Does Heterogeneous Environmental Regulation Induce Regional Green Economic Growth? Evidence from China

Abstract

Understanding the differences in the effects of heterogeneous environmental regulation (HER) can help governments select optimal environmental regulation (ER) to promote technological innovation (TI) and green economic growth (GEG). This paper chooses Chinese provincial panel data from 2003 to 2017 to investigate the intrinsic link between HER, TI, and GEG. The results show the following: (i) The effectiveness of different types of ER is different, and market incentive-based ER (MIER) is optimal; (ii) Public participation-based ER (PPER) has played a good role in promoting TI; (iii) The impact of executive order-based ER (EOER) on TI is insignificant; (iv) Only MIER-induced TI can contribute to GEG; (v) The effects of HER vary across different regions.

1. Introduction

China’s rapid industrialization has also brought about environmental pollution while promoting economic development [1]. Polluting companies have been widely criticized for their reckless discharge of pollutants. According to the 2020 Global Environmental Performance Index Report released in June 2020, China ranks 120 out of 180 countries and regions. In 2020, China’s total carbon dioxide emissions will rank first at 9.899 billion t, 1.628 billion t more than the second-ranked United States (4.457 billion t) and third-ranked Europe (3.814 billion t) combined. It shows that the current environmental situation in China is not optimistic. Given this, environmental protection has gradually become a social hotspot. The government needs to intervene reasonably due to the complex relationship between ecological protection and green economic development. ER has become one of the essential means for the government to improve environmental quality. The effects of ER are not isolated but numerous. ER will lead to a “compliance cost effect” and “innovation compensation effect [2].” Specifically, under strict ER, companies are required to treat pollutants to meet standards before they can discharge them. This adds to the cost of the business. The “cost-following effect” hinders the production and development of enterprises [3]. Moreover, ER will encourage companies to carry this out and increase R&D expenditures on sanitary products and green processes. Enterprise equipment upgrades can drive productivity and green benefits. In conclusion, the magnitude of the “innovation compensation effect” determines the direction of ER’s impact on GEG. The key to ER affecting GEG is whether the “innovation compensation effect” is enough to compensate for the “compliance cost effect.” In addition, different types of ER tools have different impacts on TI and GEG. From a practical point of view, the Chinese government has issued a series of environmental protection policies to form sustainable development. China’s ER’s intensity has continuously improved, and policy tools have constantly been enriched. The ER currently in force in China can be divided into MIER, EOER, and PPER [4].

MIER refers to the ways in which ER can improve environmental quality through market mechanisms. We summarize it into two categories. The first category includes measures to internalize the externalities of environmental pollution, including penalties for pollutant discharge projects and incentives for environmental protection projects [5]. The second category is market mechanisms to address the externalities of environmental pollution, such as emissions trading. Penalties, incentives, and market transactions can make enterprises consciously reduce pollutant emissions. On the one hand, these instruments make enterprises voluntarily take the responsibility of protecting the environment. On the other hand, companies are encouraged to adopt TI and energy-saving facilities. However, the initial investment in R&D is enormous. Its economic benefits may only sometimes compensate for the input costs [6]. TI can ultimately boost economic growth as returns to scale are realized. Therefore, the impact of MIER on the GEG is still to be determined. EOER refers to the government promulgating relevant laws and regulations to impose mandatory constraints on enterprises, such as formulating pollutant discharge standards [7]. The execution and effect of EOER are relatively fast. Similar to the influence mechanism of MIER, enterprises will increase pollution control expenses and introduce advanced production equipment. Enterprises must reduce pollutant emissions through TI to comply with regulations. Otherwise, they will be punished. However, TI has a significant investment, high cost, and long cycle. In addition, regulatory authorities have formulated more stringent environmental protection standards. Polluting companies have to reduce production or shut down. This damages business effectiveness and GEG. It can be seen that EOER is an insufficient driving force for the TI of enterprises, and its impact on TI and the green economy is still being determined. PPER refers to the public’s complaints to the relevant government departments through petitions, the Chinese People’s Political Consultative Conference (CPPCC) proposals, the National People’s Congress (NPC) proposals, and other channels when the public is dissatisfied with the quality of the environment. This feedback mechanism of public participation affects the governance environment [8]. On the one hand, the government will increase investment in environmental pollution control. On the other hand, regulators will also control the enterprise through MIER and EOER. In actively carrying out pollution control and TI, enterprises obtain certain economic benefits and may obtain social benefits due to the brand effect created by ecological protection. Since enterprises are the main constraints of ER, there is also uncertainty about the impact of PPER on the GEG.

