Dynamic Evolutionary Analysis of the Impact of Outward Foreign Direct Investment on Green Innovation Heterogeneity—From the Perspective of Binary Innovation

Abstract

In the current era of increasingly frequent international exchanges and countries’ increasing emphasis on green development, exploring the complex relationship between outward foreign direct investment (OFDI) and green innovation has become an important research topic. Taking the binary innovation perspective as an entry point, this paper is devoted to exploring the heterogeneous impact and dynamic evolution of OFDI on green innovation since China’s accession to the WTO. The purpose is to form a more comprehensive and specific understanding of how OFDI affects green innovation. By sorting out the characteristics of economic development at the early stage of China’s WTO accession and implementation of the “going out” strategy, the period of counter-trend growth of OFDI after the financial crisis, and the “new normal” period of China’s transformation and development, this paper analyzes in detail the impact of OFDI on green innovation and green binary innovation in each stage. According to the study, the coefficients of the effects of OFDI on green innovation in the three stages are 0.214 (fully significant at the 1% level), −0.057 (insignificant), and 0.137 (significant at the 5% level), showing an overall effect of promoting–insignificant–promoting; In addition, the fact that the development orientation of OFDI is different at different stages leads to significant heterogeneity in its impact on green innovation and green binary innovation. The coefficients of OFDI on green exploratory innovation at the three stages are 0.064 (insignificant), −0.107 (significant at 10% level), and 0.099 (significant at 5% level), and the coefficients of OFDI on green exploitative innovation are 0.258 (fully significant at 1% level), −0.036 (insignificant) and 0.142 (significant at 5% level), respectively. The results reveal that OFDI on green binary innovation shifts from focusing on promoting green exploitative innovation to taking green binary innovation into account, and this heterogeneous performance effect matches the development characteristics of each stage. The results of the study show more clearly the association between OFDI and green innovation in China and provide new references for subsequent academic research and management practice.

1. Introduction

The year 2020 witnessed the introduction of the goal of “carbon peaking and carbon neutrality” in China and the 20th Party Congress’ determination of the development direction of “accelerating the green transformation of the development mode and promoting the harmonious coexistence of man and nature”. These make it particularly important to continuously enhance the green innovation capability and build a green innovation system to drive the development of sustainable economic growth. At present, many scholars at home and abroad have discussed how to accelerate green innovation from different perspectives, and one important research area is the impact of OFDI on green innovation. Whether it is the expanding scale of OFDI in China or the concept that OFDI should promote green technology innovation and develop new kinetic energy for a green economy as clearly pointed out in the Guidelines on Green Development of Foreign Investment Cooperation issued by the Chinese government in 2021, it suggests that we should continue to explore the impact of OFDI on green innovation in China from both academic research and management practice levels, and continue to better explore the impact of OFDI on green innovation in the new era based on the concept of green development. In the new era, we should continue to better promote the “going out” of domestic capital based on the concept of green development.

The existing literature mainly focused on the mechanism of the role of OFDI on green innovation and the effect of OFDI on green innovation in different situations, such as environmental regulation [1,2], financial development [3], investment scope [4], the institutional environment [5], intellectual property protection [6], and innovation value chains [7], and based on this, “promotion theory”, “inhibition theory”, and “non-linear theory” [8] have been developed. However, the studies mostly start from the overall performance of green innovation, and pay less attention to the heterogeneous characteristics of green innovation in both innovation modes and development stages, which is presumed to be an important reason that the previous literature has not formed more consistent conclusions. For example, the mechanism and effect of OFDI on green exploration innovation and green exploitation innovation may be different under the binary innovation model. In addition, China has experienced a continuous development of the economic situation at home and abroad since its accession to the WTO in 2001. In this context, domestic green innovation has also shown dynamic evolutionary characteristics. At different stages of development, there are also different effects of OFDI on green innovation and even green binary innovation. Obviously, the above situation is a question that has still not been addressed or answered in previous studies. If these questions can be correctly addressed and answered, we will have a clearer understanding of how OFDI affects green innovation; at the same time, this will also be of positive significance to help China better promote the “going out” of capital and improve the level of domestic green innovation from a practical perspective, thus promoting the realization of green development.

The innovation of this paper is to explore the role of OFDI in green innovation from a binary innovation perspective, by dividing green innovation into two aspects: green exploratory innovation and green exploitative innovation (i.e., green binary innovation). It was found that the impact of OFDI on these two aspects is significantly different. Moreover, the process of change in China’s economic environment has seen a shift in the effect of OFDI on green binary innovation from a focus on green exploitative innovation to a balance of green binary innovation.

Section 2 of this paper combs through a series of documents on research issues, and puts forward two issues to be explored in depth: One is the heterogeneous impact of OFDI on green dual innovation, and the other is the impact of heterogeneity changes dynamically with the change in the economic environment. Section 3 provides an in-depth theoretical analysis of the two issues, the results of which are presented in Section 4. Section 5 presents the conclusions of the study and puts forward some reasonable suggestions.

