# Cleaner Production Standards and Increased Technical Complexity for Textile Exporters

Authors: Xiaoying Ju; Xiaoli Ge; Peng Yao
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

An important prerequisite for promoting sustainable development and value chain climbing of textile enterprises is to achieve a win-win situation between environmental protection and export technical complexity. Thus, based on Chinese enterprises’ microdata, this paper investigates the impact of cleaner production standards implementation on the export technical complexity of Chinese textile enterprises using a double difference method. The results show that the implementation of cleaner production standards significantly increases the export technical complexity of Chinese textile enterprises. The results of the mechanism test indicate that cleaner production standards increase export technical complexity by enhancing capital and labor inputs of textile enterprises, as well as by promoting enterprise innovation and increasing productivity. Heterogeneity analysis shows that cleaner production standards have a significant role in increasing the export technical complexity of textile enterprises in the eastern region and larger scale; it is not significant in increasing the export technical complexity of textile enterprises in the central and western regions and smaller scale; in terms of enterprise ownership attributes, it has the greatest role in increasing the export technical complexity of state-owned textile enterprises, followed by foreign and private textile enterprises. The results of this paper show that cleaner production standards, as an environmental regulation policy, can achieve a win-win situation for environmental protection and export technical complexity increase.


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## Body

## 1. Introduction

The report of the 19th National Congress of the Communist Party of China emphasizes on promoting China’s industry to the middle and high end of the global value chain. China is the largest producer and exporter of textiles and garments of the world, but the textile industry has long been in the middle and low ends of the global value chain; facing the challenge of production being replaced, there is the urgent need for the textile industry in the global value chain to climb up. After the reform and opening-up, China, with policy advantages and labor cost comparative advantage, has become the fourth round of transferring the destination in the global textile industry after the United Kingdom, the United States, Japan, and South Korea (Modern industrialized textile production originated in the first industrial revolution in the United Kingdom and the United States in the early 20th century to succeed the United Kingdom as the new textile manufacturing center; the global textile manufacturing center after World War II shifted to Japan, and after the 1970s, it gradually shifted to South Korea, Taiwan(China), and Hong Kong (China); subsequently, China established the development goal of socialist market economy in 1992, and the openness increased, and joined the WTO in 2001, after which the foreign trade had a rapid development, and it smoothly became the global textile manufacturing center. In recent years, Southeast Asian countries due to the advantages of labor costs and other aspects began to gradually undertake some of the low-end manufacturing capacity of other countries; textile manufacturing and export trade is growing rapidly; the textile manufacturing center has the trend of shifting to Southeast Asian countries, and from a domestic perspective, mainland China’s textile industry is moving from the eastern region to the central and western regions), and China’s textile industry is developing rapidly. In particular, after China’s accession to the WTO in 2001, the full liberalization of textile export quotas, its textile, and garment exports jumped from $54.32 billion in 2001 to $291.22 billion in 2020 (data from the China Textile Industry Development Report), accounted for more than one-third of world trade, and became a global textile manufacturing center. But China’s textile industry has a long-term low-end lock phenomenon; its products have low added value; the international competitiveness is not strong and is very easy to be replaced. Especially with the development of China’s economy, labor costs are rising year by year, losing the advantage of low labor costs. The global textile industry began the fifth round of industry transfer since 2012, coupled with the U.S.–China trade war; these all accelerated the transfer of the textile industry to Vietnam and other Southeast Asian countries. With the double squeeze of developed countries high-end back and other developing countries low-end diversion, Chinese textile enterprises can only improve the technical content and product quality of textile products, in order to get rid of the long-term low-end lock phenomenon, promote the textile industry to the high-end of the global value chain, increase the international competitiveness of textile products, and promote high-quality development of the textile industry.As a pillar industry in China, the textile industry has to achieve sustainable development while pushing it to the middle and high end of the global value chain. While the textile and garment trade is developing rapidly, its rough development mode of high pollution and high energy consumption has also brought serious environmental pollution problems to China. The textile industry ranks among the top industries in China in terms of wastewater discharge, of which wastewater discharge from dyeing and finishing processes accounts for more than 70% of textile wastewater discharge. The textile industry not only pollutes water resources seriously but its carbon dioxide emissions also cause serious pollution in the atmosphere. At the same time, with the rapid development of the global economy and the rapid expansion of the scale of world trade, global environmental pollution and ecological damage have become increasingly serious. Since the 1990s, countries have paid more and more attention to environmental protection; Europe, the United States, and other countries have implemented green trade barriers to China’s textile exports, significantly reducing the scale of China’s textile and garment exports. The loss of Chinese textile and garment exports due to green trade barriers has risen from $400 million–$500 million per year in the mid to late 20th century to about $1 billion per year at the beginning of this century (data from the statistics of the Department of Science and Technology of the Ministry of Commerce). In response to environmental pollution and green trade barriers, China introduced the cleaner production standard (dyeing and finishing of cotton) environmental regulation policy for the textile dyeing and finishing industry in 2006, aiming to reduce environmental pollution in the textile industry from the source. Compared with other environmental regulatory policies based on end-of-pipe governance models, the cleaner production standard requires the control of pollution emissions in accordance with the corresponding standards established by each production process through the use of clean energy, cleaner production equipment, advanced process technology, and improved management. The implementation of cleaner production standards can enable textile enterprises to adopt advanced production processes and equipment, force enterprises to innovate in technology, and reduce pollution emissions from the source, so as to achieve the goal of cleaner production and product environmental protection in the textile industry and promote the sustainable development of the textile industry.How to balance the textile industry to achieve sustainable development and value chain climbing? China has set the goal of achieving peak carbon emissions by 2030 and carbon neutrality by 2060, forming rigid requirements for the green development of the textile industry. The 14th Five-Year Development Plan of China’s textile industry clearly states that in the international textile supply chain procurement decisions and layout adjustments, green development has not only become a realistic influencing factor but will also be an important source of international competitiveness and discourse for the textile industry. Textile science and technology continued innovation breakthrough is the solid support for textile industry to achieve green and sustainable development. In the field of international economics, export technical complexity reflects the technical content of a country’s (region) production products and international division of labor status, the higher the technical complexity, on behalf of product technology level, the higher the product competitiveness [1, 2]. Export technical complexity can increase the international competitiveness of textile exports and promote the textile industry to achieve high-quality development.The current research related to export technical complexity mainly focuses on infrastructure construction [3], financial development [4], government subsidies [5], institutional environment [6], and foreign investment entry [7]. Most of the aforementioned literature analyzes the manufacturing industry as a whole, and little of the literature deals with a separate study of the textile industry. There are even fewer studies exploring the impact on the technical complexity of exports from the perspective of environmental regulation. The studies that have been conducted from the perspective of environmental regulation have mainly focused on the effects of environmental regulation on comparative trade advantage [8], total export trade [9], export product quality [10], and foreign investment [11]. So, how will the environmental regulation of cleaner production affect the export technical complexity of textile enterprises? Can it increase the technical complexity of exported products?Based on this, this paper uses the cleaner production standard as a quasinatural experiment and uses microdata of Chinese enterprises from 2001 to 2013, based on a double difference method, to examine the effects and mechanisms of environmental regulations on the export technical complexity of Chinese textile enterprises in depth. Compared with the existing literature, the possible marginal contributions of this paper are as follows: (1) The existing studies on environmental regulation mainly focus on the end-of-pipe treatment perspective, while this paper studies the front-end prevention perspective of cleaner production standards and supplements relevant literature research. (2) Most of the existing studies on the technical complexity of exports focused on the national or industry level, while this paper analyzes the export technical complexity of textile enterprises from the microlevel of enterprises in detail, reflecting the heterogeneity characteristics of textile enterprises in different regions, different sizes, and different ownership attributes. (3) In the context of the fifth round of global textile industry shift, this paper verifies that the implementation of cleaner production environmental regulations can improve the technical complexity of exports of Chinese textile enterprises. The research conclusions provide policy ideas for how global textile enterprises can achieve sustainable development and value chain climbing.The remainder of this paper is organized as follows: Section2 is the institutional background and theoretical mechanism; Section 3 is the model construction, indicator setting, and data description; Section 4 is the benchmark regression and robustness test; Section 5 is the heterogeneity analysis; Section 6 is the mechanism analysis; and finally, there are the conclusion and policy recommendations.