Many researchers have attempted to analyze the relationship between ER and TI, but the findings are controversial [9,10]. According to the theory of internalization of environmental pollution externalities in neoclassical economics, strict ER will increase the cost input of corporate environmental pollution control and prevention elements. The investment capital of productive resources and technical elements of enterprises will be squeezed out. The profit margin of the enterprise will be reduced. This weakens the market competitiveness of enterprises. The innovative ability of enterprises is therefore suppressed [11,12]. Neoclassical economists think in terms of statics. Many scholars also support this view. Bao and Chai [13] find that ER reduces innovation efficiency using provincial panel data for China from 2005 to 2019. Ouyang et al. [14] found that ER affects TI differently for firms with different ownership. ER holds back TI in SOEs due to higher environmental costs. Yang and Li [15] found that ER inhibits the TI of Chinese listed companies. In contrast, the revisionist school considers the relationship between ER and TI at a dynamic level. The representative figure is Porter, who proposed that appropriate ER can help push enterprises to innovate and generate “ innovation compensation “ that exceeds the cost of ER [16]. His theory is called the “Porter hypothesis” and has been confirmed by many scholars. Kaswan et al. [17] and Yadav et al. [18] found a positive impact for the Green Lean Six Sigma on environmental sustainability. Furthermore, Jaffe and Palmer [19] classified the Porter hypothesis into a broader range of categories based on the final effects of ER and discovered that ER increases R&D expenditures. Wu et al. [20] argued that formal and informal ER pressures could promote green TI. Wang et al. [21] evidenced that ER contributes to corporate environmental responsibility and green technology innovation. With the continuous refinement of research objects, the impact of different types of ER on TI has attracted the attention of scholars. They have proved that different types of ER has different effects, but MIER is more effective than PPER and EOER. Yu and Wang [22] found that MIER and EOER aid in optimizing China’s industrial structure, while PPER does not work. Guo et al. [23] believe that HER significantly impacts the green TI of heavily polluting enterprises.

Similarly, neoclassical economists and Porter have drawn opposing conclusions about the relationship between environmental policy and total factor productivity (TFP). Neoclassical economists view ER as a form of policy pressure. They argue that ER imposes additional burdens on firms, leading to a transfer of resources from “production” to “pollution control” [24]. The most representative “costly regulation hypothesis” argues that the profits from new technologies are lower than their costs under strict ER [25]. Many empirical studies also support this view. Li et al. [26] believe that ER reduces total factor productivity in the region. Xie et al. [3] considered that formal ER inhibits economic growth by increasing risks for firms and investors. Liu et al. [27] argued that EOER hinders the growth of the Chinese industry. Du et al. [28] stated that the better the region’s economy, the less ER inhibits green TI and industrial development. In contrast, economists represented by Porter have raised objections. Porter thinks companies will seize the opportunity of regulation to invest in technology. This can improve the enterprise’s production efficiency and product quality, thereby reducing the production costs of the enterprise or increasing the operating income. Therefore, the proper ER can improve the business performance of enterprises. The Porter hypothesis has been extensively tested. Lu et al. [12] found that sewage charges increase TFP by promoting green TI. Li et al. [29] suggested that appropriate ER can improve environmental quality and stimulate economic growth in China. Pang et al. [30] elaborated that the positive effects of ER on business performance vary by region and industry. They found that when ER was increased, the productivity of refineries in the region increased compared to refineries in other parts of the United States. Hamamoto [31] concluded that pollution control could increase R&D investment, thus increasing Japanese manufacturing for over 20 years. Yang et al. [32] argued that bargaining weakens the effectiveness of ER, but ER can still increase firms’ TFP. In addition, some studies revealed that the effect of ER on environmental total factor productivity (ETFP) reverses in response to changes in the intensity of ER [33,34].

Facing the multiple constraints of resources, environment, and sustainable development drivers, improving China’s production efficiency through green development and transformation is a practical problem that needs to be solved urgently. Environmental quality is a public good with external characteristics. The operability and effectiveness of relying solely on the independent emission reductions of micro-entities, such as enterprises and the operational improvement of green production efficiency, are weak. Hence, scholars have focused on whether ER is essential to China’s environmental policy system and whether it can promote GEG. Meanwhile, China is in a critical period of transition and succession of old and new dynamics. The traditional factor-driven approach is challenging to maintain, and it is urgent to rely on comprehensive policy support to promote green development. This paper explores the intrinsic association between ER, TI, and GEG in this context. The different effects and pathways of action of different ER modalities are clarified. The mechanism and feasible pathway of ER on GEG were analyzed. Our research has important practical significance for maintaining sustainable and healthy economic development. Specifically, this paper attempts to answer the following questions: Does ER have a significant facilitative effect on TI and GEG? What is the mechanism by which ER affects GEG? What role does TI play in the impact of ER on GEG? What are the differences in the impact of different types of ER tools on TI and GEG? Do the effects of HER differ across regions? Solving these problems will help the government choose the optimal ER in different regions and achieve a positive economic and environmental interaction.

