A geophysical insight of the lithostratigraphic subsurface of Rodafnidia area (Lesbos Isl., Greece)

1.   Introduction

In the past few decades, human economic activities have become more frequent. With the widespread use of fossil fuels, various extreme weather events caused by greenhouse gas emissions have frequently threatened the environment, ecological diversity, and human health (Rosa and Dietz, 2012; Ren et al., 2021; Hao et al., 2021). Extreme climate change is one of the most serious survival problems in humans (McMichael, 2013; Yang et al., 2021a; Meierrieks, 2021). The climate problem has been mainly caused by the economic growth of developed countries since the industrialization era. Although these countries have gotten rid of poverty (Wang and Shen, 2016), the vast majority of developing countries still need to rely on rapid economic growth and industrialization to improve living standards for their citizens (Ravindra et al., 2019; Shao and Yang, 2014). The global warming caused by the massive consumption of fossil energy and the environment destruction make human existence suffer from the dual threats of energy scarcity and environmental damage. For China, since the establishment of the market economy, the high GDP growth has created the material conditions for residents to live on, but it has also put pressure on ecology (Wu et al., 2021; Yang et al., 2021b). Manufacturing is an important pillar of national economy and a major industry that causes pollution in China. For a long time, although the development mode that relies on increasing the input of production factors to drive economic growth has brought China a considerable trade surplus, the "ecological deficit" has caused China to suffer from increasingly serious problems of resource depletion and environmental pollution. In 2018, the added value of China's manufacturing accounted for 31.9% of GDP, while energy consumption accounted for about 70%. The energy intensity is much higher than that of developed countries. China's manufacturing energy consumption increased from 809 million tons to 2.684 billion tons, with an average annual growth of 6.51% during the period 20002019. Huge energy consumption and inefficient use of energy have brought serious environmental pollution, making China's environmental performance index ranked at the bottom of the world. However, as China's industrialization and urbanization have not yet been fully completed, energy consumption may continue to grow for a long time, and the coal based energy pattern cannot be fundamentally changed in the short term (Liu et al., 2022). Consequently, improving the energy efficiency of the manufacturing is an inevitable path to curb the sharp increase in energy consumption. Moreover, it is directly related to the changes in residential environment and the sustainable development of human beings.

Energy efficiency (EE) covers single factor and total factor energy efficiency (Yao et al., 2021). Single factor EE is the ratio of energy input to output (energy consumption per unit of GDP), but it ignores the mutual substitution relationship between input variables such as labor and capital and energy input. To this end, scholars have also begun to incorporate pollutant emissions into the calculation of EE, and proposed the concept of green total factor energy efficiency (GTFEE). GTFEE is the improvement of single factor energy efficiency, which is measured by the ratio of target energy input to actual energy input (Hu and Wang, 2006). It considers the substitution relationship between input factors and can more comprehensively reflect the green growth quality of economic development. In order to improve GTFEE, China has implemented a carbon emission trading market, which aims to use the carbon quota system to control pollution emissions and promote sustainable growth of China's manufacturing. Moreover, some financial subsidies, tax relief, pollution control investment, and green financial measures have been implemented to reduce the cost of low-carbon transformation of the manufacturing industry and broaden financing channels (Zheng et al., 2022). These measures demonstrate China's determination to maintain ecologically sustainable development and become an important way to lead the green transformation and development of China's manufacturing industry (Figure 1).

Theoretical research believes that strict environmental regulation policies are an important approach to achieve sustainable development (Millimet and Roy, 2016; Zhu et al., 2014). Promoting technological progress and adjusting the industrial structure are crucial path to solve the problem of high pollution and high energy consumption in the industrial sector (Yang et al., 2018). Although environmental supervision policies can force enterprises to produce sewage equipment and reduce the unit energy consumption of products, this also increases business operating costs and reduces R&D investment in the short term (Ambec et al., 2013). Therefore, environmental regulations may inhibit productivity growth and the expansion of production scale (Chen et al., 2021; Wu et al., 2020a). In addition, some literature indicated that the correlation between ER and GTFEE is nonlinear (Wang et al., 2021). It can be seen that the relationship between ER and GTFEE is uncertain, and requires further empirical testing.