2. Literature Review

Green innovation, i.e., innovation consisting of new or improved products, processes, services, and management, can both add value to a firm and significantly reduce the negative impact on the environment [9]. Compared to traditional innovation, which has economic performance as its main objective, it can be found that green innovation places more emphasis on the adoption of new technologies and ideas to achieve efficient use of resources and effective reduction in pollution, and to obtain the corresponding economic performance under these premises. There are many factors that influence green innovation in a country, among which the acquisition of new products, technologies, and models from abroad through OFDI is an important way to improve the level of green innovation in the country. At present, there are basically three types of studies on the relationship between OFDI and green innovation. The first is the “promotion theory”, because some scholars believe that OFDI can promote the improvement of China’s green innovation level, and its mechanism of action mainly works through two channels: the technology spillover effect [10] and the industrial structure optimization effect [11]; the second is “inhibition theory”, because some scholars argue that OFDI does not positively promote the level of green innovation in China, and its data test finds that OFDI has a crowding-out effect on domestic investment [12,13], and the characteristic portion of OFDI used for technology seeking is relatively small. The third is the “non-linear theory”, which comes from the view of some scholars that the impact of OFDI on green innovation may be “non-linear”. It is believed that innovation itself is a complex system that covers different innovation segments and cannot be measured by a single indicator that cuts across all segments. In addition, some scholars explored green innovation in stages based on innovation value chain theory, and found that OFDI has heterogeneous effects on green innovation in different innovation stages [7,14]. Inspired by this view, the present authors argue that green innovation also has the heterogeneity of innovation modes, i.e., green binary innovation, and the influence of OFDI on green innovation in different innovation modes may also differ.

Binary innovation refers to the division of innovation activities into exploratory innovation and exploitative innovation [15], where the former refers to the development and integration of new knowledge and technology to meet new customers or emerging market needs based on the development of new products and services, while the latter refers to the optimization of existing products or services by firms through mining, refining, integrating, and improving existing knowledge and technology to meet existing [16]. A comparison of the two reveals that exploratory innovation is a large-scale, more radical innovation behavior, while exploitative innovation is a small-scale, incremental innovation behavior [15]. Even so, the two are not mutually exclusive or incompatible, and it is necessary to emphasize the balanced development of exploratory and exploitative innovation in the innovation process. The impact of OFDI on binary innovation has been studied by scholars from different perspectives. According to some scholars, there are essential differences between the home and host country binary networks in which OFDI is located. Under different network characteristics, if the home country has stronger business network relations, then the development of exploitative innovation will be more favorable [17]. However, a stable business posture for a long time may also cause firms to form inertia and hinder exploratory innovation [18]; business network relationships in the host country, on the contrary, can lead to the complexity of inter-firm relationships by providing more heterogeneous resources as well. While this favors firms undertaking exploratory innovation, it discourages exploitative innovation [19], where the impact of OFDI on binary innovation is mainly realized through the competition effect, the human capital flow effect, and the model imitation effect. Additionally, some scholars examined the dynamic evolutionary domain of OFDI affecting binary innovation. The firm life cycle has different effects when it is at different stages, specifically, the firm size, profitability, and resource redundancy are reflected in the growth, maturity, and decline stages of the firm. Therefore, OFDI has different effects on the promotion of binary innovation and binary interaction [20]; moreover, the passage of time highlights the phenomenon that OFDI has a sustainable effect on exploratory innovation represented by invention patents, while it has a smaller and less sustainable effect on exploitative innovation represented by utility models and design patents [21].

In summary, although there is a relatively mature research system on how OFDI affects green innovation, few studies have examined the impact of OFDI on green innovation and its dynamic evolution process from the perspective of binary innovation. It can be found that previous studies have not paid attention to the different characteristics of green innovation and the different effects on green innovation and different modes of green innovation that may be caused by the policy tendencies in different periods, the needs of enterprises in response to the development and changes in the external economic situation, the allocation of internal innovation resources, and the differences in the innovation level of enterprises in different periods during the continuous development of OFDI. In view of this, this paper compares the dynamic evolution of OFDI influencing green innovation from the perspective of binary innovation based on the literature of other scholars. It is hoped that the complex connection between OFDI and green innovation can be more comprehensively and clearly clarified.

3. Theoretical Mechanism and Research Hypothesis

3.1. The Influence of OFDI on Green Innovation

China’s OFDI has been growing rapidly since 2004. This period witnessed an increasing level of green innovation, with the two forms of innovation more or less demonstrating mutually-driven growth. OFDI has promoted the development of local green innovation through the reverse technology spillover effect and industrial structure optimization effect, which have contributed to the innovation of local green technology and the reduction in green innovation costs; while the improvement of the level of green innovation makes it easier for enterprises to meet the entry threshold set by the host country for local environmental protection. On the other hand, the improved level of green innovation makes it easier for enterprises to meet the entry threshold set by host countries for local environmental protection; on the other hand, it enables enterprises to increase their own profits and technological progress in order to have more capital and stronger international competitiveness, thus contributing to a wider and larger volume of OFDI.

However, the dynamic evolution over time shows a mismatch between the two in terms of growth rate. The outbreak of the financial crisis made the world’s economy as a whole enter into a downturn. Although China’s OFDI was still expanding at that stage, the growth rate of green patent output tended to be flat. The reason for this is that in the background of the financial crisis outbreak period, the world economy was in the doldrums, panic spread, and foreign demand plummeted, which made local enterprises use OFDI with the greater purpose of ensuring stable foreign demand and stable product sales. As a result, the green technology-seeking tendency of OFDI dropped dramatically, so much so that the reverse technology spillover effect was not obvious. After 2015, the growth rate of green patent output gradually raised and basically recovered to a level more closely matching OFDI. This paper argues that as a whole, China’s OFDI does play a role in promoting green innovation. However, the effect of promoting-not significant-promoting is presented due to the influencing factors such as changes in the economic environment and changes in national policies that are affected by different time periods.

