Does Environmental Regulation Promote the Upgrade of the Export Technology Structure: Evidence from China

Abstract

Rapid economic growth is accompanied by the continuous degradation of the environmental quality, low efficiency of natural-resource utilization and increasing health losses. With the growing environmental problems, countries in the world have gradually attached importance to environmental protection and regulation. As an effective means of environmental protection by the government, environmental regulation’s role in high-quality development cannot be understated. On the basis of a two-way fixed-effects model, the panel data of 276 prefecture-level cities of China during the period 2007–2016 were used to explore the effect of environmental regulation on China’s export technology structure, and the influence mechanism between the two. Moreover, panel quantile regression was used to examine the heterogeneity of the effect of environmental regulation on the export technology structures of Chinese cities with distinct technological levels. The empirical results prove that environmental regulation can boost the upgrade of China’s export technology structure by encouraging innovation. Furthermore, the impact of environmental regulation on China’s export technology structure changes according to the export technical complexity. With the improvement in the export technology structure, the boost effect appears as an inverted U-shaped change.

1. Introduction

According to the World Meteorological Organization State of the Global Climate 2021 report, four key indicators associated with climate change reached new records in 2021, including greenhouse gas concentrations, sea-level heights, the ocean pH and ocean heat. Moreover, 2015–2021 was the warmest seven years on record [1]. Furthermore, the war between Russia and Ukraine is likely to last for a much longer time, which will increase the pressure on global energy and the environment. It is urgent for environmental policymakers in many countries to consider whether they should speed up the process of “decarbonization” and strive for the future of renewable energy and sustainable development. However, strict environmental regulation is definitely a two-edged sword, which does not only protect the public environment, but also has a far-reaching impact on a country’s trade situation. Because of this, the discussion about the correlation between environmental regulation and the upgrade of the export technology structure has aroused the strong concern of researchers and has become a frontier issue in the field of international trade.

Since the Kyoto Protocol came into force, relevant countries have entered a new stage of fulfilling their mandatory emission-reduction obligations. After officially returning to the Paris Agreement, the United States promised to realize the net-zero greenhouse-gas-emissions goal before 2050. The European Parliament also approved a new proposal for a carbon border adjustment mechanism in June 2022, which clarifies the European Union’s intention to levy a full carbon tariff since 2027, and which require importers to pay for direct carbon emissions. At present, carbon tariffs are often levied directly against the intrinsic carbon emissions and intrinsic energy consumption of imported goods. It must be noted, however, that most developing countries’ export comparative advantages are mainly concentrated in labor-intensive products with high energy consumption and low efficiency. Therefore, the increasingly stringent environmental regulation may raise the export costs of many developing countries, such as China. In September 2020, the Chinese government pledged to achieve the CO₂ emissions peak before 2030, and to achieve carbon neutrality before 2060. In order to fulfill this commitment, China needs to adopt a series of strict environmental policies. As global environmental problems become progressively prominent and the international trade situation becomes increasingly complex, it will be of great significance to explore the correlation between environmental regulation and the upgrade of the export technology structure.

“You cannot eat your cake and then still have it too”. Does environmental regulation have a similar relationship with the upgrade of export technology structures? The majority view from the previous research holds that stricter environmental regulation will have a positive impact on the export technology structure. Porter et al. believed that the increased initial environmental-protection input may lead to a “Crowding-Out Effect” and weaken the competitiveness of a country’s exports in the short term. However, in the long term, the increase in the stringency of environmental policies could stimulate innovation and create an “Innovation Compensation Effect”, which will compensate for the extra costs incurred to meet environmental regulation and result in a win-win situation [2]. Simpson and Bradford improved the Spencer–Brander strategic trade model and suggested that the government can promote producers to innovate and reduce marginal costs by using environmental regulations, such as environmental-protection taxes or technical standards. These would boost the upgrading process of the export technology structure and the progress of the domestic green industry so as to exploit first-mover advantages through exports and foreign direct investment [3]. Based on the general equilibrium trade model between two countries, Marconi found that the unilateral environmental tax levied according to the pollution intensity of products could promote the improvement in the export technology structure and control environmental pollution more quickly [4]. In addition, the “Porter Hypothesis” can also be further divided into the “weak” version and “strong” version. The former believes that environmental regulation can promote enterprise innovation, and the latter believes that the benefits of technological innovation caused by environmental regulation outweigh the costs, and so it is helpful to promote the improvement in the total factor productivity and export competitiveness of enterprises. As supporters of the “Weak Porter Hypothesis”, Jaffe and Peterson suggested that the pollution-control cost of an enterprise is positively correlated with its R&D expenditure. In other words, environmental regulation will stimulate producers to carry out technological innovation and promote the upgrade of a country’s export technology structure, but it may not effectively stimulate the productivity and trade competitiveness of related export industries [5]. As a supporter of the “strong” version of the hypothesis, Hamamoto believed that a stricter change in environmental policy could indeed accelerate the technological innovation and the upgrade of the export technology structure by analyzing Japanese manufacturing industry data from the 1960s and 1970s based on the Jaffe–Palmer model of environmental policies and R&D costs. Then, he used the Cobb–Douglas production function to verify the positive correlation between stricter environmental regulation and the increase in the total factor productivity [6]. To sum up, researchers who support the “Porter Hypothesis” believe that stronger environmental regulation will stimulate producers to engage in innovative activities and will promote the upgrade of the export technology structure through the innovation-compensation effect to a certain extent.

