Effects of technological innovation on energy efficiency in China: Evidence from dynamic panel of 284 cities

Journal Pre-proof Effects of technological innovation on energy efficiency in China: Evidence from dynamic panel of 284 cities

Huiping Wang, Meixia Wang PII:

S0048-9697(19)36168-6

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136172

Reference:

STOTEN 136172

To appear in:

Science of the Total Environment

Received date:

14 September 2019

Revised date:

14 December 2019

Accepted date:

15 December 2019

Please cite this article as: H. Wang and M. Wang, Effects of technological innovation on energy efficiency in China: Evidence from dynamic panel of 284 cities, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136172

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© 2019 Published by Elsevier.

Journal Pre-proof

Effects of Technological Innovation on Energy Efficiency in China: Evidence from Dynamic Panel of 284 Cities Huiping Wanga*, MeixiaWangb a

Western Collaborative Innovation Research Center for Energy Economy and Regional Development, Xi’an University of Finance and Economics, Xi’an 710100, China; b

School of Economic and Management, Xi’an University of Technology, Xi’an 710054, China

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Abstract: Technological innovation and energy efficiency are important indicators used to measure the success of the sustainable development strategy in China. This paper aims to explore the total factor energy

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efficiency (TFEE) at the city level in China and to evaluate the impact of technological innovation on TFEE. Therefore, a two-stage analysis was conducted for the period from 2001-2013. The first stage includes an Data Envelopment

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estimation of TFEE scores using the

Analysis (DEA)

methodology and

Malmquist-Luenberger index, while the second stage includes an exploration of the impact of technological

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innovation on the TFEE scores obtained in the first stage using a system Generalised Method of Moment

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(GMM) regression analysis. Based on the results of the Malmquist-DEA, the TFEE of cities in China shows an upward trend overall, but obvious differences in the TFEE are observed among the four regions, with the highest TFEE observed in the eastern region, the second highest TFEE in the central region, a lower TFEE in

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the northeastern region and the lowest TFEE in the western region. The system GMM regression results reveal a significant positive impact of technological innovation on TFEE at the national level. According to

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the regional characteristics, the technological innovation in the eastern, western and northeastern regions is particularly important for improving TFEE, but technological innovation in the central region has inhibited the improvement of the TFEE. A logical response to these findings would be to develop different policies for different regions.

Keywords: Technological Innovation, Total Factor Energy Efficiency, Malmquist-Luenberger Index, Energy Consumption

1. Introduction Energy efficiency has attracted the attention of an increasing number of scholars due to increasingly prominent energy and environmental problems. Generally, an improvement in energy efficiency is attributed to two types of factors. One is industrial restructuring and the flow of energy from low-productivity industries to 1

Journal Pre-proof high-productivity industries, such as from industry to services and from traditional industries to new industries. The other factor is the improvement in factor utilization efficiency through technological progress (Jin et al., 2019). With the continuous improvement of industrialization in China, the demand for energy consumption is increasing, and the problem of energy shortages is becoming increasingly serious. The overall dependence of China on external energy sources has also increased rapidly from 6.0% in 2005 to 16.3% in 2015. Although China is a large energy-producing country, its total energy reserve is lower than the reserves of only the US and Russia, and its per capita energy reserve and energy utilization efficiency are low. According to World Bank statistics, the energy productivity of China in 2014 was 5.7 US dollars per kilogram of oil equivalent, while the

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productivities of the US, Japan and the UK were 7.45 US dollars, 10.75 US dollars and 13.68 US dollars per

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kilogram of oil equivalent, respectively. The gap in energy efficiency between China and developed countries remains large.

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The message from the reports reflects the potential for increasing energy efficiency in China. Therefore,

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improvements in energy efficiency and the promotion of green economic development while maintaining steady economic growth are the major issues currently being experienced in China. Therefore, the 13th

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Five-Year Plan in China emphasized that the strategic transformation of economic structure should be promoted with five development concepts: innovation, coordination, green, open and sharing. Starting from the

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11th Five-Year Plan period, the government began to employ measures to reduce energy consumption in China. During the 13th Five-Year Plan period, the Chinese government also proposed the implementation of a

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“double-control” action on total energy consumption and intensity, which requires a 15% reduction in energy consumption and a 18% reduction in carbon emissions per unit of Gross Domestic Product (GDP) in the next five years.

Currently, energy efficiency is difficult to improve by relying on industrial restructuring, and more advanced technologies are needed to solve the problems of high pollution and high energy consumption in industrial development (Shao et al., 2019). As a result, innovation-driven development strategies have become a reliable method to reduce energy intensity. In promoting an innovation-driven development strategy, technological innovation capability and energy efficiency are important indicators to measure the success of the strategy. However, can China improve its energy efficiency by improving its technological innovation capability in practice? This paper intends to use the panel data from 284 cities in China to systematically test whether technological innovation exerts the same impact on the energy efficiency of different types of cities in China in order to accurately answer this question.

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Journal Pre-proof The main contributions of the current research are two-fold. First, most of the existing studies focus on the country, provinces and industries, and rarely measure the energy efficiency at the city level in China. In particular, when using the Malmquist-DEA model to measure urban energy efficiency, power consumption is generally regarded as an alternative indicator of energy consumption, and the data lack accuracy (Yang and Wei, 2019). Therefore, the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) night-time lighting data are used to estimate the energy consumption of cities in China. Based on these values, the Malmquist-Luenberger index is used to calculate the Total Factor Energy Efficiency (TFEE) of cities in China, which addresses the shortcomings of existing research. Second, more comprehensive studies

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have been performed on many factors influencing energy efficiency. Meanwhile, fewer specific studies have

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been conducted to examine the impact of technological innovation on energy efficiency, and most are focused on the national, provincial or industrial level, and thus the research lacks specificity (Miao et al., 2018; Sun et

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al., 2019). Based on the empirical study performed at the city level, this paper not only provides a more micro

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perspective but also explores the characteristics of regional economic development information, ensuring that the empirical results have a broader space for policy applications.

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This paper is organized as described below. Section 2 reviews the existing literature. The materials and methods are described in Section 3. The results and discussion are presented in Section 4. Section 5 presents the

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2. Literature Review

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conclusions and proposed policy implications related to energy efficiency.

2.1 Research on Energy Efficiency Measurements 2.1.1 Energy Efficiency of a Single Factor The measurement method compares energy factors with total output indicators without considering other production factors or factors affecting energy consumption, such as the energy intensity, energy productivity indicators (i.e., output value per unit energy consumption), and energy ecological footprint (i.e., the ratio of energy consumption to land occupation). Long et al. (2016) used the ratio of industrial added value to total energy consumption to represent industrial energy efficiency. Li and Lin (2018) used the ratio of GDP to energy consumption to measure energy efficiency. Chang et al. (2018) and Antonietti and Fontini (2019) used the ratio of energy consumption to GDP to measure energy efficiency. Although the use of a single index for calculating energy efficiency is simple, it only considers one input factor, which neglects many factors, such as the energy input structure, energy price and other production factors and their substitution relationship. Thus, this index is limited in reflecting the “efficiency” factor. 3

Journal Pre-proof 2.1.2 TFEE This measurement method compensates for the deficiency of the single-factor measurement of energy efficiency and considers the impacts of energy, labour, capital and other production factors on output. Two different methods for estimating TFEE have been reported. One is the Stochastic Frontier Analysis (SFA). The SFA method measures the efficiency value under the condition of a known input-output and production frontier (i.e., production function). The SFA method determines the function to distinguish the statistical noise from the inefficient part, and thus it accurately reflects the inefficiency of each decision-making unit. The SFA method usually measures energy efficiency

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from the perspective of production efficiency, with capital, labour and energy as inputs and GDP as the output.

