Discussion on the Relationship between Environmental Regulation and Green Technology Innovation from the Perspective of Innovation External Cooperation: Evidence from Chinese Private Enterprises

Abstract

A number of studies have confirmed the Porter hypothesis that moderate environmental regulation can facilitate the adoption of green technology innovation strategies by cooperatives. However, the existing research has not clearly analyzed the paths of environmental regulation affecting green technology innovation and how internal factors act on the transmission path. To this end, this paper empirically examines the relationship between environmental regulation and enterprises’ green technology innovation from a collaborative innovation perspective, using data from the 13th Private Enterprise Survey in China as the sample. The findings show that: (1) the intensity of environmental regulation set by the Chinese government does promote enterprises’ green technological innovation, and this result will not change due to the difference between green process innovation and green product innovation; (2) under the constraints of environmental regulation, the allocation of resources through the mode of cooperative innovation to promote green technology innovation is an important transmission path, that is, cooperative innovation is an important mediating variable between environmental regulation and enterprises’ green technology innovation; (3) the management structure of enterprises can positively moderate the relationship between environmental regulation and collaborative innovation, i.e., the more managers, the more significant the influence of environmental regulation on collaborative innovation; (4) the stronger the social responsibility of enterprises, the more likely it is that the innovation resources obtained through collaborative forms will be allocated towards green technology innovation. However, such a moderating effect only exists in two types of cooperation: collaborative R&D and commissioned research. This research tells us that while environmental regulations continue to be strengthened, it is necessary to create an environment conducive to collaborative innovation but also to pay attention to the need for joint decision making and increase corporate social responsibility.

1. Introduction

With the continuous progress of industrialization, energy consumption and environmental pollution have become increasingly severe, leading to widespread concern about the conflict between human civilization and the natural environment [1,2,3]. To effectively address this conflict, there is an urgent need for innovative development concepts and improved development models [4,5]. Green technology innovation not only effectively reduces energy consumption and mitigates environmental pollution but also enables the recycling of resources [6,7,8]. It has become an effective way to reconcile the contradiction between environmental protection and economic growth, and achieve sustainable development of the economy and society [9,10].

A new round of global technological revolution and industrial transformation is flourishing [11,12]. Emerging energy technologies such as new energy sources [13,14], advanced nuclear power [15], energy storage [16], and hydrogen energy are rapidly advancing [17], and various new models and formats are constantly emerging. They have become the core driving force behind the global energy transition, shifting the focus from resource and capital dominance to technological innovation dominance [6,8,18]. It can be said that green and low-carbon technologies are the key to simultaneously achieving carbon peaking, carbon neutrality, and economic development [8,19,20].

As the main players in the market economy, making them the core carriers of green technological innovation [21,22,23], enterprises that successfully achieve green technology innovation can gain access to more abundant, crucial, and important resources through their unique technological competitive advantages [24,25,26]. Firstly, green technology innovation in enterprises helps reduce production costs, conserve resources, and minimize the environmental pollution caused by production [27,28]. Secondly, green innovative technologies are recognized as potential strategic resources for enterprises, bringing them unique advantages [29,30]. Thirdly, implementing green technology innovation helps enterprises alleviate environmental pressure and operational risks, optimize cost control, and further improve production efficiency [31,32,33]. Finally, by introducing products based on green technology innovation, enterprises can obtain higher product premiums, enhancing their market competitiveness [34,35].

Green technology innovation has significant characteristics of long cycles, high investment, and dual externalities, which greatly impact the motivation of enterprises [36,37,38]. Therefore, there has been widespread attention from governments, industry associations, and social groups on how to promote green technology innovation in enterprises [28,39,40]. Stakeholder theory suggests that the green technology innovation of enterprises is influenced by various subjects [41,42]. For many developing economies, the purpose of adopting green technology innovation in enterprises is to meet the legitimacy requirements of production [23,43]. For example, numerous studies have confirmed that government regulatory actions such as environmental taxes [28,44], pollution rights trading [45], and environmental legislation [46] significantly promote green technology innovation in enterprises. Unlike the neoclassical economic understanding of environmental protection policies, scholars like Michael Porter argue that appropriate environmental regulations can encourage more innovation activities in enterprises, and offset the costs brought by environmental protection [46,47].

Some scholars’ research findings indicate that the Porter hypothesis does not actually exist and argue that environmental regulations inhibit green technology innovation in enterprises [48,49,50]. Neoclassical economic theory suggests that environmental regulations increase production costs and reduce the financial flexibility of enterprises, thereby negatively impacting green technology innovation [51]. Some researchers’ conclusions strongly validate the Porter hypothesis, suggesting that environmental regulations can enhance positive effects on green technology innovation in enterprises through increased innovation compensation [52,53]. The core of this viewpoint is that there is a non-linear relationship between environmental regulations and green technology innovation in enterprises [54,55,56]. However, in the long run, the compensatory effect of innovation can promote technological progress by generating additional profits [56]. The divergent research results are likely related to the selection of samples, such as the intensity of environmental regulations where the sample companies are located, the industry to which they belong, and the level of production processes, which further emphasizes the need for more detailed research.

Many current studies focus on the relationship between environmental regulations and green technology innovation [49,50,55]. However, the inherent mechanisms through which environmental regulations affect green technology innovation are still not clear [56]. Under a certain level of environmental regulations, companies must acquire various resources necessary for green technology innovation, including funding, talent, and knowledge, through certain channels [37,57]. For companies, the channels for acquiring knowledge for green technology innovation can be independent innovation or various forms of innovation collaboration with external entities [58,59]. Open innovation theory suggests that collaborative innovation is a cost-effective approach [38]. Therefore, it is reasonable to infer that one important pathway for companies to promote green technology innovation under environmental regulations is through innovation external collaboration.

Based on this analysis, there are two important questions that need to be analyzed: why do companies choose to innovate through external collaboration strategies under the pressure of environmental regulations, and why do companies allocate various innovation resources obtained through external collaboration to green technology innovation? Perhaps there are multiple answers to these two questions, and the size of the corporate management team is an important factor influencing decisions [60,61], while corporate social responsibility is an important aspect affecting the allocation direction of resource [46,62,63].

To address the gaps in existing research, we need to not only examine the existence of the Porter hypothesis but also analyze its internal and external influencing factors. To address these questions, we use a sample of over 1000 private companies in China. First, we empirically test the existence of Porter’s hypothesis in China. Based on this, we further investigate whether environmental regulation promotes green technology innovation through innovative external collaboration. Next, we empirically test whether the management team scale influences the choice of innovation external collaboration strategies under specific levels of environmental regulations. Finally, we empirically test whether corporate social responsibility affects the relationship between innovation external collaboration and green technology innovation.

The following sections of this paper are arranged as follows: Section 2 presents a literature review and hypotheses; Section 3 presents the research methodology, including data, models, variables, etc.; Section 4 mainly describes the data analysis process, including direct effects, mediation effects, and moderation effects; and Section 5 describes the research conclusions and discussions.

2. Literature Review and Hypotheses

2.1. Environmental Regulation and Enterprises’ Technology Innovation

The relationship between environmental regulation and green technology innovation has been extensively studied, but a consensus has not yet been reached [56,64,65] were among the early researchers who pointed out the dual effects of environmental regulation on green technology innovation, namely, the cost compliance effect and innovation compensation effect [64,65]. The cost compliance effect refers to the fact that environmental regulation, to some extent, raises the standards for pollution emissions, which further increases the investment in pollution control and reduces the investment in green technology R&D, thus lowering the efficiency of green technology innovation in enterprises [66,67]. The innovation compensation effect refers to the fact that while environmental regulations improve enterprises’ emission standards, they also encourage companies to engage in green technology innovation, thereby reducing pollution control costs [68]. Therefore, under reasonable environmental regulations, in order to maximize their profits, they will increase their investment in green technology innovation [64,65,69]. In other words, when the cost compliance effect caused by environmental regulation is smaller than the innovation compensation effect, enterprises may actively engage in green technology innovation [70]. With the continuous increase in the intensity of environmental regulations, enterprises need to invest a large amount of material and labor costs to meet governmental requirements, resulting in a cost compliance effect greater than the innovation compensation effect [30,71]. At this point, enterprises would rather accept high fines than invest in green technology research and development, thus inhibiting the innovation of green technology [48]. Based on these above considerations, some studies have empirically tested the non-linear relationship between environmental regulations and green technology innovation, finding that it may be U-shaped or inverted U-shaped [54,55,56].

