Volume 19, Issue 3 pp. 353-373

RESEARCH ARTICLE

Open Access

How does factor market distortion affect green innovation? Evidence from China's sustainable development demonstration belt

Feifei Tan,

Corresponding Author

Feifei Tan

[email protected]

orcid.org/0000-0001-8243-7457

Jiangsu Industry Development Research Institute, Nanjing University of Finance & Economics, Nanjing, Jiangsu, China

Correspondence Feifei Tan, Jiangsu Industry Development Research Institute, Nanjing University of Finance & Economics, Wenyuan Rd 3#, Nanjing 210023, China.

Email: [email protected]

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Chenyu Sun,

Chenyu Sun

Jiangsu Industry Development Research Institute, Nanjing University of Finance & Economics, Nanjing, Jiangsu, China

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Feifei Tan,

Corresponding Author

Feifei Tan

[email protected]

orcid.org/0000-0001-8243-7457

Jiangsu Industry Development Research Institute, Nanjing University of Finance & Economics, Nanjing, Jiangsu, China

Correspondence Feifei Tan, Jiangsu Industry Development Research Institute, Nanjing University of Finance & Economics, Wenyuan Rd 3#, Nanjing 210023, China.

Email: [email protected]

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Chenyu Sun,

Chenyu Sun

Jiangsu Industry Development Research Institute, Nanjing University of Finance & Economics, Nanjing, Jiangsu, China

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First published: 18 June 2024

https://doi.org/10.1002/ise3.89

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Abstract

Green innovation meets the simultaneous demands of green and innovation-driven development models when it is deemed as a key to realizing a green economic transition. However, factor market distortion impedes China's green development through factor mobility and resource allocation. Under such circumstances, we detect whether and how factor market distortion affects green innovation from various perspectives in the case study of China's Sustainable Development Demonstration Belt. The findings demonstrate that the distortion has a pronounced inhibiting effect on the green innovation growth in the study area. For the eastern and more-developed cities, factor market distortion considerably inhibits green innovation improvement, while the impact is less pronounced in the western (or central) and less developed cities. Furthermore, the factor market distortion negatively affects green innovation through some effective paths, like energy efficiency and environmental regulation. From a spatial perspective, the green innovation's spillover effect could be reduced by both the distortions of the labor market and capital market. Thus, this study would provide strong theoretical support for enhancing the factor market system and improving the multiregional green innovation power in China, as well as scientific suggestions on transitioning to China's sustainable development.

1 INTRODUCTION

Since the “Chinese miracle,” economic prosperity has caused serious environmental problems and exacerbated several social issues, posing severe challenges for transitioning to sustainable development (Ling et al., 2022; Xiao & Zhao, 2017; Xie et al., 2019). China's sustainable development objectives focus on the coordination of development between environmental and economic developments (Chen & Lee, 2020; Tan et al., 2017; Wen et al., 2022). For instance, China's high-quality development plans underline a green and innovation-driven economy when green innovation is viewed as an effective instrument for balancing economic profitability and environmental sustainability (Awan et al., 2021; Dai et al., 2021). Moreover, since its action of catalyst for breaking the bottleneck of sustainable development, green innovation is more helpful for alleviating the ecological environment pressure (Aldieri et al., 2020). Consequently, the issue of how to enhance green innovation power has attracted worldwide attention.

The transformation brought about by China's economic miracle is undeniable. However, it has also left its mark on the environment and led to certain market distortions. In the quest for economic growth and environmental sustainability, a nuanced interplay has arisen between these distorted factor markets and the advancement of green innovation. Given the profound implications of this complex relationship, a thorough examination is warranted. Factor markets play a pivotal role in ensuring the optimal allocation of resources (Wang & Kong, 2019). However, when factor prices deviate from their actual scarcity or value, they lead to the misallocation of resources and environmental damage (Yang et al., 2020), stimulate the proliferation of rent-seeking behaviors, and result in the irrational allocation of resources. This consumes too many resources on nonefficient or nongreen projects, reducing the overall economy's resource use efficiency. Moreover, distortions cause excessive use and waste of energy, hindering the improvement of energy use efficiency. Since many green technologies rely on efficient energy use, this situation limits the scope for green innovation.

Moreover, the impact of distorted factor markets extends to the incentive structure for innovation within firms and the effects of environmental regulations. Faced with distorted factor prices, firms prioritize short-term cost minimization over long-term sustainable development initiatives, which may discourage investment in green innovation. As a result, the trajectory and direction of technological innovation may deviate from the requirements of sustainable development and slow down the development and application of green technologies. If firms reap additional benefits from factor price distortions, they may choose to ignore environmental regulations and continue to engage in highly polluting and energy-intensive production activities, to the detriment of the development and application of environmentally friendly technologies. Importantly, it would hinder the transition of the development model and the implementation of China's high-quality development strategy (Xiaoyang & Sheng, 2021; Zhu, 2012). During China's “14th Five-Year Plan” period, emphasis is being placed on optimizing the resource allocation of the factor market and on enhancing innovation capabilities. Therefore, addressing institutional barriers and factor market distortion has become a top priority for promoting green technological innovation.

The Yangtze River Economic Belt is a crucial area for China's economic development and plays a significant role in advancing ecological civilization and achieving green development in the country. It holds significant representativeness and exemplarity. Encompassing a variety of economic development models and stages, ranging from relatively developed areas in the East to less developed regions in the West, it showcases the diversity and imbalance of China's economic growth. Studying this region facilitates a better understanding of the characteristics and trends of China's overall economic development. Furthermore, as a sizable regional entity comprising multiple cities and provinces, the Yangtze River Economic Belt exhibits internal disparities. By comparing the development of green innovation among different cities and provinces, a deeper analysis of the influence of factor market distortion on green innovation can be conducted, leading to more universally applicable conclusions. This, in turn, aids in guiding sustainable development practices in other regions. Therefore, this study will explore the impact of factor market distortion on green innovation in China's sustainable development demonstration belt (Yangtze River Economic Belt), aiming to provide scientific recommendations for strengthening the factor market system, enhancing the capacity for green innovation in multiple regions, and promoting sustainable development in China.