## 2. Theoretical Mechanisms

The technical complexity of export products is a comprehensive reflection of the technical content and production efficiency of export products [12], and increasing the technical complexity of textile enterprises’ export products is important for the sustainable development of textile enterprises and international competitiveness increasing. So, what has been the level of technical complexity of export products of Chinese textile enterprises? Especially under the current reality of increasingly stringent environmental regulations, has the technical complexity of their export products been improved? If so, what channel they go through? This will be the next question in this paper.As an environmental regulation policy, the cleaner production standard embodies the thinking of pollution management under the whole life cycle of [13], which requires the adoption of clean energy, clean production equipment, and advanced process technology as well as improved management and increased innovation to achieve the whole process control from the source to the production process to the pollution emission and then reduce the pollution emission and improve the resource utilization efficiency. From the above analysis, the implementation of cleaner production standards will not only increase the cost of pollution control for textile enterprises but also drive technological innovation. Therefore, how cleaner production standards affect the technical complexity of export products of textile enterprises can be explained by classical environmental regulation theory, i.e., from the traditional compliance cost effect [14] and Porter’s hypothesis of the innovation compensation effect [15].The implementation of cleaner production standards will increase the capital and labor input of textile enterprises while improving the environment. The traditional compliance cost effect argues that, under constant market conditions, increased environmental regulations will increase enterprises’ pollution control expenditures, exposing them to additional costs [16]. The implementation of strict cleaner production standard policy makes the green production and operation cost of textile enterprises rise, and enterprises have to increase the capital and labor input required for cleaner production [17] to achieve the purpose of reducing pollution. On the one hand, under the implementation of cleaner production standards, textile enterprises will increase the purchase of cleaner production equipment and pollution control facilities with higher pollution control capacity, which in turn will increase the fixed asset expenditure of enterprises. On the other hand, the adoption of new machinery and equipment and advanced production processes require professional staff to operate them, as well as to reduce energy consumption and pollution, which in turn increases the labor costs of enterprises [18]. By increasing technological transformation oriented to the production process and emission ports, textile enterprises further improve the technical complexity of their export products, improve the efficiency of resource and energy use in the production process, reduce the pollution emissions per unit of enterprise output, and achieve the cleaner production standards [19].Hypothesis H1.
Cleaner production standards increase the export technical complexity of enterprises products by increasing the capital and labor inputs of textile enterprises.
The implementation of cleaner production standards will promote technological innovation and improve production efficiency of textile enterprises. Porter hypothesis says that from a long-term dynamic perspective, environmental regulations that led to an increase in production costs will force enterprises to make technological innovation, through the innovation compensation effect to compensate for the cost of expenditure brought about by the cost effect [15]. The implementation of cleaner production standards makes textile enterprises have technological innovation to avoid the cost of pollution control, technological progress to improve the production efficiency of enterprises, and then increase export technical complexity of enterprise. On the one hand, the implementation of cleaner production standards will promote a more rational allocation of resources and technological innovation through the cost effect [20], which will improve the productivity of enterprises [21, 22]. On the other hand, in the face of environmental regulations on cleaner production, smaller or low-productivity textile enterprises cannot afford the high environmental costs and choose to exit the industry, while environmental regulations also impose sunk costs on enterprises that want to enter the textile industry, thus discouraging some enterprises from entering. This makes the surviving enterprises in the textile industry face a decline in market competition, and enterprises can increase their productivity by increasing technological innovation [23]. Enterprises can improve the technological level of the production process through technological innovation, and the increase in technological level will increase the technical complexity of export products [24]. At the same time, the increase in productivity of enterprises will also increase the technical complexity of their export products, which will help them to increase their international competitiveness [25].Hypothesis H2.
By promoting technological innovation and increasing productivity of textile enterprises, the implementation of cleaner production standards increases the export products technical complexity of enterprises.


## 3. Measurement Methods and Data Processing

### 3.1. Model Setting

In order to cope with the increasingly serious environmental problems and achieve sustainable development, China has been implementing cleaner production standards in some industries since 2003. The cleaner production standards are more stringent and detailed than other environmental regulation policies, with specific environmental requirements for enterprises in different industries, and a shift from end-of-pipe treatment to front-end prevention. For the pollution problem of textile industry, the State Environmental Protection Administration issued the Cleaner Production Standard Textile Industry (dyeing and finishing of cotton) in 2006. Therefore, this paper examines the impact of the implementation of cleaner production standards on the export technical complexity of textile based on the introduction of this environmental regulation policy.The sample period for the study in this paper is from 2001 to 2013, with 2006 as the year of policy shock. It shall be noted that this paper finds the corresponding quartile industry code [26] based on the name of the cleaner production standard implementation industry with reference to the National Economic Classification of Industries (GB/T4752-2002), which is further matched with the database of Chinese industrial enterprises. Therefore, the cotton dyeing and finishing industry, which implements cleaner production standards in the textile industry, is used as the treatment group, and other quartile industries in the broad textile industry category are used as the control group, and the following double difference model is constructed to examine the effect of cleaner production environmental regulations on the exports technical complexity of textile enterprises.(1)ESIit=α+βtreati×postt+σXit+μi+λt+εit,where the subscripts i and t denote the enterprise and year, respectively. ESIit is the explanatory variable, which denotes the exports technical complexity of the enterprise. treati × postt is the core explained variable. treati represents the policy dummy variable. if enterprise i implements the cleaner production standard policy, treati = 1; otherwise, treati = 0; postt represents the time dummy variable; the value is taken as 1 for the year in which the cleaner production standard policy is implemented and later years and 0 for the year before the cleaner production standard policy is implemented. β is the core estimated coefficient, Xit represents a series of control variables, μi and λt represent the individual fixed effects and year fixed effects, respectively, and εit represents the random disturbance term.


### 3.2. Variable Selection

#### 3.2.1. Interpreted Variables

The current domestic and international measure indicators of export technical complexity mainly refer to Hausmann et al. [1, 27, 28]. However, this method does not take into account the possible variability of export products in terms of quality and may overestimate the overall export technical complexity level of a country or region [29]. In the absence of cross-country comparisons, the revised index of export technical complexity by Xu and Lu [7] can be used, using regional-level GDP per capita instead of data measuring national-level indicators [12]. In this paper, we use Chinese enterprises’ microdata to measure export technical complexity without cross-country comparisons and therefore use Xu and Lu’s [7] modified export technical complexity indicators. The export technical complexity of Chinese textile enterprises is measured in two steps, and the first step is to measure the export technical complexity of a certain textile product k, which is measured by the following formula:(2)∏yk=∑jyjk/Yj∑jyjk/Yj×gdpj,where k is the customs HS6 quantile code product, j denotes a region, yjk denotes the export value of product k of region j, Yj denotes the total export value of region j, yjk/Yj denotes the export share of product k of region j, and gdpj denotes the actual GDP per capita of the region. On the basis of obtaining the export technical complexity of a certain textile product complexity, the second step is to calculate the export technical complexity of an enterprise according to the following equation:(3)ESIi=∑kyikyi×∏yk,where yik denotes the export amount of enterprise i on product k, yi denotes the total export amount of enterprise i, and yik/yi denotes the share of exports of enterprise i’s product k in enterprise i’s total exports.


#### 3.2.2. Control Variables

In addition to the selection of the above core variable indicators, this paper also controls other influencing factors. Control variables (X): (1) enterprise age (lnage), measured by the logarithm of the current year minus the year of enterprise establishment + 1; (2) enterprise size (lnsize), measured by the logarithm of the enterprise’s annual total assets [30]; (3) the nature of the enterprise (soe), if the enterprises are state-owned enterprise, they are assigned a value of 1, while nonstate-owned enterprises are assigned a value of 0; (4) capital intensity (lncapital), measured by the logarithm of the ratio of fixed assets to the number of employees of the enterprise [31]; (5) enterprise profitability (lnprofit), measured by the logarithm of the ratio of total enterprise profit to enterprise sales revenue [26]; and (6) financing constraint (lnfinance), measured by the logarithm of the ratio of interest expense to fixed assets, referring to Sun and Li [32].


### 3.3. Data Description

This paper uses the matched data from China Industrial Enterprise Database and China Customs Trade Database from 2001 to 2013 (2010 variable data are severely missing in industrial enterprises, and 2010 is removed). The China Industrial Enterprise Database contains enterprise-level production data and is used to calculate enterprise-level control variables. However, there are many problems with this dataset, so this paper draws on Su et al. [33] to match industrial enterprise data and, at the same time, does the following: (1) remove enterprises with less than 8 employees; (2) remove enterprises with missing, zero values, or negative values of total industrial output value, industrial value added, total assets, and total fixed assets; (3) remove enterprises with unreasonable data such as total assets less than total fixed assets; and (4) adjusting prices by deflating and subtracting with 2000 as the base year. The China Customs trade database contains every product-level import and export trade of the enterprises and is used to calculate the explained variables of this paper, i.e., the exports technical complexity of the enterprises. This paper draws on Fan et al. [34] to process the customs trade data by summing the monthly data into annual data and summing the HS8 bit code to obtain the HS6 bit code. Finally, the two sets of data were matched by drawing on the practice of Tian and Yu [35] and finally combined to obtain the sample data of export enterprises in the textile industry. Meanwhile, in order to eliminate the influence of outliers and extreme values on the estimation results, the continuous variables were Winsorize shrunken tail according to the bilateral exclusion of 1% each. The statistical description of the variables is shown in Table 1.Table 1
Statistics.
VariableObsMeanStd. dev.Min.Max.ESI487742.81240.88531.13534.1047lnage487742.08380.664803.8501lnsize487745.43371.07452.94448.2674soe487740.01830.133801lnprofit487740.02390.0629−0.27260.1937lnfinance487740.05920.0952−0.01720.5786lncapital487743.59601.25880.21386.3834


