Environmental Regulations, Green Technology Innovation, and High-Quality Economic Development in China: Application of Mediation and Threshold Effects

Abstract

In this article, we consider sample data from 30 regions in China from 2004 to 2020. We use the entropy method to measure the high-quality development level, then examine the intermediary and threshold effects to verify the main paths by which green technology innovation mediates the relationship between environmental regulation intensity and the quality of economic development. Our conclusions are as follows: (1) There is a “U”-shaped relationship between environmental regulation intensity and high-quality economic development. When the environmental regulation intensity is low, there is a negative (inhibitory) relationship between the two, while there is a positive (promoting) relationship when the intensity is high. Furthermore, a high proportion of secondary industries inhibit high-quality development, perfect infrastructure and information access can promote high-quality development, and excessive population density hinders high-quality development. (2) There also exists a “U”-shaped relationship between environmental regulation intensity and green technology innovation, which forms a co-directional change relationship. Green technology innovation has a significant mediating effect on the impact of environmental regulation intensity on high-quality economic development. (3) The threshold effect test confirms the existence of double thresholds. When the green technology innovation level is not high, environmental regulations inhibit high-quality economic development. However, when green technology innovation reaches a certain level, environmental regulations will promote high-quality economic development. This paper has certain theoretical reference for achieving high-quality development goals; thus, our results are expected to provide theoretical support for China’s high-quality development.

1. Introduction

China’s GDP accounts for an ever-increasing proportion of the world’s total economy, comprising an era of rapid and sustained economic growth in human history. However, the extensive development model in this new era has been gradually abandoned, and high quality is expected to become the basic and key guiding ideology of economic development [1]. Therefore, the question of how to promote high-quality development and successfully solve the dual demands of the people for “lucid waters and lush mountains” and “gold and silver mountains” have become the focus of the government and scholars alike. The distinctive features of high-quality economic development are increased quantities; factor-driven scale expansion for quality improvement; innovation-driven structural optimization; and technological innovation with quality, efficiency, and power improvement as a core driving force [2,3,4,5].

High-quality development is economic development guided by new development concepts, which requires a shift away from the pursuit of economic growth and speed towarding quality and efficiency, as well as from pursuing scale expansion to focusing on structural optimization. In particular, innovative high-quality development is a powerful driving force. However, environmental pollution-related negative externalities make it impossible to effectively reduce environmental pollution and protect the ecological environment only by relying on market adjustment mechanisms. In the case of “market failure”, it is necessary to rely on governmental environmental regulations to achieve effective environmental governance and protection [6,7]. Then, we must ask what impact the implementation of environmental regulation policies will have on high-quality development. Does a linear or non-linear relationship exist between them? The answers to the above questions have important significance for the formulation and evaluation of environmental regulation policies.

Rapid economic growth has become increasingly inseparable from science and technology, with technological innovation serving as a driving force for rapid economic development. Although traditional technological innovation has also promoted economic growth, it has also worsened the ecological environment, which is also the result of the inability of green development and innovation to develop in harmony. Therefore, breaking the old innovation model, introducing advanced green ecology concepts, and correctly guiding technological innovation to achieve the purpose of saving resources and greening the ecological environment have become urgent requirements in economic growth. Continuous green technology innovation will be the most effective driving force for sustainable economic development. Green technology innovation focuses on providing new product processes and service markets through innovative technologies, thus achieving “green health” in China. Green technology innovation can help to relieve the disadvantages associated with the excessive use of resources and effectively alleviate the environmental pollution due to rapid economic development, thereby effectively improving production efficiency, saving energy, and reducing consumption, in order to protect the ecological environment [8,9,10,11,12,13,14].

So, can environmental regulations promote the improvement of development quality? Does green technology innovation have a mediating effect? Is there a threshold effect? These are the questions we explore in this article. This paper has three main contributions: (1) Enriching the research on high-quality economic development indicator systems; (2) we study environmental regulations, green technology innovation, and high-quality economic development under the same systematic framework, which allows for comprehensive combination of the three, to a certain extent; and (3) the panel threshold model is used to examine the different effects of environmental regulation on the quality of economic growth under various green technology innovation degrees, in order to test the threshold effect between environmental regulation and the quality of economic growth in China, thus providing theoretical support for policy-makers.

2. Literature Review

2.1. Research on High-Quality Economic Development

Many scholars have focused on the scientific connotations of high-quality development [15], as well as the associated difficulties and challenges [16], and realization paths [17]. In other respects, it is believed that high-quality development reflects the quality of economic growth, using total factor productivity [18] and/or labor productivity [19] to represent economic quality development; however, sustainable development has been neglected. With the deepening of research, scholars have expanded the connotation and extension of high-quality development, with focus on major social contradictions, new development theories, and supply and demand. The comprehensive index method has been used to construct a multi-dimensional scientific, detailed, and reasonable evaluation index system [20,21]. Train (2000) [22] pointed out that economic growth quality is not only a key factor in economic development, but also a supplement to economic development speed, the determination of which should include resource allocation, sustainability, risk management, and governance structure. Parror (2000) [23] further found that economic growth quality includes not only narrow economic growth indicators, such as inflation and investment rates, but also social development indicators such as population health, income distribution, the political system, crime, and religion. Zhang (2020) [24] systematically built a macro- and micro-integrated high-quality development measurement system for enterprises, industries, and regions. In general, there has been a lack of systematic analysis and research, especially with respect to the different driving mechanisms of high-quality economic development.

2.2. Impact of Environmental Regulations on High-Quality Development

The public goods attributes of environmental resources make it difficult for market mechanisms to regulate pollution emissions which is necessitates the use of governmental environmental regulations and policy constraints [25]. As an important policy tool for pollution control and emissions reduction, environmental regulations impose constraints on energy conservation and emissions reduction through market access and technical standards; as a result, they have an impact on the quality of economic growth. There are two main viewpoints in academic research in this field, as detailed in Table 1 below:

2.3. Impact of Green Technology Innovation on High-Quality Economic Development

The definition of green technology was first put forward by Braun and Wilder (1994) [35]. After comparing its value with traditional technology, they pointed out that green technology is an environmentally friendly technology. In 2005, Kemp defined green technology innovation as the use of improved technologies, products, processes, and systems to better prevent or reduce harm to the environment [36]. Later, “green technology innovation” was again defined, by Kemp and Pearson (2007) [37], as innovation that can reduce environmental pollution and reduce the use of resources and energy that cause negative impacts in the provision of products, processing techniques, services, and management. This kind of innovation is also relative to the concept of the system. The application and innovation of green technology should focus on the sustainability of economic, environmental, and social development. Research on green technology innovation and ecological civilization construction has focused on the promotion of various green technology innovations for improving the ecological environment. Anex (2000) [38] pointed out that sustainable industrial development depends on green industry development, which must fundamentally realize technological innovation through private enterprises. According to the policy of encouraging green technology innovation, a detailed analysis has been carried out from the aspects of technology and the innovation process of enterprises. Jaffe (2005) [39] stated that market failures related to environmental pollution, technological innovation, and so on are associated with social policy. These social and public policies refer to current policies that can reduce emissions, as well as improving the use and effective promotion of green technologies.

There are many ways by which green technology innovation can drive high-quality economic growth. Green technology innovation may also rely on leadership. The unique technological model has a certain scale effect, multiplier effect, and financial agglomeration effect on the industry and regional economy, thereby effectively promoting economic development [40,41]. Bai and Wang (2016) [42] shown that innovation, as a driver, can improve the quality of economic growth; although there were certain regional differences: compared with other regions, the effect of this promotion is more obvious in the developed eastern coastal areas. Yang and Min (2019) [43] found that technological innovation positively promotes economic growth quality. In addition, technological innovation promotes the local economic growth structure, stability, welfare distribution, and so on, but also promotes that in neighboring regions [44]. Schwerin and Werker (2003) [45] believed that innovation can promote changes in the regional industrial structure and economic growth, as well as promoting social and economic changes. Ghisetti and Rennings (2014) [46] based on the research of German enterprise micro-data, have found that green technology innovation may effectively increase the competitiveness of enterprises, as spontaneously engaging in green technology innovation activities can reduce transaction costs and improve profitability.