In this study, we selected Chinese private enterprises as the research sample, with moderately intense environmental regulation at the time of the investigation, which has not yet entered the most stringent phase of environmental protection [56]. At the same time, compared with large state-owned enterprises and central enterprises, under moderate environmental regulation, private enterprises have relatively limited resources to allocate [72]. Not engaging in green technology innovation not only fails to meet the requirements of production legitimacy but also faces the risk of market competition failure [37]. According to Porter’s hypothesis, environmental regulation has a positive impact on green technology innovation, and moderately strict environmental regulations can effectively stimulate green technology innovation in enterprises [52,53]. Environmental regulations increase production costs, while green technology innovation can generate obvious and targeted compensation effects to offset the costs of green technology innovation. At the same time, the market environment formed under environmental regulations to some extent favors the development of companies actively choosing green technology innovation, as green technology innovation is an easy way to gain a competitive advantage and beneficial information. Based on existing research, we introduce the following hypothesis:

H₁. 

Government environmental regulation has a positive impact on green technology innovation in companies.

2.2. Environmental Regulation, Innovation External Cooperation, and Green Technology Innovation

Due to the distinctiveness of green technology innovation compared to general technology innovation, it exhibits a sense of urgency and social usefulness in the research process, while facing double externalities, technological uncertainty and market uncertainty, which affect the enthusiasm of enterprises for green technology innovation [28,30,73]. On the other hand, the lack of knowledge capability, disposable funds, and human capital reserves in enterprises will also greatly limit their green technology innovation activities [28,74,75]. In order to meet the government’s production legitimacy and the green demands of market consumers, collaborating to different extents with external partners and stakeholders is one effective method to address the above challenges [35], by seeking professional knowledge, capabilities, and other resources beyond the boundaries of the company [76].

There are two main theories regarding whether the innovation subject collaborates with external parties for innovation, namely, the cost minimization theory and the resource-based theory [77]. The cost minimization theory suggests that there is a substitution relationship between internal development and external collaboration, and the subject will choose the method with the lowest cost for innovation [78]. When the relative net benefits generated by external collaboration are higher, the innovation subject will choose to collaborate externally [79,80]. The resource-based theory suggests that collaboration allows various innovation subjects to fully utilize their own resources for innovation, and there exists a complementary relationship between the resources of the subjects [81]. For example, the collaboration between industry, academia, and research institutions is an alliance with heterogeneous resources, where subjects establish a collaborative network to share their respective special heterogeneous resources and achieve efficient innovation [82]. Based on the analysis above, we can understand that the pressure from government environmental regulations will to some extent squeeze the resources of enterprises. In order to meet the pressure of government environmental regulations through green technological innovation, collaborating with external subjects is one of the most effective ways. Therefore, we propose the following research hypothesis:

H₂. 

Driven by government environmental regulations, enterprises tend to collaborate with external parties for green technology innovation.

Close collaboration with external subjects such as universities, research institutes, and peer companies helps to solve the internal knowledge, resource, and capability deficiencies in green technology innovation, and effectively share the risks of technological innovation and marketization [83,84,85]. Some scholars have pointed out that in addition to the aforementioned characteristics, external innovation collaboration also helps to stimulate synergistic effects, effectively reduce the development costs of green technology, improve the efficiency and effectiveness of the innovation process [86], and shorten the time it takes for green technology innovation to be transformed into green products and services [87,88]. The research results of scholars such as [85,87,89] indicate that compared to general technological innovation, green technology innovation particularly emphasizes the need to acquire external knowledge through external collaboration.

It can be said that innovation external collaboration, by utilizing external knowledge and resources, can complement the scarcity of elements in the internal R&D process, help improve the efficiency of green technology innovation, reduce the costs of complying with environmental regulations, enhance production legitimacy, and thereby improve the market competitiveness of enterprises [90,91]. Specifically, for private enterprises in developing countries, which may already have weak technological innovation capabilities and relatively insufficient reserves of knowledge, talent, and funds, innovation external collaboration is the most effective way of innovation [61,87], especially under the increasing pressure of government environmental regulations. It not only meets the environmental requirements of the government but also develops products and services needed by the market. It achieves economic performance while also gaining environmental and social performance. Through cooperation with universities, research institutions, other enterprises, and financial institutions, enterprises can obtain the knowledge, technology, funds, and talents that are lacking in companies, and also alleviate the negative impact of these factors on green technology innovation, indirectly promoting enterprise green technology innovation. Therefore, we propose the following research hypotheses:

H₃. 

Innovation external collaboration can alleviate the constraints of enterprises’ green technology innovation capabilities and resource shortages.

H₄. 

Innovation external collaboration is a mediation actor between environmental regulation and enterprises’ green technology innovation.

2.3. Corporate Management Team Scale and Green Technology Innovation

According to the upper echelon theory, the top management team is responsible for both formulating and executing business decisions, making it the most direct factor influencing decision making within a company [61,92] early pointed out that the characteristics of the top management team have a vital impact on the green technology innovation of enterprises. Current research has found that the proportion of female executives, total education level, tenure period, and heterogeneity within top management teams are all closely related to enterprises’ green technology innovation. Previous studies have suggested a positive correlation between the proportion of women in top management team and green technology innovation [93,94]. However, this positive correlation only occurs below a certain threshold value, and if beyond the threshold, the proportion of female executives may be negatively correlated with technology innovation outcomes [95]. In terms of the composition of the top management team, the presence of members with engineering or scientific doctoral degrees is negatively associated with the quantity and quality of new products, indicating that the transition of technical talent from the laboratory to management positions may have a detrimental effect on innovation within the company [96]. Existing research suggests a negative correlation between tenure in top management teams and innovation intensity [97]. While longer tenure provides certain advantages in acquiring knowledge and experience, as tenure lengthens, inertia in innovation increases and the rate of new product launches slows down, which is not conducive to sustained long-term investment in green technology innovation [98]. Additionally, when there is strong heterogeneity within the top management team, the proportion of external board members is more negatively correlated with research and development intensity. However, when there is low heterogeneity within the top management team, the negative correlation between the proportion of external board members and research and development intensity weakens [99].

Recently, the study on psychological cognitive factors within top management teams is no longer limited to individual psychological characteristics and cognitive processes, but also focuses on the interactive influence at the psychological level between teams. Existing research suggests that internal conflicts and disharmony caused by differences in members’ psychological characteristics within teams are important factors influencing enterprise green technology innovation decision making [100,101]. Psychological identification among the top management team plays an important role in managing complex innovation processes, and shared common vision and values, behavioral integration, and social relationship integration are all conducive to green technology innovation within the company [100,101]. Behavioral economics theory suggests that due to incomplete and asymmetric decision-making information, as well as the influence of decision-makers’ abilities and energy, the decision making of corporate managers often has bounded rationality or is the result of partially rational and partially irrational behavioral games [61,102]. Faced with government environmental regulations and market demand for green products and services, the difficulty of making green technology innovation decisions is increasing, as is the influence of decision-making failure, making group decision making an important choice [103] argue that internal collaboration, information sharing, and joint decision making within top management teams can reduce team conflicts, improve team efficiency, and enhance the output of green technology innovation. In the process of green technology innovation within an enterprise, the top management team can build network relationships, providing valuable information and resources to team members. Rich and mature network relationships contribute to green technology innovation within the company, especially in weak political and regulatory environments, where cohesive top management team social networks gain more obvious advantages [104]. Based on this, we propose the following research hypothesis:

H₅. 

Corporate management team scale positively moderates the relationship between environmental regulation and innovation external cooperation.

