The Impact of Environmental Regulation on Green Energy Technology Innovation—Evidence from China

Abstract

Green energy technology innovation (GETI) is a crucial path to achieve sustainable development. However, few studies have examined the determinants of GETI, especially from the perspective of environmental regulation. To fill this gap, this study investigates the impact of environmental regulation on GETI using the panel data of 30 provinces in Mainland China from 2001 to 2018. We first measure GETI by the latest IPC codes and patent data, and then adopt a dynamic spatial Durbin model (DSDM) to examine the relationship between environmental regulation and GETI. The main conclusions are summarized as follows: (1) There is a significant inverted U-shaped relationship between environmental regulation and GETI; (2) heterogeneity analysis shows that the inverted U-shaped correlation not only exists between environmental regulations and different types of GETI, but also exists between environmental regulations and GETI in different regions. Moreover, the results also show that the spatial spillover effect and path-dependent effect exist in all cases. The findings can provide reference for policymakers to formulate more precise environmental policies. That is, environmental policies in a province should be formulated based on its position on the inverted U-shaped curve. More specifically, when it is on the left side of the inflection point, it is reasonable to strengthen environmental policies, and when it is on the right side of the inflection point, appropriate relaxation of environmental policies should be considered.

1. Introduction

According to the statistics published by IEA (International Energy Agency https://www.iea.org/reports/world-energy-balances-overview accessed on 19 April 2022), the energy consumption in China has experienced sustained growth. As shown in Figure 1, China’s total final energy consumption rose from 657,594 thousand tonnes of oil equivalent (ktoe) in 1990 to 2,057,666 ktoe in 2018, with an average growth rate of 4.16%. China’s share of global energy consumption also increased significantly from 10.94% in 1990 to 20.71% in 2018. Now China has become the country with the largest energy consumption in the world, but it relies heavily on fossil energy such as coal and its energy efficiency is relatively low [1,2]. The extensive use of fossil energy has emitted a large number of greenhouse gases and pollutants, and has brought tremendous pressure to the environment [1,3,4,5]. Based on this fact, green energy technology innovation (GETI) has become a key path to combat climate change and reduce air pollution [6,7]. Green energy technologies refer to energy-related technologies that can save energy resources and reduce greenhouse gas and pollutant emissions [8,9]. In line with the “IPC Green Inventory” released by the World Intellectual Property Organization (WIPO) in 2010, green energy technologies mainly include alternative energy production technologies (AEPTs) and energy conservation technologies (ECTs). Clearly, innovations in both types of green energy technologies are environmental-friendly and highly efficient in improving air quality. To be specific, alternative energy production technology innovation (AEPEI) aims to promote the share of clean energy in the energy mix by developing alternative energy such as solar energy and wind energy at lower costs. Since alternative energy is carbon-free as well as pollution-free, its widespread application can slow down CO₂ and harmful gases emissions [10,11]. Moreover, increasing the proportion of alternative energy will also help to ensure the security of energy supply [12]. For energy conservation technology innovation (ECTI), it can be used to improve the reliability of energy storage system and reduce the loss in the energy conversion process, thus improving energy utilization efficiency and reducing emissions [13]. To sum up, GETI can develop alternative energy as well as improve energy efficiency, thus is crucial for green and low-carbon development.

Although the importance of GETI has raised concerns due to the negative externalities of pollution and the positive externalities of knowledge spillovers, the cost of GETI is higher than that of non-GETI [14,15]. Correspondingly, companies have little incentive to invest aggressively in this area. The lack of investment motivation from companies suggests that the rapid promotion of green energy technologies cannot be achieved by the market itself [16]. Therefore, government regulation is required. To our knowledge, environmental regulation has become one of the most important means for policymakers to stimulate the innovation of green technologies [17,18]. Generally speaking, environmental regulation has two effects on GETI. One effect is “following the cost effect”. Specifically, to comply with the regulation, companies will spend more on pollution control and thus less on production and R&D expenditure [19,20]. This will undoubtedly hurt the innovation of green energy technologies. Another effect is the “innovation compensation effect”, that is, for long-term benefits, companies tend to invest in GETI to save resources and reduce emissions [21,22,23]. Because these two effects work in opposite directions, which effect is dominant will determine the final impact of environmental regulation on GETI [24]. Therefore, there may be a nonlinear relationship between environmental regulation and GETI. However, few studies have examined the correlation between environmental regulation and GETI because of the difficulty in designing statistical indicators.

The main contributions of our study are summarized as follows. First, the latest international patent classification (IPC) codes and patent data are used to measure GETI more accurately. Second, previous studies mainly explore how environmental regulation affects general technological innovation. Although this method is simple, it ignores the huge differences that may exist within technologies. Accordingly, it is necessary to carry out a detailed investigation on specific technology innovation in order to get more accurate policy recommendations. Since energy consumption is the main source of air pollution, GETI is considered particularly important for achieving sustainable development. However, due to the lack of statistical indicators, there are a few studies on the drivers of GETI, especially from the perspective of environmental regulation. Among these few studies, GETI is only limited to renewable energy technology innovation, and the focus is on the linear relationship. Moreover, due to the flow of R&D factors, GETI may have spatial knowledge spillovers, but this also does not attract sufficient attention. Based on this, we construct a dynamic spatial Durbin model (DSDM) to explore the nonlinear impact of environmental regulation on GETI by using provincial panel data from 2001 to 2018 in China. Compared with the static model, the results of the dynamic model are more reasonable because the dynamic effect of the dependent variable is taken into account. Finally, considering the diversity of green energy technologies and the unbalanced nature of China’s economy, this paper further investigates the impact of environmental regulation on different types of GETI and GETI in different regions. The findings can expand the literature on sustainable development and provide policymakers with ideas to formulate more precise environmental policies.

The rest of the study is as follows. Section 2 reviews the existing literature. Section 3 provides the model specification. Section 4 shows data analysis. Section 5 reports the empirical findings. Section 6 reveals the main conclusions and some policy implications.

2. Literature Review

Previous research has proved that technology innovation is a realistic path to harmonize economic development and environmental quality [25,26,27]. Whether environmental regulation promotes technology innovation has been widely discussed by scholars, but no consensus has been reached. Some scholars hold that environmental regulation stimulates technology innovation of companies [28,29,30,31], while others hold the opposite viewpoint [32,33].

In recent years, academia has been concerned about the impact of environmental regulation on specific technology innovation, mainly focusing on green technology innovation. There are three popular views on how environmental regulation affects green technology innovation. One is that environmental regulation can discourage green technology innovation because of the “following the cost effect”, which indicates that environmental regulation increases companies’ expenditure on pollution control and crowds out production and R&D expenditure. In particular, to meet the regulatory requirements and avoid punishment from the government, companies will increase pollution control investment, such as purchasing end-of-pipe pollution control equipment. This will undoubtedly lead to higher production costs and lower profits. As a consequence, the production and R&D expenditures of companies will be reduced, which is not conducive to green technology innovation. For instance, You et al. [34] investigated 1961 listed industrial companies in China and found that under the influence of the fiscal decentralization system and the political promotion champion system, environmental regulation significantly hinders the company’s ecological innovation and ecological investment. Lv et al. [35] assessed the effects of dynamic environmental policies adopted by the Canadian government on general innovation and eco-innovation for oil and gas companies. The results showed that reducing the intensity of regulation can significantly expand the scale of environmental patents. Using data from 496 listed Chinese companies, Tang et al. [36] argued that environmental regulation hinders the efficiency of green technology innovation by reducing cash flow.