2.4. Environmental Regulation and Green Technology Innovation

The traditional neoclassical school states that environmental regulation will add to the production costs of manufacturers and decrease technological innovation capability [47,48]. Frondel et al. (2007) [49] found that environmental regulation will force enterprises to change their existing, familiar process and use unfamiliar processes, resulting in a decrease in productivity and, consequently, insufficient funds. Therefore, its effect was deemed to be negative. Wagner (2007) [50] analyzed the production cost minimization theoretical framework of mainstream neoclassical economics, and reinforced the crowding-out effect of environmental regulation. This is because it forces enterprises to reduce part of their costs under the condition of limited technical conditions, used in order to meet the criteria of the corresponding policies. For enterprises, the production cost increases. Due to the increased production costs of enterprises, the technological innovation development vitality in the entire market becomes seriously lacking. Leeuwen and Pierre (2016) [51] studied the relevant situation in the Netherlands, conducted an empirical test, and reached the conclusion that the “weak” Porter hypothesis was supported, but there was no evidence supporting the “strong” Porter hypothesis.

From a dynamic perspective, scholars—represented by Porter and Class [16] believed that environmental regulations can force enterprises to carry out technological innovation. The “Porter Hypothesis” has been confirmed by a large number of scholars. Castellacci et al. (2016) [52] conducted a study on green innovation, and showed that the relevant provisions of the environment-related tax law greatly promoted the technological innovation of enterprises to achieve emissions reductions. Johnstone et al. (2017) [53] studied power generation plants in 20 countries around the world, and found that environmental regulations have largely suppressed plant pollutant emissions and promoted the efficiency of thermal power generation fuel use. Miguel and Pazo (2017) [54] used the probit method to analyze the mechanism of effect of environmental regulations on local manufacturing enterprise innovation in Spain. Their results showed that there were positive effects between them, where the degree of impact is positively related to the innovation type.

Some scholars have concluded that the relationship between the two is uncertain. Peuckert (2014) [55] stated that, although environmental regulations will weaken enterprise productivity, they will have a positive effect in the long run. Peng and Lu (2017) [56] explored the green innovation effect of formal and informal environmental regulations, and showed that both play positive roles, showing different effects (“U”-shaped and inverted “U”-shaped). Rammer and Rexhauser (2011) [57] believed that environmental regulatory policies are heterogeneous, and the effects are different under different time, industry, and region conditions. Kemp et al. [58] stated that, to study the relationship between the two, we should not only focus on the positive and negative effects, but also study the individual situation of a company, including their industrial scale and relationship network.

To sum up, scholars have made great contributions to the connotation of high-quality economic development, the choice of paths, the construction of an evaluation index system, and the respective impacts of environmental regulation and technological innovation. There are relatively few research results on high-quality economic development. In view of this, based on the panel data of 30 provinces in my country from 2004 to 2020, on the basis of measuring the high-quality development of China’s regional economy, this paper systematically explains the internal mechanism and transmission mechanism of green technology innovation and environmental regulation affecting the high-quality economic development, and discusses the The driving effect of green technology innovation and environmental regulation for high-quality economic development in China. Compared with the existing literature, the main innovations of this paper are reflected in the following three aspects: (1) High-quality development is a new expression proposed by China for the first time in 2017, indicating that China’s economy has shifted from a high-speed growth stage to a high-quality development stage. Many scholars have carried out rich research on high-quality development, but the existing research has not formed a unified view on the evaluation of high-quality development, and there are few studies. This paper takes national policy as the background, from innovation, coordination, green, openness and sharing. Building an index system to evaluate high-quality development is a major difference from previous research and a major supplement to existing research. (2) Previous studies have mainly focused on the impact of technological innovation on high-quality development, while research from the perspective of green technology innovation is obviously insufficient. Green technology innovation can better reflect the high-quality requirements and is an innovation to existing research. Therefore, in terms of theoretical analysis, this paper integrates environmental regulation, green technology innovation and high-quality economic development into the same framework, and comprehensively analyzes the relationship between the three, which helps to clarify the mechanism of action between the three, and can better grasp the green technology. Theoretical evidence of the impact of innovation on high-quality economic development; (3) In terms of empirical methods, although the mediation effect model has been widely used, the literature on the impact of green technology innovation as a mediating variable on high-quality development is obviously insufficient. This is also the innovation of this paper. Therefore, this paper uses the mediation effect model to better comprehensively identify the main paths through which green technology innovation affects environmental regulation and promote high-quality economic development, and expand related research fields.

3. Material and Methods

3.1. Theoretical Mechanism

• (1). Effect of environmental regulation on high-quality economic development

Environmental regulations include major measures and means, such as the formulation of pollutant discharge standards, environmental inspections, and pollution tax collection policy. The main purpose of environmental regulation is to protect the environment, economic growth, and social coordination. Environmental regulations have a two-way effect. First, environmental regulations operate through the “cost effect”. In production and operation process, in order to comply with environmental laws and regulations, enterprises will use part of their capital, labor, and other resources for environmental governance, increasing the environmental costs and inhibiting the production activities of enterprises. At the same time, the massive increase in spending on pollution control may lead to a decrease in investment in other areas, hindering the economic sustainable development of enterprises. Second, as regulation intensity increases, the resulting “innovation compensation” effect can promote economic growth. Environmental regulation can increase the production costs of enterprises. For the purpose of improving market competitiveness, enterprises will increase their research and development funds and personnel investment, stimulating new vitality in the enterprise market and, thereby, greatly offsetting the increased costs.

Therefore, we propose the following hypotheses H1, H2, and H3.

Hypotheses 1 (H1).

Environmental regulations inhibit high-quality economic development.

Hypotheses 2 (H2).

Environmental regulations promote high-quality economic development.

Hypotheses 3 (H3).

There exists a non-linear relationship between the two.

• (2). Effect of green technology innovation on high-quality economic development

Under the national strategic deployment of green technology innovation, building a dynamic mechanism from the innovation chain may help to promote high-quality economic development. Green technology innovation focuses on maximization of the market economic system, based on governmental regulations, in order to complete the internalization of the external economy. Therefore, it is necessary to achieve a balance between supply and demand, according to the green technology innovation mechanism. Through the green technology innovation supply and demand mechanism, future societal demands can be predicted and, on this basis, the green technology research and development process can be entered. This process takes basic research as the starting point for research and development, and the resulting innovative results should lead to applied research with respect to the existing market conditions. A well-functioning R&D mechanism can enhance the dynamic potential of the green technology market. The green technology research and development process enters at the end of the innovation chain; that is, during the transformation period of technological achievements. Scientific and technological transformation is related to whether all innovation chains can finally generate economic benefits. Considering the properties of the enterprises themselves, green technology enterprises and R&D institutions may adopt a cooperative model for the achievement of scientific and technological transformation. Once the innovation is successfully completed, green innovative products that meet customer needs will quickly enter the market, thereby obtaining huge economic, ecological, and social benefits. Therefore, we propose hypothesis H4.

Hypotheses 4 (H4).

Green technology innovation can promote high-quality economic development.

• (3). Effects of environmental regulations and green technology innovation on high-quality economic development

Environmental regulations increase enterprise costs and crowd out research and development expenses; however, blindly increasing the cost of pollution treatment does not conform to the principle of maximizing corporate profits. Therefore, enterprises are expected to control their pollution emissions and improve their efficiency through green technological innovation. First, green technology innovation helps to optimize the industrial structure. There are many high-polluting and high-energy-consumption enterprises in China, and as the environmental regulation intensity has increased, the costs of many low-end and high-polluting industries have also increased, and next is the value chain climbing effect. Under environmental regulations, green technology innovation can help industries to further rise in the global value chain. Environmental regulations promote enterprises to break down the “green barriers” of developed countries, while green technology innovation can help enterprises to gain a firm foothold in the global market. By improving the quality and functions of their products, they can break out of the “low-end lock-in” in developed countries and move towards a higher global value chain. Finally, there is the collaborative innovation effect. Environmental regulations and green technology innovation synergistically enhance industrial innovation capabilities. For a long time, the manner of obtaining green technology innovation in China has been mainly through the importing of technology-intensive products and “learning by doing”, in order to imitate the technology of advanced countries. With increasing environmental regulation intensity, if enterprises with insufficient innovation ability are not eliminated, they are forced to abandon their low-end and inefficient links, and carry out technological transformation and innovation. Thus, we propose Hypothesis 5:

H5: Green technology innovation plays a positive regulatory role in the process of environmental regulations affecting high-quality economic development; that is, green technology innovation weakens the negative effect of environmental regulations, while strengthening the positive effect.

The overall impact mechanism is shown in Figure 1.

3.2. Variable Selection

3.2.1. Explained Variables

• (1). Indicator selection

For High-Quality Economic Development (HQED), we use the entropy method to evaluate its index and five sub-system indices with 19 tertiary indicators [59]. See Table 2 for further details.

