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Decomposition of energy intensity in China’s manufacturing industry using an agglomeration extended LMDI approach

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Abstract

Industrial agglomeration has considerable influence on industrial energy consumption. Exploring the factors related to the energy intensity change in the agglomeration driving sector level is crucial to support targeted energy reduction policies. This study proposes an agglomeration extended LMDI method. It uses an attribution analysis (AA) method to decompose sector-level energy intensity change into eight driving factors, including two new factors related to agglomeration, that is energy-agglomeration ratio effect and agglomeration-R & D expenditure ratio effect. A total of 27 Chinese manufacturing sectors are used to demonstrate the extended decomposition approach and AA method. Based on decomposition and attribution results, the dominant factors in decreasing industrial energy intensity are the production technological change effect and the agglomeration-R & D expenditure ratio effect, with the three sectors of manufacture of raw chemical materials and chemical products, smelting and pressing of ferrous metals, and manufacture of non-metallic mineral products being the main contributors. The energy-agglomeration ratio effect increases industrial energy intensity remarkably, primarily due to the three sectors of manufacture of raw chemical materials and chemical products, manufacture of non-metallic mineral products, and smelting and pressing of ferrous metals. Sectors are classified into four performance groups based on the attribution results. Targeted industrial energy intensity reduction policies should be performed in various sector groups.

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Notes

  1. Reviews of studies combined the PDA and IDA methods can be found in Wang et al. (2018).

  2. The data of Figs. 2, 3, 4, and 5 are presented in Tables 2, 3, and 4.

  3. The data of Fig. 6 is presented in Table 5.

  4. In this study, the result of effect of reciprocal of production technological efficiency change on MI’s energy intensity is lower than 1. Therefore, the result of effect of production technological efficiency change on MI’s energy intensity is larger than 1; namely, the production technological efficiency change improved MI’s energy intensity.

  5. The data of Fig. 7 is presented in Table 6.

  6. The average percentage portion is computed calculated as the scale of 100% to total samples (27 sectors).

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Funding

We acknowledge the financial support from the National Social Science Foundation of China (Nos. 16ZDA044).

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Authors and Affiliations

  1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics University, Nanjing, 210016, China

    Nan Wang

  2. College of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China

    Wei Zhang & YuanHai Fu

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  1. Nan Wang
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Correspondence to Wei Zhang.

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Appendix

Appendix

Table 14 Equations for seven multi-period LMDI decomposition models
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Table 15 Equations for seven multi-period attribution analysis models
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Table 16 Classification and code of industrial subsectors
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Wang, N., Zhang, W. & Fu, Y. Decomposition of energy intensity in China’s manufacturing industry using an agglomeration extended LMDI approach. Energy Efficiency 14, 66 (2021). https://doi.org/10.1007/s12053-021-09968-7

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