ER is important to ensure that promoting technological progress with low-skilled bias (Song et al., 2021). Due to the complementary effects between different skilled workers, the strengthening of environmental regulations will increase the demand for high-skilled labor in companies, pushing up the skill premium level between high-skilled and low-skilled labor (Yu et al., 2017). With the demise of the demographic dividend, China is in desperate need of high-skilled workers to drive the green upgrading of manufacturing. Improving the efficiency and sustainability of GTFEE of manufacturing can help China get rid of the resource shortage and pollution (Cai et al., 2019). In addition, the skill premium promotes the accumulation of human capital in China, which has increased the supply of high-skilled workers in the labor market, and thus realized the transformation of China's manufacturing industry from factor-dependent development to innovation-driven development.

The present research makes three major contributions. First, constructed a general analytical framework that includes environmental regulations, skills premiums, and GTFEE. Base on the visual angle of skill premium, this work deconstructs the internal mechanism of ER to GTFEE. Second, the EBM-DDF model that can incorporate undesired output are used to measure the GTFEE in China. It effectively avoids the lack of the original ratio information of the SBM model ignoring the efficiency frontier projection value, and makes the measurement results more reliable. Third, some literature demonstrates the linear impact of ER on GTFEE, but the effect of ER may show nonlinear characteristics under the influence of some potential threshold factors. Therefore, we adopt the more advanced dynamic threshold method to test the influence of ER on GTFEE under different skill premium levels. It plays an important role in spatial heterogeneity analysis and response to endogenous problems.

The remaining chapters are as follows. Section 2 is a literature review. Mechanistic analysis is reflected in the section 3. Section 4 introduces the data source, variable selection, and model setting. Sections 5 discusses the results, and we finally offer conclusions in section 6.

2.   Review of the literature

2.1.   Literature study on environmental regulation

Through the formulation of ER, the government has effectively prevented and controlled pollutant emissions and solved the dilemma of economic development and pollution (Callan and Thomas, 2013). For the type of environmental regulation, the command-and-control type regulations adopt more direct methods to prevent and control environmental pollution by formulating pollution standards and limiting the concentration of pollutants (Blackman and Liu, 2018). Because of the obvious effectiveness of command-and-control type regulations, it is widely used by local governments. Market-based type regulation mainly promotes enterprises to actively carry out environmental pollution control activities through market-oriented supervision mechanisms. (Blackman et al., 2018). It includes pollutant discharge fees, environmental protection subsidies, pollutant discharge permits and other regulatory tools. Compared with command-and-control type, market regulation is cheaper and may produce efficient policy effects (Copeland and Taylor, 2013). Voluntary environmental regulations are mainly manifested in ways such as eco-labeling and letters and visits (Blackman, 2008). For example, the US environmental protection work largely relies on citizen environmental litigation (Becker et al., 2013). With the intensification of environmental pollution and the continuous advancement of environmental protection policies, scholars have mostly focused on the command- based and market-based regulation, while ignoring the impact of voluntary ER (Guo et al., 2021).

There exists no uniform measurement method for regulations in the previous literature, which makes it difficult to obtain environmental regulation indicators. Overall, the measurement methods of environmental regulations roughly include four categories: (1) Cost indicators. It is usually used to measure the cost of abatement and is widely used by scholars in the study of environmental policies. (2) Input indicators. It includes environmental protection fiscal expenditure, pollution control investment (Naso et al., 2017). Based on the amount of investment in pollution control, some scholars have conducted a cross-provincial comparative analysis of environmental protection tools (Zhang et al., 2020a). (3) Performance indicators are measured by the effectiveness of pollution control (Alpay et al., 2010). (4) Indicator system. Based on the three main pollutants indicators, Wu et al. (2020b) employed the entropy method to calculate the comprehensive regulation indicators.