• (2). Calculation meth

The main principle of the entropy weight method is to judge the weight of an index according to its degree of dispersion. Therefore, the entropy weight method can reduce the interference of human factors in index weighting, in order to calculate the weights accurately [60,61,62,63]. The steps of the method are as follows:

The first step is to standardize the data of each indicator:

(1)$Y_{ij}=\frac{X_{ij}-min\left(X_{ij}\right)}{max\left(X_{ij}\right)-min\left(X_{ij}\right)}.$

The second step is to determine the information entropy of each indicator:

(2)$E_j=ln\frac{1}{n}{{\displaystyle \sum }}_{i=1}^n\left[\left(Y_{ij}/{{\displaystyle \sum }}_{i=1}^n{Y}_{ij}\right)ln\left(Y_{ij}/{{\displaystyle \sum }}_{i=1}^n{Y}_{ij}\right)\right].$

The third step is to calculate the weight of each indicator:

(3)$W_j=\frac{\left(1-E_j\right)}{{{\displaystyle \sum }}_{i=1}^m\left(1-E_j\right)}.$

The fourth step is to calculate the high-quality development index:

(4)$HQEG={{\displaystyle \sum }}_{j=1}^m{W}_j{X}_{ij}.$

In these equations, i denotes the province, j denotes the measurement index, n is the number of provinces, and m is the number of indicators. The final result ranges between 0 and 1. The higher the value, the the quality of economic development will be higher.

3.2.2. Explanatory Variables

Environmental Regulation (ER). According to the existing literature, the following are generally used to measure ER: (1) The number of environmental regulation policies; (2) pollutant emissions levels; (3) the government’s investment into pollution control; and (4) comprehensive indicators, such as the local wastewater treatment rate, sulfur dioxide treatment rate, smoke (dust) treatment rate, or solid waste treatment rate. In this paper, the degree of pollutant discharge is used to represent environmental regulations. At the same time, for comprehensive consideration, we also considered the industrial wastewater discharge, industrial smoke (dust) discharge, and industrial SO₂ discharge of each prefecture-level city. These three indicators were measured, and the obtained data were normalized and standardized to form a comprehensive indicator to measure environmental regulations in this paper. The specific process is as follows:

• (1). Considering the different industrial structures in different regions, we divide the three selected pollutant emissions levels by the gross domestic product of the region to normalize them.

• (2). Then, we standardize the range of emissions per unit of GDP for these three indicators, as follows:

(5)$p{x}_{il}=\frac{p_{il}-{\left(p_{il}\right)}_{min}}{{\left(p_{il}\right)}_{max}-{\left(p_{il}\right)}_{min}},$

where i denotes the region, l denotes the pollutant, p_il is the emissions of pollutant l per unit GDP of each prefecture-level city i, and $p{x}_{il}$ denotes the dimensionless emissions of a certain pollutant. The larger the final value, the lower the environmental regulation intensity.

3.2.3. Mediating Variables

Green Technology Innovation (GTE): We used the green patent list developed and provided by the IPC Expert Committee of the World Intellectual Property Organization, which allows one to search for patent information related to environmentally sound technologies (EST) listed in the United Nations Framework Convention on Climate Change (UNFCCC). Then, the number of provincial-level green technology patents was obtained, according to a search in the green patent list with respect to application area and IPC number, from the China Patent Retrieval Network of the State Intellectual Property Office.

3.2.4. Control Variables

• (1). Industrial structure (IND) is represented by the proportion of secondary output value in the GDP. (2) Infrastructure (INF) was selected to describe the number of public transport vehicles per 10,000 people. (3) Government Intervention (GOV): Fiscal expenditure is an important means of macro-control by the government, as the appropriate use of control tools can make up for market failures. The proportion of the local government’s general budget expenditure in the regional GDP was used as a proxy variable for the degree of local government intervention. (4) Population density (DES) was characterized by the population per unit area. (5) Information level (IFM) was expressed in terms of the total amount of post and telecommunications services per capita.

The selection and description of model variables are detailed in Table 3.

3.3. Model Construction

• (1). Based on the previous theoretical analysis, the econometric model was constructed, given as Formula (6):

(6) $HQE{G}_{it}={\alpha }_0+{\alpha }_{1}E{R}_{it}+{\alpha }_2{X}_{it}+{\epsilon }_{it}.$

Considering that there may be a U-shaped relationship between the high-quality economic development and environmental regulations, the square term of the environmental regulation variable was incorporated on the basis of model (6), as shown in Formula (7):

(7)$HQE{G}_{it}={\alpha }_0+{\alpha }_{1}E{R}_{it}+{\alpha }_{2}E{R}_{it}{}^2+{\alpha }_3{X}_{it}+{\epsilon }_{it},$

where the subscript i denotes the region, the subscript t denotes the year, $HQE{G}_{it}$ is the high-quality economic development index for region i in period t, $E{R}_{it}$ is the environmental regulation intensity, and $X_{it}$ represents the control variables, including industrial structure (IND), infrastructure (INF), government intervention (GOV), population density (DES), and information level (IFM).

• (2). Test of mediation effect

The three-dimensional test method was used to build a model to test the mediating effect. The model is shown in Formulas (8)–(10) below:

(8)$HQE{G}_{it}={\alpha }_0+{\alpha }_{1}E{R}_{it}+{\alpha }_{2}E{R}_{it}{}^2+{\alpha }_3{X}_{it}+{\epsilon }_{it},$

(9)$GT{E}_{it}={\beta }_0+{\beta }_{1}E{R}_{it}+{\beta }_2{X}_{it}+{\delta }_{it},$

(10)$HQE{G}_{it}={\eta }_0+{\eta }_{1}E{R}_{it}+{\eta }_{2}E{R}_{it}{}^2+{\eta }_{3}GT{E}_{it}+{\eta }_4{X}_{it}+{\gamma }_{it}.$

The mediating effect is significant if the following conditions are met: First, verify whether the regression coefficients ${\alpha }_1$ and ${\alpha }_2$ of the explanatory variables to the explained variables are significant. Second, if the correlation passes the test, then test the relationship between them and verify the coefficient ${\beta }_1$. Third, if ${\beta }_1$ passes the significance test, the correlation of the three is examined and the significance of the regression coefficients ${\eta }_1$, ${\eta }_2$, and ${\eta }_3$ is verified. If they are significant, there exists a mediating effect.

• (3). Threshold effect test

Panel threshold modeling not only helps researchers to gain more information, but also provides insight into the relationships between explanatory variables and explanatory variables. We used the panel threshold model developed by Hansen (1999) [64], including the endogenous threshold regression technique and the threshold panel regression model. The advantages of this method are: (1) It does not require that the form of a non-linear equation is given, as the threshold and the number of thresholds are completely determined from endogenous sample data; and (2) it provides a theoretical asymptotic distribution and establishes confidence intervals for parameters to be estimated, as well as using the bootstrap method to estimate the statistical significance of the thresholds. Therefore, financial agglomeration under different conditions and regions, and the dynamic relationship between economic growth and ecological efficiency can be more scientifically studied. We selected green technology innovation as the threshold variable to establish the model, as follows:

(11)$\begin{array}{ll}HQE{G}_{it}={\beta }_0& +{\beta }_{11}E{R}_{it}·I\left(GTE\le {\omega }_1\right)\\ & +{\beta }_{12}E{R}_{it}·I\left({\omega }_1<GTE\le {\omega }_2\right)+\cdots +{\beta }_{1n}E{R}_{it}·I\left({\omega }_{n-1}<GTE\le {\omega }_n\right)\\ & +{\beta }_{1n+1}E{R}_{it}I\left({\omega }_n<GTE\right)+\lambda Z_{it}+{\mu }_{it},\end{array}$

where i denotes the province, t represents the time, ${\mu }_{it}$ is a random disturbance term, I($·$) is an indicator function, and ${\omega }_1,{\omega }_2,\cdots ,{\omega }_n$ are specific threshold values. If a reasonable threshold value is selected which passes the significance test, then the coefficients ${\beta }_{11}$,${\beta }_{12}\cdots$,${\beta }_{1n+1}$ in the two models should be significantly different in sign or magnitude.

3.4. Data Sources

We used the relevant data for 30 regions from 2004 to 2020 as a sample, which were obtained from the “China Environmental Statistical Yearbook” and “China Statistical Yearbook” of the relevant years. We used the Stata 14.2 software to process the data. The descriptive variable statistics are given in Table 4.