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When an evaluated unit is located at the front of production, the energy efficiency value of the unit is equal to 1, namely, the energy efficiency is effective; when an evaluated unit deviates from the front of production, the

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energy efficiency value is less than 1, namely, the energy input of the evaluated unit is not fully utilized and

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Pareto improvement space exits (Zhou et al., 2012). However, the SFA method often only uses a single output, and the calculation is very complex for an economic system with multiple inputs and outputs. For invalid units,

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this method is only able to explain the degree of invalidity. Ouyang et al. (2018) and Li et al. (2018) used the SFA model to calculate the provincial energy efficiency in China. Sineviciene et al. (2017) applied the SFA to

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an energy efficiency assessment and cause analysis of the 11 countries in Eastern Europe. Many other scholars have used the SFA method to analyse the energy efficiency of different industries, such as the chemical (Lin and

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Long, 2015) and transportation (Xie et al., 2018) industries. The other method is the Data Envelopment Analysis (DEA). The DEA method is a non-parametric method used to measure efficiency. This method abandons the specific function form, establishes a linear plan according to the input unit and output unit, and identifies all efficient points on the production frontier and all invalid points below the production frontier by constructing a production frontier curve. Therefore, the distance from each point to the production frontier indicates the production efficiency (Charnes et al., 1978). Compared with the SFA method, the DEA method is particularly suitable for the analysis of input-output efficiency in the case of multiple inputs and outputs. A unified unit between the indicators is not required to ensure the integrity of the information contained in the original data, and thus this method is widely used to measure efficiency in various economies. However, the DEA method ignores the random errors and does not distinguish the statistical noise from the inefficient items, which affects the accuracy of the efficiency evaluation. Hu and Wang (2006) defined the framework of the TFEE and considered that energy must be combined with capital, labour and other related input factors to improve economic output. This evaluation method better reflects the objective reality. 4

Journal Pre-proof Since the publication of their study, numerous scholars have built on this idea and used the DEA method to measure TFEE at the regional and industrial levels based on the framework, including measuring TFEE in Korea (Moon and Min, 2017), Turkey (Ervural et al., 2018), Europe (Borozan, 2018), and China (Li et al., 2019), as well as for industry (Liao and He, 2018) and industrial water resources (Jin et al., 2019). From the output perspective, most studies do not consider that the production of economic output is frequently accompanied by pollutants, such as CO2, SO2, industrial waste gas, etc. The cost of pollution should be deducted from the output to reflect the actual GDP. Traditional accounting methods and production theory do not effectively address this issue. Chung et al. (1997) incorporated undesirable outputs into the DEA model

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and constructed the Malmquist-Luenberger productivity index, which accounts for both an increase in output

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and a reduction in pollution, thus providing a theoretical basis for the development of DEA technology in the field of the environment. When using the Malmquist-Luenberger index to calculate the TFEE, scholars have

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used SO2 (Wang et al., 2019) or CO2 as the undesirable output index (Wang et al., 2017; Vaninsky, 2018),

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while other researchers used SO2, CO2, industrial waste gas and other indicators as undesirable output indicators (Guo et al.,2018; Yang et al., 2018; Teng et al., 2018; Lu et al., 2019; Li et al., 2019). Although they consider

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different selection criteria and undesirable output variables, these studies all emphasize that undesirable output variables are important factors determining energy efficiency and represent a new method for measuring energy

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efficiency.

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2.2 Technological Progress and Energy Efficiency Solow (1957) attributed total factor productivity (TFP) to the remaining output growth rate generated by technological changes that cannot be explained by input growth. In fact, the Solow Residual only measures the effect of neutral technological progress, but also reflects the effect of technological progress on all factor productivity. Acemoglu (2003) believed that if technological progress affected the productivity of all factors, then technological progress was neutral. On the contrary, if the impact was differentiated, then the technological progress was biased. In fact, the Solow economic growth equation is established on the assumption of Hicks-neutral technological change, in which the alternative elasticity of capital and labour is 1, so that the production function is set in the form of the Cobb-Douglas function. Unfortunately, however, this basic assumption does not jibe with reality. In the process of real economic growth, technological change is neither exogenous nor Hicks-neutral (Schlicht, 2016), and tends to favour a certain factor of production (Doraszelski and Jaumandreu, 2018).

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Journal Pre-proof Since capital and labour are the two most important factors of production input, the early studies mainly focused on whether technological progress favoured capital or labour. The conclusions of these studies are basically the same that technological progress is biased towards capital. David and Klundert (1965) first estimated the direction of technological progress by setting the production function as a constant elastic substitution (CES) production function and estimating the alternative elasticity between labour and capital in the United States, finding that technological progress was generally skewed toward capital from 1899 to 1960. With the increase of energy consumption and the environmental pressure faced by industrial production, scholars began to integrate energy factors into the research of technological progress and put forward some

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models to measure energy-biased technological progress. For instance, according to the study of 32 industrial

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sectors in Shanghai from 1994 to 2011 by Shao et al. (2016), technological progress was biased towards energy and capital. According to the research on China's energy intensive industries from 1990 to 2012 by Zha et al.

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(2017), six industries had experienced energy-biased technological progress. Jia et al. (2018), Zha et al. (2018),

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Cheng et al. (2019), and Xiu et al. (2019) studied energy-biased technological progress. The empirical test results for technological progress and energy efficiency show that there are two

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different views. One is that there is a positive correlation between technological progress and energy efficiency (Peng et al., 2019). Technological progress improves energy efficiency by increasing the marginal productivity

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of production factors and indirectly improves the efficiency of energy allocation. At the micro level, enterprises with higher innovation activity are more willing to adopt existing energy-saving technologies and improve

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energy efficiency through process innovation (Zhu et al., 2018; Wang et al., 2019). At the regional level, local governments will improve regional energy efficiency by increasing R&D investment, introducing foreign investment, adjusting industrial structure and eliminating backward production capacity (Huang et al., 2018; Li and Lin, 2018). Another view is that there is a negative correlation between technological progress and energy efficiency. This is mainly because of the rebound effect of technological progress, that is, technological innovation will not only improve energy efficiency but also promote economic growth, which will lead to the weakening of new demand for energy and energy conservation (Liu et al., 2018; Yi et al., 2020). Some studies have also fully confirmed the existence of the rebound effect and measured the value. However, the calculation results also vary greatly due to the different assumptions, models and data (Llorca and Jamasb, 2017; Liu et al., 2018). Due to the rebound effect, whether the energy saving brought by technological innovation can offset the new energy demands brought by rebound effect is contradictory, which makes the research more complicated (Gu et al., 2019).

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Journal Pre-proof In the existing literature, although the relationship between technological innovation and total factor energy efficiency has been extensively discussed, there are still the following deficiencies. First, most of the existing studies focus on the country, provinces and industries, and rarely measure energy efficiency at the city level in China. Second, more comprehensive studies have been performed on many factors influencing energy efficiency. Meanwhile, fewer specific studies have been conducted to examine the impact of technological innovation on TFEE, and most are focused on the national, provincial or industrial level, and thus the research lacks specificity.