There is abundant literature about ER, TI, and GEG. In addition, the effects of different types of ER have been fully explored. However, their intrinsic connections have yet to be analyzed. Most literature uses mediation effect models to analyze the role of TI in ER and GEG. Direct and indirect effects of ER on GEG are not distinguished. They fail to clarify the transmission mechanism through which ER induces on TI and affects the GEG. Meanwhile, differences between regions in the effects of TI induced by different types of ER through GEG are also ignored. We investigated the intrinsic relationship between HER, TI, and GEG. The three marginal contributions of this paper are as follows: (1) This paper compares the direct and indirect effects of MIER, PPER, and EOER on GEG, emphasizing the effects of TI induced by three types of ER tools on GEG; (2) MIER was proved to be optimal because of its long-acting mechanism; (3) We found that the effects of three types of ER-induced TI on GEG were different among different regions. The choice of ER tools in different regions should be tailored to local conditions. To visually display the work of this paper, we draw a research framework diagram, as shown in Figure 1.

2. Research Design

2.1. Data and Variables

We obtained data from the China Statistical Yearbook, the China Environment Yearbook, and the China Science and Technology Statistical Yearbook. We supplemented a small amount of missing data by interpolation based on data availability, representativeness, and matching. Due to the lack of data, this study did not include Xizang, Taiwan, Hong Kong, and Macau.

In terms of TI, there are currently many indicators to measure TI. The indicator we chose is the proportion of internal R&D expenditures to GDP. On the one hand, this indicator shows how much each province attaches importance to TI and effectively measures the level of TI. (For convenience, we will refer to all provincial administrative units in China as “provinces”, including provinces, municipalities, and autonomous regions). On the other hand, this indicator can conveniently study ER’s direct and indirect effects on TI [19,31,35]. The traditional method of calculating TFP ignores the environmental pollution problems resulting from economic development [36]. In this paper, environmental factors are incorporated into the efficiency assessment system to facilitate the measurement of sustainable development [37]. As a result, this paper chooses ETFP to measure the impact of ER on the GEG. There are two models for measuring ETFP: a slacks-based measure (SBM) and an epsilon-based measure (EBM). Compared to SBM, EBM considers a combination of non-radial and radial factors, allowing for a more accurate efficiency estimate [38]. Therefore, we choose the EBM to calculate the ETFP growth index of each province. Finally, we obtain the ETFP growth index of 30 provinces in China from 2003 to 2017.

Table 1. Measurement indicators for ETFP.

Indicator System	Indicator Name	Measurement Method	Unit
Input indicator	Labor input	Employment	10,000 people
	Capital input	Physical capital stock	billion
	Energy input	Energy consumption	10,000 t of standard coal
Expected output indicator	Economic output	Real GDP	billion
Expected output indicator	Pollutants	SO2 emissions	10,000 t
		COD emissions	10,000 t

shows the measures of inputs, expected outputs, and unintended outputs involved, where capital inputs are calculated using the perpetual inventory method.

The Chinese government implements a variety of ER tools for environmental management. A single indicator does not measure the actual strength of ER, leading to a biased measure of its effectiveness. Hence, this paper focuses on the differences in the effects of HER. We classify three types of ER in China: MIER, EOER, and PPER. Sewage charges, green credits, and pollution discharge rights are the main instruments for decision makers to protect the environment through market mechanisms. Meanwhile, the data from these three tools are relatively comprehensive. Ultimately, we select them to calculate the MIER index. Considering that law, regulation, and administrative review are mandatory, this paper uses three indicators to estimate the EOER index: administrative review, local rule, and local administrative regulation. The petition letter, NPC recommendation, and CPPCC proposal reflect the public’s environmental concerns. Thus, this paper uses them to calculate the PPER index. In addition,

Table 2. Description of variables.

Index	Description
MIER	Sewage charge
	Green credit
	Pollution discharge right
EOER	Administrative review
	Local regulation
	Local administrative regulation
PPER	Petition letter
	NPC recommendation
	CPPCC proposal
Inv	Investment in industrial pollution control/GDP
FDI	Foreign direct investment/GDP
Fcf	Gross fixed capital formation/GDP

shows the definition of the control variables in this paper.

