Front. Energy Res. Frontiers in Energy Research Front. Energy Res. 2296-598X Frontiers Media S.A. 878065 10.3389/fenrg.2022.878065 Energy Research Original Research RETRACTED: How Government Corruption and Market Segmentation Affect Green Total Factor Energy Efficiency in the Post-COVID-19 Era: Evidence From China Zhou et al. Corruption, Market Segmentation and Efficiency Zhou Qingjie 1 2 Du Mingyue 1 2 Ren Siyu 3 * School of Economics, Beijing Technology and Business University, Beijing, China Institute of New Commercial Economy, Beijing Technology and Business University, Beijing, China School of Economics, Nankai University, Tianjin, China *Correspondence: Siyu Ren, rensiyuking@126.com

This article was submitted to Sustainable Energy Systems and Policies, a section of the journal Frontiers in Energy Research

Edited by: Muhammad Irfan, Beijing Institute of Technology, China

Reviewed by: Luigi Aldieri, University of Salerno, Italy

Xiaodong Yang, Xinjiang University, China

Haitao Wu, Beijing Institute of Technology, China

 31 03 2022 07 08 2025 2022 10 878065 17 02 2022 14 03 2022 Copyright © 2022 Zhou, Du and Ren. 2022 Zhou, Du and Ren

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Energy and environmental pollution have attracted wide attention, but few studies have been conducted on green total factor energy efficiency (GTFEE) from the perspective of government corruption and market segmentation. By using the panel data of 30 provinces in China for the period 2006 to 2017, this paper tests the relationship between government corruption, market segmentation, and GTFEE. Moreover, considering the threshold effect of government corruption and market segmentation on GTFEE, the system generalized method of moments and the dynamic threshold panel model are adopted to analyze the nonlinear relationship. The regression results indicate that government corruption significantly decreases GTFEE, and market segmentation also has a significant negative impact on GTFEE. Moreover, market segmentation exacerbates the negative impact of corruption on GTFEE. The more serious the government corruption, the more severe the inhibitory effect of market segmentation on GTFEE. Similarly, the higher degree of market segmentation can increase the restraining effect of corruption on GTFEE. The results are still valid after a series of robustness tests. This paper suggests that countries should adopt severe anti-corruption actions, speed up the process of regional integration, and provide a good institutional environment support for the improvement of GTFEE.

 corruption market segmentation post-COVID-19 era China energy efficiency Introduction

Since the 21st century, China’s economy has achieved remarkable achievements (Abbasi et al., 2022; Fang et al., 2022), which continue to grow despite the unexpected COVID-19 pandemic in 2020 (Razzaq et al., 2020; Iqbal et al., 2021; Yang et al., 2021; Ahmad et al., 2022; Wen et al., 2022). The GDP in China has reached 101.6 trillion yuan in 2020, with an annual growth rate of 2.3% (Irfan et al., 2021a; Lee, 2021). However, with the rapid economic growth, China’s energy demand is increasing year by year (Hao et al., 2021a), the problems of energy consumption and pollutant emissions have become increasingly prominent (Elavarasan et al., 2021a; Elavarasan et al., 2021b; Khan et al., 2021; Ren et al., 2021a; Islam et al., 2022). According to Statistical Review of World Energy, China’s consumption of primary energy attained 145.46 exajoules, accounting for 26.1% of the world. China’s CO2 emissions in 2020 are 9,899.3 million tons, accounting for 30.7% of the world’s total, making it the world’s largest carbon emitter. It is worth noting that China’s economic growth is inseparable from the extensive use of fossil energy in the short term (Hao et al., 2021b; Rauf et al., 2021). To smooth the energy and environmental dilemma, the nation has made a lot of measures in terms of energy-saving and emission reduction and has solemnly promised to achieve carbon peaking by 2030 and carbon neutrality by 2060 (Wu et al., 2021b; Zhang et al., 2021). Improving green total factor energy efficiency helps reduce pollution emissions and energy consumption, and that is the only way to protect the ecological environment and achieve economic growth (Wu et al., 2021a; Ren et al., 2022; Lee et al., 2022).