Logarithmic processing can reduce the volatility of data, thereby increasing the robustness of the obtained empirical results. After summary and descriptive statistical analysis, the maximum value of the explained variable was 0.7102, while the minimum value was only 0.1129, indicating that the economic growth quality significantly differed in different periods and regions. Among the core explanatory variables, the environmental regulation variable ranged between 0.0037 and 1.3320, with an average value of 0.1494; meanwhile, the maximum value of green technology innovation was 31.0922 and its minimum value was 0.0182. We concluded that, for the selected sample, the extreme values, mean value, and changes are in line with practical significance. As such, it was preliminarily determined as the sample for use in the next step of the empirical test.

4. Results and Discussion

4.1. Environmental Regulation Impact on High-Quality Economic Development

• (1). Benchmark regression

In view of the direct relationship between environmental regulations and high-quality economic development, there may be a dynamic impact relationship. In order to solve data endogeneity problem, we used the system GMM model to examine the impact. The results are shown in Table 5.

Models (1)–(7) add control variables in turn. From model (1), it can be seen that, for every unit change of environmental regulation intensity, the economic growth quality decreases by 2.33%; this result had a significance level of 1%. Therefore, environmental regulations can significantly inhibit high-quality economic development. Model (2) examines whether there exists a non-linear relationship between them, for which the quadratic environmental regulation term was introduced. The first-order term coefficient was −0.0431 and the quadratic term was 0.0122, both of which were significant at the 1% level, meaning that a U-shaped relationship exists between the high-quality development index and environmental regulations, in agreement with the domestic scholar Xiong [65], who arrived to the same conclusion. Through the panel model-based empirical research, we concluded that there exists a positive U-shaped relationship between environmental regulations and economic growth, rather than a direct linear relationship. Growth is the research object, which reflects the improvement in quality, so this represents a certain innovation. Model (7) adds control variables on the basis of model (2). The first-order term coefficient of environmental regulation was −0.0293, while the coefficient of the second-order term was 0.0198, both of which passed the significant test; notably, the result was lower than that for the previous environmental regulation intensity. This is consistent with the hypothesis that environmental regulations inhibit high-quality economic development; however, when the environmental regulation intensity increases, the inhibitory effect will turn into a facilitative effect. When the environmental regulation intensity is lower, pollutant discharge fees for enterprises cost less than product or process innovation. Thus, the innovation compensation effect produced by environmental regulations cannot make up for the cost effect they produce. Therefore, companies are generally not willing to carry out green technology innovation, and environmental regulation has a disincentive effect; this was the same conclusion drawn by Christainsen and Haveman [66]. They also believed that, while environmental regulation leads to increased production costs, capital investment for improving quality and reducing pollution will crowd out promising project investment, and environmental regulation standards are strict. This will occupy management time, absorb corporate financial resources, inhibit the improvement of corporate economic benefits, and have an adverse impact on economic growth. The foreign scholars Jaffe and Palmer [67] also came to the same conclusion. However, when the environmental regulations exceed a certain critical value, enterprises must make substantial innovations and develop new production methods. Polluting industries will be eliminated, which will help production materials to more easily enter those enterprises with high efficiency and less pollution. This is the same as the conclusion drawn by Porter; that is, competitive advantage should not be limited to the optimal behavior under static efficiency, but should be based on the technological improvement and innovative behavior of enterprises under dynamic constraints. Appropriate environmental regulations can motivate enterprises to carry out technological innovation, offset the increased production cost caused by the relative price increase in production factors, and thus lead to productivity improvement. It can be seen, from the above, that it is necessary to strengthen environmental regulations to promote the high-quality transformation of China’s current economic growth, which coincides with the conclusion of Huang and Gao [68]: that environmental decentralization promotes the quality of economic growth. Meanwhile, we also verified the fact that the “Porter Hypothesis” effect is established in China [69]. It can be seen that some scholars have studied the impact of environmental regulations on economic quality, who have also drawn meaningful conclusions which provide theoretical reference for this paper. However, in this article, we use green technology innovation as a mediating variable to verify its mediating effect; this supplements and improves the existing research, which has certain theoretical value.

Considering the control variables, a high proportion of secondary industries had an inhibitory effect on high-quality development, due to the country being in a stage of rapid industrialization and urbanization, while the discharges of the three considered industrial wastes was the main factor affecting the regional environmental quality development. The impact of infrastructure was significantly positive. Perfect infrastructure provides high-quality production conditions for social production, reduces transaction costs, improves resource allocation efficiency, and promotes economic quality and efficiency. The estimated coefficient of government intervention was significantly positive and, so, government fiscal expenditure can improve China’s high-quality development. First, fiscal expenditure can provide financial support for environmental governance and promote ecological environment quality; second, governmental support in education and technology can promote the transformation of economic development, as well as promoting total factor productivity growth. The population density coefficient was significantly negative, indicating that excessive population density will lead to a mismatch between the urban population and urban carrying capacity, resulting in huge pressure on public goods and, consequently, negative external factors such as rising production factor prices, resource scarcity, and ecological environment deterioration in the agglomeration area, thereby hindering high-quality development. Information had a positive effect, as improvement of information level can improve enterprise management and accelerate clean technology innovation and technological diffusion, thus reducing pollutant emissions and improving total factor productivity.

4.2. Mediation Effect Test of Green Technology Innovation

• (1). Intermediary effect test

According to the mediation effect model, an empirical analysis was carried out. The results are provided in Table 6.

As stated earlier, environmental regulation has a direct effect on high-quality economic development; at the same time, it indirectly affects high-quality economic development through green technology innovation. Again, the results are presented in Table 6.

Combined with models (1)–(3), the mediating effect of green technology innovation between environmental regulation and high-quality economic development was verified. Fang and Liu [70] also came to the same conclusion, finding that technological innovation plays a positive mediating effect. Therefore, Hypothesis 4 was verified. This is mainly as the implementation of environmental regulations is continuously being strengthened, which has increased the pressure on enterprises to comply with the cost of environmental regulations, forcing enterprises to increase their investment in scientific and technological research and development, improve the level of scientific and technological innovation of enterprises, and then promote the implementation process of industrial upgrading, ultimately promoting high-quality economic development. Liu and Fang [71] have studied the positive mediating effect of technological innovation on the impact of environmental regulation on the high-quality development of the manufacturing industry, similar to the conclusion drawn in this paper. However, it can be seen that there have been many studies carried out from the perspective of high-quality industrial research, and there is a lack of research from the perspective of high-quality economic development, such that there is room for further exploration in this area. The research in this paper can make up for this lack of theoretical research. From column (1), it can be seen that a “U”-shaped non-linear relationship exists between environmental regulation and high-quality economic development. Further, column (2) shows that the coefficient of the environmental regulation primary term was −0.1129, while that of the quadratic term was 0.0765, indicating that the impact of environmental regulations on green technology innovation presents a “U”-shaped relationship. When the environmental regulation intensity is low, on one hand, enterprises choose to invest funds to purchase production equipment or processes to reduce their emissions, and the resulting pollution control expenditure will tighten the innovation funds of enterprises; however, when the environmental regulation intensity reaches an inflection point, at this time, due to various factors (e.g., competition), it may be impossible to meet the regulatory standards simply by relying on the purchased equipment. In this case, its own green technology innovation must change the production process. Therefore, when environmental regulation intensity passes the inflection point, it will promote innovative technology adoption by enterprises. Column (3) in Table 6 shows that the technology innovation coefficient was 0.0106. At the same time, the primary term coefficient of environmental regulation becomes −0.0923, while the coefficient of the secondary term becomes 0.0855, both of which were significant at the 1% level. Therefore, hypothesis 4 was confirmed, and green technology innovation promotes high-quality economic growth; meanwhile, the partial mediation effect of green technology innovation was also established, thus confirming Hypothesis 5.

4.3. Analysis of Threshold Effect of Green Technology Innovation

To further clarify the role of green technology innovation, we constructed a threshold model to explore whether there were differences in how environmental regulations promote high-quality economic development under different levels of green technology innovation. For this, we used the non-dynamic panel model and the Bootstrap sampling method to simulate and determine the number of thresholds, their values, and related statistics. The results are provided in Table 7.

From the results in the above table, both the single threshold and the double threshold passed the F-test, while the triple threshold test did not pass the F-test. According to the assumption of the threshold effect, it can be considered that the model has double thresholds. The double threshold values, obtained according to the calculation results, are as follows: the small threshold value is 8.452, while the large threshold value is 13.241. Therefore, according to the threshold test results, it was necessary to construct a double threshold model to verify the relationships between the variables. We focused on whether different environmental regulation threshold values have different characteristics, and whether the threshold effect of green technology innovation is significant.