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2.3 Theoretical Mechanism and Hypothesis

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In the previous section, we reviewed the measurement methods and indicators of TFEE and found that input and output indicators were used in both the DEA method and SFA method. The process of improving

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TFEE is the process of reducing input or increasing expected output. Technological innovation plays an

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important role in the input and output of TFEE. Therefore, from this perspective, we will explore the

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mechanism of technological innovation on TFEE, as shown in Figure 1. Energy-biased technical progress

Productivity level

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Technological Innovation

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Energy-consumption technical progress

Energy-saving technology Clean energy technology

Input Capital input Labor input Energy consumption

Expected output GDP

Total Factor Energy Efficiency

Undesirable output Industrial waste water/Solid industrial waste/SO2/CO2

Fig.1 The impact mechanism of technological innovation on TFEE

First, the contribution of technological innovation to energy efficiency is uncertain from the input perspective. Energy-biased technological progress can improve the relative marginal productivity of energy factors, thus leading to the relative increase of energy prices at equilibrium, which will lead to the substitution of non-energy factors for energy factors and the reduction of the relative energy input. Therefore, if technological innovation promotes energy-biased technological progress, energy consumption can be effectively reduced when output and non-energy factors remain unchanged, and thus TFEE can be significantly improved. In addition, through the development of new alternative energy technologies and the improvement of existing energy technologies, new energy technologies and energy-saving technologies will be widely used. For example, the improvement of transportation technology can reduce energy loss in the 7

Journal Pre-proof transportation process, and ultimately the TFEE of the whole society will be improved (Liu et al., 2018; Jin et al., 2019). However, if technological innovation promotes energy-consumption technological progress, it will reduce the relative marginal productivity of energy, which may lead to the substitution of energy for non-energy factors, so the input share of energy will increase, resulting in the decline of TFEE. Second, technological innovation can improve the level of productivity from the perspective of expected output, that is, to achieve economic growth on the basis of maintaining the same input (Jung, et al., 2017). The theory of endogenous economic growth points out that technological progress is the leading factor for achieving sustainable economic development. Through human capital accumulation, technology imitation and

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learning by doing, production technology can improve the productivity level, that is to say, under the same

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level of input factors, it can increase the output of products or services. This also means the reduction of energy input, which is conducive to the improvement of TFEE.

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Third, technological innovation can optimize the energy consumption structure and reduce the emission

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of CO2, SO2 and other pollutants from the perspective of undesired outputs. The consumption structure of energy is restricted by the level of technology. Technological innovation promotes the development and

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utilization of clean energy such as nuclear power, wind power, hydro power and solar power (Xiao et al., 2017; Weiss et al., 2018; Alam and Murad, 2020). As an important alternative to fossil energy, the use of

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clean energy can reduce or even eliminate the emission of undesired outputs without affecting economic outputs, thus improving TFEE under environmental constraints.

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According to the above analysis, technological innovation will have a positive impact on total factor energy efficiency. However, due to the rebound effect and differences in regional technological innovation abilities, the direction and degree of technological innovation affecting energy efficiency may be different in different samples. In addition, due to market segmentation and different stages of regional development, energy allocation is mainly directed by administrative forces, and energy prices are undervalued. In order to protect local industries or economic growth, subsidies and administrative forces will hinder the free flow of energy resources at a greater level, which results in the direction and degree of impact of technological progress on TFEE differences in different regions (Pan et al., 2017; Wu and Du, 2018). Therefore, we propose the following hypotheses: Hypothesis 1: The technological innovation has a positive impact on the urban TFEE in China. Hypothesis 2: The direction and degree of technological innovation affecting TFEE may differ in different regional samples.

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Journal Pre-proof In this section, we first reviewed two methods (SFA and DEA) for measuring TFEE and the impact mechanism of technological innovation on TFEE, and then proposed the hypotheses.

3. Materials and Methods 3.1 Malmquist-Luenberger Index To address efficiency under undesirable outputs, Tone (2001) proposed the slack-based measure (SBM) model to overcome the deviation in the radial and angle measurements in the traditional DEA model. According to the theoretical analysis results in Section 2.3, the SBM model is expressed as formula (1) under

snx  n 1 xkn   x min M I y b s , s , s , sy sb 1 1 ( m   i ) M  1 m 1 ykm i 1 bki N

K

K

k 1

k 1

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1 N

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1

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constant returns to scale.

(1)

K

k 1

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snx  0 , smy  0 , sib  0 , k  0

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s.t. xkn   k xkn  snx , n ; ykm   k ykm  smy , m ; bki   k yki  sib , i

x

y

b

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where  is the TFP, xkn , ykm , and bki are the input, expected output and undesired output, respectively, and s n , sm , and si are the slack variables of input, expected output and undesired output, respectively. In

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this situation, if the decision-making unit aims to achieve optimal production, the number of input factors and undesired outputs must be reduced and the number of expected outputs must be increased. The Malmquist-Luenberger index from period t to period t + 1 is calculated using the SBM model with the following formula:

ML 

 t ( xt 1 , y t 1 , bt 1 )  t 1 ( xt 1 , y t 1 , bt 1 )   t ( xt , y t , bt )  t 1 ( x t , y t , bt )

(2)

where  t ( xt , yt , bt ) and  t ( xt 1 , y t 1 , bt 1 ) are respectively the efficiency values of periods t and t+1 of the decision-making unit with the production frontier of period t as the reference set, and  t 1 ( xt , yt , bt ) and

 t 1 ( xt 1 , yt 1 , bt 1 ) are respectively the efficiency values of periods t and t+1 of the decision-making unit with the production frontier of the period t+1 as the reference set. When ML  1 , it indicates the growth of TFP; when ML  1, it indicates a decrease in the TFP; and when ML  1 , the TFP has not changed. 3.2 System GMM Model Because of the sequential correlation of the TFP index, the dynamic panel model should be used to 9

Journal Pre-proof investigate the factors influencing this parameter. Therefore, we not only consider the inertia of the productivity index in the model but also consider the endogenous problem caused by missing variables, namely, we add a lag period of the explained variable to the right side of the regression model as the explanatory variable and build a dynamic panel model that is better able to address the lag and endogenous problems. Based on the studies by Asongu et al. (2017) and Jin et al. (2019), this paper proposes the model described below.

TFEEit    1TFEEit 1   2TI it +

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 W h 1

h

hit

 i  t   it

(3)

where i and t are the city and time, respectively, TFEEit is the energy efficiency, TFEEit 1 is the lag

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period of the explained variable, TI ij is the technological innovation, Wit is the control variable

economic openness).

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group(economic development, degree of government intervention, industrial structure, infrastructure and

t is the time-specific constant, i is the city-specific effect and  it is the error term.

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The explanatory variables on the right side of the equation contain a lag period of the explained variable,

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and the technological innovation may be endogenous. Improvement in the TFEE will promote the technological innovation of enterprises and a reverse causal relationship exists between technological innovation and TFEE,

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which may lead to endogenous problems. Therefore, the application of an ordinary panel regression model will lead to biased results. We further use differential GMM and system GMM methods to estimate the parameters

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and solve this problem. In the existing literature, the GMM method has been widely used to solve endogeneity problems, and the differential GMM method was first proposed by Arellano and Bond (1991). In this method,

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the first step is to transform the regression equation by the first-order difference to eliminate the influence of the regional fixed effect. Then, the lag variable is used as the instrumental variable to estimate the parameters. However, the differential GMM also has some limitations, namely, it is vulnerable to weak instrumental variables and small samples. Disturbances may lead to unreliable results (Bond, 2002). Therefore, Blundell and Bond (1998) proposed a system GMM method that simultaneously estimates the parameters of and increase the effectiveness of the instrumental variables. Because a two-step estimation is better than a one-step estimation, the GMM equations of the system are iterated using a two-step estimation. Therefore, this paper will use a two-step system GMM method, with the high-order lag term of the endogenous variable as its tool variable. The Sargan test and AR sequence correlation test are performed simultaneously to ensure the validity of the tool variable selection and the rationality of the model setting. The former methods test the validity of tool variable, and the latter tests whether a sequence correlation exists in the residual term of the horizontal equation.

