The nonlinear impact of R&amp;D investment on green technological progress in China’s manufacturing industry: Based on the law of diminishing marginal returns

doi:10.21203/rs.3.rs-1737944/v1

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The nonlinear impact of R&D investment on green technological progress in China’s manufacturing industry: Based on the law of diminishing marginal returns

https://doi.org/10.21203/rs.3.rs-1737944/v1

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Green technological progress is an important prerequisite for the sustainable development of the manufacturing industry, which is usually closely related to R&D investment activities. This paper explores the nonlinear relationship between R&D investment and green technological progress based on panel threshold regression model using panel data of 26 manufacturing industries in China from 2004 to 2017. The results show that the double-threshold model can better explain the nonlinear relationship between the two, and the R&D investment in the three ranges of low, medium and high levels can significantly promote green technological progress in manufacturing industry. However, with the improvement of R&D investment level, the promotion effect of R&D investment on the progress of manufacturing green technology is decreasing, which explains the low R&D intensity of China's manufacturing industry to a certain extent. When the level of R&D investment reaches a certain level, its promoting effect on green technological progress will be greatly reduced, and the motivation of enterprises to invest in R&D based on self-interest will decrease, so that the scale of R&D investment will be lower than the optimal scale of society. R&D investment can also improve green technical efficiency change. In addition, environmental regulation can promote green technological progress in manufacturing industry. However, due to the implementation of output-oriented environmental regulation policies, China’s environmental regulation can inhibit the improvement of green technical efficiency change. Based on the conclusion, this paper argues that China should implement differentiated R&D subsidy policies for manufacturing enterprises, especially to increase R&D subsidies for enterprises with a medium level of R&D investment, and formulate appropriate environmental regulatory policies, so as to promote green and low-carbon transformation of China's manufacturing sector.

Manufacturing industry

R&D investment

Green technological progress

Law of diminishing marginal returns

Super-SBM

Manufacturing industry is the economic lifeline of a country, and it is also regarded as an important way for late-developing countries to catch up with developed countries. In the past 40 years, China has become known as "the world's factory" because of its low labor costs, strong business ecosystem, lack of regulatory compliance, low taxes and duties, and competitive currency practices. However, diminishing of competitive advantage, including lower labor costs, a devalued RMB and lower export tax rates, means that China gradually lost its edge against other low-cost nations of the world. The ratio of manufacturing output to GDP has been continuing to decline, dropping to 26.2% in 2020, down 6 percentage points, becoming the country with the biggest decline among major manufacturing countries. The continuous decline in share of manufacturing value added in GDP in China has caused a wide and long-lasting discussion in academia. Huang (2021) believe that after China enters the upper-middle income stage, manufacturing industry is an important driver of economic growth, and the decline in its share of value-added will increase the risk of China falling into “middle-income trap”. Therefore, China's 14th Five-Year Plan (2021–2025) also calls for keeping the share of manufacturing value added in GDP basically stable. Based on the reality of the weakening of the competitive advantages of China's manufacturing, in order to keep the share of manufacturing value added in GDP basically stable, China's manufacturing industry must carry out industrial upgrading and realize the sustainable development of the manufacturing industry through rapid technological progress. Under the current background of China's " carbon peak and carbon neutral " strategy and the new era of green development, environmentally-friendly technological progress is also the meaning of the topic, that is, to take a green and low-carbon transformation road to achieve a win-win situation for industrial development and environmental protection.

