[Retracted] Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

Long, Renyan; Guo, Hangyuan; Zheng, Danting; Chang, Ronghua; Na, Sanggyun

doi:https://doi.org/10.1155/2020/8094247

Complexity

On this page

Abstract Introduction Literature Review Materials and Methods Conclusions and Discussion Data Availability Disclosure Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article Retraction

!

This article has been Retracted. To view the article details, please click the ‘Retraction’ tab above.

Special Issue

Complexity Problems Handled by Advanced Computer Simulation Technology in Smart Cities 2020

View this Special Issue

Research Article | Open Access

Volume 2020 | Article ID 8094247 | https://doi.org/10.1155/2020/8094247

[Retracted] Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

Renyan Long,¹Hangyuan Guo,²Danting Zheng,^2,3Ronghua Chang,^2,4and Sanggyun Na²

Academic Editor: Zhihan Lv

Received28 Jul 2020

Revised06 Sept 2020

Accepted19 Sept 2020

Published27 Oct 2020

Abstract

With the shortage of resources and the increasingly serious environmental pollution in China, green innovation has become a sustainable competition for a region. The Yangtze River Economic Belt (YREB) strategy is one of the most important strategies for the sustainable development of China’s economy under the new normal. Green innovation plays a linking role in the resources exchange and trade flow in YREB, and it is also the foundation and guarantee to implement the YREB strategy. The global environmental pollution and the weak recovery of world economy make the traditional extensive economic growth model unsustainable. Sustainable economic growth should focus on the quality of development and its external costs to the environment. In order to implement the concept of sustainable development, the improvement of logistics ecological efficiency is related to the quality of ecological civilization construction. Therefore, it is of theoretical and practical significance to study the measurement, evolution, and driving factors of coordinated development level of regional green innovation system. This paper proposes a super-slack-based measure (super-SBM) data envelopment analysis (DEA) model to measure the green innovation efficiency of 11 provinces and cities in YREB from 2008 to 2017, mastering its spatial and evolutionary characteristics, and conduct empirical analysis on the influencing factors. The empirical results indicate that economic development, government support, and industrial structure upgrading are the leading forces to directly enhance the green technology innovation ability of cities in the Yangtze River Economic belt and play the core driving role of green innovation. To further enhance the capacity of urban green innovation in the Yangtze River Economic belt, we will increase the government’s support for green innovation, optimize the environmental governance model, promote the green upgrading of industrial structure, and enhance the enthusiasm of enterprises for green innovation.

1. Introduction

With the adoption of China’s reform and open up policy, the miracle of rapid economic growth is attracting the world's attention. In the meantime, the emerging issues of environment and resource depletion also pose a huge challenge to economic development. Coordinating the relationship between economy and environment is the key to the implementation of sustainable development strategy [1], made in China 2025, and raised the “green innovation-driven development” strategy to promote the healthy development of national economy.

As an important support belt for China’s economic development in the new era, the YREB spans three regions in China, connects the Yangtze River Delta Basin with the most developed economy in China, and together with the coastal economic belt forms the T-shaped model of economy development in China, and it is important in promoting and demonstrating industrial transformation and upgrading and green development. With the diversification of innovation sources, ecological environment has become an important factor in innovation transformation and upgrading. However, there are many industries with high pollution and high-energy consumption in the YREB. Environmental pollution and resource shortage have become important factors restricting the YREB strategic development. The contradiction of ecological environment and economic society has become extremely acute. Under the rigid constraints of energy and environment, it is urgent to incorporate green development concept into technological innovation [2]. Therefore, as the integration point of innovation-driven and green development, green innovation has become an effective means to break through the constraints of resources and environment and promote sustainable development. It is of great practical significance to improve the green innovation efficiency of the YREB and realize the win-win of innovation efficiency, ecological efficiency, and economic efficiency, so as to enhance the ability of regional sustainable development and promote high-quality economic development [3].