Environmental pollution is a public product with strong negative externalities (Irfan et al., 2020; Varadarajan, 2020; Irfan and Ahmad, 2022; Irfan et al., 2022; Nuvvula et al., 2022). To reduce the harm of pollution to residents’ health, governments around the world have paid more attention to environmental governance and formulated corresponding environmental policies to improve environmental quality (Ahmad et al., 2021; Razzaq et al., 2021; Elavarasan et al., 2022a; Elavarasan et al., 2022b). However, under the economic growth “championship” among governments, local governments may relax environmental regulations and acquiesce in local polluting enterprises to discharge excessive pollutants (Ali et al., 2021; Tanveer et al., 2021; Shi et al., 2022; Xiang et al., 2022). Moreover, to obtain more resources and seek political protection, firms tend to induce local governments to erect barriers and protect businesses within their jurisdiction from other external competition through bribery tactics (Park et al., 2006). It makes enterprises lack enthusiasm for improving energy efficiency, resulting in great differences in the effects of environmental policies across countries (Hao et al., 2020; Pei et al., 2021). Therefore, energy efficiency is closely related to the effect of policy implementation. Once there is serious rent-seeking and corruption in policy implementation, it will directly affect the policy effect and energy efficiency (Ren et al., 2021a). Based on the corruption perceptions index 2021 issued by the non-governmental organizations (NGO) Transparency International, China ranks 66 out of 180 countries with a score of 45, which is higher than the average score of 43, showing that China has a severe corruption problem. However, the relationship between government corruption and energy efficiency still lacks systematic research. Moreover, few scholars study the influence of corruption on GTFEE from the view of market segmentation. Although market segmentation preserves local enterprises and markets from the competition, it prevents the free flow of factors between different regions, resulting in the misallocation of factor resources, which is not favorable to technological innovation and the enhancement of energy efficiency (Hao et al., 2020; Irfan et al., 2021b; Guo and Liu, 2022).

This paper aims to analyze the following questions. What is the influence of government corruption and market segmentation on GTFEE, respectively? Does market segmentation affect the effect of government corruption on GTFEE? Is there a threshold effect in the influence of government corruption and market segmentation on GTFEE? However, existing studies have not addressed these issues. Three variables are incorporated into the same research system to solve the above questions. It is not only useful for understanding the relationship between corruption, market segmentation, and GTFEE but also provides a reference for other developing countries to formulate anti-corruption policies and improve GTFEE policies. Overall, the innovation of this study has three main points. First, this paper brings corruption, market segmentation, and GTFEE into the same analytical framework to better understand the relationship between the three perspectives. Second, this paper examines the influence of corruption on GTFEE, market segmentation on GTFEE, and market segmentation as moderating effects. Third, this study addresses the endogeneity problem by establishing a dynamic threshold model, which separately studies the nonlinear relationship between corruption and GTFEE at different market segmentation levels.

 Literature Review The Influence of Corruption

Corruption is an important issue that countries have to deal with. There are many different interpretations of corruption, which vary by time and place, such as bribery, extortion, nepotism (Rose-Ackerman and Palifka, 2016; Mungiu-Pippidi, 2019). From the perspective of economic, corruption is mainly about government corruption or official corruption, which abuses public power for private interests (Rose-Ackerman, 2017; Igiebor, 2019). From a macro perspective, in the existing literature, there are two different opinions on the impact of government corruption on economic development. One view is that corruption distorts public resource allocation, inhibits economic and social development, reduces social welfare, and weakens capital accumulation (Liu and Mikesell, 2014; Dimant and Tosato, 2018). Cieślik and Goczek (2018) found that the interactions of corruption and investment had a significantly negative inhibition of economic development. Swaleheen (2011) analyzed the impact of corruption on the growth rate of per capita income and concluded that corruption had an inhibition effect. However, some studies have the view that corruption makes up for the shortcomings of the market mechanism, and the rent-seeking behavior of enterprises reduces the government’s inefficient control, which is beneficial to economic development, that is, corruption is a “lubricant” (Ren et al., 2021a; Gunter, 2021). Acemoglu and Verdier (1998) believed that allowing some corruption was conducive to the realization of an ideal state, which could reduce corruption, increase investment and effectively allocate talents.

Furthermore, from a micro perspective, the production and development of enterprises require a lot of capital, which induces enterprises to choose rent-seeking behavior to seek financial support. Getting better rewards through rent-seeking makes entrepreneurs spend more time paying bribes than operating, researching, and innovating, which can lead to distorted talent allocation and slow down innovation in the long-run (Dincer, 2019; Chen, 2021). In addition, Djankov et al. (2002) conducted a study on the degree of regulation in running enterprises in 85 countries found that corruption protects inefficient enterprises, hindered the entry of new enterprises, and disrupts the effective competition order of the market. De Rosa et al. (2010) believed that corruption couldn’t help companies reduce the time to deal with bureaucratic procedures and hindered the improvement of corporate productivity. However, some studies disagree. Lui (1985) proposed a formal queuing model and found that the opportunity cost of queueing people was disparate, which leaded people with high time value to pay a certain amount of bribe cost to “jump the queue” and finally achieved “Pareto optimality”. The auction model established by Beck and Maher (1986) also had a similar logic. In auction behavior, corruption improved the efficiency of resource allocation by directly assigning auction items to the most efficient enterprise. Ren et al. (2022) found that the system quality distinguished the influence of corruption on total factor productivity. In a high-quality system, corruption hinders the improvement of total factor productivity. However, in countries with institutional defects, corruption supports productivity growth and is beneficial (Aidt, 2009).