It can be seen, from Table 8, that when GTE is less than or equal to 13.241, for each unit increase in environmental regulation intensity, the high-quality economic development index decreases by 7.32%; this result was significant at the 1% level. The results indicate that, when technological innovation is at a low level, the cost of technological innovation to reduce environmental pollution is huge, and pollution control by means of updating equipment, updating processes, and so on is bound to incur costs, thereby crowding out the production funds and technological innovation of enterprises. The resulting innovation compensation effect cannot compensate for the cost effect of environmental regulation, such that blindly increasing the intensity of environmental regulation is not conducive to high-quality economic development. When the level of technological innovation increasing, the coefficient of environmental regulation changes from −0.0732 to 0.127. At this time, for each additional unit increase in technological innovation, the high-quality economic development index increases by 12.7%, indicating that, when technological innovation is at a high level, environmental regulations will stimulate technological innovation. The basic research and equipment level of technological innovation in society are at a good level, which support enterprises in carrying out technological innovation at low cost in a short period of time. Therefore, the positive impact of high-level technological innovation is greater than the negative impact of regulatory control costs. An increase in the intensity of environmental regulations at this time will increase labor productivity, promote industrial structure upgrades, enhance corporate competitiveness, and so on, ultimately promoting high-quality economic development. Thus, Hypothesis 5 is verified. Most scholars at home and abroad have also confirmed the existence of such a threshold effect. For example, Xu and Hu [72] also studied the impacts of environmental regulation and technological innovation on economic growth, but few scholars have considered this subject from the perspective of green technology innovation. Carrying out research from the perspective of high-quality economic development is an important contribution of this paper, serving as a useful supplement to the existing literature.

5. Conclusions and Suggestions

5.1. Conclusions

On the basis of using the entropy method to evaluate high-quality economic development, the intermediary and threshold effects were used to identify the impact of green technology innovation on high-quality economic development through environmental regulations. To regulate the main paths and channels, in order to promote high-quality economic development, our conclusions are as follows:

First, in the empirical results, the first-order term coefficient for environmental regulations was −0.0431, while that of the quadratic term was 0.0122, both of which were significant at the 1% level. This means that a “U”-shaped relationship exists between the high-quality economic development index and environmental regulations; furthermore, there was also a significant threshold effect. We also found that a high proportion of secondary industries inhibits high-quality development; perfect infrastructure provides high-quality production conditions for social production and promotes economic quality and efficiency; government financial expenditure can improve high-quality economic development in China; overpopulation will lead to a mismatch between the urban population and urban carrying capacity, resulting in huge pressure on public goods and hindering high-quality development; and information improvement is conducive to improving the level of business management, as well as accelerating clean technology innovation and technology diffusion, thus being conducive to improving the quality and efficiency of China’s economy.

We found that technological innovation indeed plays a mediating role between environmental regulations on high-quality economic development. Environmental regulations can promote the level of green technology innovation, through product innovation compensation effects and process innovation compensation effects. When the environmental regulation intensity is low, enterprises will reconfigure their resources and change the combination of factors to meet their pollution reduction requirements, lowering their expenditure on technological innovation and, thus, hindering economic quality; however, when the environmental regulation intensity is strong, the cost of pollution control for enterprises will be very high, the consumption structure becomes inclined toward clean products, and the government provides subsidies or tax incentives for technological innovation. At this time, enterprises will have a strong willingness to innovate their production technology, thus enhancing the economic quality.

When technological innovation is at a low level, the innovation compensation effect generated by technological innovation cannot compensate for the cost effect of environmental regulation, meaning that promote high-quality economic development cannot be promoted. When technological innovation is higher, environmental regulations will stimulate technological innovation, and the positive impact of high-level technological innovation is greater than the negative impacts of environmental regulations and control costs. At this time, high environmental regulation intensity promotes high-quality economic development. Therefore, the intensity of green technology innovation should be increased, innovation-driven strategies should be fully implemented, and the importance of green technological innovation should be realized, which will consequently stimulate the development of the real economy. The formulation and implementation of environmental regulation policies need to be selected through the lens of technological innovation improvement, making use of technological innovation advantages and making up for the disadvantages of environmental regulation-related „following costs”, in order to better utilize the intermediary effect of green technology innovation between environmental regulation and economic growth.

5.2. Suggestions

Based on the above empirical analysis, we provide suggestions for improving high-quality economic development from the following aspects:

• (1). Increase policy support for green technology innovation.

Environmental regulations should be combined with other policies, such as green ecological compensation, government financial subsidies, and so on, in order to increase environmental research and development subsidies and tax reductions to encourage enterprises to conduct independent research and development, as well as exerting policy synergy to promote green technological progress. Taking green technology innovation as the starting point, speeding up environmental policies from “whoever pollutes who controls” and “equal emphasis on environmental protection and economic development” to “protection first and prevention first” can more effectively promote high-quality economic development.

• (2). Implement differentiated environmental regulation policies.

Under the reality that environmental regulations can become the driving force for high-quality economic development, local governments should not adopt a “one-size-fits-all” approach when implementing environmental governance policies. Taking staggered production measures specifically, under-developed cities can continuously strengthen their environmental regulations through measures such as environmental access to high-polluting industries and increased investment in environmental governance, such that they can reach the peak of the “inverted U” curve as soon as possible. For cities with developed regional economies, efforts should be made to mitigate the adverse effects of high environmental regulation intensity.

• (3). Promote the high-end development of the industry and boost the high-quality development of the economy.

The focus of high-quality economic development relies on the high-end development of industries. Therefore, on one hand, we should focus on high-tech industries, accelerate the large-scale development of high-tech industries, accelerate the development of high-tech industrial clusters, and continuously improve the competitiveness and influence of advanced manufacturing clusters. On the other hand, the transformation and upgrading of traditional industries should also be promoted, adhering to the two main lines of “optimization” and “upgrading” for traditional industries, eliminating outdated production capacities. For enterprises with development advantages in traditional industries, through technological innovation, branding, service-oriented development, and so on, their competitiveness should be enhanced, increasing their added value. Traditional industries should be empowered with “high-end + intelligence”, and encouraged to promote high-end intensive development with new kinetic energy and provide better product supply.

• (4). Innovate the coordination mechanism for high-quality regional economic development.

The efforts of a single region to promote high-quality economic development may be ineffective due to “beggar-thy-neighbor” behavior. On the basis of addressing the predicament of cross-regional high-quality economic development cooperation between local governments, the cooperation compensation and benefit coordination mechanism should be clarified and local governments should be guided to plan and implement cross-regional coordination schemes for the promotion of high-quality economic development. Finally, to ensure the economic development quality and oversight of such a joint system, monitoring and early warning systems should be implemented.

• (5). Using blockchain technology to promote the green and sustainable development of society

Blockchain is the perfection of the energy dual-carbon system on the basis of low cost, high efficiency and speed in the safe and reliable, multi-value transfer and contribution distribution system [73,74]. Through blockchain technology, the credibility of carbon data can be improved, the cost of carbon verification can be reduced, and carbon finance business can be empowered [75], and help new energy transition and carbon peaking and carbon neutrality. In the field of blockchain, with the purpose of “green technology innovation, scenario breakthrough, dual-carbon pioneer, and energy transformation” [76], it will help the full implementation of new energy and dual-carbon ecosystems, accelerate the process of low-carbon development, and empower society Green and sustainable development injects green new momentum into the high-quality development of China’s economy.

Author Contributions

Conceptualization, T.L.; methodology, T.L.; software, L.W.; validation, L.W., formal analysis, T.L.; investigation, L.W.; resources, L.W.; data curation, L.W.; writing—original draft preparation, J.W.; writing—review and editing, J.W.; visualization, J.W.; supervision, T.L.; project administration, J.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figure and Tables

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Figure 1. Influence mechanism flowchart.

Table 1

Environmental regulations impact on high-quality development quality.

Effect	Authors	Views
Promoting effect	Porter and Class (2020) [26]	Enterprises actively adjust according to the orientation of environmental regulations, and develop green processes to internalize environmental costs
	Hille et al. (2020) [27]	Environmental regulations stimulate technological innovation of enterprises, achieving green economic growth.
	Ajayi and Reiner (2020) [28]	Environmental regulations reduce compliance costs, improve resource utilization efficiency, and promote economic growth.
	Borsatto and Amui (2019) [29]	The negative externalities of environmental pollution will be reduced, and the green competitiveness of enterprises will be enhanced
	Chowdhury and Das (2011) [30];Lanoie et al. (2008) [31]	Appropriate environmental regulations can reduce energy consumption and promote green total factor productivity growth
Inhibitory effect	Levinson and Taylor (2003) [32];Kheder and Zugravu (2012) [33]	Environmental regulations can increase enterprise cost and investment produces a “crowding-out effect”, thereby hindering enterprise performance improvement and inhibiting economic growth
	Unteroberdoerster (2003) [34]	The differences in regulatory standards induce pollution-intensive industrial transfer, which inhibits economic quality.

