1. Introduction
China marched greatly towards industrialization and urbanization during the 13th Five-Year planning under the Paris Agreement and strategically introduced the dual carbon goals. While proceeding in ecology and culture construction, China is facing environmental issues including resource consumption, carbon dioxide (CO
2) emission, and waste pollutant accumulation [
1], which hindered social sustainable development with extensive increase in high investment, high energy consumption, and high emission. Referring to
China Statistical Yearbook on Environment, in 2019, industrial Sulphur dioxide (SO
2) emission reached 3.954 million tons, industrial solid waste reached 4.49 million tons, and overall industrial emission of oxygen demand for chemicals was 77.2 tons [
2]. Generally, industrial emission takes the greatest proportion of carbon emissions for about 70% of the China’s total annual [
3]. President Xi Jinping pointed out at the National Conference on Innovation in Science and Technology that ecology and culture development faces increasingly serious environmental pollution and needs to rely on green technology innovation (GTI) for green development and build a beautiful China with blue sky, green land and clear water [
4]. As a new way of innovation, GTI not only pays attention to the economic growth highlighted by traditional innovation, but also concentrates on resource-saving and environmental protection such as reducing CO
2 emissions [
5,
6]. However, due to high externalities and high innovation cost, some firms lack motivations for GTI [
7]. Higher level of GTI is a response to the Paris Agreement and the United Nations Sustainable Development Goals (SDGs).
Among many factors influencing GTI, ‘Porter hypothesis’ claims that in the long run, environmental regulations (ERs) may positively promote GTI, forming “innovation compensation effect” [
8]. ER is a constraining force that provides external incentives for industry to adjust its production methods to protect the environment [
9]. It is now popularly classified to three types, command-based environmental regulation (CER), market-based environmental regulation (MER), and voluntary environmental regulation (VER) [
9]. They are differentiated according to the key actors of each type which are the government, the enterprises and the citizens respectively. Given new economic norms and high-quality economic development promoted by the government, the Chinese government has issued a series of ERs. On 1 January 2018, China’s environmental protection tax law was officially implemented, aiming to promote the construction of ecological civilization. On such basis, we recognize the necessity to delve into the influences of different types of ERs to GTI to improve towards industrial green transformation and upgrading and the dual carbon targets.
While extensive research is done around the impact of ER on GTI, studies are limited in using green technology innovation efficiency (GTIE) as the GTI process proxy. Although results can be different due to the selection of indicators and method and varied regional environment, economy, and administrative management, most of them indicate the influence of ER on GTI will be positive, negative, or in an inverted U-shape [
8,
10,
11,
12]. For example, Liu et al. [
10] found using PSM-DID approach that high-polluting firms listed in Shanghai and Shenzhen tend to file more applications for environmental patents, suggesting a growth of corporate green innovation after the implementation of the new Environmental Protection Law. Given that consumer demand is constant and the enterprises select the best choice, the “following cost effect” of ER for pollution control will increase enterprise’s burden on funding, producing the “crowding out effect” on key resources of GTI. Zhang et al. [
11] analyzed the negative impact of cost-based ER on enterprise technology innovation for China’s 30 provinces during 2003–2012. Also, an inverted U-shape relation is found between ERs and GTI. The “following cost effect” of ER on GTI is dominant in the short run, and the “innovation compensation effect” of ER is dominant in the long run. The “U” relationship between the ER intensity and the industrial production technological advancement is demonstrated for 30 provinces in China using DEA-based Malmquist productivity index with weighted generalized least squared [
12]. ER and GTI can also be independent in cases [
13]. Moreover, using systematic GMM model, Ye et al. [
14] found that dual ERs have moderation effect on the relationship between GTI and green development in urban clusters of the Yangtze River Economic Belt. Similar relations can be observed between ERs and GTIE, but rather limited. GTIE reflects capability of taking advantages of innovation investments, especially useful for evaluating the industrial benefits [
9]. Positive impact of ERs on GTIE is explored by a Tobit regression model [
15]. ER and GTIE were found independent in a study for Xi’an’s technological innovation efficiency and spatial-temporal evolution [
13]. Wu et al. [
16] note that, different types of ERs have different impacts on GTIE, with significant regional heterogeneity. Hence, we attempt to further analyze the relationship between overall ER or types of ERs and GTIE, understanding the capability of using GTIE as an indication of GTI linking with China’s reality.
