Chinese electricity demand and electricity consumption efficiency: Do the structural changes matter?

Applied Energy 262 (2020) 114505

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Chinese electricity demand and electricity consumption efficiency: Do the structural changes matter?

T

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Boqiang Lin , Junpeng Zhu School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian 361005, PR China

H I GH L IG H T S

electricity consumption and its efficiency is analyzed. • China’s regions witnessed a decline in electricity consumption efficiency. • Most are significant differences in efficiency with a range from 0.372 to 1.000. • There is a negative impact of electrification level on electricity efficiency. • There • Rationalizing industrial structure is helpful for improving electricity efficiency.

A R T I C LE I N FO

A B S T R A C T

Keywords: Electricity consumption efficiency Structural changes Stochastic frontier analysis model Electricity conservation potential

Electricity plays an important role in economic and social development. China’s coal-based power generation structure emits a large quantity of greenhouse gases. Improving electricity consumption efficiency is an important measure for energy conservation and emission mitigation. With this in mind, this study analyzes the influencing factors of electricity consumption and estimates the electricity consumption efficiency by considering the role of structural changes in China’s 30 provinces over the period 2006–2015. The following findings are obtained: (1) Per capita income, urbanization, population, the proportion of secondary industry, and electricity price have significant impacts on electricity consumption. (2) The optimization of industrial structure is conducive for improving the electricity consumption efficiency and has a significant impact, while the improvement in electrification level will lead to a decrease in efficiency score during the study period. (3) There are significant differences in electricity consumption efficiency with a range from 0.372 to 1.000, depending on different model specifications, regions, and years. This paper sheds new light on the electricity demand and its efficiency. Based on these findings, this paper proposes some targeted policy recommendations.

1. Introduction Electricity is an important energy source for ensuring the normal operation of the economy and society with the characteristic of been clean, safe, and convenient [1]. Previous studies have shown that there is a significant relationship between economic growth and electricity consumption [2,3]. In the past decades, China’s electricity consumption has increased from 623 billion kWh in 1990 to 5802 billion kWh as of 2015, with an average growth rate of 9.3%. The increase of electricity consumption has greatly improved the electrification level, and the improvement in the electrification level is regarded as an important feature of structural changes, which has significant implications for pollutant emissions mitigation and building a safe and efficient energy

⁎

consumption system [4–6]. Along with the improvement of electrification level, the change in electricity consumption efficiency should be given more attention. On the one hand, improving electricity consumption efficiency can eliminate redundant electricity consumption, which is consistent with the goal of China’s energy strategy on improving energy efficiency [1]. Existing studies have proven that improving energy efficiency is crucial to mitigate climate change [7–10] and promote the sustainable development of the society and economy. On the other hand, China’s energy endowments determine the power source structure dominated by fossil fuels, especially the coal energy [1,11,12]. The efficient use of electricity can indirectly reduce coal consumption, and further mitigate pollutant emissions and strengthen the defossilisation of the energy

Corresponding author. E-mail addresses: [email protected] (B. Lin), [email protected] (J. Zhu).

https://doi.org/10.1016/j.apenergy.2020.114505 Received 22 October 2019; Received in revised form 24 December 2019; Accepted 6 January 2020 0306-2619/ © 2020 Elsevier Ltd. All rights reserved.

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and few studies that focus on electricity consumption efficiency. Therefore, this paper contributes to the existing literature from the following ways: First, by using several novel SFA methods and constructing a series of robustness tests, this paper accurately calculates the electricity consumption efficiency and electricity saving potential. Second, compared with existing studies, this paper extends the SFA method into the analysis of total electricity consumption and gets insight into the effect of structural changes on electricity consumption and its efficiency, which enriches the literature on SFA method and structural changes. Third, this paper studies the structural changes from the aspects of energy consumption structure and industrial structure. Studying the effects of these two structural changes on electricity consumption efficiency is in line with the electricity substitution policies and industrial structure optimization policies currently advocated by Chinese government, this makes it possible to provide targeted recommendations on promoting these policies. The last but not the least, structural changes are common phenomena in developing countries, to learn from the Chinese case, this paper contains some policy implications to relevant countries that are in the process of economic transition. The rest of this paper is organized as follows (see Fig. 2): Section 2 presents an overview of existing literature. Section 3 presents the data description and model specification of the study. The empirical analysis is provided in Section 4. Section 5 concludes this paper with some policy suggestions.