Table 2

Indicator system for High-Quality Economic Development index.

Target Layer	Dimension Layer	Indicator Layer	Direction	Weights
High-quality economic development	Innovation (0.2437)	The proportion of R&D personnel in employment	+	0.0432
		Proportion of Science and Technology Expenditure to Financial Expenditure	+	0.0772
		10,000 domestic patent applications and authorizations	+	0.0394
	Coordination (0.1867)	Urban–rural income ratio	−	0.0483
		Proportion of non-agricultural industries	+	0.0263
		GDP per capita Gini coefficient	−	0.0322
		Industrial Structure Upgrading Index	+	0.0643
		Deposit balance to GDP ratio	+	0.0222
	Green (0.2027)	Green coverage in built-up areas	+	0.0433
		Production of industrial solid waste per unit of GDP	−	0.0623
		Industrial pollution control investment as a percentage of GDP	−	0.0562
		Energy consumption per unit of GDP	−	0.0823
	Opening (0.1022)	The proportion of total imports and exports to GDP	+	0.0566
		The proportion of foreign capital actually utilized in GDP	+	0.0433
		Number of international tourists received	+	0.0407
	Sharing (0.2647)	Number of medical and health institutions per 10,000 people	+	0.0463
		Years of education per capita	+	0.0853
		Urbanization rate	+	0.0684
		Railway mileage per capita	+	0.0622

Table 3

Selected variables and their descriptions.

Variables	Type	Variable Specific Indicators	Indicator Abbreviation
Environmental Regulation	Explanatory variable	The degree of pollutant discharge	ERit
Green Technology Innovation	Mediating variable	Green technology patents	GTEit
Industrial structure	Control variable	Secondary output value proportion in GDP	INDit
Infrastructure	Control variable	The number of public transport vehicles per 10,000 people	INFit
Government Intervention	Control variable	The proportion of governmental expenditure in the regional GDP	GOVit
Population density	Control variable	Population per unit area	DESit
Information level	Control variable	The total amount of post and telecommunications services per capita	IFMit

Table 4

Descriptive statistics of variables.

Variable	Mean	Standard Deviation	Minimum	Maximum
HQED	0.3382	0.0893	0.1129	0.7102
GTE	3.1231	5.3922	0.0182	31.0922
ER	0.1494	0.2301	0.0037	1.3320
IND	0.0624	0.2201	0.0012	0.7322
INF	1.1332	0.5472	0.1024	6.1382
GOV	0.2763	0.0892	0.0922	0.6328
DES	7.8871	0.3132	6.0921	11.2311
IFM	5.0221	2.3342	1.0922	9.7362

Table 5

Regression results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
ER	−0.0233 ***	−0.0431 ***	−0.0519 ***	−0.0438 ***	−0.0321 ***	−0.0181 ***	−0.0293 ***
ER2		0.0122 ***	0.0301 ***	0.0231 ***	0.0217 ***	0.0243 ***	0.0198 ***
IND			−0.1121 ***	−0.2132 ***	−0.1243 ***	−0.1563 ***	−0.1191 ***
INF				0.3332 ***	0.3367 ***	0.3114 ***	0.3299 ***
GOV					0.0153 ***	0.0144 ***	0.0201 ***
DES						−0.1603 ***	−0.2022 ***
IFM							0.2531 ***
_cons		0.3211 ***	0.2313 ***	0.1233 ***	0.1033 ***	0.2983 ***	0.0763 ***
AR(1)		0.0012	0.0001	0.0002	0.0013	0.0023	0.0001
AR(2)		0.2312	0.2234	0.3021	0.1309	0.1682	0.1290
Sargan		0.7209	0.7102	0.7634	0.7553	0.7492	0.7332
N		510	510	510	510	510	510

Note: ***, p < 0.01.

Table 6

Mediating effect of green technology innovation.

Explanatory Variables	Explained Variable HQED (1)	Explained Variable GTE (2)	The Explained Variable HQED (3)
ER	−0.1234 ***	−0.1129 ***	−0.0932 ***
ER2	0.0678 ***	0.0765 ***	0.0855 ***
GTE			0.0106 ***
IND	−0.1923 ***	−0.0131 ***	−0.1123 ***
INF	0.2301 ***	0.2104 ***	0.2012 ***
GOV	0.0124 ***	0.1429 ***	0.1239 ***
DES	−0.0922 **	−0.0201 *	−0.1024 ***
IFM	0.3402 ***	0.2973 ***	0.3018 ***
_cons	0.1233 **	−5.332 ***	0.2239
AR(1)	0.0012	0.0213	0.0122
AR(2)	0.2312	0.1321	0.1983
Sargan	0.7209	0.8772	0.8952
N	510	510	510

Note: *, p < 0.1; **, p < 0.05; ***, p < 0.01.

Table 7

Thresholds determined for various green technology innovation levels.

Model	F-Value	p-Value	Critical Value			Threshold	95%Confidence Interval
			10%	5%	1%
Single threshold	43.292 ***	0.004	35.992	29.832	17.023	8.452	(8.223,8.563)
Double threshold	23.003 **	0.012	32.092	18.882	9.673	13.241	(13.012,13.675)
Triple threshold	3.982	0.187	24.992	17.763	9.851

Note: **, p < 0.05; ***, p < 0.01.

Table 8

Threshold regression results.

Variable	Regression Coefficients	Standard Error	T-Value	p-Value	95%Confidence Interval
GTE	0.1921	0.0441	4.32	0.001	(0.082, 0.247)
IND	−0.1453 ***	0.1333	−3.93	0.012	(−0.282, −0.098)
INF	0.2123 ***	0.1584	3.45	0.013	(0.182, 0.256)
GOV	0.0215 ***	0.1392	7.09	0.002	(0.012, 0.034)
DES	−0.0453 ***	0.1268	−5.99	0.000	(−0.072, −0.026)
IFM	0.3123 ***	0.2391	4.23	0.001	(0.234, 0.367)
$\mathrm{ER}·\mathrm{I} (\mathrm{GTE}\le 8.452$)	−0.1865 ***	0.0654	5.44	0.000	(−0.256, −0.121)
$\mathrm{ER}·\mathrm{I} (8.452<$$\mathrm{GTE}\le 13.241$)	−0.0732	0.0532	−1.23	0.231	(−0.151, 0.132)
$\mathrm{ER}·\mathrm{I} (\mathrm{GTE}>13.241$)	0.127 ***	0.0670	4.22	0.000	(0.019, 0.223)
_cons	−0.543 ***	0.1234	−7.03	0.001	(−0.883, −0.123)

Note: ***, p < 0.01.

References

1. Nair, M.; Pradhan, R.P.; Bahmani, S. Is higher economic growth possible through better institutional quality and a lower carbon footprint? Evidence from developing countries. Renew. Energy; 2021; 167, pp. 132-145. [DOI: https://dx.doi.org/10.1016/j.renene.2020.11.056]

2. Suki, N.M.; Afshan, S.; Meo, M. The paradigms of technological innovation and renewables as a panacea for sustainable development: A pathway of going green. Renew. Energy; 2022; 181, pp. 1431-1439. [DOI: https://dx.doi.org/10.1016/j.renene.2021.09.121]

3. Zhang, Y.; Sun, J.; Yang, Z. Critical success factors of green innovation: Technology, organization and environment readiness. J. Clean. Prod.; 2020; 264, 121701. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.121701]

4. Sun, H.; Edziah, B.K.; Sun, C. Institutional quality, green innovation and energy efficiency. Energy Policy; 2019; 135, 111002. [DOI: https://dx.doi.org/10.1016/j.enpol.2019.111002]

5. Chen, Y.S.; Lai, S.B.; Wen, C.T. The influence of green innovation performance on corporate advantage in Taiwan. J. Bus. Ethics; 2006; 67, pp. 331-339. [DOI: https://dx.doi.org/10.1007/s10551-006-9025-5]

6. Fowlie, M.; Reguant, M.; Ryan, S. Market-Based Emissions Regulation and Industry Dynamics. Soc. Sci. Electron. Publ.; 2013; 124, pp. 249-302.