Existing theories suggest that technological progress and R&D investment (Romer 1990; Grossman and Helpman 1994) activities are closely related. In order to encourage enterprises to increase investment in research and development, China began to implement the policy of additional tax deductions for enterprise research and development in 2008, and gradually increased the deduction ratio. Figure 1 shows the change of R&D intensity in China's manufacturing industry from 2003 to 2019. It can be seen that China's manufacturing industry R&D intensity experienced a sharp decline during the 2008 financial crisis. Entering the "12th Five-Year Plan (2011–2015)" period, China began to implement the innovation-driven development strategy, and the R&D intensity of China's manufacturing industry began to increase year by year. By 2019, the R&D intensity of China's manufacturing industry reached 1.44%. However, the R&D intensity of China's manufacturing industry is still low at present. According to the "2020 China Manufacturing Power Development Index Report", the R&D intensity of the world's manufacturing powers is generally above 3%. Therefore, what is the reason for the low R&D intensity of China's manufacturing industry? This is the core issue that this paper focuses on. The traditional view holds that knowledge is non-competitive, making it impossible for enterprises to obtain all the benefits of R&D investment, resulting in the scale of R&D investment being lower than the social optimal scale (Nelson 1959; Arrow 1962), and insufficient intellectual property protection will lead to the above problems more serious (Griliches 1992). This paper believes that another important reason for the low level of R&D investment in the China’s manufacturing industry is the "law of diminishing marginal returns", that is, the marginal promotion effect of R&D investment on the progress of green technology decreases significantly with the increase of R&D intensity, which makes the R&D investment income of enterprises become smaller, resulting in a lack of incentive for enterprises to move towards higher levels of R&D investment. Methodologically, this paper explores the relationship between R&D investment and green technological progress based on panel threshold regression model using panel data of 26 manufacturing industries in China from 2004 to 2017.

Compared with the existing literature, the main contribution of this paper are as follows: First, the panel threshold regression model is extended to the research on the relationship between R&D investment and technological progress in China’s manufacturing industry. Most of the existing research mainly adopts the linear regression method (Jing and Zhang 2014; Baumann and Kritikos 2016; Liu and Xin 2019), ignoring the nonlinear relationship between the two. Second, this paper reveals an important reason for the low level of China’s manufacturing industry R&D intensity, that is, the "law of diminishing marginal returns" of R&D investment, which has certain policy guiding significance for China's relevant departments to formulate manufacturing industry development policies.

The rest of the paper is organized as follows: Section 2 reviews the relevant literature and put forward relevant theoretical hypotheses. Section 3 introduces the methods and data. Section 4 analyzes the empirical results. Section 5 ends up with conclusions and policy implications.

2.1 R&D investment and green technological progress

Generally speaking, Research and Development activities can generate new knowledge, thereby promoting technological progress (Romer 1990; Aghion et al. 1992; Grossman and Helpman 1994). All R&D activities are based on the knowledge generated by the accumulated R&D activities in the past. On the other hand, new R&D activities also contribute to the accumulation of knowledge. Therefore, technological progress of an economy also depends on its accumulated R&D efforts and effective knowledge reserves. In fact, there is compelling empirical evidence that cumulative domestic R&D is an important determinant of technological progress (Griliches 1988; Griliches 1998; Smeets-Kristkova 2017). Coe and Helpman (1995) further argue that a country's technological progress depends not only on domestic R&D activities, but also on the R&D capital stock of trading partners. Therefore, under open conditions, technological progress depends not only on the domestic R&D capital stock, but also on the foreign R&D capital stock (Griffith et al. 2004; Cheng and Chen 2017). As the constraints of climate change and energy security gradually increase, countries have strengthened environmental regulation, and technological progress tends to reduce pollutant emissions and save energy (Alam et al. 2019; Wang et al. 2020). At the industry level, researchers have studied the relationship between R&D investment and industrial green technological progress based on panel data of industrial sectors, and the results show that R&D investment can promote green technological progress in manufacturing industry (Wang and Qi 2014; Jing and Zhang 2014)

Hypothesis 1

R&D investment can promote green technological progress in manufacturing industry.