 Literature of Market Segmentation

With economic development, market segmentation has attracted widespread attention. Young (2000) found that China’s reform model enabled governments to interfere in the market through administrative power and implemented local protection. Contrary to the principle of giving play to regional comparative advantages, local governments allocate scarce resources to industries with high returns, intensifying market segmentation. However, Fan and Wei (2006) extended Young (2000) with a more comprehensive dataset and more rigorous econometric method to test the law of one price and found that the price distinction between different cities in China narrowed over time, converging to the law of one price. They also found that China’s “gradualist” reform was conducive to promoting regional market integration. The current research on market segmentation measurement method mainly includes the production method (Young, 2000), the price method (Parsley and Wei, 1996), the trading method (Naughton, 2003; Poncet, 2003), and the specialization index method. Poncet (2005) drew on the gravity model and boundary effects to calculate market segmentation and tests the effect of inter-provincial trade barriers on market segmentation by using China’s inter-provincial trade data. Parsley and Wei (1996), Parsley and Wei (2001), Parsley and Wei (2002) used price dispersion to study market segmentation and integration. In addition, many studies are currently conducted on the impact of market segmentation (Que et al., 2018; Dolnicar, 2019; Shao et al., 2019; Dolnicar, 2020).

 Research on GTFEE

Currently, many pieces of literature focus on the relative research of energy consumption and energy efficiency, it involves the categories, development, measurement methods, influencing factors (Mohsin et al., 2019). According to the number of input factors in production, there are a single factor and total factor energy efficiency. The former is usually expressed in terms of energy intensity or energy productivity, with the simple measurement method and available data, it attracts many scholars to use (Moshiri and Duah, 2016; Dong et al., 2018). However, since it cannot include the impact of other factors on output and biased metrics, many scholars have doubted single factor energy efficiency (Wilson et al., 1994; Patterson, 1996). To remedy this deficiency and measure the substitution effect between different input factors, some studies adopt total factor energy efficiency (Hu and Wang, 2006; Honma and Hu, 2009; Borozan, 2018; Ohene-Asare et al., 2020; Chen et al., 2021). Furthermore, researchers have considered the impact of resources and the environment on sustainable economic development, adding environmental factors, energy, and pollution emission into the calculation of total factor energy efficiency (Ramanathan, 2006; Baležentis et al., 2016). This input-output efficiency, which simultaneously considers energy and pollution emissions, is called GTFEE (Hao et al., 2020; Wu et al., 2020). The methods of measuring total factor productivity are generally divided into growth accounting method and econometric method. Specifically, the former includes the Solow residual method and the algebraic index method. One of the econometric methods is the potential output method which is also called Frontier production function, and mainly includes parametric or statistical methods like stochastic Frontier analysis (SFA), nonparametric methods, or mathematical programming like data envelopment analysis (DEA) (Ferrier and Lovell, 1990; Färe et al., 1997). The method of DEA is widely used by scholars (Bian et al., 2013; Mohsin et al., 2021). Furthermore, the current studies on the influencing factors of GTFEE have been carried out from multiple perspectives. including industrial structure (Guo and Yuan, 2020), energy structure (Chien and Hu, 2007), foreign investment (Pan et al., 2020), technological progress (Liu et al., 2016), government intervention (Matraeva et al., 2019), economic development (Wu et al., 2020).

 Methodology and Data Research Method Basic Linear Model

This article focuses on examining the influence of corruption and market segmentation on China’s GTFEE. Considering that GTFEE may be affected by earlier stages, the lagging one-stage variable l n g t f e e i t − 1 is added to the model. Therefore, we construct the basic econometric model: l n g t f e e i t = β 0 + β 1 l n g t f e e i t − 1 + β 2 l n c o r i t + β 3 l n r d i t + β 4 l n g d p i t + β 5 l n r d p i t + β 6 l n u r b i t + β 7 l n o p e n i t + α i + v t + ε i t l n g t f e e i t = β 0 + β 1 l n g t f e e i t − 1 + β 2 l n s e g m i t + β 3 l n r d i t + β 4 l n g d p i t + β 5 l n r d p i t + β 6 l n u r b i t + β 7 l n o p e n i t + α i + v t + ε i t l n g t f e e i t = β 0 + β 1 l n g t f e e i t − 1 + β 2 l n s e g m i t + β 3 l n c o r i t + β 4 ( l n c o r i t × l n s e g m i t ) + β 5 l n r d i t + β 6 l n g d p i t + β 7 l n r d p i t + β 8 l n u r b i t + β 9 l n o p e n i t + α i + v t + ε i t where l n g t f e e i t represents GTFEE, and i and t represent province and time. ε i t is the random disturbance term. The control variables include economic development ( g d p i t ), R&D investment ( r d i t ), R&D personnel input ( r d p i t ), trade openness ( o p e n i t ) and urbanization ( u r b i t ).