2.2.   Research on environmental regulations and pollution

Regarding the pollution control effect of environmental control, the existing literature has analyzed from different perspectives and formed some basic viewpoints. The first point of view, from the perspective of economic scale effect, believes that environmental regulation suppresses pollution emissions by reducing economic activities (Ouyang et al., 2019). However, in the case of weak regulation, high supervision costs, and asymmetric information, environmental regulation could exacerbate pollution problems by expanding the size of the hidden economy. For technological innovation perspective, reasonable ER under dynamic adjustment can promote the R&D and application of clean technologies, which is conducive to promoting the improvement of GTFEE and forming a "Porter effect" (Zhou et al., 2020). Whereas, due to the impact of ER, the cost of enterprises will rise sharply in the short term, which may squeeze research and development funds, inhibit productivity growth (Jin et al., 2019). From the perspective of industrial structure adjustment, believes that increasingly tight environmental regulations have further eliminated high-polluting industries, optimized the function of resource allocation, and ultimately promoted energy conservation and pollution reduction (Zhang et al., 2019; Chen et al., 2019). Fourth, the impact of ER on environmental pollution may be non-linear and complex. The effectiveness of ER is not only related to economic factors such as income and industry heterogeneity, but also to institutional factors such as the quality of government governance and corruption (Zhang et al., 2020b; Chen et al., 2018; Hassan et al., 2020). Finally, some scholars pointed out that once the endogenous issues of environmental protection policies are not fully considered, the effects of environmental governance fail to be accurately identified (Millimet and Roy, 2016).

2.2.   Research on environmental regulations and skill premium

The existing literature focuses on the influence of ER on economic growth (Zhang, 2016), technological innovation (Mbanyele and Wang, 2022), and industrial transformation (Liu et al., 2016), while the relationship between ER, labor wage and skill premium is rarely involved. Regarding the impact of labor wages, some studies suggested that ER increases the cost burden of enterprises and reduces the profitability of enterprises, thus forcing enterprises to adjust wage levels and suppress wage growth (Liu et al., 2021). ER can restrain real wage increases for workers by raising consumer prices (Hazilla and Kopp, 1990). However, Berman and Bui (2001) found that ER can increase labor demand in U.S. refineries, indirectly leading to higher wages in this industry. The impact of ER on wages has obvious regional heterogeneity, industry heterogeneity, and time heterogeneity (Mishra and Smyth, 2012; Liu and Wang, 2020). Although ER reduces the wages of workers in heavy pollution industries in the short term, ER can improve the average wage growth rate in high-tech and low-energy-consuming industries in the long run (Li and Chen, 2019). Kim et al (2015) found that ER has the greatest impact on wage levels in industries such as petroleum, coal, chemical and paper products. On the wage skill premium, some scholars believed that the environment increases pollution input and promotes the upgrading of green technology, which indirectly affects the demand for skilled workers and the skill premium (Wang et al., 2021). This effect can be explained from the cost effect and profit perspective. From the perspective of production costs, facing strict environmental policies, restricted companies have to invest in the treatment of pollutants, eliminate outdated production equipment and improve production processes, which directly increases the cost of pollution control (Shu et al., 2021). Enterprises can shift environmental costs by controlling the scale of production and reducing wages (Mishra and Smyth, 2012). In addition, environmental costs increase corporate profits and capital investment in research and development, which reduces the need for highly skilled labor (Song et al., 2021). Some studies, however, have also found that the high cost brought by ER also drives firms to implement green technology R&D activities. It in turn increases the demand for high-skilled labor to match R&D activities, and raises the skill premium (Zheng et al., 2022).

3.   Influence mechanisms analysis

3.1.   Environmental regulation on GTFEE

The deterioration of the ecological environment not only damages people's physical and mental health, but also threatens the green growth of the manufacturing. Green manufacturing can lead industrial green and low-carbon transformation and optimize regional ecological quality. At present, environmental regulation has become an important means to promote the green development of the manufacturing industry. The direct impact of ER on manufacturing GTFEE is explained by cost effect and innovation compensation effect.

ER refers to the legal norms formulated by the government to improve environmental quality. In the early stage of regulatory policy, to meet the corresponding environmental standards, enterprises usually choose to improve production and emission reduction equipment and increase environmental protection investment (Fan et al., 2022). When production costs are constrained, this behavior diverts productive investment, reduces corporate output, and hinders the optimization of the energy structure. Facing the government's environmental intervention, enterprises are forced to invest resources originally used for production activities into environmental governance or emission reduction activities, which indirectly changes the optimal decision of enterprises (Qiu et al., 2021). Moreover, the implementation of environmental clauses also constrains the management and decision-making behavior of enterprises, and increases management costs. The early neoclassical theory believed that the increase of costs would cause the loss of enterprise productivity and competitiveness, and hinder the transformation of enterprises (Greenstone et al., 2012). Technological innovation is crucial to the improvement of GTFEE (Acemoglu et al., 2012). To cut costs and maintain the original profit level as much as possible, companies may also reduce capital investment in technology R&D and green innovation, indirectly reducing the level of corporate GTFEE. In addition, the potential barriers to entry formed by environmental costs will also increase business risks (Reinhardt, 1999), which disrupts business operations and reduces green energy efficiency. Hence, this paper argues that cost effects may negatively affect GTFEE levels in the early stage of ER.