In early 2016, Present Xi Jinping proposed that the ecological environment of the YREB should be placed in an overwhelming position. In March 2016, the meeting of the Political Bureau of the CPC Central Committee presided over by General Secretary Xi approved the outline of the development plan of the Yangtze River Economic Belt, stressing once again that “development should be promoted on the premise of protecting the ecology, enhancing the overall planning, integrity, coordination, and sustainability of development, and improving the efficiency of essential allocation.” In March 2018, it was clearly pointed out in the work report of the 19^th National Congress of the Communist Party of China that the development orientation of the Yangtze River Economic Belt should be guided by ecological priority and green development, promote the overall layout of “five in one:” innovation, coordination, green, opening, and sharing, accelerate the construction of ecological civilization, and achieve regional coordination and sustainable development. The fundamental way to adjust the regional economic structure, change the mode of economic development, and promote high-quality economic development lies in the continuous green innovation. “Green innovation” is a complex system including resource input, innovation output, and environmental benefits. It coordinates the relationship between economic development and ecological protection, realizes the optimal benefit output with the least resource input, and creates the highest green innovation efficiency.

This paper modifies the super-SBM-DEA model to measure the green innovation efficiency of 11 provinces and cities in YREB from 2008 to 2017, mastering its spatial and evolutionary characteristics, and conduct empirical analysis on the influencing factors. The main contribution is using the undesirable super-SBM-DEA method to avoid any underestimation or overestimation of the green innovation efficiency caused by radial and nonradial DEA. The remainder of this paper is organized as follows. The literature review is presented in Section 2. Section 3 briefly describes the measurement method for green innovation efficiency. Section 4 presents the data and variables. Empirical results and analysis are reported in Section 5. Section 6 draws conclusions and policy implications. The article structure is shown in Figure 1.

2. Literature Review

According to the needs of balanced development in ecology and economy, we need to find a relationship among the rapid development of economy, excessive use of resources, and deterioration of natural environment. By measuring green innovation efficiency, we can find key influencing factors and promote the sustainable development of green ecological economy [4–6]. According to existing literatures, research on green innovation efficiency can be summarized in the following three aspects: (1) research on green innovation efficiency; (2) research on the measurement of green innovation efficiency; (3) research on the influencing factors of green innovation efficiency.

2.1. Research on Green Innovation Efficiency

Green innovation has become a popular concept, and it is often known as ecological innovation, sustainable innovation, and environmental innovation [7]. Fussier and James first introduced the term green innovation in the book driving green innovation, defining as new products or processes which provide customer and business value but significantly decrease environmental impacts [8]. Kemp et al. define green innovation as a new process technology, system, and product to avoid or reduce environmental damage [9]. Compared with traditional innovation, green innovation takes both economic and environmental benefits into account and adapts to the improvement of supply side structural reform quality and efficiency of industrial parks. From the perspective of systems theory, green innovation is a combination of industrial innovation system theory and green economy theory, in reference to both green products and green processes [10, 11], including the introduction of any new or significantly improved product, process, organizational change, or marketing solution to reduce the consumption of natural resources and the emission of harmful substances in the product life cycle [12].

In 1951, Kaufman first put forward the concept of “efficiency.” He pointed out that if technology cannot realize the increase or decrease in output or input at the given level of output or input, the input-output vector in this state was defined as technology efficiency. Then, Schumpeter combines the concept of innovation and efficiency and points out that the fundamental purpose of innovation is to maximize regional economic and social benefits. Feng Zhijun defined green innovation efficiency as an input-output efficiency that can promote the unity of “economic benefits, environmental benefits, and social benefits” [13]. The authors in [14] pointed out that green innovation efficiency should not only reflect “green” and “innovation” but also reflect its economic characteristics, that is, economic efficiency. In addition, the authors in [15] believe that green innovation efficiency is a comprehensive innovation efficiency considering the cost of resource consumption and environmental pollution.

2.2. Research on the Measurement of Green Innovation Efficiency

There are two main methods to measure green innovation efficiency: the parametric analysis method, using SFA, and nonparametric analysis method, using DEA [16, 17]. Parametric analysis assumes that the departure from the frontier of the DMU is the result of a combination of stochastic disturbances and technical inefficiencies. The application of SFA focused mainly on enterprises’ efficiency and its influencing factors and on research in the economic field [18, 19]. The authors in [20] measured the green innovation efficiency in China’s provinces based on the improved stochastic frontier model and demonstrated the spatial agglomeration characteristics and path dependence of interprovincial green innovation efficiency from a spatial perspective.