2.2 The Law of Diminishing Marginal Returns On R&D Investment

Based on the above discussion, R&D investment may promote green technological progress in manufacturing industry. However, the relationship between R&D investment and technological progress is sometimes not linear (Yu and Zhang 2015; Fan and Teo 2022; Li and Wu 2022). For example, Fan and Teo (2022) explore the relationship between R&D investment and green innovation performance using data from various regions in China from 2015 to 2019, under the effect of a threshold variable, namely, technological innovation. Their empirical results show that when the regional technological innovation level is used as the threshold variable, the R&D investment has a significant double-threshold effect with the lagging three-phase green innovation performance. Yu and Zhang (2015) observe that there is a certain trend of "R&D worship" in many regions of China. Excessive increase in R&D intensity has not brought about technological progress (Li and Zeng 2009), so they put forward put forward a discussion about a suitable innovation mode (IM). The suitable IM selection should match the endowments, the institutional environment and the stage of the economic development. Only when the regional economic development, the factor endowments and the institutional environment develop to a certain level, will choosing the endogenous IM which are relied mainly on be beneficial to increasing the level of the technical progress of this area. This paper looks at the nonlinear relationship between R&D investment and technological progress from another perspective, that is, the "law of diminishing marginal returns". The law of diminishing marginal returns means that under the condition of constant technological level, when the input quantity of a certain production factor increases to a certain level, the increase in output brought by adding one unit of this factor is decreasing. The above-mentioned diminishing marginal phenomenon was first discovered by the 18th century French Physiocrat economist Turgot (2010) through experience summary. Later, Malthus (2018) summarized the phenomenon into a general law. At the beginning of the 20th century, Marshall (2013) introduced the concept of "margin" into the theoretical framework of classical microeconomics, and the law of diminishing marginal returns has since become the basic assumption of microeconomic theory. In empirical research, R&D investment also has the law of diminishing marginal returns (Ejermo et al. 2011; Faff et al. 2013). Therefore, this paper believes that the promotion effect of R&D investment on the progress of green technology in manufacturing industry is also decreasing, which also leads to the low level of R&D investment in China's manufacturing industry to a certain extent. This is because when the level of R&D investment reaches a certain level, its promoting effect on the progress of green technology will drop significantly, and the enterprise R&D investment motivation based on self-interest will decrease, so that the scale of R&D investment will be lower than the social optimal scale.

Hypothesis 2

With the increase of R&D investment, the promotion effect of R&D investment on green technological progress in manufacturing industry is decreasing.

3.1 Model setting

In order to quantitatively analyze the relationship between R&D investment and manufacturing green technological progress, this paper draws on Jing and Zhang (2014) to construct the following basic model.

$$\text{G}{TP}_{it}={\beta }_{0}+{\beta }_{1}*{R\&D}_{it}+{\beta }_{2}*{ER}_{it}+{\beta }_{3}*T2008+{f}_{i}+{\epsilon }_{it}$$

Where $i$ stands for industry, $t$ stands for time, $GTP$ stands for green technological progress, $R\&D$ stands for R&D investment, $ER$ stands for environmental regulation.$T2008$ depicts the impact of the 2008 financial crisis, and the value is 1 in 2008 and 0 in other years. $f$ represents the individual effect, and $\epsilon$ represents the random error term. ${\beta }_{1}$ empirically studies the linear relationship between R&D investment and green technological progress.

According to the theoretical analysis in the previous section, the promotion effect of R&D investment on the progress of manufacturing green technology is decreasing. In this paper, the threshold panel regression model proposed by Hansen (1999) is used to test the nonlinear relationship between R&D investment and manufacturing green technological progress, and the following threshold regression model is constructed on the basis of formula (1).

$$\text{G}{TP}_{it}={\beta }_{0}+{\beta }_{11}*{R\&D}_{it}I\left({q}_{it}\le \gamma \right)+{\beta }_{12}*{R\&D}_{it}I({q}_{it}>\gamma )+{\beta }_{2}*{ER}_{it}+{\beta }_{3}*T2008+{f}_{i}+{\epsilon }_{it}$$

Where $I(\bullet )$ is the indicator function, $q$ is the threshold variable, this paper uses R&D investment as the threshold variable, $\gamma$ is the threshold parameter, and the meanings of other variables are the same as formula (1). There is only one threshold parameter in formula (2), which can be extended to a multi-threshold parameter model in the application process.

3.2 Variables and data sources

Due to the availability of data, this paper conducts an empirical analysis based on panel data of 26 manufacturing industries in China from 2004 to 2017.