In the long run, to better carry out production activities under environmental regulation, enterprises will try to improve green energy efficiency through green technology innovation (Li and Chen, 2019). Technological progress is the core driving force of economic growth and one of the sources of power for green total energy efficiency growth (Acemoglu et al., 2012). Once environmental policy becomes a long-term mandatory and sustainable administrative means, enterprises will actively innovate green technologies and enhance their competitiveness to offset rising costs and falling profits caused by environmental regulation. On the one hand, Long-standing ER can enhance the environmental awareness of enterprises and guide enterprises to pay attention to technological progress and innovation (Cai et al., 2020; Xu et al., 2022). On the other hand, technological innovation can also reduce the uncertainty of enterprises' investment in environmental governance (Lv et al., 2021). Once firms are unable to gain innovation gains and competitive position by circumventing environmental investments, they will also proactively increase the impetus for innovation and technological progress (Wang et al., 2018). In addition, the entry barriers and business risks caused by environmental costs will strengthen the competition mechanism between enterprises. To maintain a competitive advantage, enterprises will increase investment in innovation, triggering indirect innovation compensation effects. It reduces energy consumption and energy intensity and increases corporate GTFEE. Therefore, the implement of ER encourages enterprises to carry out green innovation on the basis of internalizing environmental costs in the long run.

Hypothesis 1: The impact of ER on GTFEE is represented by a "U"-shaped relationship.

3.2.   Environmental regulation and the skills premium

ER mainly affects skill premium through cost effect, innovation compensation effect and job creation effect. (1) Cost effect. The traditional hypothesis suggests that the increase in environmental costs may arise the "crowding out effect" of R&D investment, inhibit the technological innovation of enterprises and reduce the demand for skilled labor. Enterprises may transfer environmental costs by reducing wages. The wage of skilled labor is higher than that of unskilled labor. When the same wage is reduced, the ratio of skilled labor to unskilled labor will still increase, that is, the skill premium will increase. (2) Innovation compensation effect. The "Porter Hypothesis" holds that a reasonable intensity of ER can effectively stimulate the innovation motivation of enterprises and generate innovation compensation advantages. The high cost of ER will stimulate the R&D motivation of enterprises, and promote the green transformation of production equipment (product innovation or process innovation). It will undoubtedly increase the demand for skilled labor, lead to an increase in the price of skilled labor, and increase the skill premium. (3) Job creation effect. ER can lead to an increase in the prices of resources and energy-based factors. In this case, enterprises will tend to use labor factors to replace relatively high-priced resource and energy-based factors, thereby producing employment creation effects. Meanwhile, higher ER forces enterprises to implement cleaner production and pollution control activities, which increases the demand for skilled labor and raise the skill premium. Therefore, we propose the following research hypothesis.

Hypothesis 2: ER can improve the level of labor skill premiums.

3.3.   Environmental regulation, skills premium and GTFEE

ER can improve GTFEE by raising the level of the skills premium. (1) Hypothesis 1 put forward that ER can directly promote the improvement of manufacturing GTFEE. ER increases the production cost of high-pollution industries, forces enterprises to innovate green production technologies, thereby promoting the improvement of GTFEE in the manufacturing industry. (2) The improvement of ER increases the skill premium of enterprises. When ER are tightened, the rapid development of the environmental protection industry increases employment opportunities for skilled labor, widening the wage gap between skilled labor and unskilled labor, thereby increasing the skill premium. (3) ER has a positive impact on manufacturing GTFEE through skill premium. When the level of ER increases, the increase in the demand for skilled labor will lead to an increase in the skill premium. The skill premium increases the supply of skilled labor, provides sufficient human capital for green technology research and development. Besides, ER can accelerate the transformation of unskilled labor into skilled labor and improve the overall quality of the labor force, thereby promoting green development capabilities of the manufacturing. In addition, under different skill premium levels, the impact of ER on manufacturing GTFEE is also different. Since the supply of skilled labor is low, the effect of ER on the manufacturing GTFEE is small when the level of skill premium is low. As the level of technology premium increases, ER increases the cost of pollutant emission of manufacturing enterprises, and improves the enthusiasm of enterprises to conduct R&D and innovation in pollution reduction. By raising the skill premium level, ER can increase the supply of skilled labor and guide the knowledge update of technical talents in the industry, so as to better play the role of ER in promoting GTFEE in manufacturing. We propose the following research hypothesis.