2.4. Corporate Social Responsibility and Green Technology Innovation

Corporate social responsibility refers to the responsibility of a company to not only create economic value and fulfill legal obligations to its shareholders and employees, but also to take responsibility for consumers, communities, and the environment [94,105]. It is characterized by a high level of concern for social and environmental issues in the organization’s actions [105,106,107]. In existing research, there is limited study on the relationship between corporate social responsibility and green technological innovation [105]. The few studies that do exist have examined this relationship from perspectives such as green identity and green organizational culture, viewing corporate social responsibility as a means to stimulate and integrate green innovation resources of the company and its stakeholders, thereby enhancing the output of green technological innovation [108,109,110]. According to the stakeholder theory, when a company invests certain resources in fulfilling environmental responsibilities, it can establish closer trust relationships with various stakeholders, promote the formation of cooperative networks [111], help acquire more knowledge and talent resources, reduce the cost and risk of innovation [112], and further promote the development of green technology innovation [105]. Firstly, companies with good social responsibility are more likely to obtain valuable information and knowledge about green demand and preferences from stakeholders, effectively guiding the direction of green process and product innovation and improving the effectiveness of innovation [113]. Secondly, companies with good social responsibility help form a positive corporate culture internally, attracting more innovative talent and improving the enterprise’s technological innovation capability [105,114]. Additionally, research personnel in companies with good social responsibility usually have a higher sense of safety responsibility, which can stimulate employees’ enthusiasm for innovation [105,114,115]. Lastly, good environmental responsibility behavior helps companies gain more policy support [116], encourage increased support for core technology research and development, and improve the legitimacy of production [112,117].

The influence of corporate social responsibility on green technology innovation is not only reflected in direct impact but also in indirect effects through moderating the allocation of resources [113,118]. Companies with good social responsibility not only focus on the economic output of their production but also pay attention to environmental and social performance, which promotes the allocation of innovation resources to green technology innovation [105,106,107]. Companies that actively conduct social responsibility usually establish cooperative networks with stakeholders, and, while obtaining information and resources, they are also influenced by feedback from relevant parties, affecting the direction of resource allocation obtained through innovation external cooperation and promoting green technology innovation [114,119]. Enterprises that actively assume social responsibility have their behavioral style and decision-making methods solidified, forming a responsible corporate culture that guides the company’s efforts toward a deeper green and low-carbon direction [105]. Compared with general companies, companies with good social responsibility usually have a better market reputation and are subject to higher social expectations and supervision from investors, consumers, and government managers, which pressures the company’s green technological innovation behavior [120,121]. Therefore, it can be observed that with limited resources available for allocation, companies with good social responsibility tend to prioritize green-oriented technological innovation. Based on this, we propose the following hypothesis:

H₆. 

Corporate social responsibility positively moderates the relationship between innovation external cooperation and green technology innovation, meaning that the stronger the corporate social responsibility, the more resources obtained from innovation external cooperation will be allocated towards green technology innovation.

3. Method

3.1. Data

The data used in this article are from the 13th Chinese Private Enterprise Survey in 2018, which is one of the longest-running large-scale national surveys in China [122]. It has been conducted biennially since 1993. The overall principle of the data sampling survey is to use a certain proportion (around 0.03–0.05%, varying depending on the survey funding) for multi-stage sampling. Up to now, 2018 is the most recent publicly available data year. The database utilizes the comprehensive private enterprise directory provided by the Information Center of the State Administration for Market Regulation as the sampling frame, ensuring a more rigorous catalog sampling type, thus having a better representation. This survey obtained a total of 3973 samples from companies, covering over 10 industry sectors. In this paper, the data selection processes are as follows: firstly, due to the missing samples and low quantity in Tibet, this article retains the other 30 provinces, municipalities, and autonomous regions except for Tibet; secondly, considering the specific characteristics of the study subjects, we only keep the data of the following industries: agriculture, forestry, ranching, fishery, mining & metals, manufacturing, electricity, gas, water, construction, transportation, and storage, while the data of service enterprises are removed, including transportation and warehousing, information services, wholesale and retail trade, accommodation and food services, finance, real estate, leasing and business services, residential services and repair, science education and health, etc.; finally, we eliminate the observations with missing or obvious distortion in each variable in the regression models. The industry distribution of the sample is shown in Figure 1. It can be observed that manufacturing enterprises have the highest proportion, with a value of 68.92%. The next highest proportions are agriculture, forestry, animal husbandry, and fishery, accounting for 12.42%, and the construction industry, accounting for 12.08%. The remaining proportions total less than 10%. The above results indicate that the processed data is very suitable for the research objective, as the manufacturing industry is an important agent for green technology innovation.

3.2. Variables

Explained variables: Green process innovation refers to innovative activities in industrial production that use environmentally friendly technologies and methods to reduce resource consumption, minimize environmental pollution, and improve energy utilization efficiency. For the measurement of green process innovation (Green_Process), the main reference is the question in the questionnaire “Whether the company invested in technological innovation or process improvement last year?”; if the answer is “yes”, the value is assigned to 1, otherwise it is 0. Green product innovation can be defined as the process of developing and introducing environmentally friendly products or services that minimize the consumption of natural resources and pollution throughout their lifecycle while providing sustainable solutions. The question “Did the company invest in new product development last year?” is the main reference for the measurement of green product innovation (Green_Product); if the answer is “yes”, the value is assigned to 1, otherwise it is 0. For green technology innovation (Green_Tech), including green process innovation and green product innovation, as long as the company has invested in technology innovation, process improvement, or new product development, a value of 1 is assigned, otherwise 0.

Explanatory variables: government environmental regulation (Regulation) is the independent variable of this study. Environmental regulation refers to the system of constraining and managing the impact of human activities on the environment through laws, policies, standards, and other means. Its purpose is to protect and improve environmental quality, prevent pollution and degradation, and promote sustainable development. Its measurement method mainly refers to the question in the questionnaire, “How much did the enterprise spend on pollution control last year?”. The values obtained from the questionnaire are processed by natural logarithm in order to minimize the influence of the data itself on the estimation results.

Mediating variable: Innovation external cooperation can be defined as a cooperative relationship between organizations and external partners aimed at sharing resources, knowledge, and experience to jointly carry out innovation activities and achieve innovation goals. Innovation external cooperation (Cooperation) is the mediating variable in this study, which is measured by the questionnaire item “Did the enterprise cooperate with other research departments and technicians in the past year?”; if the answer is “yes”, the value is assigned to 1, otherwise it is 0. For the specific forms of cooperation, there are three main types: joint development (Cooperation_1), commissioned research (Cooperation_2), and co-built R&D platform (Cooperation_3). Among them, the reference for joint research is the questionnaire item “Did you invite other researchers and technicians to work part-time in your company?”; if the answer is “yes”, the value is assigned to 1, otherwise it is 0. The reference for commission research is the question “Did you purchase or commission research new products, new projects, or new technologies?”; if the answer is “yes”, then the value is 1, otherwise the value is 0. The reference for the measure of a co-built platform is the questionnaire item “Did the enterprise have a cooperative sharing R&D platform?”; if the answer is “yes”, the value of 1 is assigned, otherwise, the value of 0 is assigned.

Moderating variables: in this study, two moderating variables are included are the corporate management team scale (Manager_scale) and corporate social responsibility (CSR). In general, the scale of an enterprise management team can be defined based on the number of team members, typically including different levels of managers such as senior management, department managers, and team leaders. CSR is the responsibility that a company undertakes to society, the environment, and stakeholders during its business operations. This includes the company’s responsibilities in terms of the economy, environment, and society, namely, economic responsibility, environmental responsibility, and social responsibility. In this case, the measure of the firm’s management team scale is referred to the question item in the questionnaire “What is the proportion of managers in your company to the total number of employees?”; the measure of corporate social responsibility is referred to the question item in the questionnaire “How much did your company pay social insurance fees for employees throughout last year?” The values obtained from the questionnaire are processed by natural logarithm.