## 3.1. Model Setting

In order to cope with the increasingly serious environmental problems and achieve sustainable development, China has been implementing cleaner production standards in some industries since 2003. The cleaner production standards are more stringent and detailed than other environmental regulation policies, with specific environmental requirements for enterprises in different industries, and a shift from end-of-pipe treatment to front-end prevention. For the pollution problem of textile industry, the State Environmental Protection Administration issued the Cleaner Production Standard Textile Industry (dyeing and finishing of cotton) in 2006. Therefore, this paper examines the impact of the implementation of cleaner production standards on the export technical complexity of textile based on the introduction of this environmental regulation policy.The sample period for the study in this paper is from 2001 to 2013, with 2006 as the year of policy shock. It shall be noted that this paper finds the corresponding quartile industry code [26] based on the name of the cleaner production standard implementation industry with reference to the National Economic Classification of Industries (GB/T4752-2002), which is further matched with the database of Chinese industrial enterprises. Therefore, the cotton dyeing and finishing industry, which implements cleaner production standards in the textile industry, is used as the treatment group, and other quartile industries in the broad textile industry category are used as the control group, and the following double difference model is constructed to examine the effect of cleaner production environmental regulations on the exports technical complexity of textile enterprises.(1)ESIit=α+βtreati×postt+σXit+μi+λt+εit,where the subscripts i and t denote the enterprise and year, respectively. ESIit is the explanatory variable, which denotes the exports technical complexity of the enterprise. treati × postt is the core explained variable. treati represents the policy dummy variable. if enterprise i implements the cleaner production standard policy, treati = 1; otherwise, treati = 0; postt represents the time dummy variable; the value is taken as 1 for the year in which the cleaner production standard policy is implemented and later years and 0 for the year before the cleaner production standard policy is implemented. β is the core estimated coefficient, Xit represents a series of control variables, μi and λt represent the individual fixed effects and year fixed effects, respectively, and εit represents the random disturbance term.


## 3.2. Variable Selection

### 3.2.1. Interpreted Variables

The current domestic and international measure indicators of export technical complexity mainly refer to Hausmann et al. [1, 27, 28]. However, this method does not take into account the possible variability of export products in terms of quality and may overestimate the overall export technical complexity level of a country or region [29]. In the absence of cross-country comparisons, the revised index of export technical complexity by Xu and Lu [7] can be used, using regional-level GDP per capita instead of data measuring national-level indicators [12]. In this paper, we use Chinese enterprises’ microdata to measure export technical complexity without cross-country comparisons and therefore use Xu and Lu’s [7] modified export technical complexity indicators. The export technical complexity of Chinese textile enterprises is measured in two steps, and the first step is to measure the export technical complexity of a certain textile product k, which is measured by the following formula:(2)∏yk=∑jyjk/Yj∑jyjk/Yj×gdpj,where k is the customs HS6 quantile code product, j denotes a region, yjk denotes the export value of product k of region j, Yj denotes the total export value of region j, yjk/Yj denotes the export share of product k of region j, and gdpj denotes the actual GDP per capita of the region. On the basis of obtaining the export technical complexity of a certain textile product complexity, the second step is to calculate the export technical complexity of an enterprise according to the following equation:(3)ESIi=∑kyikyi×∏yk,where yik denotes the export amount of enterprise i on product k, yi denotes the total export amount of enterprise i, and yik/yi denotes the share of exports of enterprise i’s product k in enterprise i’s total exports.


### 3.2.2. Control Variables

In addition to the selection of the above core variable indicators, this paper also controls other influencing factors. Control variables (X): (1) enterprise age (lnage), measured by the logarithm of the current year minus the year of enterprise establishment + 1; (2) enterprise size (lnsize), measured by the logarithm of the enterprise’s annual total assets [30]; (3) the nature of the enterprise (soe), if the enterprises are state-owned enterprise, they are assigned a value of 1, while nonstate-owned enterprises are assigned a value of 0; (4) capital intensity (lncapital), measured by the logarithm of the ratio of fixed assets to the number of employees of the enterprise [31]; (5) enterprise profitability (lnprofit), measured by the logarithm of the ratio of total enterprise profit to enterprise sales revenue [26]; and (6) financing constraint (lnfinance), measured by the logarithm of the ratio of interest expense to fixed assets, referring to Sun and Li [32].


## 3.2.1. Interpreted Variables

The current domestic and international measure indicators of export technical complexity mainly refer to Hausmann et al. [1, 27, 28]. However, this method does not take into account the possible variability of export products in terms of quality and may overestimate the overall export technical complexity level of a country or region [29]. In the absence of cross-country comparisons, the revised index of export technical complexity by Xu and Lu [7] can be used, using regional-level GDP per capita instead of data measuring national-level indicators [12]. In this paper, we use Chinese enterprises’ microdata to measure export technical complexity without cross-country comparisons and therefore use Xu and Lu’s [7] modified export technical complexity indicators. The export technical complexity of Chinese textile enterprises is measured in two steps, and the first step is to measure the export technical complexity of a certain textile product k, which is measured by the following formula:(2)∏yk=∑jyjk/Yj∑jyjk/Yj×gdpj,where k is the customs HS6 quantile code product, j denotes a region, yjk denotes the export value of product k of region j, Yj denotes the total export value of region j, yjk/Yj denotes the export share of product k of region j, and gdpj denotes the actual GDP per capita of the region. On the basis of obtaining the export technical complexity of a certain textile product complexity, the second step is to calculate the export technical complexity of an enterprise according to the following equation:(3)ESIi=∑kyikyi×∏yk,where yik denotes the export amount of enterprise i on product k, yi denotes the total export amount of enterprise i, and yik/yi denotes the share of exports of enterprise i’s product k in enterprise i’s total exports.


## 3.2.2. Control Variables

In addition to the selection of the above core variable indicators, this paper also controls other influencing factors. Control variables (X): (1) enterprise age (lnage), measured by the logarithm of the current year minus the year of enterprise establishment + 1; (2) enterprise size (lnsize), measured by the logarithm of the enterprise’s annual total assets [30]; (3) the nature of the enterprise (soe), if the enterprises are state-owned enterprise, they are assigned a value of 1, while nonstate-owned enterprises are assigned a value of 0; (4) capital intensity (lncapital), measured by the logarithm of the ratio of fixed assets to the number of employees of the enterprise [31]; (5) enterprise profitability (lnprofit), measured by the logarithm of the ratio of total enterprise profit to enterprise sales revenue [26]; and (6) financing constraint (lnfinance), measured by the logarithm of the ratio of interest expense to fixed assets, referring to Sun and Li [32].


## 3.3. Data Description

This paper uses the matched data from China Industrial Enterprise Database and China Customs Trade Database from 2001 to 2013 (2010 variable data are severely missing in industrial enterprises, and 2010 is removed). The China Industrial Enterprise Database contains enterprise-level production data and is used to calculate enterprise-level control variables. However, there are many problems with this dataset, so this paper draws on Su et al. [33] to match industrial enterprise data and, at the same time, does the following: (1) remove enterprises with less than 8 employees; (2) remove enterprises with missing, zero values, or negative values of total industrial output value, industrial value added, total assets, and total fixed assets; (3) remove enterprises with unreasonable data such as total assets less than total fixed assets; and (4) adjusting prices by deflating and subtracting with 2000 as the base year. The China Customs trade database contains every product-level import and export trade of the enterprises and is used to calculate the explained variables of this paper, i.e., the exports technical complexity of the enterprises. This paper draws on Fan et al. [34] to process the customs trade data by summing the monthly data into annual data and summing the HS8 bit code to obtain the HS6 bit code. Finally, the two sets of data were matched by drawing on the practice of Tian and Yu [35] and finally combined to obtain the sample data of export enterprises in the textile industry. Meanwhile, in order to eliminate the influence of outliers and extreme values on the estimation results, the continuous variables were Winsorize shrunken tail according to the bilateral exclusion of 1% each. The statistical description of the variables is shown in Table 1.Table 1
Statistics.
VariableObsMeanStd. dev.Min.Max.ESI487742.81240.88531.13534.1047lnage487742.08380.664803.8501lnsize487745.43371.07452.94448.2674soe487740.01830.133801lnprofit487740.02390.0629−0.27260.1937lnfinance487740.05920.0952−0.01720.5786lncapital487743.59601.25880.21386.3834


## 4. Empirical Results and Analysis

### 4.1. Benchmark Regression

Table2 reports the results of estimating the average treatment effects of model (1), while controlling for enterprise and year fixed effects, where column (1) shows the effect of cleaner production standards on enterprises’ export technical complexity without control variables, and the estimation result is significantly positive, indicating that cleaner production standards significantly increase the export technical complexity of textile enterprises. Further adding each control variable in column (2), the core explanatory coefficient is still significantly positive. The results indicate that after controlling for other factors, the implementation of cleaner production standards policy significantly contributes to the export technical complexity of textile industry enterprises.Table 2
Benchmark regression.
(1)(2)ESIESITreat × post0.7035∗∗∗0.6901∗∗∗(0.0000)(0.0000)lnage0.0157(0.8431)lnsize−0.1097∗(0.0943)soe−0.2824(0.3159)lncapital−0.0798∗(0.0521)lnprofit1.1179∗∗∗(0.0044)lnfinance0.6484∗∗(0.0455)_cons4.6974∗∗∗6.4636∗∗∗(0.0000)(0.0000)Enterprise fixed effectYesYesYear fixed effectYesYesR20.95750.9578Observations4877448774P values in parentheses; ∗p<0.01, ∗∗p<0.05, ∗∗∗p<0.01, similar hereinafter.As for the control variables, the age of the enterprise has a positive effect on the exports technical complexity of the enterprise, but the results are not significant. The longer an enterprise develops, the better it is for knowledge accumulation, showing the characteristic of learning by doing [36]. Both enterprise size and enterprise nature have negative effects on the exports technological complexity of enterprise, but the results of enterprise nature are not significant, which will be analyzed in detail in the subsequent heterogeneity analysis. Capital intensity is negatively correlated with enterprise export technical complexity, which may be explained by the fact that enterprises have capital mismatch [37, 38] and do not spend capital on research and development innovation activities to increase export technical complexity. Corporate profitability has a significant positive effect on enterprises’ export technical complexity, and higher corporate profits have an incentive to invest in research and development, which increases export technical complexity. The reduction of financing constraint significantly increases the export technical complexity of enterprises. The reduction of financing constraint helps enterprises to obtain more capital, so as to increase the investment in equipment renewal and research and development innovation, which promotes the export technical complexity of enterprises.