Hypothesis 1.

OFDI plays a role in promoting green innovation in China; however, during the dynamic evolution process with time and economic environment changes, this role presents a role effect from promoting to insignificant to promoting.

3.2. Impact of OFDI on Green Binary Innovation and Its Dynamic Evolution

Green binary innovation refers to the division of green innovation into green exploratory innovation and green exploitative innovation [22]. Green exploratory innovation, which refers to the innovation behavior of breaking through the existing green technology track, is manifested by the reconstruction of existing green knowledge and technology, often accompanied by the generation of new green products and processes. It focuses on the sustainability of development and the shaping of potential competitive advantages for the future; green exploitation innovation, which refers to the innovation behavior along the existing green technology track, manifests as the improvement and perfection of existing green knowledge and technology, which is often accompanied by the upgrading of green products and services, the reduction of green development costs, etc. [23]. The difference between the two lies in whether they break out of the original green technology track, while they are at different stages of shaping the competitive advantage. However, both also take into account the social, economic, and ecological “triple bottom line” of the economic development process.

It is important to note that the green dichotomy of innovation is not a balanced development, as there are different levels of development and resource allocation between green exploratory innovation and green exploitative innovation. The reason behind this lies in the following three main points. First, although green exploratory innovation is relatively long and high-investment, its high potential benefits can help companies achieve much in innovation or competitiveness, while utilization innovation is the opposite, as its innovation level is relatively low. Although it may be useful for improving firm performance in the short term, its impact on firms’ innovation capacity improvement in the long term is uncertain. Therefore, when innovation resources face total constraints, the allocation of innovation resources in different directions will have different effects on the innovation effect; second, various economic agents represented by enterprises face different industrial policy orientations and market economic situations at different times. This inevitably leads to different business management decisions, such as considering the balance between paying costs to promote green innovation and prioritizing the economic performance of enterprises. Thirdly, it is undeniable that acquiring foreign advanced green technologies through OFDI is indeed an important way to enhance the core competitiveness of enterprises. However, since foreign advanced green technologies are often protected by the host country’s intellectual property rights, larger-scale OFDI and deeper cooperative exchanges are often the preconditions for a catalytic impact on local green innovation. In comparison, at the early stage of OFDI in China, green technologies represented by utility patents are easier to obtain than those represented by invention patents, and have less difficulty in absorbing and continuing to break through. With the expansion of the OFDI scale, the enhancement of learning and absorbing ability, and the deepening of cooperation with host countries, the learning and absorbing of foreign green technologies will gradually change from green technologies represented by utility patents to green technologies represented by invention patents. This also shows that OFDI has a different impact on green binary innovation over time. Therefore, this paper concludes that it is the policy orientation of different periods and the differences in the development needs of enterprises facing external economic situations, the differences in the allocation of innovation resources, and the differences in the scale and capacity of OFDI that lead to the dynamic evolution of the impact of OFDI on green binary innovation.

After China’s accession to the WTO, the scale of OFDI at the early stage of development was relatively small; at the same time, the lack of local innovation capacity and the urgent need to pull the economy through integration into the world market made enterprises reflect more market-seeking tendencies in OFDI activities. On the one hand, the reverse technology spillover of OFDI can be used to break through the basic green trade barriers, and it is easier to achieve or help achieve market entry through green exploitative innovation in the short term; on the other hand, at this time, China’s local independent innovation and absorption capacity are relatively weak, so even if they come into contact with foreign advanced core green technologies, it is difficult to absorb and imitate them, and the impact on green exploratory innovation is relatively weak. Therefore, relatively speaking, the gradual green utilization innovation can bring short-term benefits and optimize the allocation of resources for enterprises. This is why green innovation at this stage is more focused on improving the level of green utilization innovation.

The level of economic development in China has been rising, and at the same time, more attention has been paid to the green development path in countries around the world. As a result, the level of green exploratory innovation has been continuously improved and shown a faster development speed than green utilization innovation, no matter from the level of policy guidance, innovation organization, or learning ability. First, the country and the regions continue to emphasize the breakthrough “bottleneck” of deep-seated green technology innovation. Through the formulation of various supporting policies and innovation subsidies, the innovation orientation of innovation subjects is guided, and resources are allocated to new products, new technologies, and new processes with breakthroughs; second, the requirements of green trade barriers for import and export standards in the new era are increasing. As the use of breakthrough green emerging technologies gradually becomes necessary, enterprises themselves take the initiative to seek green technological innovations that meet the high standard requirements for better access to international markets and enhance the level of product competitiveness, which also promotes the motivation of China’s OFDI to shift in the direction of deep-seated technology seeking. Thirdly, as international cooperation becomes closer and closer, the organization, learning, and absorption ability of enterprises becomes stronger and stronger, and the possibility of accessing foreign advanced green technology through OFDI increases. This also increases the possibility of learning and absorbing in a short period of time and turning this into their own use, thus laying the foundation for enhancing the level of green exploratory innovation through OFDI. In summary, the current situation of China entering a new period of high-quality development will lead to a shift in the tendency toward green innovation activities led by OFDI, which will not only focus on improving the level of green utilization-based innovation, but also on both green utilization-based innovation and green exploration-based innovation. As a result, hypothesis two of this paper is proposed.

