Comprehensive Evaluation of China’s Input–Output Sector Status: Comparison
Please note this is a comparison between Version 2 by Camila Xu and Version 3 by Camila Xu.

Based on China's 2002–2018 input–output data, this researchtudy uses the entropy weight method to determine weights, and then combines the social network analysis method in order to construct a comprehensive index system for industry status evaluation.

  • industry sector status
  • input–output tables
  • social network analysis method

1. Introduction

The global economic system is a large network composed of national subnets. As the world’s second largest economy and the largest trading nation, a slight change in China will have a huge impact on the interconnected global economy and trade [1]. An economy is composed of interdependent departments that rely on the exchange of products and services. The industrial structure, as a concentrated expression of the level of economic development, reflects various complex proportional relationships between different industrial sectors in accordance with certain economic and technological connections, as well as the impact of each industry on the entire industrial network [2][3][4]. Understanding the economic structure (i.e., how the industrial sectors interact with each other) is crucial to determine how the economic system functions [5][6]. Therefore, identifying the key sectors and important connections in China’s economic and trade networks is crucial for also understanding the source of China’s global influence and long term sustainable development.
To deepen the application of sustainable development theory in economic and trade, “Agenda 21” has a comprehensive elaboration on sustainable development capacity building: “A country’s sustainable development capacity depends to a large extent on the capacity of its people and institutions under its ecological and geographical conditions, specifically, the capacity building includes the development and enhancement of a country’s human, scientific, technological, organizational, institutional, and resource capabilities”. The basic goal of capacity building is to improve the ability to evaluate and select policies and development models. “The application of sustainable development theory in economic and trade is to realize the sustainable development of trade, that is, to make full use of various economic resources, to ensure the lasting economic trade capacity, and to maintain the sustainable use capacity and function of resource regeneration and utilization”. China has also put forward the goal of a carbon peak by 2030 and carbon neutralization by 2060, namely, the “double carbon goal,” to support sustainable development. 

2. Comprehensive Evaluation of China’s Input–Output Sector Status

The input–output table is an effective tool for supporting the analysis of the structural relationship between national or regional economic sectors. It traces the direct and indirect supply–demand relationship between various departments in the entire economic system, and comprehensively describes the complex national economic system and the economic structure at the departmental level [7]. It is widely used in agriculture [8][9], industrial economic transformation [10], heavy industry [11], construction industries [12], etc. The social network analysis (SNA) method is a theory and technique for measuring the structure of complex systems. The SNA method can characterize the internal structure of the industry by measuring the input–output flow between industrial departments, revealing the complex relationships between departments [13], and identifying key departments at the center of power and influence. The economic system based on the input–output table can be regarded as a network, wherein nodes represent industrial sectors and the edges connecting nodes represent transactions between sectors [5]. Each department in the network acts as a producer and consumer simultaneously. On the one hand, it produces and distributes inputs provided to other departments; on the other hand, it consumes inputs from other departments to complete its own transformation process [14]. In order to better understand the relationship between inter-departmental dependence and its impact on the entire economic system, a large number of documents have been combined with input–output tables and SNA methods to investigate the industrial structure, mainly in three areas. First, the overall structure of the industrial network and its evolutionary characteristics, whether at the global level or that of a single country or region, has been fully studied [15][16][17]. This type of research mainly focuses on the topological structural characteristics of the network, and it often uses SNA indicators, such as network scale, density, degree distribution, connectivity, reciprocity, core-peripheral structure, and cohesive subgroups, to describe the overall performance of the network. An important feature of this subset of the literature is that it focuses on the analysis of mathematical and statistical characteristics of the input–output network, without paying too much attention to the meaning of the policy, or that it involves policy, but at a superficial level. The second subset of the literature focuses on the roles and functions of the industrial sector. In this literature, some scholars typically have a subjective bias. For example, to determine whether logistics and transportation sectors have become the center of the U.S. economy over time, Lyengar et al. [18] use the input–output table data of the U.S. Bureau of Economic Analysis for more than two decades in order to conduct SNA. Ma et al. [19], Li et al. [20], and Liu et al. [21] intend to study the internal evolution of China’s construction industry, evolution of the coal industry chain, and network relationship between the power industry and economic growth, respectively, as well as to provide a reference for the formulation of industrial policies. Among China’s 139 sector input–output networks in 2012, wholesale, retail, and agricultural products have the greatest impact on total output fluctuations through network connections, and can be used as key industries [22]. However, in the 42 sector input–output networks in 2017, the three major industries, namely construction, public management, and social security and transportation equipment, showed clear advantages and achieved a higher status than other industries [23]. It can be found that because various scholars may use data from different years, different statistical calibers, different evaluation indicators, or diverse conclusions are often obtained. The third subset of the literature is the application of ecological network analysis to input–output data with environmental expansion, by analyzing the interaction between the economy and the environment through the flow of energy, resources, and emissions. In order to understand how the economic structure affects the country’s environmental conditions [5], this type of research is often related to energy flow and carbon emissions. According to the input–output relationship between industrial sectors, energy flow and carbon emission networks can be constructed indirectly, and the source of energy demand and carbon emissions can be determined by analyzing the flow of various substances in the network. Departments have provided evidence to improve global environmental issues [24][25][26][27][28][29][30][31][32][33]. Because research topics closely follow leading real-world issues in today’s world, research results in this field are quite rich. However, an important flaw is that the data used for empirical explanations often lag in timeliness. These results have laid the foundation for this rpapesearchr, which has also made the following expansions. Firstly, in terms of data, this researchpaper uses China’s latest input–output table data (2002–2018), which, currently, has not yet appeared in the literature. It is believed that the analysis results based on the latest data can provide new insights regarding China’s economic reality. Secondly, in terms of industrial sector selection, this researchstudy uses the input–output data of sub-industrial sectors that have not been merged for calculation and evaluation, which overcomes the limitations that the previous literature had using the input–output table and the social network method to analyze the industrial structure. This makes it easy to miss the original input–output information between industries. In terms of research methods, this researchpaper solves the problem that the previous literature had drawing different conclusions based on various evaluation indicators. The entropy weight method is used to determine the weight, and various measurement indicators of the SNA method are combined in order to construct a set of comprehensive evaluation indicator systems. It provides effective ideas for future research and the development of new methods for individual status evaluation. Finally, in terms of the analytical framework, cross-sectional analysis and time-series analysis were combined. On the one hand, a horizontal comparison of the status of industry sectors is carried out under three strength relationship networks. On the other hand, vertical changes in the status of specific sectors are discussed under the industry category. This provides a comprehensive and detailed industrial cognition for the formulation of comprehensive and systematic national economic strategies and targeted industrial policies.