The nonparametric analysis method constructs a minimum output possibility set that can accommodate all individual production modes according to the input and output of all decision-making units in the sample and measures the input-output efficiency based on the production possibility set. The authors in [21] compared the innovation efficiency of 185 regions in 23 European countries with the multiobjective DEA model and pointed out that there were differences in the innovation efficiency between different regions and different innovation stages. The authors in [22] used DEA to measure the innovation efficiency values of hospitals in 29 OECD countries between 2000 and 2010 and then applied the panel Tobit model to determine the environmental factors affecting hospital efficiency scores. By decomposing the Malmquist Productivity Index Decomposition, the change in the efficiency decomposition value was analyzed. The authors in [23] used the DEA method to calculate the overall efficiency, patent production efficiency, and scientific paper production efficiency of 32 Mexican states. The authors in [24] used the SBM model to measure the green innovation efficiency of Chinese industrial enterprises without considering the nonexpected output and analyzed the regional differences of the green innovation efficiency of industrial enterprises in the regions. Luo et al. [3] applied the Malmquist Index and data envelopment analysis to evaluate the efficiency of green technology innovation in strategic emerging industries. Du et al. [25] used a two-stage network DEA with shared input to measure the efficiency of regional enterprises’ green technology innovation and explored the regional differences in industrial enterprises’ green technology R&D and the efficiency of green technology achievement transformation.

2.3. Research on the Influencing Factors of Green Innovation Efficiency

The influencing factors of green innovation efficiency can be classified into direct factors and indirect factors. Direct factors included labor quality, industrial structure, resource consumption, and technological innovation. The authors in [26, 27] conducted a dynamic evaluation on the efficiency of technological innovations in OECD countries and 20 member states of the European Union based on the Malmquist Index. Guan and Zuo [28] applied dual network DEA model to compare technological innovation efficiency of 35 countries. Yu et al. [29] considered the direct factors such as human capital, enterprise nature, and industrial structure when measuring the efficiency of technological innovation in China and found that they all have a significant impact on the efficiency of technological innovation. Wang et al. [30] found that R&D investment intensity has double threshold effect on green innovation efficiency of high-tech industry based on provincial panel data from 2006 to 2012.

The indirect factors included economic development, government funding, regional infrastructure, foreign direct investment, opening up, and environmental regulation. Yu et al. [31] reveal that environmental regulation can significantly improve the green innovation efficiency of the YREB, but different environmental regulation models have different effects on the green innovation efficiency of the YREB. Yang et al. [32] further analyzed the driving mechanism of green innovation efficiency in the YREB and found that the cost of enterprise pollution, the maturity of technology market, and the openness of market are conducive to promoting the efficiency of green innovation in the YREB, while the industrial structure has no significant impact on the efficiency of green innovation. Luo et al. [3] revealed the impact of international R&D capital technology spillover on the efficiency of green technology innovation by building a spatial model. According to the agglomeration effect of FDI, Gong et al. [33] demonstrated the effect and transmission mechanism of industrial green innovation efficiency.

According to the existing literatures on green innovation, there are still some shortcomings: Renyan Long concentrated on the level of enterprises, industrial, or provinces. The research on the efficiency of regional green innovation or specific economic zones and economic basins is of great practical significance for promoting the coordinated development of regional economy. At the same time, factors flow, technology spillover, pollution discharge, and innovation all show spatial interdependence and mutual influence. Existing literatures ignore the spatial correlation of green innovation as data with spatial correlation. Using the 11 provinces/cities’ panel data in the YREB from 2008 to 2017, this paper uses the super-slack-based measure (super-SBM) model to evaluate the green innovation efficiency, using Global Moran Index to analyze the spatial correlation and spatial agglomeration characteristics and finally use the spatial measurement model to analyze the influencing factors of green innovation efficiency.

3. Materials and Methods

3.1. Super-SBM Model

Traditional DEA models, such as the CCR and BCC models, are radial projection constructs by Cook and Seiford [34], which assumes that all the outputs of a production system are valuable and should be maximized for given inputs. Nevertheless, the undesirable output will have significant effects on the efficiency in the whole process [35, 36].Tone [37] developed a nonradial measurement to solve the problems of input and output slacks by proposing the slack-based measure (SBM). Compared with the traditional DEA, the efficiency value of this method is distributed in the (0, 1) interval, and the efficiency value of the effective DMU is 1. Therefore, when there are multiple effective DMUs, further comparison cannot be made. Then, Tone [38] developed a superefficiency SBM-DEA model which solves the problem of effective sorting and allows the efficiency score to be greater than 1 and can be easily rank-efficient DMUs. Super-SBM model can not only deal with the unexpected output more appropriately but also make further comparison in effective decision-making units, so it is more accurate and rigorous. Therefore, the super-SBM with undesirable outputs is introduced into measuring green innovation efficiency in this study, and the model is as follows:where is the green innovation efficiency and is the constant vector. The super-SBM model is simultaneously able to measure DMU efficiency and can also calculate DMU input and undesirable output redundancy rates, and it fully considers and effectively solves the problem with undesirable output and is more accurate to evaluate and analyze regional sustainable development.