The other viewpoint is that environmental regulation facilitates green technology innovation due to the “innovation compensation effect”, which means that proper environmental supervision will encourage companies to innovate in green technologies to control total cost and improve competitiveness. In this spirit, environmental regulation increases the cost of emissions and stimulates companies to invest more resources in R&D activities. The purpose is to seek more environmentally sound technologies and transition to cleaner production. With cleaner production, companies can improve the intermediate production processes, enhance competitiveness, and ultimately increase company profits [37]. Consequently, in the long term, environmental regulation can induce green technology innovation. Cai et al. [17] investigated the data of listed companies in heavy pollution industries in China and confirmed that the direct environmental regulation imposed by the government can significantly stimulate green technology innovation. Wang et al. [7] proposed a Steinberg game model and analyzed it using the mathematical deduction method. The results also showed that environmental regulation can facilitate green technology innovation, and this relationship is affected by enterprise cooperation. Raza [38] proposed a structural equation model to inspect the role of external institutional drivers on green innovation in companies. Through a survey of 101 European short sea shipping companies, he found that regulatory pressure significantly promotes green technology innovation. Using data from companies in 32 countries, Herman and Xiang [39] investigated environmental regulatory spillovers on clean technology innovation. The findings revealed that foreign environmental regulation has a positive impact on domestic clean technology innovation, and this impact is moderated by the institutional quality distance.

The third point of view is that how environmental regulation affects green technology innovation is a comprehensive result of the “following the cost effect” and “innovation compensation effect”. If the influence of the “following the cost effect” is greater than that of the “innovation compensation effect”, then there will be a negative relationship between environmental regulation and green technology innovation, otherwise, there will be a positive correlation. With the dynamics of environmental regulation, the comprehensive effect may be different, so there may be a non-linear influence between the two. Dong et al. [40] adopted the spatial Durbin model to investigate the ripple effect of environmental regulation in China. The results displayed a U-shaped correlation between environmental regulation and green technology innovation, and this correlation exists in both local and adjacent areas. Using data from 30 Chinese provinces, Song et al. [41] also found a U-shaped relationship between environmental regulation intensity and green innovation. Namely, environmental regulation hinders green innovation in the early stage, but when a certain threshold value is reached, environmental regulation will facilitate green innovation. Du et al. [42] also came to a similar conclusion. On the contrary, Song and Xue [43] conducted a survey of various industries in China, and the results showed that in heavy industry, the relation between environmental regulation and green technology innovation is an inverted U-shaped curve. Shang et al. [44] carried out an empirical investigation of 30 provinces in China and found that environmental regulation can promote green technology innovation in the early stage, but with the increase of environmental regulation intensity, the promotion effect gradually turned into restriction.

For GETI, due to the lack of statistical indicators, related research is rather inadequate. Meanwhile, the few studies only focus on renewable energy technology innovation. For instance, using data from OECD countries and BRICS countries, Herman and Xiang [45] explored the innovation-induced effects of environmental policy spillovers. The results showed that foreign environmental regulation can stimulate domestic clean energy technology innovation. Bai et al. [46] inspected the trend of renewable energy technology innovation in China from 1997 to 2005. They argued that provinces with higher levels of environmental regulation are more likely to innovate in renewable energy technologies. Li and Shao [47] investigated the data of OECD countries from 1990 to 2018, and found that the Kyoto Protocol, a global regulatory tool, did not significantly affect the innovation of renewable energy technologies. By using the data of 32 countries from 1998 to 2016, Zhu et al. [48] explored the driving factors of renewable energy technology innovation. The results proved that environmental regulation can promote domestic renewable energy technology innovation. It can be seen that scholars mainly examine the linear relationship between environmental regulation and GETI, ignoring the possible nonlinear relationship, and they have not reached a consistent conclusion. As mentioned earlier, environmental regulation has a dual impact on GETI. On the one hand, the “following the cost effect” is not conducive to GETI. On the other hand, the “innovation compensation effect” caused by appropriate environmental regulation is beneficial to GETI. Obviously, the impact of environmental regulation on GETI may not simply promote or hinder, but change with the intensity of environmental regulation. In other words, there may be a nonlinear relationship between environmental regulation and GETI.

In general, although scholars have begun to study how environmental regulation affects green technology innovation in recent years, no consistent conclusions have been drawn. As a specific type of green technology innovation, the role of GETI in alleviating fossil energy depletion, combating climate change, and reducing environmental pollution has been highly regarded. However, in reality, due the difficulty in setting statistical indicators, relevant research is extremely limited. In these limited studies, scholars focused on the linear impact of environmental regulation on GETI, ignoring the possible nonlinear impact. Meanwhile, innovation activities are often accompanied by knowledge spillover effect, but it also has not received sufficient attention [49,50]. Therefore, this paper attempts to fill the gap by incorporating environmental regulation and GETI into the same analytical framework and constructing a dynamic spatial econometric model to reveal the spatial-temporal relationship between them.

3. Model Specification

To explore the influence of environmental regulation on GETI, we use the spatial econometric method. There are two basic spatial econometric models. One is the spatial autoregressive model (SAR), whose spatial autocorrelation is caused by the dependent variable (SEM). The other is the spatial error model, whose spatial autocorrelation is due to the spatial error term. In fact, the autocorrelation due to the dependent variable and the autocorrelation due to the error term may exist simultaneously. In order to deal with this situation, Lesage and Pace [51] proposed a more widely applicable spatial Durbin model (SDM). Combined with the variables in this paper, the SDM is set as shown in Equation (1).

$$\ln GET{I}_{it}=\alpha +\rho W\ln GET{I}_{it}+{\beta }_1\ln E{R}_{it}+{\beta }_2\ln E{R}_{it}^2+\varphi X_{it}+{\theta }_{1}W\ln E{R}_{it}+{\theta }_{2}W\ln E{R}_{it}^2+\vartheta W{X}_{it}+{\mu }_i+{\eta }_i+{\epsilon }_{it}$$

(1)

where GETI_it is the number of green energy technology patents applied by area i in year t; ER_it refers to environmental regulation; ER_it² is the square term of ER_it, which is used to explore the possible nonlinear relationship; X_it represents a set of control variables; μ_i defines individual fixed effect, η_i defines time fixed effect, and ε_i means error term; ρ is the spatial lag parameter of the dependent variable (GETI), θ_i (i = 1,2) is the spatial lag parameter of environmental regulation, and ϑ is the spatial lag parameter of a set of control variables. W expresses the spatial weight matrix. There are two types of spatial weight matrices. One is geographical distance weight matrix, the other is economic distance weight matrix [52,53,54]. Since the economic distance weight matrix may cause endogeneity problems, this study uses the geographic distance weight matrix, which is set as shown in Equation (2). Referring to Deng et al. [55], the matrix shows that a shorter geographical distance leads to greater spatial interaction between areas.

$$W_{ij}=\{\begin{array}{l}0,i=j\\ \frac{1}{d^2},i\ne j\end{array}$$

(2)

where d_ij represents the distance between province i and province j.

