Abstract
The purpose of this paper is to investigate the driving force behind industrial energy productivity growth in China from 2005 to 2010. The model of Wang (Energy, 32(8), 1326–1333, 2007) is extended to accommodate different technologies by relaxing the constant return to scale (CRS) assumption. In the empirical study, we find that (1) technological changes and capital-energy substitution are the main contributors to the increase in industrial energy productivity; (2) impacts of changes in technical efficiency, energy composition, and output structure are relatively trivial; (3) the effect of labor-energy substitution is unfavorable to industrial energy productivity growth.
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Notes
The numerical values are calculated by the author according to the data from China Statistical Yearbook.
According to China Statistical Yearbook, China’s industrial energy productivity declined from 5.5 thousand RMB/TCE in 2000 to 4.9 thousand RMB/TCE in 2005 (2005 price).
We thank an anonymous reviewer for pointing out this.
Ang (2004) provided a useful summary and comparison of the various methods.
We thank an anonymous reviewer for pointing out this.
Zhou and Ang (2008a) also provided a brief comparison between SDA, IDA and PDA.
We follow Wang (2007) to decompose energy productivity change based on the distance function. We also notice that a recent study, Wang et al. (2013) conducted the decomposition of total energy productivity in the context of directional distance function (DDF). It is a nice idea since DDF can take into account undesirable outputs. However, the energy productivity defined in our paper belongs to PFEE indicators, different from that in Wang et al. (2013). The decomposition based on the DDF is not applicable in our case.
Under CRS technology, the output distance function is homogeneous degree of −1 in inputs.
One can choose a specific type of technology based on the judgment of the real technologies to apply the model.
Due to the unavailability of the data, Tibet is not considered in this study.
The heavy and light industries are classified according to the criteria of Chinese National Bureau of Statistics which can be found in Appendix 3.
The gross output measure has the double counting problem. Hence, the gross output data should not be directly used.
They are provided by China Industrial Economy Statistical Yearbook. Enterprises above designated size are defined by enterprises of which total annual turnover exceed 5 million RMB.
Due to data limitation, we only have observations in 2005 and 2010. Thus, in our empirical studies, we actually use observations in 2005 to construct the frontier for 2005 and use observations both in 2005 and 2010 to construct the frontier for 2010. But, this is essentially in line with the sequential DEA method.
In practice, the LPs for cross-period distance function (specially when the technology in previous year is used to estimate the distance function in a later year) may become infeasible which would challenge the empirical analysis. We thank an anonymous reviewer for pointing out this issue. We discuss this in Appendix 4. But note that our empirical study does not encounter infeasibility.
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Acknowledgments
We thank three anonymous reviewers for their helpful comments and suggestion which led to an improved version of this paper. Financial support from Yinxing Economic Research Fund is gratefully acknowledged.
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Highlights
• The original PDA model is extended to accommodate different technologies.
• Technological progress is the main driver of industrial energy productivity growth.
• Capital-energy substitution contributes to industrial energy productivity growth.
Appendices
Appendix 1
The whole decomposition of Eq. (7):
Appendix 2
The whole decomposition of Eq. (9):
Appendix 3
Appendix 4
Note that for the Malmquist-type quantity index, geometric mean of distance changes relative to different production activities are calculated to avoid the ambiguity of choosing reference. The idea can be depicted in Fig. 4. The curves S t and S t + 1 represent the frontiers for time period t and t + 1, respectively. Points A and B represent the production activities at time period t and t + 1, respectively. Technological change between time period t and t + 1 can be measured as \( \frac{O{A}_1/O{A}_2}{O{A}_1/O{A}_3} \) when taking point A as reference. However, relative to point B, it is estimated as \( \frac{O{B}_1/O{B}_2}{O{B}_1/O{B}_3} \). To avoid different results initiated from different choosing references, geometric mean of \( \frac{O{A}_1/O{A}_2}{O{A}_1/O{A}_3} \) and \( \frac{O{B}_1/O{B}_2}{O{B}_1/O{B}_3} \) is calculated as the proxy of technological change. Furthermore, suppose that the production activity at time period t + 1 is point C instead of point B. It can be seen that point C is out of the production possibility set at time period t so that technological change cannot be measured based on point C. But, the calculation based on point A1 can partly reflect the movement of the frontier. This is the only useful information we can use. Thus, in this case, technological change can be measured as the distance change taking the production activity at time period t as reference. That is to say, technological change is estimated as \( \frac{O{A}_1/O{A}_2}{O{A}_1/O{A}_3} \), neglecting the information of point C.
Generally speaking, infeasibility occurs when production activity at time period t + 1 is out of the production possibility set at time t (like the case we described above). In our model, the indices of KE, LE, ES, OS, and TC are all Malmquist-type quantity index. Therefore, when some subcomponents are infeasible, we can exclude them from the calculation of geometric mean.
Another solution to the issue of infeasibility is conducting our decomposition based on the global DEA method proposed by Pastor and Lovell (2005). Analog to the derivation in section “Methodology”, one can easily get another version of our decomposition model with the global DEA method in which calculating cross-period distance functions is not needed so that it is free from the issue of infeasibility. However, the weakness of decomposing energy productivity change based on the global DEA method is that it cannot preclude technology regress.
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Du, K., Huang, L. & Yang, Z. Understanding industrial energy productivity growth in China: a production-theoretical approach. Energy Efficiency 8, 493–508 (2015). https://doi.org/10.1007/s12053-014-9304-4
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DOI: https://doi.org/10.1007/s12053-014-9304-4