### 4.2. Parallel Trend and the Dynamic Effect Test

An important prerequisite for using the double difference model is to satisfy the parallel trend assumption, i.e., before the implementation of the cleaner production standard, the export technical complexity of enterprises in the treatment groups and control groups is consistent in time trend. This paper tests the parallel trend assumption while examining the dynamic treatment effects of the implementation of cleaner production standards over time. Therefore, this paper tests whether model (1) satisfies the parallel trend assumption and dynamic effect based on event analysis and introduces the interaction term of year dummy variable and treatment variable to construct the following test model:(4)ESIit=a+∑t=20012013bttreati×yeart+σXit+μi+λt+εit.The explained variables and other control variables in model (4) are defined in the same way as in model (1).bt is the core explanatory coefficient. Before the implementation of the cleaner production standard policy, if bt is not significantly different from 0, it satisfies the parallel trend. Column (1) of Table 3 shows the results without control variables, and column (2) adds control variables. The results show that before the implementation of the cleaner production standard policy, the coefficients of the interaction terms treat × 2001, treat × 2002, treat × 2003, treat × 2004, and treat × 2005 are not significant. It indicates that before the implementation of the cleaner production standard policy, there is no significant difference between the treatment groups and control groups in terms of the exports technical complexity of enterprises, which satisfies the parallel trend assumption. And after the implementation of cleaner production standards, the coefficients of the interaction terms treat × 2007, treat × 2008, treat × 2009, treat × 2011, treat × 2013 are all significantly positive except for 2012. It indicates that the implementation of cleaner production standards has a significant effect on the exports technical complexity of textile enterprises.Table 3
Parallel trend and dynamic tests.
(1)(2)ESIESITreat × 20010.77610.6466(0.7234)(0.7658)Treat × 20020.61500.5474(0.7791)(0.8009)Treat × 2003−0.1072−0.0789(0.6337)(0.7234)Treat × 20040.17350.2324(0.3553)(0.2127)Treat × 20050.10010.1436(0.5957)(0.4429)Treat × 20060.3719∗∗0.4096∗∗(0.0439)(0.0255)Treat × 20071.0842∗∗∗1.1259∗∗∗(0.0000)(0.0000)Treat × 20080.7269∗∗∗0.7718∗∗∗(0.0000)(0.0000)Treat × 20090.3082∗0.3649∗∗(0.0892)(0.0434)Treat × 20111.6724∗∗∗1.5991∗∗∗(0.0000)(0.0000)Treat × 20120.23070.2632(0.1955)(0.1413)Treat × 20130.7980∗∗∗0.7768∗∗∗(0.0000)(0.0000)Control variablesNotYesEnterprise fixed effectYesYesYear fixed effectYesYesR20.97470.9749Observations4877448774


### 4.3. The Robustness Test

#### 4.3.1. Tightening Policy Year Identification Conditions

In the previous regression analysis, the policy occurrence time is identified according to the year when the cleaner production standard policy is implemented in the enterprise’s industry. However, the reality is that the textile (dyeing and finishing cotton) industry started to implement cleaner production standards on October 1, 2006, and the policy was implemented for only three months in 2006. To make the results more reliable, this paper draws on Lu et al. (2017) [39] to further constrain the identification condition of the policy year. Since the implementation of cleaner production standards in the textile (dyeing and finishing cotton) industry was in October 2006, the year before 2006 is assigned as 0, the year after 2006 is assigned as 1, and the year 2006 is assigned as 1/4. The empirical results are presented in column (1) of Table 4. And it can be seen that after constraining the policy year identification condition, the core explanatory coefficient is still significantly positive, which is consistent with the baseline regression results. This indicates that the conclusion that the implementation of cleaner production standards policy significantly increases the exports technical complexity of enterprises is robust.Table 4
Robustness test.
(1) Tightening policy year identification conditions(2) Excluding policy interference(3) Placebo test(4) Pure samplesESIESIESIESITreat × post0.6967∗∗∗0.5959∗∗∗0.01180.8251∗∗∗(0.0000)(0.0024)(0.8423)(0.0016)Control variablesYesYesYesYesEnterprise fixed effectYesYesYesYesYear fixed effectYesYesYesYesR20.95780.95630.60120.9615Observations4877417863141666200


#### 4.3.2. Excluding Other Policy Disturbances

This paper tests the effect of cleaner production standard policy implementation on the exports technical complexity of textile enterprises, but as the country pays more and more attention to environmental protection, it also promulgates numerous environmental regulation policies one after another. The environmental regulation policies in the same period may interfere with the effect of cleaner production standard policy implementation on the exports technical complexity of enterprises. China implemented a pilot system of paid use and trading of emission rights in 2007, taking the lead in emissions trading mechanism for enterprises in 11 pilot provinces (regions and cities), including Hebei, Tianjin, Inner Mongolia, Shaanxi, Shanxi, Chongqing, Henan, Hubei, Hunan, Jiangsu, and Zhejiang. Therefore, in this paper, to control the interference of this policy on the regression results, the sample of enterprises in these 11 pilot provinces is excluded and then regression analysis is conducted. The empirical results are presented in column (2) of Table4, and it can be seen that the core explanatory coefficient is still significantly positive, which is consistent with the baseline regression results. This indicates that after excluding the interference of the pilot policy of the paid use and trading system of emission rights, the implementation of the cleaner production standard policy significantly increases the exports technical complexity of textile enterprises. The conclusions of this paper are robust.


#### 4.3.3. The Placebo Test

The implementation of the cleaner production standard policy has an impact on the exports technical complexity of enterprises. The other policy or random factors also affect the exports technical complexity of enterprises and are not related to the implementation of cleaner production standards, leading to the conclusion of this paper as not valid. Therefore, to exclude the influence of such factors, a robustness test is conducted by changing the policy implementation time, drawing on Liu and Zhao [40]. In this paper, the implementation of cleaner production standards in the textile industry (dyeing and finishing cotton) is advanced by three years, while the sample scope is controlled from 2001 to 2005 to ensure the preciseness of the test. If the core explanatory coefficient is significantly positive, it indicates that the increase in exports technical complexity of enterprises may be influenced by other factors rather than the implementation of cleaner production standards. If the core explanatory coefficient is insignificant, it indicates that it is due to the implementation of cleaner production standards that increases the exports technical complexity of enterprises. The empirical results are presented in column (3) of Table 4, which shows that the core explanatory coefficient is insignificant. It shows that the increase in exports technical complexity of textile enterprises is not caused by other factors, but because of the implementation of cleaner production standards, and the conclusions of this paper are robust.


#### 4.3.4. Pure Sample

The previous baseline regression analysis shows that the implementation of cleaner production standards has a significant effect on the exports technical complexity of textile enterprises. The sample of the baseline regression analysis, however, contains enterprises entering and exiting during the implementation of cleaner production standards, which may interfere with the analysis results of the impact of policy implementation on the exports technical complexity of enterprises. Therefore, in this paper, the surviving enterprises in the sample period are selected from the baseline regression sample, and the regression analysis is conducted on the surviving enterprises to examine the impact of cleaner production standards on the exports technical complexity of enterprises. The empirical results are presented in column (4) of Table4, which shows that the core coefficient of explanation is significantly positive and greater than the results of the baseline regression. It indicates that even if the regression is conducted with surviving enterprises, the implementation of cleaner production standards also significantly increases the exports technical complexity of textile enterprises, and the conclusions of this paper are robust.