In summary, this study makes three main contributions. First, this study utilizes a comprehensive evaluation framework to analyze the distortion and green innovation and broadens the green innovation evaluation framework. This integrated evaluation approach helps to understand the impact of factor market distortion on green innovation more comprehensively. It provides a more comprehensive methodological paradigm for similar studies in the future. Second, this study demonstrates that the distortion negatively impacts green innovation, as well as identifies the influence path in a systematical way, which provides strong theoretical support for the study area and other similar multiscale areas to enhance regional green development level and self-dependent innovation capability. By deeply analyzing the impact pathways, this study provides clues to policymakers on solutions and improvement measures for factor market distortion and provides guidance for achieving more sustainable development goals. Finally, it will confirm the spatial spillover of the distortion on green innovation and clarify regional variability of green innovation, which will help to pinpoint the problem more precisely and formulate more effective policy measures. The following sections make up the remaining portion of this study: After introducing the data and methodology, the regression results are presented. Finally, the fifth section draws conclusions and offers policy recommendations.

2 LITERATURE REVIEW

Currently, research on factor market distortion primarily focuses on the essence, measurement, and influencing factors of factor markets. Factor market distortion is the term used to describe the inefficient distribution of productive factors in a national economy as a result of market imperfections, which are induced by information asymmetry, market segmentation, industry monopolies, government intervention, and institutional constraints. Research on factor market distortion typically focuses on several perspectives, including labor (Tikoudis & Kurt, 2021), capital (Beladi et al., 2019), land (Britos et al., 2022), and energy (Gao & Yi, 2022; Xu & Lin, 2022). A series of adverse social and economic effects are caused by factor market distortion. First, there is evidence that distorted factor markets lead to exacerbating environmental pollution, such as the increase of industry pollution intensity (Ji, 2020), the deterioration of haze pollution, and CO₂ emissions performance (Lin & Du, 2015). Second, factor market distortion inhibits the effective allocation of resources, restrains the enterprise innovation efficiency, and prevents the upgrading and transformation of economic growth mode (Li et al., 2017; Xiaoyang & Sheng, 2021). Third, the factor market distortion has inevitably resulted in a sharp reduction of industrial total factor productivity (Brandt et al., 2013). Additionally, other scholars have confirmed that factor market distortion also contributes to income inequality and rent-seeking behavior. As such, it is imperative to correct the factor of market distortion to optimize resource allocation. Besides， the measurement methods for factor market distortion mainly include the production function approach (Hsieh & Klenow, 2009), stochastic frontier analysis (Ouyang & Sun, 2015), shadow pricing method (Atkinson & Halvorsen, 1980), and marketization index method (Kong et al., 2021; Yin et al., 2018).

A great deal of studies are emphasized due to the increasing interest in green innovation efficiency all over the world. The Slack-Based Model (SBM) and traditional Data Envelopment Analysis (DEA) model are widely used to evaluate green innovation efficiency (Cheng & Yin, 2016; Zhao et al., 2021). A considerable body of in-depth research has led to some specific models for measuring the efficiency of technology innovation, including the DEA-Malmquist productivity index approach (Liu, 2015), super-SBM model (Li & Zeng, 2020), SBM-DDF model (Zhang et al., 2020), the SBM-DEA model (Miao et al., 2021), the Malmquist Data Envelopment Analysis Index (Luo et al., 2019), and the Epsilon-Based Measure global Malmquist-Luenberger (ML) model (Long et al., 2020). There is also a great deal of influencing factors of green innovation efficiency, including industrial agglomeration (Zeng et al., 2021), foreign capital structures (Cheng & Yin, 2016), environmental regulation (Fan et al., 2021), corporate environmental R&D investments (Dangelico et al., 2017), high-speed rail (Huang & Wang, 2020), and internet development and entrepreneurship (Hong et al., 2016; Kafouros, 2006; Wu et al., 2021). With the initiation of the national smart city project, research indicates that smart city pilot initiatives can enhance the efficiency of urban green innovation, demonstrating distinctive regional variations. Furthermore, in terms of market-driving factors, financial efficiency can have adverse effects on green innovation, whereas financial structure, financial investments, and policies can give a positive one (Luo et al., 2020).

By summarizing a great deal of literature, in general, prior research establishes a theoretical framework and logical basis for this study. The link between factor market distortion and green innovation, however, has received little attention. At the same time, most empirical studies on factor market distortion and green innovation efficiency remain on panel data at the provincial level, and the exploration at the city level is yet to be enriched. Therefore, in seeking global sustainable development, detecting whether and how factor market distortion affects green innovation is crucial for improving the factor market system and enhancing green development level and self-dependent innovation capability. This study takes China's sustainable development demonstration belt as an example and adopts the production function method to evaluate factor market distortion and the Super-SBM model to calculate green innovation efficiency. On this basis, the impact of factor market distortion on urban green innovation efficiency is empirically investigated. The regional heterogeneity and spatial correlation of the impact are explored to identify its impact mechanism.

3 DATA AND METHODOLOGY

3.1 Study area and data resource

3.1.1 Study area

The Yangtze River Economic Belt (China's Sustainable Development Demonstration Belt), which is called China's “golden belt” and “ecological corridor,” serves as a treasure for environmental security as well as an important economic radiation belt (Luo et al., 2020). The rapid economic growth, however, places pressure on YREB to simultaneously advance the economy and safeguard the environment (Xu et al., 2020). Under this circumstance, YREB should explore how to successfully coordinate innovation, economy, and environmental development while giving priority to ecology and green development. To meet the dual needs between the environmental and economic benefits, the enhancement of green innovation should be one of the key issues in efficiency and quality reform. It is noteworthy that in the YREB region, few studies have examined the long-run evolution of green innovation, especially taking factor market distortion into account. Consequently, under China's practical background of green development strategies and innovation-driven development, this study detects whether and how factor market distortion affects green innovation when applying the YREB region as a case study.

3.1.2 Data resource

From 2006 to 2020, 104 cities' panel data from the Yangtze River Economic Belt are used for this study. Therein, the original data for evaluating factor market distortion and green innovation are gathered from many regional and urban statistical yearbooks of China (2007−2021) and some other statistical yearbooks from the corresponding regions or government reports. Besides, the People's Bank of China offered the 1-year bank loan interest rate. Due to a fraction of missing data in individual cities and years, the interpolation method was used to make some supplements in the data process.

