*Article* **Do Environmental Regulations Promote or Inhibit Cities' Innovation Capacity? Evidence from China**

**Xiaowen Zeng <sup>1</sup> , Ming Jin <sup>2</sup> and Shuang Pan 3,\***


**\*** Correspondence: pasan2016@163.com

**Abstract:** The "Porter Hypothesis" proposes that appropriate environmental regulations would promote firm innovation. This study aims to build a theoretical model for illustrating the impact and mechanism of environmental regulation on urban innovation through a panel of 281 Chinese prefecture-level cities during 2003–2016. The results indicated that an increase in environmental regulation markedly suppressed the innovative capacity of Chinese cities during the sample period. This inhibitory effect is primarily transmitted through two mediating variables: lower regional fiscal revenue and reduced manufacturing output. Moreover, improved regional economic development level helps generate positive incentives for environmental regulation and mitigate its inhibitions to innovation. Environmental regulation and urban innovation might have a non-linear U-shape relation, with the former helping improve urban innovation capacity upon reaching a particular level.

**Keywords:** environmental regulation; urban innovation; mediating effect

**Citation:** Zeng, X.; Jin, M.; Pan, S. Do Environmental Regulations Promote or Inhibit Cities' Innovation Capacity? Evidence from China. *Int. J. Environ. Res. Public Health* **2022**, *19*, 16993. https://doi.org/10.3390/ ijerph192416993

Academic Editors: Hongxiao Liu, Tong Wu and Yuan Li

Received: 27 October 2022 Accepted: 13 December 2022 Published: 17 December 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

### **1. Introduction**

Recently, environmental problems have become more and more serious in the world. Global climate changes, acid rain, water pollution, air pollution and other types of environmental degradation are becoming increasingly common. China is not exempt from such environmental problems either. Relying on massive inputs of production factors from the reform and opening to foreign investment in 1978, the Chinese economy has achieved remarkable success, growing at over 9% per year from 1978 to 2021. It is undeniable that while the extensive growth approach has brought huge economic gains, it has also caused severe environmental pollution. Recently, China has been deepening sustainable development strategies and promoting a comprehensive green transformation of its development. This has led to the introduction of numerous environmental policies and regulations throughout China to prevent and control environmental risks tightly. Environmental quality keeps improving with the implementation of "closure, suspension, merger, or shifting to different line of production" of high-energy-consuming and high-polluting enterprises. However, this inevitably has a significant impact on regional economic development. When governments implement stringent environmental regulations and low-carbon policies, enterprises face various risks when transitioning to more environmental practices due to uncertainty about the future and increased operating costs. Moreover, local development may also be affected. Thus, the study of environmental regulation is essential to regional development.

Whether environmental protection and economic growth can be achieved concurrently has been extensively debated in both domestic and international academic circles. It is suggested that environmental regulation would aggravate the financial burden on firms and thus reduce their international competitiveness [1] and affect firms' total factor growth rate [2]. However, several researchers proposed that environmental regulation can have positive effects. For example, Porter argued that appropriate environmental regulation stimulated innovation and increased the competitiveness of firms, despite raising their

<sup>1</sup> The Faculty of Economics, Guangdong University of Finance & Economics, Guangzhou 510320, China

costs, which is subsequently summarised as the "Porter hypothesis" [3]. However, there is still no clear consensus on whether this view is applicable to China. When firms face environmental regulation, a more effective solution is to reduce corporate pollution and increase revenue through innovative means. Some thus argued that incentive-based environmental regulation in China could enhance corporate innovation [4]. However, firms face transition risks such as lower revenues and higher costs under environmental regulations. Additionally, research and development (R&D) activities are inherently risky corporate behaviours, and enterprises may also lower R&D expenditures due to difficulty in providing sufficient funding or reducing corporate risk [5]. Moreover, poorer economic development can cause environmental regulations to inhibit innovation [6].

