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Total factor energy efficiency in Chinese manufacturing industry under industry and regional heterogeneities

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Highlights

  • Total factor energy efficiency is analyzed from perspective of dual heterogeneity.

  • A new three-level meta-frontier SBM approach is developed for modeling dual heterogeneity.

  • 19 Chinese manufacturing sub-sectors in 9 provinces in 2001–2011 are evaluated.

  • Dual heterogeneity leads to significant difference of technology gap rates and energy inefficiency mainly arises from management inefficiency.

  • Impact of various influencing factors on energy inefficiency is identified.

Abstract

At present, there are serious problems of excessive energy consumption and carbon emissions in Chinese manufacturing industry. To solve these problems, it is of great importance to measure and improve total factor energy efficiency reasonably. At the same time, Chinese manufacturing industry differs significantly in various sub-sectors and regions, which means the prevalence of dual heterogeneity. Such strong heterogeneities can have a significant impact on the accurate measurement of total factor energy efficiency. However, it is unfortunately neglected in existing studies. In this paper, we empirically analyze the total factor energy efficiency of 19 Chinese manufacturing sub-sectors in 9 provinces from 2001 to 2011 and further analyzes its influencing factors from the perspective of the dual heterogeneity. To model industry and regional heterogeneities, we propose a three-level meta-frontier slacks-based measure (SBM) approach based on data envelopment analysis (DEA). The results show that: (1) The energy efficiency of Chinese manufacturing industry is generally low, and varies significantly across different industries and regions. Moreover, the energy inefficiency mainly comes from management inefficiency; (2) The dual heterogeneity affects the technology gap rate (TGR) deeply. The technology gap rate caused by industry heterogeneity (ITGR) is larger than that caused by regional heterogeneity (RTGR); (3) Enterprise scale, foreign direct investment, economy development level and industrial structure affect energy efficiency positively, while energy consumption structure and energy price affect energy efficiency negatively. In response to the above research findings, some policy recommendations are put forward in the end.

Introduction

According to the World Bank, the manufacturing value added of the world accounted for 15.7% of its GDP in 2016. A strong manufacturing industry can bring strong economic development of a country. However, the conflict between economic development and environmental protection is becoming increasingly serious. According to the BP World Energy Statistical Yearbook 2018, due to the slowdown in the improvement of energy efficiency, both the growth rate of energy consumption and the carbon emissions increased in 2017. And Chinese energy consumption increased by 3.1%, becoming the country with the largest increase in global energy consumption for 17 consecutive years.

From an industry perspective, industrialization and urbanization are driving the rapid development of Chinese manufacturing industry and energy-intensive industries. Li and Lin (Li and Lin, 2017) pointed out that Chinese six energy-intensive sectors’ energy consumption is close to half of its total energy consumption. In 2012, the energy consumption of Chinese heavy industry exceeded 65% of the final energy consumption. The energy consumption gap between heavy and light industry was significant, and there was also a clear industry difference in energy efficiency.

As pointed out by the "13th Five-Year Plan for Energy Development", Chinese manufacturing industry faced new challenges as resources and environmental constraints were strengthened continuously. And the problems of development quality and efficiency were prominent. Therefore, how to reasonably measure and improve energy efficiency is an important factor that affects China's goal as a manufacturer of quality. China has a vast territory and there are huge differences in resource endowments and regional development. How to effectively distinguish the impact of regional heterogeneity is also an important aspect for reasonably measuring energy efficiency.

In recent years, many scholars have focuses on Chinese total factor energy efficiency. Most of the literature investigate the issue from the perspective of either industry or regional, few studies integrate the two aspects into a unified analysis. Due to the existence of regional and industry heterogeneities, it is more reasonable to consider the due heterogeneity of industries and regions when measuring energy efficiency (C. Feng et al., 2018). Moreover, from a practical point of view, the sub-sectors have different production technology frontiers. Without considering the dual heterogeneity, there may be some deviations in studying the influencing factors of energy efficiency. On the one hand, if we only consider the energy efficiency under the industry heterogeneity, we will inevitably ignore the regional efficiency differences, e.g. the impact of differences in location conditions and regional capabilities on energy efficiency; On the contrary, if we only consider the regional heterogeneity, the industry efficiency differences in resource and labor intensiveness, capital and technology intensiveness are also ignored. Therefore, in order to make up for the above flaws, based on the dual perspectives of regional and industry heterogeneities, this paper proposes an improved three-level meta-frontier SBM model to measure Chinese total factor energy efficiency, which not only solves the problem of slack variables, but also overcomes the contradiction that the technology gap rate (TGR) is greater than 1, and then we use Tobit regression to explore the factors affecting energy efficiency.