Control variables: we controlled for some variables along two dimensions, the entrepreneur and the firm itself, including the entrepreneur’s gender (Sex), age (Age), level of education (Education), political status (Politic), and membership of a trade association (Industry_embedding) [123]. Firm-level control variables include: how long the firm has been founded (Firm_age), employee scale (Scale), operating income (Income), enterprise asset-liability ratio (Leverage), and profits (Profit) [124]. The reasons for selecting these control variables are mainly as follows: Firstly, the entrepreneur’s gender, age, level of education, and political status will have an impact on the green and low-carbon awareness of the enterprise, relevant knowledge reserves, and the ability to obtain innovative knowledge. Secondly, joining industry associations can provide enterprises with a comprehensive support platform, helping them understand industry development trends, obtain resource support, promote policy advocacy and standard setting, and promote the development of green technological innovation. Thirdly, enterprise age also has a certain influence on green technological innovation. Young enterprises may have advantages in terms of innovation culture, resource input, technological capabilities, and market opportunities. Established enterprises can also adapt and promote green technological innovation through cooperation, strategic adjustments, and transformation. Fourthly, enterprise size also has a certain influence on green technological innovation. Large enterprises have advantages in terms of R&D resources, market share, and influence, but may face limitations in flexibility and innovative capabilities. Lastly, A lower asset: liability ratio helps enterprises improve their funding capacity, stabilize their financial conditions, implement long-term strategies and investments, and promote green technological innovation. Enterprises with high debt ratios may face financial risks and challenges in financing costs and may adopt a conservative attitude towards green technological innovation.

3.3. Models

Considering that both the explained variables and mediating variables are binary variables, therefore, the Logit model was chosen to estimate the research hypotheses. First, we test the relationship between environmental regulation and enterprises’ green technology innovation, as shown in Equation (1); based on this, we further test whether innovation external cooperation is a mediating variable between environmental regulation and green technology innovation, as shown in Equation (2); then, we test the moderating effect of corporate management team scale on the mediating path, as shown in Equation (3); finally, we test the moderating effect of corporate social responsibility on the relationship between innovation external cooperation and green technology innovation, as shown in Equation (4). Fixed effects such as province, industry, and type of firm are controlled for in all models.

$$GT{I}_i={\alpha }_0+{\alpha }_{1}ERI+{\displaystyle \sum {\alpha }_{i+1}{x}_i}+\epsilon$$

(1)

$$GT{I}_i={\beta }_0+{\beta }_{1}ERI+{\beta }_{2}COR+{\displaystyle \sum {\beta }_{i+2}{x}_i}+\epsilon$$

(2)

$$GT{I}_i={\gamma }_0+{\gamma }_{1}ERI+{\gamma }_{2}COR+{\gamma }_{3}CMS\times ERI+{\displaystyle \sum {\gamma }_{i+3}{x}_i}+\epsilon$$

(3)

$$GT{I}_i={\phi }_0+{\phi }_{1}ERI+{\phi }_{2}COR+{\phi }_{3}CMS+{\phi }_{4}CSR+{\phi }_{5}CMS\times ERI+{\phi }_{6}CSR\times COR+{\displaystyle \sum {\phi }_{i+6}{x}_i}+\epsilon$$

(4)

In the above equation, GTI denotes green technology innovation, which can be divided into green process innovation and green product innovation; ERI denotes the intensity of government environmental regulation; COR denotes innovation external cooperation; CMS denotes corporate management team scale; and CSR denotes corporate social responsibility. ${\alpha }_i$, ${\beta }_i$, and ${\phi }_i$ denote the regression coefficient for estimated. x_i denotes the control variable; $\epsilon$ denotes the random error term.

3.4. Framework

Based on the above content, we draw a research framework diagram as shown in Figure 2. Specifically, there are several aspects: (1) the impact of environmental regulations and green technology innovation, including green process innovation and green product innovation; (2) the mediating effect of innovative external collaboration, including joint development, commissioned research, and co-built R&D platforms; (3) the moderating effect of management team scale on environmental regulations and innovative external collaboration; (4) the moderating effect of corporate social responsibility on the relationship between innovative external collaboration and green technological innovation.

4. Data Analysis

4.1. Descriptive Statistics

Table 1. Descriptive statistics of variables.

Variable	Obs	Mean	SD	Min	Max
Green_Tech	884	0.411	0.492	0.000	1.000
Green_Process	884	0.324	0.468	0.000	1.000
Green_Product	884	0.308	0.462	0.000	1.000
Cooperation	884	0.462	0.499	0.000	1.000
Cooperation_1	884	0.215	0.411	0.000	1.000
Cooperation_2	884	0.173	0.379	0.000	1.000
Cooperation_3	884	0.180	0.384	0.000	1.000
Regulation	884	0.194	0.385	−6.908	12.000
Manager_scale	884	0.170	0.364	0.001	0.536
CSR	884	3.876	2.074	−4.600	16.000
Sex	884	0.880	0.325	0.000	1.000
Age	884	47.000	8.993	22.000	73.000
Education	884	3.342	1.107	1.000	6.000
Politic	884	0.415	0.493	0.000	1.000
Industry_embedding	884	0.878	0.328	0.000	1.000
Firm_age	884	17.000	8.127	0.000	59.000
Scale	884	4.878	1.387	0.693	12.000
Income	884	8.578	2.079	0.698	19.000
Leverage	884	38.000	27.000	0.000	260.000
Profit	884	0.676	11.000	−13.835	300.000

shows the descriptive statistics of the main variables. The results show that 41% of enterprises have adopted green technology innovation strategies in the past year, and the green process innovation strategy is more attractive than the green product innovation; the former is 31% and the latter is 32%. In the past year, the proportion of enterprises choosing innovation external cooperation reached 46%, of which the proportion of cooperative R&D was the largest, accounting for 22%, and the smallest share of commissioned research at a value of 17%. In 2017, the average investment expenditure of enterprises to clean up pollution was CNY 3.7 million, and the gap between the maximum and minimum values was large. In terms of the size of the management of the enterprise, the number of managers accounts for approximately 17% of the total number of employees of the enterprise. The average annual social insurance costs paid by enterprises for employees is CNY 75 million, and the maximum value is CNY 60.9 billion, which to a certain extent shows the social responsibility of enterprises.

In terms of corporate characteristics, 88% of entrepreneurs are men, and the proportion of women is smaller. The average age of entrepreneurs was 47, with the youngest being 22 and the oldest 73. Among the enterprises surveyed, 42% of the heads of enterprises are members of the National People’s Congress or the Chinese People’s Political Consultative Conference. From the perspective of industry embedding of enterprises, 88% of enterprises have joined industry associations. The average number of years of enterprise establishment was 17 years, with the largest being 59 years and the smallest being the year of new start-up. The average number of employees in an enterprise is 517.

4.2. Correlation Analysis

Table 2. Variable correlation coefficients.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)
(1) Green_Tech	1
(2) Green_Process	0.799 *	1
(3) Green_Product	0.829 *	0.561 *	1
(4) Regulation	0.259 *	0.256 *	0.201 *	1
(5) Cooperation	0.367 *	0.263 *	0.393 *	0.229 *	1
(6) Manager_scale	−0.035	−0.041	−0.042	−0.087 *	−0.068 *	1
(7) Sex	0.032	0.020	0.017	0.098 *	0.027	0.008	1
(8) Age	−0.002	−0.003	−0.006	0.099 *	0.031	−0.044	0.132 *	1
(9) Education	0.125 *	0.080 *	0.125 *	0.003	0.241 *	−0.021	−0.012	−0.170 *	1
(10) Politic	0.011	0.040	0.021	0.130 *	0.081 *	0.039	0.085 *	0.076 *	0.018	1
(11) Industry_embedding	0.101 *	0.099 *	0.059	0.062	0.096 *	−0.040	0.054	0.118 *	0.015	0.062	1
(12) Firm_age	0.033	0.028	0.030	0.100 *	0.085 *	−0.075 *	0.103 *	0.558 *	−0.195 *	−0.033	0.152 *	1
(13) Scale	0.260 *	0.228 *	0.248 *	0.359 *	0.328 *	−0.253 *	0.091 *	0.169 *	0.219 *	0.108 *	0.145 *	0.186 *	1
(14) Income	0.210 *	0.196 *	0.189 *	0.307 *	0.252 *	−0.142 *	0.096 *	0.140 *	0.243 *	0.082 *	0.073 *	0.125 *	0.718 *	1
(15) Leverage	0.056	0.056	0.029	0.131 *	0.059	−0.093 *	0.013	0.144 *	0.070 *	−0.007	−0.003	0.084 *	0.261 *	0.221 *	1
(16) Profit	0.068 *	0.031	0.016	−0.034	0.060	−0.017	0.009	0.039	0.054	−0.005	−0.007	0.029	0.056	−0.150 *	−0.032	1