3.2 Evaluation of factor market distortion

There are many evaluation methods for factor market distortion, including the shadow price method, marketization index method (Kong et al., 2021; Yin et al., 2018), stochastic frontier analysis (Ouyang & Sun, 2015), and production function method. Among such approaches, the production function model can accurately depict the actual meaning of the distortion by assessing the marginal output of the production factor in a direct manner. In addition, the production function model has an easy calculation process and can well evaluate the absolute degree of distortion in different factor markets. Therefore, we select the Cobb-Douglas production function model to construct and calculate the factor market distortion's degree.

Equation (1) shows the C-D production function:

{Y}_{it}={A}_{it}{K}_{it}^{\alpha }{L}_{it}^{\beta }.

()

Taking logarithm as follows:

ln{Y}_{it}=ln{A}_{it}\,+\alpha ln{K}_{it}\,+\beta ln{L}_{it}.

()

In Equations (1)–(2), ${Y}_{it}$ is measured by regional real GDP and denotes the output of the ith city in the tth period. ${K}_{it}$ presents the capital input determined by each city's total fixed asset investment, and it is further estimated as capital stock using the perpetual inventory method. ${L}_{it}$ denotes the labor input calculated by the total employed population at each year. $\alpha$ and $\beta$ denote the elasticity of capital output and labor output, which can be evaluated through the regression analysis. And we assumed $\alpha +\beta =1$ , which means the return to scale of factor input is constant.

Further, Equations (3)–(4) show the marginal output of capital and labor:

MP{K}_{it}\,=A\alpha {K}_{it}^{\alpha -1}{L}_{it}^{\beta }=\alpha \frac{{Y}_{it}}{{K}_{it}},

()

MP{L}_{it}\,=A\beta {K}_{it}^{\alpha }{L}_{it}^{\beta -1}=\beta \frac{{Y}_{it}}{{L}_{it}}.

()

In Equations (5)–(6), we compute

Dis{K}_{it}

and

Dis{L}_{it}\,

using the capital and labor input and output's deviation degree. As shown in Equations (5)–(6), if the ratio exceeds 1, it presents that there is a negative distortion. If it is less than 1, it forecasts that there is a positive distortion, and if the ratio is equal to 1, it presents that there is no distortion. At last, based on

MP{L}_{it}

and

MP{K}_{it}

, the total factor market distortion (

Di{s}_{it}

) is received. The models are as in Equations (5-7).

Dis{K}_{it}\,=\frac{MP{K}_{it}}{{r}_{it}},

()

Dis{L}_{it}\,=\frac{MP{L}_{it}}{{w}_{it}},

()

Di{s}_{it}\,=Dis{K}_{it}^{\frac{\alpha }{\alpha +\beta }}Dis{L}_{it}^{\frac{\beta }{\alpha +\beta }}.

()

The 1-year loan interest rate is used to calculate the real capital price of city i in period t as denoted by ${r}_{it}$ . ${w}_{it}$ is the actual labor price, which is evaluated by the average income of urban employees.

3.3 Evaluation of urban green innovation efficiency

3.3.1 Super-SBM model

In this study, we decided to use the Super-SBM model to measure the efficiency of urban green innovation. One of the representative methods to calculate green innovation efficiency is nonparametric (DEA). The redundancy and relaxation of input–output, however, are not considered in the calculation process for the traditional DEA model. To overcome the measurement deviation caused by the above problems, a coupled model of SBM and DEA, as well as considering the relaxation variables, is proposed (Tone, 2001). Furthermore, to avoid the situation where the efficiency of multiple DMUs ultimately equals 1, Tone combined the super-efficiency method proposed by Anderson and Peterson with the SBM model to create the Super-SBM model. Thus, evaluation and ranking can be performed in the event of multiple effective decision-making units with an efficiency value of 1, which enhances the objectivity and accuracy of the comparison results (Tone, 2002). The features of nonangular, nonradial, and convexity of the Super-SBM model have an advantage in measuring the efficiency within resource or environmental constraints (Du et al., 2010). The specific Super-SBM evaluation model is expressed by Equation (8):

\min \,\rho =\frac{1-\frac{1}{m}{\sum }_{i=1}^{m}\frac{{s}_{i\,}^{-}}{{x}_{ik}}}{1+\frac{1}{{q}_{1}+{q}_{2}}\left({\sum }_{r=1}^{{q}_{1}}\frac{{s}_{r}^{+}}{{y}_{rk}}+{\sum }_{t=1}^{{q}_{2}}\frac{{s}_{t}^{b-}}{{b}_{tk}\,}\right)},

()

\begin{array}{c}{\text{s.t.}}\,\sum _{j=1,j\ne k}^{n}{x}_{rj}{\lambda }_{j}-{s}_{i\,}^{-}\le {x}_{ik}\\ \,\sum _{j=1,j\ne k}^{n}{y}_{ij}{\lambda }_{j}+{s}_{i\,}^{+}\ge {y}_{rk}\\ \,\sum _{j=1,j\ne k}^{n}{b}_{tj}{\lambda }_{j}-{s}_{i\,}^{b-}\le {b}_{tk}\\ \,\lambda ,{s}^{-},{s}^{+},{s}^{b-}\ge 0\\ \,i=1,2,\text{\unicode{x02026}},m\\ \,j=1,2,\text{\unicode{x02026}},n\\ \,t=1,2,\text{\unicode{x02026}},{q}_{2}\\ \,r=1,2,\text{\unicode{x02026}},{q}_{1}\end{array},

wherein

{b}_{t}

indicates the unexpected output variable,

{x}_{i}

represents the input variable, and

{y}_{r}

denotes the expected output variable.

\rho

is the core explanatory variable (Urban green innovation).

{q}_{1}

{q}_{2}

, and

m

are the quantities of the expected output variable, unexpected output, and input variables. The weight vector is

\lambda

s=\left({s}_{i\,}^{-},{s}_{r}^{+},{s}_{t}^{b-}\right)

denotes the relaxation vectors of the input variable, expected and unexpected output variables; ρ is the core explanatory variable Urban Green Innovation Efficiency (UGI).