90 80 70

%

60 50 40 30 20 10 0 2005

2006

2007

Primary industry

2008

2009

2010

2011

Secondary industry

2012

2013

2014

2015

Tertiary industry

Fig. 1. The proportion of electricity consumption in different industries.

sector. To control pollution from the fountainhead and ameliorate atmospheric quality as well as achieve defossilisation, the Chinese government issued the “Guidance on Promoting Electricity Substitution” in 2016, which is designed to increase electricity consumption and simultaneously reduce fossil energy consumption in terminal energy consumption in the field of industrial production process and residential sector [13]. Electricity substitution will further increase the electrification level and change the energy consumption structure. In order to identify possible problems in the process of promoting electricity substitution policy, it is of great significance to explore how the electricity consumption efficiency changes with the electrification level. A noteworthy feature of China is that there are huge differences in electricity consumption among different industries. Fig. 1 shows that the secondary industry consumes most of the electricity with a proportion of over 70% between 2005 and 2015, and other industries only consume less than 30% of the electricity. In developed countries, the electricity consumption structure is more reasonable, where transportation, construction, and manufacturing industries consume about onethird each respectively. In order to realize the coordinated development of different industries and ensure the sustainable growth of the economy, China has given high priority to the adjustment of industrial structure. Thus, changes in industrial structure can be recognized as another important feature of structural changes. Changes in industrial structure comprises advanced development and rationalized development of industrial structure. When the economy is gradually transformed into an economic structure dominated by the tertiary industry with lower energy consumption, it can gradually realize the advanced development of the industrial structure. When society achieves the maximum utilization of resources among different industries, the industrial structure will be rationalized gradually. Considering the important role of changes in structural changes, an in-depth analysis of their effects on electricity consumption efficiency has a positive guiding significance on promoting the electricity substitution policy and industrial structure optimization policies. Motivated by this, this paper adopts several novel Stochastic Frontier Analysis (SFA) models to estimate the electricity consumption efficiency by using China’s provincial data over the period 2006–2015. Different from prior studies, this paper contributes to incorporate structural changes into the SFA model and attempts to explore the impact of structural changes on electricity consumption and its efficiency. The results of this study show that the rationalized development of industrial structure is indeed helpful for the improvement in electricity consumption efficiency, while improvement in electrification level inhibits the enhancement in electricity consumption efficiency. Based on these findings, this paper proposes relevant policy recommendations to improve electricity consumption efficiency. The existing literature mainly emphasizes the role of improving energy efficiency in energy conservation and emission mitigation [4,7],

2. Literature review 2.1. Energy efficiency As an important index to measure the energy consumption level and energy utilization, energy efficiency has attracted the attention of many scholars. The current research can be divided into three main categories. The former mainly uses single factor indexes to represent energy efficiency, such as the energy intensity, which is calculated by dividing the total energy consumption by real GDP [14–16]. However, the single factor index does not take into account the impact of other production factors on energy efficiency. Therefore, the change in energy intensity cannot fully reflect the efficiency change, and hence it is regarded as a weak proxy for energy efficiency [17]. The second category adopts a non-parametric Data Envelopment Analysis (DEA) method to estimate energy efficiency. The DEA method can avoid the model misspecification problems because it does not need to assume any conditions about the model setting. Thus, the DEA method has been widely used in the analysis of energy efficiency [9,10,18–22]. Even though the DEA method has these advantages, it does not consider the impact of statistical errors [23]. Specifically, the DEA method is very sensitive to outliers, it will produce estimated errors when there exist statistical errors, thus this method has been criticized by some scholars [24]. The SFA model can solve this problem properly. Different from the DEA methods, the SFA model is a parametric approach and usually assumes a specific model setting. The SFA model has been widely adopted in previous studies to estimate energy efficiency [25,26]. The stochastic energy demand frontier model was first proposed by Filippini and Hunt [27]. In their paper, they assumed that the aggregate energy demand is a function of income level, energy price, economic structure, and other factors. Compared with the traditional stochastic frontier used in the production process, the stochastic energy demand frontier function measures the minimum energy consumption to produce a given energy service. At present, this method has been widely adopted to estimate energy efficiency. For example, Filippini and Hunt [6] adopted this SFA method to estimate the residential electricity efficiency of US-48 states. Filippini et al. [28] estimated the energy efficiency of the EU-27 residential sector by evaluating the impact of policies on electricity efficiency. Marin and Palma [5] went beyond the above works and further discussed the role of policies and innovation in 2

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Introduction and literature Review Data description

Data description and model Baseline estimates

specification

MMLWE Model Specification MMSLE

Robustness checks

Empirical analysis Estimated electricity efficiency

Electricity concervation potential

Conclusion and policy implication Fig. 2. The research framework of this study.