Moreover, in Equation (1), if we set θ_i = 0, the SAR model can be obtained; likewise, if we set ${\theta }_i+\rho {\beta }_i=0$, the SEM model can be obtained. Obviously, SAR and SEM are special cases of SDM.

Given that technology innovation is a dynamic process, current innovation activities may be influenced by prior technical knowledge. In this case, the results obtained by the static model may be biased. Therefore, we introduce dynamic effects into SDM. The expected benefits are as follows: first, the relationship between environmental regulation and GETI can be estimated more reliably. Second, it can help us investigate whether there is a path-dependent effect in GETI. The dynamic spatial Durbin model (DSDM) is set as follows.

$$\begin{array}{l}\ln GET{I}_{it}=\alpha +\lambda \ln GET{I}_{it-1}+\rho W\ln GET{I}_{it}+{\beta }_1\ln E{R}_{it}+{\beta }_2\ln E{R}_{it}^2+\varphi X_{it}+{\theta }_{1}W\ln E{R}_{it}+{\theta }_{2}W\ln E{R}_{it}^2\\ +\vartheta W{X}_{it}+{\mu }_i+{\eta }_i+{\epsilon }_{it}\end{array}$$

(3)

4. Data Analysis

4.1. Variables and Data

The data used in this article come from 30 provinces in mainland China during 2001–2018, and Tibet was excluded due to lack of data. The sample contains a total of 540 observations. The related variables are as follows:

(1)

Dependent variable

The dependent variable of our model is GETI. Taking into account the importance of technology innovation in high-quality development, how to find a reliable way to measure it has become a hot topic in academia. However, the effective indicators are still very insufficient and these available indicators are more focused on input, such as R&D personnel investment and R&D capital investment. The limitation is that the input and the output of innovation are actually not equal. Since innovation output is directly linked to the results of innovation activities, the number of patent applications can more effectively reflect innovation activities. Furthermore, in certain technical fields such as green energy, input data are often difficult to obtain. In contrast, patent data are easily available and countable, and therefore suitable for statistical analysis. In practice, patent data are not perfect because there may be some measurement errors in patent applications. However, it is still a very effective and widely used measurement [56,57,58].

The National Intellectual Property Administration of China provides three types of patent data, namely inventions, utility models, and designs. Consistent with Wang, et al. [59], we merely count inventions and utility models. We attempt to retrieve green energy-related patents with the latest IPC codes in reference to the “IPC Green Inventory” formulated by WIPO in 2010.

Table 1. The technical field of green energy.

Alternative Energy Production

Bio-fuels; Integrated gasification combined cycle; Fuel cells; Pyrolysis or gasification of biomass; Harnessing energy from manmadewaste; Hydro energy; Ocean thermal energy conversion; Wind energy; Solar energy; Geothermal energy; Other production or use of heat, not derived from combustion; using waste heat; Devices for producing mechanical power from muscle energy.

Energy Conservation

Storage of electrical energy; Power supply circuitry; Measurement of electricity consumption; Storage of thermal energy; Low energy lighting; Thermal building insulation, ingeneral; Recovering mechanical energy.

details the technical field of green energy, and the patent data come from China National Intellectual Property Administration.

(2)

Core independent variables

Environmental regulation is a common tool for governments to control pollution and promote innovation. For the measurement of environmental regulation, the most widely used indicators are environmental pollution control investment, pollution discharge fees, and the number of penalties for environmental cases [19,60,61]. However, these indicators are endogenous with GETI to some extent, so the results may be biased. To overcome this shortcoming, text analysis method is used to construct environmental regulation measurement index. That is, the intensity of environmental regulation is expressed by the proportion of words related to environmental governance in the work report issued by provincial governments at the beginning of each year [62]. A higher frequency of environment-related words means that the government pays more attention to environmental regulation. Since the government work report is completed at the beginning of the year and does not change with subsequent economic activities, there is no bilateral causal relationship between environmental regulation and companies’ innovation, thus avoiding endogenous problems. In other words, the government work report released at the beginning of the year affects the subsequent GETI activities, but the adverse effect do not exist, thus avoiding the endogenous issues that other proxy indicators may induce. Referring to Chen, et al. [63], the search keywords related to environmental regulation are ecological protection (sheng tai bao hu), environmental protection (huan jing bao hu, huan bao), low carbon (di tan), energy consumption (neng hao), emissions reduction (jian pai), and pollution (wu ran). It is worth noting that the frequency data are standardized according to Equation (4).

$$x_{it}^{\prime }=\frac{x_{it}-x_{\min }}{x_{\max }-x_{\min }}$$

(4)

where $x_{it}^{\prime }$ is the normalized value of the current observation; $x_{it}$ indicates the current observation; $x_{\min }$ means the smallest observation; $x_{\max }$ denotes the largest observation; i is area and t stands for year.

(3)

Control variables

The relevant control variables mainly include economic development level, trade openness, industrial structure, and R&D investment.

Economic development level. Economic development level is closely related to innovation input and the risk tolerance of innovation failure [64]. GDP per capita is an indicator for economic development level. Given that GDP is related to prices, we deflated it into the 2001 constant price.

Trade openness. The role of trade openness on GETI is uncertain, and the final effect mainly depends on whether the products of foreign trade come from polluting industries or cleaner production sectors [44]. We use the ratio of total amount of regional import and export trade to GDP as a proxy for the level of trade openness.

Industrial structure. The secondary industry, which emits a large amount of CO₂ and pollutants, is the main demander of green innovation [65]. The ratio of the added value of the secondary industry to GDP is selected as an indicator of industrial structure.

R&D investment. As a direct driving factor of innovation activities, R&D investment is also included in the control variables [66]. We use the ratio of R&D spending to GDP as a proxy for R&D investment.

All the data are from China Statistical Yearbook (2002–2019), and the descriptive characteristics of variables of variables are reported in

Table 2. Descriptive characteristics of variables.

Variable	Definition	Obs.	Mean	Std. Dev	Min	Max
lnGETI	Green energy technology innovation	540	6.0290	1.8321	0.0000	10.2786
lnER	Environmental regulation	540	0.3044	0.1496	0.0000	0.6931
lnER2	The square term of lnER	540	0.1150	0.0974	0.0000	0.4804
lnAGDP	The level of economic development	540	9.8343	0.7018	7.9535	11.5163
lnIS	Industrial structure	540	3.8107	0.2071	2.9232	4.1190
lnOPEN	The level of trade openness	540	2.8847	1.0150	0.52059	5.2423
lnRD	R&D investment	540	−1.5071	1.0720	−3.5751	2.4898

.

4.2. Data Analysis

(1)

Patent count

Note: Green energy technologies consist of alternative energy production technologies (AEPTs) and energy conservation technologies (ECTs).