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Journal Pre-proof 3.3 Variable Selection 3.3.1 Explained variable: Total Factor Energy Efficiency Most of the early evaluations of energy efficiency did not consider the pollutants that accompanied economic production. The emission of these pollutants would pollute the environment; thus, the impact of pollutants and the cost of environmental pollution control must be considered to reflect the actual GDP. However, the traditional DEA and SFA models used in early studies did not consider the impact of undesirable outputs and were unable to reflect the true level of energy efficiency. Scholars have proposed various methods to address energy efficiency under undesirable outputs, such as environmental pollutants, and to solve these

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problems. Therefore, this paper incorporates environmental pollution as an undesirable output in the framework

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used to calculate the TFEE, constructs a SBM model that considers pollutant emissions, and uses the Malmquist-Luenberger index to calculate the TFEE of cities in China, thus avoiding the possible deviation in

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the results caused by the neglect of environmental factors. The input index of the model is capital, labour and

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energy consumption, the expected output is GDP, and the undesirable output is SO2. The input-output indicators are defined as described below.

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Capital input (K). Since no official survey data of the capital stock in China are currently available, we use the perpetual inventory method to estimate the capital stock of cities. The following formula is used:

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Kt  (1   ) Kt 1  It

 is the

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Where K t is the capital stock of period t , I t is the fixed assets investment of period t , and

(4)

depreciation rate. All correlative data are handled by setting a fixed asset investment price, which is calculated based on data from 2000, with the depreciation rate set to 9.6% to eliminate the effect of price fluctuation factors (Zhang and Yu, 2019).

Labour input (L). The ideal index of labour input should include the time and quality of labour input, but due to the lack of relevant statistical data, this paper refers to Guo et al. (2018) and uses the number of employees at the end of the year as the index to evaluate labour input. Energy consumption (EC). Due to the lack of energy consumption statistics at the municipal level in China, we are unable to directly obtain the energy consumption of various cities. As a good data source for monitoring the intensity of human activities, the OLS sensor on the DMSP effectively detects the night-time low-intensity light produced by the urban lights or lights from small-scale residential areas, traffic flow, etc. Therefore, since the 1980s, many scholars at home and abroad have successfully applied DMSP/OLS night light data to many research fields, such as urban development, GDP estimation, population simulation, carbon 11

Journal Pre-proof emission, energy consumption, etc. (Ma et al., 2015 ; Shi et al.,2016; Li et al., 2017; Ji et al., 2019). Energy consumption is closely related to the intensity of human activities, and the DMSP/OLS night light data have been used to estimate energy consumption, which has been confirmed by scholars in other countries (Wang et al., 2019). Therefore, the DMSP/OLS night-time data are used to estimate the energy consumption in various cities in China. Expected output (EO). The GDP of cities is used as the expected output index, and the GDP deflator index is used to convert these values into the real GDP at the constant price recorded in 2000. Undesirable output (UO). The existing studies generally use SO2, CO2, solid wastes, industrial wastewater

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3.3.2 Core Explanatory Variables: Technological Innovation (TI)

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and other pollutants as indicators. According to the availability of data, SO 2 is used as the undesirable output.

Technological innovation is a complex process involving economic input-output (Kontolaimou et al.,

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2016). Therefore, the existing research generally measures technological innovation from the perspective of

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input and output. Input variables mainly include financial expenditures on science and technology, R&D expenditures, and the number of scientific researchers (Kontolaimou et al., 2016; Liu et al., 2018; Jin et al.,

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2019). Output variables mainly include patent applications and patent authorizations, which are the two most commonly used indicators to measure technological innovation and are widely used in research (Wurlod and

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Noally, 2018). Therefore, this paper uses the number of invention patent authorizations in each city to measure the technological innovation capability. The main reason is that patent application information has

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been included in patent authorizations (Song et al., 2015), and patent authorization is the result of research activities and the source of industrialization technology, which may truly reflect the current level of technological innovation (Sun et al., 2019; Pan et al., 2019). 3.3.3 Control Variables

The level of economic development (RGDP). In the definition and calculation of energy efficiency, a high level of economic development has a positive role in promoting energy efficiency. On the one hand, an improvement in the level of economic development leads to the emergence of a series of resources and environmental problems that are generally inconsistent with the goal of sustainable development. These environmental problems create higher requirements for energy efficiency (Kang and Lee, 2016). On the other hand, an improvement in production efficiency encourages enterprises to use more energy elements, which may reduce the positive impact of an improvement in energy efficiency. The GDP per capita is used to measure the level of economic development. Because no GDP deflator index exists at the urban level, this paper uses the 12

Journal Pre-proof GDP deflator index of the province where the city is located to deflate the urban GDP at the constant price recorded in 2000. The degree of government intervention (GOV). The energy efficiency of each city is closely related to government intervention measures, such as attracting investment and formulating preferential policies. This paper uses the proportion of fiscal revenue to GDP as a proxy for the intensity of government policy implementation. Industrial structure (IS). Different industrial structures exhibit different energy demands, and the proportion of the industrial output value in China is still very large, particularly in the heavy industrial sectors,

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such as power, steel and metallurgy, which consume enormous amounts of energy. At the same time, industrial

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sectors will use energy more effectively due to industrial agglomeration under the effect of economies of scale. This paper chooses the proportion of the added value of secondary industry in GDP to express the industrial

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structure.

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Infrastructure (INF). The improvement in transportation infrastructure is conducive to reducing the transportation costs of energy resources flowing across regions, thus improving the efficiency of resource

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allocation and energy use. This paper uses the per capita road mileage to measure infrastructure. Economic openness (OPEN). The global circulation of factors of production is conducive to enterprises

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seeking resources globally and promotes the optimal allocation of funds, technology and manpower. According to Doytch et al. (2016), foreign direct investment (FDI) improves energy efficiency through innovation. This

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paper chooses the ratio of FDI to GDP as the proxy variable of the degree of economic openness.

3.4 Data description

The main object of this study is cities in China (excluding Tibet, Hong Kong, Macao and Taiwan). Because of the adjustment of some administrative regions and the substantial lack of data, the final object of the study is 284 cities, and the sample data cover the period from 2000-2013. The DMSP/OLS night-time data used to estimate urban energy consumption in these cities were obtained from the National Geophysical Data Center (NGDC) website of the National Oceanic and Atmospheric Administration (NOAA) in the US. The number of patents granted for inventions were obtained from the patent cloud database. Other indicators were derived from China’s Urban Statistics Yearbook for each year. Descriptive statistics of variables are shown in Table 1A in Appendix A. This section introduced the Malmquist-Luenberger (ML) index, the basic model, the selection of explained variables and explanatory variables, and the data descriptions, among other parameters. 13

Journal Pre-proof 4. Results and Discussion 4.1 Results of TFEE Measurements 4.1.1 Overall Characteristics of Changes in the TFEE All input variables and output variables are imported into Maxdea software to obtain the TFEE of 284 cities in China from 2001 to 2013. The descriptive statistics for each year are shown in Table 1. Table 1. Descriptive statistics of China's urban TFEE Mean

Min

Max

S.D.

