Environmental Regulation, Green Technology Innovation, and Industrial Green Transformation: Empirical Evidence from a Developing Economy

Abstract

In this paper, to facilitate a green transition in developing economies globally, China is utilized as a standard case study. China has proposed the dual objectives of “carbon peaking” and “carbon neutrality”, where industrial green transformation has emerged as a critical avenue for high-quality industrial development. This paper assesses the extent of China’s industrial green transformation utilizing the SBM-ML model based on provincial panel data from 2004 to 2018 and empirically analyzes the intrinsic mechanism of environmental regulation and green technology innovation in China’s industrial green transformation. Three primary research results are drawn in this paper, which are as follows: (1) innovation compensation effects are generated and environmental regulation and industrial green transformation are positively correlated; (2) environmental regulation’s effect on the industrial green transformation is greatly enhanced under the moderating role of green technology innovation; (3) environmental regulations’ effects on the industrial green transformation differ in time and space. This influence is extremely prominent in eastern and western China but minimal in central China. On account of the introduction of inappropriate environmental restraints, China’s inter-regional pollution transfer has been intensified, leading to the general situation of China’s industrial green development being poor. This research seeks to put forward suggestions from the perspectives of policy applicability, green innovation system formation, and regional policy execution precision, so as to give reference methods for China and global industrial green transformation.

1. Introduction

When industrialization moves forward, developed countries’ environmental issues evolve in an increasingly obvious manner. With an increasing amount of environmental consciousness, mankind has started to consider the unsustainable economic development paradigm at the expense of the environment. Launched by the United Nations Environment Programme (UNEP) in 2008, “Green Economy” or “Green New Deal” initiatives have been unanimously agreed to by developed countries. The Paris Agreement raises the bar for all parties, both developed and developing, to formulate national emission reduction targets and guidelines to be conformed to in future decisions, signifying that the “greening” development trend in the economy has gradually been given higher priority by the international community and national governments.

Particularly since the People’s Republic of China was founded, China has maintained a rapid rate of industrial and economic development, making it the largest developing nation in the world. China’s industrial economy has grown at one of the fastest rates globally, with an average yearly rate of 6.7% based on data from the United Nations Industrial Development Organization. Industry has become an essential pillar supporting China’s economic growth. However, conflicts between the environment and the economy resulting from inefficient and blind development modes characterized by overdependence on inputs from resource factors have not been fully addressed. According to the Industrial Energy Efficiency Improvement Action Plan, which was published in July 2022, industrial energy consumption makes up around 65% of the overall energy consumption of the entirety of Chinese society. Accordingly, energy dependence inevitably causes a certain amount of pollutant emissions, which greatly jeopardizes the quality and efficiency of Chinese industrial economic growth.

Given the pressing need for green development, both domestically and globally, as well as the fact that the economy of China has entered a green development phase during the “14th Five-Year Plan” timeframe, the current industrial growth mode characterized by excessive consumption of energy and high pollution is no longer appropriate for the demands of modern times. Accelerating the industrial transition to a greener model is imperative, including aiming to improve the quality and efficiency related to China’s industrial development and reaching the dual goals of promoting industrial growth and lowering emissions. As a crucial part of the “green economy”, industrial green transformation relates to the intensive energy use, the reduction in environmental impacts, the improvement of input–output efficiency, and the enhancement of sustainable development momentum driven by green technological innovation. The core objective is to increase the effectiveness and quality of industrial development while realizing the “synergies” that exist between protecting the environment and economic expansion.

Among the present approaches to environmental management, environmental regulation is the most significant policy system. A substantial amount of research has been carried out regarding the connection involving industrial green transformation and environmental regulation. However, due to the heterogeneity of the actual situation, different nations have varied degrees of adaptation in terms of the forms and intensities of environmental regulation, leading to the fact that scholars’ findings on this issue are not highly consistent. This represents an important starting point for this paper in carrying out research.

Against the background of the current global advocacy for industrial green development, this paper takes China as the object of study and focuses on two issues, namely the connection regarding environmental regulation, green technology innovation, and industrial green transformation, and determining how to more effectively develop industrial green transformation through reasonable and appropriate environmental policy constraints and the construction of perfect green technology innovation systems. Based on the principle of problem orientation, this study aims to deliver useful policy recommendations for industrial green transformation, striving to provide beneficial policy information for the improvement of the quality and efficiency of industry in China, further contributing China’s strength toward worldwide industrial green transformation.

The structure of this paper is as follows. The relevant classical literature is discussed in Section 2. The research hypotheses and theoretical analysis are covered in Section 3. Section 4 introduces the methodology, along with the measurement of variables in this paper. After presenting the model setting and variable selection in Section 5, we discuss the empirical findings in Section 6. Section 7 comprises findings and policy recommendations. Finally, Section 8 points out the deficiencies and prospects of this study.