7. Su, Y.W.; Jiang, Q.Q.; Khattak, S.I.; Ahmad, M.; Li, H. Do higher education research and development expenditures affect environmental sustainability? New evidence from Chinese provinces. Environ. Sci. Pollut. Res.; 2021; 28, pp. 66656-66676. [DOI: https://dx.doi.org/10.1007/s11356-021-14685-w]

8. Gramkow, C.; Anger-Kraavi, A. Could fiscal policies induce green innovation in developing countries? The case of Brazilian manufacturing sectors. Clim. Policy; 2018; 18, pp. 246-257. [DOI: https://dx.doi.org/10.1080/14693062.2016.1277683]

9. You, C.D.; Khattak, S.I.; Ahmad, M. Do international collaborations in environmental-related technology development in the US pay off in combating carbon dioxide emissions? Role of domestic environmental innovation, renewable energy consumption, and trade openness. Environ. Sci. Pollut. Res.; 2022; 29, pp. 19693-19713. [DOI: https://dx.doi.org/10.1007/s11356-021-17146-6]

10. Liguo, X.; Ahmad, M.; Khattak, S.I. Impact of innovation in marine energy generation, distribution, or transmission-related technologies on carbon dioxide emissions in the United States. Renew. Sustain. Energy Rev.; 2022; 159, 112225. [DOI: https://dx.doi.org/10.1016/j.rser.2022.112225]

11. Khattak, S.I.; Ahmad, M.; Haq, Z.; Gao, S.F.; Jiang, H. On the goals of sustainable production and the conditions of environmental sustainability: Does cyclical innovation in green and sustainable technologies determine carbon dioxide emissions in G-7 economies. Sustain. Prod. Consum.; 2022; 29, pp. 406-420. [DOI: https://dx.doi.org/10.1016/j.spc.2021.10.022]

12. Khattak, S.I.; Ahmad, M.; Khan, S.; Li, H. The cyclical and asymmetrical impact of green and sustainable technology research (GSTR) on carbon dioxide emissions (CO₂) in BRICS economies: Role of renewable energy consumption, foreign direct investment, and exports. 2021; preprint [DOI: https://dx.doi.org/10.21203/rs.3.rs-280754/v1]

13. Khattak, S.I.; Ahmad, M. The cyclical impact of green and sustainable technology research on carbon dioxide emissions in BRICS economies. Environ. Sci. Pollut. Res.; 2022; 29, pp. 22687-22707. [DOI: https://dx.doi.org/10.1007/s11356-021-17368-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34797535]

14. Ding, Q.; Khattak, S.I.; Ahmad, M. Towards sustainable production and consumption: Assessing the impact of energy productivity and eco-innovation on consumption-based carbon dioxide emissions (CCO2) in G-7 nations. Sustain. Prod. Consum.; 2021; 27, pp. 254-268. [DOI: https://dx.doi.org/10.1016/j.spc.2020.11.004]

15. Manish, G.P. Market Reforms in India and the Quality of Economic Growth. Soc. Sci. Electron. Publ.; 2016; 18, pp. 241-262. [DOI: https://dx.doi.org/10.2139/ssrn.2822874]

16. Popkova, E.; Mamontov, V.; Kozhevnikova, T. New quality of economic growth in the tree of goals of economic development. Rev. Appl. Soc. Econ. Res.; 2014; 2, pp. 151-160.

17. Kuprianov, A. GDP Upgrade: How Can One Measure the Quality of Economic Growth?. World Econ.; 2020; 21, pp. 1-11.

18. Baier, S.L.; Dwyer, G.P., Jr.; Tamura, R.F. How Important Are Capital and Total Factor Productivity for Economic Growth?. Econ. Inq.; 2010; 44, pp. 23-49. [DOI: https://dx.doi.org/10.1093/ei/cbj003]

19. Farnham, M.; Hutchinson, E. The Effect of Multiskilling on Labor Productivity, Product Quality, and Financial Performance. Adv. Econ. Anal. Particip. Labor Manag. Firms; 2011; 12, pp. 35-62.

20. Carillo, F.J.; Rodriguez, B.M.; José, M.V.M. The region’s intellectual capital benchmarking system: Enabling economic growth through evaluation. J. Knowl. Manag.; 2006; 10, pp. 41-54.

21. Kahia, M.; Kadria, M.; Aissa, M.B. Modelling the treatment effect of renewable energy policies on economic growth: Evaluation from MENA countries. J. Clean. Prod.; 2017; 149, pp. 845-855. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.02.030]

22. Train, R.E. The Quality of Growth; Oxford University Press: New York, NY, USA, 2000.

23. Barro, R.J. Quantity and quality of economic growth. Economic Growth: Sources, Trends, and Cycles; Central Bank of Chile: Santiago, Chile, 2000; pp. 135-162.

24. Zhang, T. Research on Theoretical Interpretation and Measurement Methods of High-Quality Development. Quant. Tech. Econ. Res.; 2020; 37, pp. 23-43.

25. Munir, K.; Ameer, A. Nonlinear effect of FDI, economic growth, and industrialization on environmental quality. Manag. Environ. Qual. Int. J.; 2020; 31, pp. 223-234. [DOI: https://dx.doi.org/10.1108/MEQ-10-2018-0186]

26. Porter, M.E.; Class, V.D. Towards a new conception of the environment competitiveness relationship. J. Econ. Perspect.; 1995; 9, pp. 97-118. [DOI: https://dx.doi.org/10.1257/jep.9.4.97]

27. Hille, E.; Althammer, W.; Diederich, H. Environmental regulation and innovation in renewable energy technologies: Does the policy instrument matter?. Technol. Forecast. Soc. Chang.; 2020; 153, 119921. [DOI: https://dx.doi.org/10.1016/j.techfore.2020.119921]

28. Ajayi, V.; Reiner, D. European Industrial Energy Intensity: Innovation, Environmental Regulation, and Price. Effects. Energy J.; 2020; 41, pp. 105-128. [DOI: https://dx.doi.org/10.5547/01956574.41.4.vaja]

29. Borsatto, J.; Amui, L. Green innovation: Unfolding the relation with environmental regulations and competitiveness. Resour. Conserv. Recycl.; 2019; 149, pp. 445-454. [DOI: https://dx.doi.org/10.1016/j.resconrec.2019.06.005]

30. Chowdhury, I.R.; Das, S.K. Environmental regulation, green R & D and the Porter hypothesis. Indian Growth Dev. Rev.; 2011; 4, pp. 142-152.

31. Lanoie, P.; Patry, M.; Lajeunesse, R. Environmental regulation and productivity: Testing the porter hypothesis. J. Product. Anal.; 2008; 30, pp. 121-128. [DOI: https://dx.doi.org/10.1007/s11123-008-0108-4]

32. Levinson, A.; Taylor, M.S. Unmasking the pollution haven effect. Int. Econ. Rev.; 2008; 49, pp. 223-254. [DOI: https://dx.doi.org/10.1111/j.1468-2354.2008.00478.x]

33. Kheder, S.B.; Zugravu, N. Environmental regulation and French firms location abroad: An economic geography model in an international comparative study. Ecol. Econ.; 2012; 77, pp. 48-61. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2011.10.005]

34. Unteroberdoerster, O. Trade Policy and Environmental Regulation in the Asia-Pacific: A Simulation. World Econ.; 2003; 26, pp. 73-95. [DOI: https://dx.doi.org/10.1111/1467-9701.00511]

35. Braun, E.; Wield, D. Regulation as a means for the social control of technology. Technol. Anal. Strateg. Manag.; 1994; 6, pp. 259-273. [DOI: https://dx.doi.org/10.1080/09537329408524171]

36. Kemp, R.; Arundel, A.; Smith, K. Survey Indicators for Environmental Innovation; Springer: Berlin/Heidelberg, Germany, 2005.

37. Kemp, R.; Pearson, P. Final Report of the MEI Project Measuring Eco-Innovation; UM Merit: Maastricht, The Netherlands, 2007.

38. Annex, R.P. Stimulating Innovation in Green Technology Policy Alternatives and Opportunities. Am. Behav. Sci.; 2000; 44, pp. 188-212. [DOI: https://dx.doi.org/10.1177/00027640021956161]

39. Jaffe, A.B.; Newell, R.G.; Stavins, R.N. A tale of two market failures: Technology and environmental policy. Ecol. Econ.; 2005; 54, pp. 164-174. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2004.12.027]

40. Sohag, K. Dynamics of energy use, technological innovation, economic growth and trade openness in Malaysia. Energy; 2015; 90, pp. 1497-1507. [DOI: https://dx.doi.org/10.1016/j.energy.2015.06.101]

41. Islam, M.S. Dynamics of energy use, technological innovation, economic growth, and trade openness in Bangladesh. Econ. Bull.; 2021; 41, pp. 997-1008.

42. Bai, J.H.; Wang, L.D. Does innovation drive improve the quality of economic growth?. Sci. Res.; 2016; 34, pp. 1725-1735.

