production and trade flows. Am. Econ. Rev. 102 (3), 412–416. Barabasi, A.L., Oltvai, Z.N., 2004. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5 (2), 101–113. Bergmann, A., Zaharieva, I., Dau, H., Strasser, P., 2013. Electrochemical water splitting by layered and 3D cross-linked manganese oxides: correlating structural motifs and catalytic activity. Energy Environ. Sci. 6 (9), 2745–2755. BP, 2018. BP statistical review of world energy 2018. In: B.S.R.o.W (Ed.), Energy, 67th ed. BP, UK. Brown, M.T., Herendeen, R.A., 1996. Embodied energy analysis and EMERGY analysis a comparative view. Ecol. Econ. 19, 219–235. Bullard, C.W., Herendeen, R.A., 1975. The energy cost of goods and service. Energy Policy 3, 268–278. Chen, S., 2009. Energy consumption, CO2 emission and sustainable development in Chinese industry. Econ. Res. J. 4, 41–55. Chen, G.Q., Chen, Z.M., 2010. Carbon emissions and resources use by Chinese economy 2007: a 135-sector inventory and input–output embodiment. Commun. Nonlinear Sci. Numer. Simul. 15 (11), 3647–3732. Chen, Z.-M., Chen, G.Q., 2013. Demand-driven energy requirement of world economy 2007: a multi-region input–output network simulation. Commun. Nonlinear Sci. Numer. Simul. 18 (7), 1757–1774. Cheng, H., Dong, S., Li, F., Yang, Y., Li, S., Li, Y., 2018. Multiregional input-output analysis of spatial-temporal evolution driving force for carbon emissions embodied in interprovincial trade and optimization policies: case study of northeast industrial district in China. Environ. Sci. Technol. 52 (1), 346–358. Conant, G.C., Wagner, A., 2003. Convergent evolution of gene circuits. Nat. Genet. 34 (3), 264–266. Du, Q., Xu, Y., Wu, M., Sun, Q., Bai, L., Yu, M., 2018. A network analysis of indirect carbon emission flows among different industries in China. Environ. Sci. Pollut. Res. Int. 25 (24), 24469–24487. Geard, N., Bullock, S., Lohaus, R., Azevedo, R.B.R., Wiles, J., 2011. Developmental motifs reveal complex structure in cell lineages. Complexity 16 (4), 48–57. Grant, C.E., Bailey, T.L., Noble, W.S., 2011. FIMO: scanning for occurrences of a given motif. Bioinformatics 27 (7), 1017–1018. Guan, D., Peters, G.P., Weber, C.L., Hubacek, K., 2009. Journey to world top emitter: an analysis of the driving forces of China’s recent CO2 emissions surge. Geophys. Res. Lett. 36 (4). Guan, Q., An, H., Liu, N., An, F., Jiang, M., 2017. Information connections among multiple investors: evolutionary local patterns revealed by motifs. Sci. Rep. 7 (1), 14034. IEA, 2009. CO2 Emissions From Fuel Combustion Highlights. Paris, France. . Jalil, A., Mahmud, S.F., 2009. Environment Kuznets curve for CO2 emissions: a cointegration analysis for China. Energy Policy 37 (12), 5167–5172.

411

Resources, Conservation & Recycling 149 (2019) 391–412

N. Ma, et al. Chinese supply chains. J. Clean. Prod. 226, 28–36. Sole, R.V., Valverde, S., 2006. Are network motifs the spandrels of cellular complexity? Trends Ecol. Evol. 21 (8), 419–422. Squartini, T., Garlaschelli, D., 2012. Triadic motifs and dyadic self-organization in the world trade network. Lect. Notes Comput. Sci. 7166, 24–35. Su, B., Thomson, E., 2016. China’s carbon emissions embodied in (normal and processing) exports and their driving forces, 2006–2012. Energy Econ. 59, 414–422. Tian, Y.H., Zhu, Q.H., Geng, Y., 2013. An analysis of energy-related greenhouse gas emissions in the Chinese iron and steel industry. Energy Policy 56, 352–361. Wang, Y.L., Zhu, Q.H., Geng, Y., 2013. Trajectory and driving factors for GHG emissions in the Chinese cement industry. J. Clean. Prod. 53, 252–260. Wang, S.J., Fang, C.L., Ma, H.T., Wang, Y., Qin, J., 2014. Spatial differences and multimechanism of carbon footprint based on GWR model in provincial China. J. Geogr. Sci. 24 (4), 612–630. Wang, Z., Cui, C., Peng, S., 2018a. Critical sectors and paths for climate change mitigation within supply chain networks. J. Environ. Manage. 226, 30–36. Wang, Z., Yang, Y., Wang, B., 2018b. Carbon footprints and embodied CO2 transfers among provinces in China. Renew. Sustain. Energy Rev. 82, 1068–1078. WBCSD, WRI, 2009. The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard: Revised Edition. . https://ghgprotocol.org/corporate-standard. Wernicke, S., Rasche, F., 2006. FANMOD: a tool for fast network motif detection. Bioinformatics 22 (9), 1152–1153. Wu, S.M., Lei, Y.L., Li, S.T., 2017. Provincial carbon footprints and interprovincial transfer of embodied CO2 emissions in China. Nat. Hazards 85 (1), 537–558. Xie, R., Hu, G.X., Zhang, Y.G., Liu, Y., 2017. Provincial transfers of enabled carbon emissions in China: a supply-side perspective. Energy Policy 107, 688–697.

Yang, J., Chen, B., 2014. Carbon footprint estimation of Chinese economic sectors based on a three-tier model. Renew. Sustain. Energy Rev. 29, 499–507. Yang, S., 2015. Evaluation and prediction on carbon emissions transferring across the industrial sectors in China. China Ind. Econ. 6, 55–67. Zha, D.L., Zhou, D.Q., Zhou, P., 2010. Driving forces of residential CO2 emissions in urban and rural China: an index decomposition analysis. Energy Policy 38 (7), 3377–3383. Zhang, Y., 2013. The responsibility for carbon emissions and carbon efficiency at the sectoral level: evidence from China. Energy Econ. 40, 967–975. Zhang, X.-P., Cheng, X.-M., 2009. Energy consumption, carbon emissions, and economic growth in China. Ecol. Econ. 68 (10), 2706–2712. Zhang, M., Mu, H.L., Ning, Y.D., 2009. Accounting for energy-related CO(2) emission in China, 1991–2006. Energy Policy 37 (3), 767–773. Zhang, Q., Nakatani, J., Moriguchi, Y., 2015. Compilation of an embodied CO2 emission inventory for China using 135-sector input-output tables. Sustainability 7 (7), 8223–8239. Zhao, Q., Yan, Q., 2017. Research on properties and structural characters of carbon flows network among industries in China based on SNA. Sci. Technol. Manage. Res. 37 (09), 233–239. Zhao, R., Min, N., Geng, Y., He, Y., 2017. Allocation of carbon emissions among industries/sectors: an emissions intensity reduction constrained approach. J. Clean. Prod. 142, 3083–3094. Zhu, B.Z., Su, B., Li, Y.Z., 2018. Input-output and structural decomposition analysis of India’s carbon emissions and intensity, 2007/08–2013/14. Appl. Energy 230, 1545–1556.

412