Backward and forward multilevel indicators for identifying key sectors of China’s intersectoral CO2 transfer network

Abstract

Many countries face a dilemma of economic growth and carbon emission mitigation, which is highly associated with energy consumption. In order to initiate effective policies for controlling carbon emissions, it is important to identify the key sectors in the value chain, thus proposing corresponding measures. To date, however, energy and carbon emissions have been studied mainly from a production or consumption perspective, with important interactions between sectors being seldom considered. In response, a new CO2 flow model is presented in which input-output analysis and network theory are combined with multilevel indicators to identify the key sectors affecting carbon emissions in terms of total, immediate, and mediative centrality effects. The model is demonstrated with an analysis of 2007 and 2012 China sectoral data, showing that Production & Supply of Electric Power, Steam and Hot Water (PESH), Nonmetal Mineral Products (NMMP), and Coal Mining & Dressing (CMDG) played key roles in China’s carbon transfer network; the roles of Electronic & Telecommunications Equipment (ETET), Instruments & Office Machinery (IOMY), and Electric Equipment & Machinery (EEMY) had the largest immediacy effect; and, acting as key transmission sectors, PESH, Smelting & Pressing of Metals (SPOM), and NMMP controlled a large share of CO2 transfer. The measures used are closely related to, and provide new insights into, the traditional indicators of sector centrality. At the same time, the proposed multilevel indicators are supplements for techniques that aim to instruct sector-level carbon mitigation policies.

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Acknowledgements

We appreciate the financial support of National Natural Science Foundation of China (No: 71834005).

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Correspondence to Zhen Wang or Xiaoling Zhang.

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Wei, L., Wang, Z. & Zhang, X. Backward and forward multilevel indicators for identifying key sectors of China’s intersectoral CO2 transfer network. Environ Sci Pollut Res 26, 9661–9671 (2019). https://doi.org/10.1007/s11356-019-04350-8

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Keywords

  • Carbon emissions
  • Environmental input-output analysis
  • Network theory
  • Key sectors