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Can sustainable operations achieve economic benefit and energy saving for manufacturing industries in China?

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Abstract

China has launched energy quota right trading system, as one of the sustainable operations, since 2016 to cap the total energy consumption, however the economic and energy saving effect of this new policy is unclear. Based on the input and output panel data of China’s 29 manufacturing sub-industries, this paper constructs the non-parametric optimization model under the command-and-control and the energy quota right trading scenario, to compare their potential economic gains and energy savings of these two polices for Chinese manufacturing industries. Results show that, whether at overall level or at the sub-industries level, the economic potential and energy-saving potential from energy quota right trading are higher than those from command-and-control. However, a part of energy-saving potential will be squeezed out due to the negative externality of the energy market trading. We suggest the well integration of both command-and-control and the market mechanism to achieve a Win–Win development.

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Notes

  1. According to Fujii et al.(2016), inclusive capital stocks such as human, environmental and produced capital stocks can be set evaluation indicators to evaluate urban sustainable development. Considering the research objects (manufacturing industries) and the availability of data, we refer to Fujii et al.(2016) and measure the sustainable development from economic (gross industrial product value) and environmental (energy-saving) performance two perspectives in this paper.

  2. In the model (1) and model (6), we use the inequality for the bad output to avoid the unreasonable case where the more undesirable outputs, the higher environmental efficiency (Chen 2013).

  3. The stochastic frontier model can set specific function form. On one hand, the model can separate the technical inefficiency from residual error (in this paper, the technical inefficiency implies that energy technical inefficiency), and all estimates have precise standard errors. On the other hand, according to Battese and Coelli (1995), we can also examine influence factors of the energy technical inefficiency and explore how these factors affect the energy technical inefficiency. Based on the above considerations, we decide to change the non-parametric method and employ the stochastic frontier method.

  4. Including raw coal, cleaned coal, other washed coal, coke, coke oven gas, other gas, crude oil, gasoline, kerosene, diesel oil, fuel oil, liquefied petroleum gas, refinery gas, natural gas, heat power and electricity.

  5. In this paper, we do not consider the Power generation, which is the sixth energy-intensive industry in China.

  6. Detailed information about the top five energy-intensive industries can be found in Table 7.

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Acknowledgements

We gratefully acknowledge the financial support from the National Natural Science Foundation of China (No. 91746112). Research Center on Low-carbon Economy for Guangzhou Region.

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Authors and Affiliations

  1. Department of Economics, Jinan University, No. 601, West of Huangpu Avenue, Tianhe District, Guangzhou, 510632, Guangdong, China

    Ning Zhang & Weijie Zhang

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  1. Ning Zhang
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  2. Weijie Zhang
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Correspondence to Ning Zhang.

Appendix A

Appendix A

See Tables 6 and 7.

Table 6 29 manufacturing industries and IDs
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Table 7 The top five energy-intensive industries and IDs
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Zhang, N., Zhang, W. Can sustainable operations achieve economic benefit and energy saving for manufacturing industries in China?. Ann Oper Res 290, 145–168 (2020). https://doi.org/10.1007/s10479-018-2955-3

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