3.2. Spatial Econometric Model

3.2.1. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is a kind of spatial data analysis method that is used for the estimation and analysis of spatial dependency and heterogeneity among objects, which is commonly indicated by Moran Index (Moran’s I) [39–42]. Before using spatial econometric methods, it is needed to be constructed to examine whether the green innovation efficiency in YREB has spatial dependence.

Global spatial autocorrelation is used to measure the distribution characteristics of the entire research unit among spatial elements, and it can effectively test the autocorrelation of adjacent units. The global Moran’s I value ranges from [−1, 1]. If I < 0, there is a negative spatial correlation, which indicates that the efficiency in the study area is in a discrete state. If I > 0, there is a positive correlation, indicating an agglomeration state. If I = 0, demonstration is made that the treatment efficiency is random, and the formula is as follows:where is the variance value of green innovation efficiency, n represents the total 11 provinces/cities in YREB, and show province i and province s green innovation efficiency, represents the average green innovation efficiency, and is the spatial weighting matrix.

3.2.2. Spatial Weighting Matrix

Setting spatial weighting matrix is the basis of the spatial autocorrelation test and spatial econometric model. It reflects the spatial distance between two regions, usually including geographical distance and socioeconomic distance. At present, geographic distance is more common in research. The spherical distance (d) between provincial capitals can be used to construct the spatial weighting matrix of geographical distance [43]. It uses the reciprocal of the square of the central distance between regions. The specific formula is as follows:

3.2.3. Spatial Econometric Model

Following Elhorst and Geogr [44], there are mainly three kinds of spatial econometric models: spatial lag panel model (SLM), spatial error panel model (SEM), and spatial Durbin panel model (SDM). The SLM model hypothesizes that the value of the dependent variable observed at a particular location is partially determined by a spatially weighted average of neighboring-dependent variables.

If the level of green innovation efficiency in the region is not only affected by some variables in the region and by the level of green innovation efficiency in neighboring regions, the spatial lag model (SLM) can be set up, which can be expressed as follows:where is the constant term and is the spatial weighting matrix. is the variable matrix of the corresponding influencing factors after the logarithmic treatment, and is the influencing coefficient of the local influencing factors on the local green innovation efficiency. i represents the corresponding region, t represents the corresponding year, and is the random error term. is the spatial lag variable influence coefficient of green innovation efficiency development, which reflects the spillover effect of green innovation efficiency development on green innovation development in the surrounding areas of the target area.

If the spatial dependence of green innovation behavior is affected by some error disturbance terms which are difficult to observe and have certain spatial structure, and to effectively measure the impact of this error impact on the efficiency of green innovation in this region, the spatial error model (SEM) can be expressed as follows:where the parameter reflects the regional spillover effects caused by the error term and is the residual term.

If the level of green innovation efficiency in the region is not only affected by the spatial spillover effect of green innovation efficiency in neighboring regions but also by other variables in neighboring regions, the spatial Durbin model (SDM) can be considered, which can be expressed as follows:where reflects the weighted influence of other regional factors on the efficiency of green innovation in this region, which is defined as other spillover effect in this paper.

