How Do Local Environmental Regulations Affect the Export Technological Complexity of Enterprises?

Abstract

This paper investigates the impact of environmental regulations in the cities where enterprises are located on the technological complexity of exports from Chinese manufacturing companies. Currently, China is promoting high-quality development in foreign trade, which encompasses various aspects, including the enhancement of the technological content of export products. This study begins by examining the environmental regulations in the cities of enterprises and their effects on the technological content of exported products. It utilizes government work reports from various cities in China to evaluate the strength of environmental regulations in each city, subsequently analyzing their impact on the export technological complexity of Chinese manufacturing enterprises. Empirical results indicate that environmental regulations drive an increase in the technological complexity of exports from these enterprises, and this conclusion is validated through several robustness tests, including substitution variables and instrumental variable regression. Heterogeneity tests reveal that the effect of environmental regulations on export technological complexity is not significant for enterprises located in central cities compared to those in eastern and western cities. Furthermore, the export technological complexity of processing-trade enterprises and capital-intensive industries is not significantly influenced by environmental regulations. Additionally, the analysis of the moderating effect finds that a city’s innovation capacity plays a reinforcing role in the relationship between environmental regulations and the export technological complexity of enterprises. Based on these research findings, this paper offers corresponding recommendations from the perspective of environmental regulations to promote the high-quality development of China’s foreign trade.

1. Introduction

Rapid economic development around the world is often accompanied by a corresponding degree of environmental pollution, and China is no exception. Since the onset of reform and opening-up, China’s economy has experienced rapid growth. However, similar to other nations, it has increasingly confronted challenges related to air and water pollution. Environmental pollution not only adversely affects natural ecosystems but also poses significant risks to human health. As a result, environmental protection has emerged as a global concern, prompting nations, including China, to implement measures aimed at safeguarding the environment. The principle that “lucid waters and lush mountains are invaluable assets” has been integrated into China’s economic development strategy, benefiting both the country and the global community. In recent years, the Chinese government has introduced a series of initiatives focusing on air and water pollution control and the promotion of low-carbon energy, thereby driving the development of a green economy. As a developing country, China is ranked as the world’s second-largest economy. Additionally, China is a significant trading nation, according to the latest statistics from China Customs, the total value of goods trade in imports and exports reached CNY 42 trillion in 2022, maintaining its position as the world’s largest goods trading nation for several consecutive years. However, while China is a prominent trading nation, it is still in the process of becoming a strong trading nation, focusing on various aspects such as optimizing and upgrading the structure of its exported products. Research indicates that improving the structure of export products can yield numerous benefits for both the economy and society, including economic growth [1], reducing output fluctuations [2], stabilizing export relationships [3], and reducing the impacts of exchange rate fluctuations [4]. Currently, as China seeks to strengthen its status as a trading nation, it also emphasizes environmental protection, recognizing that effective environmental governance significantly influences various facets of its economy and society. Given China’s role as a major trading nation, its foreign trade is inevitably affected by environmental regulations. Thus, it is essential to elucidate the relationship between environmental regulations and the development of foreign trade. Understanding whether these two aspects are mutually exclusive or mutually beneficial is a vital area for exploration, as it will aid China in charting a clearer path for its present and future development.

The existing literature indicates that environmental regulations can significantly impact various aspects of export trade. Some scholars have specifically examined the influence of these regulations on the technological complexity of exports. For instance, Gao and Yuan investigated the industry-level effects of environmental regulations on the export technological complexity of enterprises [5], while Wang et al. conducted research at the provincial level [6]. However, the relationship between environmental regulations and the export technological complexity of enterprises requires further investigation. This paper aims to initiate research in this area. China’s foreign trade has evolved to make it the world’s largest goods trading nation, a success attributed to the efforts of numerous enterprises engaged in international trade. Notably, enterprises do not operate in isolation; they are influenced by their industry and geographical location. Previous studies have addressed the impact of industry-level environmental regulations on the technological complexity of exports [5]. The geographical categorization of enterprises spans provincial, city, and district levels. Given that the provincial level is too broad and the district level often lacks sufficient detailed data, the city level emerges as a more suitable focus for research. Consequently, this paper explores the effects of environmental regulations at the city level on the export technological complexity of enterprises. This study enhances our understanding of the relationship between the environment and the economy, confirming that environmental protection and economic development are not mutually exclusive. Furthermore, the findings provide valuable insights for the formulation of government policies in various cities. Governments that recognize the potential of environmental policies to promote green development can also foster high-quality economic growth. Compared to previous studies, this paper presents an innovative discussion of the relationship between environmental regulation and the technological complexity of enterprise exports, thereby enriching the existing body of research. Additionally, by examining the environmental regulations in the cities where enterprises are located, this study highlights that, beyond the industry context, geographical location significantly influences the technological complexity of exports. Moreover, the innovation fostered in cities plays a positive regulatory role in enhancing the export technological complexity of enterprises, even in the face of environmental regulations.

The remaining sections of this paper are organized as follows: Section 2 is the literature review. Section 3 discusses the mechanism of influence. Section 4 presents the econometric model design and variable definitions. Section 5 analyzes the empirical results. Section 6 presents the conclusion and policy recommendations.