Figure 2 reflects the patent count of green energy in China from 2001 to 2018. We can see that the number of patents related to green energy has experienced rapid growth since 2001. More specifically, the number of patents for AEPTs increased from 1423 in 2001 to 84,758 in 2018, and the number of patents for ECTs increased from 926 in 2001 to 81,956 in 2018. The average annual growth rates of AEPTs patents and ECTs patents are 27.18% and 30.18%, respectively.

Figure 3 shows the spatial distribution of green energy-related patents in China in 2001 and 2018. Since the number of patent applications varies greatly in different years, we use the quantile breakpoint method to divide areas into five categories. It is easy to see that the areas with prominent advantages in GETI are mainly concentrated in the southeast coastal areas such as Beijing, Shandong, Shanghai, Jiangsu and Guangdong. In contrast, the areas with fewer patent applications for green energy technologies are mainly distributed in northwest China, such as Xinjiang, Qinghai, Gansu, Ningxia and Inner Mongolia. Innovations in green energy technologies across the country have made great strides over time. However, the dominance of the southeast coastal areas over the northwest areas has also expanded. One obvious fact is that more of the northwestern provinces are in the 1/5 quintile for green patent applications in 2018. We can also find a divergence in the development of green energy technologies in central areas from 2001 to 2018. Among the six central provinces, Anhui, Jiangxi, and Henan have made great progress, and their relative positions in the country have all risen. Specifically, Anhui province has made the greatest progress, as it has made the leap from an innovation-deficient area to an innovative highland. On the contrary, the relative positions of Shanxi province and Henan province have declined. Generally speaking, GETI in central areas develops faster than that in northwest areas. From a national perspective, the southeast coastal areas have more advantages in GETI than northwest areas, and the gap between the southeast coastal areas and the central areas is gradually narrowing.

(2)

Environmental regulation

Figure 4 shows the kernel density distribution of environmental regulation intensity in 2001, 2006, 2011, 2016, and 2018, respectively. It can be seen that the kernel density curve of environmental regulation intensity significantly shifted to the right from 2001 to 2006, indicating a considerable increase in environmental regulation intensity during this period. At the same time, it can also be found that these two density curves are relatively steep, suggesting a highly concentrated distribution of environmental regulation intensity. From 2006 to 2011, the kernel density curve of environmental regulation intensity continues to move sharply to the right, implying that the intensity of environmental regulation increased greatly during this period. It is worth noting that compared with 2006, the Kernel density curve of environmental regulation intensity in 2011 became flatter. It indicates that the distribution of environmental regulation intensity has changed from highly concentrated to more dispersed. From 2011 to 2018, the Kernel density curve of environmental regulation intensity gradually moved to the left, revealing that the intensity of environmental regulation decreased in this period. In short, the intensity of environmental regulation in China increased rapidly from 2001 to 2011. During this period, the Chinese government carried out comprehensive environmental governance and issued a series of policies and regulations. In particular, the implementation of the Environmental Impact Assessment Law reflects the transformation of China’s environmental protection from “pollution first and then treatment” to “assessment first and then construction”. After 2011, the intensity of environmental regulation has decreased slightly.

5. Empirical Consequences

This section reports the empirical findings of this study. Section 5.1 displays the results of unit root tests and co-integration tests; Section 5.2 shows the consequences of spatial correlation test; Section 5.3 reveals the results of the empirical equation; Section 5.4 is heterogeneity analysis.

5.1. Unit Root Tests and Co-Integration Tests

Before the empirical analysis, we need to perform the unit root test to check whether the data are stable. If the data are nonstationary, the estimation result may produce a pseudo-regression problem [67]. Due to the short period of this research, the HT test and the IPS test are employed for unit root test. The outcomes are shown in

Table 3. HT and IPS unit root tests.

Methods	HT		IPS
	Level	First-Difference	Level	First-Difference
GETI	0.5919 ***	−0.3510 ***	−3.7729 ***	−11.8358 ***
lnER	0.3205 ***	−0.3689 ***	−7.3517 ***	−13.1023 ***
lnER2	0.3023 ***	−0.3616 ***	−7.2798 ***	−13.1088 ***
lnAGDP	0.9648	0.5505 ***	6.5079	−3.7584 ***
lnIS	0.9125	0.1380 ***	1.8738	−8.2134 ***
lnOPEN	0.8814	0.0222 ***	1.8639	−8.9432 ***
lnRD	0.4433 ***	−0.0874 ***	−7.9411 ***	−13.0420 ***

*** p < 0.01.

. It is not difficult to find that some variables are unstable. However, we can also find that the first-order differences of all variables are stable. In this condition, the next step is to perform a co-integration test to investigate whether there is a long-term equilibrium relationship between the variables. Mainstream co-integration testing methods include the Pedroni test [68], and the Westerlund test [69]. We represent the results of these two methods in

Table 4. Co-integration tests.

Pedroni Test	Statistic	Westerlund Test	Statistic
Modified Phillipse-Perron t	7.4224 ***	Variance ratio	−1.6108 *
Phillipse-Perron t	−19.1058 ***
Augmented Dickeye-Fuller t	−20.2247 ***

* p < 0.1, *** p < 0.01.

. The results revealed that all statistics in the Pedroni test and the Westerlund test are significant. Therefore, it can be considered that there is a long-term stable relationship between the variables in this study.

5.2. Spatial Correlation Test

Figure 3 shows that the dominant areas of GETI in China are concentrated in the southeastern provinces, while the areas with GETI deficiency are mainly distributed in the northwestern provinces. This initially suggests a spatial correlation of GETI in China, but it needs to be further examined. According to Wang et al. [70] and Yang et al. [65], the global Moran’s I index is selected to uncover the spatial autocorrelation of GETI, and the results are displayed in

Table 5. Global Moran’s I index test.

	Moran’s I	Sd(I)	Z Value
2001	0.176 **	0.092	2.279
2002	0.210 ***	0.095	2.570
2003	0.245 ***	0.095	2.949
2004	0.231 ***	0.094	2.807
2005	0.214 ***	0.095	2.609
2006	0.262 ***	0.095	3.126
2007	0.251 ***	0.094	3.030
2008	0.232 ***	0.096	2.793
2009	0.256 ***	0.096	3.029
2010	0.237 ***	0.096	2.840
2011	0.255 ***	0.095	3.062
2012	0.254 ***	0.096	3.011
2013	0.242 ***	0.096	2.879
2014	0.253 ***	0.095	3.017
2015	0.295 ***	0.096	3.426
2016	0.283 ***	0.096	3.306
2017	0.270 ***	0.096	3.176
2018	0.268 ***	0.096	3.159

** p < 0.05, *** p < 0.01.

. It is clear that there is a positive spatial autocorrelation of GETI between regions, and it is significant at least at the 5% level. The global Moran’s I index examines the overall spatial autocorrelation of GETI, but fails to reflect the spatial autocorrelation of a certain area. Referring to Fan and Xu [71], we then explore the local spatial autocorrelation of GETI with the Getis-Ord index. The Getis-Ord index divides areas into cold spots and hot spots according to the spatial autocorrelation of variables. The cold spots are the areas where low and low values are clustered, and the hot spots are the areas where high and high values are clustered. Figure 5 shows the distribution of cold and hot spots for the average GETI from 2001 to 2018. The results revealed that Sichuan province in western China is the cold spot, while Jiangsu, Shanghai, Zhejiang, Fujian, Anhui, Hubei and Jiangxi provinces in eastern and central China are the hot spots. Obviously, GETI also has spatial agglomeration effect in local areas. To demonstrate the spatial autocorrelation more precisely, we also performed Lagrange multiplier (LM) statistical tests [72]. The results are shown in

Table 6. Spatial autocorrelation test of DSDM model.