Hypothesis 3: Labor skill premium plays a moderation effect and threshold effect in the impact of ER on GTFEE.

4.   Methods design

4.1.   Model settings

4.1.1.   Baseline model

To analyze the potential nonlinear effect of environmental regulations on GTFEE, the quadratic term of ER is adopted to our baseline model as follow:

where subscript i is industry, and t represent year. ${GTFEE}_{it}$ is the green total factor energy efficiency, ${GTFEE}_{it-1}$ represents the lag term of GTFEE. The main control variables include skill premium (${SP}_{it}$), capital structure (${CS}_{it}$), property rights structure (${PRS}_{it}$), energy consumption structure (${ECS}_{it}$), technological innovation (${TI}_{it}$), trade import (${IM}_{it}$), sales scale (${SS}_{it}$) and trade export (${EX}_{it}$). ${\alpha }_{0}{, \alpha }_{1}, {\alpha }_{2}, \dots, {\alpha }_{n}$ are the coefficients. ${\mu }_{i}$ and ${\theta }_{t}$ are industry and year fixed effects, and ${\varepsilon }_{it}$ stands for the error term.

4.1.2.   Mediation effect model

To test whether the environmental regulation can increase the GTFEE through skill premiums, following Ren et al., (2022), we perform a step-by-step regression with equations (2), (3), and (4).

Among them, ${Med}_{it}$ is the mediating variable (${SP}_{it}$). Formula (2) tests the impact of ER on GTFEE; formula (3) estimates the effect of ER on skill premium; formula (4) further examines whether skill premium plays a mediating role between ER and the GTFEE.

4.1.3.   Threshold panel model

We further analyze threshold effect between ER and GTFEE. Referring to the research methods of Wu et al. (2020b), the dynamic threshold model is set as follows.

4.2.   Variables selected

4.2.1.   GTFEE

Presently, some statistical methods such as Solow growth kernel algorithm, and data envelopment analysis (DEA) are used to quantify GTFEE (Jia et al., 2021; Su et al., 2021). Among them, the data envelopment analysis method demonstrates the tremendous potential of a nonparametric method to handle efficiency calculations that do not have fixed data units and do not specify production function form (Ren et al., 2022). The Epsilon-Based Measure (EBM) model not only distinguishes the magnitude of decision unit efficiency, but also tackles the potential for desired and undesired outputs in the computation process. As such, the EBM model is employed to calculate the GTFEE. The variable measurement method is shown in Table 1.

The manufacturing GTFEE is measured by MAXDEA software (Table 2), and decomposed into green technological efficiency (GEFFCH) and green technological progress (GTECH) (see Appendix for specific industry name). The calculation results display that the average value of GTFEE of 27 industries in China's manufacturing is 1.020, indicating that the GTFEE of China's manufacturing has increased by an average of 2% per year. In terms of efficiency decomposition value, the annual average value of GEFFCH is 0.988, and the annual average value of GTECH is 1.037. We found that the green development of the manufacturing mainly benefits from green technological progress rather than green technical efficiency. Therefore, compared with green technology, the efficiency of green technology has great potential for GTFEE. Besides, the GTFEE of 22 sectors has increased, and the efficiency of 5 sectors has decreased. The specific industry name is in the appendix file.