3.3.2 Indicator selection of urban green innovation efficiency

The indicators for evaluating urban green innovation efficiency are constructed, which include seven components: capital inputs, labor inputs, resource inputs, economic outputs, innovation outputs, green outputs, and undesired outputs. First, in terms of inputs, besides considering the traditional R&D costs of new products, environmental governance investments were also considered. Second, in terms of outputs, we chose GDP and the number of green patent applications as indicators of economic and innovation outputs and introduced a new indicator for green outputs, specifically the green coverage of built-up urban areas. Meanwhile, industrial wastewater emissions, smog emissions, and industrial sulfur dioxide are indicated as undesired outputs. The specific details of the green innovation efficiency indicator system are shown in Table 1.

Table 1. Index system for evaluating green innovation efficiency.

Level indicators	Secondary indicators	Tertiary indicators
Input aspects	Capital investment	New product R&D expenditure (10,000 yuan)
	Capital investment	Investment in environmental governance (10,000 yuan)
	Labor input	Number of people engaged in technological and scientific activities (10,000 people)
	Labor input	Number of people engaged in water conservancy public services and urban environment (10,000)
	Resource input	Total water supply (10,000 tons)
	Resource input	Power consumption of the whole society (100 million kWh)
Output aspects	Economic output	GDP of the year (10,000 yuan)
	Innovation output	Number of green patent applications
	Green output	Green coverage rate of built-up area (%)
	Unexpected output	Industrial wastewater discharge (10,000 tons)
		Emission of industrial sulfur dioxide (t)
		Emission of industrial smoke (powder) (t)

3.4 Basic model construction

The baseline regression model (Equation 9) is created to explore whether the distortion of the factor market affects green innovation in China's Sustainable Development Demonstration Belt (Yangtze River Economic Belt).

UG{I}_{it}={\alpha }_{0}+{\alpha }_{1}Di{s}_{it}+{\varphi }_{j}\sum _{j=1}^{n}{Z}_{jit}+{\mu }_{i}+{\gamma }_{t}+{\varepsilon }_{it},

()

where i and t denote city and year. UGI is the explanatory variable of the efficiency of urban green innovation, and Dis represents factor market distortion.

{\gamma }_{t}

represents the fixed effect of year, and

{\mu }_{i}

means the fixed effect of urban. One variable that needs to be noticed in this study is the coefficient of factor market distortion, which is represented by

{\alpha }_{1}

. If

{\alpha }_{1}

is less than 0, it represents that the distortion inhibits the growth of UGI, and vice versa.

{\varepsilon }_{it}

means the stochastic perturbation term. The intragroup autocorrelation is controlled by the robust standard error.

Z represents other control variables that may have some effects on UGI. What's more, open (the degree of external openness), fdi (foreign direct investment), edu (human capital level), inf (infrastructure level), and fin (financial development level) represent control variables in the model. Thereinto, the ratio of total trade to GDP can represent open. The ratio of foreign direct investment to GDP is selected to assess fdi. edu should be reflected by the proportion of college students per 10,000 population. inf should be reflected by the ratio of post and telecommunication services to GDP. The ratio between the deposits and loans of financial institutions and the GDP can help estimate fin.

3.5 Spatial regression model

This study selects Moran's I test (the index is expressed by Equation 10) to measure the spatial linkage between the distortion of factor market and the efficiency of urban green innovation.

Moran^{\prime} s\,I=\frac{n}{{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{W}_{ij}}\times \frac{{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{W}_{ij}({X}_{i}-\bar{X})({X}_{j}-\bar{X})}{{\sum }_{i=1}^{n}{({X}_{i}-\bar{X})}^{2}},

()

n is the number of cities in the YREB. The matrix of spatial weight is represented by

{W}_{ij}

{X}_{i}

and

{X}_{j}

are the observed values of ith and jth city. Given that a range of [−1, 1] for Moran's I index, the value of 0 represents no spatial correlation; a value less than 0 means negative correlation, and a value greater than 0 is positive effect.

Generally, there are some spatial correlations among the geographical and economic behaviors of various areas. This study incorporates the spatial attributes into the analysis framework for detecting the impact of factor market distortion on the green innovation efficiency of the nearby cities. What's more, several econometric models can be adopted according to the influence pattern of the spatial correlation. For example, the Wald test is selected to test the applicability of SDM (Spatial Dubin Model), while the LM test is used to compare SEM (Spatial Error Model) and SLM (Spatial Lag Model). Consequently, our findings show that the first model (SDM) is the most appropriate for the spatial estimation, while SDM can achieve good data fitting effect and its expression is as follows.

UG{I}_{it}=\rho WUG{I}_{it}+{\alpha }_{0}+{\alpha }_{1}Di{s}_{i,t}+{\alpha }_{2}WDi{s}_{it}+{\varphi }_{j}\sum _{j=1}^{n}{Z}_{jit}+{\varepsilon }_{it},

()

UG{I}_{it}=\rho WUG{I}_{it}+{\alpha }_{0}+{\alpha }_{1}Dis{K}_{i,t}+{\alpha }_{2}WDis{K}_{it}+{\varphi }_{j}\sum _{j=1}^{n}{Z}_{jit}+{\varepsilon }_{it},

()

UG{I}_{it}=\rho WUG{I}_{it}+{\alpha }_{0}+{\alpha }_{1}Dis{L}_{i,t}+{\alpha }_{2}WDis{L}_{it}+{\varphi }_{j}\sum _{j=1}^{n}{Z}_{jit}+{\varepsilon }_{it},

()

{\varepsilon }_{it}=\lambda W{\varepsilon }_{it}+{\mu }_{it}.

()

where ρ denotes the spatial autocorrelation coefficient. It illustrates the spatial dependence between some observations, that is, the impact direction and impact degree of UGI. The spatial error autocorrelation coefficient is denoted by

\lambda

when

{\mu }_{it}

is the random error vector. The spatial adjacency weight matrix can be denoted by W when

{\varepsilon }_{it}

represents a random error sequence vector.