Skewness

Dispersion

Kurtosis

2001

0.8395

0.4430

1.1699

0.1251

-0.5273

0.1490

3.5804

2002

0.8645

0.3219

1.1209

0.1121

-1.1849

0.1296

5.6385

2003

0.8335

0.3793

1.1277

0.1177

-0.8129

0.1412

3.8789

2004

0.8962

0.4533

1.1972

0.1193

-0.3460

0.1331

3.4127

2005

0.8730

0.5339

1.1770

0.1153

-0.1002

0.1321

2.9275

2006

0.8475

0.5372

1.1800

0.1025

-0.2287

0.1209

3.4684

2007

0.8773

0.5501

1.1309

0.0932

-1.3167

0.1062

5.2619

2008

0.9388

0.3778

1.1921

0.1006

-1.6101

0.1071

7.9751

2009

0.9104

0.5075

1.0963

0.1027

-1.0738

0.1128

4.4202

2010

0.9428

0.4723

1.1142

0.0737

-2.3076

0.0781

12.7202

2011

0.9640

0.4926

1.1847

0.0781

-2.0595

0.0810

12.8644

2012

0.9249

0.4479

1.1188

0.0677

-2.3383

0.0732

15.3476

2013

0.8354

0.3822

1.0172

0.0824

-1.4075

0.0986

6.7376

Mean

0.8883

0.4538

1.1405

0.0993

-1.1780

0.1125

6.7872

na

lP

re

-p

ro

of

Year

The average change range of the urban TFEE in China is 0.8335-0.9640 from 2001 to 2013, showing an

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overall growth trend. Among these values, the mean value of TFEE in 2003 is the lowest and the value in 2011 is the highest of all years. The mean value of TFEE of all cities is 0.8883 from 2001 to 2013, which is a significantly higher value than the result estimated by Yang and Wei (2019). The possible explanation for the difference is that in this paper, night-time lighting data are used to estimate energy consumption as an input indicator of TFEE, while Yang and Wei (2019) use electricity consumption instead of energy consumption. In addition, the mean value of TFEE of all cities in this paper is also higher than the value calculated using provincial data (Huang and Wang, 2017; Zhao et al., 2019), consistent with the conclusions of Li and Li (2010) and Wu and Li (2016). The dispersion coefficient of TFEE shows a general decreasing trend and reaches the minimum in 2012, indicating that the difference in the TFEE values between cities is gradually decreasing. Significant differences are observed in the specific values between cities. Taking 2013 as an example, the average TFEE of the 284 cities is 0.8354. Among these cities, the TFEE is greater than 0.9 in 48 cities, 0.8–0.9 in 161 cities, 0.7–0.8 in 52 cities and less than 0.7 in 23 cities. The TFEE of most cities in China is low, indicating substantial room for improvement. 14

Journal Pre-proof In addition, the top 10 cities ranked by TFEE are mainly located in the eastern coastal areas, including Beijing, Shanghai, Zhejiang and Jiangsu. The 10 cities with the lowest rankings are mainly located in the central and western areas, including Gansu, Ningxia, Guizhou and Shaanxi. Thus, the central and western regions have low energy efficiency and great energy-saving potential, which will be the challenge and focus of building a

re

-p

ro

of

resource-saving society.

lP

Fig.2 Nuclear density curve of total factor energy efficiency Furthermore, the non-parametric nuclear density estimation method is used to visually describe the

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distribution pattern of TFEE, and the Kernel density curve of urban TFEE is drawn, as shown in Figure 2. The nuclear density curve of TFEE presents an obvious left-skewed distribution overall, with a peak value between

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0.8 and 1. From the perspective of the skewness and kurtosis coefficients, a more serious migration degree of the distribution is observed at greater absolute values of skewness. Therefore, the degree of skewness of the urban TFEE in China is becoming increasingly serious. In the nuclear density map, the peak is entirely located on the right side of 0.5. Over time, the peak moves to the right in general and gradually increases, and the nuclear density graph becomes steep. 4.1.2 Regional Differences in the TFEE The 284 cities are divided into four regions, eastern, central, western and northeast, and the mean values of urban TFEE from 2001 to 2013 are calculated to describe the regional differences in the urban TFEE in China. The changing trends of urban energy efficiency in the different regions are shown in Figure 3.

15

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Fig.3 Trends in TFEE in different regions

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The mean value of the TFEE ranges from 0.8417 to 0.9690, 0.8168 to 0.9622, 0.7913 to 0.9661 and 0.8244

re

to 0.9592 for the cities in the eastern, central, western and northeast regions, respectively. Before 2011, the TFEE of the cities in the eastern region was significantly higher than in other regions. The TFEE of the cities in

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the northeast showed a decreasing trend during 2001-2005, but it was significantly higher than that in the central and western regions before 2003. The TFEE of this region gradually increased after 2005, reaching its

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maximum in 2011. The TFEE of the cities in the central region showed an overall increasing trend and reached a maximum in 2011, a value that was significantly higher than the western and northeast regions in 2003-2010.

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The TFEE of the cities in the western region increased and displayed fluctuations, reaching its maximum in 2011, but generally remained lower than the values of the other three regions. The high TFEE in the cities in the eastern region may be attributed to the higher level of economic development, the more reasonable industrial structure, the closer attention paid by local governments and the application of energy-saving technologies, among other factors. The low TFEE in the cities in the northeast and western regions may be attributed to the lower level of economic development and the weight of resource-based industries and heavy chemical industries in the industrial structure. The demand for energy in these regions is substantial, and the innovation level of enterprises is low. After 2011, the TFEE values of all four regions show a trend of a gradual decrease and convergence, indicating that the regional differences in TFEE are narrowing. 4.2 Results of the Regression Analysis of the Total Sample We estimate five models, fixed effect, random effect, mixing effect, differential GMM and system GMM, and compare their results. The results of the regression analysis of the whole sample are shown in Table 2. Based on the regression results of the fixed effect, random effect and mixed effect models, although the R2 16

Journal Pre-proof values are small, the regression coefficients of each equation are generally significant, and the coefficient symbols and expectations are relatively consistent. Overall, the model has strong robustness. Table. 2 Total sample regression results Variables

Fixed effect Random effect Mixing effect Differential GMM

TFEE（-1）

C R-squared AR(1) AR(2) Hansen-test

0.0011***

0.1461**

(2.25)

(4.94)

(7.12)

(2.82)

(3.62)

(2.11)

-0.2864***

-0.2786***

-0.1685***

-0.0459***

-0.0042***

-0.1182***

(-34.38)

(-34.97)

(-23.01)

(-26.84)

(-6.36)

(-4.46)

-0.0034

-0.1718***

-0.7578***

-0.0189***

-0.0378***

-0.0760*

(-0.10)

(-5.15)

(-19.55)

(-38.66)

(-45.03)

(-1.87)

0.2472***

0.3264***

0.2657***

0.1232***

0.0456***

0.0017

(4.56)

(6.51)

(6.74)

(14.68)

(13.22)

(0.06)

-0.0496***

-0.0599***

-0.0819***

-0.0444***

-0.0072***

-0.0186*

(-6.97)

(-8.39)

(-10.01)

(-27.32)

(-11.39)

(-1.71)

0.2675***

0.4713***

1.3158***

0.1233***

0.1950***

0.1582**

(11.06)

(45.47)

(50.54)

(2.07)

(2.99)

(5.18)

3.1238***

2.9981***

2.0083***

0.0626***

(56.50)

(55.07)

(38.03)

(11.26)

0.6613

0.6572

0.3029

0.6730 0.015

0.014

0.154

0.182

0.197

0.101

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DWH test [p-value] Kleibergen-Paap LM [p-value]

0.0013***

of

OPEN

0.0202***

ro

INF

(615.82)

0.0171***

-p

IS

(201.27)

re

GOV

0.8068***

lP

RGDP

0.6536***

2SLS

0.0084**

na

TI

System GMM

5.323 [0.024] 12.567 [0.000]

Kleibergen-Paap Wald F

32.547

Stock-Yogo-10%

16.38

Note: *, **, ***Significant at 10, 5 and 1 percent levels, respectively. T-statistics are in parentheses.