4.2.2.   Environmental regulation

Although the construction of environmental regulation (ER) index has matured, the measurement of ER at the industry level is still more difficult. Previous studies have used the ratio of pollutant treatment costs to operating income to measure environmental regulation. However, this measurement method cannot truly reflect the burden of pollution control. This article uses the following method to measure environmental regulation (Huang et al., 2018).

where, ${\mathrm{c}\mathrm{o}\mathrm{s}}_{1}$ and ${\mathrm{c}\mathrm{o}\mathrm{s}}_{2}$ represent the treatment costs of industrial waste water and waste gas; ${\mathrm{e}\mathrm{m}\mathrm{i}}_{1}$ and ${\mathrm{e}\mathrm{m}\mathrm{i}}_{2}$ represent waste water emissions and waste gas emissions; ${\mathrm{f}\mathrm{a}\mathrm{c}}_{1}$ and ${\mathrm{f}\mathrm{a}\mathrm{c}}_{2}$ represent waste water treatment facilities and waste gas treatment facilities. For the convenience of statistical analysis of the data, based on the division standard of national economic industries (GB/T4754-2011), we divide China's manufacturing sector into 28 sub-sectors. The specific pollution indicators (eg., waste water, waste gas, etc.) in this paper are from China Environmental Yearbook.

4.2.3.   Skill premium

Since this article focuses on the analysis of skill premium differences within the manufacturing industry, it is not suitable for division according to the distribution of technical personnel. Reference to the research of Huang et al. (2018), this article treats scientific and technological personnel as skilled laborers. The ratio of the total remuneration of scientific and technological personnel to the number of scientific and technological activities represents the per capita salary of skilled labor. Non-scientific and technical personnel are uniformly regarded as unskilled laborers, which are measured by the difference between the annual average number of all employees and the number of scientific and technical personnel. The average wage of unskilled workers is measured by the ratio of the total wages of the manufacturing industry excluding the wages of scientific and technical personnel to the number of unskilled workers.

4.2.4.   Control variables

The capital structure is expressed by the ratio of foreign investment in various industries to paid-in capital (Wang et al., 2018). The number of enterprise patents is used as a proxy variable of the technological innovation. The R&D level of an enterprise is expressed as the ratio of the R&D capital investment in various industries to industrial sales output value (Wei and Liu, 2006). The property right structure is expressed as the ratio of the total assets of state-owned enterprises to the total assets of industrial enterprises (Shen and Lin, 2020). The energy consumption structure is expressed by the ratio of coal consumption to total energy consumption. The level of imports is expressed as the ratio of the value of sub-industry imports to the value of industrial sales. The trade export is expressed by the ratio of the industry's total export value to the industry's total output value. The statistical results of all variables are shown in Table 3.

5.   Results and analysis

5.1.   Benchmark model

We use the OLS, FE, and generalized least squares (GLS) regression methods to estimate basic model. Additionally, to address endogeneity problem between ER and GTFEE, the GMM method is used to re-estimate equation (1). The results in Table 4 display the significance and coefficient sign of the explanatory variables (ER) show no marked difference, only the coefficient values are different. We focus on the empirical results of the GMM estimation method. From the empirical results, regardless of the estimation method adopted, the coefficient of ER² is significantly positive, indicating that the relationship between ER and GTFEE is "U-shaped". This result is consistent with Wu et al. (2020a). Environmental regulation policy leads to an increase in corporate pollution control costs and production factor costs under low-intensity environmental regulation, which produced a "crowding out effect" on innovation investment and technology R&D investment, and indirectly hinders the improvement of GTFEE (Yuan and Xiang, 2018). However, in the long run, to reduce costs and obtain more profits, companies usually take the initiative to develop energy-saving technology R&D, optimize the input structure of factors, and internalize the external costs of environmental regulations. Additionally, under the increasing pressure of economic sustainability, firms tend to use clean energy to replace fossil energy. Therefore, the adjustment of energy structure further promotes the increase of GTFEE.

5.2.   Regression results of environmental regulation on skill premium

To test the impact of ER on skill premium, the OLS, FGLS and GMM models are applied to empirical test the relationship between them. The empirical results point out that ER can increase the level of skill premiums in the labor market (see Table 5). It is consistent with the empirical conclusions of Wang et al. (2021a). An important reason is that Lax environmental supervision policies will not only lead to the expansion of the production scale of polluting industries, but also attract the inflow of polluting industries in developed countries. Pollution industries are mainly labor-intensive industries, which require relatively low skills for workers. Since unskilled workers can adapt to jobs in polluting industries, the expansion of polluting firms will inevitably increase the demand for unskilled workers, thereby reducing the level of skill premiums. With the continuous increase of the environmental regulations intensity, enterprises may think highly of green production and pollutant control in the production process (Yu et al., 2017). Whether it is the upgrading and improvement of the production process or the end pollution control, it is necessary to introduce skilled workers who master the corresponding technology. Therefore, it increases the demand for skilled labor, produces employment substitution effects, and raises the skill premium.