## 4.1. Benchmark Regression

Table2 reports the results of estimating the average treatment effects of model (1), while controlling for enterprise and year fixed effects, where column (1) shows the effect of cleaner production standards on enterprises’ export technical complexity without control variables, and the estimation result is significantly positive, indicating that cleaner production standards significantly increase the export technical complexity of textile enterprises. Further adding each control variable in column (2), the core explanatory coefficient is still significantly positive. The results indicate that after controlling for other factors, the implementation of cleaner production standards policy significantly contributes to the export technical complexity of textile industry enterprises.Table 2
Benchmark regression.
(1)(2)ESIESITreat × post0.7035∗∗∗0.6901∗∗∗(0.0000)(0.0000)lnage0.0157(0.8431)lnsize−0.1097∗(0.0943)soe−0.2824(0.3159)lncapital−0.0798∗(0.0521)lnprofit1.1179∗∗∗(0.0044)lnfinance0.6484∗∗(0.0455)_cons4.6974∗∗∗6.4636∗∗∗(0.0000)(0.0000)Enterprise fixed effectYesYesYear fixed effectYesYesR20.95750.9578Observations4877448774P values in parentheses; ∗p<0.01, ∗∗p<0.05, ∗∗∗p<0.01, similar hereinafter.As for the control variables, the age of the enterprise has a positive effect on the exports technical complexity of the enterprise, but the results are not significant. The longer an enterprise develops, the better it is for knowledge accumulation, showing the characteristic of learning by doing [36]. Both enterprise size and enterprise nature have negative effects on the exports technological complexity of enterprise, but the results of enterprise nature are not significant, which will be analyzed in detail in the subsequent heterogeneity analysis. Capital intensity is negatively correlated with enterprise export technical complexity, which may be explained by the fact that enterprises have capital mismatch [37, 38] and do not spend capital on research and development innovation activities to increase export technical complexity. Corporate profitability has a significant positive effect on enterprises’ export technical complexity, and higher corporate profits have an incentive to invest in research and development, which increases export technical complexity. The reduction of financing constraint significantly increases the export technical complexity of enterprises. The reduction of financing constraint helps enterprises to obtain more capital, so as to increase the investment in equipment renewal and research and development innovation, which promotes the export technical complexity of enterprises.


## 4.2. Parallel Trend and the Dynamic Effect Test

An important prerequisite for using the double difference model is to satisfy the parallel trend assumption, i.e., before the implementation of the cleaner production standard, the export technical complexity of enterprises in the treatment groups and control groups is consistent in time trend. This paper tests the parallel trend assumption while examining the dynamic treatment effects of the implementation of cleaner production standards over time. Therefore, this paper tests whether model (1) satisfies the parallel trend assumption and dynamic effect based on event analysis and introduces the interaction term of year dummy variable and treatment variable to construct the following test model:(4)ESIit=a+∑t=20012013bttreati×yeart+σXit+μi+λt+εit.The explained variables and other control variables in model (4) are defined in the same way as in model (1).bt is the core explanatory coefficient. Before the implementation of the cleaner production standard policy, if bt is not significantly different from 0, it satisfies the parallel trend. Column (1) of Table 3 shows the results without control variables, and column (2) adds control variables. The results show that before the implementation of the cleaner production standard policy, the coefficients of the interaction terms treat × 2001, treat × 2002, treat × 2003, treat × 2004, and treat × 2005 are not significant. It indicates that before the implementation of the cleaner production standard policy, there is no significant difference between the treatment groups and control groups in terms of the exports technical complexity of enterprises, which satisfies the parallel trend assumption. And after the implementation of cleaner production standards, the coefficients of the interaction terms treat × 2007, treat × 2008, treat × 2009, treat × 2011, treat × 2013 are all significantly positive except for 2012. It indicates that the implementation of cleaner production standards has a significant effect on the exports technical complexity of textile enterprises.Table 3
Parallel trend and dynamic tests.
(1)(2)ESIESITreat × 20010.77610.6466(0.7234)(0.7658)Treat × 20020.61500.5474(0.7791)(0.8009)Treat × 2003−0.1072−0.0789(0.6337)(0.7234)Treat × 20040.17350.2324(0.3553)(0.2127)Treat × 20050.10010.1436(0.5957)(0.4429)Treat × 20060.3719∗∗0.4096∗∗(0.0439)(0.0255)Treat × 20071.0842∗∗∗1.1259∗∗∗(0.0000)(0.0000)Treat × 20080.7269∗∗∗0.7718∗∗∗(0.0000)(0.0000)Treat × 20090.3082∗0.3649∗∗(0.0892)(0.0434)Treat × 20111.6724∗∗∗1.5991∗∗∗(0.0000)(0.0000)Treat × 20120.23070.2632(0.1955)(0.1413)Treat × 20130.7980∗∗∗0.7768∗∗∗(0.0000)(0.0000)Control variablesNotYesEnterprise fixed effectYesYesYear fixed effectYesYesR20.97470.9749Observations4877448774


## 4.3. The Robustness Test

### 4.3.1. Tightening Policy Year Identification Conditions

In the previous regression analysis, the policy occurrence time is identified according to the year when the cleaner production standard policy is implemented in the enterprise’s industry. However, the reality is that the textile (dyeing and finishing cotton) industry started to implement cleaner production standards on October 1, 2006, and the policy was implemented for only three months in 2006. To make the results more reliable, this paper draws on Lu et al. (2017) [39] to further constrain the identification condition of the policy year. Since the implementation of cleaner production standards in the textile (dyeing and finishing cotton) industry was in October 2006, the year before 2006 is assigned as 0, the year after 2006 is assigned as 1, and the year 2006 is assigned as 1/4. The empirical results are presented in column (1) of Table 4. And it can be seen that after constraining the policy year identification condition, the core explanatory coefficient is still significantly positive, which is consistent with the baseline regression results. This indicates that the conclusion that the implementation of cleaner production standards policy significantly increases the exports technical complexity of enterprises is robust.Table 4
Robustness test.
(1) Tightening policy year identification conditions(2) Excluding policy interference(3) Placebo test(4) Pure samplesESIESIESIESITreat × post0.6967∗∗∗0.5959∗∗∗0.01180.8251∗∗∗(0.0000)(0.0024)(0.8423)(0.0016)Control variablesYesYesYesYesEnterprise fixed effectYesYesYesYesYear fixed effectYesYesYesYesR20.95780.95630.60120.9615Observations4877417863141666200


### 4.3.2. Excluding Other Policy Disturbances

This paper tests the effect of cleaner production standard policy implementation on the exports technical complexity of textile enterprises, but as the country pays more and more attention to environmental protection, it also promulgates numerous environmental regulation policies one after another. The environmental regulation policies in the same period may interfere with the effect of cleaner production standard policy implementation on the exports technical complexity of enterprises. China implemented a pilot system of paid use and trading of emission rights in 2007, taking the lead in emissions trading mechanism for enterprises in 11 pilot provinces (regions and cities), including Hebei, Tianjin, Inner Mongolia, Shaanxi, Shanxi, Chongqing, Henan, Hubei, Hunan, Jiangsu, and Zhejiang. Therefore, in this paper, to control the interference of this policy on the regression results, the sample of enterprises in these 11 pilot provinces is excluded and then regression analysis is conducted. The empirical results are presented in column (2) of Table4, and it can be seen that the core explanatory coefficient is still significantly positive, which is consistent with the baseline regression results. This indicates that after excluding the interference of the pilot policy of the paid use and trading system of emission rights, the implementation of the cleaner production standard policy significantly increases the exports technical complexity of textile enterprises. The conclusions of this paper are robust.


### 4.3.3. The Placebo Test

The implementation of the cleaner production standard policy has an impact on the exports technical complexity of enterprises. The other policy or random factors also affect the exports technical complexity of enterprises and are not related to the implementation of cleaner production standards, leading to the conclusion of this paper as not valid. Therefore, to exclude the influence of such factors, a robustness test is conducted by changing the policy implementation time, drawing on Liu and Zhao [40]. In this paper, the implementation of cleaner production standards in the textile industry (dyeing and finishing cotton) is advanced by three years, while the sample scope is controlled from 2001 to 2005 to ensure the preciseness of the test. If the core explanatory coefficient is significantly positive, it indicates that the increase in exports technical complexity of enterprises may be influenced by other factors rather than the implementation of cleaner production standards. If the core explanatory coefficient is insignificant, it indicates that it is due to the implementation of cleaner production standards that increases the exports technical complexity of enterprises. The empirical results are presented in column (3) of Table 4, which shows that the core explanatory coefficient is insignificant. It shows that the increase in exports technical complexity of textile enterprises is not caused by other factors, but because of the implementation of cleaner production standards, and the conclusions of this paper are robust.


### 4.3.4. Pure Sample

The previous baseline regression analysis shows that the implementation of cleaner production standards has a significant effect on the exports technical complexity of textile enterprises. The sample of the baseline regression analysis, however, contains enterprises entering and exiting during the implementation of cleaner production standards, which may interfere with the analysis results of the impact of policy implementation on the exports technical complexity of enterprises. Therefore, in this paper, the surviving enterprises in the sample period are selected from the baseline regression sample, and the regression analysis is conducted on the surviving enterprises to examine the impact of cleaner production standards on the exports technical complexity of enterprises. The empirical results are presented in column (4) of Table4, which shows that the core coefficient of explanation is significantly positive and greater than the results of the baseline regression. It indicates that even if the regression is conducted with surviving enterprises, the implementation of cleaner production standards also significantly increases the exports technical complexity of textile enterprises, and the conclusions of this paper are robust.