2. Literature Review

Export technological complexity measures the product structure of export trade and serves as an indicator of the high-quality development of foreign trade. A substantial body of literature exists on the factors influencing export technological complexity, with scholars examining this phenomenon from various perspectives, including those of countries, provinces, and individual enterprises. For instance, Lapatinas utilized country-level data to illustrate the positive impact of the Internet on the complexity of exported products [7]. Zhang and Chen employed provincial-level data in China to demonstrate that outbound direct investment (OFDI) significantly affects the technological complexity of exports [8]. Liu and Wang explored the influence of foreign direct investment (FDI) on the technological complexity of Chinese firms’ exports using firm-level data spanning from 2000 to 2010 [9]. Additionally, Song et al. analyzed data from Chinese industrial enterprises between 2000 and 2013, investigating how the importation of intermediate goods impacts the technological complexity of exports [10].

This paper begins by examining environmental regulations and their effect on the technological complexity of exports. Previous research has established that environmental regulations can have diverse effects on economic and social development. For example, Du empirically tested the impact of environmental regulations on achieving high-quality economic development using data from 284 prefecture-level cities in China [11]. Ye and Cao analyzed the influence of environmental regulations on industrial development through a difference-in-differences approach, utilizing provincial panel data from 2009 to 2017 [12]. Zhou et al. studied the effects of environmental regulations on green innovation by leveraging China’s “Eleventh Five-Year Plan” as a quasi-natural experiment [13]. The impact of environmental regulations on export trade is the primary focus of this article. Relevant literature has evidenced that environmental regulations influence export decision-making, export scale, export quality, and export value added.

Environmental regulations can significantly influence participation in export trade. Shi and Xu analyzed data from Chinese enterprises and found that in heavily polluting industries, more stringent environmental regulations reduce the likelihood of companies exporting their products [14]. Similarly, Zhang et al. focused on water pollution regulations and discovered that such environmental regulations negatively impact export decisions among enterprises [15]. Conversely, Lu et al. argued that the public disclosure of environmental information can encourage export decisions by businesses [16]. Environmental regulations also affect the scale of export trade. Hering and Poncet investigated policies related to sulfur dioxide (SO₂) emissions at the city level and demonstrated that implementing environmental regulations leads to a decrease in export volume [17]. Fang et al. utilized a Chinese enterprise database from 2003 to 2013 to reveal the existence of a pollution haven effect, indicating that the public disclosure of environmental information policies reduces the scale of enterprise exports [18]. Zhang et al. examined the 2003 urban air pollution control plan in China and found that environmental regulations contributed to a decline in the scale of enterprises’ exports [19]. However, Xie et al. analyzed manufacturing enterprise data from 2000 to 2013 in China and reported a positive impact of environmental information disclosure on export value [20]. Additionally, scholars suggest that the effects of environmental regulations on the export trade scale may differ in the short and long term. Qiang et al. studied data from 30 provinces in China between 2008 and 2017, finding that environmental regulations inhibit export trade in the short term while promoting it in the long term [21].

Environmental regulations not only influence export decisions and scale but also impact domestic value added in exports. Sun et al. examined the implementation of clean production standards in China and found that these standards increased the domestic value-added ratio of Chinese enterprises [22]. Ma et al. constructed a spatial econometric model using data from Chinese cities, revealing a nonlinear effect of environmental regulations on the domestic value-added ratio of exports, initially decreasing before subsequently increasing [23]. Furthermore, environmental regulations can affect the quality of export products. Deng et al. argued that the environmental regulations enacted during China’s “Eleventh Five-Year Plan” negatively influenced export product quality [24]. Similarly, He and Tang suggested that local environmental constraints related to emission reduction targets diminished the quality of export products for enterprises [25]. Conversely, some scholars presented opposing views. Jiang et al. analyzed enterprise-level data from 2000 to 2013 and found that the key air pollution control zone policy implemented in China in 2010 improved export product quality [26]. Xiong and Zhu treated the COD emission reduction task stipulated in the “Eleventh Five-Year Plan” in 2006 as an exogenous shock and concluded that environmental regulations significantly enhanced export product quality [27]. Moreover, some researchers propose that the impact of environmental regulations on the quality of export products is nonlinear. Ning et al. examined industry data from China covering 2003 to 2010 and verified a “U-shaped” relationship, indicating that early-stage regulations, where cost effects outweigh innovation effects, reduce export product quality [28]. In contrast, in later stages, when innovation effects surpass cost effects, environmental regulations facilitate improvements in export product quality. The literature most relevant to this study focuses on the impact of environmental regulations on the complexity of export technology. Gao and Yuan found that the implementation of clean production standards in certain Chinese industries in 2003 increased the complexity of export technology among enterprises [5]. Wang et al., based on provincial panel data from China between 2004 and 2016, identified a nonlinear effect of environmental regulations on export technology complexity, characterized by a U-shaped relationship marked by an initial decrease followed by an increase [6].

The literature review reveals that scholars have examined the impact of environmental regulations on exports from various perspectives. Gao and Yuan explored the influence of environmental regulations on the technological complexity of exports at the industry level [5], while Wang et al. focused on this issue at the provincial level [6]. These studies are highly relevant to the current research; however, analyzing the effects of environmental regulations solely at the provincial level limits the potential for a nuanced micro-level analysis. Thus, it is imperative to investigate how environmental regulations influence export technological complexity at the enterprise level. Environmental regulations applicable to the industry in which an enterprise operates can significantly affect its export technological complexity, as can regulations imposed by the city in which the enterprise is located. Consequently, this paper expands upon prior research by addressing this issue at the enterprise level.