Green Technological Progress (GTP): This paper measures green total factor productivity of China’s manufacturing industry based on the Super-SBM model, and then decomposes the index into Green Technological Progress and Green Technical Efficiency Change (GEC). In the specific application process of the method, the idea of global Malmquist-Luenberger index proposed by Oh (2010) is used for reference, which has no infeasible solution and is transitive. Table 1 explains the specific calculation process of input-output indicators.

Table 1

Input-output indicators calculation method
	Indicator	Calculation Method	Data Sources
Input	Labor	The average annual number of employees of industrial enterprises above designated size in the manufacturing industry.	China Industrial Statistics Yearbook
	Capital stock	The calculation method of capital stock adopts the method of Chen (2011). Different capital depreciation rates are set for different industries in different years (depreciation in the current year/original value of fixed assets in the previous year). The initial capital stock in 2003 was directly taken from the research results of Chen (2011). The price adopts the 2003 constant price.	China Industrial Statistical Yearbook, China Fixed Asset Investment Statistical Yearbook
	Energy consumption	The energy consumption of industrial enterprises above designated size in the manufacturing industry.	China Energy Statistical Yearbook
output	Expected output	The industrial output of industrial enterprises above designated size in the manufacturing industry.	China Industrial Statistics Yearbook, 2012 China Input-Output Table, 2017 China Input-Output Table
output	Unexpected output	The CO2 emissions of manufacturing sub-sectors.	China Emission Accounts and Datasets (CEADs)

R&D investment (R&D): It is measured by the ratio of R&D expenditure to main business income in the manufacturing industry. Relevant data come from the China Science and Technology Statistical Yearbook.

Environmental Regulation (ER): On the basis of considering the relative rationality of the index and the availability of data, this paper uses the pollution emission control fee rate to describe the intensity of environmental regulation, which can measure the emission reduction efforts of various industries in the manufacturing industry. Specifically, this paper uses the proportion of the current operating cost of waste gas and wastewater treatment facilities in a manufacturing industry to the industry's main business income to measure environmental regulation. Relevant data come from the China Environmental Statistical Yearbook.

Financial crisis indicator variable (T2008): Considering that the 2008 global financial crisis was included in the sample period, the relevant indicators changed greatly, so the financial crisis indicator variable was included in the econometric model to reduce the interference of sample extreme values on the estimation results. T2008 is 1 in 2008 and 0 in other years.

Table 2 shows the descriptive analysis of the variables.

Table 2

Descriptive statistics
Variables	Observations	Mean	Std	Minimum	Maximum
GTP	364	1.0348	0.0816	0.6527	1.4598
GEC	364	0.9882	0.0719	0.6639	1.3364
R&D	364	0.0092	0.0063	0.0012	0.0301
ER	364	0.0128	0.0191	0.0001	0.1150

Unit roots usually appear in time series data, resulting in pseudo regression. In order to avoid pseudo regression, this paper uses LLC test and IPS test to perform panel unit root test on the data before estimating the model. The results show that the variables are stationary.

4.1 Basic Regression Analysis

Models (1)-(2) in Table 3 are the estimation results of the formula (1). Model (1) uses green technological progress (GTP) as the explained variable. It can be seen that R&D coefficient is positive and significant at the 1% level, indicating that R&D investment can promote GTP in manufacturing industry, thus validating Hypothesis 1. This conclusion shows that during the sample period, China's R&D investment shows a certain green technological progress bias, and the main reason behind it may be the strengthening of environmental regulations. For example, the 11h Five-Year Plan (2006–2010) took the reduction of energy consumption per unit of GDP as a binding indicator. The 12th Five-Year Plan (2011–2015), while taking the reduction of energy consumption per unit of GDP as a binding indicator, put forward the requirement to reasonably control the total energy consumption. During the "13th Five-Year Plan (2016–2020)" period, the "double control" action of total energy consumption and intensity was implemented, and it was clearly required that by 2020, the energy consumption per unit of GDP should be reduced by 15% compared with 2015, and the total energy consumption should be controlled within 5 billion tons of standard coal. The 14th Five-Year Plan (2021–2025) further proposes to improve the dual control system of total energy consumption and intensity, and focus on controlling fossil energy consumption consumption. In 2025, the energy consumption per unit of GDP will be reduced by 13.5% compared with 2020. As a comparison, model (2) uses green technical efficiency change (GEC) as the explained variable, and it can be seen that R&D investment can also improve GEC. According to Schumpeter's innovation theory (Schumpeter 1934), innovation is to introduce a new combination of production factors and production conditions into the production system, that is, recombination of production factors. From the perspective of the microscopic mechanism, the process of promoting technological progress through research and development will involve the optimization of the entire production process of the enterprise, thereby reducing waste and improving green technical efficiency change.