Some scholars also believe that stronger environmental regulation will have a negative effect on the upgrade of the export technology structure. The “Pollution Haven Hypothesis” holds that production activities with high energy-consumption levels and high pollution intensity will migrate from countries with stricter environmental regulation to countries with relatively easy environmental regulation [7]. In the meantime, changes in environmental policies can alter existing trade patterns, and the stringency of environmental regulation will increase with a country’s economic growth. As a result of the generally less stringent environmental regulation, developing countries usually have the comparative advantage in exporting pollution-intensive products [8]. Enterprises will prefer to choose to produce in countries with relatively loose environmental regulations rather than increase more R&D investment and technological innovation to meet environmental regulations. Although there is no consensus on the relevant research about the “Pollution Haven Hypothesis”, Hettige et al. supported the “Pollution Haven Hypothesis” by their empirical analysis. According to their findings, developing countries exported pollution-intensive products more often when OECD members adopted stricter environmental regulations. [9]. The study of Mani and Wheeler showed that the “Pollution Haven” was only temporary [10]. Other studies analyze the export industry by using the gravity model with environmental regulation. Some researchers argue that there is no evidence that stricter environmental regulation is the decisive factor of the growth in the net exports of pollution-intensive goods [11,12].

Generally speaking, scholars believe that environmental regulation is used to solve environmental problems and to avoid the negative external effects caused by environmental pollution to a certain extent. Environmental regulation can be either explicit or implicit [13]. Explicit environmental regulation refers to those mandatory environmental-protection laws and regulations issued by legislatures and governments. Implicit environmental regulation generally refers to the awareness of environmental protection at the individual and national levels. There is no unified standard method to measure the stringency of environmental regulation. Most relevant studies measure it by the quantity of the laws and regulations related to environmental protection [14], the emission intensity of certain kinds of pollutants [15], the per capita income level [16] and the ratio of the costs of pollution control to the value of the whole industrial production [17].

The export technical complexity is the most widely used measurement method of the export technology structure, and it was first proposed and applied by Hausmann et al. [18]. It comprehensively reflects the technology structure and the volume of a country’s exports by combining the displayed comparative advantage, total factor productivity and export share of each export commodity of the sample countries. On this basis, many scholars have improved the measurement method due to their own research needs [19,20,21]. Because the export technical complexity can truly reflect the technology structure and technical level of the export products, it has been widely used in relevant research fields, such as international trade and industrial structures. This paper will also continue to adopt Hausmann’s method to measure the level of the export technology structure by the export technical complexity.

More and more scholars have paid attention to the impact of environmental regulation on international exports. The existing literature provides a solid theoretical basis for this study. However, due to different methods and data, relevant studies have not reached a consistent conclusion. However, most existing studies focus on the upgrade of the export technology structure at the national level and ignore the differences in the export technology among the different regions within a country. Meanwhile, previous studies usually used a single indicator to measure the stringency of environmental regulation, which could not fully reveal the comprehensive situation of regional-level environmental regulation, and may not be able to eliminate the endogenous influence. In addition, it is necessary to mention that most scholars only explain the impact mechanism of environmental regulation on the export technology structure in a theoretical way, without providing empirical evidence, and only a few scholars discuss the heterogeneity of the relationship between environmental regulation and the export technology structure.