Hypothesis 2.

From the perspective of binary innovation, the effects of OFDI on green exploratory innovation and green exploitative innovation are different; and throughout the dynamic evolution process, the effect shown by OFDI is more inclined to promote green exploitative innovation to the direction of balancing green binary innovation.

Based on the events or policies that have caused significant changes in China’s domestic and international macroeconomic environment, this paper divides the analysis cycle into the following three stages, with the aim of showing more clearly the impact of OFDI on China’s green innovation. (1) The first stage is the initial development stage of China’s accession to the WTO (2004–2008). During this period, China’s economy was in the early stage of development, characterized by its low research and innovation capacity and insufficient intrinsic motivation for economic development. Therefore, China’s need to integrate into the world economy is very strong, and OFDI reflects a strong market-seeking motive; relying on abundant local labor resources, the industry flow of Chinese OFDI is mainly concentrated in the mining, service, and manufacturing industries. The limitation of investment volume and its own research level makes it difficult for Chinese companies to enter industries with advanced green technologies. At this time, the source of the OFDI’s driving force for green innovation is more in the learning and imitation of foreign production management methods, green services, green product usage, etc. (2) The second stage is the recovery stage after the financial crisis in 2008 (2009–2014). This phase is characterized by a major turnaround in the world economy and a serious decline in foreign demand affected by the world economic environment. At this time, China’s OFDI showed a trend of counter-trend growth, and its purpose was mainly to respond to the various policy strategies introduced by the national state to stabilize foreign demand, so not much consideration was given to the issue of green innovation. (3) The third stage is the “new normal” stage of China’s transformation and development (15–19 years). In this phase, China began to shift its focus on high-quality development and put forward the concept of “new normal” development. The emphasis on green development issues makes the green technology-seeking orientation of OFDI increasingly obvious. In addition, after 2015, the proportion of OFDI investment in mining and service industries gradually decreased and went to industries such as technology, electricity, and fuel. By investing directly in these sectors, China is exposed to more advanced green technologies, and this facilitates breakthroughs in green technologies, rather than limiting itself to green cost reduction and production optimization. Based on the above three different cycles of dynamic evolution, the impact of OFDI on green innovation and green binary innovation is further analyzed.

4. Research Design

4.1. Model Construction

Based on the above theoretical analysis, the following regression analysis model is constructed:

Zl_it = β₀ + β₁Ofdisto_it + β₂Rd_it + β₃Rdr_it + β₄Edu_it + β₅Fe_it + β₆Op_it + μ_it + ε_it

(1)

Fm_it = β₀ + β₁Ofdisto_it + β₂Rd_it + β₃Rdr_it + β₄Edu_it + β₅Fe_it + β₆Op_it + μ_it + ε_it

(2)

Sy_it = β₀ + β₁Ofdisto_it + β₂Rd_it + β₃Rdr_it + β₄Edu_it + β₅Fe_it + β₆Op_it + μ_it + ε_it

(3)

where Zl, Fm and Sy are the explanatory variables, which represent the level of green innovation, the level of green exploratory innovation, and the level of green utilization-based innovation, respectively. The core explanatory variable Ofdisto is the level of foreign direct investment. The remaining control variables are as follows: Rd is the local research and development expenditure, Rdr is the local research and development personnel input, Edu is the education level, Fe is the local factor endowment, and Op is the degree of openness to the outside world. Regression model (1) is used to analyze the impact of OFDI on overall green innovation; regression models (2) and (3) are used to analyze the impact of OFDI on green exploratory innovation and green exploitative innovation from the perspective of binary innovation, respectively.

4.2. Variable Selection

4.2.1. Explained Variables

The explained variables cover the level of green innovation (Zl), the level of green exploratory innovation (Fm), and the level of green exploitative innovation (Sy). Referring to [24], this paper uses the respective number of green patents granted, the number of green invention-based patents granted, and the number of green utility model patents granted to measure them. Patents are a good measure of a country’s technological innovation level, where inventive patents refer to patents with high innovation and technology levels, while utility model patents refer to patents with lower innovation and technology levels than inventive patents, but with higher utility value. The choice of two different patent grant volumes can better reflect the different nature of exploratory and exploitative innovation in innovation [25]. According to the approach of “Autonomous research and development, technology spillover and green technology innovation in China”, this paper classifies the industries where green patents are located as follows: biofuels, other heat manufacturing or use, rail vehicles, energy supply lines, general building insulation, recovered mechanical energy, wind energy, and fuel cells [12].

4.2.2. Core Explanatory Variables

The core explanatory variable is the level of OFDI (Ofdisto). In this paper, the annual OFDI stock of each province is chosen to measure the regional OFDI level. Given that stock data can better overcome the short-term fluctuations of flow data, the reverse dynamic green innovation spillover of OFDI can be more accurately reflected, and therefore stock data are more suitable for analyzing the long-term effects of OFDI [8].