4 RESULTS AND ANALYSIS

4.1 Can factor market distortion affect green innovation development?

A regression analysis is intended to determine whether the change of green innovation efficiency is hindered by the distortion of factor market at urban scale in China's Yangtze River Economic Belt region. Following the Hausman test, we adopt a double fixed effect model when controlling the individual and time samples. Table 2 summarizes the regression results when column (1) does not contain control variables. At a 5% significance level, the correlation coefficient is −0.0672, denoting that when factor market distortion increases by 1%, UGI decreases by 0.0672%. In column (2), after adding the control variables, factor market distortion is negative at the 5% significance level. The correlation coefficient is −0.0678, indicating that for every 1% increase in factor market distortion, the UGI decreases by 0.0678%. The baseline regression results indicate that, without considering regional heterogeneity, factor market distortion exerts a dampening effect on green innovation efficiency across the entire YREB region.

Table 2. The regression results of factor market distortion on GIE in YREB.

	YREB		East	Middle and west	Big cities	Small and medium cities
	(1)	(2)	(3)	(4)	(5)	(6)
Dis	−0.0672**	−0.0678**	−0.0803***	−0.0740**	−0.301***	−0.0740**
	(0.0261)	(0.0267)	(0.0259)	(0.0312)	(0.0822)	(0.0312)
edu		−0.000104	−0.000385	−0.0000449	−0.000385	−0.0000449
		(0.000129)	(0.000364)	(0.000138)	(0.000364)	(0.000138)
fdi		−0.00210	−0.0735	−0.0118	−0.0735	−0.0118
		(0.00561)	(0.0480)	(0.0337)	(0.0480)	(0.0337)
open		0.0000526	0.00561	0.00203	0.00561	0.00203
		(0.000960)	(0.00778)	(0.00824)	(0.00778)	(0.00824)
inf		−0.00826	0.000503	−0.0000669	0.000503	−0.0000669
		(0.00675)	(0.00117)	(0.00187)	(0.00117)	(0.00187)
fin		−0.00582	−0.0108	−0.0107	−0.0108	−0.0107
		(0.0270)	(0.0167)	(0.00702)	(0.0167)	(0.00702)
Cons_	0.0791	0.0791	0.989**	0.478***	0.989**	0.478***
	(0.0794)	(0.0794)	(0.400)	(0.171)	(0.400)	(0.171)
Year FE	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
N	1560	1560	375	1185	375	1185
R²	0.614	0.615	0.613	0.633	0.613	0.633

Note: (1) “***”, “**”, and “*” represent significance at the levels of 1%, 5%, and 10%, respectively; (2) Standard errors in parentheses.

As the YREB encompasses the eastern, central, and western regions of China, cities with different resource endowments and scales implement varied practices of green innovation, leading to significant heterogeneity in green innovation efficiency outcomes. Therefore, this study empirically examines the heterogeneous impact of factor market distortion on green innovation efficiency. First, we investigate the effects of the distortion on UGI in the western, central, and eastern cities of the YREB region. In columns (3)–(4), factor market distortion passed the 1% level test in the eastern region and the 5% level test in the central and western regions. This indicates a significant inhibitory effect of factor market distortion on urban green innovation efficiency. The coefficients of distortion to UGI are −0.0803 in the east region and −0.0740 in the middle and west regions. This indicates that factor market distortion has a stronger negative effect on urban green innovation efficiency in the eastern region compared with the central and western regions. The disparity may be attributed to several factors. First, the industrial structure in the eastern region is likely more diversified and highly developed, involving a greater presence of high-tech and innovative industries. Consequently, factor market distortion may more directly affect the development and innovation within these sectors. Second, cities in the eastern region typically have higher population density and scarcer land resources, leading to more pronounced environmental pollution issues. Factor market distortion could result in improper resource allocation and environmental degradation, thereby affecting the implementation and efficiency of green innovation.

In addition, considering that the impact of factor market distortion on the efficiency of urban green innovation may vary with city size, we divided all the cities into two types: Small and medium-sized cities with a population of less than 1 million and big cities with a population exceeding 1 million. Based on column (5), the coefficient is −0.301 with a 1% significance level, denoting that the distortion obviously hinders the growth of UGI in Big cities. UGI will be reduced by 0.301% with every 1% increase in factor market distortion. In column (6), it can be found that the coefficient is −0.0740, with a significance level of 5%, and that the inhibition of big cities is four times that of small and medium-sized cities, indicating that big cities are more inhibited by the distortion of the factor market. This may be related to factors such as industrial structure, market competition, resource allocation, government policies, and the clustering effect of talent.

4.2 How does factor market distortion inhibit green innovation development?

4.2.1 The intermediate path of energy efficiency

The lower prices of production factors do not adequately represent the costs of externality and scarcity, which stimulates energy waste (Ouyang et al., 2018) and also hinders the enhancement of energy efficiency such as electricity and fossil (Yin et al., 2018). Additionally, in the absence of a sound monitoring system for government officials, absolute government control over the factors allocation would bring some rent-seeking links between some enterprises and governments. If some enterprises focus on pursuing political connections rather than increasing innovation investment, the breeding of rent-seeking behavior and the reduction of innovation investment (Kong et al., 2021) will bring about a deterioration in green innovation.

Energy efficiency is often denoted by the ratio of energy consumption and economic output, and the value is processed by logarithm. It means the higher ratios indicate higher energy efficiency. In Table 3, the specific intermediate effects resulting from factor market distortion, energy efficiency, and green innovation efficiency are denoted in columns (1) and (2). Obviously, the coefficient from the distortion level to energy efficiency is −0.0400, which is negative at the 1% level. Energy efficiency decreases by 0.0400% for every 1% increase in factor market distortion. After including the control variables, the coefficient is −0.0349, which is significantly negative at a 5% significance level. This is consistent with the research findings of Ouyang et al. and Yin et al., indicating that distortions in the factor market will lead to a decrease in energy efficiency. Hence, excessively low factor prices and government control of factor allocation can give rise to wasted energy and decreased investment in innovation, thus influencing green innovation.

Table 3. The regression results of the intermediate mechanism of energy efficiency and environmental regulation.