Currently, evaluation to GTI, setting GTIE as the indicator, using data envelopment analysis (DEA) is limited. GTI evaluation research is mainly done in two ways. One is using the number of green patent licenses to construct singular index [
7] or to following weighted average of sub-indexes such as green product innovation and green process innovation that are treated dimensionless to construct comprehensive index [
17]. The other is, naming the process performance GTIE, measuring with stochastic frontier analysis [
18] or DEA [
13]. However, non-parametric DEA methods are rarely used for evaluating GTIE. Non-parametric method effectively avoids the subjectivity of parameter weighting by not requiring establishing a function form nor assuming prior conditions. The efficiency measured with DEA is relative efficiency which is closely related to the concept of productivity in classic economics [
19]. Comparing with stochastic frontier analysis, DEA takes the advantages of being suitable for multi output-input analysis and not requiring function for input-output transformation nor assuming weights. Efficiency score of the overall can be given merely using input and output data [
20]. Relational network DEA model is built by Kao [
21]. Since the assumption of constant returns to scale is too stringent and the influence of scale effect cannot be eliminated, DEA model with radial measurement has the deficiency of measuring slacks [
19]. SBM-DDF model is used under variable returns to scale (VRS) assumption to measure GTIE showing the high degree of discrimination to data with non-radial measure and VRS [
22]. Cases are the same in Zeng et al. [
5] measuring using both SBM model and Malmquist Luenberger (GML) index. Based on the theoretical framework of innovation value chain by Hansen and Birkinshaw [
23], this article decomposes green technology development into two stages, R&D and commercialization, and applies the non-oriented two-stage network SBM-DEA model [
24] under VRS assumption to calculate the GTIE. This model is higher in the degree of discrimination of the data and models the process of GTI is better detail, testing the possibility for application of more advanced DEA models for GTI.
On such basis, this article observes the impact of ER on the industrial GTIE of 29 provinces in China from 2005 to 2017. GTI process is understood by calculating the industrial CO2 emission during raw material consumption, processing, and conversion, respecting regional and industrial differences. With the undesirable outputs including energy consumption and CO2 emission in the framework for measuring efficiency, two-stage network SBM-DEA model is used to measure overall and stage-wise GTIEs. This is supported by constructing a clear R&D price index with closer integration with GTIE. Finally, considering the time lag existed in stage-wise transformation process of GTI, and different natural and economic conditions may influence the effect of ERs, generalized method of moments (GMM), fixed effect model, and threshold effect model are used to analyze the effects of different types of ERs on GTIE, its dynamic effect, threshold effect, lag effect and regional differences.
4. Discussions and Conclusions
4.1. Discussions
In the measure of GTIE, with the theoretical framework of innovation value chain [
23], the time lag between the transformation of the R&D stage and commercialization stage is set to two years. On the one hand, this enriches the application of innovation value chain. On the other, the handle is consistent with previous research result [
9,
34,
54], expanding the relevant research.
ER has a nationwide positive impact on industrial GTIE. ‘Porter hypothesis’ exists in China, which is consistent with existing studies [
10,
15]. Researchers found that ‘Porter hypothesis’ also holds in European manufacturing sectors, Pakistani manufacturing firms and the Australian oil and gas industry [
54,
55,
56]. However, when the midland and northeastern regions are studied alone, ER has a negative impact. It may be explained by the results from Qiu et al. [
57]. Using monopolistic competition model with linear demand and pollution tax, they derived that porter hypothesis holds for more capable firms but fails for the less capable within the same industry [
57]. Due to the strong support of the China to the western region and the developed economy in the eastern region, the enterprises in the midland and northeastern regions are relatively less capable. Besides, different types of ERs have differentiated impacts on the GTIE, consistent with [
9]. In the analysis of heterogeneity, lag effect is confirmed, consistent with Martínez-Zarzoso et al. in OECD countries, that environmental policy stringency can foster innovation and productivity with one-year and five-year lag [
58].