Therefore, on the basis of the double fixed-effect model, the panel data of 276 Chinese prefecture-level cities from 2007 to 2016 were used in this paper to conduct empirical results. Moreover, this paper chose indicators that provide a more comprehensive and rational depiction of the stringency of environmental regulation on the basis of the characteristics of the Chinese regulatory system and environmental governance experience. The proportion of the frequency of environment-related concepts in annual government work reports are used to reflect the intensity of environmental regulation, which will effectively solve the endogenous problem, if there is one. The environmental-regulation composite index was calculated by three environmental governance indicators and was used to test the robustness of the conclusion. In the meantime, the mediation-effect analysis was used to investigate whether environmental regulation creates an innovation-compensation effect in the upgrading progress of China’s export technology structure. Meanwhile, panel quantile regression was used to explore the heterogeneity of the effect of environmental regulation on the export technology structure of cities that have diverse technological levels. There is no doubt that these efforts will compensate for the lack of previous literature and will provide a novel perspective for future research.

This paper consists of four sections, which are organized as follows: Section 1 introduces the study motivation and reviews the previous literature; Section 2 presents the data sources, methodology and variable definition, which are explained in as specific a way as possible way; Section 3 shows the empirical results of the basic model, with the mediating-effect model the same as the panel quantile regression; Section 4 discusses the empirical research conclusions and policy implications from our research.

2. Materials and Methods

2.1. Data and Methodology

In order to investigate the impact of environmental regulation on the export technology structure, this paper bases the basic econometric model on the existing research on the influencing factors of the export technology structure. In recent years, the influencing factors of the export technology structure have also become a research hotspot. Many scholars have conducted much research. Rodrik (2006) believed that rapid economic growth would increase a country’s export technical complexity [22]. Zhu Shujin et al. (2010) found that both the capital–labor ratio and human capital could significantly promote the improvement in a country’s export technology structure, and that these influencing factors have different effects on countries with different economic-development levels [23]. Generally, foreign direct investment can promote the optimization and upgrading of the export structure through technology spillovers [24]. However, the technology-spillover level of foreign direct investment depends on the specific situation of both the source country and host country [25]. The improvement in informatization would improve a country’s export technology structure by reducing the international trade cost and stimulating technology innovation [26,27]. Yu Yongze et al. (2014) added urbanization to the control variables to control the influence of the regional development level and population-agglomeration degree on the manufacturing export technical complexity [28]. The basic econometric model is as follows:

$$\begin{array}{ll}lnet{s}_{it}={\alpha }_0+& {\alpha }_{1}EnGo{v}_{it}+{\alpha }_{2}huma{n}_{it}+{\alpha }_{3}urba{n}_{it}+{\alpha }_{4}informatio{n}_{it}\\ & +{\alpha }_{5}fd{i}_{it}+{\alpha }_{6}growt{h}_{it}+{\alpha }_{7}lnrati{o}_{it}+{\epsilon }_{it}\end{array}$$

(1)

where $i$ and $t$ refer to cities and years, respectively; $lnets$ denotes the export technology structure measured by the export technical complexity; $EnGov$ refers to the environmental regulation; $human$ refers to the human capital; $urban$ refers to the urbanization level; $information$ refers to the level of informatization; $fdi$ refers to the foreign direct investment; $growth$ refers to the economic-growth rate; $lnratio$ refers to the factor input structure; ${\alpha }_0$ denotes a constant; ε refers to the random disturbance term.

2.2. Variable Definition

2.2.1. Dependent Variable

The dependent variable of this paper is the export technology structure, which is measured by the logarithm of the export technical complexity. On the basis of the calculation method applied by Hausmann et al. (2007) [18], this paper calculates the export technical complexity of prefecture-level cities in China. First, the export technical complexity of each commodity can be calculated according to Equation (2):

$$Prod{y}_c={{\displaystyle \sum }}_i\frac{E{X}_{i,\mathrm{c}}/E{X}_i}{{{\displaystyle \sum }}_{i}E{X}_{i,\mathrm{c}}/E{X}_i}{Y}_i$$

(2)

where the subscripts i and c denote cities and export commodities, respectively; $E{X}_{i,c}$ refers to the export value of a commodity (c) in the city (i); $E{X}_i$ represents the total export value of the city (i); $Y_i$ denotes the per capita GDP of the city (i).