3.2.4. Decomposition of Direct and Indirect Effects

Due to the spatial correlation in the spatial regression models, the authors in [45] point out that the coefficients of the explanatory variables in the regression model cannot accurately reflect the marginal effect. Spatial spillover effect is an important analysis tool in the spatial econometric model. Because spillover effect has a certain direction of source and source, there will be other spillover effects of other regional influencing factors on innovation efficiency in the region, and there will be other spillover effects of regional relevant variables on green innovation efficiency in the surrounding regions. In the spatial econometric model, the independent variable and the dependent variable will interact. At this time, the marginal effect of the independent variable on the dependent variable cannot be regressed by the linear model. Further deconstruction is needed to simplify the above spatial Durbin model into a vector expression at a specific time point:where is the vector of -order dependent variable, is the constant term, is the cross-section, random, and period error term, and is the dimension matrix composed of all independent variables. At a specific time point, the derivative matrix expression of the dependent variable to the independent variable is

The mean value of the elements on the main diagonal of the right matrix in the formula reflects the influence degree of the independent variable on the dependent variable in the province, that is, the effect of a province on the efficiency of green technology innovation in the region through a certain influencing factor, which is called direct spillover effect. It is expressed as , where is the trace of matrix , and it is the sum of main diagonals. The mean value of other elements on the nonmain diagonal of the right matrix in this formula reflects the spillover effect of a province on the green innovation efficiency of other provinces through its own relevant influencing factors, which is called the indirect spillover effect [46]. In this paper, it is defined as the spillover effect; that is, . Finally, direct spillover effect and indirect spillover effect are summed up as the total spillover effect.

3.3. Variables and Data Description

3.3.1. Variables for Green Innovation Efficiency

According to the principles of comprehensiveness, scientific, and availability of data, the index system for evaluating the efficiency of industrial green technology innovation is constructed by referring to the relevant research of green technology innovation:(1)Inputs: including labor input (number of R&D employees), capital input (total investment in R&D), and resource input (total energy consumption), which represent the consumption degree of innovation activities on resources.(2)Desirable outputs: including new product sales revenue and patent applications, which, respectively, reflect the economic benefits, living standards, and output level of scientific research achievements of each region.(3)Undesirable outputs: industrial pollution is the main source of environmental pollution, so the undesired output variable adopts the industrial wastewater discharge, industrial smoke (dust), and industrial sulfur dioxide discharge of each city in the YREB and uses the entropy method to calculate an environmental pollution index, which is used to explain the comprehensive impact of innovation activities on the ecological environment.

The input-output index system of green innovation efficiency in YREB is constructed in Table 1.

3.3.2. Influential Factors on Green Innovation Efficiency

There are many driving factors for the coordinated development of green innovation system. These factors will affect the development level of the subsystem invested in the regional green innovation subsystem and then affect the coordinated development level of the regional green innovation system. However, these factors cannot be used as the direct investment of each subsystem, and these factors are often not directly measured [47]. Therefore, in order to systematically and comprehensively study the driving factors of regional green innovation system, the factors that affect the efficiency of green innovation in the YREB are summarized as direct and indirect factors, including environmental regulation and industrial structure, and the indirect factors include the level of economic development, the strength of government support, and the level of opening to the outside world [48]. Consider that the YREB, as a strategic region of our country, has different responsibilities and requirements in its upper, middle, and lower reaches. Based on the previous study, the following five factors are used to examine the impact on the green innovation efficiency:(1)Economic development (ED): green innovation has a higher threshold than traditional innovation. A higher level of economic development is conducive to the improvement of environmental protection needs and environmental human capital of residents and provides the necessary material basis and social environment for promoting the green innovation achievements. The exhibition has green incentive effect and cumulative effect of innovation ability, and it can promote the promotion of green innovation ability. The YREB is a national key construction inland river economic belt with global influence. Its economic development speed is at the national leading level, which should promote the ability of green innovation and enhance the competitiveness of regional green innovation development.(2)Environmental regulation (ER): Porter believes that environmental regulation can drive green innovation, which is the famous “Porter Hypothesis” [49]. Porter believes that appropriate environmental regulations can stimulate enterprises to increase investment in technology research and development, promote green innovation, and achieve a win-win situation of technological progress and environmental protection. Since the “Porter Hypothesis” was put forward, a large number of empirical research results show that environmental regulation is one of the important driving factors of green innovation [50–52]. Under the restriction of environmental regulation, the innovation subject in the region should increase the investment in technological innovation, promote the improvement of the development level of technological innovation system and then reduce the expected output of the cost of achievement transformation subsystem, and promote the coordinated development of regional green innovation system. Therefore, environmental regulation is also an important driving factor for the coordinated development of regional green innovation system.(3)Government support (GS): technology driving factors are considered to be the fundamental cause of green innovation, and the improvement of technology capability triggers green innovation. This paper chooses government support for technology innovation to represent technology driving factors. The government’s financial expenditure on science and technology improves the national green innovation ability and promotes economic growth. The government’s support for scientific and technological innovation has created a good external environment for regional green innovation. To a large extent, the government’s financial support also reflects the strength of the government’s policy support. This paper chooses government funds from R&D funds as the indicator of government support for technological innovation. To a large extent, the government's financial support also reflects the strength of the government’s policy support. Choose government funds from R&D funds as the indicator of government support for green innovation.(4)Foreign direct investment (FDI): the degree of market opening reflects the degree of exchange between a region and other regions in the fields of economy, science, and technology. The impact of market openness on green innovation is still controversial in academia. One of the most famous hypotheses is the “pollution shelter” hypothesis [53]. According to the “pollution shelter” hypothesis, companies in developed countries will transfer their pollution intensive industries to developing countries with relatively low regulation so that developing countries will become “pollution shelter paradise” and bear more environmental pollution [54]. But another hypothesis, pollution halo hypothesis, holds that market opening can reduce environmental pollution [55]. Through the spillover effect of foreign investment, developing countries bring advanced foreign green technologies, which can significantly improve the level of regional technological innovation and the level of regional unexpected output, thus promoting the coordinated development of regional green innovation system [56, 57]. The YREB covers the three major economic zones of the East, the middle, and the West. The introduction of foreign investment may promote the local technological progress, and at the same time, there will be competition for foreign investment, which will make the surrounding cities backward in production capacity.(5)Industrial structure (IS): optimizing the internal allocation of the industry is conducive to stimulating the vitality of industrial innovation and enhancing the capacity of industrial green technology innovation. With the gradual upgrading of industrial structure, the secondary industry with strong pollution production capacity has transformed into a clean and low-carbon service industry, and the secondary and tertiary industries have accelerated the pace of integrated development [58, 59]. The close connection is promoted between green technology R&D services and industrial green transformation and enhanced the technological innovation ability with industrial characteristics. The YREB actively promotes the optimization and upgrading of industrial structure, promotes the integrated development of urban productive service industry and manufacturing industry, and requires enterprises to strengthen the research and development of green production technology to meet the technical requirements of industrial structure upgrading, and low-end production capacity may be forced to move to surrounding areas.