 Threshold Model

Furthermore, we refer to the research of Ren et al. (2021a) and use a dynamic threshold model to separately discuss their impact on GTFEE with corruption and market segmentation as threshold variables. The dynamic threshold panel model combines generalized method of moments (GMM) with a threshold model to study spatial heterogeneity as well as to solve endogeneity problems between variables. Moreover, the dynamic threshold panel model can determine the threshold variable through grid search under different threshold variables and solve potential internal factors in the model. The specific dynamic threshold is modeled as follows. l n g t f e e i t = β 0 + β 1 l n g t f e e i t − 1 + β 2 l n c o r i t ⋅ I ( l n s e g m i t ≤ c ) + β 3 l n c o r i t ⋅ I ( l n s e g m i t > c ) + ∑ k = 1 5 β k X k i t + α i + v t + ε i t l n g t f e e i t = β 0 + β 1 l n g t f e e i t − 1 + β 2 l n s e g m i t ⋅ I ( l n c o r i t ≤ c ) + β 3 l n s e g m i t ⋅ I ( l n c o r i t > c ) + ∑ k = 1 5 β k X k i t + α i + v t + ε i t where l n s e g m i t and l n c o r i t are the threshold variable, respectively. c is the threshold value to be estimated. I ( ⋅ ) is an instruction function.

 Explanation of Variables China’s Green Total Factor Energy Efficiency

Tone (2001) proposed a non-radial super-efficiency SBM model that includes slack variables, calling it the super-efficiency Slack Based Model (SBM). It is a type of super-efficiency DEA model. Compared with other radial models, the super-efficiency SBM model not only measures all the slack variables, but in the case of considering all inefficiency sources it can accurately measure the efficiency level—that is, the effectiveness of all DMUs (decision-making units) is analyzed first, and then the super-efficiency analysis is performed for the effective DMU. This paper constructs a super-efficiency SBM model that includes all influencing factors of resource consumption, expected output, and undesired output to evaluate green eco-efficiency. Therefore, based on Wu et al. (2020), we establish an SBM model: γ ∗ = 1 m ∑ i = 1 m X ¯ X i 0 1 + 1 S 1 + S 2   ( ∑ r = 1 S 1 S r g y r 0 g   +   ∑ r = 1 S 2 S r b y r 0 b )   S . t . ∑ k = 1 K λ k x n k ≤   x n ;   ∑ k = 1 K λ k y m k     ≥ y m   ; ∑ k = 1 K λ k u j k =   u j   ;   λ   ≥   0 where X denotes the input variable (Capital, labor, and energy); Y indicates the expected output (GDP); B denotes the unexpected output (wastewater, waste gas, and waste solid); n, M, and j represent the number of types of input, expected output, and unexpected output. The calculation of GTFEE requires the inclusion of inputs, desired and undesired outputs, in the calculation framework.

 Corruption

Government corruption (COR). Corruption not only stimulates rent-seeking activities but also reduces the motivation of firms for energy technology innovation (Del Monte and Papagni, 2001; Svensson, 2005; Ceva and Ferretti, 2021). Given the availability of data, we draw on the study of Hao et al. (2020) and adopt the number of public officials involved in corruption per 10,000 as a proxy variable for regional corruption. The calculation results are shown in Figures 1, 2.

Corruption levels in Chinese provinces in 2006.

Corruption levels in Chinese provinces in 2017.

 Market Segmentation

One of the difficulties studied in this article is the calculation of the market segmentation indicators of China’s provinces. There are currently five types of measurement methods for market segmentation: the production method, trade method, price method, business cycle method, and market survey method. This article refers to the research of Shao et al. (2019) and uses relative price information to calculate the market segmentation in each province. Specifically, it analyzes market segmentation through the difference in commodity prices between regions.

Before measuring the relative price variance, a 3-dimensional (t × m × k) data set needs to be constructed, where t, m, and k represent the year, region and commodity, respectively. We sort out the retail price index of eight categories 1 of commodities of 30 provinces in China.

1) Considering that the original data of this article are a chain index of retail prices of commodities, the relative price is measured by the logarithmic first difference of the price ratio. We define the following:

Δ Q i j t k = ln ( p i t k / p j t k ) − ln ( p i t − 1 k / p j t − 1 k ) = ln ( p i t k / p i t − 1 k ) − ln ( p j t k / p j t − 1 k )

In this way, from the data of 435 pairs of eight types of commodities in the province and city combination in the sample from 2006 to 2017 (12 years in total), the relative prices of 41,760 differential forms can be calculated as Δ Q i j t k .

2) Changes in commodity prices between regions may be caused by some characteristics of the commodities themselves | Δ Q i j t k | . This means that not all changes are caused by the differences in the market environment between regions, and differences may also include the nonadditive effects caused by the heterogeneity of commodities, leading to the overestimation of the actual market segmentation index formed by trade barriers.

Therefore, we draw on the de-mean method proposed by Parsley and Wei (2001) and assume | Δ Q i j t k | = a k + ε i j t k ,

Where a k is the price change caused by the kth commodity itself, and ε i j t k is related to the special market environment of the two regions i and j. In this article, to eliminate the fixed effect a k , the relative prices between the provincial and municipal combinations | Δ Q i j t k | are averaged to obtain | Δ Q ¯ t k | , and then these 435 | Δ Q i j t k | subtract the mean value. q i j t k = ε i j t k − ε ¯ i j t k = | Δ Q i j t k | + | Δ Q ¯ t k | = ( a k − a ¯ k ) + ( ε i j t k − ε ¯ i j t k )

In this formula, q i j t k is the final relative price change, and it is only related to the market segmentation factors between regions and some random factors.