	Energy efficiency		Environmental regulation intensity	Environmental regulation effect
	(1)	(2)	(3)	(4)
Dis	−0.0400***	−0.0349**	−0.0632*	−0.0312**
	(0.0155)	(0.0142)	(0.0357)	(0.0130)
edu		0.000525***	−0.000195***	−0.000219***
		(0.000167)	(0.0000593)	(0.0000571)
fin		−0.173***	−0.00926	−0.00305
		(0.0154)	(0.0131)	(0.0130)
fdi		−0.00806**	0.00499**	0.00257
		(0.00378)	(0.00245)	(0.00241)
open		0.00292***	0.000917***	0.000476**
		(0.000581)	(0.000340)	(0.000232)
inf		−0.00824***	0.000494	0.000491
		(0.00292)	(0.00335)	(0.00318)
_cons	9.379***	9.685***	−0.498*	0.682***
	(0.0452)	(0.119)	(0.279)	(0.0854)
Year FE	YES	YES	Year FE	YES
City FE	YES	YES	City FE	YES
N	1560	1560	1560	1560
R²	0.979	0.983	0.591	0.718

Note: (1) “***”, “**”, and “*” represent significance at the levels of 1%, 5%, and 10%, respectively; (2) Standard errors in parentheses.

4.2.2 The intermediate path of environmental regulation

In the long run, according to the theory of Porter hypothesis, some enterprises will be encouraged by environmental regulations and participate in technological innovation and development activities for costs and sustainable profits (Porter & Linde, 1995). In fact, effective environmental regulation provides the guidelines for improving innovation efficiency and alleviating environmental pollution. Therefore, both the green development level and technology innovation level can be improved by good environmental legislation (Peng et al., 2021; Qiao et al., 2022). Nevertheless, when there are weaker and weaker positive effects from environmental regulation on the green innovation level, factor market distortion may hinder the implication of environmental regulation policies and exacerbate regional environmental damage.

A comprehensive index of environmental regulations is selected to estimate the intensity and impact of environmental regulation. Higher values of the comprehensive index indicate a better status of environmental regulation implementation. In Table 3, columns (3)–(4) show the specific intermediate effects brought by environmental regulation, factor market distortion, and green innovation efficiency. The coefficients of the distortion level on environmental regulation and its intensity are −0.0312 and −0.0632, both negative at the 10% and 5% significance levels. The role of environmental regulation is weakened, which would cause environmental pollution, and affect the designation and implementation of environmental regulatory policies. In summary, the study shows that factor market distortion makes the implementation of environmental regulations more difficult. Moreover, they weaken the positive effects of environmental regulations on the efficiency of urban green innovation and the effectiveness of their implementation.

The empirical results of the mechanism test above indicate that factor market distortion can lead to the locking of extensive modes and deviations from optimal allocation, which hinder the improvement of energy utilization efficiency. Furthermore, they can further generate environmental pollution, increase the difficulty of environmental regulation implementation, and weaken the positive effects and effectiveness of environmental regulation. Through these pathways, the factor market distortion ultimately has a negative impact on urban green innovation efficiency, hindering the development of green innovation.

4.3 How does factor market distortion inhibit green innovation development from the spatial perspective?

4.3.1 Results of Moran's I index

The findings of Moran's I index for factor market distortion and green innovation development are presented in Table 4. At a 1% significance level, the distortion and efficiency both pass the test. The distortions and efficiency have relative strong spatial correlations, which demonstrated by the positive results for Moran's I index. As a result, while analyzing the relations between distortion and efficiency, the spatial spillover effect test was considered after establishing the corresponding econometric model.

Table 4. The results of Moran's I index.

	Moran's I	Z-value	p Value
UGI	0.202	13.910	0.000
Dis	0.100	6.929	0.000
DisK	0.132	9.081	0.000
DisL	0.473	32.483	0.000

4.3.2 Results of spatial regression

This study uses the SDM to detect how the distortion of the factor market impacts the change in green innovation efficiency. The spatial econometric regression results of the two aspects are shown in Table 5. At the 1% significance level, the spatial coefficients for $Dis$ , $DisL$ , and $DisK$ are all positive, showing that UGI possesses clear spatial positive spillover characteristics. So GIE in the Yangtze River Economic Belt can promote that in the adjacent cities.

Table 5. Results of spatial regression from the distortion on UGI.

	(1)	(2)	(3)
WxDis	−0.00938*
	(0.00569)
WxDisK		−0.0419***
		(0.00357)
WxDisL			0.0168***
			(0.00313)
edu	−0.0000283	0.000108***	0.00000102
	(0.0000349)	(0.0000349)	(0.0000348)
fin	−0.0540***	−0.0500***	−0.0559***
	(0.00623)	(0.00594)	(0.00619)
fdi	−0.00858***	−0.0112***	−0.00641***
	(0.00157)	(0.00144)	(0.00148)
open	−0.0132***	−0.0101***	−0.0139***
	(0.00115)	(0.00111)	(0.00111)
inf	−0.00740**	−0.00370	−0.0113***
	(0.00302)	(0.00284)	(0.00297)
ε	0.243***	0.243***	0.243***
	(0.000691)	(0.000720)	(0.000722)
ρ	0.243***	0.243***	0.243***
	(0.000691)	(0.000720)	(0.000722)
sigma2_e	3.472***	3.154***	3.427***
	(0.140)	(0.128)	(0.139)
Year FE	YES	YES	YES
City FE	YES	YES	YES
N	1560	1560	1560
R²	0.001	0.002	0.001

Note: (1) ***, **, and * represent significance at the levels of 1%, 5%, and 10%, respectively; (2) Standard errors in parentheses.

Column (1) shows that the distortion of the factor market has a negative effect on the change of green innovation efficiency at the 1% level, demonstrating there exists an inhibitory relationship between the two when the spatial spillover is considered. The distortions of factor markets not only hinder the local efficiency of green innovation but also reduce the spatial spillover effect of green innovation in the surrounding areas. In columns (2)–(3), specific to the capital and labor markets, the market distortions likewise have a significant negative impact on the green innovation efficiency of neighboring cities. Consequently, the factor market distortion not only reduces local urban green innovation efficiency but also weakens the spatial spillover effect of green innovation efficiency, thereby negatively affecting the improvement of green innovation efficiency in neighboring cities.