2. Literature Review

2.1. Studies on How Environmental Regulation Impacts the Green Development of Industry

According to Jing Weimin and Zhang Lu [1], moderate environmental policy constraints are of benefit to the green development of China. Qi Yawei [2] argued that strict environmental policies are more likely to help Chinese industries achieve green development. Stavropoulos et al. (2018) [3] found that environmental regulations improve industrial competitiveness by accelerating innovation. Li Ling and Tao Feng [4] empirically confirmed the presence of a non-linear relationship between environmental regulation and industrial green development, relying on data for 28 manufacturing industries. Yin Baoqing [5] also drew a similar conclusion in the context of businesses’ involvement in global specialization. Regarding a potential mechanism for the environmental management of industrial green development, Du Longzheng et al. [6] argued that environmental control can advance industrial green progress in three aspects, including cost savings, structure optimization, and value enhancement. Du et al. [7] stated that environmental policies force industries to evolve toward rationalization and advancement.

2.2. Studies on How Technological Innovation Supports Industrial Green Development

Lin Chunyan and Kong Fanchao [8] empirically examined the spatial influencing mechanism of technological advancements on industrial hierarchy by applying the spatial Durbin model (SDM) and came to the conclusion that technological innovation makes a difference in encouraging the industrial landscape to become greener. Increased R&D spending, according to Yi Xin and Liu Fengliang [9], can hasten the change in the industrial structure. According to Ding Yibing et al. [10], R&D expenditure expansion will increase technology-intensive firms’ economic share. Transformation of China’s industrial structure can thus be realized through the significant engine of technological innovation [11], and several academics have confirmed the critical role that industrial upgrading plays in promoting green development. For instance, as shown by Shen et al. (2023) [12], industrial structure upgrading can lessen the reliance on energy in the production process, raising resource-based cities’ green productivity.

Using patent application data, Barbieri (2015) [13] found that the presence of an environmental transport policy portfolio in Europe accelerated the invention of green patents, hence lowering the emissions of pollutants during the production process. According to Yang et al. (2012) [14], innovative incentives brought about by environmental legislation have the potential to boost total factor productivity in the manufacturing sector. Bitat (2018) [15], using German firms as research objects, found that the innovation incentives generated by environmental policies can help to realize the twin objectives of environmental preservation and economic growth. Therefore, it is clear that technological innovation is crucial to “green economics” globally.

2.3. Studies on the Connection Regarding Environmental Regulation and Technological Innovation

Borsatto and Amui (2019) [16] argued that the implementation of strict environmental restraint policies can spur industrial green technology innovation in both developing and developed countries. Scholars mostly take the “Porter Hypothesis” as the theory and conduct research from the perspectives of “compliance cost” and the “innovation compensation” effect. After reviewing the existing literature, we discovered that scholars have classified the causality between environmental regulation and technological innovation into three categories: a positive relationship [17,18], an inhibitory [19] or non-significant relationship [20], and a non-linear relationship, which is characterized by a “U-shaped” [21], “inverted U-shaped” [22], or multi-threshold [23] relation.

Upon reviewing the pertinent literature, several limitations were discovered in the following areas: First, scholars have typically examined the effects of technological innovation and environmental regulation separately when it comes to industrial green development. Second, the data regarding technological innovation have rarely been extended to the green technologies that participate practically in the industrial green transformation process. Third, not much analysis has been carried out on the regional and temporal disparities in industrial green transformation, and most research on this topic is performed at the industry level. Considering pertinent research, some possible contributions made by this paper are as follows: First, this paper is based on the theory of industrial ecology, drawing an integrated analytical framework including environmental regulation, green technology innovation, and industrial green transformation. Second, with respect to the data, the ratio of internal expenditure on R&D funding to total energy consumption is utilized to measure green technology innovation, bringing the research data closer to reality and reducing the possibility of estimation errors in the empirical results. Third, based on the research content, considering that there are great regional differences in the results, this paper separates the samples into several groups, namely eastern, central, and western China, with the purpose of investigating the regional variations in the impact of green technology innovation and environmental regulation on industrial green transformation. The challenge of reducing emissions has increased further since the “11th Five-Year Plan” was implemented. Thus, we take the implementation time of stricter environmental constraints as the segmentation point and divide the research samples into two groups to explore the temporal heterogeneity of the effect.

3. Theoretical Assumptions

3.1. Theoretical Analysis

The United Nations Industrial Growth Organization first introduced the idea of industrial ecology in 1989, and it was later defined as an approach to industrial growth that balances the needs of the environment and the economy. It puts forward higher requirements for achieving greater economic benefits and better environmental resource protection with minimum factor input and energy consumption, which fits the definition of sustainable development, making it green. Meanwhile, the emergence of green technological innovation can achieve benign interactions between environmental friendliness and industrial development, enhancing the efficiency of input–output. As an economy develops industrially, there will inevitably be some degree of market failure, especially in developing economies, and market failure in relation to environmental pollution is particularly prominent, due to the fact that environmental resources are non-exclusive and non-competitive public goods. Coercive policy constraints can make up for market failure and promote the green transformation of industry. As a consequence, drawing from the industrial ecology concept and market failure theory, it is of constructive significance to position environmental regulation, green technology innovation, and industrial green transformation as a unified research system to explore the possible relationship among the three.