	Spatial Error Test		Spatial Lag Test
	Statistic	p-Value	Statistic	p-Value
Lagrange multiplier	1.668	0.0900	12.894	0.0000
Robust Lagrange multiplier	0.175	0.6760	11.598	0.0010

. It can be seen that the LM statistic and the robust LM statistic of the spatial lag test are both significant at the 1% level. The LM statistic of the spatial error test is significant, but the robust LM statistic is not significant. Overall, at least both statistics of the spatial lag test are highly significant, suggesting that it is suitable to adopt the spatial econometric model.

5.3. Baseline Results

In our model, there is no bilateral causal association between environmental regulation and GETI because the measurement indicator of environmental regulation is obtained by text analysis. Meanwhile, the dynamic spatial panel model is adopted in this paper, which can better deal with the possible endogeneity issues by using the generalized moment estimation method.

The empirical results are reported in

Table 7. Main results.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
	DSAR	DSDM	DSAR	DSDM	DSAR	DSDM	DSAR	DSDM
Main
L.GETI	0.5470 ***	0.5379 ***	0.5647 ***	0.5610 ***	0.6372 ***	0.5569 ***	0.5933 ***	0.5784 ***
	(0.0332)	(0.0332)	(0.0331)	(0.0330)	(0.0304)	(0.0329)	(0.0308)	(0.0315)
lnER	1.1011 ***	1.0083 ***	1.1267 ***	0.9464 ***	1.2585 ***	1.1473 ***	1.2827 ***	1.1202 ***
	(0.3231)	(0.3533)	(0.3260)	(0.3573)	(0.3317)	(0.3463)	(0.3222)	(0.3462)
lnER2	−1.5401 ***	−1.4114 ***	−1.5218 ***	−1.2690 ***	−1.6806 ***	−1.5208 ***	−1.7506 ***	−1.5785 ***
	(0.4445)	(0.4600)	(0.4492)	(0.4672)	(0.4580)	(0.4534)	(0.4454)	(0.4558)
lnAGDP	0.2170 **	0.2168	0.3032 ***	0.3160 **	0.2125 **	0.1919	0.2586 ***	0.2880 **
	(0.0964)	(0.1333)	(0.0921)	(0.1458)	(0.1051)	(0.1335)	(0.0926)	(0.1287)
lnIS	0.1596	0.2482 **	0.1543	0.2097	0.1926	0.3721 ***	0.1357	0.2554 **
	(0.1087)	(0.1263)	(0.1103)	(0.1287)	(0.1181)	(0.1299)	(0.1080)	(0.1283)
lnOPEN	−0.0573 *	0.0097	−0.0711 **	−0.0321	−0.0517	0.0342	−0.0565 *	0.0194
	(0.0329)	(0.0373)	(0.0330)	(0.0362)	(0.0343)	(0.0357)	(0.0331)	(0.0391)
lnRD	−0.0170	−0.0638	−0.0170	−0.0733	−0.0212	−0.0867 *	−0.0179	−0.0491
	(0.0406)	(0.0478)	(0.0410)	(0.0485)	(0.0420)	(0.0516)	(0.0408)	(0.0489)
Wx
lnER		−0.6537		0.8091		0.4872		−0.0317
		(0.5969)		(0.5443)		(0.5578)		(0.5036)
lnER2		1.4611 *		−0.8867		−0.1453		0.4100
		(0.8505)		(0.7435)		(0.7863)		(0.7080)
lnAGDP		−0.0652		−0.0976		0.1636		−0.0446
		(0.1840)		(0.1716)		(0.1934)		(0.1602)
lnIS		−0.3608		−0.5343 **		−1.0291 ***		−0.2738
		(0.2266)		(0.2271)		(0.2565)		(0.1869)
lnOPEN		−0.1704 **		−0.0028		−0.1917 ***		−0.1492 **
		(0.0785)		(0.0589)		(0.0716)		(0.0613)
lnRD		0.0655		0.0115		−0.0263		0.0537
		(0.0829)		(0.0772)		(0.0827)		(0.0715)
Spatial rho(ρ)	0.3338 ***	0.3530 ***	0.2786 ***	0.2789 ***	0.2378 ***	0.1988 ***	0.2640 ***	0.2748 ***
	(0.0447)	(0.0526)	(0.0404)	(0.0452)	(0.0414)	(0.0504)	(0.0359)	(0.0419)
LR test		0.0016		0.0929		0.0000		0.0094
Wald test		0.0017		0.1003		0.0000		0.0169
R2	0.9721	0.9735	0.9721	0.9731	95.86	96.07	96.52	97.21
N	510	510	510	510	510	510	510	510

* p < 0.1, ** p < 0.05, *** p < 0.01.

. Model 2 shows the estimation results of DSDM. As a benchmark, the DSAR method is also employed and the results are shown in Model 1. Since DSDM includes DSAR to some extent, the LR test and the Wald test are used to investigate whether DSDM can be degraded into DSAR. The test results in Table 6 reject the null hypothesis, indicating that DSDM cannot be degraded into DSAR. Therefore, it is reasonable to choose DSDM for analysis.