## 4.3.1. Tightening Policy Year Identification Conditions

In the previous regression analysis, the policy occurrence time is identified according to the year when the cleaner production standard policy is implemented in the enterprise’s industry. However, the reality is that the textile (dyeing and finishing cotton) industry started to implement cleaner production standards on October 1, 2006, and the policy was implemented for only three months in 2006. To make the results more reliable, this paper draws on Lu et al. (2017) [39] to further constrain the identification condition of the policy year. Since the implementation of cleaner production standards in the textile (dyeing and finishing cotton) industry was in October 2006, the year before 2006 is assigned as 0, the year after 2006 is assigned as 1, and the year 2006 is assigned as 1/4. The empirical results are presented in column (1) of Table 4. And it can be seen that after constraining the policy year identification condition, the core explanatory coefficient is still significantly positive, which is consistent with the baseline regression results. This indicates that the conclusion that the implementation of cleaner production standards policy significantly increases the exports technical complexity of enterprises is robust.Table 4
Robustness test.
(1) Tightening policy year identification conditions(2) Excluding policy interference(3) Placebo test(4) Pure samplesESIESIESIESITreat × post0.6967∗∗∗0.5959∗∗∗0.01180.8251∗∗∗(0.0000)(0.0024)(0.8423)(0.0016)Control variablesYesYesYesYesEnterprise fixed effectYesYesYesYesYear fixed effectYesYesYesYesR20.95780.95630.60120.9615Observations4877417863141666200


## 4.3.2. Excluding Other Policy Disturbances

This paper tests the effect of cleaner production standard policy implementation on the exports technical complexity of textile enterprises, but as the country pays more and more attention to environmental protection, it also promulgates numerous environmental regulation policies one after another. The environmental regulation policies in the same period may interfere with the effect of cleaner production standard policy implementation on the exports technical complexity of enterprises. China implemented a pilot system of paid use and trading of emission rights in 2007, taking the lead in emissions trading mechanism for enterprises in 11 pilot provinces (regions and cities), including Hebei, Tianjin, Inner Mongolia, Shaanxi, Shanxi, Chongqing, Henan, Hubei, Hunan, Jiangsu, and Zhejiang. Therefore, in this paper, to control the interference of this policy on the regression results, the sample of enterprises in these 11 pilot provinces is excluded and then regression analysis is conducted. The empirical results are presented in column (2) of Table4, and it can be seen that the core explanatory coefficient is still significantly positive, which is consistent with the baseline regression results. This indicates that after excluding the interference of the pilot policy of the paid use and trading system of emission rights, the implementation of the cleaner production standard policy significantly increases the exports technical complexity of textile enterprises. The conclusions of this paper are robust.


## 4.3.3. The Placebo Test

The implementation of the cleaner production standard policy has an impact on the exports technical complexity of enterprises. The other policy or random factors also affect the exports technical complexity of enterprises and are not related to the implementation of cleaner production standards, leading to the conclusion of this paper as not valid. Therefore, to exclude the influence of such factors, a robustness test is conducted by changing the policy implementation time, drawing on Liu and Zhao [40]. In this paper, the implementation of cleaner production standards in the textile industry (dyeing and finishing cotton) is advanced by three years, while the sample scope is controlled from 2001 to 2005 to ensure the preciseness of the test. If the core explanatory coefficient is significantly positive, it indicates that the increase in exports technical complexity of enterprises may be influenced by other factors rather than the implementation of cleaner production standards. If the core explanatory coefficient is insignificant, it indicates that it is due to the implementation of cleaner production standards that increases the exports technical complexity of enterprises. The empirical results are presented in column (3) of Table 4, which shows that the core explanatory coefficient is insignificant. It shows that the increase in exports technical complexity of textile enterprises is not caused by other factors, but because of the implementation of cleaner production standards, and the conclusions of this paper are robust.


## 4.3.4. Pure Sample

The previous baseline regression analysis shows that the implementation of cleaner production standards has a significant effect on the exports technical complexity of textile enterprises. The sample of the baseline regression analysis, however, contains enterprises entering and exiting during the implementation of cleaner production standards, which may interfere with the analysis results of the impact of policy implementation on the exports technical complexity of enterprises. Therefore, in this paper, the surviving enterprises in the sample period are selected from the baseline regression sample, and the regression analysis is conducted on the surviving enterprises to examine the impact of cleaner production standards on the exports technical complexity of enterprises. The empirical results are presented in column (4) of Table4, which shows that the core coefficient of explanation is significantly positive and greater than the results of the baseline regression. It indicates that even if the regression is conducted with surviving enterprises, the implementation of cleaner production standards also significantly increases the exports technical complexity of textile enterprises, and the conclusions of this paper are robust.


## 5. Heterogeneity Analysis

### 5.1. Different Regions

In the previous study, the implementation of the cleaner production standards policy can increase the exports technical complexity of textile enterprises. However, the clean production standard policy is implemented nationwide, and the effect of policy implementation in each region on the exports technical complexity of enterprises is unclear. Considering the differences between the eastern region and central and western regions in terms of policy implementation, economic development level, and pollution level, the impact of their cleaner production standards implementation on the exports technical complexity of enterprises may also differ. Therefore, this paper divides the sample into eastern region and central-western regions for analysis according to the three major regions of China, namely east and central and west. The empirical results are shown in Table5, from which it can be seen that the core explanatory coefficient in column (1) is significantly positive. It indicates that the implementation of the cleaner production standard policy significantly increases the export technical complexity of textile enterprises in the eastern region; the core coefficient in column (2) is negative but not significant, indicating that the implementation of the cleaner production standard policy does not increase the export technical complexity of textile enterprises in the central and western regions. Possible reasons are most of the eastern region are located in coastal developed areas with convenient export trade, advanced technology, and sufficient talents, which make it easier to carry out technological innovation activities and increase the exports technical complexity of enterprises. In contrast, the central and western regions are relatively less developed. And in order to attract and retain enterprises, the central and western regions may lower the environmental regulation policy standards. It is not conducive to the purchase of clean equipment and technological innovation and makes it difficult to promote the exports technical complexity of enterprises. Therefore, the clean production standards policy significantly increases the exports technical complexity of textile enterprises in the eastern region.Table 5
Regions and sizes.
(1)(2)(3)(4)Eastern region ESIMidwest region ESIAbove the average ESIBelow the average ESITreat × post0.7082∗∗∗−0.31340.5998∗∗∗0.3418(0.0000)(0.4397)(0.0000)(0.2003)Control variablesYesYesYesYesEnterprise fixed effectYesYesYesYesYear fixed effectYesYesYesYesR20.95930.93940.95790.9519Observations4482039542721921555


### 5.2. Scale of Compliance Costs

When enterprises are subject to environmental regulations, they pay for the costs related to environmental regulations such as equipment, materials, and personnel, i.e., compliance costs. So, does the implementation of cleaner production standards policy affect export technical complexity differently for enterprises with different compliance cost scales? In this paper, the logarithm of fixed assets is used to measure the asset size of enterprises, and the sample is divided into above-average size enterprises and below-average size enterprises for analysis. The empirical results are shown in Table5, from which it can be seen that the core explanatory coefficient in column (3) is significantly positive. It indicates that the implementation of cleaner production standards significantly contributes to the exports technical complexity of above-average size enterprises; the core explanatory coefficient in column (4) is positive but not significant. It indicates that the implementation of cleaner production standards policy has less impact on the exports technical complexity of below-average size enterprises. The possible reason is that larger scale enterprises have more production workshops compared to smaller scale enterprises, which can share their compliance costs equally through multiple workshops and thus have lower compliance costs, which is conducive to reducing production costs [26] and further enhancing the exports technical complexity of enterprises. Therefore, the implementation of cleaner production standards policy can significantly increase the exports technical complexity of textile enterprises above the mean size.


### 5.3. Ownership Attributes

The implementation of environmental regulations is stricter for state-owned enterprises, while it is relatively lax for foreign-owned and private enterprises [41]. Therefore, in this paper, according to the ownership attributes of the enterprises, the sample is divided into state-owned (collective) enterprises, foreign-owned enterprises, and private enterprises to explore the impact of the implementation of cleaner production standards policy on the exports technical complexity of enterprises with different ownership. The empirical results are shown in Table 6, from which it can be seen that the core explanatory coefficients of columns (1), (2), and (3) are all significantly positive. It indicates that the implementation of cleaner production standards policy significantly promotes the export technical complexity of state-owned enterprises, foreign-funded enterprises, and private enterprises; however, the core explanatory coefficients of state-owned enterprises are significantly larger than those of foreign-owned enterprises and private enterprises. It indicates that the impact on state-owned enterprises is the largest, foreign-owned enterprises the second largest, and private enterprises is the smallest. Possible reasons are as follows: environmental responsibility is an important part of social responsibility of state-owned enterprises in China, and state-owned enterprises have to play a leading role in green development and are an important target of government environmental regulation. At the same time, state-owned enterprises are the most supported by the government and have the incentive and funds to purchase clean equipment and technological innovation to increase the exports technical complexity. Through their own research and development of advanced technology and production of advanced equipment, the foreign-owned enterprises promote the exports technical complexity of enterprise. Private enterprises are relatively lax in environmental regulations, but in order to increase the competitiveness of enterprises, they will also increase the exports technical complexity. Therefore, the implementation of cleaner production standard policy promotes the increase of export technical complexity of state-owned enterprises, foreign-owned enterprises, and private enterprises.Table 6
Enterprise ownership.
(1)(2)(3)State-owned ESIForeign-funded ESIPrivate ESITreat × post2.8344∗∗∗0.8504∗∗∗0.5443∗∗∗(0.0066)(0.0000)(0.0000)Control variablesYesYesYesEnterprise fixed effectYesYesYesYear fixed effectYesYesYesR20.94190.96280.9532Observations8902100023000