3.2. Research Hypotheses

3.2.1. Environmental Regulations’ Direct Influence on Industrial Green Transformation

Environmental regulation can be punitive to some extent. Based on the goal of profit maximization, industrial enterprises will try to lower their pollution fines by reducing their emission of pollutants, thereby significantly reducing the adverse effects of industrial activity on the environment. In addition, for the industrial businesses that are accelerating their green transformation, their social reputation can be widely improved in the era of network media, making them more likely to win the favor of more consumers. The resulting strong competitiveness in the product market will partially offset the cost of pollution control, resulting in the so-called “innovation compensation” effect.

Environmental regulation emphasizes moderation. If the environmental policy restrictions are too tight, the pollution control investment of industrial enterprises will squeeze out some of the productive investment and technology research and development investment because of the limited amount of available investment, resulting in the reduction in its market size and the stagnation of technological innovation. More seriously, it may cause the bankruptcy of enterprises, jeopardizing green transformation. Therefore, the first hypothesis of this study is as follows:

Hypothesis 1 (H1). 

The impact of environmental regulation on industrial green transformation is contingent upon the suitability of the regulatory policy’s intensity.

3.2.2. Moderating Effect of Green Technology Innovation

Innovation in green technologies can encourage the modernization of industrial production machinery, which is advantageous for the change in manufacturing toward high efficiency and low emissions [24]. Generally, with a greater extent of green technology innovation, less pollution emissions will be emitted from industrial production. Under the watchful eye of the authorities concerning environmental contamination, firms will suffer heavy penalties for excessive pollution emissions, pushing industrial organizations to innovate in green technologies. Industrial businesses are incentivized to pursue green technology innovation, which is often driven by regulation pertaining to environmental limitations. This is concretely manifested as breaking the path of dependence on traditional production and realizing technological transformation throughout the whole process of production, thereby greatly contributing to the transformation of traditional industries. Under policy constraints, green technology innovation causes the proportion of clean industries and products to increase significantly, so as to achieve the green transformation of regional industry. Given this, Hypothesis 2 is put forward:

Hypothesis 2 (H2). 

Environmental regulation positively influences the green transformation of industry through innovation in green technologies.

4. Methodology and Measurement

4.1. Measurement Method and Indicators

Because of the increasingly serious environmental issues around the world, the idea of green and low-carbon growth is starting to gain traction, which is key to keeping the economy and environment in balance and is an important part of industrial transformation. When measuring the extent of green transformation of China’s industry, it is vital to incorporate environmental factors into the traditional analytical framework. Therefore, green total factor productivity is the variable adopted to measure the extent of industrial green transformation precisely.

Currently, the Solow residual method, stochastic frontier analysis, and data envelopment analysis (DEA) are the three main ways to measure green total factor productivity. Among them, DEA has multiple advantages, such as modeling, which can occur without a particular type of production function. Meanwhile, the dimensionless treatment of data and non-desired outputs being incorporated into the productivity analysis framework are also among its most prominent advantages. However, the radial DEA model may overestimate actual efficiency if over-input or under-output creates non-zero slack. In this regard, the SBM model proposed by Tone (2003) [25] can effectively address the measuring deficiencies of the radial DEA model. With the further introduction of the Malmquist index, we can measure the total factor productivity of each DMU depending on the change in its relative position concerning the production frontier and the change in the production frontier itself (Tian and Feng, 2022) [26]. Based on the outstanding contribution of Chung et al. (1997) [27] in model extension, green total factor productivity can now be estimated more accurately using the SBM-ML model that takes environmental factors into account.

In this study, multiple inputs and outputs, including expected and undesired, were included in a unified analysis framework. With the VRS assumption, we applied a non-radial SBM-ML model to measure the variation in green total factor productivity from t to t + 1. With the exception of the indicator in the base period set to 1, the green total factor productivity for the other periods was obtained using cumulative multiplication. Despite the limitation of the ML index, only reflecting the variation in green total factor productivity over adjacent periods, it accounts for increases in expected output and decreases in undesired output, as well as the input of factors such as manpower, capital, and energy, which can effectively reflect the current balance status in industrial development and environmental protection at this point. Therefore, we could not reject SBM-ML as a suitable model that can comprehensively and accurately measure the degree of China’s industrial green transformation.

Considering each province as a DMU, we assumed that each DMU has an input $X$, an expected output $Y^e$, and an undesired output $Y^u$. Among them, $X=\left(x_1,\cdots ,x_n\right)\in R^{m\times n},Y^e=\left(y_1^e,\cdots ,y_n^e\right)\in R^{s_1\times n}$, and $Y^u=\left(y_1^u,\cdots ,y_n^u\right)\in R^{s_2\times n},X,Y^e,Y^u>0$. The production possibility frontier is expressed as follows:

$$\mathrm{P}=\left\{\left(x,y^e,y^u\right)|x\ge X\lambda ,y^e\le Y^e\lambda ,y^u\ge Y^u\lambda ,\lambda \ge 0\right\}$$

(1)