In model 2, the spatial autocorrelation coefficient ρ is 0.3530, which is highly significant at 1% level, illustrating that the local GETI is significantly affected by the neighboring area. This is mainly because green energy technologies can spread to neighboring areas through such mechanisms as human mobility, investment, and industrial transfer. The positive spatial lag coefficient indicates that the spatial knowledge spillover will narrow the gap of GETI between areas, which is beneficial to the overall improvement of GETI [73]. As for the core independent variable, the coefficient of environmental regulation is positive, and the coefficient of the square term of environmental regulation is negative, and both are significant at 1% level. It shows that there is a significant inverted U-shaped relationship between environmental regulation and GETI. This is consistent with the results of Shang et al. [44] and Wang et al. [74]. The possible explanation is that in the early stage, stronger regulation not only increases the cost of compliance, but also reduces the effectiveness of traditional terminal governance measures [17]. For companies, GETI has become a more effective way to enhance competitiveness by improving their production processes and productivity. In this sense, the compliance cost induced by environmental regulation is offset by the innovation compensation effect [75]. Companies are willing to engage in innovations related to green energy technologies and improve their core competitiveness through cleaner production. When the intensity of environmental regulation reaches a certain threshold, environmental regulation will have a negative impact on GETI. On the one hand, under strict supervision, any non-compliance behavior of companies may incur high fines. This will crowd out production and R&D spending, thus hindering GETI activities. Clearly, the compliance cost effect caused by environmental regulation in this period exceeds the innovation compensation effect. On the other hand, the bottleneck effect also exists in GETI [76]. As the intensity of environmental regulation increases, corporate innovation may shift to other technical fields where it is easier to succeed. More concretely, strict environmental regulation will reduce the risk-bearing capacity of companies, then companies will spare no effort to seek lower-cost green technologies to reduce compliance costs. Therefore, when there is a bottleneck effect in GETI, strict environmental regulation may cause companies to switch to other green innovations with lower costs or less risks, which is not conducive to GETI. We can calculate that the inflection point of the inverted U-shaped curve is 0.3572. In the sample population, 47.96% of the observations are located to the left of the inflection point, and 52.04% of the observations are distributed to the right of the inflection point. Accordingly, provinces should appropriately adjust environmental policies according to their positions to promote GETI. Moreover, the coefficient of L.lnGETI is 0.5379 and the significance level is 1%, suggesting that GETI of the current period will be positively affected by the previous GETI [77]. Clearly, there is a path-dependent effect in GETI. That is, areas with a larger stock of green energy knowledge are more likely to pursue similar technologies in the future. In terms of control variables, industrial structure plays a significant role in promoting GETI. This is mainly because the companies related to energy are concentrated in the secondary industry. For this reason, a higher share of the secondary industry means more energy companies and more likely to obtain relevant innovation patents. The effects of other control variables on GETI are not significant. Regarding the spatial interaction term, the spatial interaction coefficients of ER and ER² are not all significant, indicating that spatial spillover effect is not significant. As for the spatial interaction term, the coefficient of W × ER is negative and the coefficient of W × ER² is positive, indicating a U-shaped relationship between environmental regulations in adjacent areas and local GETI. That is to say, the environmental regulation of neighboring areas first hinders and then promotes local GETI. However, the U-shaped spatial spillover effect of environmental regulation is not significant because the coefficient of ER is not significant. This may be due to the two-way influence of environmental regulation in neighboring areas. On the one hand, the strict environmental regulation in neighboring areas may lead to the transfer of polluting industries to the local area, which will adversely affect the local GETI [78]. On the other hand, environmental regulations in neighboring areas may have a demonstration effect, which will make local governments adopt similar policies to promote GETI [40]. The insignificant U-shaped relationship may be caused by the fact that neither effect is dominant. Among the control variables, only the coefficient of trade openness is significantly negative, implying that a higher level of trade openness in adjacent areas is not conducive to local GETI. It indicates that the higher concentration of foreign companies in neighboring areas has a siphon effect on the local area.

The results of Model 1 are similar to those of Model 2. The spatial autoregressive coefficient of GETI is positive, demonstrating that local GETI will be comprehensively affected by adjacent areas. The coefficient of ER is positive and the quadratic coefficient of ER is negative, indicating that an inverted U-shaped correlation existed between environmental regulation and GETI. Moreover, GETI has a significant path-dependent effect.

To further explore the reliability of model 2, the following robustness tests are performed. (1) Replace the spatial weight matrix. In this method, three spatial weight matrices are adopted, namely, adjacency weight matrix, economic distance weight matrix, and geographical economic distance weight matrix. Specifically, the setting of adjacency weight matrix is as follows. $w_{ij}=1$ if area i and area j are adjacent areas, and $w_{ij}=0$ if area i and area j are not adjacent areas. The estimation results of DSAR model and DSDM model of adjacency weight matrix are shown in Model 3 and Model 4 respectively. The economic distance weight matrix is set as follows. $w_{ij}=1/|{\overline{agdp}}_i-\overline{agd{p}_j}|$ if i ≠ j, and $w_{ij}=0$ if i = j, where agdp_i is the GDP per capita of area i. The estimation results of DSAR model and DSDM model of economic distance weight matrix are shown in Model 5 and Model 6 respectively. The geographical economic distance weight matrix is set as follows. $w_{ij}=w_{ij}^d\times w_{ij}^e$, where $w_{ij}^d$ represents the geographical distance weight matrix in Equation (2), $w_{ij}^e$ represents the economic distance weight matrix. The estimation results of DSAR model and DSDM model of geographical economic distance weight matrix are shown in Model 7 and Model 8 respectively. (2) Replace the measurement index of GETI. First, in line with Cheng et al. [79], the number of invention patents is taken as the proxy variable of GETI. Then, the estimation methods of model 1 and model 2 are repeated to obtain model 9 and model 10. Second, referring to Wu et al. [80], the number of patents granted is taken as an indicator of GETI, and the estimation results are shown in Model 11 and Model 12. (3) Education is added to the model as an additional control variable. Education level, to some extent, determines the quality of employees in a region and thus can be considered as one of the factors affecting GETI. The level of education is measured by the number of college students per 10,000, and the data can be obtained from China Statistical Yearbook (2002–2019). The estimation methods of DSAR and DSDM are repeated to obtain model 13 and model 14. As shown in Table 7 and

Table 8. Robustness tests (continued).

	Model9	Model 10	Model 11	Model 12	Model 13	Model 14
	DSAR	DSDM	DSAR	DSDM	DSAR	DSDM
Main
L.GETI	0.5339 ***	0.5093 ***	0.4928 ***	0.4663 ***	0.5443 ***	0.5230 ***
	(0.0342)	(0.0345)	(0.0338)	(0.0340)	(0.0331)	(0.0338)
lnER	0.8936 **	0.8823 **	0.8173 **	0.8192 *	1.0282 ***	0.9391 ***
	(0.3992)	(0.4398)	(0.4073)	(0.4498)	(0.3244)	(0.3488)
lnER2	−1.4325 ***	−1.3704 **	−1.1584 **	−1.1840 **	−1.4622 ***	−1.2972 ***
	(0.5506)	(0.5733)	(0.5627)	(0.5862)	(0.4450)	(0.4543)
lnAGDP	0.3757 ***	0.1713	0.3798 ***	0.1498	0.1393	0.0396
	(0.1199)	(0.1663)	(0.1098)	(0.1704)	(0.1055)	(0.1495)
lnIS	0.2980 **	0.5270 ***	0.4117 ***	0.6047 ***	0.1062	0.1605
	(0.1328)	(0.1569)	(0.1312)	(0.1604)	(0.1125)	(0.1270)
lnOPEN	−0.0426	0.0200	−0.0337	0.0025	−0.0700 **	0.0067
	(0.0406)	(0.0465)	(0.0416)	(0.0476)	(0.0336)	(0.0368)
lnRD	−0.0355	−0.0763	−0.0668	−0.0274	−0.0028	−0.0433
	(0.0503)	(0.0596)	(0.0530)	(0.0611)	(0.0412)	(0.0475)
lnEDU					0.1306 *	0.3096 ***
					(0.0738)	(0.1061)
Wx
lnER		−1.2546 *		−2.0445 ***		−0.9078
		(0.7435)		(0.7614)		(0.6133)
lnER2		2.1933 **		3.3189 ***		1.8037 **
		(1.0602)		(1.0861)		(0.8736)
lnAGDP		0.3697		0.5108 **		−0.0563
		(0.2288)		(0.2183)		(0.2171)
lnIS		−0.3210		−0.1200		−0.4491 **
		(0.2804)		(0.2867)		(0.2272)
lnOPEN		−0.2144 **		−0.1817 *		−0.2096 **
		(0.0975)		(0.0998)		(0.0839)
lnRD		0.0913		−0.1219		0.0522
		(0.1030)		(0.1069)		(0.0820)
lnEDU						−0.0837
						(0.1689)
Spatial rho(ρ)	0.2962 ***	0.2670 ***	0.2965 ***	0.2089 ***	0.3399 ***	0.3807 ***
	(0.0482)	(0.0586)	(0.0480)	(0.0591)	(0.0446)	(0.0521)
LR test		0.0020		0.0005		0.0000
Wald test		0.0050		0.0014		0.0001
R2	0.9615	0.9631	0.9192	0.9471	0.9723	0.9744
N	510	510	510	510	510	510