improvement in the electrification level is in line with the electricity substitution policy proposed by China in recent years. The purpose of electricity substitution policy is to use electricity instead of coal or oil in terminal energy and eventually mitigate pollutants emission caused by the burning of fossil fuels [40]. The Chinese government attaches great importance to the electricity substitution policy. Previous studies have shown that the increase in electrification level can change the mode of the production process to a certain extent, thereby increase the production costs and affect the productivity of enterprises. For instance, Jung and Lee [41] showed that the improvement of the electrification level will reduce the productivity of manufacturing companies in the short term. The reason may be that the technical progress caused by the improvement in electrification is not immediate, it usually needs some time to materialize [42,43]. Under the condition that the technical level has not reached the current optimum, rapid electricity substitution may lead to a decline in electricity consumption efficiency. Notwithstanding, in the long term, when the energy consumption structure gradually changes to high-quality energy, such as electricity, and technological progress continues to mature, it may bring about an increase in energy efficiency. On the one hand, the technological progress caused by the improvement in electrification level can effectively improve the industries’ production efficiency in the long term [41]. On the other hand, different energy sources have different productivity, with higher quality fuels having higher productivity [44]. Therefore, the relationship between electricity consumption efficiency and electrification level is surprisingly subtle, this paper takes the electrification level as an explanatory variable and discusses the effect of electrification on electricity consumption efficiency. This paper focuses on the impact of structural changes on energy consumption efficiency. These two structural changes are closely related to China’s current economic transformation as well as the economic development status in other developing countries. Therefore, the findings of this research will provide evidence on the subject.

the energy conversion of the residential sector of ten EU countries. Zhang and Adom [29] applied this method to estimate the energy efficiency transitions of Chinese provinces. In the foregoing studies, the adoption of the SFA method to estimate electricity efficiency has mostly focused on the residential sector [5,6,28]. However, residential electricity consumption accounts for a small part (13.04% in 2015) of China’s total electricity consumption. Most of the electricity consumption is consumed during the production process of industrial sectors, thus it is certainly important to analyze the total electricity consumption efficiency. Following the framework of Marin and Palma [5], this paper formulates a demand frontier of the electricity consumption by taking structural changes into account and attempts to estimate the “underlying electricity consumption efficiency” [27].

2.2. The role of structural changes in electricity consumption The structural changes defined in this paper contain two aspects: i.e., the change in industrial structure and the change in energy structure. We first consider the change in industrial structure, prior studies mainly used the proportion of economically-dominated industries or the ratio of tertiary industry to secondary industry to represent the change in industrial structure, and they found that they are important factors that affect energy consumption, pollutant emissions [30–34], and energy efficiency [35–37]. However, some studies revealed that change in industrial structure contains two aspects [38]. Apart from the change in economically-dominated industries, the optimization of industrial structure is also one important part of change in industrial structure, but the impact of the optimization of industrial structure on the economy and society is rarely studied before, and most existing works focus on its impact on single factor efficiency. For example, Cheng et al. [39] found that the optimization of industrial structure can efficiently reduce carbon intensity. However, the impact of industrial structural changes on energy consumption efficiency is largely unknown, while studying the impact of changes in industrial structure on electricity consumption is crucial because China is at the key stage of the economic transformation process. In terms of change in energy structure, this paper uses the electrification level to represent the change in energy structure. The

3. Data description and model specification 3.1. Data description China’s five-year plan is of great guiding significance to China’s 3

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rising in the residents’ electricity consumption. Moreover, there has been serious air pollution in winter mainly resulted by the burning of coal from small-scaled stoves by residents located in the areas where the pipe network is unavailable and distinct heating is economically unfeasible [12,46]. Therefore, the Chinese government has vigorously promoted electric heating in recent years. The electric heating will increase the requirements for power load capacity and electric grid infrastructures, and eventually improve the electrification level [47,48]. It is significant for China to implement the electricity substitution policy, but it is still worth exploring whether the increase of electrification level is beneficial to the improvement of electricity consumption efficiency. The electrification level (Ele) can be calculated as follows:

economic development. In different planning periods, the government’s policy objectives are different. Considering that now it is in the period of “13th-Five Year Plan”, this paper only covers the complete period of “11th-Five Year Plan” (2006–2010) and “12th-Five Year Plan” (2011–2015). Thus, the dataset used in this paper is limited from 2006 to 2015. In addition, due to data limitation, the dataset only includes 30 provinces of China. The empirical analysis is carried out to develop an electricity demand frontier of China’s provinces, the variables are described as follows: (1) Changes in industrial structure Changes in industrial structure in an economy comprised of two aspects. Firstly, the change in leading industries. Referring to the experience of developed countries, the economy is gradually dominated by the tertiary industry and realizes the advanced development of the industrial structure. Secondly, under the premise of continuous optimization of resource allocation, resources will gradually shift from lowproductivity industries to high-productivity industries and eventually achieve rationalized development of the industrial structure. The influence of the advancement of industrial structure and the rationalization of industrial structure on electricity consumption is different. China’s secondary industry includes the heavy industry, light industry, and the construction industry. In 2015, the secondary industry consumed 72.82% of the total electricity. When the industrial structure gradually reaches the advanced form dominated by the tertiary industry, China’s electricity consumption will slow down. Therefore, the advanced industrial structure mainly affects the stochastic frontier of electricity consumption. The rationalization of industrial structure measures changes in relative productivity, when resources flow from low-productivity industries to high-productivity industries, this will not only increase the productivity of the entire society but also improves the efficiency of resource usage. Therefore, the rationalization of the industrial structure can affect electricity consumption efficiency via continuous optimization of resources. Following Gan et al. [38], this paper uses the Theil index to measure the rationalization of industrial structure (TS ): n

TS =

Y

Y Li ⎞ L⎠

∑ Yi ln ⎛ Yi i=1

⎝

Ele =

Electricity consumption ∗ 100% Total energy consumption

(2)

(3) Other control variables Other variables in the datasets comprise Real per capita GDP (RPCGDP), population (Pop), the proportion of secondary industry (SI), urbanization rate (Urb), average wholesale electricity price (EP), and average temperature (Tem). This paper takes income, population, and electricity price as the inputs of the electricity demand, and real per capita GDP and average electricity price as indicators of income level and electricity price. The real per capita GDP is normalized in 2000 constant price. Considering the important role of the advancement of industrial structure on electricity demand [49], this paper uses the proportion of secondary industry to reflect the influence of industrial structure on electricity consumption. The secondary industry contains many energy-intensive industries, such as iron and steel industry and metallurgical industry, and existing studies found that the secondary industry is the main sector of electricity consumption [11,50]. Therefore, this paper expects that a higher proportion of secondary industry is usually accompanied by higher electricity demand. Additionally, some studies have proved that the urbanization process also plays an important role in determining the electricity demand via the increase in income level and changes in consumption behaviour [50,51]. Thus, the urbanization rate, which is calculated by the ratio of urban population to the total population, is included in the electricity demand frontier function. Lastly, the temperature is the climate factor that affects electricity consumption [52,53]. It is also an important factor that distinguishes the north and the south of China. In order to reflect the different electricity consumption behaviours in different regions, the temperature, which is measured by the annual average temperature of the capital cities, is included in this paper to control for regional differences. Data on average electricity price is collected from the Wind database1, other data are collected from China Provincial Statistical Yearbook. The descriptive statistics of all the variables are presented in Table 1. This paper uses the Chinese Yuan in the main analysis because it is an accurate unit of measurement representation of the Chinese issue. Table 1 reveals that there are large differences in all variables. For example, the maximum value of the RPCGDP is 97695.203 Chinese Yuan, while the minimum value is only 5139.9 Chinese Yuan; The TS ranges between 19.74 and 59.05, with the standard deviation, 7.794. The huge variations ensure that this paper can thoroughly identify the impact of these variables on electricity consumption and its efficiency.

(1)

where, Yi represents the added value of different industries. Li is the number of employees in different industries. The Theil index considers the different weights of the industrial sectors and distinguishes the importance of different sectors, it can effectively measure the development of the rationalization of the industrial structure [38,45]. A larger TS means that the economic structure deviates from equilibrium and the industrial structure is unreasonable. The economic structure achieves an ideal equilibrium when TS = 0 . (2) Change in energy consumption structure This paper attempts to analyze the electricity consumption efficiency when the electrification level continually improves, therefore the electrification level is used as the proxy of energy consumption structure. This article measures changes in electrification level by the proportion of electricity consumption to total energy consumption. The improvement of electrification level mainly comes from the following two aspects: (1) Industrial sectors. The industrial sector is the major consumer of electricity, the industrial electricity consumption accounts for more than 70% of total electricity consumption. With the implement of several environmental protection policies, the government has further promoted the using of electric boiler and electric-furnace in the industrial production process, which has increased electricity usage. (2) Residential sector. The improvement of residents’ living standards has increased the demand for electricity, which has resulted in a continuous

3.2. Model specification The SFA method adopted is a parametric method to estimate the technology efficiency given a specified production or demand function. 1 Wind database is a leading economic data service provider in China (http:// www.wind.com.cn/).