According to the definition of DMU, the solution process of the non-radial SBM model containing unanticipated outputs is as follows:

$$\begin{gathered} P^{*}=\min {\displaystyle \frac{1-\frac{1}{m}{\displaystyle {\sum }_{i=1}^m\frac{S_{i0}^{-}}{x_{i0}}}}{1+\frac{1}{S_1+S_2}\left({\displaystyle {\sum }_{r_1=1}^{S_1}\frac{S_{r_1}^e}{y_{r_{1}0}^e}+{\displaystyle {\sum }_{r_2=1}^{S_2}\frac{S_{r_2}^u}{y_{r_{2}0}^u}}}\right)}} \\ \mathrm{s}.\mathrm{t}. x_0=X\lambda +s^{-};y_0^e=Y^e\lambda -s^e;y_0^u=Y^u\lambda +s^u \\ {s}^{-}\ge 0,s^e\ge 0,s^u\ge 0,\lambda \ge 0 \end{gathered}$$

(2)

where $s^{-}$ indicates excess inputs, $s^u$ indicates excessive pollution emission, and $s^e$ indicates a shortfall in expected outputs. Following Färe et al. (2007) [28], the ML index is decomposed into technical progress and technical efficiency.

$$\begin{gathered} GTFP\left(x_{t+1},y_{t+1},b_{t+1};x_t,y_t,b_t\right)=\sqrt{{\displaystyle \frac{E^t\left(x^{t+1},y^{t+1},b^{t+1}\right)}{E^t\left(x^t,y^t,b^t\right)}}\times {\displaystyle \frac{E^{t+1}\left(x^{t+1},y^{t+1},b^{t+1}\right)}{E^{t+1}\left(x^t,y^t,b^t\right)}}} \\ ={\displaystyle \frac{E^{t+1}\left(x^{t+1},y^{t+1},b^{t+1}\right)}{E^t\left(x^t,y^t,b^t\right)}}\times \sqrt{{\displaystyle \frac{E^t\left(x^t,y^t,b^t\right)}{E^{t+1}\left(x^t,y^t,b^t\right)}}\times {\displaystyle \frac{E^t\left(x^{t+1},y^{t+1},b^{t+1}\right)}{E^{t+1}\left(x^{t+1},y^{t+1},b^{t+1}\right)}}} \\ =TEC\left(x_{t+1},y_{t+1},b_{t+1};x_t,y_t,b_t\right)\times EFF\left(x_{t+1},y_{t+1},b_{t+1};x_t,y_t,b_t\right) \end{gathered}$$

(3)

In the above formula, $x_t$ represents a series of input indicators, including manpower input (number of employees), capital input (capital stock), and energy input (energy consumption); $y_t$ represents the expected output, which is estimated by the real GDP (at constant prices with 2004 as the base period) of each province, city, and autonomous region; $b_t$ represents the undesired output, which is the composite index calculated by the entropy value method; and selected detailed indicators are the CO₂ emissions and SO₂ emissions of industry.

GTFP can be decomposed into two parts, TEC and EFF. Values of GTFP, TEC (green technology progress), and EFF (green technology efficiency) greater than 1 indicate the growth of green total factor productivity, the enhancement of green technology progress, and the improvement of green technology efficiency from t to t + 1, respectively; meanwhile, a reduction in green total factor productivity, lagging in green technological progress, and a decline in green technological efficiency from t to t + 1 are indicated when the values of GTFP, TEC, and EFF are less than 1.

4.2. Measurement Results

We measured the regional industrial green transformation using the SBM-ML index to calculate green total factor productivity in 30 provinces (Tibet is not included in this study for lack of data). Figure 1 depicts trends in the green transition in 30 provinces. Values above 1 in Figure 1 indicate that the province’s green transition efficiency is in a state of growth, and values below 1 indicate that it is in a state of decline. Looking closely at Figure 1, the green total factor productivity of Beijing showed great fluctuations from 2004 to 2018, and that of Guangdong, Zhejiang, Henan, Tianjin, Shanghai, and other regions presented a peak value during the rising process between 2015 and 2018. The green total factor productivity of Inner Mongolia, Guangxi, Xinjiang, Liaoning, and other regions was generally in a stable state from 2004 to 2018. To sum up, it is evident that China has exhibited a steady and generally encouraging development trend with its industrial green transformation. The letters in Figure 1 are abbreviated Chinese letters for the names of the provinces, and the provinces represented by these letters are described in Appendix A of this article.

Based on the calculation results of the average value of regional green total factor productivity shown in Figure 2, the green total factor productivity of Ningxia, Shanxi, Jiangxi, Hainan, Gansu, Guizhou, Chongqing, and Qinghai is less than 1, indicating a slow rate of industrial green growth. Because of the weak industrial base and industrial structure dominated by high energy consumption, there is still a long way to go in the process of industrial green transformation in these regions, including abandoning resource dependence, reducing pollution emissions, and finding a green growth model that is more appropriate for their development. The letters in Figure 2 have the same meanings as in Figure 1, and the provinces represented by these letters are described in Appendix A in this article.

