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Inputs optimization to reduce the undesirable outputs by environmental hazards: a DEA model with data of PM2.5 in China

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Abstract

Currently, the hazy weather in China is increasingly serious. It is urgent for China to reduce haze emissions in environmental governance. A feasible way is to control haze emissions by optimizing the input sources. This paper proposed an innovative method in which the haze emission is controlled by readjusting input indicators. The output efficiency of input indicators in 29 provinces in China is calculated through 7 input indicators (namely, SO2 emissions, NOX emissions, soot emissions, coal consumption, car ownership, capital, and labor force) as well as GDP (desirable output) and PM2.5 emissions (undesirable output). The results showed that the input indicators are excessive in redundancy on condition that PM2.5 emissions and GDP are equal. The input indicators are high in redundancy rate except labor force. The redundancy rates of soot emissions, SO2 emissions and coal consumption are relatively high and, respectively, are 78, 67.18, and 61.14%. Moreover, all the provinces are redundant in inputs except Beijing, Tianjin, and Shanghai which are optimal in input–output efficiency. The redundancy of middle and western provinces, such as Ningxia, Guizhou, and Shanxi, is relatively large. The ideas and methods proposed in this paper can provide a reference for the future researches that aim to reduce the input indicators of undesirable output, and the empirical results can provide empirical support for the PM2.5 abatement in China.

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Notes

  1. Comprehensive technical efficiency is composed of two parts. Comprehensive technical efficiency = pure technical efficiency × scale efficiency. Pure technical efficiency refers to the production efficiency which is influenced by the management and technology of enterprise. Scale efficiency refers to the production efficiency which is influenced by the scale of enterprise.

  2. According to formula (1), the reason for the difference of DEA efficiency can be divided into three aspects: 1. From the perspective of input: The denominators of formula (1) are the input indicators. From the calculation results of the input indicators, except Beijing, Tianjin, and Shanghai, the decision making units of other provinces have one or more input indicators that are more than DEA effective input, resulting in DEA efficiency value less than 1. Take Hebei Province for example, the redundancy rates of SO2 emissions, NOX emissions, soot emissions, coal consumption, car ownership, capital and labor force are 964,686.041, 1,157,308.897, 1,093,714.577, 22,252.012, 712.265, 8418.193 and 0, respectively, indicating that the amount of input must be reduced to achieve effective DEA. 2. From the perspective of output: It can be seen from the molecular part of GDP (desirable output) and PM2.5 (undesirable output) (see Table 2) that the provinces with relatively low efficiency values are Gansu, Qinghai, and other provinces that are relatively backward in economic development. The desirable output GDP of these provinces is quite low and the undesirable output PM2.5 is relatively high, which is one of the reasons why DEA is not effective. However, since this paper focuses on the improvement of the DEA efficiency of each evaluation unit by reducing the input indicator, how to raise the desirable output (GDP) and reduce the undesirable output (PM2.5) is not yet considered. Of course, this is the next step that needs to be studied. 3. Emission efficiency values are relative values. The DEA efficiency values of the DMUs obtained in Table 3, and the redundancies of the input indicators of DMUs obtained in Table 4 are relative values compared with other “leading” DMUs. As mentioned in this paper, the three DMUs in Beijing, Tianjin, and Shanghai are on the frontier whose efficiency value is 1 and the efficiency values of other DMUs and the redundancies of their input indicators are relative to these DMUs on frontier.

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Acknowledgements

This research was supported by: Project of National Social and Scientific Fund Program (16ZDA047). The Natural Science Foundation of China (91546117, 71373131). National Social and Scientific Fund Program (15BTJ019). National Industry-specific Topics (GYHY 201506051). The Ministry of Education Scientific Research Foundation for the returned overseas students (No. 2013-693, Ji Guo). This research was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. Collaborative Innovation Center on Climate and Meteorological Disasters, School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Xianhua Wu

  2. School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China

    Yufeng Chen

  3. Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China

    Ji Guo

  4. Collaborative Innovation Center on Climate and Meteorological Disasters, National Climate Center, Beijing, 10081, China

    Ge Gao

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  1. Xianhua Wu
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Correspondence to Xianhua Wu.

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Wu, X., Chen, Y., Guo, J. et al. Inputs optimization to reduce the undesirable outputs by environmental hazards: a DEA model with data of PM2.5 in China. Nat Hazards 90, 1–25 (2018). https://doi.org/10.1007/s11069-017-3105-y

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