3) Calculate the variance var ( q i j t k ) of the relative price fluctuation q i j t k (k = 1, 2, … ,8) of the eight types of commodities in each of the two regions. Then, calculate the relative price variance of the 435 pairs of provinces and obtain the market segmentation index from other provinces and cities across the country, Var ( q n t )=( ∑ i ≠ j v a r ( q i j t ) / N , where n is the region and N is the number of combined provinces and cities. The above calculation process has a total of 360 (=30 × 12) observations.

 Control Variables and Data Sources

R&D personnel ( r d p i t ) and R&D capital ( r d i t ) can affect the level of technological innovation, which can contribute to the improvement of GTFEE (Song and Oh, 2015; Yang et al., 2021a). This paper uses R&D capital investment and R&D personnel investment in each province to represent, respectively.

The level of economic development ( g d p i t ) can influence the industrial structure and energy utilization efficiency and plays an important role in GTFEE. This paper adopts the GDP of each province to measure it (Yang et al., 2021b).

Trade openness ( o p e n i t ) can affect the regional GTFEE by introducing the leading energy-saving technology in developed countries. This paper uses the proportion of the total import and export to GDP to measure it (Li and Hu, 2012; Ren et al., 2021b).

The regional urbanization level ( u r b i t ) can adjust the industrial structure, optimize the allocation of resources, and improve the GTFEE (Lv et al., 2020). In addition, it can promote the improvement of residents’ living standards and economic growth, but changes in residents’ consumption patterns and the construction of urbanization infrastructure will also increase the energy demand. These have important implications for GTFEE. We use the proportion of the urban population to express it.

This paper uses panel data of 30 provinces from the Wind database, China Energy Statistical Yearbook, China Statistical Yearbook, China Inspection Yearbook, provincial inspection reports and the statistical yearbooks of various provinces in China. The specific descriptive statistics are shown in Table 1.

The variables description.

Variable Definition Obs Mean Std. Dev Min Max
GTFEE Green total factor energy efficiency 360 0.5516 0.1800 0.2324 1.1084
SEGM Market segmentation 360 0.3479 0.2782 0.0114 1.4764
COR Regional corruption 360 24.7129 6.8828 7.8750 46.3227
RDP R&D personnel 360 6.2481 8.9534 0.0085 45.7342
RD R&D capital 360 2.1496 3.2228 0.0040 18.6503
GDP Economic Development 360 1.4276 1.2059 0.0638 6.6918
OPEN Trade openness 360 0.3122 0.3750 0.0086 1.7215
URB Urbanization level 360 0.5353 0.1371 0.2745 0.8961
Results and Discussion Corruption and GTFEE

First, we examine the impact of corruption on GTFEE. This study uses OLS, FE, RE, and feasible generalized least square (FGLS) to analyze the relationship between corruption and GTFEE. The regression results show that the coefficient of corruption is significantly negative, indicating that corruption inhibits the increase of GTFEE. Another cause of the endogeneity problem is that conventional models such as fixed effects (FE) and random effects can have estimation bias. To account for this, the lagged term of GTFEE is introduced in our empirical model. Moreover, the GMM approach, which can obtain consistent estimation results, was applied. To test whether the instrumental variable is overidentified, this paper reports the Hanse test. Table 2 shows the corresponding estimation results based on the GMM estimators. We find that the lag term of GTFEE is positive, which means the GTFEE has obvious dependency features. An important reason is that the corruption of officials seeking profit by power exacerbates the misallocation of resources such as labor and capital, as well as market information asymmetry, which reduces GTFEE. This conclusion is consistent with Hao et al. (2020).

The results of corruption on GTFEE.

Variable OLS FE RE FGLS GMM
L.lngtfee — — — — 0.568***
— — — — (43.070)
Lncor −0.077*** −0.055** −0.066** −0.062*** −0.047***
(−2.883) (−2.119) (−2.427) (−4.829) (−6.292)
Lnrd −0.270*** −0.091*** −0.136*** −0.254*** −0.002
(−8.450) (−3.076) (−4.677) (−24.889) (−0.201)
Lngdp 0.292*** 0.298*** 0.241*** 0.302*** −0.015
(10.139) (6.286) (6.049) (22.294) (-0.683)
Lnrdp 0.169*** 0.060*** 0.100*** 0.148*** 0.011***
(5.718) (2.660) (4.264) (13.117) (3.457)
Lnopen 0.119*** 0.015 0.039*** 0.115*** 0.023***
(10.341) (1.426) (3.647) (24.411) (7.106)
Lnurb 0.510*** −0.547*** −0.134 0.527*** 0.065**
(9.018) (−5.844) (−1.638) (18.922) (2.246)
_cons −0.077 −0.882*** −0.582*** −0.099** —
(−0.945) (−8.370) (−5.718) (−2.329) —
AR(1) — — — — −1.54
— — — — [0.124]
AR(2) — — — 0.89
— — — — [0.371]
Hansen test — — — — 28.67
— — — — [0.683]
R 2 /Wald test 0.7900 0.2264 0.4796 15,738.81*** 4,412.36***
N 360 360 360 360 360