5. Model Setting

5.1. Economic Modeling

Considering Hypothesis 1 (H1), a dual fixed-effect panel is designed.

$$\ln GTF{P}_{i,t}=\alpha +{\beta }_{1}E{R}_{i,t}+{\beta }_2{X}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(4)

where i and t stand for the province and year, respectively.${ GTFP}_{i,t}$ represents the green total factor productivity province i in year t, ${ER}_{i,t}$ represents the environmental regulation intensity of province i in year t, and $X_{i,t}$ denotes the combination of control variables that affect the green total factor productivity. ${\mu }_i$ symbolizes the regional fixed effect, ${\delta }_t$ signifies the time fixed effect, and ${\epsilon }_{i,t}$ indicates the stochastic perturbation item.

In addition, with the goal of testing Hypothesis 2 (H2), we further introduce green technology innovation (TEH), adding the interaction terms of ER and TEH to those in Equation (4), as expressed in Equation (5).

$$\ln GTF{P}_{i,t}=\alpha +{\beta }_{1}E{R}_{i,t}+{\beta }_{2}TE{H}_{i,t}+{\beta }_{3}E{R}_{i,t}\times TE{H}_{i,t}+{\beta }_4{X}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(5)

In Equation (5), ${TEH}_{i,t}$ illustrates the degree of innovation in green technologies across various time and provinces. ${GTFP}_{i,t,},{ER}_{i,t}$, $X_{i,t}$, ${\mu }_i$, ${\delta }_t,{\mathrm{and} \epsilon }_{i,t}$ all have the same definitions as in Equation (4). Notably, if ${\beta }_3$ exceeds zero, Hypothesis 2 (H2) is confirmed.

5.2. Variable Selecting

In this paper on the extent of industrial green transition calculated using GTFP, GTFP is the explained variable. Regarding the two central explanatory variables, ER is determined by dividing the total amount invested in mitigating industrial pollution by the secondary industry’s augmented value, while TEH is determined using the ratio of internal R&D expenditure to total energy consumption of industrial enterprises above the designated size. Considering the time lag between the generation of innovation and its practical application, this paper delays the green technological innovation indicator by one period in empirical research. To prevent the omission of variables associated with industrial green transformation, this study selects industrial structure, urbanization level, government scale, and enterprise scale as the control variables. Industrial structure (ind) is measured by the ratio of the tertiary industry to the secondary industry. Urbanization (urb) represents the percentage of individuals living in urban areas in relation to the whole population size. Government size (gov) is represented by the ratio of region fiscal expenditure to GDP. Enterprise scale (size) refers to the percentage of the primary business revenue of industrial enterprises above the scale in the number of industrial enterprises of the above-designated size in the region.

Table 1. Variable definitions.

Variable Types	Variable Symbols	Variable Calculations
Explained Variable	GTFP	Input	Capital Stock
			Labor Input
			Energy Consumption
		Expected Output	Real GDP
		Unexpected Output	Industrial CO2 Emission
			Industrial SO2 Emission
Explanatory Variable	ER	Environmental Regulation	Investments in Industrial Pollution Governance/Value Added of Secondary Sector
	TEH	Green Technology Innovation	Internal R&D Spend /Total Energy Consumption
Control Variable	ind	Industrial Structure	Value Added (Tertiary Industry)/Value Added (Secondary Industry)
	urb	Urbanization Level	Urban Population/Total Population
	gov	Government Scale	Local Fiscal Expenditure/Regional GDP
	size	Enterprise Scale	Principal Business Revenue/Number of Firms beyond Designated Size

Source: organized by author.

gives a detailed account of the meaning of each variable and how it was measured.

5.3. Data Acquisition and Treating

Panel data from 2004 to 2018 across 30 provinces were applied in this study (data for Tibet were not included because of their unavailability). Data were collected mainly from the China Statistical Yearbook, the China Statistical Yearbook of the Environment, the China Industry Statistical Yearbook, the China Energy Statistical Yearbook, the official website of the National Bureau of Statistics, the CSMAR database, and the EPS database. Variable symbols as well as measurement methods can be obtained from Table 1. Meanwhile, this study used linear interpolation to supplement some missing data. Outliers are instrumental in causing bias in the regression results, so we shrank extremity data before regression.

5.4. Descriptive Statistics Analysis of Variables

The sample’s descriptive statistical results, including sample size, mean, standard deviation, quartiles, as well as minimum and maximum values, are presented in

Table 2. Descriptive statistical results.

Variable Name	Obs	Mean	SD	25%	50%	75%	Min	Max
lnGTFP	450	0.00	0.09	0.00	0.00	0.01	−0.62	0.20
ER	450	0.00	0.00	0.00	0.00	0.00	0.00	0.02
TEH	450	138.19	138.57	31.50	81.66	212.93	0.00	639.96
ind	450	1.03	0.53	0.73	0.88	1.12	0.54	3.66
gov	450	0.45	0.67	0.08	0.20	0.48	0.00	3.71
urb	450	0.52	0.15	0.42	0.50	0.58	0.23	0.89
size	450	2.38	1.27	1.25	2.21	3.21	0.54	5.67

Data: calculated by author.