4.2.3. Control Variables

Since other factors besides OFDI can also have an impact on regional green innovation performance, the following control variables are included in this paper. The specific settings are as follows:

• Independent innovation investment (Rd). This term represents the research and development expenditure input. In addition to the reverse technology spillover effect of foreign direct investment, domestic research and development investment are also key factors affecting green innovation.
• Research and development personnel input (Rdr). Like research and development expenditure, personnel input in research and development exerts a large influence on the level of green innovation in China.
• Educational attainment (Edu). Since the quality of human capital is not exactly the same across regions due to different education levels, the effect on local innovation is not the same. Therefore, this paper includes the educational attainment of each region in the control variables, which is expressed by the ratio of undergraduate and graduate students to the employed population in each region.
• Regional factor endowment (Fe). To control for the different effects of different levels of economic development and factor endowments in different regions, the capital stock per capita in each region is chosen to be the control variable [6].
• Degree of openness to the outside world (Op). Different regions with different degrees of openness to the outside world may have different OFDI scales, which are measured here by the ratio of total imports and exports to regional GDP [8].

4.2.4. Data Sources

Given the lack of OFDI data from 01–04, the research sample used in this paper is the balanced panel data of 30 Chinese provinces from 2004 to 2019 (excluding Hong Kong, Macao, and Taiwan, and Tibet is excluded due to serious missing values). The data in this paper are obtained from the China Statistical Yearbook, China Science and Technology Yearbook, China Labor Yearbook, China Outward FDI Statistical Bulletin, National Bureau of Statistics of the People’s Republic of China, the WDI database, and statistical yearbooks for each province. For individual missing values, this paper uses the linear interpolation method to complete them.

4.2.5. Descriptive Statistics

According to the statistical results in

Table 1. Descriptive statistics.

Variables	Sample Size	Mean	Standard Deviation	Min	Max
Zl	480	2536	4444	2	34,063
Fm	480	601.9	1096	0	7126
Sy	480	1934	3465	2	28,490
Ofdisto	480	535.3	1452	0.08	13,271
Rd	480	3,295,130	4,791,027	1262	3 × 107
Rdr	480	171,114.4	453,432.5	1209	5,053,870
Edu	480	6.600	6.320	0.780	40.70
Fe	480	64.27	37.85	13.45	205.9
Op	480	0.320	0.380	0.010	1.730

, the standard deviations of green patents (Zl, Fm, Sy), foreign direct investment (Ofdisto), research and development expenditure (Rd), and research and development personnel investment (Rdr) are 4444, 1096, 3465, 1452, 4,791,027, 453, and 432.5, respectively, and these very high values are obviously due to the inconsistent economic level of the provinces and the gradual increase in the level of development of the country over time. At the same time, there are large fluctuations in the values of these variables. In addition, educational attainment (Edu), regional factor endowment (Fe), and openness to the outside world (Op) are measured as ratios and therefore have relatively small values. In view of the possible influence of the data magnitude on the overall regression results, the data in the article are taken to be standardized.

4.3. Empirical Analysis

With the help of the Hausman test, this paper selects the form of the model setting. The original hypothesis of the Hausman test is to use a random effects model. Since the test found that the p-value (λ = 0.000) was lower than 0.010, the original hypothesis was rejected, and the fixed-effects model was used.

Given the problem of endogeneity of the model, the problem of endogeneity in the model mainly lies in the fact that the core explanatory variable OFDI may have a reciprocal causal relationship with the explained variables. Although higher OFDI promotes the development of green innovation, a higher level of green innovation may also make it easier for regional OFDI to reach the access threshold of other countries and achieve a larger volume of OFDI. Therefore, the lagged terms of international tourism foreign exchange earnings (tour) and core variables (Ofdisto) were chosen as instrumental variables in this paper for estimation [7]. The results are shown in

Table 2. Results of overall regression analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
	Zl	Fm	Sy	Zl	Fm	Sy
Ofdisto	0.080 *	−0.060	0.122 **	0.145 ***	0.021	0.179 ***
	(1.85)	(−1.34)	(2.43)	(6.49)	(0.84)	(6.87)
Rd	0.913 ***	0.869 ***	0.896 ***	1.021 ***	0.862 ***	1.038 ***
	(25.57)	(23.48)	(21.70)	(33.18)	(25.14)	(28.81)
Rdr	0.085 ***	0.080 ***	0.084 ***	0.105 ***	0.077 ***	0.110 ***
	(6.13)	(5.58)	(5.21)	(8.54)	(5.67)	(7.65)
Edu	−0.001	0.360 ***	−0.114 ***	−0.047	0.329 ***	−0.164 ***
	(−0.03)	(18.65)	(−5.32)	(−1.25)	(7.82)	(−3.72)
Fe	0.022	−0.147 ***	0.075 ***	0.028	−0.136 ***	0.079 ***
	(1.51)	(−9.68)	(4.41)	(1.55)	(−6.68)	(3.71)
Op	−0.118 ***	−0.203 ***	−0.088 ***	0.090 *	−0.096 *	0.146 ***
	(−6.83)	(−11.30)	(−4.37)	(1.91)	(−1.82)	(2.64)
_cons	−0.000	−0.000	−0.000	−0.047	−0.046	−0.046
	(0.00)	(0.00)	(0.00)	(0.01)	(0.01)	(0.02)
N	480	480	480	480	480	480
r2_a	0.921	0.915	0.894	0.911	0.876	0.881

Note: *, **, and *** represent the 10%, 5%, and 1% significance levels, respectively. (The same below).