4.4 Results of several robustness tests

Since the green innovation efficiency is restrained by the factor market distortion, a series of robustness tests are adopted to confirm and verify the related conclusion. Tables 6 and 7 present the results of the endogeneity test and several other robustness checks, respectively.

4.4.1 Results of the endogeneity test

To mitigate potential endogeneity issues, geographic environmental factors are used as instrumental variables to estimate factor market distortion. Regions with favorable geography facilitate transportation, enabling factors to move more freely, thus reducing distortion levels. Furthermore, geographic factors fulfill the “exclusivity constraint” criterion required of an instrumental variable. This study argues that average altitude is linked to factor market distortion, as locational factors influence the agglomeration and dispersion of these factors. Consequently, we select the region's average altitude as the instrumental variable for our analysis. Table 6 presents the regression outcomes for the instrumental variables.

Table 6. The regression results of instrumental variables.

Variables	(1)	(2)
	First stage	Second stage
	Dis	UGI
iv	−0.0000909***
	(0.0000282)
Dis		−1.023**
		(0.451)
F	10.36
Control variables	YES	YES
Year FE	YES	YES
City FE	YES	YES
N	1560	1560
R²		−1.826

Note: (1) “***”, “**”, and “*” represent significance at the levels of 1%, 5%, and 10%, respectively; (2) Standard errors in parentheses.

The first-stage regression results show that the F-statistic is greater than the critical value of 10 for weak instrumental variable tests, indicating no issue with weak instrumental variables. Additionally, the association between regional average altitude and factor market distortion is significantly negative at the 1% level, demonstrating a linkage between the two. Furthermore, as seen in column (2), the significance is negative at the 5% level, reaffirming the substantial inhibitory effect of factor market distortion on urban green innovation efficiency.

4.4.2 Robustness test with removing extreme values

The selected and minorized variables at the 1% and 99% levels can prevent the deviation from the results caused by some extreme values. So, the extreme values can be eliminated from the data in a more reasonable range. It presents the result of a robust test of extreme values removed in column (1). The coefficient is −0.0611, showing that factor market distortion is negatively associated with urban green innovation at the 5% level.

4.4.3 Reduction of omitted variable bias

To reduce the estimation bias caused by omitted variables, we will incorporate the following control variables into our analysis to further elucidate the relationship between factor market distortion and urban green innovation efficiency.

Urban transportation infrastructure

To foster the development of an innovation network, transportation infrastructure improvement is essential. On the one hand, the improvement of transport conditions has a favorable impact on the free flow of factors, thus enhancing the efficiency of resource allocation and alleviating the degree of factor market distortion; on the other hand, the investment in transport infrastructure construction will increase the urban–rural gap, resulting in regional development imbalance and triggering factor market distortion; some scholars have argued that the opening of high-speed rail facilitates the flow of innovation factors, which in turn contributes to the enhancement of the efficiency of green innovation (Huang & Wang, 2020). Therefore, additional controls on traffic infrastructure factors including the opening of hig (high-speed railway) and roa (highway density) are made based on the baseline model (1). To be specific, the former is denoted by 0 or 1, 0 indicating unopened and 1 indicating opened. The latter is represented by the highway mileage index per square kilometer administrative area.

Policy environment

A favorable policy environment can motivate enterprises to embrace green innovation to alleviate the strain of emission reduction and energy conservation and, subsequently, to enhance regional green innovation capability (Peng et al., 2021; Qiao et al., 2022). Therefore, the policy environment indicator is added to reduce the bias brought by the level of policy environment to the empirical test. The proportion of word frequency of environmental protection-related terms in local government work reports to the number of words in the full report is used as a proxy indicator of the policy environment, denoted as epf.

In columns (3)–(5) of Table 7, the coefficients are −0.0670, −0.0649, and −0.0659 at the 5% significant level. After accounting for hig, the relationship between Dis and UGI remains negatively significant at the 5% level, with UGI declining by 0.0670 units for every unit increase in Dis. Similarly, when adjusting for roa, the effect of Dis on UGI is consistently negative and significant at the 5% level, resulting in a decrease of 0.0649 units in UGI for each unit increase in Dis after considering the basic control variables and roa. Additionally, after factoring in epf, the coefficient of Dis on UGI continues to be negative and significant at the 5% level. Specifically, UGI decreases by 0.0659 units for every unit increase in Dis, while controlling for basic variables and epf. The results indicate that after further controlling for omitted variables related to transportation infrastructure and the policy environment, the conclusions from the basic tests remain unchanged. The negative correlation between market distortions and green innovation efficiency persists, reinforcing the robustness of the findings in the article.

Table 7. Results of several robustness tests.

	Winsor		Adding control variables
	(1)	(2)	(3)	(4)	（5）
Dis	−0.0611**	−0.0579**	−0.0670**	−0.0649**	−0.0659**
	(0.0266)	(0.0272)	(0.0266)	(0.0268)	(0.0269)
edu		−0.0000820	−0.000100	−0.000103	−0.000100
		(0.000131)	(0.000129)	(0.000128)	(0.000131)
fin		−0.00220	−0.00202	−0.00285	−0.000929
		(0.00628)	(0.00561)	(0.00569)	(0.00568)
fdi		0.000668	0.0000198	0.0000824	−0.0000108
		(0.000892)	(0.000969)	(0.000949)	(0.000967)
open		−0.00461	−0.00818	−0.00736	−0.00619
		(0.00650)	(0.00675)	(0.00693)	(0.00691)
inf		−0.0121	−0.00527	−0.00549	−0.000660
		(0.0272)	(0.0272)	(0.0270)	(0.0274)
hig			−0.0187
			(0.0209)
roa				−0.0649**
				(0.0268)
epf					0.505
					(0.370)
_cons	0.0627	0.0799	0.0190	0.0358	0.125
	(0.0806)	(0.210)	(0.216)	(0.244)	(0.216)
Year FE	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES
N	1560	1560	1560	1560	1560
R²	0.615	0.618	0.615	0.616	0.618

Note: (1) “***”, “**”, and “*” represent significance at the levels of 1%, 5%, and 10%, respectively; (2) Standard errors in parentheses.