. These results demonstrate China’s poor green total factor productivity level: its logarithmic mean is 0, its standard deviation is 0.09, its lower quartile is 0, and its upper quartile is 0.01. ER intensity has a maximum value of 0.02 and a mean value of 0. The sample data generally satisfy the needs of the follow-up study, and also align with the basic situation of China, which is undergoing economic transformation and tightening environmental policy constraints. The degrees of correlation between each of the variables are listed in

Table 3. Variable correlation coefficient.

	lnGTFP	ER	TEH	ind	gov	urb	size
lnGTFP
ER	−0.02
TEH	0.17 ***	−0.15 ***
ind	0.07	0.10 **	0.01
gov	0.04	0.71 ***	−0.06	0.18 ***
urb	0.08 *	−0.48 ***	0.20 ***	0.43 ***	−0.36 ***
size	0.21 ***	−0.32 ***	0.29 ***	0.19 ***	−0.13 ***	0.35 ***

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Data from authors’ calculations.

, initially demonstrating the validity of this paper’s hypotheses.

6. Empirical Results

6.1. Benchmark Regression Results

The regression results are shown in

Table 4. Benchmark regression.

	(1)	(2)	(3)
	lnGTFP	lnGTFP	lnGTFP
ER	3.456 **	4.067 ***	4.863 ***
	(1.418)	(1.454)	(1.573)
TEH		−0.000 *	−0.016
		(0.000)	(0.041)
Interaction			0.083 ***
			(0.020)
ind	−0.020	−0.016	0.086
	(0.035)	(0.041)	(0.274)
gov	0.045 **	0.076 ***	0.013
	(0.019)	(0.024)	(0.010)
urb	−0.060	0.073	−0.000
	(0.242)	(0.272)	(0.000)
size	0.011	0.015	−0.000 *
	(0.008)	(0.010)	(0.000)
_cons	0.004	−0.161	−0.169
	(0.135)	(0.162)	(0.165)
Observed Value	450	420	420
Adjusted R2	0.244	0.256	0.259
Province Effect	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes

Note: * p < 0.1, ** p < 0.05, *** p < 0.01, with standard errors in parentheses. Data from authors’ calculations.

. Considering the control variables, column (1) shows the beneficial effects of environmental regulations on the green transformation of industry, including industrial structure, government size, urbanization level, and enterprise size. The regression coefficient of the key explanatory variables, as demonstrated in Table 4, is equal to 3.456. Our research demonstrates that the efficiency of industry’s green transformation increases by 3.456% for every 1% increase in the magnitude of environmental regulation. Hypothesis 1 (H1) can therefore be effectively acknowledged. According to the aforementioned research, economies with a comparable economic structure to China’s can encourage a green shift in their industries by tightening environmental regulations.

Comparing this with the existing research results found in other countries, we found that scholars generally display different perspectives regarding what part environmental regulation plays in industrial green transformation. Ghosal et al. (2019) [29] studied the pulp industry in Sweden, concluding that the government’s environmental policy contributed to the industry’s green transformation by driving technological change. Meanwhile, Murshed et al. (2021) [30] selected four developing countries as their research objects, including Bangladesh, India, Pakistan, and Sri Lanka, and argued that well-designed environmental regulation can help to better promote the green transformation of the economy. Conversely, Kraus et al. (2020) [31] contended that such regulations will drive up additional operating costs for businesses and reduce their ability to compete in the market. These different research conclusions could possibly be the reason for the different prosperity stages and the different environmental regulations applied in each country. In any case, we believe that, under the current circumstances, environmental regulation is indeed beneficial to the greening of Chinese industry, a typical representative of developing countries, and this may provide a useful policy reference for the industrial green transformation of countries with similar development circumstances to China.

There is an extensive body of literature on the validity of Porter’s hypothesis within the context of different countries. For instance, Ramanathan et al. (2010) [32] argued that environmental regulation inhibits enterprises’ technological innovation, with a greater “compliance cost” than “innovation compensation” effect. Chintrakarn (2008) [33] also drew similar conclusions. In contrast to the existing studies, based on Chinese context, this paper verified environmental regulations’ beneficial effects on green development. Column (3) shows that the environmental regulation coefficient does not change significantly with the introduction of the interaction term. The interaction term’s coefficient value is 0.083, and the core explanatory variable has a coefficient value of 4.863, which is greater than it was prior to the introduction of the interaction term. It can be concluded that the influence of environmental regulation on industrial green transformation seems to be positively moderated by green technological innovation, which effectively verifies Hypothesis 2 (H2) proposed above. This means that countries with similar development circumstances to China should vigorously activate green technological innovation to offset the potential crowding-out impact of environmental regulations on innovation resources, thus enhancing the capacity of environmental regulation during the industrial green transformation period.

6.2. Heterogeneity Discussion

6.2.1. Regional Heterogeneity

The research hypotheses proposed above in this paper are supported by the benchmark regression results. However, there exist notable disparities in the degree of economic growth and resource endowment among regions, which may lead to differentiated impacts of environmental regulation on industrial green transformation in different regions. The study by Lin et al. (2017) [34] showed that China’s green overall productivity of factors is uneven, and Hu et al. (2005) [35] noted that it is higher in the east than in the central and western regions of China. Therefore, it is necessary to conduct grouping regression based on the three geographical regions of eastern, central, and western China.

Table 5. Regional heterogeneity results.