According to the results of the system GMM regression analysis, the P values of the Hansen test in the model are significantly greater than 0.1, reflecting the validity of the instrumental variable setting in the GMM estimation. The P values of AR (2) in the Arellano-Bond test are also greater than 0.1, indicating that a significant autocorrelation in the residual terms of the second-order difference equation does not exist, and thus the estimation results are valid. At the same time, the coefficients estimated using the static panel model and the dynamic panel model are consistent with the signs, which further reveals that the variable selection is reasonable and the model is robust. In the system GMM model, the estimated coefficient of the TFEE lag period is positive and significant at the level of 1%, indicating that the TFEE of the previous period exerts a positive 17

Journal Pre-proof impact on the current TFEE and displays an obvious transfer effect. Moreover, the TFEE accumulated in the early stage will adopt a demonstration role and a virtuous circle to exert a continuous driving effect. The estimated coefficient of the variable TI is significantly positive at the level of 1%. An increase in the number of invention patents promotes technological progress and TFEE. The instrumental variable method is used to estimate the model, test the robustness of the model, and identify the causal relationship between technological innovation and TFEE. In this paper, the intensity of intellectual property protection (IPP) in each region is used as a tool variable for technological innovation. The data were derived from the Marketization Index of China, and the impact of economic scale is excluded. Strengthening of the protection

of

of intellectual property rights will improve the innovation level of enterprises (Estrin et al, 2013). On the one

ro

hand, enterprise innovation requires a large amount of early investment and bears a large risk. Under the environment of strong IPP, investors have a more optimistic attitude towards future innovation income and are

-p

willing to continue to increase investments in technological innovation. At the same time, when the

re

innovation achievements of enterprises are protected, they are exclusive and thus they are difficult to be plagiarized and copied by competitors. Enterprises obtain greater profits from these achievements and

lP

improve their innovation enthusiasm. On the other hand, a perfect IPP system might also help to increase the brand value of enterprises. Using an intellectual property pledge, enterprises are more likely to obtain credit

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from banks for subsequent innovation activities. In other words, the intensity of IPP meets the requirements of the relevance of instrument variables. In addition, IPP policies are usually formulated and implemented by the

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government. For enterprises, these policies are exogenous, and enterprises are unlikely to affect IPP policies. Column 7 of Table 3 reports the test results and regression results using the 2SLS method. The statistic of the DWH test is 5.323. The result shows that the original hypothesis of exogeneity of technological innovation is rejected at the level of 5%, indicating that the model has endogeneity. Using Kleibergen-Paap LM statistics to test the unrecognizability, the result shows that the original hypothesis of insufficient identification of instrumental variable is rejected at the level of 1%, indicating that the instrumental variable is related to the explanatory variable. The Kleibergen-Paap Wald F statistic is 32.547, which is larger than the critical value of the Stock-Yogo test at 10% level, rejecting the hypothesis of weak identification of the instrumental variable. All of the above statistical tests show that the instrumental variable is reasonable. Based on the regression results, the coefficient of technological innovation of the core explanatory variable is still significantly positive at the level of 5% after the use of the instrumental variable, indicating that after considering the endogenous problem, technological innovation still has a positive role in promoting TFEE and the results are stable. Therefore, in the future, we should take full advantage of the role of technological innovation in 18

Journal Pre-proof promoting industrial transformation and upgrades, and ensure that technological innovation is the main force to improve TFEE. Next, we present the results for the other control variables. The estimated coefficient of RGDP is significantly negative at the level of 1%, indicating that the improvement in the economic development level contributes negatively to TFEE. This finding is consistent with the first stage described by the environmental Kuznets curve; in this case, the increase in per capita income in the early stage of the economic development of a country is often accompanied by an increasing pollution level, namely, a decrease in energy efficiency. This result may be due to the relatively short research period, as the level of economic development is far removed

of

from the turning point of energy efficiency improvement. In addition, economic development has promoted the

ro

development of industrialization and urbanization and increased energy consumption, resulting in imbalances in energy supply and demand, unstable energy prices, unreasonable energy consumption structure and other

-p

issues, which will exert a more obvious rebound effect. Thus, the improvement in production efficiency

re

prompts enterprises to use more energy elements, which may weaken the positive impact of improvements in productivity on energy efficiency. The estimated coefficient of GOV is significantly negative, indicating that

lP

government intervention in the economy will inhibit the increase in TFEE. Due to the increasing intervention of the city government, the level of marketization is decreasing, which leads to a distortion of energy prices and a

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reduction of energy efficiency in the city. In addition, the government may relax the requirements for energy-saving measures and emission reduction for enterprises to promote local economic growth; the

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government may introduce heavy industry, which will also lead to an increase in energy consumption. The estimated coefficient of IS is significantly positive, indicating that as the proportion of secondary industry in GDP increases, energy utilization will become more effective under the effect of economies of scale. The estimated coefficient of INF is significantly negative, indicating that the construction of infrastructure has a negative impact on TFEE. As a result of the rebound effect, the scale of energy consumption is expanding. The estimated coefficient of FDI is significantly positive at the 1% significance level, indicating that foreign investment promotes the improvement in energy efficiency through its effects on the industrial structure and technological progress. 4.3 Regression Results of Regional Samples As shown in Table 2, technological innovation plays an important role in the process of improving the urban TFEE in China. However, due to the different geographical locations and economic development levels across cities, the impact of technological innovation on TFEE inevitably varies. The National Bureau of Statistics divides the whole country into four regions with similar characteristics: the eastern region, the central 19

Journal Pre-proof region, the western region, and the northeastern region. Given the limited space, the TFEE values of all 284 cities in China are impossible to list. Therefore, only the mean values of TFEE for the 4 regions and 30 provinces from 2001 to 2013 are shown in Table 2A in Appendix A. The system GMM method is used to estimate the parameters, and the results are shown in Table 3.

IS

INF

OPEN

C

0.7156***

0.7977***

0.7878***

0.9041***

(112.72)

(106.47)

(209.96)

(169.35)

0.0082***

-0.0057***

0.0077***

0.0013**

(4.01)

(-5.13)

(7.09)

(2.36)

-0.0489***

0.0132***

-0.0033*

-0.0119***

(-7.40)

(4.11)

(-1.66)

(5.08)

0.0289***

-0.2093***

-0.0228***

0.2049***

(3.95)

(-10.80)

(-5.47)

(6.45)

0.1127***

0.0073

0.0026

0.0228*

ro

GOV

Northeast

-p

RGDP

West

(7.61)

(0.77)

(0.20)

(1.88)

-0.0167***

-0.0079***

-0.0161***

0.0131***

(-4.60)

(-4.87)

(4.24)

-0.3631***

-0.1058

-0.0006

(-5.36)

(-1.2)

(-0.02)

-0.0082

0.0472***

-0.1782***

(-0.28)

(3.68)

(-10.54)

re

TI

Central

(-4.76) 0.1601*** (5.13) 0.4921*** (9.15)

lP

TFEE（-1）

East

na

Variables

of

Table. 3 Systematic GMM Regression Results of Regional Samples

0.078

0.020

0.000

0.001

AR(2)

0.129

0.341

0.798

0.272

Hansen-test

0.186

0.175

0.123

0.420

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AR(1)

Note: *,**,***Significant at 10, 5 and 1 percent levels, respectively. T-statistics are in parentheses.