43. Yang, K.J.; Min, C.Z. Industrial Green Total Factor Energy Efficiency under the Requirement of High-quality Development—Based on the Empirical Cases of the Provinces Along China’s “Belt and Road”. Manag. Mod.; 2019; 4, pp. 114-117.

44. Rafique, M.Z.; Li, Y.; Larik, A.R. The effects of FDI, technological innovation, and financial development on CO₂ emissions: Evidence from the BRICS countries. Environ. Sci. Pollut. Res.; 2020; 27, pp. 23899-23913. [DOI: https://dx.doi.org/10.1007/s11356-020-08715-2]

45. Schwerin, J.; Werker, C. Learning innovation policy based on historical experience. Struct. Change Econ. Dyn.; 2003; 14, pp. 385-404. [DOI: https://dx.doi.org/10.1016/S0954-349X(03)00031-6]

46. Ghisetti, C.; Rennings, K. Environmental innovations and profitability: How does it pay to be green? An empirical analysis on the German innovation survey. J. Clean. Prod.; 2014; 75, pp. 106-117. [DOI: https://dx.doi.org/10.1016/j.jclepro.2014.03.097]

47. Yarime, M. Promoting Green Innovation or Prolonging the Existing Technology. J. Ind. Ecol.; 2007; 11, pp. 117-139. [DOI: https://dx.doi.org/10.1162/jiec.2007.1151]

48. Liu, Y.; Li, Z.; Yin, X. Environmental regulation, technological innovation and energy consumption—A cross-region analysis in China. J. Clean. Prod.; 2018; 203, pp. 885-897. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.08.277]

49. Frondel, M.; Horbach, J.; Rennings, K. End-of-Pipe or Cleaner Production? An Empirical Comparison of Environmental Innovation Decisions Across OECD Countries. Bus. Strategy Environ.; 2007; 16, pp. 571-584. [DOI: https://dx.doi.org/10.1002/bse.496]

50. Wagner, M. On the relationship between environmental management, environmental innovation and patenting: Evidence from German manufacturing firms. Res. Policy; 2007; 36, pp. 1587-1602. [DOI: https://dx.doi.org/10.1016/j.respol.2007.08.004]

51. Van Leeuwen, G.; Mohnen, P. Revisiting the Porter hypothesis: An empirical analysis of Green innovation for The Netherlands. Econ. Innov. New Technol.; 2017; 26, pp. 63-77. [DOI: https://dx.doi.org/10.1080/10438599.2016.1202521]

52. Castellacci, F.; Lie, C.M. A taxonomy of green innovators: Empirical evidence from South Korea. J. Clean. Prod.; 2016; 143, pp. 1036-1047. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.12.016]

53. Johnstone, N.; Managi, S.; Rodriguez, M.C. Environmental policy design, innovation and efficiency gains in electricity generation. Energy Econ.; 2017; 63, pp. 106-115. [DOI: https://dx.doi.org/10.1016/j.eneco.2017.01.014]

54. Miguel, C.D.; Pazo, C. Environmental protection, innovation and price-setting behavior in Spanish manufacturing firms. Energy Econ.; 2017; 68, pp. 116-124. [DOI: https://dx.doi.org/10.1016/j.eneco.2017.10.030]

55. Jan, P. What shapes the impact of environmental regulation on competitiveness? Evidence from Executive Opinion Surveys. Environ. Innov. Soc. Transit.; 2014; 10, pp. 77-94.

56. Peng, W.B.; Cheng, F.F.; Lu, J.L. Research on the threshold effect of environmental regulation on the efficiency of provincial green innovation. South. Econ.; 2017; 9, pp. 73-84.

57. Rammer, C.; Rexhauser, S. Unmasking the Porter hypothesis: Environmental innovations and firm profitability. Environ. Resour. Econ.; 2011; 57, pp. 145-167. [DOI: https://dx.doi.org/10.2139/ssrn.1865249]

58. Kemp, R.; Smith, K.; Becher, G. How Should We Study the Relationship between Environmental Regulation and Innovation. Theor. Approaches Empir. Anal.; 2000; 10, pp. 43-66.

59. Wei, J.J.; Wu, G.; Ban, L. Evaluation and Analysis of Industrial Transformation and Upgrading Capability in Western Regions—Based on the Perspective of High-quality Development. Ningxia Soc. Sci.; 2021; 1, pp. 111-119.

60. Wang, R.; Tan, J. Exploring the coupling and forecasting of financial development, technological innovation, and economic growth. Technol. Forecast. Soc. Chang.; 2021; 163, 120466. [DOI: https://dx.doi.org/10.1016/j.techfore.2020.120466]

61. Lam, W.S.; Liew, K.F.; Lam, W.H. Investigation on the performance of construction companies in Malaysia with entropy-TOPSIS model. IOP Conf. Ser. Earth Environ. Sci.; 2019; 385, 012006. [DOI: https://dx.doi.org/10.1088/1755-1315/385/1/012006]

62. Delgado, A.; Romero, I. Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A case study of a mining project in Peru. Environ. Model. Softw.; 2016; 77, pp. 108-121. [DOI: https://dx.doi.org/10.1016/j.envsoft.2015.12.011]

63. Kaynak, S.; Altuntas, S.; Dereli, T. Comparing the innovation performance of EU candidate countries: An entropy-based TOPSIS approach. Econ. Res.; 2017; 30, pp. 31-54. [DOI: https://dx.doi.org/10.1080/1331677X.2016.1265895]

64. Hansen, B. Testing for Linearity. J. Econ. Surv.; 1999; 13, pp. 551-576. [DOI: https://dx.doi.org/10.1111/1467-6419.00098]

65. Xiong, Y. The relationship between environmental regulation and economic growth based on provincial data. China Popul. Resour. Environ.; 2011; 21, pp. 126-131.

66. Christainsen, G.B.; Haveman, R.H. The Contribution of Environmental Regulations to the Slowdown in Productivity Growth. J. Environ. Econ. Manag.; 1981; 8, pp. 381-390. [DOI: https://dx.doi.org/10.1016/0095-0696(81)90048-6]

67. Jaffe, A.B.; Palmer, K. Environmental regulation and innovation: A panel data study. Rev. Econ. Stat.; 1997; 79, pp. 610-619. [DOI: https://dx.doi.org/10.1162/003465397557196]

68. Huang, Q.H.; Gao, M. Quantitative and qualitative effects of environmental regulation on economic growth: A test based on simultaneous equations. Economist; 2016; 4, pp. 53-62.

69. Yuan, Y.J.; Xie, R.H. Environmental Regulation and Industrial Green Productivity Growth: Re-examination of “Strong Porter Hypothesis”. China Soft Sci.; 2016; 7, pp. 144-154.

70. Fang, Z.Y.; Liu, Y.J. Can Heterogeneous Environmental Regulation Boost the Industrial Upgrading of the Yangtze River Economic Belt?—Testing the Mediation Effect Based on Scientific and Technological Innovation. Econ. Perspect.; 2021; 40, pp. 99-108.

71. Liu, Y.; Fang, Z.Y. Can environmental regulation boost the high-quality development of manufacturing?—Intermediary and spillover effect test based on scientific and technological innovation. Resour. Dev. Mark.; 2021; 37, 1409−1416+1496.

72. Xu, C.X.; Hu, L.Y. Environmental Regulation, Technological Innovation and Economic Growth—An Empirical Analysis Based on China’s Provincial Panel Data from 2008 to 2015. Resour. Dev. Mark.; 2019; 35, pp. 1-6.

73. Centobelli, P.; Cerchione, R.; Vecchio, P.D.; Oropallo, E.; Secundoc, G. Blockchain technology for bridging trust, traceability and transparency in circular supply chain. Inf. Manag.; 2021; 103508.in press [DOI: https://dx.doi.org/10.1016/j.im.2021.103508]

74. Centobelli, P.; Cerchione, R.; Del Vecchio, P.; Oropallo, E.; Secundo, G. Blockchain technology design in accounting: Game changer to tackle fraud or technological fairy tale?. Account. Audit. Account. J.; 2021; ahead-of-print [DOI: https://dx.doi.org/10.1108/AAAJ-10-2020-4994]

75. Shen, C.; Pena-Mora, F. Blockchain for Cities—A Systematic Literature Review. IEEE Access; 2018; 6, pp. 76787-76819. [DOI: https://dx.doi.org/10.1109/ACCESS.2018.2880744]

76. Kshetri, N. Blockchain and sustainable supply chain management in developing countries. Int. J. Inf. Manag.; 2021; 60, 102376. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2021.102376]