Furthermore, the threshold effect analysis shows that the influences of ERs on the industrial GTIE are affected by the intensity of traffic convenience, VER and MER. This finding enriches the researches of the threshold effect on the relationship between ER and GTIE. The existing studies rarely use traffic awareness as the threshold variable.
Moreover, our results also conclude that inertia features exist in industrial GTI. This finding is in line with the literature pointing to a positive influence of previous innovation experience has on the innovation capacity in European manufacturing sectors [
54]. However, the difference is that in China’s industrial sector, after removing the inertial influence of GTIE, ER no longer has an effect. In the European manufacturing sectors, the ER with one-year and two-year lag is still significant [
54], indicating that China’s ER needs to be further improved. More detailed comparisons can be done in future research.
4.2. Conclusions
Under the background of Paris Agreement and the United Nations Sustainable Development Goals (SDGs), in this paper, we studied the influences of ER on the industrial GTIE in China from 2005 to 2017. The influences are observed for the overall GTIE and for separate stages. The GTIE of provinces are studied and ranked. Considering the regional features of China, GTIE of regions are also calculated using simple averaged and ranked. The results are analyzed from the perspective of dynamic evolution of GTIE, GTIE1, and GTIE2, also in provinces and regions. Following that, the influences of GTIEs and types of ERs are analyzed with regression. Dynamic effect model and threshold effect models are used for further in-sights. Eventually, as China present typical regional industrial functions, heterogeneity analysis is applied to study GTIEs in eastern, middle, northeastern, and western regions.
The empirical results show that the development of GTI is unbalanced for the two stages. Kernel density diagram indicates that industrial GTIE in China is overall low, with a steadily growing trend. Initial results have been achieved in industrial green transformation. Also, gradient changes can be observed for provinces and its regional difference become increasingly prominent.
Country-wise, ER brings positive influences on GTIE in China, mainly in MER. On the contrary, the VER may cause negative influences on GTIE2. To note, CER has no influence on both overall and stage-wise GTIEs. This is consistent with Hypothesis 3 that different types of ER could have different effects on GTIE. And none of them has the lag effect. To balance the relationship between economy and environment, we need to choose the appropriate type of ER. As for control variables, the effect of FDI on GTIE is not stable, and enterprise scale and traffic convenience are positively related to GTIE.
Considering the stage-wise transformation process of GTI, we further analyze the possible characteristics of inertia of GTI using two-step system GMM. As the dynamic effect model indicates, inertia features exist in GTI, and itis a yearly continuous process. This confirms the prediction of Hypothesis 1, that is, there are dynamic effect in the impact of the ER on GTIE. Although ER can encourage enterprises to save energy and reduce emissions, promoting green industrial transformation in Chinese cities takes long time.
The threshold effect analysis shows that the nonlinear relationship exists between different types of ERs and GTIEs. In other words, their relationship easily be affected by different natural and economic conditions. Which is consistent with Hypothesis 2. In the area with extremely inconvenient traffic, all ERs would bring financial burden on local enterprises that adversely affects GTIEs. When the traffic convenience is improved, the favorable impacts of all ERs on industrial GTI began to prevail. Additionally, the effect of ER and MER is sensitive to the strength of CER, suppressively, but to MER, promotively. The effects of CER and VER are not easily influenced by other types of ERs. Additionally, Stage 2 of GTI is more sensitive to the nonlinear impact of ERs than Stage 1. Therefore, appropriately reducing CER and increasing MER and traffic convenience would improve GTIE, especial GTIE2.
The impact of ERs on GTIE varies regionally, confirming Hypothesis 4. Eastern and western regions are higher in GTIEs both overall and stage-wise. In general, ER positively influence GTIE in eastern and western regions while it presents negative impacts in the midland and northeastern regions. Specifically, in the east, with good market environment and advanced ideology of development, MER and VER promote GTIE and GTIE1 significantly and CER inhibits GTIE2. In the west, provinces actively use regional geographical advantages, featuring unique in their economic development. Hence, VER showing high flexibility is more suitable for promoting GTIEs, but MER and CER could be effectless. In the midland, as accountable measures to ERs mature, the current negative impacts of MER and VER are gradually turning positive as time lags. In the northeast, the inhibitive effects of MER and VER are obvious, while CER has no significant impact. All regions exhibit lag effect in the impacts of types of ERs on overall and stag-wise GTIEs. This is consistent with Hypothesis 1.