Note: * p < 0.05.

presents the Pearson correlation coefficient matrix of the main variables. The results indicate a significant positive correlation between environmental regulation and green technology innovation. Specifically, the correlation coefficients between environmental regulations and green process innovation, as well as green product innovation, are 0.256 and 0.201, respectively. Innovation external collaboration is also significantly positively correlated with both green process innovation (correlation coefficient of 0.263) and green product innovation (correlation coefficient of 0.393). Additionally, a significant positive relationship is observed between environmental regulation and innovation external collaboration, with a correlation coefficient of 0.229. Furthermore, it is found that there are significant correlations between corporate management team scale and both environmental regulations and innovation external collaboration. Similarly, there are significant correlations between corporate social responsibility and innovation external collaboration, as well as green technology innovation. These findings strongly support the research hypotheses and provide a basis for subsequent empirical testing.

4.3. Main Effects

Table 3. Direct effects of environmental regulation on green technology innovation.

Variable	M1	M2	M3
	Green_Tech	Green_Process	Green_Product
Regulation	0.073 ***	0.071 ***	0.055 ***
	(4.257)	(3.897)	(3.051)
Sex	−0.030	−0.231	−0.173
	(−0.127)	(−0.949)	(−0.721)
Age	−0.017	−0.018 *	−0.012
	(−1.628)	(−1.663)	(−1.092)
Education	0.146 **	0.074	0.156 **
	(2.043)	(1.013)	(2.119)
Politic	−0.172	−0.048	−0.048
	(−1.133)	(−0.305)	(−0.308)
Industry_embedding	0.858 ***	0.841 ***	0.454 *
	(3.307)	(2.936)	(1.723)
Firm_age	0.002	0.001	0.001
	(0.208)	(0.086)	(0.090)
Scale	0.157 *	0.114	0.307 ***
	(1.762)	(1.266)	(3.299)
Income	0.128 **	0.130 **	0.033
	(2.116)	(2.133)	(0.531)
Leverage	−0.002	−0.001	−0.004
	(−0.701)	(−0.482)	(−1.301)
Profit	0.151 **	0.011	0.003
	(2.199)	(1.426)	(0.434)
Industry type	Y	Y	Y
Province	Y	Y	Y
Enterprise ownership	Y	Y	Y
Constant	−2.959 ***	−3.376 ***	−2.453 ***
	(−3.370)	(−3.589)	(−2.730)
Observations	1030	1030	1019
Chi2	247.5	179.8	223.0
R2_p	0.176	0.140	0.170

Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. M1 is used to examine the relationship between government environmental regulations and green technology innovation; M2 is used to examine the relationship between government environmental regulations and green process innovation; M3 is used to examine the relationship between government environmental regulations and green product innovation.

presents the direct effects of environmental regulation on green technology innovation in enterprises. The first column of the table shows the impact of government environmental regulation on green technology innovation, with a regression coefficient of 0.073, which is significant at the 1% confidence level. The second and third columns respectively display the effects of environmental regulations on green process innovation and green product innovation in enterprises, with corresponding regression coefficients of 0.071 and 0.055, both significant at the 1% confidence level. The empirical results indicate that environmental regulations have a significant positive influence on both green process innovation and green product innovation, with a stronger impact on the former. Research hypothesis 1 is confirmed by sample data. It is consistent with the reality of the research subjects, as under the pressure of environmental regulations, most enterprises always choose to meet legal production requirements through innovative processes. Therefore, the influence of environmental regulation on green process innovation in Chinese private enterprises is greater than that of green product innovation.

4.4. Mediating Effects

Table 4. The mediating effects of innovation external cooperation on the relationship between environmental regulation and enterprises’ green technology innovation.

Variables	M1	M2	M3	M4
	Green_Tech
Regulation	0.057 ***	0.060 ***	0.063 ***	0.057 ***
	(3.002)	(3.228)	(3.359)	(3.065)
Cooperation	1.151 ***
	(6.497)
Cooperation_1		0.363 *
		(1.898)
Cooperation_2			1.204 ***
			(5.323)
Cooperation_3				0.955 ***
				(4.440)
Control variables	Y	Y	Y	Y
Industry type	Y	Y	Y	Y
Province	Y	Y	Y	Y
Enterprise ownership	Y	Y	Y	Y
Constant	−3.480 ***	−3.634 ***	−3.397 ***	−3.807 ***
	(−3.479)	(−3.741)	(−3.439)	(−3.845)
Observations	884	884	884	884
Chi2	256.9	217.1	243.8	234.0
R2_p	0.215	0.181	0.204	0.195

Note: z-statistics in parentheses; *** p < 0.01, * p < 0.1. M1 is used to test the mediating effect of innovative external collaboration on government environmental regulations and green technology innovation; M2 is used to test the mediating effect of joint development on government environmental regulations and green technology innovation; M3 is used to test the mediating effect of commissioned research on government environmental regulations and green technological innovation; M4 is used to test the mediating effect of co-built R&D platforms on government environmental regulations and green technological innovation.

presents the mediating effects of innovation external collaboration on the relationship between environmental regulation and green technology innovation in enterprises. In the first column, the coefficient for innovation external collaboration is 1.151, significant at the 1% confidence level. Meanwhile, the coefficient for environmental regulation is 0.057, which is smaller than the scenario without the inclusion of the mediating variable (β = 0.073, p < 0.01). It can be seen that innovation external collaboration is an effective mediating variable in the relationship between environmental regulation and green technology innovation in enterprises. It means that under a certain intensity of environmental regulations, enterprises may choose to establish collaborative relationships with external partners to meet the conditions for green technology innovation. The mediation test results for three different types of collaboration modes are shown in columns 2 to 4. The results indicate that the coefficients for joint development, commissioned research, and co-built R&D platforms are 0.363 (p < 0.1), 1.204 (p < 0.01), and 0.955 (p < 0.01), respectively. The corresponding coefficients for environmental regulations are all significant and smaller than the coefficient without the inclusion of the mediating variable (β = 0.0733, p < 0.01). Research hypotheses 2 to 4 are confirmed by sample data. These results demonstrate that joint development, commissioned research, and co-built R&D platforms are mediating variables in the impact of environmental regulations on green technology innovation. The relationship between environmental regulation and green technology innovation can be classified as partial mediation.

Table 5. The mediating effects of innovation external cooperation on the relationship between environmental regulation and enterprises’ green process innovation.

Variables	M1	M2	M3	M4
	Green_Process
Regulation	0.061 ***	0.063 ***	0.065 ***	0.062 ***
	(3.053)	(3.153)	(3.242)	(3.098)
Cooperation	0.741 ***
	(4.016)
Cooperation_1		0.469 **
		(2.440)
Cooperation_2			0.737 ***
			(3.459)
Cooperation_3				0.726 ***
				(3.488)
Control variables	Y	Y	Y	Y
Industry type	Y	Y	Y	Y
Province	Y	Y	Y	Y
Enterprise ownership	Y	Y	Y	Y
Constant	−3.767 ***	−3.933 ***	−3.675 ***	−4.026 ***
	(−3.558)	(−3.731)	(−3.483)	(−3.781)
Observations	884	884	884	884
Chi2	184.0	173.5	179.6	179.7
R2_p	0.169	0.159	0.165	0.165

Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05. M1 is used to test the mediating effect of innovative external collaboration on government environmental regulations and green process innovation; M2 is used to test the mediating effect of joint development on government environmental regulations and green process innovation; M3 is used to test the mediating effect of commissioned research on government environmental regulations and green process innovation; M4 is used to test the mediating effect of co-built R&D platforms on government environmental regulations and green process innovation.

provides the mediating effects of innovation external collaboration on the relationship between environmental regulation and green process innovation. The results indicate a significant mediating effect of innovation external collaboration on the relationship between environmental regulations and green process innovation, with a corresponding coefficient of 0.741 (p < 0.01). Meanwhile, the coefficients for joint development, commissioned research, and co-built R&D platforms are 0.469 (p < 0.05), 0.737 (p < 0.01), and 0.726 (p < 0.01), respectively. The corresponding coefficients for environmental regulation are all significant and smaller than the coefficient without the inclusion of the mediating variable (0.071, p < 0.01).