  1. Sun, X.; An, H.; Liu, X. Network analysis of Chinese provincial economies. Phys. A 2018, 492, 1168–1180.
  2. Li, Z.; Sun, L.; Geng, Y.; Dong, H.; Ren, J.; Liu, Z.; Tian, X.; Yabar, H.; Higano, Y. Examining industrial structure changes and corresponding carbon emission reduction effect by combining input-output analysis and social network analysis: A comparison study of China and Japan. J. Clean. Prod. 2017, 162, 61–70.
  3. Sun, L.; Xue, B.; Geng, Y. Comparative Analysis of Regional Industrial Structure Based on Input-Output Table and Social Network Analysis: Taking Seven Provinces (Cities) in East China as Examples. J. East China Normal Univ. Nat. Sci. Ed. 2015, 1, 224–233.
  4. Singh, T. On the sectoral linkages and pattern of economic growth in India. J. Asia Pac. Econ. 2016, 21, 257–275.
  5. Xu, M.; Liang, S. Input-output networks offer new insights of economic structure. Phys. A 2019, 527, 121178.
  6. Tadjoeddin, M.Z. Productivity, wages and employment: Evidence from the Indonesia’s manufacturing sector. J. Asia Pac. Econ. 2016, 21, 489–512.
  7. Miller, R.E. Input-Output Analysis: Foundations and Extensions; Cambridge University Press: London, UK, 2009; pp. 151–168.
  8. Zhu, Y.; Li, M.; Lu, S.; Wang, H.; Wang, J.; Wang, W. Research on the Input-Output Model of the Rural Agricultural Eco-Economic System Based on Emergy Theory. Sustainability 2022, 14, 3717.
  9. Ashraf, M.N.; Mahmood, M.H.; Sultan, M.; Banaeian, N.; Usman, M.; Ibrahim, S.M.; Butt, M.U.B.U.; Waseem, M.; Ali, I.; Shakoor, A. Investigation of Input and Output Energy for Wheat Production: A Comprehensive Study for Tehsil Mailsi (Pakistan). Sustainability 2020, 12, 6884.
  10. Shi, Q.; Chen, S.; Shi, C.; Wang, Z.; Deng, X. The Impact of Industrial Transformation on Water Use Efficiency in Northwest Region of China. Sustainability 2015, 7, 56–74.
  11. Ma, F.J.; Eneji, A.E.; Wu, Y.B. An Evaluation of Input-Output Value for Sustainability in a Chinese Steel Production System Based on Emergy Analysis. Sustainability 2018, 10, 4749.
  12. Lin, L.; Fan, Y.; Xu, M.; Sun, C. A Decomposition Analysis of Embodied Energy Consumption in China’s Construction Industry. Sustainability 2017, 9, 1583.
  13. Xing, L.; Dong, X.; Guan, J. Global industrial impact coefficient based on random walk process and inter-country input-output table. Phys. A 2017, 471, 576–591.
  14. Guan, J.; Xu, X.; Wu, S.; Xing, L. Measurement and simulation of the relatively competitive advantages and weaknesses between economies based on bipartite graph theory. PLoS ONE 2018, 13, e0197575.
  15. Xing, L.; Wang, D.; Li, Y.; Guan, J.; Dong, X. Simulation analysis of the competitive status between China and Portuguese-speaking countries under the background of one belt and one road initiative. Phys. A 2020, 539, 122895.
  16. Cerina, F.; Zhu, Z.; Chessa, A.; Riccaboni, M. World Input-Output Network. PLoS ONE 2015, 10, e0134025.
  17. Han, Y.; Goetz, S.J. Predicting US county economic resilience from industry input-output accounts. Appl. Econ. 2019, 51, 2019–2028.