## 5.1. Different Regions

In the previous study, the implementation of the cleaner production standards policy can increase the exports technical complexity of textile enterprises. However, the clean production standard policy is implemented nationwide, and the effect of policy implementation in each region on the exports technical complexity of enterprises is unclear. Considering the differences between the eastern region and central and western regions in terms of policy implementation, economic development level, and pollution level, the impact of their cleaner production standards implementation on the exports technical complexity of enterprises may also differ. Therefore, this paper divides the sample into eastern region and central-western regions for analysis according to the three major regions of China, namely east and central and west. The empirical results are shown in Table5, from which it can be seen that the core explanatory coefficient in column (1) is significantly positive. It indicates that the implementation of the cleaner production standard policy significantly increases the export technical complexity of textile enterprises in the eastern region; the core coefficient in column (2) is negative but not significant, indicating that the implementation of the cleaner production standard policy does not increase the export technical complexity of textile enterprises in the central and western regions. Possible reasons are most of the eastern region are located in coastal developed areas with convenient export trade, advanced technology, and sufficient talents, which make it easier to carry out technological innovation activities and increase the exports technical complexity of enterprises. In contrast, the central and western regions are relatively less developed. And in order to attract and retain enterprises, the central and western regions may lower the environmental regulation policy standards. It is not conducive to the purchase of clean equipment and technological innovation and makes it difficult to promote the exports technical complexity of enterprises. Therefore, the clean production standards policy significantly increases the exports technical complexity of textile enterprises in the eastern region.Table 5
Regions and sizes.
(1)(2)(3)(4)Eastern region ESIMidwest region ESIAbove the average ESIBelow the average ESITreat × post0.7082∗∗∗−0.31340.5998∗∗∗0.3418(0.0000)(0.4397)(0.0000)(0.2003)Control variablesYesYesYesYesEnterprise fixed effectYesYesYesYesYear fixed effectYesYesYesYesR20.95930.93940.95790.9519Observations4482039542721921555


## 5.2. Scale of Compliance Costs

When enterprises are subject to environmental regulations, they pay for the costs related to environmental regulations such as equipment, materials, and personnel, i.e., compliance costs. So, does the implementation of cleaner production standards policy affect export technical complexity differently for enterprises with different compliance cost scales? In this paper, the logarithm of fixed assets is used to measure the asset size of enterprises, and the sample is divided into above-average size enterprises and below-average size enterprises for analysis. The empirical results are shown in Table5, from which it can be seen that the core explanatory coefficient in column (3) is significantly positive. It indicates that the implementation of cleaner production standards significantly contributes to the exports technical complexity of above-average size enterprises; the core explanatory coefficient in column (4) is positive but not significant. It indicates that the implementation of cleaner production standards policy has less impact on the exports technical complexity of below-average size enterprises. The possible reason is that larger scale enterprises have more production workshops compared to smaller scale enterprises, which can share their compliance costs equally through multiple workshops and thus have lower compliance costs, which is conducive to reducing production costs [26] and further enhancing the exports technical complexity of enterprises. Therefore, the implementation of cleaner production standards policy can significantly increase the exports technical complexity of textile enterprises above the mean size.


## 5.3. Ownership Attributes

The implementation of environmental regulations is stricter for state-owned enterprises, while it is relatively lax for foreign-owned and private enterprises [41]. Therefore, in this paper, according to the ownership attributes of the enterprises, the sample is divided into state-owned (collective) enterprises, foreign-owned enterprises, and private enterprises to explore the impact of the implementation of cleaner production standards policy on the exports technical complexity of enterprises with different ownership. The empirical results are shown in Table 6, from which it can be seen that the core explanatory coefficients of columns (1), (2), and (3) are all significantly positive. It indicates that the implementation of cleaner production standards policy significantly promotes the export technical complexity of state-owned enterprises, foreign-funded enterprises, and private enterprises; however, the core explanatory coefficients of state-owned enterprises are significantly larger than those of foreign-owned enterprises and private enterprises. It indicates that the impact on state-owned enterprises is the largest, foreign-owned enterprises the second largest, and private enterprises is the smallest. Possible reasons are as follows: environmental responsibility is an important part of social responsibility of state-owned enterprises in China, and state-owned enterprises have to play a leading role in green development and are an important target of government environmental regulation. At the same time, state-owned enterprises are the most supported by the government and have the incentive and funds to purchase clean equipment and technological innovation to increase the exports technical complexity. Through their own research and development of advanced technology and production of advanced equipment, the foreign-owned enterprises promote the exports technical complexity of enterprise. Private enterprises are relatively lax in environmental regulations, but in order to increase the competitiveness of enterprises, they will also increase the exports technical complexity. Therefore, the implementation of cleaner production standard policy promotes the increase of export technical complexity of state-owned enterprises, foreign-owned enterprises, and private enterprises.Table 6
Enterprise ownership.
(1)(2)(3)State-owned ESIForeign-funded ESIPrivate ESITreat × post2.8344∗∗∗0.8504∗∗∗0.5443∗∗∗(0.0066)(0.0000)(0.0000)Control variablesYesYesYesEnterprise fixed effectYesYesYesYear fixed effectYesYesYesR20.94190.96280.9532Observations8902100023000


## 6. Mechanism Analysis

The previous analysis shows that the implementation of cleaner production standards has improved the export products technical complexity of textile. So how do cleaner production standards increase the export technical complexity of textile products? What is its mechanism of action? According to the aforementioned theoretical analysis, textile enterprises, under the rigid pressure of environmental regulations, may improve their production processes by investing in cleaner production equipment and professionals to increase the export products technical complexity on the one hand; on the other hand, they may improve their export products technical complexity by promoting technological innovation and increasing enterprise productivity. Drawing on existing studies [19, 42], this paper uses an econometric model to test the proposed mechanisms and uses them as intermediate variables to directly examine the impact of cleaner production standards implementation on the intermediate variables. And it constructs the following mechanism test equation:(5)lnfixed=α+βtreati×postt+σXit+μi+λt+εit,lnlabor=α+βtreati×postt+σXit+μi+λt+εit,pro=α+βtreati×postt+σXit+μi+λt+εit,innov=α+βtreati×postt+σXit+μi+λt+εit,where lnfixed represents the investment in purchasing cleaner production equipment, measured by the logarithm of total fixed assets; lnlabor represents the labor cost, measured by the logarithm of year-end employees [18]; pro represents the enterprise productivity, measured by the ratio of product sales revenue to all employees [43]; and innov represents the enterprise innovation, measured by the ratio of new product output value to product sales revenue, and this variable is only available for the 2005–2009 sample due to missing data in the industrial enterprise database. β is the core explanatory coefficient, Xit represents a set of control variables, μi and λt represent individual fixed effects and year fixed effects, respectively, and εit represents the random disturbance term. If the core explanatory coefficients are significant, it indicates that the proposed mechanism is reasonable.The effect of cleaner production standard implementation on enterprises’ input equipment and personnel: If the implementation of cleaner production standards increases the investment in cleaner production equipment, then the fixed assets of enterprises will increase. As seen in column (1) of Table7, the core explanatory coefficient is significantly positive. It indicates that the implementation of cleaner production standards significantly increases the fixed assets of enterprises and increases their investment in cleaner production equipment. Column (2) of Table 7 examines the impact of the implementation of cleaner production standards on labor input, and the core coefficient is also significantly positive. It indicates that the implementation of cleaner production standards significantly increases the labor cost of enterprises and increases their investment in professionals. The above results confirm that by increasing the investment in cleaner production equipment and professionals, the mechanism of the effect of cleaner production standards increases the export products technical complexity of textile enterprises.Table 7
Mechanism analysis.
(1)(2)(3)(4)lnfixed ESIlnlabor ESIInnov ESIPro-ESITreat × post0.0051∗∗0.0039∗∗∗0.0121∗16.9957∗∗(0.0359)(0.0000)(0.0544)(0.0324)Control variablesYesYesYesYesEnterprise fixed effectYesYesYesYesYear fixed effectYesYesYesYesR20.96400.99290.31740.3435Observations48774487742472148774The impact of cleaner production standards implementation on enterprises innovation and productivity: If the implementation of cleaner production standards can promote enterprises technological innovation, then the ratio of new product output to product sales revenue of enterprises will increase. As seen in column (3) of Table7, the core explanatory coefficient is significantly positive. It indicates that the implementation of cleaner production standards increases the ratio of new product output value to product sales revenue of enterprises and promotes their technological innovation. Column (4) of Table 7 examines the impact of the implementation of cleaner production standards on enterprises productivity, and the core explanatory coefficient remains significantly positive. It indicates that the implementation of cleaner production standards significantly increases enterprises productivity. The above results confirm the mechanism of action by which cleaner production standards can increase the exports technical complexity of textile enterprises by promoting enterprise innovation and increasing enterprise productivity.