First, we normalize individual indicators, such as sewage charges and the number of petition letters.

$${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{j}}^{\mathrm{s}}=\frac{{\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{j}}-\mathrm{M}\mathrm{i}\mathrm{n}\left({\mathrm{U}\mathrm{R}}_{\mathrm{j}}\right)}{\mathrm{M}\mathrm{A}\mathrm{X}\left({\mathrm{U}\mathrm{R}}_{\mathrm{j}}\right)-\mathrm{M}\mathrm{i}\mathrm{n}\left({\mathrm{U}\mathrm{R}}_{\mathrm{j}}\right)}$$

(1)

${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{j}}$ represents the raw data of the j-th indicator in the i-th province. $\mathrm{M}\mathrm{i}\mathrm{n}\left({\mathrm{U}\mathrm{R}}_{\mathrm{j}}\right)$ and $\mathrm{M}\mathrm{a}\mathrm{x}\left({\mathrm{U}\mathrm{R}}_{\mathrm{j}}\right)$ represent each province’s minimum and maximum values, respectively. Then, we calculate three ER indices.

$${\mathrm{E}\mathrm{R}}_{\mathrm{i}}^{\mathrm{m}}={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{U}\mathrm{E}}_{\mathrm{i}\mathrm{j}}^{\mathrm{s}}/\mathrm{n}$$

(2)

where $\mathrm{n}$ represents the number of indicators. ${\mathrm{E}\mathrm{R}}_{\mathrm{i}}^{\mathrm{m}}$ represents the intensity of different ER types in different provinces, where $\mathrm{m}$ represents different types of ER.

Table 3. Descriptive statistics.

Variables	Definition	Obs	Mean	Std	Min	Max
Tech	RD internal expenditure/GDP	450	1.3668	1.0465	0.1749	6.0137
ETFP	Environmental TFP growth index	450	0.9698	0.1215	0.4855	2.0590
MIER	Market incentive-based ER index	450	1.0997	0.8180	0.0000	3.0000
EOER	Executive order-based ER index	450	0.4719	0.5058	0.0000	2.7572
PPER	Public participation-based ER Index	450	0.7122	0.6561	0.0000	3.0000
Inv	Investment in industrial pollution control/GDP	450	0.1629	0.1358	0.0067	0.9918
FDI	Foreign direct investment/GDP	450	0.0238	0.0190	0.0004	0.1051
Fcf	Gross fixed capital formation/GDP	450	0.5555	0.1689	0.3109	1.4928

also shows the description of the control variables.

2.2. Model Design

This paper refers to Hamamoto’s two-stage model to discuss the relationship between ER, TI, and GEG [31]. The advantage of this model is that it can analyze not only the direct impact of ER on TI but also the impact of ER-induced TI on GEG. Therefore, this model can distinguish the direct and indirect effects of ER on GEG and deeply analyze the mechanism of ER’s effect on GEG.

2.2.1. The Effect of ER on TI

First, we study the relationship between ER and TI, referring to the structure–behavior–performance (SCP) paradigm. We set up the following model:

$${\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{E}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{I}\mathrm{n}\mathrm{v}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{F}\mathrm{c}\mathrm{f}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

$\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}$ represents technical innovation; $\mathrm{E}\mathrm{R}$ represents MIER, PPER and EOER; $\mathsf{\beta }$ is the parameter to be estimated. On the one hand, we consider that explanatory and control variables may have endogeneity problems. On the other hand, the impact of ER on TI and GEG may lag. As a result, we analyze the explanatory and control variables with a one-period lag.

2.2.2. The Effect of TI Induced by ER on GEG

Next, we distinguish TI induced by ER from TI induced by non-ER and focus on the relationship between TI induced by ER and GEG [31].

$${\mathrm{E}\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}1}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_1{\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}2}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{I}\mathrm{n}\mathrm{v}}_{\mathrm{i}\mathrm{t}}+{{\mathsf{\beta }}_4\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{F}\mathrm{c}\mathrm{f}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(4)

$${\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}1}_{\mathrm{i}\mathrm{t}}=\widehat{{\mathsf{\beta }}_{\mathrm{E}\mathrm{R}}}\times \left[\frac{{∆\mathrm{E}\mathrm{R}}_{\mathrm{i}\mathrm{t},\mathrm{t}-1}}{{\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}}\right]\times {\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{t}}$$

(5)

$${\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}2}_{\mathrm{i}\mathrm{t}}={\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{t}}-{\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}1}_{\mathrm{i}\mathrm{t}}$$

(6)

$\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}1$ represents TI induced by ER. $\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}2$ represents TI induced by non-ER.

2.2.3. The Direct Impact of ER on GEG

Finally, we use TI as a control variable to analyze the direct impact of ER on GEG.

$${\mathrm{E}\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{E}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{I}\mathrm{n}\mathrm{v}}_{\mathrm{i}\mathrm{t}}+{{\mathsf{\beta }}_4\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{F}\mathrm{c}\mathrm{f}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(7)

ETFP is the explained variable. MIER, EOER, and PPER are explanatory variables.