* p < 0.1, ** p < 0.05, *** p < 0.01.

, the results of the three robustness tests are similar to model 2. Namely, GETI has significant spatial spillover effect and path-dependent effect, and there is a significant inverted U-shaped correlation between environmental regulation and GETI. Furthermore, both spatial spillover effect and path dependence effect exist in GETI. Therefore, our empirical results are both reliable and robust. It is worth noting that in model 4, the LR test passes, whereas the Wald test fails. This does not affect the conclusion, because the main conclusions of Model 3 and Model 4 are consistent.

5.4. Further Analysis

As mentioned earlier, green energy technologies include AEPTs and ECTs. Does environmental regulation have the same impact on these two types of technologies? To answer this question, we further examine the effects of environmental regulation on the innovation of AEPTs and ECTs, respectively, and the results are displayed in

Table 9. Heterogeneous analysis of AEPTI and ECTI.

	Model 15	Model 16	Model 17	Model 18
	AEPTI	AEPTI	ECTI	ECTI
Main
L.lnD	0.5241 ***	0.5154 ***	0.5983 ***	0.5849 ***
	(0.0346)	(0.0349)	(0.0346)	(0.0350)
lnER	0.8890 **	0.9009 **	1.4228 ***	1.1795 **
	(0.3642)	(0.4003)	(0.4173)	(0.4616)
lnER2	−1.1993 **	−1.1304 **	−1.9983 ***	−1.7672 ***
	(0.5016)	(0.5212)	(0.5756)	(0.6012)
lnAGDP	0.3138 ***	0.3464 **	0.1967 *	0.0683
	(0.1046)	(0.1517)	(0.1194)	(0.1742)
lnIS	0.1625	0.2321	0.1431	0.3127 *
	(0.1215)	(0.1431)	(0.1396)	(0.1635)
lnOPEN	−0.0675 *	0.0026	−0.0529	−0.0045
	(0.0369)	(0.0421)	(0.0429)	(0.0488)
lnRD	−0.0069	−0.0735	−0.0149	−0.0359
	(0.0456)	(0.0542)	(0.0527)	(0.0625)
Wx
lnER		−0.5193		−0.8436
		(0.6767)		(0.7846)
lnER2		1.0652		1.9327 *
		(0.9657)		(1.1151)
lnAGDP		−0.1543		0.3105
		(0.2046)		(0.2271)
lnIS		−0.4239 *		−0.1920
		(0.2567)		(0.2958)
lnOPEN		−0.1351		−0.2309 **
		(0.0891)		(0.1024)
lnRD		0.0683		0.0900
		(0.0942)		(0.1082)
Spatial rho(ρ)	0.3188 ***	0.3556 ***	0.2908 ***	0.2421 ***
	(0.0454)	(0.0537)	(0.0502)	(0.0584)
LR test		0.0127		0.0060
Wald test		0.0136		0.0116
R2	0.9619	0.9633	0.9611	0.9626
N	510	510	510	510

Note: D stands for dependent variable; * p < 0.1, ** p < 0.05, *** p < 0.01.

. We can see that the dependent variables of both Model 15 and Model 16 are alternative energy production technology innovation (AEPTI). The difference is that the estimation method of Model 15 is DSAR, while the estimation method of Model 16 is DSDM. When the dependent variables of model 15 and model 16 are replaced by energy conservation technology innovation (ECTI), then obtain model 17 and model 18 can be obtained. It not difficult to find that the spatial autocorrelation coefficients of all models are positively significant, indicating that both AEPTI and ECTI are affected by similar innovations in neighboring areas. For environmental regulation, the outcomes of all models are similar to model 2. That is, as the intensity of environmental regulation increases, both AEPTI and ECTI increase first and then decrease. Although the inverted U-shaped correlation exists between environmental regulation and AEPTI as well as between environmental regulation and ECTI, the location of the inflection point is different. Using the mathematical calculation of the parabola, it can be known that the inflection points of environmental regulation on AEPTI and ECTI are 0.3985 and 0.3337, respectively. As shown in Figure 6a, ECTI is more likely to reach the inflection point as environmental regulation increases. The possible reason is that in a certain period, with the enhancement of environmental regulation, the direction of GETI in companies is more focused on AEPTI. This is reflected in Figure 6a as AEPTI continues to rise when ECT reaches the inflection point. Finally, it can also be found that the lag terms of all dependent variables are significantly positive, suggesting the existence of path dependence in both AEPTI and ECTI.

Considering the unbalanced characteristics of China’s economy, we also wanted to know whether the role of environmental regulation on GETI is heterogeneous in different regions. As a result, we further explore the influence of environmental regulation on GETI in eastern China as well as in central and western China. According to the National Bureau of Statistics of China, eastern China consists of 11 provinces (cities) including Beijing, Tianjin, Hebei, Liaoning, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, and Hainan. The other 19 provinces (cities) belong to central and western China. The results are reported in

Table 10. Heterogeneous analysis of GETI in eastern China and central and western China.