The estimated coefficients of TI in the eastern and western regions are significantly positive at the 1% level, the coefficient in the northeast region is significantly positive at the 5% level and the coefficient in the central region is significantly negative at the 1% level. Based on these findings, technological innovation promotes the improvement in TFEE in the eastern, western and northeast regions, consistent with the estimated results for the whole sample. However, in the central region, technological innovation inhibits the improvement in TFEE. In this region, technological innovation does not tend to save energy, and the energy consumption bias is more obvious; a potential explanation is the large number of energy-consuming industries, such as iron, steel, non-metallic minerals, chemicals and electricity. The technological progress of these industries tends to lead to obviously increased energy consumption. Due to the imperfection of price control and market forces, the role of market regulation of the energy price is restrained, and the energy price does not truly reflect the scarcity of energy factors. This situation results in long-term market distortion and low energy prices that will inevitably 20

Journal Pre-proof hinder and weaken the price effect on technological innovation bias, thus reducing the tendency of technological innovation to save energy. In addition, due to the rapid expansion in investment scale led by the local governments, market participants will take advantage of the factors of production, and the market scale effect will aggravate the energy consumption bias of technological innovation. The results of the other control variables reveal significant differences among the four regions. The estimated coefficients of RGDP in the eastern, western and northeast regions are significantly negative, but the coefficient in the central region is significantly positive. The estimated coefficients of GOV in the eastern and northeast regions are significantly positive, while those in the central and western regions are

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significantly negative. The estimated coefficients of IS in the eastern and northeast regions are

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significantly positive, while those in the central and western regions are not significant. The estimated coefficients of INF in the eastern, central, and western regions are significantly negative, while the

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coefficient in the northeast region is significantly positive. The estimated coefficient of FDI in the eastern

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region is significantly positive, the coefficient in the central region is significantly negative, and those in the western and northeast regions are not significant.

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4.4 Robustness Check

In order to ensure the robustness of the research results, this paper also conducts a robustness test.

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This paper uses the city innovation index in China's City and Industry Innovation Report 2017 to measure the technological innovation capability, and takes it as an independent variable to estimate the

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model. The System GMM estimation results are shown in Table 4. The results are basically the same. Table. 4 Robustness Test Results Variables

TFEE（-1） TI RGDP GOV IS INF OPEN C AR(1)

National

East

Central

West

Northeast

0.7987*** (95.45) 0.0071*** (30.68) -0.0088*** (-19.44) -0.0364*** (-12.17) 0.0721*** (19.65) -0.0052*** (-8.63) 0.2090*** (9.47) 0.1026*** (21.79)

0.7495*** (92.82) 0.0035*** (15.44) -0.0216*** (-23.52) 0.0314*** (6.36) 0.0705*** (11.75) -0.0168*** (-13.74) 0.1630*** (6.52) 0.2737*** (28.20)

0.8305*** (82.96) -0.1263*** (-52.59) 0.0163*** (16.41) -0.0166*** (-6.42) 0.0852*** (10.58) 0.0086*** (10.69) 0.5583*** (14.12) -0.1015*** (-10.06)

0.8136*** (85.11) 0.0687*** (17.13) 0.0018* (1.81) -0.1487*** (-12.43) 0.0407*** (3.75) -0.0115*** (-11.04) -0.5256*** (-16.16) 0.0447*** (5.89)

0.9107*** (110.45) 0.0581*** (5.36) 0.0119** (2.25) 0.1954*** (7.83) 0.0493 (1.46) 0.0257* (1.90) 0.1591*** (2.57) -0.1924*** (-5.24)

0.014

0.078

0.000

0.019

0.002 21

Journal Pre-proof AR(2)

0.184

0.129

0.873

0.340

0.307

Hansen-test

0.113

0.514

1.000

1.000

1.000

Note: *,**,*** Significant at 10, 5 and 1 percent levels, respectively. T-statistics are in parentheses.

This section describes and discusses the results for the calculation of the TFEE at the urban level and the impact of technological innovation on TFEE at the national and regional levels.

5. Conclusions and Policy Implications Based on the panel data of 284 cities in China, this paper uses the Malmquist-Luenberger index to measure

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TFEE while considering energy input and undesirable output. Then, by constructing a dynamic panel model, this paper empirically tests the impact of technological innovation on TFEE. The main conclusions and policy

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implications are listed below.

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5.1 Conclusions

First, the TFEE shows an increasing trend in China overall, and the differences between cities gradually

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decrease. Based on the specific values for each city, the top 10 cities ranked by TFEE are located in the eastern

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coastal areas, while the bottom 10 cities are located in the central and western regions. Regionally, obvious differences in urban TFEE are identified among the four regions, with the highest efficiency observed in the

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eastern region, followed by the central, northeast and western regions. Second, technological innovation has a significant role in promoting TFEE. However, significant

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differences in the effect of technological innovation on TFEE are observed. Among these regions, technological innovation in the eastern, western and northeastern regions significantly promotes the improvement in TFEE, but technological innovation has inhibited the improvement in TFEE in the central region. Third, the level of economic development, the degree of government intervention and infrastructure construction inhibit the improvement in the TFEE, while industrial structure and FDI promote its improvement at the national level. However, the effects of these factors on TFEE are restricted by the economic development of different regions. 5.2 Policy Implications First, technological innovation in energy development and utilization should be accelerated. We should learn from the advanced energy conservation and emission reduction technologies of developed countries and strengthen the technological innovation of harmless coal mining, deep-sea oil and gas development, and the clean and efficient utilization of coal. Around the goal of improving energy efficiency, clean energy technology, low-carbon energy technology, and key material technology should be developed, so as to realize 22

Journal Pre-proof the positive promotion of manufacturing enterprises' technological progress on energy efficiency. A science and technology innovation strategy should be comprehensively deployed in the energy field, and the continuous development of the energy technology field should be promoted. Second, the support of energy conservation and consumption reduction policies should be strengthened in the western and northeastern regions. Due to the differences in the distribution of the TFEE and the influences of technological innovation on TFEE in China, the research, introduction, digestion and absorption of energy-related technologies should be a focus of policies in the western and northeastern regions. Third, economic development, government intervention, infrastructure construction, industrial structure

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and FDI should not be ignored, and improvements in the TFEE and energy conservation and a reduction in

should be comprehensively considered at the same time.

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emissions are long-term adjustment processes. Therefore, other supporting policies and multiple measures

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Finally, according to the Energy Efficiency 2018-Analysis and Outlooks to 2040, published by the

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International Energy Agency (IEA), China has made great progress in energy efficiency. The improvement in energy efficiency in industry, the service industry and housing sector in China saved more than 10 EJ of energy

lP

in 2017. Energy demands in major emerging economies, such as China, Brazil, India, Mexico, South Africa and Indonesia, has increased rapidly, accounting for approximately one-third of the global total (IEA, 2018).

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Therefore, in the major emerging economies described above, measures and methods to improve energy efficiency should be constructed through technological innovation and relevant policies, which will not only

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reduce energy-related greenhouse gas emissions and help to achieve the emission reduction targets of the Paris Agreement but also reduce emissions of major air pollutants to substantially reduce the annual number of deaths caused by a deteriorating air quality. Thus, the research methods and conclusions of this paper are also an important reference for other emerging economies. Although this paper quantitatively analyses the dynamic relationship between technological innovation and TFEE, there are still some problems worthy of further study. For example, technological innovation may have spatial spillover effects between cities, and the spatial econometric model can be used to test the direct and indirect effects of technological innovation on TFEE. In addition, technological innovation is greatly influenced by government policies, and the impact of technological innovation on TFEE can also be considered under the constraints of government environmental regulations.

Acknowledgments: This work was supported by the National Natural Science Foundation of China for Young Scholars(No. 71603202), the National Natural Science Foundation of China (No.71972153), the Shaanxi Soft Science Foundation (Grant 23

Journal Pre-proof No.2019KRM129), the Shaanxi Province Education Department Philosophy and Social Science Key Institute Base Project (No. 19JZ048, the Xi'an Social Science Planning Fund Project (No.19J13) and Xi’an Soft Science Foundation (Grant No. 2019111813RKX002SF006-6).