3. Empirical Analysis

3.1. The Effect of ER on TI

The results show that MIER and PPER significantly affect TI, but EOER has no significant effect on TI [7,39,40]. MIER is the use of market forces by the government to regulate the environment. MIER can provide more significant incentives for enterprises to conduct research and development. Therefore, this has encouraged TI activity to a large extent. Society is currently in the stage of multi-governance, and the public has gradually formed green awareness in the process of social development. The public actively participates in environmental governance. In order to meet the consumption needs of the public and create an excellent corporate image, companies often conduct TI to reduce pollution. Market conditions and the public opinion environment can incentivize TI more than the constraints of concrete policy terms. Strict policy terms will cause enterprises to reduce production to meet strict ER but will not stimulate innovation, transformation, and innovation investment.

Table 4. The effect of ER on TI.

Variables	National		East	Central	West
	At Sight	Lag 1
MIER	0.275 ***	0.269 ***	0.382 ***	0.204 ***	0.122
	(5.08)	(5.38)	(3.68)	(4.58)	(1.46)
EOER	0.012	−0.028	0.024	−0.032	−0.012
	(0.39)	(−0.80)	(0.45)	(−0.64)	(−0.91)
PPER	0.074 **	0.034	0.044	0.165 ***	0.052
	(2.46)	(−1.25)	(−1.1)	(−3.45)	(−1.40)
Inv	−0.173	−0.23	0.248	−0.067	−0.142
	(−1.26)	(−1.62)	−0.53	(−0.37)	(−1.53)
FDI	−6.476 *	−5.976 *	−8.504 **	13.596	−1.878
	(−2.03)	(−1.97)	(−2.78)	(1.61)	(−0.64)
Fcf	−0.05	−0.062	0.551	0.296	0.055
	(−0.24)	(−0.31)	(0.94)	(0.93)	(0.27)
C	1.215 ***	1.313 ***	1.516 ***	0.339	0.788
	(7.69)	(8.59)	(3.93)	(1.68)	(8.16)
R2	0.4481	0.4479	0.5702	0.644	0.2915
Obs	450	420	165	120	165

Note: t statistics in parentheses; *, **, and *** indicate a significance level of 0.1, 0.05, and 0.01, respectively.

also contains regression data with a one-period lag. We can see that the EOER and PPER effects have changed. The EOER coefficient turns positive to negative, but it is still insignificant. The coefficient of PPER is still positive but not significant. We can see that only the EOER effect has changed. MIER still promotes TI, but EOER and PPER are ineffective for TI. These findings suggest that MIER has effective, long-term incentives for TI when compared to EOER and PPER. This is consistent with the views of Ma et al. [41] and Jia et al. [42]. In addition, the coefficient of the control variable Inv is not significant. This indicates that investment in industrial pollution control has no significant effect on TI. Moreover, the coefficient of the control variable $\mathrm{I}\mathrm{n}\mathrm{v}$ is insignificant. This suggests that the investment in industrial pollution control fails to boost TI. The idea of pollution first and then treatment is harsh for solving environmental problems. It may also hurt TI. Whether FDI can bring about technological progress is controversial. Some scholars believe that FDI promotes local economic growth and brings advanced technology [43,44,45]). Other scholars argue that FDI inhibits technological progress, especially in developing countries [46,47]. The results show that FDI significantly inhibits TI. Following the pollution haven hypothesis, developing countries have attracted many low-tech and high-polluting enterprises due to their loose ER and abundant resources. This also verified the viewpoint of Song et al. [48]. Instead, these companies will inhibit TI [49,50]. The coefficient of the control variable $\mathrm{F}\mathrm{c}\mathrm{f}$ is also insignificant.

Furthermore, this paper finds that there are regional differences in the effect of ER on TI. MIER can significantly contribute to TI in the eastern and central regions, while PPER is only effective in the central region [39]. MIER, PPER, and EOER do not work in the west. Regional differences exist in China’s economic development, with the eastern and central regions outpacing the west. The determination of the local government to implement ER is relatively strong. In addition to ER, they have introduced other policies that can foster TI. In addition to ER, they have also introduced a series of policies to promote TI. Due to synergies between policies, ER tools come into play. The western region’s local economic foundation is weak, and the government’s ER enforcement is insufficient. Meanwhile, as ER becomes stricter in the eastern and central areas, the cost of pollution for enterprises becomes higher. However, the western regions have set low ER thresholds to attract investment for economic development, thereby attracting some low-tech polluting enterprises [51]. Therefore, ER cannot work in the west area. The research results of Zhang et al. [36] were verified.