3. Impact Mechanisms

The impact of environmental regulations on enterprise production and operations can be considered from two perspectives. On one hand, these regulations may negatively affect enterprises by increasing production and operational costs. On the other hand, they may yield positive outcomes. According to the “Porter Hypothesis”, environmental regulations can incentivize enterprises to adopt more environmentally friendly and efficient production methods, leading to increased productivity, reduced costs, and enhanced competitiveness. This study investigates the relationship between environmental regulations and the export technological complexity of enterprises. Export technological complexity is intricately linked to a firm’s technological innovation and the characteristics of its export products. Therefore, the mechanisms through which environmental regulations affect the export technological complexity of enterprises may be multifaceted:

• Promoting independent innovation by enterprises [29]. When the government implements environmental regulations, these measures inevitably influence the operational dynamics of enterprises. To mitigate potential adverse effects and avoid economic losses, businesses must actively engage in innovation. Consequently, the independent innovation spurred by environmental regulations contributes to an increase in the export technological complexity of enterprises.
• Government support for enterprise technological innovation [30]. Environmental protection yields positive externalities, yet environmental regulations can also impose challenges on enterprise operations. To address these challenges, the government provides support for technological innovation through subsidies, tax incentives, and technological assistance. Hence, the implementation of environmental regulations not only initiates government support for enterprise innovation but also enhances the export technological complexity of these businesses.
• Environmental regulations will affect the use of intermediate goods by enterprises [31]. Environmental regulations influence not only individual enterprises that export products but also affect various production processes, particularly for high-tech goods that require complex intermediate inputs. In response to these regulations, enterprises producing intermediate goods enhance their offerings, which in turn leads to upgrades in the final products exported by these enterprises. Thus, environmental regulations increase export technological complexity by affecting the utilization of intermediate goods.
• Environmental regulations will enhance the human capital of enterprises [32]. Cities with stringent environmental governance often become more attractive places to live, drawing in talented individuals. Consequently, enterprises located in these cities benefit from increased access to skilled labor. Human capital is a critical factor in enhancing the export technological complexity of enterprises. Therefore, environmental regulations elevate the export technological complexity of local businesses by positively influencing their human capital.

Based on the above analysis, this paper hypothesizes that environmental regulations will lead to an increase in the export technological complexity of local enterprises.

4. Design of Econometric Models and Explanation of Variables

4.1. Econometric Model Design

This paper explores the impact of environmental regulations in the cities where enterprises are located on the export technological complexity of enterprises. Drawing on relevant previous research, the econometric model designed in this paper is as follows:

$$lnExp{y}_{it}={\beta }_0+{\beta }_{1}lnE{r}_{mt}+{\beta }_{2}Control{s}_{it}+{\mu }_m+{\mu }_j+{\mu }_t+{\epsilon }_{it}$$

(1)

The variable lnExpy_it represents the logarithmic form of the export technological complexity of enterprises, which is the explained variable in this paper. The variable lnEr_mt represents the logarithmic form of the intensity of environmental regulations in cities, which is the core explanatory variable in this paper. Controls_it represents the control variables, which involve the enterprise level, industry level, and city level, specifically including enterprise size, enterprise age, capital intensity, financing constraints, industry competition level, and city industrial structure. μ_m represents city fixed effects, μ_j represents industry fixed effects, μ_t represents year fixed effects, and ε_it represents the random error term.

4.2. Selection and Explanation of Variables

The explained variable is the export technological complexity of enterprises. It is often used by scholars to measure the technological content of a company’s export products. The method proposed by Hausmann et al. for measuring export technological complexity has been adopted by most scholars [1]; however, this indicator has certain deficiencies. Subsequently, Hausmann and Hidalgo proposed using the reflection method to calculate export technological complexity [33], and then Tacchella et al. improved upon this method [34]. This article adopts this method to calculate the technological complexity of enterprise export, and the calculation process is as follows.

$$RC{A}_{cp}={\displaystyle \frac{(X_{cp}/X_c)}{{\displaystyle {\sum }_c(X_{cp}/X_c)}}},M_{cp}=\left\{\begin{array}{ll}1,& {RCA}_{cp}\ge 1\\ 0,& Others\end{array}\right.$$

(2)

$${\tilde{F}}^{(n)}={\displaystyle {\sum }_p{M}_{cp}{Q_p}^{(n-1)}},{{\tilde{Q}}_p}^{(n)}={\displaystyle \frac{1}{{\displaystyle {\sum }_c{M}_{cp}(1/{F_c}^{(n-1)})}}}$$

(3)

$${F_c}^{(n)}={\displaystyle \frac{{{\tilde{F}}_c}^{(n)}}{{[{{\tilde{F}}_c}^{(n)}]}_c}},{Q_p}^{(n)}={\displaystyle \frac{{{\tilde{Q}}_p}^{(n)}}{{[{{\tilde{Q}}_p}^{(n)}]}_p}}$$

(4)

$$Exp{y}_{it}={\displaystyle \sum \frac{x_{ipt}}{x_{it}}\times {Q_p}^{(n)}}$$

(5)

In Formulas (2)–(5), c represents the country, p represents the product, X_cp represents the export value of country c in product p, X_c is the total export value of all products of country c, and RCA_cp is the revealed comparative advantage index. When RCA_cp is greater than or equal to 1, M_cp is 1; otherwise, it is 0. n represents the number of iterations, ${{\tilde{F}}_c}^{(n)}$ represents the appropriateness of country c, ${{\tilde{Q}}_p}^{(n)}$ represents the export technological complexity of product p, and ${[{{\tilde{F}}_c}^{(n)}]}_c$ and ${[{{\tilde{Q}}_p}^{(n)}]}_p$ represent the mean of the two, respectively. F_c⁽ⁿ⁾ and Q_p⁽ⁿ⁾ are the final calculation results of the appropriateness of country c and the technological complexity of product p at the nth iteration, respectively. When the country appropriateness or product technological complexity sequence obtained by the nth and n + 1th time is stable, the iteration is stopped, and the final F_c⁽ⁿ⁾ and Q_p⁽ⁿ⁾ are obtained. Here, Q_p⁽ⁿ⁾ is the export technological complexity of the product required by this study. When calculating the export technological complexity at the enterprise level, the export technological complexity at the product level is matched with the export data of enterprises in the Chinese Customs database, and then the export technological complexity (Expy) at the enterprise level is calculated according to Formula (5), where it represents the export value of enterprise i in product p in year t and the total export value of enterprise i in year t.