5.3.   The results of the mediation effect

To study the transmission mechanism between ER and GTFEE, skill premium is used as an intermediary variable to test the influence channel (see Table 6). Specifically, the effect of ER on GTFEE shows "U" shaped relationship. Moreover, the estimated coefficient of ER on the skill premium is significantly positive (0.076), indicating that ER can increase the wage gap between skilled and unskilled workers. And, skill premium can significantly promote GTFEE in column (3). Referring to the analysis criteria of the mediation effect, we believe that ER can indirectly improve GTFEE by increasing the skill premium. This study proves that skill premium plays an important role in environmental governance.

5.4.   The results of the threshold model

This article uses skill premium as the threshold variable to test the effect of ER on GTFEE under different skill premium levels. Before estimating the model, we test the existence of the threshold. This paper uses the Bootstrap sampling method to estimate thresholds and associated statistics. According to the estimated results in Table 7, Wald statistics with a single threshold is significant at the 1% level, reflecting the skill premium has a single threshold effect. When the skill premium is regarded as a threshold variable, the ER exerts a significant threshold effect on the GTFEE (Table 8). In other words, when skill premium crosses the threshold, there is an increase in the regression coefficient of the ER. This can be attributed to the fact that when the wage gap between skilled and unskilled labor is small, the low skill premium will increase the relative supply of unskilled labor and reduce overall labor costs. It not only makes companies tend to rely on low-cost advantages to increase output, but also leads to a lack of endogenous power for product structure upgrades and green innovation, which reduces the pollution control effect of ER. However, expanding the skill premium will effectively stimulate the supply of highly skilled laborers. It provides high-quality human capital for the company's green technology R&D and product structure upgrade, and better exerts the innovation compensation effect and structural dividend effect of ER. Therefore, with the sharp increase in skill premiums, companies tend to hire highly skilled labor, which increases the possibility of companies engaging in green production and technological innovation. In addition, the conclusion also means that to achieve sustainable development under environmental constraints against the backdrop of a declining demographic dividend, China needs to change its low-cost advantage of labor force and focus on the role of environmental regulation in improving the accumulation of highly skilled workers and human capital.

5.5.   Robustness and endogeneity test

(1) Robustness test. The Global Malmquist-Luenberger index is used to recalculate the GTFEE based on the processing method of Kumar (2006) and Arabi et al. (2014). Furthermore, the impact of ER on GTFEE is "U" shaped, indicating that the empirical results are robust (Table 9).

(2) Endogeneity treatment. The panel vector autoregressive model (PVAR) treats all variables as endogenous variables, and analyzes the influence of each variable on other variables in the model without setting the causal relationship between the variables. It treats all variables in the system as endogenous variables to prevent model setting errors and endogenous problems. Therefore, we use the PVAR model to deal with the endogeneity of ER and GTFEE. After performing the panel unit root test and choosing the optimal lag order, we used Monte Carlo simulations 500 times to obtain the impulse response function plot of environmental regulation on GTFEE (Fig. 2). We found that GTFEE exhibits a negative response in the first period and then shifts to a positive shock in the second period under a positive shock of one standard error of environmental regulation. It verifies the "U"-shaped relation between ER and GTFEE.

5.6.   Quantile regression and heterogeneity analysis

To test whether there is a structural change in the impact of the skills premium on GTFEE, a quantile regression is adopted to measure the effect of skill premium on different GTFEE level. Quantile regression can not only exclude the interference of extreme values, but also fully describe the overall picture of the conditional distribution. To this end, this paper selects 4 representative quantiles (25%, 50%, 75%, and 90%) to estimate the effect of the skills premium on GTFEE. From the change trend of the regression coefficient of skill premium at each quantile, the effect of skill premium on GTFEE is 0.134, 0.169, 0.148, and 0.131 (Table 10). This shows that compared with industries with high GTFEE, the skill premium plays a greater role in promoting GTFEE in industries with low GTFEE.