***, **, and * are significance at the 1, 5, and 10% levels; t or z statistics in ().

 Market Segmentation and GTFEE

Given the endogeneity problems, this paper uses system GMM to examine the impact of market segmentation on GTFEE (Table 3). It can be found that the negative relation between market segmentation and GTFEE is clear. Obviously, after increasing the control variable continuously, we find the estimated coefficient is still negative, that is the above conclusion is valid. According to the regression coefficient, every 1% increase in market segmentation can reduce the GTFEE of China by 0.003%, which shows that the region can improve GTFEE through the process of market integration and industrial agglomeration. The reason for this result may be that market segmentation reduces the flow of factors such as labor and technology. Local governments ignore comparative advantages and carry out local protection, which leads to misallocation of resources, hinders technological innovation and GTFEE (Ren et al., 2021a). Therefore, the government can increase GTFEE by breaking the market segmentation, allowing the free flow of production factors, and enhancing regional market integration.

The empirical results of market segmentation on GTFEE.

Variable SYS-GMM SYS-GMM SYS-GMM SYS-GMM SYS-GMM SYS-GMM
L.lngtfee 1.025*** 1.011*** 1.030*** 1.031*** 0.968*** 0.964***
(1,066.198) (474.876) (303.568) (303.205) (179.048) (178.149)
lnsegm −0.007*** −0.003*** −0.005*** −0.005*** −0.003*** −0.003***
(−121.775) (−4.642) (−6.382) (−5.925) (−4.384) (−3.937)
Lnrd — 0.003*** 0.010*** 0.013*** 0.009*** 0.004
— (7.659) (14.188) (20.603) (3.579) (0.928)
lngdp — — −0.016*** −0.015*** −0.004 −0.001
— — (−8.589) (−8.426) (−1.102) (−0.334)
lnrdp — — — −0.005*** −0.005* -0.001
— — — (−5.598) (−1.723) (−0.282)
Lnopen — — — — 0.022*** 0.020***
— — — — (16.165) (11.298)
Lnurb — — — — — 0.018*
— — — — — (1.828)
_cons −0.015*** −0.034*** −0.020*** −0.010* 0.001 0.004
(−3.404) (−57.662) (−3.788) (−1.784) (0.081) (0.396)
AR(1) −1.71 −1.67 −1.70 −1.69 −1.69 −1.69
[0.087] [0.095] [0.089] [0.091] [0.090] [0.091]
AR(2) 0.74 0.78 0.76 0.77 0.74 0.74
[0.458] [0.438] [0.446] [0.441] [0.461] [0.458]
Hansen test 29.69 28.78 28.44 28.15 28.21 27.50
[0.328] [0.321] [0.288] [0.254] [0.208] [0.193]
N 360 360 360 360 360 360
The Moderating Effect of Market Segmentation and Corruption on GTFEE

The conclusion of the above tests show that corruption and market segmentation have a significant negative impact on GTFEE. In this part, we analyze the moderating effect of market segmentation through the SYS-GMM method and the stepwise regression method. Table 4 indicates the empirical result of the moderating effect. We found that the interaction term coefficients are all significantly negative. It shows that as market segmentation increases, the negative effect of corruption on the GTFEE is also increasing. Moreover, local government corruption exacerbates market fragmentation and reduces innovation resource flows, resource allocation efficiency, and GTFEE (Zhao et al., 2021).

The estimated results of moderating effect.

Variable SYS-GMM Model
L.lngtfee 1.019*** 1.011*** 1.007*** 1.018*** 0.974*** 0.963***
(730.061) (178.951) (88.893) (151.642) (125.911) (85.650)
Lnsegm 0.357*** 0.304*** 0.323*** 0.358*** 0.317*** 0.312***
(12.791) (21.689) (10.786) (12.780) (13.854) (14.042)
Lncor −0.962*** −0.828*** −0.865*** −0.956*** −0.823*** −0.800***
(−13.087) (−22.466) (−11.005) (−13.257) (−13.882) (−13.283)
lncor*lnsegm −0.115*** −0.098*** −0.103*** −0.114*** −0.100*** −0.098***
(−13.043) (−23.114) (−10.532) (−12.887) (−13.444) (−13.368)
Lnrd — 0.002** 0.004* −0.004 0.006 −0.002
— (2.082) (1.832) (−0.872) (1.584) (−0.199)
Lngdp — — −0.003 −0.001 0.004 0.011
— — (−0.394) (−0.126) (0.488) (0.893)
Lnrdp — — — 0.006 −0.007 −0.003
— — — (1.255) (−1.355) (−0.433)
lnopen — — — — 0.019*** 0.019***
— — — — (7.149) (6.170)
Lnurb — — — — — 0.028
— — — — — (0.746)
_cons 3.009*** 2.579*** 2.730*** 3.013*** 2.629*** 2.574***
(12.958) (21.444) (11.194) (13.152) (14.475) (14.668)
AR(1) −1.83 −1.79 −1.76 −1.78 −1.82 −1.81
[0.067] [0.073] [0.079] [0.075] [0.069] [0.070]
AR(2) 0.33 0.41 0.44 0.37 0.41 0.42
[0.743] [0.681] [0.662] [0.710] [0.685] [0.678]
Hansen test 27.71 28.22 24.89 26.64 25.84 25.62
[0.321] [0.850] [0.356] [0.775] [0.213] [0.179]
N 360 360 360 360 360 360
The Results of the Threshold Model