Table 3

Regression results
	(1) GTP	(2) GEC	(3) GTP
R&D	5.453*** (1.787)	2.835*** (0.692)
ER	0.606** (0.240)	-0.177 (0.159)	0.619** (0.242)
T2008	-0.033*** (0.006)	-0.004 (0.006)	-0.032*** (0.007)
R&D_1			15.98*** (3.895)
R&D_2			10.15*** (2.205)
R&D_3			6.096*** (1.382)
Constant	0.979*** (0.016)	0.964*** (0.007)	0.927*** (0.021)
Industry fixed effect	Yes	Yes	Yes
Time fixed effect	No	No	No
Observations	364	364	364
Note: *, , and * respectively indicate significance at the 1%, 5%, and 10% levels. Robust standard errors are in parentheses.

In addition, environmental regulation (ER) can promote GTC, which further verifies the "Porter Hypothesis" (Porter 1991; Porter and van der Linde 1995), that is, environmental regulation can motivate enterprises to carry out green technology innovation. However, environmental regulation can inhibit the improvement of GEC, although the coefficient is not significant, which indicates that environmental regulation can reduce the efficiency of resource allocation to a certain extent (Tombe and Winter 2015). This is mainly because China usually implements output-oriented environmental regulation policies which limit the energy use or emissions per unit of industry output. It is more difficult for low-productivity industries to meet policy goals than high-productivity industries. Enterprises in low-productivity industries may sacrifice resource allocation efficiency in order to achieve policy goals. Considering that China's manufacturing industry is still "big but not strong", and low-productivity industries are still in the majority, so overall, environmental regulation will reduce green technical efficiency change, which is contrary to the conclusion of existing research (Han Chao et al. 2017; Li and Sheng 2018). The 2008 financial crisis caused a slight setback in green technological progress.

4.2 Threshold Regression Analysis

Applying the threshold regression model requires two basic tests: one is the threshold effect test, that is, to test whether there is a threshold effect. The other is the threshold parameter test, that is, to test whether the threshold parameter is significant. Specifically, the threshold effect test is carried out sequentially according to the absence of a threshold, the existence of 1 threshold, the existence of 2 thresholds, and the existence of 3 thresholds. The null hypothesis of the threshold parameter test is: ${H}_{0}$: $\widehat{{\gamma }}={{\gamma }}_{0}$. When likelihood ratio test statistic$\text{L}\text{R}\left({\gamma }\right)\le -2\text{l}\text{n}(1-\sqrt{1-\alpha })$, the null hypothesis cannot be rejected, where $\alpha$ represents the significance level, and this paper takes 5%, then the critical value of the corresponding LR is 7.35 (Hansen, 1999).

Based on formula (2), the above two tests are carried out, and the results are shown in Table 4. It can be seen that the single threshold model is significant at the 10% significance level, the self-sampling p-value is 0.0933, and neither the double threshold model nor the triple threshold model is significant. At the same time, combined with the likelihood ratio function graph corresponding to each model, the confidence interval of a single threshold model is too large, so this paper believes that the double threshold model is more suitable for analysis.