	(1)	(2)	(3)
	lnGTFP	lnGTFP	lnGTFP
ER	2.667 **	1.992	6.443 **
	(0.891)	(1.997)	(2.768)
TEH	−0.000	−0.000	−0.000
	(0.000)	(0.000)	(0.000)
Interaction	0.000	0.000	0.000
	(0.000)	(0.000)	(0.000)
Control Variable	Yes	Yes	Yes
Observed Value	154	112	154
Adjusted R2	0.139	0.185	0.164
Province Effect	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes

Note: * p < 0.1, ** p < 0.05, *** p < 0.01, with standard errors in parentheses. Data from authors’ calculations.

presents the findings from the heterogeneity regression. The findings of the regression for the eastern region are displayed in column (1), those for the central region are displayed in column (2), and those for the western region are displayed in column (3). It is found that environmental regulation in the eastern and western areas has a substantial effect in fostering industrial green transformation. However, this influence is negligible in the central region. The possible reasons leading to this result are as follows: the eastern region boasts a strong economic base and great achievements in sci-tech innovation. It is capable of quickly completing the task of transforming energy-intensive and pollution-producing industries and giving birth to a number of new industries. For the western region, powerful state policy can effectively help in industrial transformation. The economic strength of the central region is not high enough, while policy support is relatively weak, and as a result, the industrial transformation process as a whole in this region frequently moves slowly.

Therefore, when establishing environmental regulatory policies, decision-makers must take full account of national and regional carrying capacities. Excessive penalties that are not adapted to the current state of regional development will weaken the development vitality of industrial enterprises in some countries and regions, to the detriment of the industrial green transition, which is an important reason for the differences in the findings of scholars from various countries. Therefore, each region should take into account its own special circumstances and dynamically adjust the strength of its environmental regulations in order to better support the industrial transformation process, providing successful examples of global sustainable development.

6.2.2. Temporal Heterogeneity Analysis

With the aim of establishing whether more stringent environmental policies can effectively accelerate China’s industrial transformation following the promulgation of the task of emissions reduction in the “11th Five-Year Plan”, we took 2010 as a time segmentation point and divided the research sample data into two groups for further regression.

Table 6. Temporal heterogeneity results.

	(1)	(2)
	lnGTFP	lnGTFP
ER	16.059 *	−4.760 ***
	(9.181)	(1.447)
TEH	−0.001 **	0.000
	(0.001)	(0.000)
Interaction	0.000	0.000 *
	(0.000)	(0.000)
Control Variable	Yes	Yes
Observed Value	Yes	Yes
Adjusted R2	0.110	0.302
Province Effect	Yes	Yes
Year Effect	Yes	Yes

Note: * p < 0.1, ** p < 0.05, *** p < 0.01, with standard errors in parentheses. Data from authors’ calculations.

presents the regression results prior to 2010 (including 2010) and subsequent to 2010 in column (1) and column (2), respectively. This study finds that environmental regulations impede the industrial green transition in the time period following the promulgation of stronger environmental policies overall. The potential explanations for this result are as follows: the “11th Five-Year Plan” represented a critical phase in China’s industrial green reform, and significant progress in green industrial development was made during this period. Strict environmental policies can cause regional pollution migration, with highly polluting enterprises having a tendency to relocate from coastal provinces to western regions with less strict policies, thus causing the overall pace of the green transformation of China’s industry to slow down.

6.3. Robustness Test

Focusing on improving the credibility and precision of the results, this study adopts the methods of replacing the environmental regulation variables and removing the values of abnormal years for the robustness test. Columns (1) to (3) in

Table 7. Robustness check.

	(1)	(2)	(3)	(4)	(5)	(6)
	lnGTFP	lnGTFP	lnGTFP	lnGTFP	lnGTFP	lnGTFP
lnER	0.020 **	0.023 **	0.027 **	0.020 **	0.025 *	0.030 **
	(0.009)	(0.011)	(0.011)	(0.010)	(0.013)	(0.014)
TEH		−0.000 *	−0.000		−0.000 *	−0.000
		(0.000)	(0.000)		(0.000)	(0.000)
Interaction			0.000 **			0.000 **
			(0.000)			(0.000)
ind	−0.018	−0.013	−0.012	−0.019	−0.012	−0.012
	(0.032)	(0.038)	(0.038)	(0.035)	(0.045)	(0.045)
gov	0.039 *	0.067 **	0.073 ***	0.042 *	0.078 **	0.086 ***
	(0.021)	(0.027)	(0.024)	(0.022)	(0.030)	(0.026)
urb	−0.089	0.036	0.044	−0.083	0.096	0.107
	(0.220)	(0.242)	(0.238)	(0.232)	(0.283)	(0.278)
size	0.013	0.017	0.016	0.014	0.020	0.019
	(0.008)	(0.011)	(0.011)	(0.009)	(0.012)	(0.012)
_cons	0.142	0.001	0.020	0.141	−0.021	0.003
	(0.140)	(0.147)	(0.147)	(0.146)	(0.168)	(0.169)
Observed Value	450	420	420	420	360	360
Adjusted R2	0.248	0.262	0.265	0.250	0.270	0.274
Province Effect	Yes	Yes	Yes	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes	Yes	Yes	Yes

Note: * p < 0.1, ** p < 0.05, *** p < 0.01, with standard errors in parentheses. Data from authors’ calculations.

show the robustness check results with the replacement of environmental regulation variables. Meanwhile, columns (4) and (5) show the results excluding samples during financial crisis periods. The coefficients of industrial green transformation in Table 7 are always favorable, reaching statistical significance, therefore confirming how reliable this study’s conclusions are.