Table 6. The mediating effects of innovation external cooperation on the relationship between environmental regulation and enterprises’ green product innovation.

Variables	M1	M2	M3	M4
	Green_Product
Regulation	0.037 *	0.041 **	0.044 **	0.038 *
	(1.800)	(2.088)	(2.179)	(1.935)
Cooperation	1.461 ***
	(7.687)
Cooperation_1		0.383 **
		(2.006)
Cooperation_2			1.283 ***
			(5.896)
Cooperation_3				1.059 ***
				(4.985)
Control variables	Y	Y	Y	Y
Industry type	Y	Y	Y	Y
Province	Y	Y	Y	Y
Enterprise ownership	Y	Y	Y	Y
Constant	−2.927 ***	−3.260 ***	−2.963 ***	−3.403 ***
	(−2.789)	(−3.230)	(−2.886)	(−3.309)
Observations	873	873	873	873
Chi2	245.6	186.7	218.9	208.1
R2_p	0.222	0.169	0.198	0.188

Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. M1 is used to test the mediating effect of innovative external collaboration on government environmental regulations and green product innovation; M2 is used to test the mediating effect of joint development on government environmental regulations and green product innovation; M3 is used to test the mediating effect of commissioned research on government environmental regulations and green product innovation; M4 is used to test the mediating effect of co-built R&D platforms on government environmental regulations and green product innovation.

provides the mediating effects of innovation external collaboration on the relationship between environmental regulation and green product innovation, yielding the same conclusions. These test results indicate that environmental regulations not only have a direct impact on green technology innovation in enterprises but also can influence it through the indirect pathway of innovation external collaboration. Furthermore, this direct and indirect impact does not change with different types of green technology innovation.

4.5. Moderating Effects

As designed in the study, we first examined whether the management team scale of enterprises moderates the relationship between environmental regulations and innovation external collaboration. In terms of method selection, following the approach outlined in Equation (3), we assessed the significance of the coefficient for the interaction term between environmental regulations and management team scale, as shown in

Table 7. Moderating effects of enterprise’s management team scale.

Variables	M1	M2	M3	M4
	Green_Tech
Regulation	0.024	0.028	0.031	0.026
	(0.990)	(1.175)	(1.325)	(1.101)
Manager_scale	1.555 **	1.522 **	1.492 **	1.486 **
	(2.412)	(2.491)	(2.440)	(2.454)
Regulation × Manager_scale	0.212 **	0.201 **	0.198 **	0.195 **
	(2.131)	(2.147)	(2.119)	(2.109)
Cooperation	1.142 ***
	(6.418)
Cooperation_1		0.357 *
		(1.853)
Cooperation_2			1.205 ***
			(5.299)
Cooperation_3				0.954 ***
				(4.419)
Control variables	Y	Y	Y	Y
Industry type	Y	Y	Y	Y
Province	Y	Y	Y	Y
Enterprise ownership	Y	Y	Y	Y
Constant	−3.882 ***	−4.034 ***	−3.788 ***	−4.193 ***
	(−3.795)	(−4.056)	(−3.749)	(−4.143)
Observations	884	884	884	884
Chi2	264.9	226.0	252.5	242.8
R2_p	0.221	0.189	0.211	0.203

Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. M1 is used to test the moderating effect of management team scale on the relationship between environmental regulations and innovation external collaboration; M2 is used to test the moderating effect of management team scale on the relationship between environmental regulations and joint development; M3 is used to test the moderating effect of management team scale on the relationship between environmental regulations and commissioned research; M4 is used to test the moderating effect of management team scale on the relationship between environmental regulations and co-built R&D platforms.

. The results indicate that the coefficient for the interaction term in the first column is 0.212, significant at the 5% confidence level, suggesting that the scale of the management team positively moderates the relationship between environmental regulation and innovation external collaboration. Furthermore, we examined whether the moderating effects vary depending on the type of innovation external collaboration. The results indicate that the coefficients for joint development, commissioned research, and co-built R&D platforms are 0.201 (p < 0.05), 0.198 (p < 0.05), and 0.195 (p < 0.05), respectively. Research hypothesis 5 is confirmed by sample data. It suggests that the moderating effect of the scale of the management team is significant and robust and does not vary based on different mediating variables (innovation external collaboration).

Based on the above research, we further examined the moderating effect of corporate social responsibility (CSR). Following the approach in Equation (4), we introduced the interaction term between CSR and innovation external collaboration based on Equation (3) and then determined the presence of the moderating effect by evaluating the significance of the interaction term coefficient. The results in the first column of

Table 8. Moderating effects of corporate social responsibility.

Variables	M1	M2	M3	M 6
	Green_Tech
Regulation	0.020	0.022	0.029	0.023
	(0.823)	(0.895)	(1.187)	(0.973)
Manager_scale	1.564 **	1.554 **	1.448 **	1.444 **
	(2.386)	(2.484)	(2.371)	(2.380)
Regulation × Manager_scale	0.208 **	0.202 **	0.190 **	0.189 **
	(2.055)	(2.116)	(2.036)	(2.035)
CSR	0.133 **	0.100 **	0.073	0.051
	(2.229)	(2.085)	(1.527)	(1.082)
Cooperation	1.717 ***
	(4.918)
Cooperation × CSR	0.150 **
	(2.061)
Cooperation_1		1.078 ***
		(2.662)
Cooperation_1 × CSR		0.154 *
		(1.836)
Cooperation_2			1.231 ***
			(2.608)
Cooperation_2 × CSR			0.137 *
			(2.401)
Cooperation_3				0.609
				(1.326)
Cooperation_3 × CSR				0.069
				(0.731)
Control variables	Y	Y	Y	Y
Industry type	Y	Y	Y	Y
Province	Y	Y	Y	Y
Enterprise ownership	Y	Y	Y	Y
Constant	−3.990 ***	−3.957 ***	−3.596 ***	−3.963 ***
	(−3.715)	(−3.824)	(−3.457)	(−3.786)
Observations	861	861	861	861
Chi2	270.6	236.1	250.7	246.2
R2_p	0.232	0.203	0.215	0.211

Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. M1 is used to test the moderating effect of CSR on the relationship between environmental regulations and innovation external collaboration; M2 is used to test the moderating effect of CSR on the relationship between environmental regulations and joint development; M3 is used to test the moderating effect of CSR on the relationship between environmental regulations and commissioned research; M4 is used to test the moderating effect of CSR on the relationship between environmental regulations and co-built R&D platforms.

show that the CSR coefficient is 0.133, significant at a 5% confidence level, and the interaction term coefficient is 0.150, also significant at a 5% confidence level. It indicates that CSR indeed plays a positive moderating role in the relationship between innovation external collaboration and green technology innovation. In other words, the stronger the CSR, the more innovation external collaboration can promote green technology innovation. Next, we further tested whether this mediating moderating effect would vary with different types of collaborative innovation, as shown in columns 2 to 4. The results indicate that the moderating effect of CSR only exists in the relationships between joint development and green technology innovation, and between commissioned research and green technology innovation, and is not significant for the remaining type of innovation external collaboration and green technology innovation. Research hypothesis 5 is mostly confirmed by sample data.