According to the existing studies, the change in environmental regulation intensity will have a significant impact on the innovation behaviour of enterprises, and the overall regional innovation level is closely related to the innovation ability of local enterprises. Therefore, it is not difficult to guess that environmental regulation is likely to have a corresponding impact on regional innovation. Although some scholars have analysed the micro-impact of environmental regulation from the perspective of enterprise behaviour, they mainly focus on listed companies or enterprises above a designated size, which will bring sample selection bias and make it difficult to identify the impact of environmental regulation. In addition, the relationship between urban innovation and environmental regulation will be more complex. On the one hand, local enterprises will be forced to transform due to stricter environmental regulations, which will significantly increase regional innovation. On the other hand, when the intensity of environmental regulations has been enhanced, polluting enterprises may shut down or reduce production in order to reduce the cost of pollution control, which will lead to the reduction of enterprise innovation activities and the decline of regional innovation ability. These changes are difficult to observe only through enterprise-level data. Therefore, this paper believes that the empirical study on the panel data of 281 prefecture-level cities is helpful to further explore the micro-impact mechanism of environmental regulation policies on regional innovation ability. Based on the city as an innovation carrier, this paper made several contributions: (1) Being different from the Porter hypothesis, this study finds that an increase in environmental regulation significantly suppressed cities' innovation capacity. At the same time, this paper adopts urban innovation data at the prefecture level, which can avoid sample selection bias. It can reflect the overall impact of environmental regulation policies on urban innovation ability and provide research support for investigating the spillover effect of environmental regulation policies. (2) This paper innovatively explores how Chinese environmental regulation inhibits urban innovation capacity. On the one hand, with the increase of environmental regulations, the decline in business efficiency of enterprises in the short term will bring a significant decline in local fiscal revenue, and the local government may be forced to reduce the subsidy support for enterprises' innovation, which will bring a restraining effect on urban innovation ability. On the other hand, since most Chinese manufacturing enterprises are still in the transition stage from extensive production to efficient production, blindly strengthening environmental regulation intensity is easy to lead to the decline of local manufacturing output and obstacles to technology research, thus restricting the improvement of urban innovation ability. (3) This paper conducts a variety of robustness tests on urban heterogeneity, environmental regulation policy categories, green technology innovation and other aspects and draws a series of new conclusions: in areas with poor economic development and less fixed investment, environmental regulation has a more significant inhibiting effect on urban innovation capability. In non-knowledge-intensive cities, the impact of environmental regulation is significantly negative, while in knowledge-intensive cities, the impact is not significant. Both market-oriented and command-and-control environmental regulation policies have a significant inhibitory effect on urban innovation ability in the sample period. (4) This study demonstrated the specific non-linear association between environmental regulation and urban innovation, which lends empirical proof for the Porter hypothesis at the mesolevel. At the same time, it is clearly pointed out that China is still in a painful period of transition from extensive development to high-quality development, and the negative effects of environmental regulation policies will temporarily outweigh the positive effects. It is necessary to pay attention to the negative spillover effects of environmental regulation on technological innovation. This manuscript is organised below: Chapter 2 reviewes the relevant works. Chapter 3 introduces the theory model derivation. Chapter 4 provides the empirical test analysis and further research. The final section offers conclusions and policy advice.

#### **2. Literature Review**

#### *2.1. Environmental Regulation and Urban Innovation*

Environmental protection and economic growth are enduring topics of academic debate, and the loss of economic benefits due to environmental protection has long been thought-provoking for numerous scholars. Environmental regulation by governments can be a valuable motivator for firms to implement environmental initiatives [7]. Magat found that environmental regulation can influence firm innovation, but the effects vary depending on the environmental regulation type [8]. Scholars also argued that environmental regulation could lower the productivity level and growth rate of industries [1]. Porter countered that environmental regulation does not necessarily result in economic losses, suggesting that appropriate environmental regulation may spur 'innovation compensation' to enhance firm innovation [3]. Subsequently, Porter and Linde specified that innovation may occur when organisations attempt to increase the environmental efficacy in resource use, thus helping the production processes and product quality improvement [9].