7. Conclusions and Enlightenment

7.1. Main Findings

As the world’s industrial development process proceeds, regardless of the kind of economy, be it developed or developing, there exists a certain degree of market failure in environmental pollution control. Under the guidance of the international strategy of sustainable development, we took China, the largest developing country, to systematically review the internal impact mechanisms of environmental regulation or green technology innovation on industrial development, putting forward a series of theoretical hypotheses to be tested. On the basis of the panel data from 2004 to 2018, we assessed the level of industrial green transformation in 30 Chinese provinces using the SBM-ML model. This study’s findings illustrate that environmental policies promote industrial green transformation, and the validity of this conclusion is verified by a series of robustness tests. Green technology innovation also generates beneficial moderating influences during the industrial shift toward sustainability. Regarding geographically specific heterogeneity, environmental regulation promoted the green transformation in eastern and western China. However, this effect is not evident in central China. Regarding temporal heterogeneity, following China’s “11th Five-Year Plan” period, which involved stronger environmental regulations, the regional transfer of industrial pollution began to intensify, indicating that environmental regulation policies that exceed certain limits have an inhibitory effect on China’s transition toward a greener economy.

7.2. Policy Suggestions

7.2.1. Formulate Moderate Environmental Policies

Industry currently contributes significantly to China’s economic growth, but its non-green development mode does not fulfill the demands of modern economic development. Environmental policy is crucial to alleviating pollution externality. To ensure that environmental constraints contribute to the transformation of industry, using the maximum extent, the government should take each province’s industrial bearing capacity into consideration when making environmental policies. Penalties for violating relevant policies cannot be applied leniently, otherwise the restraints of industrial pollution emissions would be ineffective. At the same time, the punishment cannot override the actual bearing capacity of the industry, thus destroying the original industrial layout of China. Therefore, while drafting policies, relevant departments should take into account the actual development of China’s industry, progressively improve the environmental regulation framework, and establish appropriate environmental policies.

7.2.2. Construct an Industrial Innovation System

The constructive impact of green technology innovation on industrial green transformation cannot be ignored. However, innovation in green technology involves an elevated level of risk, and the majority of developing countries lack independent innovation vitality and rely heavily on the introduction of technologies. Furthermore, green technology innovation has not been demonstrated to fully play an intrinsic driving role in industrial green transition. To expedite a country’s industrial green transformation, the government should make a difference by stimulating green technology innovation and creating an innovative atmosphere, forming a synergy between the government and enterprises to create an industrial green innovation system. In this process, the government should provide sufficient factor guarantees to boost the anti-risk capacity of industrial enterprises. First, from the perspective of innovation policy, the government should implement multi-type green subsidy policies to encourage leading enterprises in the industrial field to actively participate in innovation. Second, from the perspective of enterprise participation, the government ought to concentrate more on minor enterprises, guiding them to participate in the technical innovation process. Third, in terms of financial support, financial credit reform ought to be expedited to mitigate the issue of inadequate funding for technological research and development faced by enterprises.

7.2.3. Flexibly Adjust Measures in Accordance with Regional Circumstances

The industrial structure and resource endowments of eastern, central, and western China vary significantly, which causes significant regional differences in the manner that environmental regulations affect the industrial green revolution. Therefore, when designing relevant constraints, one-size-fits-all management should be avoided to the greatest extent. Enhancing the ecological environment and fostering regional economic development should be the common goals of environmental policies of different regions. Meanwhile, it is imperative to ensure that the focus of environmental policy varies from region to region. For the eastern region, the main task is to eliminate outdated production capacity and force the closure of high-polluting industrial enterprises. The major objective in the central and western regions is to fully utilize government financial subsidies, increase investment in innovation research, continuously enhance the capacity for green technology innovation, and actively accept some industries with development advantages and prospects transferred from the eastern region.

8. Research Deficiencies and Prospects

Providing a policy governance perspective for the study of industrial green transformation, this study mainly concentrates on the direct influence of environmental regulation on industrial greening, based on China’s current development context, and further discusses the regulating role of green technology innovation, which provides theoretical reference and practical guidance for traditional regional industrial transformation. Because obtaining pertinent data and information is difficult, there is still some room for further research. In this regard, we put forward some valuable ideas, hoping to provide a reference for future research in related fields.

In the future, scholars can deeply explore the multiple dynamic relationships among explained and explanatory variables by improving and expanding the model, which may lead to new research directions and interesting findings. In addition, the type of environmental policy applied varies from country to country. Scholars can comb through the main environmental regulation types in each country and comparatively discuss their heterogeneous influence on the synergistic promotion of industrial development and environmental protection.