3.2. The Effect of TI Induced by ER on GEG

Table 5. The effect of TI on GEG.

Variables	National			East
	MIER	EOER	PPER	MIER	EOER	PPER
Tech1	0.048 ***	0.022	0.028	0.0003 **	0.035	0.008
	(2.86)	(1.51)	(1.05)	(2.30)	(0.86)	(0.79)
Tech2	0.047 ***	0.025	0.040 **	−0.003	0.0003	−0.006
	(2.83)	(1.68)	(2.35)	(−0.40)	(0.47)	(−0.33)
Inv	0.209 *	0.201	0.208 *	−0.0976	−0.083	−0.097
	(1.80)	(1.65)	(1.78)	(−1.13)	(−1.03)	(−1.11)
FDI	−0.786 *	−1.074 ***	−0.899 **	−1.367 ***	−0.1.364 ***	−1.338 ***
	(−1.97)	(−3.04)	(−2.25)	(−5.84)	(−5.55)	(−3.93)
Fcf	−0.067	−0.042	−0.0562	0.010	0.001	0.001
	(−1.40)	(−0.91)	(−1.21)	(0.09)	(0.01)	(0.02)
C	0.927 ***	0.953 ***	0.9354 ***	1.034 ***	1.033 ***	1.033 ***
	(25.20)	(27.36)	(24.54)	(16.39)	(16.23)	(12.33)
R2	0.0437	0.0402	0.0408	0.4545	0.0656	0.0615
Obs	450	450	450	165	165	165
Variables	Central			West
	MIER	EOER	PPER	MIER	EOER	PPER
Tech1	0.096 *	0.112 **	0.093	0.012	−0.008	−0.096
	(1.90)	(2.91)	(1.83)	(0.22)	(−0.25)	(−1.80)
Tech2	0.114 **	0.112 **	0.111 **	0.012	0.015	−0.012
	(2.83)	(2.75)	(2.72)	(0.22)	(0.47)	(−0.27)
Inv	0.291 ***	0.321 **	0.283 ***	0.348 *	0.352 *	0.355 **
	(4.16)	(2.80)	(3.63)	(2.16)	(2.16)	(2.36)
FDI	−3.164	−0.024	−3.000	1.522	1.405	1.774
	(−1.64)	(−0.32)	(−1.32)	(0.72)	(0.69)	(0.87)
Fcf	−0.080	−0.024	−0.084	−0.036	−0.033	−0.023
	(−0.81)	(−0.32)	(−0.87)	(−0.45)	(−0.46)	(−0.35)
C	0.919 ***	0.899 ***	0.921 ***	0.890 ***	0.887 ***	0.904 ***
	(12.68)	(14.70)	(12.88)	(13.67)	(14.28)	(14.79)
R2	0.0661	0.0902	0.0628	0.0829	0.0844	0.0966
Obs	120	120	120	165	165	165

Note: t statistics in parentheses; *, **, and *** indicate a significance level of 0.1, 0.05, and 0.01, respectively.

shows that the TI induced by MIER promotes GEG, while the TI induced by PPER and EOER has no significant effect on GEG. It shows that after MIER significantly enables TI, the TI generated by it further promotes GEG [31,32]; this verifies the viewpoint of Fan and Sun [52]. Inv can significantly promote GEG among the control variables, while FDI significantly adversely affects the immediate green economy. This is consistent with the conclusion of Li et al. [53]: direct investment in pollution control can also significantly promote GEG.

Likewise, the effect of ER on GEG varies in different regions. TIs resulting from MIER have contributed significantly to GEG in the eastern region. TIs induced by EOER and PPER have no significant impact on GEG. TI induced by non-ER also works in the east. We guess that the development of MIER in the eastern region is more mature. The government relies more on market mechanisms to adjust the environment, and the transformation mechanism of TI achievements is also better, thus driving GEG. The entry of many low-tech polluting enterprises has inhibited the local green economy. MIER- and EOER-induced TI significantly boosted GEG in the central region, while PPER-induced had less significant impact on the green economy. The coefficients of Tech1 for MIER and EOER are 0.112 and 0.096, which are significant at 5% and 10%, respectively. It implies that GEG in the central region is more dependent on government constraints and investment due to the imperfect market incentive mechanism. In the western region, the effects of MIER, PPER, and EOER on GEG are all insignificant. We guess that with the increasingly stringent ER in the eastern region, many polluting enterprises have moved westward, inhibiting the green economy of the west. Local governments are pursuing economic growth, making ER difficult to exert its effects. At the same time, non-ER-induced TI in the west will not encourage GEG. It means that the western region lacks a system for transforming TI into green products. Ultimately, TI cannot promote GEG. The control variable $\mathrm{I}\mathrm{n}\mathrm{v}$ can promote GEG in the central and western regions. Combining data from the east, we argue that fiscal and environmental governance spending may dampen GEG in regions with well-developed MIER systems. Instead, budgetary expenditures in ecological governance can contribute considerably to GEG in the central and western areas where the MIER system needs to be better developed.