The core explanatory variable of this paper is environmental regulations. The indicator of environmental regulations in each city is borrowed from the practice of Chen et al. using the frequency of environmental-protection-related words in the government work reports of each city (including “green”, “low carbon”, “carbon dioxide”, “emission reduction”, “environmental protection”, “pollution”, “energy consumption”, “air”, “sulfur dioxide”, “ecology”, “PM10”, and “PM2.5”) to characterize the intensity of environmental protection regulation [35].

The control variables in this article are included at three levels: enterprise, industry, and city. At the enterprise level, the control variables include enterprise size (Size), enterprise age (Age), capital intensity (Capital), and financial constraint (Finance). Size is represented by the number of employees in the enterprise, which is logarithmically transformed and included in the econometric model. Age is measured by subtracting the year from the year the enterprise was established and adding 1 and then logarithmically transformed and included in the econometric model. Capital is obtained by dividing fixed assets by the number of employees in the enterprise, logarithmically transformed, and included in the econometric model. Finance is represented by the ratio of current liabilities to total assets. At the industry level, the control variable chosen is the four-digit industry concentration of enterprises (HHI), which reflects the level of competition in the industry where the enterprises operate, measured using the Herfindahl–Hirschman Index (HHI). At the city level, the control variable selected is the industrial structure of the city (Indcity), represented by the proportion of the value added of the tertiary industry in each city to the regional GDP.

4.3. Data Sources

The original data for calculating the export technological complexity at the product level are sourced from the United Nations Comtrade Database, which includes trade data at the product level for various countries or regions. The data required for calculating the export technological complexity at the product level are the HS six-digit code data. Following the method of Tacchella et al. [34], this paper used these data to measure the export technological complexity of HS six-digit-coded products. The data for calculating the export technological complexity at the enterprise level are sourced from the China Customs database, which covers the import and export data of eight-digit-coded products at the enterprise level in China. Additionally, as the product codes change every five years and to align with the United Nations Commodity Trade Statistics website’s product code conversion table, they are uniformly converted to the HS 1996 version. Furthermore, since some enterprises are only engaged in commodity circulation and not in the manufacturing of goods, enterprises with names containing “export”, “trade”, “commerce”, and similar terms are excluded. The data at the enterprise level are sourced from the Industrial Enterprise Database, which contains basic information and financial data at the enterprise level in China and is the main database for studying Chinese enterprise issues. This paper matches the Industrial Enterprise Database and the Customs database based on enterprise names, telephone numbers, and area codes and removes samples with fewer than 10 employees, missing key indicators, and financial indicator errors, retaining only manufacturing enterprises. The city environmental regulations are measured based on keywords in the government work reports of each city, and therefore the data are sourced from the “Government Work Reports” of each city annually. The city-level data related to the control variables are sourced from the “China City Statistical Yearbook”. Due to data availability limitations, the time span of this study is from 2003 to 2013.

Table 1. Variable description.

Variable	Description	Measurement	Sources
lnExpy	export technological complexity	Tacchella et al. [34]	China Customs database
lnEr	environmental regulations	Chen et al. [35]	Government Work Reports
lnSize	enterprise size	number of employees	Industrial Enterprise Database
lnAge	enterprise age	age of enterprise	Industrial Enterprise Database
lnCapital	capital intensity	fixed assets/employees	Industrial Enterprise Database
Finance	financial constraint	current liabilities/assets	Industrial Enterprise Database
HHI	competition in the industry	H-H Index	Industrial Enterprise Database
Cityind	industrial structure	tertiary sector/GDP	City Statistical Yearbook

presents a description of the variables in this paper.

4.4. Statistical Description

Table 2. Statistical description.

Variable	Obs	Mean	Std. Dev.	Min	Max
lnExpy	628,903	−0.90	1.20	−14.66	3.15
lnEr	628,903	−3.97	1.61	−13.95	3.56
lnSize	628,903	5.38	1.09	2.89	8.30
lnAge	628,903	2.21	0.67	0.69	3.99
lnCapital	628,903	3.64	1.38	0.14	7.03
Finance	628,903	0.51	0.27	0.00	1.17
HHI	628,903	0.01	0.03	0.00	0.90
Cityind	628,903	0.42	0.09	0.11	0.77

provides the statistical description of the main variables in this paper.

5. The Regression Results and Analysis

5.1. Baseline Regression

Table 3. Baseline regression.