The reminder of this paper is organized as follows: Section 2 is a literature review; Section 3 introduces the models, variables and data. Section 4 analyzes the heterogeneity of total factor energy efficiency in Chinese manufacturing industry. Section 5 explores the factors that influence the total factor energy inefficiency. The paper concludes with some policy implications in Section 6.

Section snippets

The heterogeneity of energy efficiency in Chinese manufacturing industry: a brief overview

The problem of heterogeneity has always been a hot topic in existing studies. And the research of the impact of heterogeneity on Chinese energy efficiency is gradually growing. Wang et al. (2013) and C. Feng et al. (2018) considered the differences in industry characteristics, production technologies, development levels in different regions and resource endowments. They argued that the comprehensive study of the heterogeneity of regions and industries is crucial for the correct measurement of

Traditional three-level meta-frontier model

Following existing literature, this paper divides the Chinese manufacturing industry into sub-sectors of light and heavy industry. Each sub-sector is divided into three groups according to different regions: eastern, central and western, as shown in Fig. 1.

Suppose there are three regions (i, ii, iii) and two sub-sectors (I, II), y represents outputs and e represents energy inputs. Under the meta-frontier, there are two industry sub-sector frontiers, i.e. I and II. And there are three regional

Analysis of technology gap rate caused by dual heterogeneity

Based on the proposed approach in Section 3.2, this section analyzes the technology gap caused by dual heterogeneity and divides it into two parts: the technology gap rate caused by industry heterogeneity (ITGR) and the technology gap rate caused by regional heterogeneity (RTGR). Specifically, according to the type of industry, Table 3 shows the results of the two technology gap rates in Chinese manufacturing industry, which are calculated by models (3) and (5) in Section 3.2.

Table 3 shows the

Indicator selection and regression model

Because the total factor energy efficiency score is between 0 and 1, this paper uses the Tobit model for regression analysis. According to Eq. (7) in this paper, EI and energy efficiency are essentially the same in the regression analysis. The factors that affect energy efficiency will inevitably have opposite effects on EI. Therefore, this paper takes EI and its decomposition as dependent variables. The regression model constructed is as follows:Yi,t*=C+β1scalei,t+β2prsi,t+β3fdii,t+β4ecsi,t+β5l

Conclusions and policy implications

Based on the dual heterogeneity perspectives of region and industry, we propose an improved three-level meta-frontier SBM model, and further analyzes the total factor energy efficiency in Chinese manufacturing industry. We find that Chinese manufacturing industry have a low energy efficiency. And the large energy inefficiency is mainly due to management inefficiency. From a regional perspective, the eastern region has the highest energy efficiency while the TGR of the eastern region is the

CRediT authorship contribution statement

Ya Chen: Methodology, Software, Formal analysis, Writing - original draft, Writing - review & editing. Mengyuan Wang: Data curation, Software, Formal analysis. Chenpeng Feng: Data curation, Software, Formal analysis. Huadong Zhou: Data curation, Software, Formal analysis. Ke Wang: Conceptualization, Validation, Investigation, Writing - review & editing, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by National Natural Science Foundation of China [grant numbers 71601064, 72071067, 71601062, 71871081, 71801067, 71971072, 71871022, 71828401]; Major Project of the National Social Science Foundation of China [grant number 18ZDA064]; Fok Ying Tung Education Foundation (161076), Joint Development Program of Beijing Municipal Commission of Education, and National Program for Support of Top-notch Young Professionals. We also thank Zhiqiang Zhang for the computation and

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