The influencing factors of green innovation efficiency in YREB is constructed in Table 2.

4. Empirical Analysis

4.1. Green Innovation Efficiency of YREB

Considering that there will be a certain time lag when green innovation input is converted into output, using other research results for reference, the input-output time lag is set as 1 year [60]; that is, the time interval of input index is set as 2008–2017, and the output index is set as 2008–2017. All the data were directly derived from the China Statistical Yearbook (2008–2017), the China Energy Statistical Yearbook (2008–2017), and the China Statistical Yearbook (2008–2017), the carbon dioxide emissions were estimated using the method provided by the Intergovernmental Panel on Climate Change [61]. Descriptive statistics of related variables are shown in Table 3. It can be preliminarily judged that the green innovation efficiency of 11 provinces and cities may also be significantly different, and further empirical analysis will be carried out in the future.

This paper relies on Max DEA PRO 8.0 software by using super-SBM model to measure the green innovation efficiency of 11 provinces in YREB from 2008 to 2017. The results are summarized in Table 4.

From 2008 to 2017, the overall green innovation efficiency of YREB was relatively stable. From 2008 to 2010, there was a slight downward trend. It increased significantly in 2013 and decreased slightly in 2013–2017. There are significant regional differences in green innovation efficiency level and time evolution trend in the upper, middle, and lower reaches of the YREB. During the research period, the green innovation efficiency of the middle and lower reaches of the province showed a trend of increasing first and then decreasing slightly, while that of the upper and lower reaches showed a trend of decreasing first and then increasing and then decreasing, but the fluctuation range was small. Since the promulgation of several opinions of the State Council on promoting the rise of the central region in 2005, the industrial undertaking policies to promote the rise of the central and western regions have promoted the inflow of a large number of capital and labor factors, which has led to the economic growth of the central and western regions. However, the industrial undertaking has brought economic benefits as well as unexpected output, making the green innovation efficiency of the middle and upper reaches of provinces at it is low and declining, and only in recent years, does it show an upward trend. In 2014, the policy of building the YREB into a leading demonstration zone of ecological civilization was issued. Since then, the state and local governments of the YREB have successively issued relevant policies, and the construction of ecological civilization in the YREB has achieved initial results.