Variable	(1) lnExpy	(2) lnExpy	(3) lnExpy	(4) lnExpy
lnEr	0.0090 *** (0.003)	0.0099 *** (0.003)	0.0099 *** (0.003)	0.0087 *** (0.003)
lnSize		0.0070 ** (0.003)	0.0070 ** (0.003)	0.0070 *** (0.003)
lnAge		−0.0159 *** (0.004)	−0.0159 *** (0.004)	−0.0163 *** (0.004)
lnCapital		0.0376 *** (0.002)	0.0376 *** (0.002)	0.0380 *** (0.002)
Finance		−0.0487 *** (0.009)	−0.0488 *** (0.009)	−0.0492 *** (0.009)
HHI			0.0184 (0.090)	0.0170 (0.090)
Cityind				0.4025 *** (0.065)
City fixed effect	Yes	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes
Observations	628,903	628,903	628,903	628,903
R2	0.274	0.276	0.276	0.276

Note: Robust standard errors clustered at the firm level are reported in parentheses; ** and *** indicate significance at the 5% and 1% levels.

presents the baseline regression results. From the results in the first column of the table, it can be seen that under the control of fixed effects such as city, industry, and year, environmental regulations significantly promote the complexity of technological exports for enterprises, passing a significance test at the 1% level. Columns 2, 3, and 4 of Table 3, respectively, indicate that even after gradually introducing control variables at the enterprise, industry, and city levels, the impact of environmental regulations on the complexity of technological exports for enterprises remains significantly positive, also passing a significance test at the 1% level. The empirical results are consistent with the previous hypothesis that environmental regulations can promote the improvement of the technological complexity of enterprise exports. Column 4 of Table 3 shows the regression results after adding all control variables. According to this column, enterprise size, capital intensity, and city industrial structure have a significant promoting effect on the complexity of technological exports for enterprises, while enterprise age and financing constraints have a significantly negative impact. The level of industry competition does not have a significant impact on the export technological complexity of enterprises.

5.2. Robustness Test

To test the robustness of the research conclusions, this study conducts robustness tests on the relationship between environmental regulations and the complexity of export technology from multiple perspectives. The dependent variable in column (1) of

Table 4. Robustness test.

Variable	(1) lnExpyShen	(2) lnExpyHau	(3) lnExpy	(4) lnHtvol	(5) Htratio
lnEr	0.0060 ***	0.0019 **	0.0104 **	0.0359 ***	0.0021 ***
	(0.002)	(0.001)	(0.005)	(0.010)	(0.001)
lnSize	0.0093 ***	−0.0042 ***	0.0071 **	0.5377 ***	0.0110 ***
	(0.002)	(0.001)	(0.003)	(0.012)	(0.001)
lnAge	−0.0243 ***	−0.0090 ***	−0.0221 ***	−0.1959 ***	−0.0067 ***
	(0.004)	(0.001)	(0.005)	(0.017)	(0.001)
lnCapital	0.0380 ***	0.0281 ***	0.0403 ***	0.0786 ***	−0.0002
	(0.002)	(0.001)	(0.002)	(0.008)	(0.000)
Finance	−0.0250 ***	−0.0157 ***	−0.0531 ***	−0.0689 *	−0.0026
	(0.008)	(0.003)	(0.010)	(0.036)	(0.002)
HHI	0.1243	0.4632 ***	−0.0344	4.5074 ***	0.1254 ***
	(0.077)	(0.035)	(0.103)	(0.456)	(0.027)
Cityind	0.3636 ***	−0.0837 ***	0.4404 ***	3.0287 ***	0.0316 **
	(0.056)	(0.024)	(0.080)	(0.261)	(0.016)
City fixed effect	Yes	Yes	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes
Observations	628,903	628,903	467,046	628,903	628,903
R2	0.294	0.446	0.291	0.401	0.373

Note: Robust standard errors clustered at the firm level are reported in parentheses; *, **, and *** indicate significance at the 10%, 5%, and 1% levels.

is replaced by the export technology complexity (ExpyShen) calculated by Shen and Huang, which reclassifies RCA_cp: when RCA_cp ≥ 1.25, the product has a strong revealed comparative advantage; when 0.8 ≤ RCA_cp < 1.25, the product has a weak revealed comparative advantage; and when RCA_cp < 0.8, the product has a revealed comparative disadvantage [36]. The regression results show that environmental regulations have a significantly positive impact on the complexity of export technology for enterprises, indicating that environmental regulations promote the improvement of the complexity of export technology for enterprises, and this result passes the 1% significance test.

The dependent variable in column (2) of Table 4 is replaced by the export technology complexity (ExpyHau) calculated by Hausmann et al. [1], which provides a method for measuring the technological content of goods. The logic behind it is “the higher the technological content of products exported by high-income countries”, and its calculation formula is as follows:

$$Prod{y}_p={\displaystyle \sum _{c}RC{A}_{cp}\times {Pgdp}_c}$$

(6)

$$ExpyHa{u}_{it}={\displaystyle \sum \frac{x_{ipt}}{x_{it}}\times {Prody}_{pt}}$$

(7)

In Formula (6), Prody_p represents the export technological complexity of product p, RCA_cp represents the index of revealed comparative advantage, and Pgdp_c represents the per capita GDP of a country. After calculating the export technological complexity of the product, the export technological complexity (Expy) of the enterprise is weighted by Formula (7), where x_ipt represents the export value of enterprise i on product p in year t, and x_it represents the total export value of enterprise i in year t. After using the method of Hausmann et al. to re-measure the export technological complexity of enterprises [1], the regression results show that the impact of environmental regulations on the export technological complexity of enterprises is significantly positive.

The presence of endogeneity may lead to bias in the regression results, so it is necessary to address endogeneity to some extent. In Table 4, column (3), endogeneity is considered, and a lagged core explanatory variable is used as an instrumental variable for 2SLS regression. The results of the instrumental variable unidentifiability test and weak instrumental variable test were passed. The regression results show that after addressing endogeneity, the impact of environmental regulations on the technological complexity of enterprise exports did not change significantly.