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Table 1 Descriptive statistics of all variables. Variables

Unit

Obs

Mean

Std. Dev.

Minimum

Maximum

Electricity consumption (EC) Real per capita GDP (RPCGDP) Population (Pop) The proportion of secondary industry (SI) Urbanization rate (Urb) Average selling electricity price (EP) Average temperature (Tem) Electrification level (Ele) Optimization of industrial structure (TS)

Billion kWh Yuan Million % % Yuan/kWh °C % –

300 300 300 300 300 300 300 300 300

147.141 26712.98 44.394 47.525 52.215 0.542 14.537 13.842 0.248

109.255 16692.306 26.629 7.794 13.708 0.115 5.054 3.528 0.147

9.77 5139.9 5.477 19.74 27.96 0.272 4.28 7.2 0.016

531.07 97695.203 108.49 59.05 89.6 0.777 25.36 22.78 0.758

estimator when the time period T is fixed. By using the Monte Carlo simulations, the authors found that the MMSLE method performs well when the sample is small. Therefore, this paper adopts the MMLWE and MMSLE methods in the main estimation. The level of electricity consumption efficiency may differ over different provinces since it is affected by various factors, including industrial structure upgrading, distinct technical level and human lifestyle. Among these candidate indicators, this paper focuses on the effect of structural changes on electricity consumption efficiency. In our case, the inefficient term is expressed as the function of structural changes. Thus, the model is specified as follows:

With regard to the aggregate energy demand function, the frontier gives the minimum energy necessary to produce any given level of energy services [27]. Referring to the method adopted in Filippini and Hunt [27], the SFA model used in this paper is based on the assumption that the electricity consumption efficiency can be approximated by a onesided non-negative term, thus, a log–log electricity demand function can be defined as follows [27,54]:

lnECit = αi + lnf (Xit , β ) + vit + uit

(3)

where EC represents the observed actual electricity demand, X is a vector of control variables that determine electricity demand, β is a parameter vector of control variables that need to be estimated in this paper. f (X , β ) is the optimal electricity demand based on the existing technology, which represents the minimum electricity demand determined by these control variables on the consumer side, i.e., the deterministic frontier of electricity demand. v is the idiosyncratic error that affects electricity demand, which is a symmetric distribution that follows iid N (0, σv2) . u represents the inefficiency term, reflecting the distance from the efficiency frontier. It is usually assumed that u follows truncated normal distribution, i.e., iid N+ (μ, σu2) . u and v are mutually independent. Define ε = u + v , Thus, for a random sample with N observations, the likelihood function is specified as follows:

lnL = constant − Nlnσ −

1 2

N

ε

2

∑i =1 ⎛ σ ⎞ ⎝ ⎠

+

N

∑i =1 lnΦ(−ελ

σ)

lnECit = αi + Xit' β + vit + uit

(5)

Xit' = [lnRPCGDPit , lnPopit , lnSIit , lnUrbit , lnEPit , lnTemit ]

(6)

β = [β1, β2, β3, β4 , β5, β6]

(7)

uit = z it' γ + ωit

(8)

z it' = [lnEleit , lnTSit ]

(9)

where RPCGDPit represents the real GDP per capita, Popit is the total population, SIit represents the proportion of secondary industry, Urbit is the urbanization rate, EPit represents the average electricity price, Temit is the average temperature. u is modelled by the auxiliary variables, i.e., structural changes Els and TS in our case. Eleit is the electrification level, TSit represents the rationalization of industrial structure, γ is a coefficient vector of these two auxiliary variables. The stochastic frontier framework reveals that the actual electricity demand EC equals to the optimal electricity demand f (X , β ) plus the inefficiency term u .

(4) σ

where constant represents the constant term, σ = σv2 + σu2 , λ = σu v measures the relative contribution of inefficiency term u to the compound perturbation term v , Φ(∙) represents the cumulative distribution function of standard normal. This paper adopts the fixed effect SFA method to estimate Eq. (3), this method can be divided into the following two main types when considering whether u changes over time. The first one assumes that the inefficiency term u does not change with time, which is called “timeinvariant technical efficiency” model [55]. However, when the time span of the panel data is very large, the assumption that the inefficiency term u does not change with time seems to be not in line with reality. The second case assumes the inefficiency term u change with time. The True Fixed Effect (TFE) model proposed by Greene [56] separates the inefficiency term from other time-invariant components by adopting the maximum likelihood dummy variable (MLDV) method, but the TFE method has two major disadvantages. On the one hand, using the MLDV method means that a large number of parameters need to be estimated [5], which may cause parameter estimation errors in short panels [57]. On the other hand, since the intercept term captures all time-invariant variables in the TFE method, the inefficiency term is no longer affected by any time-invariant variables [5]. Chen et al. [58] proposed the “Marginal Maximum Likelihood Within Estimator (MMLWE)” method, they proved that this method is consistent when facing the incidental parameters problem and performs better than the TFE method. Furthermore, the “Marginal Maximum Simulated Likelihood Within Estimator (MMSLE)”, which is proposed by Belotti and Ilardi [59], has proved capable to obtain a consistent