## 7. Conclusion and Policy Recommendations

### 7.1. Conclusion

Although China is a global textile manufacturing center, it is facing the challenges of environmental pollution and the fifth industry transfer of the global textile industry, and it is urgent to achieve green production and enhance the international competitiveness of textile products. How to achieve a win-win situation between China’s environmental protection and enhancing the international competitiveness of textile products is a key issue for the current government. Based on this, this paper takes the cleaner production environment regulation as the research object, uses the microdata of Chinese enterprises, and investigates the impact of the implementation of cleaner production standards on the complexity of export technology of Chinese textile enterprises through theoretical analysis and empirical testing. The results show that the implementation of cleaner production standards significantly increases the exports technical complexity of Chinese textile enterprises. Cleaner production standards increase the complexity of export technology by increasing the capital and labor input of textile enterprises, adopting advanced processes and cleaning equipment, and carrying out technological transformation. At the same time, the implementation of strict environmental regulation policies for cleaner production standards will force textile enterprises to carry out technological innovation in order to reduce the cost of pollution control and improve productivity to enhance the complexity of export technology. This paper also examines the policy implications of the heterogeneity characteristics of firms in different regions, sizes, and ownership attributes. Due to the different resource allocation and development speed of each region, the cleaner production standard has a significant effect on the export technology complexity of textile enterprises in the eastern region, but the export technology complexity of textile enterprises in the central and western regions is not obvious. Cleaner production standards can improve the technical complexity of large-scale textile enterprises, but the role of smaller enterprises is not significant; it may be that compared with small enterprises, large enterprises have more sufficient funds for technological transformation. From the perspective of the ownership attributes of enterprises, cleaner production standards have the greatest effect on the improvement of the technical complexity of state-owned textile enterprises, followed by foreign-funded and private textile enterprises. In general, the implementation of cleaner production environmental regulations can improve environmental pollution and enhance the technical complexity of textile enterprises’ exports.


### 7.2. Policy Recommendations

Overall, this paper empirically investigates that cleaner production environmental regulations can increase the exports technical complexity of textile enterprises, which has important implications for China’s textile industry and the global textile industry to move to the middle and high end of the global value chain and achieve sustainable development. In this regard, the following policy recommendations are proposed:(1)
Improve cleaner production standards and expand the industry coverage of cleaner production standards. According to the empirical analysis, the implementation of cleaner production environmental regulations can significantly improve the exports technical complexity of textile enterprises and achieve the win-win situation of environmental improvement and increase international competitiveness. Therefore, the government shall gradually increase the environmental regulation of cleaner production, improve cleaner production standards, and promote textile enterprises to improve the efficiency of resource utilization and internal technological innovation. At the same time, it shall expand the coverage of cleaner production industries. The current cleaner production standards are only implemented in a small category of cotton dyeing and finishing industry of the textile industry; the government can gradually implement cleaner production standards for other small categories of industries with serious pollution in the textile industry to achieve cleaner production in the entire textile industry.(2)
According to the actual development of different regions, formulate regional differentiated cleaner production environmental regulation policies. The analysis of empirical results shows that because of the differences in the development of different regions, the implementation of cleaner production standards can significantly increase the exports technical complexity of textile enterprises in the eastern region, while the textile enterprises in the central and western regions have no significant role in increasing. Because the eastern region is economically developed, innovative technology and sufficient talent is conducive for textile enterprises to increase the exports technical complexity. The development foundation in the central and western regions is relatively weak, which is not conducive to the improvement of the technical complexity of textile enterprises. Therefore, the government shall be based on the actual development of different regions, develop the reasonable cleaner production standards, and implement the regional differentiated regulatory policy.(3)
Strengthen environmental subsidy policies and strengthen financing support for small and medium-sized enterprises that implement cleaner production standards. According to the empirical analysis, the implementation of cleaner production standards on the larger textile enterprises export technology complexity has significant increase, but for the smaller textile enterprises is not obvious for increase because the implementation of cleaner production standards requires enterprises to purchase cleaner production equipment and energy and increase the cost of investment. Larger enterprises with strong assets have the ability to cope with the implementation of cleaner production standards and technological innovation. While, smaller enterprises do not have enough capital for technological innovation due to their own lack of funds, financing difficulties, and other problems. Therefore, in view of the plight of small-scale textile enterprises, increase their environmental subsidies and financing support and improve their ability to cope with the implementation of cleaner production standards.(4)
Provide a good environment and talents for textile enterprises’ technological innovation and strengthen the protection of intellectual property rights. By increasing capital, labor inputs, and channels for technological innovation, the implementation of cleaner production standards can increase the exports technical complexity of textile enterprises. Enterprises to carry out their own technological innovation is more conducive to the long-term export competitiveness of enterprises. Therefore, the intellectual property protection system shall be improved to provide good institutional protection for enterprises’ independent technological innovation, stimulate enterprises to increase their research and development investment, and carry out internal independent innovation. At the same time, encourage the universities to open textile-related majors to provide relevant professionals and innovative talents for enterprise technological innovation and promote the exports technical complexity of enterprise.


## 7.1. Conclusion

Although China is a global textile manufacturing center, it is facing the challenges of environmental pollution and the fifth industry transfer of the global textile industry, and it is urgent to achieve green production and enhance the international competitiveness of textile products. How to achieve a win-win situation between China’s environmental protection and enhancing the international competitiveness of textile products is a key issue for the current government. Based on this, this paper takes the cleaner production environment regulation as the research object, uses the microdata of Chinese enterprises, and investigates the impact of the implementation of cleaner production standards on the complexity of export technology of Chinese textile enterprises through theoretical analysis and empirical testing. The results show that the implementation of cleaner production standards significantly increases the exports technical complexity of Chinese textile enterprises. Cleaner production standards increase the complexity of export technology by increasing the capital and labor input of textile enterprises, adopting advanced processes and cleaning equipment, and carrying out technological transformation. At the same time, the implementation of strict environmental regulation policies for cleaner production standards will force textile enterprises to carry out technological innovation in order to reduce the cost of pollution control and improve productivity to enhance the complexity of export technology. This paper also examines the policy implications of the heterogeneity characteristics of firms in different regions, sizes, and ownership attributes. Due to the different resource allocation and development speed of each region, the cleaner production standard has a significant effect on the export technology complexity of textile enterprises in the eastern region, but the export technology complexity of textile enterprises in the central and western regions is not obvious. Cleaner production standards can improve the technical complexity of large-scale textile enterprises, but the role of smaller enterprises is not significant; it may be that compared with small enterprises, large enterprises have more sufficient funds for technological transformation. From the perspective of the ownership attributes of enterprises, cleaner production standards have the greatest effect on the improvement of the technical complexity of state-owned textile enterprises, followed by foreign-funded and private textile enterprises. In general, the implementation of cleaner production environmental regulations can improve environmental pollution and enhance the technical complexity of textile enterprises’ exports.


## 7.2. Policy Recommendations

Overall, this paper empirically investigates that cleaner production environmental regulations can increase the exports technical complexity of textile enterprises, which has important implications for China’s textile industry and the global textile industry to move to the middle and high end of the global value chain and achieve sustainable development. In this regard, the following policy recommendations are proposed:(1)
Improve cleaner production standards and expand the industry coverage of cleaner production standards. According to the empirical analysis, the implementation of cleaner production environmental regulations can significantly improve the exports technical complexity of textile enterprises and achieve the win-win situation of environmental improvement and increase international competitiveness. Therefore, the government shall gradually increase the environmental regulation of cleaner production, improve cleaner production standards, and promote textile enterprises to improve the efficiency of resource utilization and internal technological innovation. At the same time, it shall expand the coverage of cleaner production industries. The current cleaner production standards are only implemented in a small category of cotton dyeing and finishing industry of the textile industry; the government can gradually implement cleaner production standards for other small categories of industries with serious pollution in the textile industry to achieve cleaner production in the entire textile industry.(2)
According to the actual development of different regions, formulate regional differentiated cleaner production environmental regulation policies. The analysis of empirical results shows that because of the differences in the development of different regions, the implementation of cleaner production standards can significantly increase the exports technical complexity of textile enterprises in the eastern region, while the textile enterprises in the central and western regions have no significant role in increasing. Because the eastern region is economically developed, innovative technology and sufficient talent is conducive for textile enterprises to increase the exports technical complexity. The development foundation in the central and western regions is relatively weak, which is not conducive to the improvement of the technical complexity of textile enterprises. Therefore, the government shall be based on the actual development of different regions, develop the reasonable cleaner production standards, and implement the regional differentiated regulatory policy.(3)
Strengthen environmental subsidy policies and strengthen financing support for small and medium-sized enterprises that implement cleaner production standards. According to the empirical analysis, the implementation of cleaner production standards on the larger textile enterprises export technology complexity has significant increase, but for the smaller textile enterprises is not obvious for increase because the implementation of cleaner production standards requires enterprises to purchase cleaner production equipment and energy and increase the cost of investment. Larger enterprises with strong assets have the ability to cope with the implementation of cleaner production standards and technological innovation. While, smaller enterprises do not have enough capital for technological innovation due to their own lack of funds, financing difficulties, and other problems. Therefore, in view of the plight of small-scale textile enterprises, increase their environmental subsidies and financing support and improve their ability to cope with the implementation of cleaner production standards.(4)
Provide a good environment and talents for textile enterprises’ technological innovation and strengthen the protection of intellectual property rights. By increasing capital, labor inputs, and channels for technological innovation, the implementation of cleaner production standards can increase the exports technical complexity of textile enterprises. Enterprises to carry out their own technological innovation is more conducive to the long-term export competitiveness of enterprises. Therefore, the intellectual property protection system shall be improved to provide good institutional protection for enterprises’ independent technological innovation, stimulate enterprises to increase their research and development investment, and carry out internal independent innovation. At the same time, encourage the universities to open textile-related majors to provide relevant professionals and innovative talents for enterprise technological innovation and promote the exports technical complexity of enterprise.



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*Source: 3646949-2022-09-29.xml*