  18. Iyengar, D.; Rao, S.; Goldsby, T.J. The Power and Centrality of the Transportation and Warehousing Sector within the US Economy: A Longitudinal Exploration Using Social Network Analysis. Transport. J. 2012, 51, 373–398.
  19. Ma, X.; Huang, C.; Fu, Y.; Gao, J.; Qin, B. Study on Evolution of China’s Construction Industry Based on Input-Output Analysis and Complex Network. The Vjesn. 2019, 26, 208–216.
  20. Li, Y.; Zhang, B.; Wang, B.; Wang, Z. Evolutionary trend of the coal industry chain in China: Evidence from the analysis of I-O and APL model. Resour. Conserv. Recycl. 2019, 145, 399–410.
  21. Liu, D.; Zeng, X.; Su, B.; Wang, W.; Sun, K.; Sadia, U.H. A social network analysis regarding electricity consumption and economic growth in China. J. Clean. Prod. 2020, 274, 122973.
  22. Gong, J.; Xu, J.; Hu, F. Key industries in the input-output network. J. Shandong Univ. Sci. Ed. 2019, 5, 61–67.
  23. Fan, L.; Zhang, S.; Chang, X.; Qing, F. Input-output Sector Evaluation from the Perspective of Complex Network. In Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 12–14 June 2020; pp. 904–907.
  24. Sun, L.; Qin, L.; Taghizadeh-Hesary, F.; Zhang, J.; Mohsin, M.; Chaudhry, I.S. Analyzing carbon emission transfer network structure among provinces in China: New evidence from social network analysis. Environ. Sci. Pollut. Res. 2020, 27, 23281–23300.
  25. An, Q.; An, H.; Wang, L.; Gao, X.; Lv, N. Analysis of embodied exergy flow between Chinese industries based on network theory. Ecol. Model. 2015, 318, 26–35.
  26. Chen, B.; Li, J.S.; Wu, X.F.; Han, M.Y.; Zeng, L.; Li, Z.; Chen, G.Q. Global energy flows embodied in international trade: A combination of environmentally extended input-output analysis and complex network analysis. Appl. Energy 2018, 210, 98–107.
  27. Li, Y.L.; Chen, B.; Chen, G.Q. Carbon network embodied in international trade: Global structural evolution and its policy implications. Energy Policy 2020, 139, 111316.
  28. Ma, N.; Li, H.; Tang, R.; Dong, D.; Shi, J.; Wang, Z. Structural analysis of indirect carbon emissions embodied in intermediate input between Chinese sectors: A complex network approach. Environ. Sci. Pollut. Res. 2019, 26, 17591–17607.
  29. Shi, J.; Li, H.; Guan, J.; Sun, X.; Guan, Q.; Liu, X. Evolutionary features of global embodied energy flow between sectors: A complex network approach. Energy 2017, 140, 395–405.
  30. Sun, X.; An, H. Emergy network analysis of Chinese sectoral ecological sustainability. J. Clean. Prod. 2018, 174, 548–559.
  31. Sun, X.; An, H.; Gao, X.; Jia, X.; Liu, X. Indirect energy flow between industrial sectors in China: A complex network approach. Energy 2016, 94, 195–205.
  32. Wang, X.; Yu, J.; Song, J.; Di, X.; Wang, R. Structural evolution of China’s intersectoral embodied carbon emission flow network. Environ. Sci. Pollut. Res. 2021, 28, 21145–21158.
  33. Lv, K.; Feng, X.; Kelly, S.; Zhu, L.; Deng, M. A study on embodied carbon transfer at the provincial level of China from a social network perspective. J. Clean. Prod. 2019, 225, 1089–1104.
Video Production Service