4. Empirical analysis 4.1. Baseline estimates Table 2 reports the baseline estimation results. The MMLWE method is adopted in model m1 and the MMSLE method is adopted in model m2 and m3. The distribution of the inefficiency term is assumed as half normal in all models. To be specific, model m1 presents the estimation result of the MMLWE method, this case does not regress the inefficiency term on the auxiliary variables. Model m2 considers the important role of electrification level on the inefficiency term. In addition to the electrification level, the indicator of the optimization of industrial σ structure is also included in model m3. Theλ = σu is ranged between v 0.937 and 2.497, suggesting that the inefficiency term u occupies a dominant position in the compound perturbation term. Real per capita GDP has a positive effect on electricity consumption, and significant at 1% level in all three models, with the estimated elasticities of 0.693, 0.744 and 0.676 for model m1, m2, and m3, respectively. This means that the increase in income level will lead to a significant increase in electricity consumption, which is consistent with prior studies [60,61]. The population has a significant positive impact on electricity demand frontier, and the estimates are similar in different 5

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Table 2 Main estimation results. Dependent variable ln(electricity consumption) Frontier ln(real per capita GDP) ln(population) ln(share of secondary industry) ln(urbanization rate) ln(electricity price) ln(average temperature)

Table 3 Robustness checks. m1

m2

m3

0.693*** (9.60) 1.140*** (5.97) 0.378*** (4.06) 0.871*** (4.17) −0.530*** (−3.82) −0.157 (−1.54)

0.744*** (10.99) 1.129*** (6.22) 0.298*** (3.42) 0.962*** (4.86) −0.854*** (−6.37) −0.117 (−1.23)

0.676*** (10.43) 1.137*** (7.13) 0.284*** (3.52) 1.011*** (5.45) −0.761*** (−5.76) −0.178* (−1.96)

3.027*** (5.48)

4.297*** (11.22) 0.724***

Usigma ln(electrification level)

Frontier ln(real per capita GDP) ln(population) ln(share of secondary industry) ln(urbanization rate) ln(electricity price) ln(average temperature)

r1

r2

0.716*** (11.70) 1.070*** (6.76) 0.921*** (5.16) −0.731*** (−5.98) 0.309*** (3.88) −0.116 (−1.35)

0.628*** (7.73) 1.128*** (24.12) 0.750*** (3.42) −0.479*** (−3.13) 0.324*** (8.59) −0.0979*** (−3.82) −9.904*** (−12.71)

3.636*** (8.12) 0.628*** (3.20) −11.65*** (−8.88) MMSLE Exponential 0.0610 0.0733 0.833

4.861*** (4.47) 1.197* (1.79) −15.13*** (−4.46) TRE Half-normal 0.135 0.0469 2.881

Constant

ln(optimization of industrial structure) Constant Model Distribution of the inefficiency term σu σv λ

Dependent variable ln(electricity consumption)

MMLWE Half-normal 0.134 0.0538 2.497

−10.71*** (−6.72) MMSLE Half-normal 0.0767 0.0818 0.937

Usigma ln(electrification level) ln(optimization of industrial structure)

(4.25) −12.58*** (−11.16) MMSLE Half-normal 0.134 0.0714 1.873

constant Model Distribution of the inefficiency term σu σv λ

Note: t statistics in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01. Note: t statistics in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01.