Appendix A Table 1A Statistical description of variables. Variable

Unit 8

K

10 yuan 4

L

10 persons

Obs

Mean

Std. Dev.

Min

Max

3,976

2039.566

3691.461

6.110

49119.140

3,976

43.492

63.671

4.050

923.300

3,976

933.272

843.180

37.741

6294.673

3,976

407.953

549.435

17.930

5940.215

62453.322

435

683162

6265.290

0

106645

4

of

10 ton standard EC 108 yuan

EO

4

ro

coal

10 ton

3,976

60830.731

TI

pieces

3,692

1713.883

RGDP

yuan

3,692

24208.421

21795.972

1320

196728

GOV

%

3,692

0.139

0.096

0.008

2.186

IS

%

3,692

0.483

0.115

0.090

0.910

0.759

0.508

0.024

12.429

0.021

0.031

0.0001

1.103

km/km

3,692

FDI

%

3,692

lP

INF

re

2

-p

UO

na

Table 2A

Total factor energy efficiency in different regions. Provice Beijing Tianjin Hebei Shanghai Jiangsu East

Zhejiang

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

0.8669 0.8113 0.9830 0.8261 0.8359 0.8910 0.8973 0.9162 0.9543 1.0183 0.9659 0.9914 0.8223

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Region

0.9489 0.9853 0.8089 0.8895 1.0245 0.9286 0.9184 0.9192 1.0610 0.9627 0.9705 0.9330 0.8613 0.8454 0.8974 0.8918 0.8830 0.9030 0.8078 0.8995 0.8495 0.9298 0.9155 0.9702 0.8975 0.8760 0.9890 1.1209 0.9447 0.8696 0.8951 0.8865 0.9447 1.0134 0.9458 1.0278 0.9840 1.0005 0.8703 0.8608 0.8915 0.8629 0.8310 0.9006 0.9172 0.8476 0.9326 0.9129 0.9318 0.9864 0.9819 0.7069 0.9192 0.8821 0.8284 0.8421 0.9032 0.8895 0.9053 1.0109 0.9668 0.9768 0.9999 0.9217 0.9052

Fujian

0.8642 0.8819 0.9204 0.9020 1.0220 0.9041 0.8984 0.9587 0.9070 0.9438 0.9428 0.8794 0.8828

Shandong

0.8243 0.8691 0.8408 0.9339 0.8552 0.8532 0.9018 0.9904 0.8574 0.9367 0.9539 0.9363 0.8588

Guangdong 0.9394 0.8800 0.8970 0.9801 0.8045 0.8585 0.8548 0.8834 0.9074 0.9937 0.9701 0.9367 0.8365 Hainan

0.8913 0.8898 0.8178 0.8889 0.8174 0.9051 0.8900 0.9246 0.9317 0.9827 0.9035 0.9057 0.7970

Mean

0.8950 0.9109 0.8796 0.8846 0.8961 0.8842 0.8958 0.9399 0.9374 0.9690 0.9647 0.9384 0.8417

Shanxi

0.7795 0.9172 0.8963 0.8846 0.9584 0.8937 0.9079 0.9090 0.9818 0.9346 0.9777 0.9017 0.8138

Anhui

0.8495 0.8629 0.9145 0.8749 0.9098 0.8672 0.8175 0.8882 0.9114 0.9487 0.9304 0.9181 0.8257

Jiangxi

0.6548 0.7643 0.8907 0.8517 0.9073 0.9013 0.8407 0.9231 0.9056 0.9630 0.9518 0.9118 0.8419

Henan

0.8343 0.8275 0.7752 0.8084 0.8734 0.8274 0.9097 0.9114 0.9007 0.9288 0.9328 0.9157 0.8390

Hubei

0.9372 0.9181 0.8313 0.8650 0.8091 0.8352 0.8845 0.9090 0.8736 0.9114 1.0015 0.9341 0.8563

Hunan

0.8453 0.8682 0.8484 0.8926 0.8473 0.8538 0.9030 0.9094 0.8775 0.9455 0.9788 0.9487 0.8904

Central

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Inner Mongolia

0.6620 0.7768 0.8812 0.8165 0.9927 0.8271 0.8956 0.8854 0.9192 0.9693 0.9794 0.8948 0.7825

Guangxi

0.7185 0.8503 0.7922 0.7965 0.8151 0.8855 0.8944 0.8485 0.8284 0.9654 0.9388 0.9025 0.8669

Chongqin

0.8338 0.8681 0.9197 0.9034 1.1260 0.9697 0.8762 0.8650 1.0571 0.9207 1.0136 0.9635 0.8705

Sichuan

0.7763 0.7573 0.8470 0.8530 0.8696 0.8584 0.8575 0.8511 0.8626 0.9067 1.0040 0.9309 0.7997

Guizhou

0.8558 0.9550 0.8230 0.8244 0.7596 0.8048 0.7970 0.8009 0.8915 0.8805 0.9272 0.9366 0.9107

Yunnan

0.8034 0.9414 0.7927 0.7808 0.8567 0.8613 0.7519 0.9842 0.8378 0.8886 0.8880 0.9520 0.8568

Shaanxi

0.8378 0.9073 0.7229 0.7791 0.8444 0.8354 0.8012 0.9425 0.8730 0.9007 0.9851 0.9488 0.8289

Gansu

0.8757 0.8975 0.7408 0.8042 0.8307 0.9097 0.8449 0.8021 0.8593 0.9217 0.9534 0.9304 0.7479

Qinghai

0.8395 0.6276 0.7729 0.8002 0.8117 0.8687 0.8180 0.8153 0.8251 0.9366 1.0077 0.9128 0.8594

Ningxia

0.8872 0.9098 0.7276 0.8487 0.7784 0.7141 0.7692 0.7903 0.8448 0.7989 0.9218 0.8355 0.8312

Xinjiang

0.8760 0.9238 0.6848 0.7719 0.6697 0.7449 0.9041 0.8939 0.7754 0.9430 1.0082 0.8530 0.7644

Mean

0.8151 0.8559 0.7913 0.8162 0.8504 0.8436 0.8373 0.8617 0.8704 0.9120 0.9661 0.9146 0.8290

Liaoning

0.8718 0.9213 0.8236 0.8769 0.8573 0.8388 0.8945 0.8990 0.9034 0.9885 0.9498 0.9523 0.8468

Jilin

0.9096 0.8861 0.8566 0.8691 0.8540 0.8710 0.9183 0.9358 0.9035 0.9825 1.0113 0.9709 0.8274

of

0.8168 0.8597 0.8594 0.8629 0.8842 0.8631 0.8772 0.9084 0.9084 0.9387 0.9622 0.9217 0.8445

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West

Mean

Northeast

0.9142 0.8860 0.8571 0.8525 0.8355 0.8561 0.8746 0.8969 0.9059 0.9480 0.9592 0.9387 0.8244

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Mean

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Heilongjiang 0.9612 0.8505 0.8912 0.8115 0.7951 0.8584 0.8111 0.8559 0.9106 0.8732 0.9165 0.8931 0.7990

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Journal Pre-proof Declaration of interests  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.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Graphical abstract

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Highlights • We use the Malmquist-Luenberger productivity index to estimate total factor energy efficiency(TFEE) considering energy input and undesirable output. • We identify the relationship between technological innovation and TFEE. • We find the technological innovation has a significant positive impact on TFEE. • The government should pay attention to the role of technological innovation in energy saving and

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emission reduction.

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