#### *2.2. Environmental Regulation and Economic Development*

At present, China continues to implement low-carbon environmental policies, accompanied by increased environmental regulation in various regions. Many scholars have begun to discuss the link between environmental regulation and economic development. Several academics argued that environmental regulations caused the operating costs to increase and productivity to decline to some extent. For example, Guo discovered that environmental regulations fail to enhance green growth straightforwardly [10]. Yuan verified that environmental regulation reduces R&D investment over a long period utilizing panel data on Chinese manufacturing [5]. He utilised a Chinese water quality monitoring system and found that local governments implement more rigorous environmental criteria for enterprises upstream of monitoring sites [2]. Wu identified a significant U-shape relationship between environmental regulation and the green factor productivity of China's energy sector [11]. In contrast, Du argued that poor economic development can cause environmental regulation to stifle green technological innovation [6]. However, several academics hold the view that environmental regulation could prompt technological innovation and productivity improvement, especially in clean production industries [12]. Fu and Li found that environmental regulations promote innovation while increasing firms' costs, thereby improving their competitiveness [13]. Pan found that as market-based environmental regulations progressively enhance the energy efficiency, technological innovation will also be impacted by these regulations [4]. Additionally, market-based and voluntary environmental regulations possess a greater incentive effect on business innovation than that from command/control-based environmental regulations. The above thereby validated "Porter's hypothesis".

#### *2.3. Environmental Regulation and Industries' Innovation Capacity*

Concurrently, numerous researchers addressed the connection between environmental regulation and industries' innovation capacity in the Chinese manufacturing sector. Yuan conducted a study on Chinese manufacturing firms and determined that environmental regulation decreases their R&D investment [5]. Studies based on industry classification have apparent advantages: as performance measures tend to be consistent across industries, industry classification studies specify environmental regulation's impact on industries with

varying development and pollution levels. Thus, their findings are more credible. However, there are limitations in that multiple industries exist in the same region. Thus, local governments need to consider multiple industries when making decisions, as different types of industries are affected by environmental regulations to different degrees. Hence, the regional role of environmental regulation also requires investigation. For companies of the same region environmentally regulated with similar degrees, an examination of regional environmental regulation has greater potential to highlight its regional role and be more informative for the implementation of local governmental policy.

Additionally, other scholars have conducted region-based studies about environmental regulation and firm innovation. Nie found that environmental regulation fostered innovation among less developed regions of western China and demonstrated the applicability of Porter's hypothesis in less developed regions of developing countries [14]. Li revealed that environmental regulations did not significantly affect the efficiency of urban science and technology innovation in Xi'an, suggesting the inapplicability of the Porter hypothesis in Xi'an [15].

In summary, focusing on Porter's hypothesis, the established research mainly examined the influence of environmental regulations on innovation at a micro level. However, research at the prefecture level has been insufficient and thus needs to be expanded. Concurrently, studies at the regional level centred on the correlation of environmental regulation to regional economic development but have been unable to identify and validate the mechanisms of its impact. Thus, this study examined the effects and transmission mechanisms of environmental regulation towards urban innovation using meso-level data to serve as a reference for setting regional environmental regulation policies.

#### **3. Mathematical Model Analysis**

The ability of cities to innovate depends on economic support and talent development. Many firms are at the forefront of urban innovation. "Porter's hypothesis" suggests that there exists a possible non-linear relationship between environmental regulation and firm innovation, i.e., environmental regulation may inhibit firm innovation in the short term but increase firm innovation and competitiveness in the long run. Similarly, this study argues that environmental regulation may ultimately have a corresponding impact on the innovation capacity of urban areas by influencing firms' production and business behaviour. Thus, the following mathematical model was derived by drawing on the research method of Zhang et al. (2011) [16]. We mainly extended Zhang's theoretical model to the city level and shifted the research perspective to the field of urban innovation ability.

Assumptions: Manufacturers conduct production activities in a perfectly competitive product and factor market; as production expands, the pollution generated by the manufacturers increases accordingly.

Let the vendor's revenue function be:

$$R = P \ast A(K\_A) f(K\_P)$$

where *P* denotes the price of products, *K<sup>A</sup>* denotes the capital input used in production for technological innovation, and *K<sup>P</sup>* represents the capital input to the daily production of the firm. *A*(*KA*) represents the level of technological innovation in production, and *f*(*KP*) represents the level of output at the given level of innovation.

Then, the output function of the manufacturer can be expressed as *F* = *A*(*KA*)*f*(*KP*) = *A f* . Here, it is assumed that the level of innovation in production with technological innovation is Hicks neutral.