5 CONCLUSIONS AND DISCUSSIONS

5.1 Main achievements

Understanding the linkage between factor market distortion and green innovation is crucial for improving the factor market's resource allocation and enhancing YREB's green development level and innovation capabilities. The effects of the distortion on green innovation, however, have not gotten much attention. Therefore, this study puts the distortion and green innovation into an integrated framework and examines whether and how the distortion affects green innovation. The distortion and green innovation of YREB's 104 cities are measured in this study using a comprehensive index system. Furthermore, it detects how the distortion affects green innovation with the heterogeneity effect, spatial effects, and intermediate paths considered, which can help achieve regional green innovation co-integration. The findings reveal that factor market distortion significantly obstructs the green development and technological innovation levels within China's sustainable development demonstration zones. Specifically, urban green innovation efficiency decreases by 0.0678% for every 1% deeper distortion in factor markets. Notably, regional heterogeneity is evident, while the capacity of green innovation is much more inhibited by factor market distortion in the eastern and more developed cities than in the western (or central) and less developed cities. Furthermore, this study investigates the distortion negatively affects green innovation through some effective pathways, such as energy efficiency and environmental regulation. Finally, this study reveals that urban green innovation and factor market distortion in YREB have a highly positive spatial link. Distortions in labor and capital markets can both weaken green development and innovation levels from the spatial perspective.

5.2 Policy implications and limitations

Our study suggests some policy recommendations according to the research results. First, deepening the reform of factor markets would help narrow the gap between factor markets and product markets to alleviate factor market distortion. The primary task of factor market reform is to enhance the management of factor prices, which requires the government to eliminate absolute and relative factor pricing distortion caused by excessive or improper intervention, as well as the re-establishment of response mechanisms from factor prices (Zhu & Lin, 2022). Simultaneously, factor market distortion involves not only absolute distortions but also relative distortions. In the process of advancing market reform, it is essential to not only reduce barriers to the free flow of factors and minimize overall resource allocation losses but also consider the unequal allocation losses among various factors. Market reform efforts should be approached from the perspectives of multiple factors to prevent an exacerbation of relative factor distortions. It is a top priority to reform the urbanization policies in the labor market, such as the household registration system, when it can reduce the distortion of the labor market. Some well-constructed financial policies may promote the market-based mechanism and accelerate the interest rate marketization on the capital market. In addition, the Government should also formulate scientific urban and population development plans to provide a certain number of jobs.

Second, some efforts should be taken to encourage green technology innovation and advancement. First and foremost, it is essential for both governments and businesses to embrace a philosophy of green and sustainable development. On the one hand, the government's top objective is to expand funding for some green development and technology innovation projects. On the other hand, enterprises need to learn the advanced eco-technologies and processes, and need to introduce the eco-efficient production equipment. What's more, the local government should improve the preferential policies and establish supportive measures associated with green innovation, such as some attractions for the inflow of high-quality talent (Seo et al., 2020). Additionally, establishing an ecological scoring system that links pollution emissions to corporate financing and loans can be considered. This system aims to incentivize green innovation capabilities and elevate the level of green innovation through a series of measures. By tying pollution emission behavior to corporate financing activities, it serves as a mechanism to stimulate improvements in regional green innovation efficiency.

Third, various policies should be introduced to guide the coordination development of environment and technology according to local conditions. In terms of the eastern and more developed cities, the government should take advantage of their comparative advantages, hasten the transition of industrial structure, strengthen the construction of second industries that are compatible with ecological protection and technological advancement, and drive urban green sustainable development through green innovation. In terms of the central (or western) and less developed cities, it is essential to open to the outside world and strive to attract more green and innovative enterprises to develop (Tan et al., 2022). Seizing the significant development opportunities presented by the “Belt and Road” initiative, it is crucial to enhance efforts and policy support for the introduction of energy-efficient, eco-friendly, and technology-innovative industries. This will create favorable conditions for local green innovation development and lay a solid foundation for future growth.

However, the present study still has some limitations. When constructing evaluation indicators or regression models, the choice of control variables and indicators is not comprehensive enough. Considering the availability of relevant data, there are some missing data for some samples, which may lead to some discrepancies with the actual situation. Moreover, the evaluation system for green innovation efficiency should be better established. Also, the discussion of the intermediate route may not be described in detail.

ACKNOWLEDGMENTS

We are grateful for support from the fund project: National Natural Science Foundation of China (72074107).

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

ETHICS STATEMENT

We claim that there is no ethic problem in our work published in International Studies of Economics.

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Volume19, Issue3

September 2024

Pages 353-373

How does factor market distortion affect green innovation? Evidence from China's sustainable development demonstration belt

Abstract

1 INTRODUCTION

2 LITERATURE REVIEW

3 DATA AND METHODOLOGY

3.1 Study area and data resource

3.1.1 Study area

3.1.2 Data resource

3.2 Evaluation of factor market distortion

3.3 Evaluation of urban green innovation efficiency

3.3.1 Super-SBM model

3.3.2 Indicator selection of urban green innovation efficiency

3.4 Basic model construction

3.5 Spatial regression model

4 RESULTS AND ANALYSIS

4.1 Can factor market distortion affect green innovation development?

4.2 How does factor market distortion inhibit green innovation development?

4.2.1 The intermediate path of energy efficiency

4.2.2 The intermediate path of environmental regulation

4.3 How does factor market distortion inhibit green innovation development from the spatial perspective?

4.3.1 Results of Moran's I index

4.3.2 Results of spatial regression

4.4 Results of several robustness tests

4.4.1 Results of the endogeneity test

4.4.2 Robustness test with removing extreme values

4.4.3 Reduction of omitted variable bias

Urban transportation infrastructure

Policy environment

5 CONCLUSIONS AND DISCUSSIONS

5.1 Main achievements

5.2 Policy implications and limitations

ACKNOWLEDGMENTS

CONFLICT OF INTEREST STATEMENT

ETHICS STATEMENT

REFERENCES

References

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