Then, the export technology complexity of each city can be calculated according to Equation (3):

$$ET{S}_i={{\displaystyle \sum }}_i\frac{E{X}_{i,\mathrm{c}}}{E{X}_i}Prod{y}_c$$

(3)

Equation (3) presents that the export-technical-complexity index of each city is the weighted average of the export technology complexity of all commodities ($Prod{y}_c$) in the city, and the weight is the proportion of the exports of commodities to the total exports of the city. To eliminate the impact of heteroscedasticity, we take the logarithm form of the export technology complexity as the dependent variable.

2.2.2. Independent Variable

At the beginning of each year, the municipal governments submit their work reports to the National People’s Congress and the Chinese People’s Political Consultative Conference. The government reports reflect the priorities of prefecture-level governments, which serve as decisive guidance for government services each year. The proportion of words related to specific policies in the government work report is often used to measure the importance that the government attaches to the policy [29]. The more environment-related words that appear in the government work report, the more the government attaches importance to environmental governance, which means that environmental regulation becomes stronger. Based on the perspective of Chen et al. (2018) [30], we construct an index to calculate the implementation stringency of environmental regulation on the basis of the occurrence frequency of environment-related words in municipal government work reports, as shown in Equation (4):

$$EnGo{v}_{it}=\frac{environment\_relate{d}_{it}}{tota{l}_{it}}$$

(4)

where $i$ and $t$ refer to cities and years, respectively; $environment\_related$ refers to the frequency of environment-related terms in every municipal government work report; $total$ refers to the total word frequency in those reports. The environment-related terms include environmental protection (huanjingbaohu/huanbao), ecology (shengtai), green (lvse), energy consumption (nengyuanxiaohao), contamination (wuran), energy saving and emission reduction (jienengjianpai), low carbon (ditan), air (kongqi), chemical oxygen demand (huaxuexuyangliang), SO₂ (eryanghualiu), CO₂ (eryanghuatan), PM10 and PM2.5.

2.2.3. Control Variables

Based on the previous research, the corresponding control variables are selected very carefully to make sure that other factors’ effects on the export technical complexity are considered to the fullest extent possible [22,23,24,25,26,27,28,31,32,33,34,35]. The control variables include human capital, informatization, urbanization, foreign direct investment, economic growth and the factor input structure. Human capital ($human$) is calculated by the quantity of college students per 10,000 people; the urbanization level ($urban$) is measured by the ratio of the urban population to the total population; foreign direct investment ($fdi$) is mensurated according to the ratio of the production value of foreign-invested industrial firms to the total industrial production value; economic growth ($growth$) is measured by the annual economic-growth rate; the factor input structure ($lnratio$) is measured by the logarithm of the capital–labor ratio. It has to be mentioned that this paper also controls the city-level fixed effect and time fixed effect.

In this paper, we examine the impact of environmental regulation on China’s export technology structure by using panel data from 276 prefecture-level cities from 2007 to 2016. The export data used to calculate the export technical complexity are from the RESSET Customs Import and Export Statistics Database. Other data for empirical analysis were attained from the China City Statistical Yearbook over certain years. The summary statistics of the sample data are shown in

Table 1. Summary Statistics of Sample Data.

Variable Name	Label	N	Mean	S.D.	Min	Max
Export technology structure	lnets	2773	10.48	0.380	8.810	11.53
Environment regulation	EnGov	2773	0.0050	0.0021	0.00025	0.0228
	ECI	2773	0.797	0.212	0.099	1.650
Human capital	human	2773	9.108	1.130	0.395	11.784
Urbanization level	urban	2773	50.99	15.47	18.43	100
Informatization level	information	2636	3.640	1.040	0.546	8.550
Foreign direct investment	fdi	2636	0.0900	0.100	0.000032	0.540
Economic-growth rate	growth	2636	11.38	4.110	−19.38	32.90
Factor input structure	lnratio	2636	12.29	0.600	9.740	13.950
Innovation	lnpatent	2607	6.610	1.710	1.610	11.530

.

3. Results

3.1. Estimation Results of Basic Model

The Hausman test results indicate that the fixed-effects model performs better than the random-effects model. Therefore, the former will be used in future empirical studies. Considering the reliability of the estimation results, the number of control variables is gradually increased in regressions.