For the double-threshold model, the two thresholds are 0.0071 and 0.0222, or 0.71% and 2.22% of R&D intensity, respectively. According to the above two thresholds, this paper divides R&D investment into low level (R&D ≤ 0.0071), medium level (0.0071 < R&D ≤ 0.0222), and high level (R&D > 0.0222). Table 5 shows the number of manufacturing industries in different R&D investment levels in different years. It can be seen that nearly half of China's manufacturing industry R&D investment has been at a low level for a long time, but since the "12th Five-Year Plan (2011–2015)" period, China has begun to implement an innovation-driven strategy, which has made the manufacturing industry increase R&D investment, and the number of industries at a low level has begun gradually decrease. By 2017, 12 industries were at low level, 14 were at medium level, and no industry was at high level. It can be seen that since the "12th Five-Year Plan (2011–2015)" period, the upgrading of China's manufacturing industry has been very effective. The overall manufacturing industry has begun to move from a low level of R&D investment to a medium level of R&D investment, but few industries have moved from a medium level to a high level. The level of momentum still needs to be strengthened.

Table 4

Threshold effect test
Model	F value	P value	Threshold Value
Model	F value	P value	Ⅰ		Ⅱ	Ⅲ		1%	5%	10%
single threshold	11.68*	0.0933	0.0222					18.8939	14.2345	11.4923
double threshold	5.48	0.6567	0.0071	0.0222				31.0096	19.4205	14.0653
triple threshold	10.45	0.6833	0.0030	0.0071			0.0222	54.3284	42.9645	29.7276
Note: , , ** indicate significance at the 10%, 5%, and 1% levels, respectively, and the p value is the result of repeated sampling 300 times using the "bootstrap" method.

Table 5

The number of manufacturing industries in each interval in different years
	2004	2005	2006	2007	2008	2009	2010
low	11	8	9	9	9	16	17
medium	14	15	14	14	14	10	9
high	1	3	3	3	3	0	0
	2011	2012	2013	2014	2015	2016	2017
low	16	15	15	14	12	12	12
medium	10	11	11	12	14	14	14
high	0	0	0	0	0	0	0

Model (3) in Table 3 is the estimation result of formula (2). In model (3), R&D_1, R&D_2, and R&D_3 correspond to the R&D investment levels in three intervals: low level, medium level and high level, respectively. It can be seen that the R&D investment (R&D) coefficients of the three intervals are 15.98, 10.15, and 6.096, which are all significant at the 1% level, indicating that R&D investment can promote green technological progress in manufacturing industry. This is consistent with the estimation of the formula (1). However, as the level of R&D investment reaches a certain level, the promotion effect of R&D investment on the progress of green technology in the manufacturing industry decreases significantly, thus verifying Hypothesis 2, which explains the low level of R&D investment in China's manufacturing industry to a certain extent. When the level of R&D investment reaches a certain level, its promoting effect on the progress of green technology will be greatly reduced, and the motivation of enterprises to invest in R&D based on self-interest will decrease, so that the scale of R&D investment will be lower than the social optimal scale.

This paper uses panel data of 26 manufacturing industries in China from 2004 to 2017, and studies the nonlinear relationship between R&D investment and green technological progress based on the threshold regression model. The results show that the double threshold model can better explain the nonlinear relationship between the two. The R&D investment coefficients in the three ranges of low level, medium level and high level are all positive and significant at the 1% level, indicating that R&D investment can promote the progress of green technology in manufacturing. However, with the improvement of R&D investment level, the promotion effect of R&D investment on the progress of manufacturing green technology is decreasing, which explains the low R&D intensity of China's manufacturing industry to a certain extent. This is because when the level of R&D investment reaches a certain level, its promoting effect on the progress of green technology will be greatly reduced, and the incentives of enterprises to invest in R&D based on self-interest will decline, resulting in the scale of R&D investment being lower than the socially optimal scale. At present, since the "12th Five-Year Plan (2011–2015)" period, China's manufacturing industry as a whole has moved from a low level of R&D investment to a medium level of R&D investment, but few industries have moved from a medium level to a high level, and the driving force for the industry to enter a high level is still needs to be strengthened. R&D investment can also improve green technical efficiency change. From the perspective of the microscopic mechanism, the process of promoting technological progress through research and development will involve the optimization of the entire production process of the enterprise, thereby reducing waste and improving the efficiency of green technology. In addition, environmental regulation can promote the progress of green technology in manufacturing industry. However, environmental regulation inhibits the improvement of green technical efficiency change, although the coefficient is not significant, which indicates that environmental regulation can reduce the efficiency of resource allocation to a certain extent. One of the reasons is that China usually implements output-oriented environmental regulation policies, which limit the Energy use or emissions per unit output of an industry, low-productivity industries are more difficult to meet policy goals than high-productivity industries, and companies in low-productivity industries may sacrifice resource allocation efficiency in order to achieve policy goals. Considering that China's manufacturing industry is still "big but not strong" and low-productivity industries are still in the majority, overall, environmental regulation will reduce the green technical efficiency change.