As manufacturers produce emissions in the production process and pollution has negative externalities, the government will regulate manufacturers' pollution behaviour by specifying a level of pollution, i.e., environmental regulation (ERS). Existing research suggests that, under government environmental regulation, manufacturers may first reduce the level of pollution emissions from their production process through technological innovation in their production processes to achieve a lower level of pollution; then, manu-

facturers may increase their R&D investment to increase their output level. Although this will lead to more pollution emissions, firms can increase their pollution control expenditure in response to environmental regulation, benefiting from the increased scale of output and profits. It can be deduced that manufacturers' technological innovation is related to their own production technology A and pollution control technology E. Based on this, the present study argues that urban innovation in the context of environmental regulation can also be composed of two parts: regional productive technological innovation (*C IA*) and the pollution control technology innovation induced by environmental regulation (*C IE*). This study further assumes that the urban innovation function is separable; thus, *C I* = *C I<sup>A</sup>* + *C IE*, which satisfies *C I*<sup>0</sup> = (A, ·) > 0, *C I*<sup>0</sup> = (·, *E*) > 0.

Additionally, the manufacturer's emission function is assumed to be *W* = (*F*, *E*), which is a function of the level of output and the pollution control technology. This function has the following properties:

First, pollution emissions increase with the increase in the scale of output, i.e., *W*0 = (*F*, ·) > 0. Second, pollution emissions decrease as pollution control technology improves, i.e., *W*<sup>0</sup> = (·, *E*) > 0. Apparently, *E* is positively correlated with the intensity of environmental regulation; it is believed that an increase in the intensity of environmental regulation will be followed by an increase in technological innovation in pollution treatment.

Assuming that the portion of firms' total output devoted to pollution control *α* denotes the intensity coefficient of environmental regulation, where *α* represents a real number between 0 and 1, then *αA*(*KA*)*f*(*KP*) = *E*. Thus, the final profit function of the manufacturer is as follows: *π* = *P*[*A*(*KA*)*f*(*KP*) − *αA*(*KA*)*f*(*KP*)]. The constraint under which the manufacturer produces is then as follows:

*ERS* = *W*[*A*(*KA*)*f*(*KP*), *αA*(*KA*)*f*(*KP*)], i.e., the pollution emissions are equal to the environmental regulation.

Constructing Lagrangian functions:

$$L = P[A(K\_A)f(K\_P) - \mathfrak{a}A(K\_A)f(K\_P)] + \lambda \{ \mathcal{W}[A(K\_A)f(K\_P), \mathfrak{a}A(K\_A)f(K\_P)] - ERS \}$$

The first-order optimality condition for the manufacturer is solved by the Lagrangian function as:

$$P(1 - \alpha)A'f + \lambda \frac{\partial \mathcal{W}[A(\mathcal{K}\_A)f(\mathcal{K}\_P), \alpha A(\mathcal{K}\_A)f(\mathcal{K}\_P)}{\partial \mathcal{K}\_A} = 0\tag{1}$$

$$P(1 - \alpha)Af' + \lambda \frac{\partial \mathcal{W}[A(\mathcal{K}\_A)f(\mathcal{K}\_P), \alpha A(\mathcal{K}\_A)f(\mathcal{K}\_P)}{\partial \mathcal{K}\_P} = 0\tag{2}$$

$$-PAf + \lambda \frac{\partial \mathcal{W}[A(K\_A)f(K\_P), \,\alpha A(K\_A)f(K\_P)}{\partial \alpha} = 0 \tag{3}$$

From Equation (3), we obtained

$$P = \lambda \cdot \partial \mathcal{W} / \partial E \tag{4}$$

Bringing Equation (4) into Equation (1) yielded the following:

$$
\partial \mathcal{W} / \partial \mathcal{E} = -\partial \mathcal{W} / \partial \mathcal{F} \tag{5}
$$

This equation demonstrates that the optimal option for a manufacturer facing environmental regulations is to make the increase in marginal pollution in production equal to the decrease in marginal pollution from pollution control inputs, i.e., the level of emissions of the manufacturer decreases as the intensity of environmental regulation increases.

From Equations (2), (3) and (5), we derived the following: *∂W*/ *∂K<sup>A</sup>* > 0, and since *P*(1 − *α*)*A* 0 *f* > 0, it is introduced that *λ* < 0.

Substituting this into Equation (3), we obtained W/*∂*α < 0. This implies that the manufacturer's pollution emissions will keep decreasing as its investment in pollution control keeps increasing during the production process due to the effects of environmental regulations.

Next, the impact of environmental regulation on urban innovation was examined from a technological innovation perspective.