This paper chooses corruption and market segmentation as threshold variables, respectively. The p-values of Wald statistic and AR 2) are greater than 0.1 (Table 5), showing that the variables have no serial correlation and the instrumental variable is valid. The specific threshold results are shown in Table 6.

The threshold tests.

Variable Threshold value Wald p-Value 95% Confidence interval
Corruption −9.2276 498.4594 0.0000 −9.5527 −7.3389
−9.2276 498.4594 0.0000 −9.5527 −7.3389
Market segmentation 3.3965 80.99856 0.0000 2.6452 3.6106
3.3965 80.99856 0.0000 2.6452 3.6106

The results of threshold model.

Variable Cor Segm
SYS-GMM DIF-GMM SYS-GMM DIF-GMM
lngtfee it-1 0.981*** 0.561*** 1.018*** 0.506***
(111.27) (27.39) (107.25) (24.57)
lnrd −0.010*** −0.002 0.016*** −0.038***
(3.40) (−0.13) (5.14) (−4.00)
lngdp −0.011* −0.001 −0.045*** 0.056***
(−1.75) (−0.03) (−5.60) (3.67)
lnrdp 0.001 0.007 0.012*** 0.025***
(0.22) (1.54) (3.78) (4.17)
Lnopen 0.011*** 0.008*** 0.005** -0.029***
(5.33) (9.69) (2.05) (−7.39)
q i t ≤ C −0.033*** −0.069*** −0.0037*** −0.006***
(−3.44) (−4.45) (−.13) (−3.89)
q i t > C −0.040*** −0.074*** −0.0044*** −0.007***
(−3.95) (−4.67) (−4.05) (−4.79)
_cons 0.134*** — −0.017*** —
(4.89) — (−3.08) —
AR (1) −1.84 −1.73 −1.71 −1.68
[0.066] [0.083] [0.088] [0.094]
AR (2) 0.58 0.72 0.78 0.82
[0.563] [0.472] [0.437] [0.412]
Hansen test 27.87 28.11 27.70 27.75
[0.926] [0.512] [0.684] [0.889]
Wald test 392,589.80*** 6,106.34*** 692,995.96*** 1,499.68***
[0.000] [0.000] [0.000] [0.000]
N 360 360 360 360

Corruption is being considered as the threshold variable to investigate the effect of market segmentation on GTFEE under different levels of corruption. We find that as corruption exacerbates, the regression coefficient of market segmentation increases, indicating that the more serious the corruption is, the greater the negative effect of market segmentation on GTFEE. The reason for this may be because with the increase of corruption, to prevent the entry of other competitors, local government officials often choose to set up administrative barriers to protect local enterprises to obtain local GDP growth, which further hinders the free flow of resources such as technology and talent, and reduces energy efficiency.

Furthermore, this paper examines the role of corruption on GTFEE under different market segmentation levels. The study found that with the exacerbation of market segmentation, the regression coefficient of corruption also increases, indicating that severe market segmentation leads to the stronger negative effect of corruption on GTFEE. The reason for this phenomenon is that in areas with severe market segmentation, local officials excessively use administrative power to interpose the allocation of resources. In this case, the market divides the competition of enterprises, restricts the expansion of the production possibility Frontier of enterprises, and distorts the technical efficiency of firms, thereby reducing the GTFEE.

 Robustness Test

To verify the reliability of the above results (Tables 1–5), the robustness test is conducted on the relevance between corruption, market segmentation, and GTFEE. The internal relevance between the three variables is investigated by taking the GTFEE that is measured through DDF-GML model as the explained variable. Furthermore, we re-estimate the model using the OLS and the spatial econometric approach. The results in Table 7 show that the main variables (lncor, lnsegm, and lncor×lnsegm) are negative, which is consistent with the results above and confirms the robustness of the conclusion.

The results of the robustness test.