Export technological complexity is used to measure the technological content of enterprise export products. Therefore, the more high-tech products a company exports, the higher the technological complexity of its export. Taking this into account, this study examines the impact of environmental regulations on the export of high-tech products by enterprises. In Table 4, column (4), the dependent variable is replaced with the export value of high-tech products, with the identification of high-tech products referring to the product technology classification standard defined by Lall [37]. The export value of high-tech products is measured in the form of a natural logarithm plus one (lnHtvol). The regression results show that environmental regulations promote an increase in the export value of high-tech products by enterprises. In Table 4, column (5), the dependent variable is replaced with the proportion of high-tech products in the export products of enterprises (Htratio), and environmental regulations significantly promote an increase in the proportion of high-tech product exports by enterprises. It can be seen that when the dependent variables in columns (4) and (5) are replaced with the export value and proportion of high-tech products, the impact of environmental regulations on the export of high-tech products is significantly positive. As the technological complexity of enterprise exports is closely related to the export of high-tech products, the promoting effect of environmental regulations on the technological complexity of enterprise exports is further validated.

5.3. Heterogeneity Test

Based on the trade mode of enterprises, the sample is divided into general-trade enterprises, processing-trade enterprises, and mixed-trade enterprises. Processing-trade enterprises involve the processing and assembly of products, with research, production, and sales being handled by foreign enterprises. Mixed-trade enterprises engage in both general and processing trade. The results in

Table 5. Heterogeneity: trade patterns.

Variable	(1) lnExpy (General)	(2) lnExpy (Processing)	(3) lnExpy (Mixed)
lnEr	0.0112 *** (0.003)	−0.0118 (0.008)	0.0094 ** (0.005)
lnSize	−0.0074 **	0.0223 ***	0.0157 ***
	(0.003)	(0.007)	(0.005)
lnAge	−0.0067	−0.0482 ***	−0.0274 ***
	(0.005)	(0.012)	(0.008)
lnCapital	0.0292 ***	0.0590 ***	0.0489 ***
	(0.002)	(0.006)	(0.004)
Finance	−0.0284 ***	−0.0955 ***	−0.0788 ***
	(0.011)	(0.024)	(0.016)
HHI	−0.0524	0.8420 ***	−0.0514
	(0.116)	(0.243)	(0.146)
Cityind	0.1927 **	0.3967 **	0.9252 ***
	(0.092)	(0.180)	(0.117)
City fixed effect	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
Observations	390,112	68,400	170,361
R2	0.244	0.355	0.355

Note: Robust standard errors clustered at the firm level are reported in parentheses; ** and *** indicate significance at the 5% and 1% levels.

indicate that general-trade enterprises’ export technological complexity is significantly and positively influenced by environmental regulations, passing the 1% significance test. However, the export technological complexity of processing-trade enterprises is not significantly affected by environmental regulations, failing to pass the 10% significance test. Mixed-trade enterprises’ export technological complexity is restricted by environmental regulations, with a decreased significance compared to general-trade enterprises, only passing the 5% significance test. This phenomenon may be attributed to the fact that processing trade only involves a part of the product’s complete production chain, which consists of relatively simple labor, resulting in limited technological innovation. Environmental regulations drive enterprises to innovate; hence, low-tech processing trade is not significantly affected by environmental regulations. Consequently, when environmental regulations impact the export technological complexity of enterprises, general-trade enterprises show the most significant effect, while mixed-trade enterprises, due to their involvement in some processing trade, exhibit a slightly decreased significance, and processing-trade enterprises, focusing solely on processing trade, are not significantly affected by environmental regulations.

Industries can be classified into three categories based on the intensity of factors: labor-intensive industries, capital-intensive industries, and technology-intensive industries. The labor-intensive industries mainly include food, textiles, and furniture, while the capital-intensive industries mainly include metal, chemical, and plastic sectors. The technology-intensive industries mainly comprise electronics, machinery, and pharmaceuticals. These industries exhibit certain differences, and this paper conducts a heterogeneity test on these three types of industries. The results in column (1)–(3) of

Table 6. Heterogeneity: factor intensity.

Variable	(1) lnExpy (Labor)	(2) lnExpy (Captial)	(3) lnExpy (Tech)
lnEr	0.0158 ***	−0.0072	0.0091 ***
	(0.005)	(0.005)	(0.004)
lnSize	−0.0231 ***	0.0300 ***	0.0147 ***
	(0.005)	(0.006)	(0.004)
lnAge	0.0012	−0.0215 **	−0.0301 ***
	(0.008)	(0.008)	(0.006)
lnCapital	0.0530 ***	0.0443 ***	0.0265 ***
	(0.004)	(0.004)	(0.003)
Finance	0.0043	−0.0862 ***	−0.0920 ***
	(0.016)	(0.019)	(0.013)
HHI	1.8134 ***	−0.6366 ***	−0.5129 ***
	(0.228)	(0.233)	(0.108)
Cityind	1.0671 ***	0.1768	0.0234
	(0.126)	(0.130)	(0.095)
City fixed effect	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
Observations	190,033	153,025	252,323
R2	0.229	0.117	0.059

Note: Robust standard errors clustered at the firm level are reported in parentheses; ** and *** indicate significance at the 5% and 1% levels.

show that the export technological complexity of labor-intensive and technology-intensive industries is promoted by environmental regulations. However, the impact on the export technological complexity of capital-intensive industries is not significant, failing to pass the 10% significance test. These capital-intensive industries, such as papermaking, printing, petroleum, chemical, plastic, metal, and non-metal sectors, are heavily polluting industries. Compared to other industries, capital-intensive industries are more likely to generate pollutants such as wastewater, exhaust gas, and solid waste during the production process due to their industry characteristics. Enterprises in capital-intensive industries need to invest more in environmental protection equipment and R&D, which puts greater financial pressure on them, making it more difficult for them to address environmental pollution. Therefore, under the influence of environmental regulations, enterprises in capital-intensive industries find it difficult to quickly enhance the export technological complexity. In the future process of environmental pollution control, the government should pay special attention to environmental governance in capital-intensive industries.