. Among them, models (1)–(3) are fixed effects models; model (1) aims to explore the impact of OFDI on the overall green innovation level, model (2) is the impact of OFDI on the level of green exploratory innovation, model (3) aims to investigate the impact of OFDI on the level of green exploitative innovation, and models (4)–(6) correspond to the results after endogeneity treatment using instrumental variables, respectively.

The above model (1) and model (4) reveal that OFDI plays a positive role in promoting green innovation in China, which also confirms to some extent the views of some scholars [6,11]. In China, OFDI has indeed enhanced China’s green innovation through reverse technology spillover, industrial structure optimization, and learning absorption effects. By comparing model (2) with model (3), model (5), and model (6), it can be found that OFDI has different effects on green exploratory innovation and green exploitative innovation. Among them, OFDI still plays a facilitating role in green exploitative innovation, while it has a less significant effect on green exploratory innovation. In addition, both in terms of the impact on green innovation as a whole and green binary innovation, China’s local research and development expenditure and research and development personnel investment play a facilitating role. However, factors such as education level, resource endowment, and openness make this effect uncertain. The above findings are, to a certain extent, consistent with the previous analysis of this paper: since China’s accession to the WTO, the whole development cycle has been characterized by different policy orientations, different development needs of enterprises facing the external economic situation, different allocation of innovation resources, and different scale and capacity of OFDI, which have led to a dynamic evolution of the impact of OFDI on green innovation.

Taking the considerations in the third part of the paper as a premise, the next part of the paper will explore the effect of OFDI on green binary innovation from three time periods in turn. In this way, the whole dynamic evolution process with macro-environmental changes is more clearly demonstrated.

4.3.1. The Early Period of China’s WTO Accession and the Implementation of the “Going Out” Strategy

The results in

Table 3. The stage regression results of the effect of OFDI on green binary innovation from 2004 to 2008.

	(1)	(2)	(3)	(4)	(5)	(6)
	Zl	Fm	Sy	Zl	Fm	Sy
Ofdisto	0.214 ***	0.064	0.258 ***	0.259 ***	0.054	0.323 ***
	(5.31)	(1.38)	(5.91)	(5.58)	(1.29)	(5.63)
Rd	0.978 ***	0.713 ***	1.008 ***	0.768 ***	0.670 ***	0.747 ***
	(20.53)	(13.10)	(19.52)	(18.39)	(17.71)	(14.51)
Rdr	0.647 ***	0.438 ***	0.681 ***	0.450 ***	0.362 ***	0.451 ***
	(18.85)	(11.16)	(18.29)	(13.46)	(11.96)	(10.92)
Edu	0.186 **	0.353 ***	0.103	0.030	0.563 ***	−0.190 **
	(2.38)	(3.96)	(1.21)	(0.49)	(10.35)	(−2.57)
Fe	−0.007	−0.037	0.006	−0.012	−0.176 ***	0.056
	(−0.31)	(−1.52)	(0.27)	(−0.30)	(−4.73)	(1.09)
Op	−0.138	0.107	−0.227 *	−0.020	−0.119	0.022
	(−1.12)	(0.76)	(−1.70)	(−0.20)	(−1.37)	(0.19)
_cons	0.000	0.000	0.000	0.117 **	0.145 ***	0.096 *
	(0.00)	(0.00)	(0.00)	(2.50)	(3.42)	(1.67)
N	150	150	150	150	150	150
r2_a	0.927	0.799	0.924	0.960	0.962	0.940

Note: *, **, and *** represent the 10%, 5%, and 1% significance levels, respectively.

show that OFDI has a significant positive impact on green innovation as a whole and green utilization innovation, but not on green exploratory innovation. As mentioned in the previous analysis, since China’s accession to the WTO in 2001, OFDI and FDI have shown a dramatic increase, and the two-way flow of capital has enabled enterprises to access an unprecedented high level of the technology market and more advanced technological innovation paradigms. Obviously, this also provides a direction for China’s green technology innovation to learn and imitate. If enterprises have the opportunity to learn advanced foreign green management practices, they will effectively remove or optimize relatively backward production technologies and production processes, which not only improve production efficiency, but can also reduce energy consumption and pollution. This stage witnessed the remarkable promotion of OFDI to improve the level of green innovation in China.

At this stage, there is still a large gap between the level of domestic innovation in China and that of developed countries. Although there has been a direction of imitation, China’s learning and absorption capacity is still insufficient, so it cannot make a breakthrough in deep green core technology. It can be seen that OFDI does not have a significant impact on green exploratory innovation, while it positively promotes green exploitative innovation that is easier to learn from and brings a competitive advantage in the short term. The fact that domestic products have better access to foreign markets with this support is also due to the overall green innovation orientation of the country at this stage. According to the results of the analysis in Table 3, the level of impact of research and development investment and research and development human capital investment on green exploitative innovation in China is much higher than that of green exploratory innovation. In addition, the level of education is also an important point, because it not only plays a significant role in promoting green exploratory innovation in China, but also lays the foundation for the subsequent deepening of OFDI to be more conducive to learning and using green high-precision technology and thus promoting green exploratory innovation.