Given the large differences in the manufacturing structure and development level in different regions, the above manufacturing data are replaced by province (or municipalities or autonomous regions) panel data from 2011–2019 to reanalyze the regional heterogeneity of environmental regulation on GTFEE (Table 11). We find that the influence coefficients of ER on GTFEE of the eastern, central and western region are positive, while it is not significant in the central region. The impact coefficient of ER on GTFEE in eastern region is the largest. An important reason is that the eastern coastal areas have convenient transportation, high level of openness, sufficient research and development funds, and strong technological innovation capabilities. These advantages lead to more obvious compensation effect of technical progress in the eastern region.

6.   Conclusions

This research tests the correlation between ER, skill premium and GTFEE of the manufacturing. Moreover, we empirically analyze whether the influence of ER on GTFEE is affected by the skill premium. The research findings include: The effect of ER on GTFEE has an "U" shape. Additionally, threshold model results display that the improvement in the degree of skill premium can alleviate the positive effect of ER on GTFEE. We present some insights:

(1) Formulate scientific and appropriate environmental regulation. Due to the relation between ER and GTFEE is characterized by "U" shape, the environmental department needs to flexibly adjust the policy intensity to give full play to the long-term innovative compensation effect of ER. The government should formulate reasonable industrial development policies, improve corporate green energy efficiency, and gradually weaken the "crowding out effect" of ER on the green transition of the manufacturing industry. In addition, there has been an unbalanced economic level in the three regions of China for a long time, and conditions such as natural resources, factor endowments, market environment, and industrial structure are different in different regions. It makes different effects appear under the guidance of the same intensity of ER. It is necessary for each region to flexibly adjust environmental policies in light of the actual level of local economic development. According to the actual situation in different regions, the environmental protection department should introduce detailed pollution control measures to avoid inefficient environmental protection behaviors that disturb the normal operation and production of enterprises and inhibit the enthusiasm of enterprises for production. In particular, our study shows that ER has not yet exerted an innovative compensation effect in the central region. Therefore, to make up for the loss of environmental protection costs caused by corresponding policies, the state should arouse enterprises to engage in green and clean production projects through policy subsidies and special support.

(2) This paper confirms that ER can raise GTFEE through skills premium. Therefore, the government should give full play to the positive effect of environmental regulation on the evolution of the skill structure, and stimulate the transformation of unskilled labor into skilled labor through the technological spillover effect. Besides, the government should encourage the supply of high-skilled labor, optimize the structure of human capital, and provide the ultimate impetus for green development. Besides, the government and enterprises may increase education and training expenditures, improve the professional skills of low-skilled workers. It is conducive to providing high-quality talents for industrial upgrading and technological change, reducing structural unemployment, and achieving a coordination between full employment and environmental governance. The increase of the skill premium can not only upgrade the employment skills structure and encourage the transformation of unskilled labor into skilled labor, but also help to the restrictions on the free flow of labor, and solve the dilemma of the mismatch between labor skills and job demand. To this end, the government needs to establish a complete information docking platform between enterprises and high-skilled labor, so as to avoid the waste of labor resources caused by asymmetric employment information. Moreover, enterprises also need to strengthen income subsidies and skills training for unskilled workers, promote the transformation of unskilled workers into skilled workers, and increase the supply of high-skilled labor. In addition, in the process of implementing environmental policies, the government should pay attention to the issue of income distribution and continuously improve the social security system. In order to minimize the income imbalance and narrow the income gap, the government should pay attention to the heterogeneous impact of industrial upgrading on the remuneration of labor with different skills, and alleviate the wage differentiation and wage inequality of laborers.

Although we have analyzed the effects of the ER and skill premium on GTFEE, manufacturing panel data may not be as accurate as enterprise data. Furthermore, due to sample selection and space constraints, we did not explore the industry heterogeneity of the effects of the ER and skill premium on GTFEE in detail. Therefore, future research can analyze the more microscopic impact of the ER on sustainable economy, or explore the impact of the ER and skill premium on sustainable development in terms of industry heterogeneity. In addition, scholars can also focus on the technology effect of the skill premium.