From an international perspective, EOER is still the primary method of ER in the United States. Similarly, EOER has many problems in the United States, such as high costs and insufficient incentives for TI. Due to the many disadvantages of ER, developed countries have begun to introduce more diverse ER methods based on EOER. MIER is more widely used in Organization for Economic Cooperation and Development countries. The most apparent change in US environmental policy is the use of market instruments. The tradable license system is one of the most commonly used MIER methods in the United States. Most European countries mainly rely on sewage charges to control enterprises’ discharge behavior. Due to the cost and efficiency advantages of MIER, economists have praised it highly. The United States advocates adopting market-based, cooperative, and voluntary environmental protection as well as mandatory regulatory measures.

3.3. Direct Effect of ER on GEG

The results suggest that MIER can also directly facilitate GEG. MIER can play a positive boosting effect. By adhering to the market-regulating principle of “whoever shall be treated, and the polluters shall be charged,” it aims to guide enterprises to independently control the level of pollution discharge and the cost of pollution control. MIER can effectively supplement and improve administrative tools. Enterprises are forced to improve energy efficiency. In addition, the results of the one period lag show that EOER and PPER directly and significantly promote GEG [54]. It indicates that the direct effect of MIER on GEG is time-sensitive, while the effect of EOER and PPER is time-lagged [39]. The possible reason is that after implementing MIER, such as sewage charges, it will act on polluting enterprises immediately, reducing the discharge of pollutants and promoting the transformation of enterprises. Nevertheless, there is a time lag between EOER and PPER from inception to implementation. For example, ER, such as administrative reconsideration and petitioning, takes a period from acceptance to final processing. Ultimately, it will take time to promote GEG. From a regional perspective, the direct effect of MIER on GEG was significant only in the central region. We speculate that the eastern region is more likely to promote GEG through induced TI. In contrast, due to the weaker ER in the western region, the direct impact on GEG is insignificant.

3.4. Robustness Test

To make the research in this paper more convincing, we adopted three methods for robustness testing, including supplementary variables, adjusting the sample period, and replacing variables.

First, although this paper controls for the effects of the variables $\mathrm{I}\mathrm{n}\mathrm{v}$, $\mathrm{F}\mathrm{D}\mathrm{I}$, and $\mathrm{F}\mathrm{c}\mathrm{f}$ on ER, industrial structure, regional economies, and trade openness may also make a difference in the effects of ER. To avoid serious endogeneity problems caused by omitted variables, we added three control variables to the baseline regression for robustness testing. The industrial structure is measured by the share of secondary industry value-added in GDP; the logarithm of GDP per capita counts the regional economy; and the percentage of total exports and imports in GDP measures trade openness. The results show no change in the effects of MIER, EOER, and PAPER on TI and GEG. As a result, the results of this paper are robust.

Second, we test our conclusions by adjusting the sample period. When analyzing the entire data set, we will find that the conclusions obtained may be completely different by changing different periods. An inevitable conclusion is that results have been achieved within a certain period per our expectations. However, we find that conclusions are entirely different when the sample time is pushed back ten years or ten years and then returned. Therefore, choosing the correct research period is also very important. We can test our conclusions in the robustness test by widening or shortening the sample period. In this paper, the sample period was shortened to 2005–2014. The new regression results show that the effect of HER does not change. This indicates that our findings are reliable.

Third, we replaced the core variables for robustness testing. Although R&D expenditure is widely used in the research of TI, considering the possible endogeneity problems, this paper chooses the number of patent applications to represent TI. The coefficients of the variables in the re-regression results are generally consistent with those in Table 4, Table 5 and

Table 6. The direct impact of ER on GEG.