According to *C I*<sup>0</sup> = (*A*, ·) > 0, then *∂C I*/*∂A* > 0, and it can be deduced that:

$$\frac{\partial CI}{\partial A} = \frac{\partial CI}{\partial W} \cdot \frac{\partial W}{\partial A} + \frac{\partial CI}{\partial W} \cdot \frac{\partial W}{\partial E} \cdot \frac{\partial E}{\partial A} > 0 \tag{6}$$

and because *<sup>∂</sup><sup>W</sup> <sup>∂</sup><sup>A</sup>* <sup>=</sup> *<sup>∂</sup><sup>W</sup> ∂E* · *<sup>f</sup>* <sup>+</sup> *<sup>∂</sup><sup>W</sup> ∂E* · *α f* , and *C I* = *C I<sup>A</sup>* + *C IE*, we eventually derived that:

$$\frac{\partial CI}{\partial A} = \left(\frac{\partial CI\_E}{\partial W} + \frac{\partial CI\_A}{\partial W}\right) \cdot \left[\frac{\partial W}{\partial F} f(1 - 2a)\right] > 0 \tag{7}$$

where *α* denotes the intensity of the environmental regulation. From 1 − 2*α* > 0, *∂W*/*∂F* > 0, *∂W*/*∂E* < 0, we obtained *∂W*/*∂α* < 0.

From Equation (7), it can be deduced that when 0.5 > *α* > 0, then *∂C IA*/*∂W* > 0. This means that when the level of environmental regulation faced by enterprises is low, with the increase in the intensity of environmental regulation, the emissions of enterprises will decline. However, this will also lead to a decline in technological innovation in enterprise production, which is ultimately detrimental to the improvement of the level of urban innovation. When *α* > 0.5 and tends to 1, *<sup>∂</sup><sup>W</sup> ∂F f*(1 − 2*α*) < 0 and *∂C IE*/*∂W* tends to 0, we obtain *∂C IA*/*∂W* < 0. At this time, the improvement of technological innovation in enterprises is negatively correlated with pollution emissions. This suggests that the higher the intensity of environmental regulation, the lower the emissions of enterprises, which will increase the technological innovation in enterprise production, leading to a further rise in the level of urban innovation. Therefore, it can be deduced that the effect/impact of the level of environmental regulation on urban innovation is not unique, and further empirical test analysis is required.

#### **4. Empirical Design and Analysis**

#### *4.1. Data Sources and Variable Descriptions*

The main sources of panel data for the 281 prefecture-level cities for the period from 2003–2016 are the China Urban Statistical Yearbook, the China City and Industry Innovation Report 2017, the CSMAR database, the WIND database, and www.zhuanli.com (accessed on 24 March 2020).

The explained variable, the urban innovation index (innovation), was measured using the urban innovation index, which is currently a more standardised indicator for measuring innovation capacity at the city level, in addition to patent data. This study also used the patent grant numbers data from www.zhuanli.com (accessed on 24 March 2020) for robustness testing.

The core explanatory variable, i.e., environmental regulation intensity (ERS), was obtained by measuring five indicators using the entropy method with reference to Wang [17]. These five indicators include the sulphur dioxide removal rate, soot removal rate, comprehensive utilisation rate of industrial solid waste, domestic wastewater treatment rate, and domestic waste harmless treatment rate. The specific treatment methods are listed below:

(1) Raw data standardisation.

Positive indicator: *x* 0 *ij* = *xij* − *x* /*s<sup>j</sup>* Reverse indicator: *x* 0 *ij* = *<sup>x</sup>* <sup>−</sup> *<sup>x</sup>ij* /*s<sup>j</sup>*

where *xij* indicates the raw data of the *j*th indicator of the *i*th city, *x* 0 *ij* represents the standardised indicator values and *x* and *s<sup>j</sup>* denote the mean and standard deviation of the *j*th indicators, respectively. As there were negative values in the standardised data and the entropy method requires logarithmic processing, the standardised data were thus converted to positive values by adding the following constants: *Zij* = *x* 0 *ij* + *A*

(2) Isomorphism of the indicators and calculating the proportion (*pij* ) of the *i*th city in the *j*th indicator (*pij* )

$$p\_{ij} = \frac{Z\_{ij}}{\sum\_{i=1}^{n} Z\_{ij}} (i = 1, 2, \dots, 281; j = 1, 2, \dots, 5)$$