4.3.2. A Period of Counter-Trend Growth of OFDI in China after the Financial Crisis

Although the occurrence of the financial crisis led to global disinvestment from Western developed countries, China’s OFDI has shown an average annual increase of 5% in consecutive years since 2009. In the view of domestic and foreign scholars, the high growth of China’s OFDI in that period was attributed to the search for resources, markets, and the exploitation of comparative advantages [25]. The reasons are, first, the low energy prices after the financial crisis made China’s energy-oriented OFDI increase rapidly; second, trade protection suddenly intensified since 2009, which forced China’s trade-substituting OFDI to increase; thirdly, hampered by labor costs, China’s cost-oriented OFDI mostly went to developing countries rather than developed countries with high technology levels [26]. The above view shows from the side that the motivation of technology demand of OFDI in China at that stage is very weak, which is confirmed by the results in

Table 4. The stage regression results of the effect of OFDI on green binary innovation from 2009 to 2014.

	(1)	(2)	(3)	(4)	(5)	(6)
	Zl	Fm	Sy	Zl	Fm	Sy
Ofdisto	−0.057	−0.107 *	−0.036	−0.038	−0.038	−0.036
	(−1.06)	(−1.81)	(−0.59)	(−0.70)	(−0.71)	(−0.54)
Rd	0.822 ***	1.047 ***	0.707 ***	0.148	0.237 *	0.110
	(4.53)	(5.21)	(3.39)	(1.07)	(1.69)	(0.65)
Rdr	0.498 ***	−0.065	0.684 ***	0.916 ***	0.628 ***	0.985 ***
	(2.71)	(−0.32)	(3.24)	(6.94)	(4.70)	(6.08)
Edu	0.316 ***	0.568 ***	0.211 **	0.364 ***	0.677 ***	0.235 ***
	(4.10)	(6.66)	(2.38)	(5.67)	(10.41)	(2.98)
Fe	−0.089 ***	−0.135 ***	−0.069 *	−0.099 ***	−0.145 ***	−0.079 **
	(−2.81)	(−3.84)	(−1.88)	(−3.39)	(−4.89)	(−2.18)
Op	−0.265 **	−0.477 ***	−0.176	−0.270 ***	−0.257 ***	−0.264 ***
	(−2.12)	(−3.45)	(−1.23)	(−3.38)	(−3.18)	(−2.69)
_cons	0.000	−0.000	−0.000	0.070 *	0.053	0.074
	(0.00)	(−0.00)	(−0.00)	(1.91)	(1.42)	(1.63)
N	180	180	180	180	180	180
r2_a	0.840	0.796	0.790	0.939	0.940	0.907

Note: *, **, and *** represent the 10%, 5%, and 1% significance levels, respectively.

. The regression coefficient of OFDI becomes insignificant both for overall green innovation and green binary innovation; moreover, the impact of China’s research and development expenditure and research and development personnel investment on green innovation is greatly reduced. It can be seen that in the years after the emergence of the financial crisis, China’s OFDI focused on solidifying the economy rather than on technological innovation, leaving no room for green innovation [27]. It should be noted that the regional factor endowment and the level of foreign openness show a negative impact on green innovation in this period. The reason for this is that the higher the level of openness and the higher the level of regional factors, the easier it becomes for domestic capital to go out to achieve market and resource-seeking goals; the greater deviation from the technological goals also means innovation resources may be misallocated to a certain extent [28].

4.3.3. China Enters the “New Normal” Period of Transformation and Development

Table 5. The stage regression results of the effect of OFDI on green binary innovation from 2015 to 2019.

	(1)	(2)	(3)	(4)	(5)	(6)
	Zl	Fm	Sy	Zl	Fm	Sy
Ofdisto	0.137 **	0.099 **	0.142 **	0.123 **	0.315 ***	0.203 ***
	(2.38)	(2.39)	(2.03)	(2.72)	(0.78)	(1.78)
Rd	0.727 ***	0.599 ***	0.736 ***	0.208 ***	0.381 **	0.059
	(4.85)	(5.51)	(4.01)	(2.56)	(6.23)	(0.56)
Rdr	0.627 ***	0.022	0.790 ***	0.794 ***	0.617 ***	0.903 ***
	(4.23)	(0.21)	(4.35)	(9.91)	(2.83)	(9.81)
Edu	−0.224 *	−0.112	−0.250	0.075	0.661 ***	0.075
	(−1.73)	(−1.19)	(−1.57)	(4.83)	(10.64)	(1.46)
Fe	0.162 ***	0.069 *	0.184 ***	0.011	−0.159 ***	0.056 **
	(3.34)	(1.96)	(3.11)	(0.56)	(−5.54)	(2.14)
Op	−0.202	−0.310 **	−0.161	−0.214	−0.637 ***	−0.207 ***
	(−1.19)	(−2.51)	(−0.77)	(−3.63)	(−5.55)	(−2.74)
_cons	0.000	0.000	0.000	0.023	0.149 ***	0.019
	(0.00)	(0.00)	(0.00)	(1.18)	(4.08)	(0.79)
N	150	150	150	150	150	150
r2_a	0.772	0.659	0.728	0.953	0.927	0.930

Note: *, **, and *** represent the 10%, 5%, and 1% significance levels, respectively.