4.6. Endogeneity Issue

In the previous section, our study found a significant positive impact of government environmental regulations on green technology innovation in enterprises. However, this effect may arise from self-selection bias. Additionally, considering the issue of reverse causality and omitted variables, we selected the average pollution control investment in the industry where the firms operate as an instrumental variable (Regulation_) for 2SLS estimation. Generally, the average pollution control investment in the industry where firms operate to a large extent compels firms to invest in environmental pollution control, thus satisfying the relevance requirement of the instrumental variable. Moreover, this judgment is unrelated to the error term in the econometric model, fulfilling the homogeneity requirement. Therefore, this variable can serve as a suitable instrumental variable for environmental regulations. The 2SLS estimation results, as shown in

Table 9. Results of 2SLS estimate.

Variables	Green Process Innovation		Green Product Innovation
	First Stage	Second Stage	First Stage	Second Stage
	M1	M2	M3	M4
Regulation		0.831 *** (0.279)		1.098 *** (0.278)
Regulation_	−0.112 *** (0.022)		−0.112 *** (0.022)
Control variables	Y	Y	Y	Y
Industry type	Y	Y	Y	Y
Province	Y	Y	Y	Y
Enterprise ownership	Y	Y	Y	Y
Constant	Y	Y	Y	Y
Wald test	100.58		552.19
Observations	1030	1030	1019	1019
R2	0.195	0.203	0.195	0.203

Note: *** p < 0.01. M1 is used to demonstrate the first stage test results of the impact of environmental regulations on green process innovation; M2 is used to display the second stage test results of the impact of environmental regulations on green process innovation; M3 is used to demonstrate the first stage test results of the impact of environmental regulations on green product innovation; M4 is used to display the second stage test results of the impact of environmental regulations on green product innovation.

, reveal that the coefficients for environmental regulations are positive and significant at the 1% level in the second and fourth columns. This further confirms the robustness of our conclusions.

In addition, in order to further examine the robustness of the results, this study conducted analyses from two aspects: sample size and research methods. On one hand, the sample was narrowed down to manufacturing companies, excluding samples from other industries, and the Logit model was used to repeat the aforementioned testing process. On the other hand, while keeping the sample size constant, the testing method in the empirical process was replaced by the Probit model. Furthermore, we narrowed down the sample size to manufacturing companies in the central and eastern provinces, taking into consideration the limited number of samples in the western and northwestern provinces, as well as the significant industry differences. We found that the empirical results mentioned above did not change due to different research samples or different research methods, indicating that the research results of this study possess strong robustness.

5. Discussions and Conclusions

Our research contributions are reflected in three aspects: firstly, we identify an important pathway for the existence of Porter’s hypothesis, namely, the implementation of it through the mediation factor of innovative external collaboration, which further enriches the theory of Porter’s hypothesis; secondly, we identify the impact of the scale of the management team on the adoption of external innovation collaboration strategies under environmental regulations, further improving upper echelon theory; thirdly, we provide evidence that corporate social responsibility promotes green technology innovation by optimizing the direction of technological resource allocation, deepening the understanding of corporate social responsibility theory.

5.1. Theoretical Implications

Firstly, we analyze the internal reasons for the existence of Porter’s hypothesis from the dual dimensions of the policy signal of environmental regulation and the inherent characteristics of private enterprises. Many existing studies have verified the existence of Porter’s hypothesis [48,56] However, due to factors such as the inherent characteristics of enterprises and the economic and social environments in which they are located, a consensus has not been reached on the relationship between environmental regulation and green technological innovation by enterprises. Two key factors are crucial here: how strong the environmental regulation set by the government is and how the enterprise perceives and responds to the pressure [52,53]. This is also one of the considerations based on the inherent characteristics of the enterprise, which is one of the important factors influencing the Porter hypothesis, and also a deepening of existing research.

China, as the world’s largest developing country, has experienced rapid economic development over the past forty years, but it has also brought problems such as environmental pollution, resource consumption, and ecological destruction [69]. Since 2013, the Chinese government has attached great importance to environmental, resource, energy, and ecological issues, and proposed the goal of ecological civilization construction [125,126]. Compared with 2015, the average concentration of PM_2.5 in cities at or above the prefecture level in 2021 has decreased by 34.8%, and the proportion of good air quality days has reached 87.5%. In the past decade, China’s carbon intensity has decreased by 34.4%, reversing the trend of rapid growth in carbon dioxide emissions. Currently, China has set the goals of carbon peaking by 2030 and achieving carbon neutrality by 2060, thereby promoting systematic changes in the economic and social system [8,18]. It can be seen that China’s environmental regulation is continuously tightening, which is also an important signal and will provide a stable policy environment for enterprises’ green technology innovation.

The position and inherent characteristics of private enterprises in the economic and social system of China also determine the effectiveness of environmental regulation in promoting green technology innovation. In China’s economic system, although the reform of central and state-owned enterprises has been promoted, it is undeniable that these enterprises surpass most private enterprises in terms of resource endowment, innovation capability, and social network relationships. Therefore, for the same level of environmental regulation pressure, central and state-owned enterprises have greater resistance than private enterprises, and their ways of coping with pressure are also diverse. For Chinese private enterprises, on the basis of meeting the environmental legality of production, they rely on the market to obtain more resources and further improve their market competitiveness, and innovation is the most cost-effective way [127,128]. Especially with the strengthening of public preference for green and low-carbon products and services, enterprises are more enthusiastic about improving production processes and developing green products [37,69]. This is also an important reason why Porter’s hypothesis significantly exists in the field of private enterprises in China.

Secondly, this study further opens the black box of Porter’s hypothesis from the perspective of innovation external cooperation. Green technology innovation refers to the development of new technologies, products, services, and solutions that can reduce resource consumption, decrease environmental pollution, improve resource utilization efficiency, and promote sustainable development in response to environmental issues and challenges. The adoption of green technology innovation by enterprises is influenced by many factors, including cognitive level, resource endowment, and innovation capability [62]. It can be said that an important aspect influencing the behavior of enterprise green technology innovation lies in the resources that enterprises can allocate. To deeply understand Porter’s hypothesis, an important aspect is how enterprises can obtain sufficient resources for green technology innovation in the face of environmental regulation pressure imposed by the government [45]. Existing research shows that innovation cooperation with external entities is an important form of acquiring green technology innovation elements. For example [85] found that R&D cooperation promotes environmental innovation to a greater extent than other innovations, and compared with other innovations, R&D cooperation with suppliers promotes environmental innovation to a greater extent. For green technology innovation, adopting forms of innovation external cooperation can not only reduce transaction costs and share innovation risks but also compensate for the deficiencies in internal resources and innovation capabilities [22]. The research results of [129] show that network embedding may be the main driving force for enterprises to carry out ecological innovation, even more important than the company’s structural characteristics. Furthermore, Jens & Horbach’s analysis of German companies shows that R&D cooperation is more important for green innovation than non-green innovation [87].

Although there is diversity in the types of innovation external cooperation, this does not affect its mediating role in the relationship between environmental regulation and enterprise green technology innovation. The forms of innovation external cooperation can include joint development, commissioned research, co-built R&D platform, and joint training of talent [85,130,131]. Cooperative development involves multiple parties jointly investing funds, technology, and manpower in a critical area of a project through contracts, and jointly participating in creative activities to produce intellectual achievements and complete research and development projects. Joint research and development can give full play to the advantages of enterprise funds and industrialization and the research advantages of research institutions, forming complementary advantages. Commissioned research refers to projects developed by the assignee based on others’ commissions, and the assignor obtains ownership of the research and development results by paying remuneration to the assignee. Due to the large fluctuation in the amount of technical research and development work, many times, enterprises do not need to form a dedicated technical team, and they can achieve technological innovation even when their own capabilities are insufficient. Joint development platforms are led by enterprises and cooperate with universities or research institutes to jointly build laboratories, research centers, and other research and development platforms within the enterprise. Enterprises can obtain research resources, advanced technologies, and innovative ideas from universities through laboratory and research center platforms, thereby enhancing their technical capabilities. At the same time, they can also absorb university talent to participate in technical activities through research and development interactions, thereby improving talent reserves. Therefore, although the forms of innovative external cooperation may differ, they share common characteristics in terms of acquiring more innovation resources, reducing innovation risks and costs, and improving innovation quality, which can promote enterprise green technological innovation.

