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Measurement of production efficiency and environmental efficiency in China’s province-level: a by-production approach

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Abstract

The nonconventional by-production approach with respect to the freely disposable inputs but without the weakly disposable and null-joint outputs has been proposed to describe the pollution generating technologies since 2012. To amend the contradictory trade-offs among inputs, intended and unintended outputs, which generate in the previous conventional pollution-generating technologies, the new by-production approach decomposes the general pollution generating technology as classical intended production technology and nature’s residual-generation mechanism. In this paper, some production and environmental efficiency indexes will be extended and firstly applied in the study of regional technical efficiency level with considering the energy utilization and air pollutants emission in China. Based on our calculating results, there exists the obvious variation in the regional technical efficiency level with the regional geographic separation. Eastcoast area ranks the highest in production efficiency measurement, and the West has the lowest levels in both production and environmental efficiency. Through conducting some reason discussions, our new efficiency results based on the by-production approach are consistent with the fact of Chinas unbalanced regional development pattern and also reveal the ineffectiveness of current environmental policy implementations.

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Notes

  1. Inputs used in production process can lead to some negative or positive effects on environment (outputs). The environmental efficiency index aims to ranking the economic units by taking account the environmental efficiency level. See, e.g. (Caves et al. 1982; Färe et al. 1989, 2005).

  2. See, e.g. (Coggins and Swinton 1996; Färe et al. 1989, 1993, 2005; Grosskopf 1996; Murty and Kumar 2002, 2003).

  3. See, e.g. (Färe et al. 1996; Tyteca 1997; Färe and Grosskopf 2003).

  4. The standard single-equation representation of weak-disposability approach shows the non-positive trade-off between input and undesirable output when desirable output held fixed and the non-negative trade-off between desirable output and undesirable output with fixed input. These two trade-offs can be argued to be counter to the emission generation fact (Murty et al. 2012).

  5. Here, the authors only consider the undesirable outputs (emissions) from the production process (e.g. tons of \({\rm SO}_{2}\) and \({\rm CO}_2\)) and not the externality they might cause.

  6. This paper does not considerate the pollution reductions, but the model can also be extended to include abatement activities. See, (Murty et al. 2012; Murty 2015).

  7. Murty (2015) provides a generalisation of this where emissions from a firm may affect its own desirable production in a beneficial or detrimental manner.

  8. These two efficiency measures indexes have been widely used in study WD approach. In this paper, they will be modified and employed to measure technical efficiency under the BP approach.

  9. The output-oriented version index takes up all slack in output spaces and leaves the slack in inputs spaces.

  10. We denote \(y\oslash \theta =\langle y_{1}/\theta _{1},\ldots y_{M}/\theta _{M}\rangle\) and \(b\otimes \gamma =\langle b_{1}\gamma _{1},\ldots b_{K}\gamma _{K}\rangle\).

  11. Due to lack of some data on regions such Tibet, Hongkong, Macau and Taiwan, we only consider 30 provincial level regions, including 22 provinces, 4 municipalities and 4 autonomous regions.

  12. CNY is an abbreviation for Chinese currency “Yuan”.

  13. The PIM could be straightforward as

    $$\begin{aligned} {K_{i,t}} = {K_{i,t - 1}}\left( {1 - {\delta _i}} \right) + {I_{i,t}}. \end{aligned}$$

    where, i and t represent the \(i{\rm th}\) province and \(t{\rm th}\) year, respectively. K denotes the capital stock. \(\delta\) and I denote the depreciation rate and capital asset investment of year, respectively. The initial capital stock (based year: 2000) and depreciation rates are derived from Zhang et al. (2004). The annual capital asset investment is obtained from the “China Statistical Yearbook”.

  14. The definition of \({\rm SO}_2\) variable can be found in the National Bureau of Statistics of China. Net \({\rm SO}_2\) emission refers to volume of sulphur dioxide emission from burning fossil-fuel during production in the premises of enterprises in each region for a given period of time.

  15. The reference approach to calculate the \({\rm CO}_2\) emission is designed as

    $$\begin{aligned} {{\rm CO}_2}_{\rm emission} = \sum \limits _i {\left( {{\rm AC}_i \cdot {\rm CF}_i \cdot {\rm CC}_i} \right) } \cdot {\rm COF} \cdot 44/12. \end{aligned}$$

    Here, \({\rm AC}_i\) represents the apparent energy consumption for fossil fuel i . \({\rm CF}_i\) is the conversion factor for fuel i to energy. \({\rm CC}_i\) is the carbon content for i fuel. COF is the carbon oxidation factor, usually the value is 1. And 44 / 12 equals to molecular weight ratio of \({\rm CO}_2\) to C. The data on energy consumption are taken from the “China Energy Statistical Yearbook”.

  16. Due to the only one desirable output is chosen (\(M=1\)) in this paper, the results of decomposition of FGL production efficiency \(D_{\rm FGL(1)}\) for each region in every year are exactly same with \(D_{\rm HYP(1)}\) in HYP. Hence, the differences between integrated efficiency scores could be mainly attributed to calculation of environmental efficiency scores under these two methods. If \(M\ge 2\), the programming for production efficiency calculation should be designed to take the coordinate-wise distances from each desirable output observation to the corresponding possibility frontier. Hence, \(D_{\rm HYP(1)}=D_{\rm FGL(1)}\) is the occasional case with \(M=1\).

  17. Source from: China National Energy Administration.

  18. Statistical data come from the report of “Coal use contribution to China’s air pollution” by China’s coal consumption control scheme and policy research, 2014.

  19. Zeng (2011) uses the input-oriental variable return to scale (VRS) model based on the DEA efficiency measurement of Charnes et al. (1978) to measure China’s regional total technical efficiency with bad output consideration. Then, it also employs Tobit regression and finds the technology innovation has a negative effect on regional technology efficiency.

  20. Data from: statistical departments in Ministry of Environmental Protection and Ministry of Housing and Urban-rural Development, P. R. China.

  21. According to the latest statement from Ministry of Environmental Protection of Peoples’ Republic of China, the new discharge rate will increase to 1.20 CNY/kg for main air pollutants.

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Acknowledgments

The author is indebted to Dr. Sushama Murty and an anonymous referee; participants at the The 5th Congress of East Asian Association of Environmental and Resource Economics (EAAERE) 2015 in Taipei helpful suggestions on earlier versions of the paper. All remaining errors are the author’s responsibility.

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  1. Economics Department of Business School, University of Exeter, Exeter, UK

    Zuoxiang Zhao

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Zhao, Z. Measurement of production efficiency and environmental efficiency in China’s province-level: a by-production approach. Environ Econ Policy Stud 19, 735–759 (2017). https://doi.org/10.1007/s10018-016-0172-3

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