shows that OFDI has had a significant positive impact on the level of green innovation since China entered the “new normal” stage of transition development in 2015. Moreover, from the perspective of binary innovation, both green utilization-based innovation and green exploration-based innovation have a significant positive impact. This result is inextricably linked to the development orientation of China’s macroeconomic situation in that period. With China’s proposal to actively adapt to the new normal of economic development in 2015, China’s economic development has entered a medium-high growth stage with high efficiency, low costs, and sustainable development [29]. In this context, China’s development of the green low-carbon cycle has changed from emphasizing the development concept to becoming a realistic demand [30], and economic development has shifted from factor-driven and investment-driven to innovation-driven and green-driven [31]. This national macro-level economic development orientation makes enterprises focus on green innovation and green industries to open up new profit growth points, which also drives to improve the rough development of China’s economy. In this stage, the motivation of OFDI also changed accordingly, from market-seeking to using their competitive advantage to shift the low-end production link to the green technology-seeking level. In addition, the improvement in the level of local innovation and the accumulation of OFDI technology demand capabilities mean that OFDI absorption and borrowing can bring breakthrough green exploratory technology innovation that is stronger. This also indicates that China’s OFDI in the “new normal” period of transformation and development has shifted from focusing on promoting green utilization-based innovation to taking into account green binary innovation. Additionally in this period, China’s research and development investment and research and development personnel investment still positively influence the green level, and regional factor endowment also shows a significant promotion effect on green innovation. Thus, China’s innovation resource allocation has been optimized in the development process, which reduces the cost of green innovation and thus promotes the development of green innovation.

4.3.4. Robustness Test

The Sargen test accepted the original hypothesis and instrumental variables were strictly exogenous, see

Table 6. Robustness test results.

	(1)	(2)	(3)	(4)
	04–19	04–08	09–14	15–19
L.Zl	0.144	−0.284	−0.035	−0.0143
L.Fm	0.199	0.184	0.001	0.194
L.Sy	0.244 *	−0.049	−0.126	0.026
Ofdi(Zl)	0.457 ***	0.384 ***	0.065	0.542 ***
Ofdi(Fm)	−0.002	0.064	−0.002	0.083 ***
Ofdi(Sy)	0.587 ***	0.483 ***	0.038	0.698 ***
Sargen(Zl)	1.246	3.595 *	0.199	0.810
Sargen(Fm)	1.785	5.333 *	0.009	7.410 *
Sargen(Sy)	0.736	3.449 *	0.282	2.950 *

Note: * and *** represent the 10% and 1% significance levels, respectively.

.Considering that there may be path dependence, green innovation in the previous period may affect green innovation in the next period [32,33], so this paper adds lagged terms (L.Zl, L.Fm, and L.Sy) of the explanatory variables (Zl, Fm, and Sy) and applies the generalized method of moments estimation (Gmm) to the model. It was found that none of the lagged terms were significant in the model, indicating that there is no path dependence to influence the level of green innovation. The results of the re-regression after replacing the core explanatory variables (replacing the stock with OFDI flow) and deleting the control variables remain consistent with the original results, indicating the robustness of the results.

5. Conclusions and Implications

This paper explores the impact of OFDI on green innovation in China, taking binary innovation and dynamic evolution as the entry point. The analysis finds that throughout the time cycle from China’s accession to the WTO to the present, there are changes in the domestic and international economic situation and economic development orientation. The impact of OFDI on the level of green innovation is not constant, but follows a dynamic evolutionary process. In addition, the analysis from the perspective of binary innovation also shows that the impact of OFDI on green binary innovation evolves from focusing on promoting green utilization-based innovation to balancing green utilization-based innovation and green exploration-based innovation. This result helps us to have a clearer understanding of how OFDI affects green innovation.

The fact that China has entered a new stage of high-quality development reminds us that we also need to combine the concept of high-quality development and the heterogeneous characteristics of green innovation to optimize resource allocation in the process of continuously promoting OFDI. On this basis, we can achieve targeted improvement of China’s green innovation level. First, it is necessary to continue to adhere to the “going out” strategy under the new openness pattern, and at the same time to make reasonable OFDI policies to optimize the OFDI’ structure according to the local economic development base; in addition, it is also indispensable to allocate innovation resources reasonably, because it can ensure that the OFDI technology spillover effect and industrial structure optimization effect can be maximized [34,35]. Second, it is necessary to use the heterogeneity of green innovation as a base, reasonably plan the OFDI tendency, and seek the most suitable path for enterprise development. Relevant management departments should also pay attention to the investment-led effect brought by OFDI, and avoid neglecting the improvement of green exploratory innovation with deeper impact because of focusing on economic growth. Third, it is necessary to pay attention to the dynamic evolutionary effect of OFDI on green innovation. Relevant management departments should follow the dynamic changes in economic development to avoid the loss of resources caused by the mismatch between static policies and it. Fourth, in the whole dynamic evolution process of OFDI affecting green innovation, green innovation evolves from focusing on utilization-based innovation to taking into account binary innovation, in which the ability to absorb advanced technology plays an important role. In view of this, management can act to strengthen the absorptive capacity by increasing trade openness, promoting industrial upgrading, and strengthening government science and technology subsidies to further amplify the reverse technology spillover effect of OFDI and finally realize the breakthrough of green technology.