models (between 1.129 and 1.140). This means that an increase in population will lead to more electricity consumption, which is in line with reality. The effect of the share of secondary industry on electricity consumption is positive and significant at 1% level in all models. This confirms the important role of the secondary industry in electricity consumption. It is well known that the secondary industry is a highenergy consuming industry, thus higher proportion of secondary industry tends to be associated with higher electricity consumption. The estimated coefficient of urbanization is positive and statistically significant in all the three models, suggesting that the urbanization process is associated with higher electricity consumption [62]. Electricity is an important force supporting the urbanization process. The urbanization process requires the construction of large-scale urban infrastructure and housing transportation system, which will drive the rapid development of energy-intensive industries and increase electricity consumption in industrial, agricultural, and residential sectors [63]. The price elasticity of the electricity demand is negative and significant at 1% level. This means that the rise in electricity prices will lead to a decline in electricity consumption, which is in line with economic theory. Meanwhile, the estimate of electricity price varies in the range of −0.530 and −0.854 in the different models. This indicates that a 1% rise in electricity prices will result in a 0.53–0.854% fall in electricity demand. The absolute values of the elasticities below 1 means that electricity consumption is price-inelastic. Whereas, for temperature, the results show that there is a negative impact on electricity demand. The coefficient is not significant in model m1 and m2, and only significant at 10% level in model m3, suggesting that the temperature has a limited effect on electricity demand. The influencing factors of the inefficiency term in model m2 and model m3 are the focus of this paper. The coefficients of the electrification level are positive in model m2 and m3. This finding indicates that improvement in electrification level will lead to a decrease in electricity consumption efficiency, which seems not to be in line with reality. However, this result is feasible when there is insufficient energy utilization of electrified equipment. This means that the increase in electricity consumption may result in a decrease in electricity

consumption efficiency, the government need to pay attention to this when promoting the electricity substitution policy. Further, the indicator of the optimization of industrial structure is added into the inefficient term, and the result is presented in model m3. The significant positive correlation between the indicator of the optimization of industrial structure and the inefficiency term indicates that the optimization of industrial structure contributes to the improvement in electricity consumption efficiency, which is in line with our a priori expectation.

4.2. Robustness checks Two robustness tests are conducted to make the results more reliable. The results are shown in Table 3. The first robustness check is presented in model r1, which assumes the distribution of the inefficient term is exponential rather than the half-normal distribution. Compared with the estimated results in Table 2, the sign of all the coefficients has not changed. In the estimation of the inefficiency term, both electrification level and the optimization of the industrial structure are significant at the 1% level. This means that under different assumptions of the inefficiency term, the results obtained in this paper are robust. The True Random Effect (TRE) model [56] is adopted for the second robustness check. Compared with the TFE model, the TRE model allows the direct individual-specific estimates of the inefficiency term. However, the TRE model depends on the strong assumption that the effects are uncorrelated with the variables entered the model [56], but this is always an unreasonable assumption in the stochastic frontier framework. Anyway, this paper adopts the TRE model as a robustness test and assumes that the inefficient term follows half-normal distribution. The estimation result is shown in model r2, it is noted that the result is similar to that obtained in the TFE model. Overall, the robustness checks prove that the model estimation in Table 2 is efficient and the estimated results are reliable.

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Fig. 3. Estimated electricity efficiency score.

obtained from model m3 in Table 2 is highly correlated with the score from model r1 in Table 3. The difference between these two models is the different assumptions of the inefficiency term. Besides, the correlation of the efficiency score between the two robustness check models is also high with the correlation coefficient been 0.854.

4.3. Estimated electricity efficiency Following [27], the estimated electricity consumption efficiency scores can be calculated by EFit = exp(−uit̂ ) , and the results of different models are reported in Fig. 3. For comparison, this paper only reports the efficiency score in 2006 and 2015. The top-left panel of Fig. 3 is the result of model m1 in Table 2: the model which use the MMLWE method without modelling the inefficiency term. The top-right panel of Fig. 3 is the result of model m3 in Table 2: the model which adopt the MMSLE method by modelling the inefficiency term with all auxiliary variables. The two bottom panels of Fig. 3 are the efficiency scores from the robustness checks in Table 3 respectively. The average estimated efficiency scores are large and the scores are 0.902, 0.906, 0.945 and 0.898 for the four models. Less than half of China’s provinces witnessed an increase in electricity consumption efficiency, but the increase is not obvious. In contrast, most provinces experienced some decrease in electricity consumption efficiency, which is consistent with the result of He et al. [1]. Xinjiang has a significant decrease in technology efficiency score, with which ranges from 0.372 to 0.669 in different models. This suggests that Xinjiang should pay more attention to the decline in electricity consumption efficiency. In order to identify whether there is a correlation between the estimated efficiency of different models, we report the correlation matrix of the estimation efficiency of different models in Table 4. We observe that the correlations are always positive and the efficiency score

4.4. Electricity conservation potential Understanding the energy conversation potential is of great significance to provide targeted policy suggestions to saving energy [64]. In order to fully understand the electricity consumption situation in different regions and provide a reference for future electricity conservation, this paper further calculates the electricity conservation potential. If the actual electricity demand is higher than the deterministic frontier, there will be excessive electricity consumption, and electricity savings potential is defined as electricity consumption that can be reduced by increasing electricity consumption efficiency through certain means. Following Battese and Coelli [65], Lin and Yang [66], and Lin and Long [67], the electricity conservation potential can be calculated as follow:

ENCON = E (EC ) ∗ (1 − INE )

whereENCON represents the electricity conservation potential, E (·) denotes the conditional expectation and INE is the electricity inefficiency scores. This paper regards the energy inefficiency score, which is obtained from model m3 (column 3, Table 2), as a baseline result to calculate the electricity conservation potential. Fig. 4 shows the changes in electricity consumption efficiency and cumulative energy savings potential over the past decade. We observe that the average electricity consumption efficiency fluctuated in the study period but remains above 0.85. It shows downtrends from 2006 to 2013 but gradually increases from 2014 to 2015. This reveals that there is still much room for improvement in electricity consumption efficiency. Besides, even though the electricity savings potential has

Table 4 Correlation matrix of estimation efficiency.

eff_m1 eff_m3 eff_r1 eff_r2

eff_m1

eff_m3

eff_r1

eff_r2

1 0.417 0.622 0.813

1 0.943 0.727

1 0.854

1

(10)

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Applied Energy 262 (2020) 114505

1.00

800

0.98

700

0.96

600

0.94 0.92

500

0.90

400

0.88

300

0.86

200

0.84

100

0.82

differences in electricity consumption efficiency. Therefore, targeted policies should be implemented in different regions. The local governments should design electricity saving policies that are tailored to the regional level. For example, for these regions with lower efficiency score, the local government can pay timely attention to energy efficiency changes caused by industrial transfer, integrate the electricity saving policy with industrial structure upgrading policies, intensify its support policies for energy-saving and higher-output industries, eliminate high-energy-consuming and high-pollution industries, promote the rationalized development of industrial structure and eventually accelerate rational allocation of resources. This paper provides a preliminary discussion on China’s electricity consumption and its efficiency as well as the impact of structural changes. Much remains to be done. On the one hand, future work should segregate the industrial sector, further evaluate the differences in electricity consumption efficiency across sectors, and thoroughly analyze the impact of structural changes. On the other hand, it would be very interesting to analyze the electricity consumption efficiency by adding some socio-demographic and geographic variables to encompass the whole household sector and its bottom-layer.

Electricity saving potential (Billion KWh)

Average efficiency score

B. Lin and J. Zhu

0

0.80 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Electricity saving potential

Average efficiency score

Fig. 4. Average efficiency score and electricity conservation potential in each year.

decreased significantly in 2015, it was still large and reached to 627.38 billion kWh.

5. Conclusion and policy implication

CRediT authorship contribution statement

This study analyzes the influencing factors of electricity consumption and estimates the electricity consumption efficiency by considering the role of structural changes in China’s 30 provinces over the period 2006–2015. Two novel estimators, namely Marginal Maximum Likelihood Within Estimator and Marginal Maximum Simulated Likelihood Within Estimator, are used to estimate the Stochastic Frontier Analysis model, the robustness tests confirm the reliability of the results. Based on the baseline estimation result, this study further calculates the electricity conservation potential. The following conclusions are obtained: (1) Per capita income, urbanization, population, the proportion of secondary industry and electricity price have a significant impact on electricity consumption. (2) In the study period, it is found that the rationalization of the industrial structure is conducive for improving electricity consumption efficiency. In contrast, the improvement in electrification level will lead to a decrease in the efficiency score. (3) As for the electricity consumption efficiency score, there are significant differences in electricity consumption efficiency with a range from 0.372 to 1.000, depending on different model specifications, regions, and years. (4) The baseline estimation reveals that the average electricity consumption efficiency was 0.906 over the period 2006–2015, with the cumulative electricity savings potential been 4440.37 billion kWh in this decade. Based on these findings, this paper proposes the following policy recommendations. On the one hand, the central government is vigorously promoting the electricity substitution policy in the field of industrial production process and residential heating. However, this paper reveals there is a negative effect from electrification level to electricity consumption efficiency from the overall aspect. Therefore, enough attention should be paid to the improvement of electricity consumption efficiency in the process of promoting electricity substitution policy. In industrial production process, the central government should strengthen R&D investment in energy-saving technology and promote the key technical level. In addition, these regions with low-efficiency score should be noted to facilitate the technical reconstruction and replace the high energy-consuming equipment with energy-efficient equipment. In the field of residential heating, the government should provide certain subsidies for residential electric heating equipment according to the actual situation, and promote low-energy consumption and high-efficiency equipment as much as possible. Moreover, the renovation of residential buildings should also be intensified so as to improve electricity consumption efficiency in electric heating. On the other hand, this paper also finds that most regions witness a decline in the efficiency score, and there are marked regional

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