Table 2. Basic Model Estimation Results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	FE	FE	FE	FE	FE	FE	FE
EnGov	2.990 **	3.015 **	2.542 *	2.545 *	2.695 **	2.594 **	2.401 *
	(1.347)	(1.348)	(1.340)	(1.341)	(1.268)	(1.262)	(1.236)
human		0.0214 *	0.0199 *	0.0191 *	0.0258 **	0.0253 **	0.0270 **
		(0.0112)	(0.0111)	(0.0109)	(0.0108)	(0.0108)	(0.0109)
urban			0.00349 ***	0.00314 ***	0.00285 ***	0.00256 ***	0.00216 ***
			(0.000780)	(0.000778)	(0.000760)	(0.000766)	(0.000743)
information				0.0282 ***	0.0242 ***	0.0234 ***	0.0168 ***
				(0.00806)	(0.00740)	(0.00737)	(0.00632)
fdi					0.146 **	0.154 **	0.186 ***
					(0.0633)	(0.0657)	(0.0646)
growth						0.00148 *	0.000122
						(0.000868)	(0.000857)
lnratio							0.0599 ***
							(0.00820)
Constant	10.46 ***	10.36 ***	10.19 ***	10.11 ***	10.11 ***	10.11 ***	9.424 ***
	(0.00694)	(0.0508)	(0.0634)	(0.0661)	(0.0658)	(0.0654)	(0.118)
City Fixed Effect	YES	YES	YES	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES	YES	YES	YES
N	2773	2773	2773	2769	2644	2639	2636
R2	0.944	0.945	0.945	0.946	0.951	0.951	0.953
Hausman	37.76 ***	38.18 ***	29.14 ***	29.16 ***	22.07 ***	26.14 ***	59.06 ***
F statistics	5.63 ***	6.25 ***	12.36 ***	14.53 ***	12.93 ***	11.37 ***	21.21 ***

Note: (1) standard errors in parentheses; (2) asterisks indicate significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the results of our basic regression. Column (1) examines how environmental regulation affects China’s export technology structure without adding any control variables. The regression result suggests that environmental regulation could promote the improvement in the export technology structure. That is to say, the increased environmental-regulation stringency in China will eventually improve its export technical complexity. Based on Column (1), Columns (2)–(7) further add the control variables, step by step. In the process of gradually introducing the control variables, the significance and signs of the regression coefficients are all in accordance with the baseline model, demonstrating the robustness of our estimation results.

3.2. Mechanism Analysis

The “Porter Hypothesis” suggests that environmental regulation could stimulate technology innovation [35]. Specifically, enterprises are encouraged to increase R&D investment for technological innovation so as to make up for the costs brought on by environmental regulation, which could produce the innovation-compensation effect and improve the export technical complexity. To further study the mechanism between environment regulation and the export technology structure, this paper takes the city’s technical innovation level as the mediating factor, and it uses the causal-step approach, which was applied by Baron et al. (1986) [36], to examine the mediating effect. The mediating-effect model is set as Equations (5)–(7).

Table 3. Mechanism analysis.

Variable	(1)	(2)	(3)
	Lnets	Lnpatent	Lnets
EnGov	2.401 *	13.41 ***	2.309 **
	(1.236)	(4.942)	(1.158)
lnpatent			0.0215 ***
			(0.00545)
human	0.0270 **	0.273 ***	0.0236 ***
	(0.0109)	(0.0269)	(0.00824)
urban	0.00216 ***	0.305 ***	0.104 ***
	(0.000743)	(0.0846)	(0.0200)
information	0.0168 ***	0.349 ***	0.0106 *
	(0.00632)	(0.0229)	(0.00582)
fdi	0.186 ***	1.407 ***	0.188 ***
	(0.0646)	(0.251)	(0.0672)
growth	0.000122	0.0125 ***	0.000848
	(0.000857)	(0.00303)	(0.000710)
lnratio	0.0599 ***	−0.00540	0.0573 ***
	(0.00820)	(0.0283)	(0.00681)
Constant	9.424 ***	2.905 ***	8.979 ***
	(0.118)	(0.340)	(0.0885)
City Fixed Effect	YES	YES	YES
Time Fixed Effect	YES	YES	YES
N	2636	2596	2596
R2	0.953		0.935

Note: (1) standard errors in parentheses; (2) asterisks indicate significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the estimation results of the mechanism analysis.