Thirdly, it is recognized that under environmental regulatory pressure, group decision making tends to adopt innovative external collaboration strategies. As demonstrated above, under environmental regulatory pressure, innovative external collaboration can also promote green technological innovation in companies. However, although this path has been well supported both theoretically and practically, the decision-making model of companies can influence the choice of innovative external collaboration strategies under environmental regulatory pressure. Compared with individual centralized decision making, group decision making allows for the best and most suitable ideas to be found through brainstorming within a specified time frame, and consensus to be reached among group members. Specifically: (I) With the continuous progress of society and technology, the problems encountered by companies in their daily decision making become increasingly complex, involving various disciplines. Group decision making is beneficial for gathering wisdom from different fields and is of great help in solving increasingly complex decision-making problems; (II) Decision-making group members come from different functional departments and are familiar with different business knowledge, allowing for a more comprehensive understanding of the company’s development situation and facilitating complementary interactions among members; (III) Group decision-making solutions more easily accepted by the majority of members, thereby improving the quality and efficiency of decision implementation; (IV) During group decision-making processes, members are more willing to take risks compared to when making individual decisions, and they demonstrate a stronger sense of responsibility and mission.

The larger the size of the corporate management team, the greater the likelihood of adopting group decision making, and the better the quality and implementation of the decisions. Environmental regulations, as an important external pressure source for corporate managers, to some extent determine the job requirements of corporate managers and change their focus on available information and risk preferences, thereby affecting decisions regarding green technological innovation [92]. Psychological identification among corporate management teams plays an important role in managing complex innovation processes. Shared vision, shared values, behavioral integration, and social relationship integration are all behaviors that are beneficial for green technological innovation in companies [101]. Ou et al. (2018) [103] believe that internal collaboration, information sharing, and joint decision making within corporate management teams can reduce team conflicts, improve team efficiency, and enhance green technological innovation output. Therefore, under the pressure of environmental regulations, the larger the size of the corporate management team, the more comprehensive their understanding of green technological innovation opportunities and risks, and the greater the possibility of taking external innovative collaboration.

Fourthly, corporate social responsibility significantly influences the allocation direction of innovation resources in companies, meaning that stronger corporate social responsibility tends to allocate innovation resources towards environmentally friendly technologies. Due to the lag in relevant institutional construction and the ability to account for benefits, the social and environmental benefits generated by green technological innovation often cannot be directly converted into the company’s economic benefits. Therefore, the subject of green technological innovation needs to integrate social responsibility strategies into the production and manufacturing process to achieve long-term development. On one hand, companies with stronger social responsibility can foster a positive innovation atmosphere internally and positively impact corporate innovation. On the other hand, corporate social responsibility can effectively resolve conflicts between companies and various stakeholders, correctly handle the economic, legal, ethical, and other aspects, and obtain necessary resources through effective interaction with the external environment. Additionally, corporate social responsibility serves as a new way of information transmission and can effectively reduce information asymmetry, promoting the implementation of green technological innovation. Therefore, companies with good social responsibility can guide the allocation direction of innovation resources by effectively managing relationships with internal and external stakeholders and promoting the development of green technological innovation.

Looking at the three types of external collaboration models observed, joint research and development and commissioned research are directly involved in the process of green technological innovation, while the influence path of jointly building research and development platforms is relatively longer. At the same time, jointly building research and development platforms requires relatively large investments and incurs higher costs for acquiring innovation resources, and, especially when the green benefit accounting system is not sound, the enthusiasm of companies to engage in cooperative research and development platforms is affected. Compared to joint research and development and commissioned research, the relationship between corporate social responsibility and the three types of external collaboration is not significantly influenced by corporate social responsibility due to the high risks, large investments, and high operational requirements of jointly building research and development platforms. Joint research and development and commissioned research, which are company-oriented, adhere to problem-oriented and goal-oriented approaches, with clear expectations for innovation input and output. The stronger the corporate social responsibility, the higher the enthusiasm for allocating resources obtained through external collaboration towards green technological innovation.

5.2. Practical Implications

Firstly, environmental regulations have significantly positive impacts on promoting green technological innovation in private enterprises. Currently, the global economy is in the recovery phase after the pandemic, and there is significant downward pressure on the economy. However, this should not lead to a relaxation of environmental regulations. Otherwise, some regions and companies may initiate projects that are not environmentally friendly, which usually face the risk of asset lock-in and hinder the future green transformation of the economy and society.

Secondly, on the basis of strengthening environmental regulatory requirements, policy and market innovations should be employed to support stable cooperation between innovative entities and external stakeholders. Promoting innovative cooperation requires a correct balance between the government and the market. It is necessary to leverage the scientific guidance role of the government while valuing the fundamental role of market resource allocation, achieving optimal allocation of innovation resources and effective sharing of innovative outcomes, and enabling various innovative entities to create value through coupling and interactive collaboration. Furthermore, continuous improvement of laws, regulations, and policies can guide market entities to carry out orderly development based on their functional positioning.

Thirdly, at the corporate level, a collective decision-making model should be advocated, actively absorbing information and wisdom from different business departments. Leaders should create a democratic atmosphere conducive to collective decision making, mobilize the enthusiasm of decision-making members, and maintain a relative balance of interests within the group. Leaders should define their own boundaries of power, acting as coordinators rather than commanders in the decision-making process. In addition, attention should be paid to harnessing the guiding function of organizational culture, promoting alignment between individual and organizational goals, and enhancing the cohesion and centripetal force of the organization.

Finally, efforts should be made to stimulate corporate social responsibility and guide enterprises towards eco-friendly allocation. The government should establish a comprehensive and dynamic assessment system for corporate social responsibility and improve the system of social responsibility information disclosure. Through comprehensive governance and guidance at the national level, a platform for social responsibility information disclosure should be established, and the assessment system and information disclosure mechanism for environmental performance should be improved. By rewarding enterprises that achieve significant green results and encouraging them to actively assume social responsibilities, policies should tilt towards companies with a high level of accountability, forming an industry benchmark and promoting cooperation and communication between enterprises and stakeholders, thereby driving green technological innovation in enterprises.

5.3. Limitations and Future Research

However, this study also has certain limitations. Firstly, we cannot determine whether the green process innovation and green product innovation claimed by company managers are substantively real or just a form of appeasement strategy. Secondly, the policy effects of any economic policy have a certain lag and delay, and environmental regulation policies are no exception. The sample data used in this study is annual cross-sectional data of private enterprises, which specifically reflect short-term effects and cannot effectively capture the long-term dynamic impact of environmental regulations on green technological innovation in companies. This will be a hot point of future research. Thirdly, the indirect pathways through which government environmental regulations affect green technological innovation in companies may be diverse. We only analyze the transmission pathway between the two from the perspective of innovation external collaboration, which can be considered as a narrow aspect. In the future, we need to further open the black box of Porter’s hypothesis from the perspectives of open innovation theory and resource-based view theory. Fourthly, this study only focuses on the moderating role of corporate social responsibility in the relationship between innovative external collaborations and green technology innovation. It is necessary to further analyze whether corporate social responsibility affects the relationship between environmental regulation pressure and innovative external collaborations, as well as the moderating role of management team scale in the relationship between innovative external collaborations and green technology innovation. Lastly, although we have analyzed how factors such as the management team scale and corporate social responsibility amplify the impact of environmental regulations on green technological innovation from an internal trait perspective, this analysis is relatively singular. In future research, we will focus on the dynamic evolutionary relationship between environmental regulations and green technological innovation of enterprises. Based on this, we will further identify the impact of these factors on the relationship between environmental regulations and green technological innovation from dimensions such as internal traits and external environmental factors. In addition, we also need to innovate in research methods and sample selection to improve the accuracy and robustness of research conclusions.