Variables	National		East	Central	West
	At Sight	Lag 1
MIER	0.025 **	−0.006	0.002	0.032 *	0.023
	(2.40)	(−0.59)	(0.20)	(2.06)	(1.58)
EOER	0.011	0.031 **	0.045	0.014	−0.023
	(0.79)	(2.13)	(1.69)	(0.77)	(−0.73)
PPER	0.011	0.026 **	−0.006	0.031	0.005
	(1.12)	(2.39)	(−0.52)	(0.95)	(0.32)
Inv	0.243 **	−0.180 ***	−0.067	0.345 **	0.339 *
	(2.12)	(−3.95)	(−0.82)	(3.23)	(2.20)
FDI	−0.842 **	−0.162	−1.088 ***	−2.744	0.994
	(−2.28)	(−0.31)	(−4.13)	(−1.18)	(0.50)
Fcf	−0.105 *	−0.195 ***	0.0003	−0.111	−0.071
	(−1.95)	(−2.81)	(0.00)	(−1.36)	(−0.96)
Tech	0.017	0.005	0.018	0.05	−0.049
	(1.01)	(0.27)	(1.17)	(0.97)	(−1.11)
C	0.945 ***	1.094 ***	0.963 ***	0.925 ***	0.959 ***
	(28.57)	(20.37)	(14.35)	(16.72)	(14.25)
R2	0.0545	0.1022	0.1048	0.0829	0.0919
Obs	450	420	165	450	450

Note: t statistics in parentheses; *, **, and *** indicate a significance level of 0.1, 0.05, and 0.01, respectively.

, indicating that the findings are convincing.

Finally, to avoid the resulting error caused by endogeneity in the regression, we use the system GMM to re-estimate the original equation. Systematic GMM estimation is a commonly used method for endogeneity testing, which is used to improve the problem of weak instrumental variables with lagged variables. Consistency estimates are obtained by incorporating the lagged items of endogenous explanatory variables into the estimation equation as instrumental variables. This paper uses the system GMM estimation method to test endogeneity. The GMM estimation results are about the same as the baseline regression results. Therefore, the conclusion of this paper is reliable.

4. Conclusions and Policy Recommendations

This paper selects the panel data of 30 provinces in China from 2003 to 2017 to study the relationship between ER, TI, and GEG. Compared with the existing literature, the advantage of this paper is that we calculate the extent to which three types of ER induce TI. On this basis, we analyzed the impact of three types of ER-induced TI on GEG. In addition, regional differences in the effect of ER-induced TI on GEG were also analyzed. In contrast, the direct effect of ER on GEG was focused. Analyzing the regional differences between the three types of ER helps to choose the optimal strategy according to local conditions. The main research conclusions are the following: (1) MIER significantly promoted TI and GEG. MIER-induced TI promoted GEG. Therefore, this is consistent with Porter’s view that moderate ER can bring about TI and promote economic growth. That is, MIER positively verified Porter’s hypothesis. A one-period lagged regression of MIER revealed a long-term mechanism of MIER effects; (2) PPER can only promote TI on the spot, but the direct impact on GEG has a time lag. PPER-induced TI has no significant effect on GEG; (3) EOER has no significant effect on TI. It also has a time lag in driving the green economy; (4) The effects of MIER, PPER, and EOER differed regionally. In the eastern region, MIER can promote TI and GEG. MIER and PPER foster TI in the central region but did not affect GEG. In the western region, MIER, ERER, and PPER are ineffective for TI and GEG; (5) FDI significantly suppressed TI and GEG. $\mathrm{I}\mathrm{n}\mathrm{v}$ only affects GEG in the central and western regions.

Hence, we make the following recommendations: (1) MIER should be a priority in environmental management. In this paper, MIER is found to be the optimal ER tool. Compared to PPER and EOER, MIER effectively promotes TI and GEG in the long term. When faced with the choice of ER tools, the government should be more inclined to adopt MIER. In addition, the government should pay attention to the implementation of supporting policies in the transformation process from MIER to TI and from TI to the green economy to speed up the construction of a long-term MIER system; (2) Environmental public opinion monitoring platforms should be regulated. The current public opinion supervision needs more continuity. A unified national public opinion supervision platform should be established. The government has increased publicity efforts to encourage more public participation in environmental monitoring; (3) Environmental agencies should be cautious about using EOER to achieve a “one size fits all” approach. Instead, environmental authorities should limit polluting firms while encouraging them to innovate and operate in a greener way; (4) The government should fully utilize the power of pollution control investment in environmental governance in the central and western regions. Due to the relatively lagging economic development, the government faces the dual problems of economic development and environmental governance. Increasing direct investment in pollution control can effectively solve this problem. However, in the long run, the national level should help the central and western regions to establish an ER system through guidance and assistance. (5) The role of FDI in environmental management should be emphasized. On the one hand, decision makers should attract more investment in high-tech industries and clean enterprises. On the other hand, they should increase their support for innovative local enterprises and rely more on the TI of local enterprises.

This paper still has some limitations. COVID-19 has had a significant impact on China’s environment and economy. Future research can focus on the impact of HER on TI and the green economy under the impact of COVID-19. In addition, the digital economy has become another focus of China’s economic development. Focusing on the synergy between the digital economy and HER for the environment is also very meaningful.