$$lnet{s}_{it}={\alpha }_0+{\alpha }_{1}EnGo{v}_{it}+{\alpha }_j\sum control+{\epsilon }_{it}$$

(5)

$$lnpaten{t}_{it}={\beta }_0+{\beta }_{1}EnGo{v}_{it}+{\beta }_j\sum control+{\xi }_{it}$$

(6)

$$lnet{s}_{it}={\gamma }_0+{\gamma }_{1}EnGo{v}_{it}+{\gamma }_{2}lnpaten{t}_{it}+{\gamma }_j\sum control+{\zeta }_{it}$$

(7)

where $lnpatent$ refers to the mediating-factor innovation measured by the logarithm of the applied patent quantity, and $control$ denotes all the control variables.

In Column (1), there is a positive correlation between environmental regulation and the export technology structure, indicating that environmental regulation can promote the upgrade of the export technology structure. In Column (2), the coefficient of environmental regulation is significantly positive, indicating a significant positive correlation between environmental regulation and the mediating-variable innovation. In Column (3), the coefficient still presents as significantly positive, but the absolute value is smaller. Moreover, the coefficient of innovation is significantly positive, demonstrating that environmental regulation can certainly boost the upgrade of the export technology structure by encouraging innovation in China. A Sobel test was conducted to further examine the mediating effect. The Z value (3.359) is significant at a 1% significance, which further verified the significance of the mediating effect of innovation.

3.3. Heterogeneity Analysis

To further study the relationship between environmental regulation and the export technology structure, this paper uses panel quantile regression to investigate the heterogeneity of the effect of environmental regulation on the export technology structures of cities with diverse technological levels. Different from the traditional regression-analysis method, quantile regression usually pays attention to the functional correlation of the independent variable and the conditional quantile of the dependent variable. It uses multiple quantiles of the independent variable to obtain the corresponding quantile equation of the conditional distribution of the dependent variable, which can describe the statistical distribution of the variable in more detail [37]. We will reexamine the impact on the basis of panel quantile regression with nonadditive fixed effects [38]. The quantile-regression model is helpful to discern the impact on the export technology structures of Chinese cities with diverse technological levels. This section selects five quantiles for analysis, including 0.10, 0.25, 0.50, 0.75 and 0.90. The estimation results are presented in

Table 4. Quantile Regression Results.

	(1)	(2)	(3)	(4)	(5)
	$\mathbf{Quantile}\left(\mathit{q}\right)$
Variable	0.10	0.25	0.50	0.75	0.90
EnGov	5.633 **	7.339 ***	9.872 ***	10.47 ***	6.727 ***
	(2.733)	(0.898)	(0.213)	(0.250)	(2.374)
human	0.0473 ***	−0.000466	−0.0112 ***	−0.0277 ***	−0.0185 ***
	(0.00744)	(0.000922)	(0.000476)	(0.00334)	(0.00147)
urban	0.00878 ***	0.00937 ***	0.00908 ***	0.00943 ***	0.0101 ***
	(0.000223)	(0.000359)	(0.000159)	(0.000243)	(0.000316)
information	0.0792 ***	0.0953 ***	0.105 ***	0.0858 ***	0.0924 ***
	(0.00330)	(0.00179)	(0.00131)	(0.0131)	(0.00396)
fdi	−0.0708	−0.208 ***	0.100 ***	0.00507	−0.133 ***
	(0.170)	(0.0111)	(0.0135)	(0.0768)	(0.0162)
growth	−0.0125 ***	−0.0242 ***	−0.0188 ***	−0.0215 ***	−0.0198 ***
	(0.00332)	(0.00105)	(0.000851)	(0.00214)	(0.000508)
lnratio	0.281 ***	0.284 ***	0.290 ***	0.219 ***	0.193 ***
	(0.0307)	(0.00190)	(0.00391)	(0.0172)	(0.0131)
City Fixed Effect	YES	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES	YES
N	2638	2638	2638	2638	2638
Number of cities	276	276	276	276	276

Note: (1) standard errors in parentheses; (2) asterisks indicate significance levels: ** p < 0.05, *** p < 0.01.

.

Table 4 shows that environmental regulations have different effects on the export technology structures of cities with different export technology levels. With the change in the quantile ($q$), the elasticity coefficient of environmental regulation to the export technology structure shows a corresponding regular change. To be specific, the promotion effect of environmental regulation on the export upgrade exhibits an inverted U-shape with the improvement in the export technology structure. When q = 0.10 and q = 0.90, the environmental regulation has a relatively smaller impact on the export technical complexity.