Variable OLS SAR OLS SAR SAR
lncor −0.050* −0.063** — — −0.231
(−1.86) (v2.36) — — (−1.49)
Lnsegm — — −0.032* −0.033* 0.083
— — (−1.67) (−1.78) (1.47)
lncor×lnsegm — — — — −0.030*
— — — — (−1.66)
Lnrd 0.047 0.001 0.010 -0.001 0.034
(1.46) (0.03) (0.24) (-0.02) (1.08)
Lngdp −0.039 −0.040 −0.050* −0.043 −0.099
(−1.36) (−1.45) (−1.73) (−1.52) (−1.58)
Lnrdp 0.034 0.070** 0.069* 0.068** −0.074***
(1.14) (2.04) (1.91) (1.96) (−2.90)
Lnopen 0.106*** 0.113*** 0.126*** 0.121*** −0.009
(9.18) (8.76) (9.54) (9.42) (−0.84)
Lnurb 0.318*** 0.252*** 0.302*** 0.279*** −0.032
(5.60) (4.50) (5.21) (4.96) (−0.30)
_cons −0.012 — −0.463*** — —
(−0.15) — (−2.96) — —
Spatial_rho — 0.198*** — 0.165*** -0.369***
— (3.83) — (3.26) (-4.36)
Sigma2_e — 0.020*** — 0.020*** 0.003***
— (13.42) — (13.44) (13.27)
R 2 0.703 0.784 0.714 0.792 0.223
N 360 360 360 360 360
Conclusion and Policy Implication

This study first establishes an SBM model to measure GTFEE by using considering undesirable outputs, environmental supervision indicator system as unexpected output, and then use the price-relative price method to calculate the market segment index. Second, taking China as the research object, this paper conducts the basic linear regression model to discuss the relationship between corruption, market segmentation, and GTFEE. Finally, the threshold model is adopted to analyze the effect of corruption and segmentation on GTFEE. The main empirically test conclusion is as follows.

First, we found that corruption has a significantly negative effect on GTFEE. With the increase of corruption, the GTFEE will be reduced. Second, when adding different control variables gradually, the stepwise regression method is adopted to test the effect between market segmentation and GTFEE. This study found that market segmentation reduces the GTFEE. In the robustness test, the conclusion about the influence of corruption on GTFEE, market segmentation on GTFEE are both valid. Finally, corruption and market fragmentation significantly reduce GTFEE when they are regarded as threshold variables. Based on the above research, this paper puts forward some recommendations.

1) GTFEE has obvious spatial dependence and is greatly affected by the GTFEE of adjacent areas. If the GTFEE of adjacent areas is higher, the local GTFEE will increase faster. To promote market integration and achieve regional integration, the government should increase the intensity of supervision and governance of official corruption, which can make government governance more transparent, prevent local governments from taking bribes, and reduce officials’ incentives to formulate local protection policies. Furthermore, corruption and market segmentation are alleviated, factor resources can flow freely, and market integration is promoted, which is conducive to the improvement of GTFEE.

2) In the process of economic transformation, the government should focus on market-oriented reforms, reduce excessive intervention, release more power to the market, and ensure the free flow of resources. The main reason is that if the government intervenes too much, the local government will use the resources and power at its disposal to set up and seek rents, breed corruption, and exacerbate market segmentation. The government should improve the degree of market integration, and increase the flow of resources, talents, and technology between regions. Moreover, break regional protectionism, learn from regions with high GTFEE, introduce talents, learn advanced technology, promote the free flow of factor resources and improve GTFEE.

3) Local governments should find and solve the key factors restricting the improvement of local GTFEE according to their regional energy advantages. Implement differentiated energy efficiency improvement policies for different development stages. In addition, optimize the energy consumption structure, reduce the demand for fossil energy such as coal, and strengthen the development and utilization of green and clean energy and renewable energy. As the developed eastern regions have the advantages of geographical location, traffic conditions, and economic opening, it is necessary to maintain the regional advantages, reduce the degree of market segmentation, promote the free flow of energy factors between regions, and establish a unified, and orderly energy market. Moreover, these regions need to vigorously develop low-energy-consuming industries. For the central and western regions with relatively backward economic and technological development, it is necessary to strengthen inter-regional cooperation and promote the free flow of production factors and technology. They also need to appropriately raise local energy prices and improve the pricing mechanism in the energy market.

Although this paper examines the relationship between corruption, market segmentation, and GTFEE, there are still some limitations. For example, this article uses provincial data due to data availability. It would be interesting to study this question at the industry or city level in the future if more detailed data are available. In terms of research methods, the spatial econometric models and techniques can be used to analyze the spatial spillover effects of corruption and market segmentation on the GTFEE in future research. In addition, future research can focus on the impact of corruption, market segmentation on carbon emissions, economic growth or excess capacity.

 Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: http://www.stats.gov.cn/tjsj/ndsj/.

 Author Contributions

MD: Data curation, Formal analysis, Writing-original draft. SR: Software, Visualization, Empirical analysis. QZ: Funding acquisition, Supervision, Writing–original draft.

 Funding

This work was supported by the Beijing Commecial Development Research Center (JD-YB-2022-050) and Major Program of National Social Science Foundation of China (21&ZD151).

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

The eight categories of commodities included are grain; clothing; shoes and hats; beverages, tobacco and alcohol; cultural and sports goods; medicines; books, newspapers and magazines; daily necessities and fuel.

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