The economic development in China exhibits a certain degree of regional imbalance. Geographically, it can be divided into three regions: the eastern, central, and western regions. The eastern region is the most developed, followed by the central region, while the western region lags behind in economic development. This study categorizes sample cities into eastern, central, and western regions, and examines the impact of different regional environmental regulations on the technological complexity of enterprise exports. The corresponding regression results are shown in

Table 7. Heterogeneity: geographic location.

Variable	(1) lnExpy (Eastern)	(2) lnExpy (Central)	(3) lnExpy (Western)
lnEr	0.0080 *** (0.003)	0.0028 (0.009)	0.0264 ** (0.013)
lnSize	0.0085 ***	0.0013	−0.0052
	(0.003)	(0.009)	(0.014)
lnAge	−0.0134 ***	−0.0138	−0.0513 **
	(0.005)	(0.013)	(0.021)
lnCapital	0.0376 ***	0.0456 ***	0.0386 ***
	(0.002)	(0.007)	(0.010)
Finance	−0.0488 ***	0.0165	−0.1371 ***
	(0.010)	(0.032)	(0.050)
HHI	0.0389	−0.1366	−0.4287
	(0.092)	(0.386)	(0.590)
Cityind	0.4754 ***	−0.2654	0.3731
	(0.075)	(0.286)	(0.370)
City fixed effect	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
Observations	565,858	41,922	21,123
R2	0.280	0.259	0.278

Note: Robust standard errors clustered at the firm level are reported in parentheses; ** and *** indicate significance at the 5% and 1% levels.

. It is evident that environmental regulations in eastern cities significantly promote the technological complexity of enterprise exports, passing a 1% significance test. In central cities, environmental regulations do not have a significant impact on the technological complexity of enterprise exports. In western cities, environmental regulations also significantly promote the technological complexity of enterprise exports, but the significance is slightly lower than that of eastern cities, passing only a 5% significance test. The reason for this phenomenon may be that the level of urban development in the eastern region is higher, with greater innovation capabilities. Therefore, under the constraint of environmental regulations, enterprises are encouraged to innovate and improve the technological content of their export products. The overall development of the western region is relatively lagging, with lower levels of environmental pollution that are easier to control, thus achieving better results. The development of the central region lies between the eastern and western regions. While the central region has made some progress compared to the western region, there still exists a certain gap in development compared to the eastern region. This makes it more challenging for environmental regulations to drive enterprise technological innovation and enhance the technological complexity of exports.

5.4. Moderating Effect

The environmental regulations do not directly promote the export technological complexity of enterprises. It is only when the innovation capability is enhanced during the implementation of environmental regulations that it can play a better role. Therefore, this article establishes a moderation-effect econometric model to test the moderation effect of urban innovation capability on the relationship between environmental regulations and the export technological complexity of enterprises, as in Formula (8).

$$\begin{array}{l}lnExp{y}_{it}={\beta }_0+{\beta }_{1}lnE{r}_{mt}+{\beta }_{2}lnPa{t}_{mt}+{\beta }_{3}ln{Er}_{mt}\times ln{Pat}_{mt}\\ +{\beta }_4{Controls}_{it}+{\mu }_m+{\mu }_j+{\mu }_t+{\epsilon }_{it}\end{array}$$

(8)

In testing the moderation effect, special attention should be paid to the significance and sign of the interaction term, i.e., the significance and sign of β₃. The innovation capability of a city is measured by the number of patent applications for inventions per total population of the city and is entered into the econometric model in logarithmic form. Patent data are sourced from the statistical yearbooks of various cities, and population data are sourced from the “China City Statistical Yearbook”.

Table 8. Moderating effects of urban innovation.

Variable	(1) lnExpy	(2) lnExpyHau	(3) lnHtratio	(4) lnHtvol
lnEr	0.0069 *** (0.003)	0.0009 (0.001)	0.0008 (0.001)	−0.0009 (0.010)
InPat	−0.0013 (0.005)	0.0024 (0.002)	−0.0029 ** (0.001)	−0.0088 (0.022)
lnEr × lnPat	0.0019 ** (0.001)	0.0014 *** (0.000)	0.0013 *** (0.000)	0.0438 *** (0.004)
lnSize	0.0070 ***	−0.0042 ***	0.0110 ***	0.5389 ***
	(0.003)	(0.001)	(0.001)	(0.012)
lnAge	−0.0162 ***	−0.0090 ***	−0.0066 ***	−0.1940 ***
	(0.004)	(0.001)	(0.001)	(0.017)
lnCapital	0.0380 ***	0.0281 ***	−0.0002	0.0788 ***
	(0.002)	(0.001)	(0.000)	(0.008)
Finance	−0.0493 ***	−0.0157 ***	−0.0026	−0.0700 *
	(0.009)	(0.003)	(0.002)	(0.036)
HHI	0.0241	0.4667 ***	0.1256 ***	4.5066 ***
	(0.090)	(0.034)	(0.027)	(0.456)
Cityind	0.4209 ***	−0.0713 ***	0.0438 ***	3.4277 ***
	(0.066)	(0.025)	(0.016)	(0.268)
City fixed effect	Yes	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes
Observations	628,866	628,866	628,866	628,866
R2	0.276	0.446	0.373	0.401