This shows that, when the level of the export technical complexity appears low, the effect of environmental regulation on China’s export technology structure gradually increases with the improvement in the export technical complexity. When the export technical complexity reaches a high level, the boost effect on China’s export technology structure from environmental regulation gradually decreases with the increase in the quantile. This means that environmental regulation has a higher promoting effect on China’s export technology structure in those regions with medium and high levels of export technology complexity.

3.4. Robustness Check

This paper uses an alternative index of environmental regulation to examine the robustness of the basic estimation results above. The composite index method is applied to mensurate the environmental-regulation intensity, which is presumed to be more accurate and comprehensive [38,39]. Three indicators are selected to calculate the environmental-regulation comprehensive index, including the industrial-smoke and dust-removal rate, industrial SO2-removal rate and general industrial-solid-waste comprehensive utilization rate. The calculation formula is as follows:

$$P{T}_{ij}^s=\frac{P{T}_{ij}-minP{T}_j}{maxP{T}_j-P{T}_{ij}}$$

(8)

$$W_j=\frac{P{T}_{ij}}{aveP{T}_{ij}}$$

(9)

$$EC{I}_i=\frac{1}{3}{{\displaystyle \sum }}_j^3{W}_{j}P{T}_{ij}^s$$

(10)

where $j$ denotes the pollution-treatment indicators; $P{T}_{ij}^s$ refers to the standardized value of the pollution-treatment indicators (j) in the city ($i$); $minP{T}_j$ and $maxP{T}_j$ refer to the minimum and maximum of the pollution-treatment indicator ($j$), respectively; $aveP{T}_{ij}$ is the average value of the pollution-treatment indicator; $W_j$ is the weight calculated by the adjustment coefficient method; $EC{I}_i$ is the composite index of environmental regulation. The higher the composite index, the stronger the environmental regulation becomes. The results are shown in

Table 5. Robustness Test.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
ECI	0.0421 **	0.0427 **	0.0414 **	0.0396 **	0.0284 *	0.0302 *	0.0247 *
	(0.0181)	(0.0180)	(0.0180)	(0.0179)	(0.0165)	(0.0165)	(0.0145)
human		0.0215 *	0.0201 *	0.0192 *	0.0261 **	0.0258 **	0.0270 ***
		(0.0111)	(0.0110)	(0.0108)	(0.0109)	(0.0109)	(0.00820)
urban			0.00360 ***	0.00327 ***	0.00301 ***	0.00268 ***	0.00226 ***
			(0.000780)	(0.000777)	(0.000763)	(0.000765)	(0.000659)
information				0.0277 ***	0.0242 ***	0.0233 ***	0.0168 ***
				(0.00807)	(0.00752)	(0.00747)	(0.00577)
fdi					0.138 **	0.148 **	0.179 ***
					(0.0627)	(0.0651)	(0.0668)
growth						0.00167 *	0.000379
						(0.000857)	(0.000705)
lnratio							0.0590 ***
							(0.00678)
Constant	10.44 ***	10.34 ***	10.16 ***	10.09 ***	10.09 ***	10.09 ***	9.418 ***
	(0.0144)	(0.0503)	(0.0634)	(0.0662)	(0.0668)	(0.0663)	(0.0936)
City Fixed Effect	YES	YES	YES	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES	YES	YES	YES
N	2799	2799	2799	2795	2665	2660	2657
R2	0.944	0.945	0.945	0.946	0.951	0.951	0.953

Note: (1) standard errors in parentheses; (2) asterisks indicate significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01.

and are in accordance with the basic regression results above, which suggests that our final conclusions are trustworthy.

4. Discussion

Trade between countries is becoming closer and closer as a result of economic globalization. Moreover, among those studies that manifest the positive correlation between exports and economic growth, it is well known that the export-led growth exists catholically [40], and even in some cases, increased exports lead to economic growth, and more economic growth somehow stimulates more exports as a feedback effect [41]. With the rapid development in international trade, many problems have emerged, one after another, such as deteriorating environmental quality and the inefficient utilization of natural resources [42,43]. These problems have become the focus of public concern. Countries around the world are gradually paying attention to environmental protection and environmental regulation. It is of cardinal importance for all countries to have an equilibrium between environmental protection and international trade development. There has been an increasing focus on the correlation between environmental regulation and international trade. On the basis of the double fixed-effects model, the panel data of 276 prefecture-level cities during the period 2007–2016 were used to examine the impact of environmental regulation on the export technology structure, and the mechanism between the two. Furthermore, panel quantile regression was applied to explore the heterogeneity of the impact of environmental regulation on the export technology structures of cities with different technological levels.