Based on the above conclusions, this paper puts forward the following policy recommendations:

First, implement a differentiated R&D subsidy policy for manufacturing enterprises, especially for enterprises with a medium level of R&D investment (0.71%~2.22%), to make the level of R&D investment in the manufacturing industry reach a high level. This is mainly because when the level of R&D investment reaches a certain level, its promotion effect on the progress of green technology will drop significantly, and the incentives of enterprises to invest in R&D based on self-interest will decline, so the industry has insufficient motivation to enter a high level. Considering that China's manufacturing industry is facing "double-end squeeze" (Wang et al. 2019), in the field of low-end and mid-end manufacturing, China's manufacturing industry is moving to Southeast Asia, South Asia and other regions. In the high-end manufacturing industry, China faces fierce competition with Europe, the United States, Japan and other developed countries. Based on the reality of the irreversible weakening of China's traditional competitive advantages such as low labor costs, in order to keep the share of manufacturing value added in GDP basically stable, China's manufacturing industry must upgrade upwards and achieve sustainable development of the manufacturing industry through rapid technological progress, which depends on the level of R&D investment improvement.

Second, formulate appropriate environmental regulation policies. Environmental regulation can promote the progress of green technology in manufacturing industry. However, environmental regulation can inhibit the improvement of green technology efficiency. One of the reasons is that China usually implements output-oriented environmental regulation policies which limit the energy use or emissions per unit of industry output. It is more difficult for low-productivity industries to meet policy goals than high-productivity industries. Enterprises in low-productivity industries may sacrifice resource allocation efficiency in order to achieve policy goals. Considering that China's manufacturing industry is still "big but not strong", and low-productivity industries are still in the majority, so overall, environmental regulation will reduce the efficiency of green technology. Therefore, it is necessary to implement differentiated environmental regulation policies for enterprises with different productivity level.

Author contributions

Xi Zhang: Conceptualization; Methodology; Formal analysis and investigation; Supervision; Funding acquisition; Writing - Review & Editing

Rui Li: Methodology; Formal analysis and investigation; Resources; Writing - Original Draft; Writing - Review & Editing

Jinglei Zhang: Conceptualization; Formal analysis and investigation; Resources; Writing - Original Draft; Writing - Review & Editing

Funding

This work is supported by National Natural Science Foundation of China (Grant No. 72003009).

Data availability

All materials and data which are generated or analyzed during this study are included in this article.

Ethics approval Ethical approval was not required for this research.

Consent to participate Not applicable.

Consent for publication Not applicable.

Competing interests The authors declare no competing interests

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Editorial decision: Major Revision
28 Jul, 2022
Reviewers agreed at journal
22 Jun, 2022
Reviewers invited by journal
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21 Jun, 2022
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The nonlinear impact of R&D investment on green technological progress in China’s manufacturing industry: Based on the law of diminishing marginal returns

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Abstract

Figures

1. Introduction

2. Theoretical Analysis And Research Hypothesis

2.1 R&D investment and green technological progress

2.2 The Law of Diminishing Marginal Returns On R&D Investment

3. Methods And Data

3.1 Model setting

3.2 Variables and data sources

4. Empirical Analysis

4.1 Basic Regression Analysis

4.2 Threshold Regression Analysis

5. Conclusion And Policy Implications

Declarations

References

Status:

Version 1