	Model 19	Model 20	Model 21	Model22
	Eastern China	Eastern China	Central and Western China	Central and Western China
Main
L.lnGETI	0.5156 ***	0.4558 ***	0.6267 ***	0.5959 ***
	(0.0524)	(0.0558)	(0.0426)	(0.0460)
lnER	1.2607 ***	1.5868 ***	1.2863 ***	1.0624 **
	(0.4786)	(0.4966)	(0.4398)	(0.4883)
lnER2	−1.2489 *	−1.4927 **	−1.9229 ***	−1.6915 ***
	(0.6674)	(0.6609)	(0.5955)	(0.6255)
lnAGDP	0.7428 ***	0.6319 ***	0.1093	−0.0878
	(0.1663)	(0.1986)	(0.1332)	(0.2103)
lnIS	−0.4546 **	−0.2240	0.2828 **	0.3816 **
	(0.1788)	(0.2043)	(0.1420)	(0.1774)
lnOPEN	−0.0824	0.0282	−0.0849 **	−0.0522
	(0.0796)	(0.0927)	(0.0401)	(0.0473)
lnRD	−0.1329 **	−0.2556 ***	0.0485	0.0989
	(0.0583)	(0.0702)	(0.0576)	(0.0698)
Wx
lnER		−0.4568		−0.8246
		(0.7998)		(0.7391)
lnER2		0.4754		1.6825 *
		(1.0631)		(1.0093)
lnAGDP		−0.0338		0.3586
		(0.2663)		(0.2819)
lnIS		−0.1195		−0.3065
		(0.3182)		(0.2902)
lnOPEN		−0.2199 *		−0.1088
		(0.1294)		(0.0938)
lnRD		0.2426 ***		−0.0896
		(0.0936)		(0.1271)
Spatial	0.1023 *	0.2229 ***	0.3195 ***	0.2766 ***
rho	(0.0578)	(0.0801)	(0.0563)	(0.0610)
LR test		0.0113		0.0390
Wald test		0.0150		0.1016
R2	0.9788	0.9804	0.9688	0.9705
N	187	187	323	323

* p < 0.1, ** p < 0.05, *** p < 0.01.

. Specifically, the areas in Model 19 and Model 20 are both eastern China. Model 19 is a benchmark model based on DSAR, and Model 20 shows the estimation results of DSDM. By replacing the eastern areas in model 19 and model 20 with the central and western areas, model 21 and model 22 are obtained. Clearly, the spatial spillover effect and path-dependent effect of GETI are significant in both eastern China and central and western China. The inverted U-shaped relationship is also significant, suggesting that the role of environmental regulation on GETI in different regions are similar. Further investigation of the inverted U-shaped relationship shows that the inflection point in eastern China is quite different from that in central and western China, as the inflection point values are 0.5315 and 0.314 respectively. From Figure 6b, we can see that, with the strengthening of environmental regulation, eastern China reaches the inflection point later than central and western China. This may be due to the fact that eastern China has gathered a lot of capital and R&D resources, and has higher affordability in cost compliance and greater advantages in innovation. Table 10 also shows that the spatial lag coefficient of GETI in eastern China is smaller than that in central and western China, implying that the spatial spillover effect of GETI in eastern China is smaller. The possible explanation is that the higher innovation level makes companies in eastern China pay more attention to internal improvement mechanisms. That is, rather than absorbing knowledge spillovers from neighboring areas, local companies are more inclined to increase internal R&D expenditures to improve their overall innovation level. In contrast, due to the weak capacity and the poor risk bearing capacity, companies in central and western China are more willing to absorb knowledge spillovers from neighboring areas to improve their innovation level.

6. Conclusions and Policy Implications

This paper examines the effects of environmental regulation on GETI in China from a spatial perspective. Green energy technologies are first measured by the latest IPC codes and patent data, and then the DSDM method is adopted to conduct our investigation. Finally, the possible heterogeneous impact of environmental regulation on GETI is discussed. The main conclusions are summarized as follows. (1) Environmental regulation exhibits an inverted U-shaped impact on GETI. Namely, when the intensity of environmental regulation is lower than a certain threshold, stricter environmental supervision will stimulate GETI, and when the intensity of environmental regulation exceeds the threshold, stricter environmental supervision will hinder GETI. Heterogeneity analysis results show that the inverted U-shaped correlation also exists between environmental regulation and different types of GETI, as well as between environmental regulation and GETI in different regions. A more detailed analysis shows that the inflection point of the U-shaped curve of ECTI is closer to the left than that of AEPTI. Similarly, compared with eastern China, the inflection point of the U-shaped curve in central and western China is closer to the left. (2) There are spatial spillover effect and path-dependent effect in GETI, and these two effects are significant in all cases. In other words, local GETI will be positively influenced by similar innovations in neighboring regions, implying that GETI has spatial agglomeration. Meanwhile, the current GETI is also influenced by similar innovations in the past, indicating that GETI accumulates over time.

In order to promote China’s GETI and build a low-carbon economy, this study proposes the following implications.

Considering the tremendous heterogeneity in China, policymakers should formulate differentiated environmental policies in the light of the characteristics of each province to promote GETI. According to the empirical findings, environmental regulation displays an inverted U-shaped effect on GETI. Therefore, rather than a one-size-fits-all policy, environmental policies in a province should be formulated based on its position on the inverted U-shaped curve. More specifically, when on the left side of the curve, it is reasonable to encourage GETI by increasing the intensity of environmental regulation; when on the right side of the curve, the adverse effects of strict regulation on GETI should be fully considered. From the internal composition of GETI, when the environmental regulation intensity of an area is between 0.3337 and 0.3985, the impact of environmental regulation on APETI is still positive, while the impact on ECTI is negative. Therefore, when formulating regulatory policies in such areas, it is necessary to distinguish between the AEPT field and the ECT field. For the AEPT field, environmental regulation intensity can be increased to encourage innovation, while for the ECT field, the intensity of environmental should be appropriately reduced to facilitate innovation. Moreover, heterogeneity analysis also demonstrates that the inflection points of environmental regulation in eastern China and central and western China are quite different (One is 0.314 and the other is 0.5314.). In comparison, the central and western provinces are more likely to reach the inflection point, which is detrimental to the sustainable development of GETI. Therefore, policymakers in the central and western regions should actively learn from the advanced experience of the eastern regions based on their characteristics, and spare no effort to push the inflection point of the curve to the right. Specifically, the cooperation between the central and western regions and the eastern region in the field of GETI should be vigorously promoted, such as joint construction of industrial parks and joint project development. Local companies in the central and western regions should be encouraged to learn from advanced companies in the eastern provinces, so as to improve their own research and development capabilities.

For green energy technologies, due to the high cost and high risk in the initial stage, companies have insufficient motivation for innovation. Under the path-dependent assumption, the more knowledge accumulated in the initial stage, the better for the subsequent innovation. Therefore, decision-makers should take effective measures to support the initial innovation of green energy technologies. For instance, provide financial and tax subsidies, set up government investment funds, and encourage private venture capital to enter this field. Furthermore, the positive spatial spillover effect of green energy technologies means that GETI in adjacent areas can significantly contribute to local GETI. Consequently, it is necessary to take vigorous measures to absorb the knowledge spillovers related to green energy in adjacent areas. To be specific, local governments should improve transport infrastructure and facilitate the flow of R&D elements. For western provinces with insufficient GETI, knowledge spillovers are relatively small. An important way to improve this situation is to create virtual neighbors. By building a remote collaboration platform, the local area can gain more opportunities to communicate with provinces rich in GETI resources, thus absorbing more knowledge spillovers.

Several interesting conclusions have been drawn in this paper, but there are still some limitations. On the one hand, this study mainly focuses on the impact of regulation on GETI, but there may be other determinants, such as government subsidies and energy price. Investigating the impact of these variables on GETI may yield some valuable research. On the other hand, although patent count is considered a good indicator of technology innovation, it does not reveal the differences in patent value. In fact, patent values are likely to vary widely. For this reason, if there is a convenient way to obtain patent citation data in the future, then more comprehensive research can be carried out.