The basic regression results show that environmental regulation has a positive effect on the export technology structure. This means that stricter environmental regulation can surely stimulate the upgrade of the export technology structure. Many studies back up this conclusion, including Costantini and Mazzantib (2012), Greenstone et al. (2012), Zhou (2015) and so on [44,45,46]. In the process of the upgrade of China’s export trade structure in the future, the effective improvement in environmental-regulation standards can promote the optimization of the export technology structure and the improvement in the export product quality. Moreover, through the mediating-effect analysis, this paper further validates that stricter environmental regulation can boost the upgrade of the export technology structure by encouraging innovative activities, which is consistent with the “Porter Hypothesis” [2]. The “Porter hypothesis” emphasizes the stimulating effect of environmental regulation on enterprise innovation, and it believes that appropriate environmental regulation can motivate enterprises to engage in technological innovation. As the stringency of environmental regulation increases, the costs of contamination control in the production processes of enterprises will increase significantly so that they can meet the standard of new environmental policies. At this time, environmental regulation will become an external pressure that will force enterprises to engage in technology innovation activities more to break through the constraints of stricter environmental regulations. Moreover, this will improve the export competitiveness and reduce the negative impact of the severe regulation situation to a certain extent. In this case, the improvement in environmental regulation has a strong compensation effect on the R&D investment and innovative activities of enterprises, thereby significantly promoting the upgrade of the export technology structure. This conclusion is supported by many other studies, such as Simpson and Bradford (1996), Marconi (2009) and Jaffe and Palmer (1997) [3,4,5]. To some extent, this conclusion accords with the research of Ouyang et al. (2020) [47]. Ouyang used the quantile-regression method based on panel data from 30 provinces in China to study the impact of environmental regulation on export trade. They also verified that the “Porter hypothesis” has been confirmed in China.

Another important conclusion obtained from heterogeneity analysis is that environmental regulation tends to have a different impact on the export technology structures of cities with different export technology levels. With the improvement in the export technology structure, the boost effect of environmental regulation on the export upgrade presents an inverted U-shaped change. This shows that environmental regulation has the greatest improvement effect on the export-technology-structure upgrade of those cities with medium export technological levels, and then the boost effect gradually decreases. This is mainly because, when the export technical complexity is low, the promotion effect is weakened and limited due to the lack of an innovation environment. As the export technical complexity gradually increases, the level of human capital and the innovation capacity also increase, and the innovation-compensation effect caused by environmental regulation becomes stronger. However, in the case of high export technical complexity, enterprises are less sensitive to the costs brought on by environmental regulation due to their technology-intensive characteristics, and so they prefer to undertake independent and active innovation voluntarily, rather than be incentivized by environmental regulation. Therefore, when the export technical complexity is high, the positive influence on the upgrade of the export technology structure will be weakened. This conclusion is an innovation of this paper. We use quantile regression to explore ways to effectively improve the export technology structure under different export technology structures.

The conclusions above show that there is no need to worry about the negative effect of the stricter environmental-policy situation on China’s exports. Therefore, it is particularly necessary to discuss how to play the role of environmental regulation to promote the upgrade of export trade. Environmental regulation can coordinate green development and innovative development simultaneously. It does not only protect the ecological environment, but also promotes the upgrade of the export technology structure. The government should make space for the potential of environmental-regulation policies and encourage the development of energy-saving and emission-reduction technologies, the same as the development of clean technologies. The government should strengthen the enforcement of environmental regulation so as to stimulate enterprises to draw advanced technology into the promotion of the upgrade of the product technology structure to make up for the costs brought on by stricter environmental policies. It is necessary to mention that the government should formulate environmental policies that are suitable for the regional economic development level and technological level so as to maximize the innovation-compensation effect for accelerating industrial transformation and upgrading export trade. Then, governments should also increase the investment in human capital and subsidies for R&D activities to maintain the continuous supply of innovative human capital, which could promote the export-trade upgrade through improvements in the innovation ability and innovation performance.