4.3. Policy Implementations, Limitations and Future Insights
The section is focused on possible policy implementations and the limitations and prospections to this research.
4.3.1. Policy Implications
Firstly, the Chinese government should, integrating the spatial distribution of GTIE, promote coordinative development across regions and county, and establish a group of energy-saving and environmental protection demonstration zones. It is essential to reduce the excessive reliance over past development pathways for inefficient regions, by promoting cross-regional and cross-national flow of key factors, accelerating the extension of industrial connections, and cultivating competitiveness in new aspects.
Secondly, analysis on the dynamic effect suggest that the government should encourage GTI as the highlight in its long-term strategy. In detail, while optimizing the R&D investment scale and increasing labor for R&D, introducing advanced management experience also increases the financial benefits for enterprises and fundamentally alters mode of “terminal management” for pollution control. Consequently, industrial green transformation can be promoted, shifting globally towards sustainable development directed by the environmental aspect.
Thirdly, international trade should be encouraged to globally popularize green energy-saving products and technology. Also, the threshold study shows that traffic convenience should be enhanced, such as promoting clean energy, intelligent, and digitally advanced transportation equipment. For FDI, especially with on the lower end of global value chain, the government should introduce FDI reasonably to guide foreign investment to high-tech rather than high pollution enterprises. Meanwhile, enterprise scale should be expanded.
Finally, it is necessary to distinguish the impact of three ERs on green technology innovation activities in deferent regions. According to the threshold effect analysis, the government should moderately reduce command-based ER and leverage the market’s leading role, thereby promoting technology innovation upgrade. For example, the government should strengthen the overall connection between trading energy consumption rights and trading carbon emission rights. In eastern regions, command-based ER should be reduced, and market-based and voluntary ER should be strengthened. On the one hand, market mechanism needs perfection; on the other hand, the public should be guided to spontaneously fulfill the responsibility of energy saving and emission reduction. In the central region, the government should reduce current ERs and explore new pathways of ecological lead exploitation and environmental hosting. In the west, market-based ER is in the negative influence range under the threshold, and voluntary ER plays a promoting role. The government build and develop emission trading market and open channels for public environmental supervision. For the northeast regions, reduction to environmental pollution and improvement enterprise innovation should be done through technology support rather than ER. Establishing a comprehensive scientific and technological service system is believed to eliminate lagged production and excessive production capacity, enhance clean and efficient resource use, comprehensively supporting the green transformation of enterprises.
4.3.2. Limitations and Future Insights
Considering progress of updating the China statistical yearbook, the data, especially data of gross industrial output, are only fully updated to 2017 and much data on industrial wastewater discharges is absent after 2017. Concerning the integrity and accessibility of data and the reliability of the conclusions, 2017 is the currently most up-to-date year. The influences of ERs to GTI, especially on GTIE, can be interesting and acutely different as the lifestyles and industrial production structure changes in China under the global pandemic.
However, although dramatic changes are brought by the pandemic, in recent years, the basic process of GTI has not changed significantly, and the transformation of GTI can still be developed in to two stage process, the R&D stage and commercialization stage [
59]. Meanwhile, it is recognized that the social and environmental background caused by the epidemic may affect GTI [
60], especially the GTI projects started in and after 2019, which requires ER to be more efficient and more conducive to save resources. Comparative calculation can be further made after gaining the complete data.
Additionally, the accuracy of this article can be improved, calling for further efforts. In mechanism, the carbon emission of cement production and different CO2 emission factor for electricity in different regions can be considered into CO2 calculation. In statistics, the significance of estimation coefficients can be influenced by the selection of indicators. Moreover, taking policy as the proxy variable of ER can better deal with the problem of endogeneity.