(3) Calculation of the entropy value (*e<sup>j</sup>* ) of the *j*th indicator:

$$e\_j = -k \sum\_{i=1}^n p\_{ij} \ln(p\_{ij}), \text{ where } k = \frac{1}{\ln(n)}, \ e\_j \ge 0$$

(4) Calculation of the differentiation factor (*g<sup>j</sup>* ) of the *j*th indicator: *g<sup>j</sup>* = 1 − *e<sup>j</sup>*

(5) Normalising the coefficient of variation and calculating the weights (*w<sup>j</sup>* ) of the *j*th indicator: *w<sup>j</sup>* = *g<sup>j</sup>* / ∑ *m j*=1 *g<sup>j</sup>* (*j* = 1, 2, . . . , *m*)

(6) Calculation of the environmental regulation intensity (*ERS<sup>j</sup>* ) of the *i*th city: *ERS<sup>j</sup>* = ∑ *m <sup>j</sup>*=<sup>1</sup> *w<sup>j</sup> pij*

Referring to related studies for other control variables, the ratio of secondary and tertiary industries in cities was selected for measuring industrial structure (Industry). The proportion of loan balance in the gross domestic product (GDP) was used to measure financial development (Finde). The natural rate of population growth was used to measure the population growth rate (Growth). The natural logarithm of population size was used to measure the population structure (Lnpeosize). The proportion of financial spending on science and education was used for measuring regional government behaviour (Sciedu), and the Log GDP for measuring regional economic development (LnGDP), among others. These were all control variables in this study. To reduce the estimation bias caused by heteroskedasticity, the standard errors of clustering to the city level were used in this study.

The explained, explanatory, and control variables mentioned above were set up as listed in Table 1. Descriptive statistics of variables are shown in Table 2.


**Table 1.** Setting of the main variables.

**Table 2.** Variables description.


#### *4.2. Empirical Regression Results*

#### (1) Baseline regression model

To examine how environmental regulation intensity affects urban innovation capacity, a benchmark regression model was set in this study as follows:

$$Innovation\_{\rm it} = a + \beta\_1 ERS\_{\rm it} + \sum\_{j} \beta\_j \, control\_{\rm it} + \mu\_i + \gamma\_t + \varepsilon\_{\rm it} \tag{8}$$

where *Innovation* denotes the explanatory variables, *ERS* as the environmental regulation intensity, *µ<sup>i</sup>* denotes the city fixed effect, *γ<sup>t</sup>* is the year fixed effect and controlit denotes the control variable and was set as Table 1.

Table 3 presents the baseline regression results. Column (1) denotes results without control variables and fixed effects, Column (2) denotes results without fixed effects and Column (3) represents results having control variables and city fixed effects, while Column (4) denotes results with control variables and city, as well as time fixed effects. The regression results indicate the negative coefficients of environmental regulation intensity and the negative correlation between environmental regulation intensity and China's urban innovation capacity over the sample period. This result suggests that while intensive environmental regulation may be beneficial to energy conservation and emission reduction at present, but is detrimental to the enhancement of urban innovation capacity. This, in turn, may undermine the ability of cities to achieve long-term energy conservation and emission reduction through innovation over time. Moreover, when two-way city and time fixed effects are added, the coefficient signs of control variables like industrial structure and economic development change significantly. This suggests that heterogeneity may still exist in the way environmental regulation affects the innovation capacity of regional cities and that further mechanism analysis is needed. Additionally, the Porter hypothesis suggests that environmental regulation may enable firms to avoid the cost effects of environmental regulation by promoting innovation for high-quality development.


**Table 3.** Baseline regression results.

Note: \*\*\*, \*\* and \* represent the significance at the 1%, 5% and 10% levels, respectively. The *t*-values are in parentheses.

#### (2) Robustness test

<sup>1</sup> The impact of price fluctuations

In this paper, we used the GDP deflator index to convert cities' GDP data into the constant price based on 2003, which can eliminate the influence of price factors on the regression results and then test the robustness of the baseline regression result. The result report is shown in Table 4. It can be seen that the result after price treatment is still consistent with the baseline regression result, indicating that our baseline regression result is still robust.



Note: \*\*\*, \*\* and \* represent the significance at the 1%, 5% and 10% levels, respectively. The *t*-values are in parentheses.