Note: Robust standard errors clustered at the firm level are reported in parentheses; *, **, and *** indicate significance at the 10%, 5%, and 1% levels.

presents the regression results of the moderation effect test. The dependent variable in column (1) is measured using the method of Tacchella et al. [34]. The dependent variable in column (2) is measured using the method of Hausmann et al. [1]. The dependent variable in column (3) is the export value of high-tech products, while the dependent variable in column (4) is the proportion of high-tech product exports. The high-tech products mentioned here are defined by Lall [37]. The regression results show that regardless of the method used to measure the export technological complexity of enterprises, the interaction term between a city’s innovation capability and urban environmental regulations has a significant positive impact on the export technological complexity of enterprises. It can be seen that the enhancing effect of environmental regulations on the export technological complexity of enterprises is strengthened with the improvement of a city’s innovation capability. Therefore, in the process of promoting the enhancement of urban environmental regulations on the export technological complexity of enterprises, it is crucial to enhance the city’s innovation capability. This also gives us insight that the positive effects of environmental regulations on the economic and social aspects require innovation capability to play a role.

5.5. Discussion

This study aims to investigate the impact of environmental regulations in the cities where enterprises are located on the technological complexity of their exports. Italso explores the moderating effect of urban innovation. Empirical analysis indicates that environmental regulations in a city’s context exert a positive influence on the technological complexity of exports. These findings are consistent with previous research on the impact of industry-specific environmental regulations on export technological complexity [5]. However, unlike Gao and Yuan’s study [5], which analyzed the issue from an industry perspective, this analysis takes a city-level approach. The development of enterprises is not only related to their respective industries but also to their geographical locations. Moreover, the research findings reveal heterogeneity in the influence of environmental regulations on the technological complexity of enterprise exports.

6. Conclusions and Policy Recommendations

6.1. Conclusions

The results from the baselineregression indicate that environmental regulations enhance the export technological complexity of enterprises. The positive impact of regulations in the city where the enterprise is located was verified through various robustness tests, including substitution variable and instrumental variable regressions. Notably, there exists a degree of heterogeneity in how environmental regulations influence export technological complexity across different enterprises. For instance, the impact of environmental regulations on processing-trade enterprises is less significant than on general-trade and mixed-trade enterprises. Similarly, enterprises in capital-intensive industries experience a lesser impact compared to those in labor-intensive and technology-intensive sectors. Additionally, the geographical location of the city plays a critical role; the effect of environmental regulations on export technological complexity is less pronounced in central cities than in eastern and western cities. Moderating effect indicates that stronger innovation capabilities within a city correlate with a more pronounced impact of environmental regulations on enhancing enterprises’ export technological complexity. The findings offer valuable insights for policymakers, illustrating that the development of environmentally friendly cities can positively contribute to enterprise advancement.

6.2. Policy Recommendations

Based on the research findings, this study proposes the following policy recommendations.

Environmental regulations serve as a crucial mechanism for fostering harmonious coexistence between humans and nature. High-quality economic development cannot be achieved without robust environmental protection. In the current pursuit of high-quality foreign trade development, environmental regulations not only do not hinder progress but also positively contribute to it. These regulations can encourage enterprises to enhance the complexity of their export technologies. Consequently, it is essential to further implement policies related to environmental regulations, as they are beneficial for both the nation and its citizens.

The impact of environmental regulations on the enhancement of export technological complexity among enterprises demonstrates heterogeneity. Therefore, it is important to consider this variability when promoting improvements in export technological complexity through environmental regulations. Notably, the influence of these regulations on the export technological complexity of enterprises in central cities, processing-trade sectors, and capital-intensive industries is minimal; thus, these areas warrant focused attention in future initiatives.

In addition to enforcing environmental regulations, it is imperative to bolster urban innovation. The moderation effect analysis indicates that city innovation significantly amplifies the positive impact of environmental regulations on the export technological complexity of enterprises. Therefore, the government should invest in human capital, funding, and technological support while implementing environmental regulations to foster urban innovation.

To ensure the effective implementation of environmental regulation policies, the government should adopt appropriate measures. First, relevant departments must establish clear environmental laws and standards. Second, enhancing transparency in policy implementation is crucial to encourage public participation in environmental governance. Additionally, providing necessary financial support and technical assistance will facilitate the adoption of environmental protection measures by enterprises and local governments.

6.3. Limitation and Future Guidelines

Similar to other studies, this research has some limitations. First, the study utilizes the Chinese industrial enterprises database, which is large enough to provide a comprehensive overview of issues related to Chinese companies. However, the slow updating of enterprise-level databases has led to somewhat outdated data, a common issue encountered in similar research. In the future, studies can be carried out using updated databases once they are available. Second, this paper only considers data from Chinese enterprises. As a developing country, China’s economic development has unique characteristics, so it remains uncertain whether the conclusions of this study apply to other developing countries. Future research could leverage data from other developing nations to validate the findings of this study. Lastly, this research only explores the moderating effect of innovation on the relationship between environmental regulation and the technological complexity of company exports. Other potential moderating factors could be examined in future studies.