4.2. Spatial Autocorrelation Analysis

4.2.1. Global Spatial Autocorrelation

This paper uses MATLAB to calculate the Global Moran’s I Index of logistics green innovation efficiency in YREB. Table 5 presents the results.

The results show that the Global Moran’s I Index of the green innovation efficiency passed the test at 10% significance level in 2008–2017, indicating that the green innovation efficiency displays a positive spatial correlation, it is not distributed randomly, the index is basically between 0.3 and 0.6, and it indicates that the green innovation efficiency shows a weak agglomeration state. In the main, the Global Moran’s I Index moves upward along a wave-like curve, this may be related to the macroeconomic environment in which the Chinese economy enters the “new normal,” and the industrial structure is transformed and upgraded.

The high and low efficiency neighboring provinces show a spatial cluster. The higher green innovation efficiency regions were adjacent, and the regions with lower green innovation efficiency were close to each other.

4.2.2. Spatial Effect of Green Innovation Efficiency

Because the data used in this paper are panel data, it is necessary to determine whether the fixed effect model or the random effect model should be used before regression analysis of the model. The Hausman test was carried out for SLM and SEM by MATLAB, and the test results are shown in Table 6.

According to Table 6, both SLM and SEM passed the Hausman test at 5% significance level, so the panel model with fixed effect was selected for regression analysis.

According to the test of spatial correlation, Table 7 shows that the test of LM_lag is greater than LM_error, so the estimation method of spatial lag model is studied and analyzed.

The general OLS regression coefficient is smaller than the spatial Durbin model (SDM), which shows that OLS regression ignores the spatial interaction between independent variables and dependent variables and overestimates the influence of related variables. From the regression results of the SDM model, the log-L and R² of the spatiotemporal fixed model are obvious, and it is larger than the fixed time model and the fixed space model, so the double fixed model has the best estimation results. Table 8 is an analysis of the influencing factors of green innovation efficiency based on the empirical results of the time-space fixed model. The results are shown in Table 8.(1)Economic development (ED) has a significant positive role in promoting the green innovation efficiency of the YREB. For every 1% increase in GDP per capita, the efficiency of green innovation will increase by an average of 0.3487%. It shows that economic growth will improve the green innovation efficiency. Economically developed regions in the YREB, on the one hand, will pay more attention to the development of environmental quality; on the other hand, the R&D investment in the field of green innovation will increase, and the investment subsidies and production subsidies for products and services will be greater. The coefficient of the spatial lag term of economic growth is −0.1593, and through the 1% significance test, it shows that the economic growth of the neighboring areas in the YREB has negative spatial spillover effects to the green innovation efficiency of the region. This is because the economic development of the neighboring areas will have a certain siphon effect on the relevant innovation elements of the region, which is not conducive to the improvement of the green innovation efficiency of the region.(2)Industrial structure (IS) has a significant positive role at the level of 5%. In the TF model, the coefficient is also significantly positive. This is mainly because in the adjustment and upgrading of industrial structure in the YREB, those high energy consumptions and high pollution situation have improved. However, in the process of promoting the industrial structure to achieve a high degree and rationalization in a certain region in the YREB, it may cause the imitation of neighboring regions and promote the coordinated development of regional green systems in different regions.(3)Foreign direct investment (FDI) has no significant effect on the green innovation efficiency of the YREB. At the same time, the corresponding spatial lag has not passed the significance test, which means that when the YREB regions introduce FDI, they neither promote the efficiency of local green innovation nor bring spillover effects to the efficiency of green innovation in the surrounding areas. The reason may be that FDI does not really consider regional environmental technology innovation in most provinces or even occupies the provincial R&D innovation of the YREB and inhibits the technological innovation ability, and the green innovation efficiency of introducing foreign investment is not ideal. This also means that the purpose of most FDI entry is to pursue low cost and tax advantages. It does not really consider environmental technology innovation, and the quality of investment still needs to be further improved.(4)Government support (GS) has a significant positive role in promoting the green innovation efficiency of the YREB. The government’s support for green innovation activities can improve the development level of scientific and technological research and development subsystem, so as to reduce the unexpected output in the process of achievement transformation, increase the expected output, and promote the coordinated development of green innovation system. The government should continue to increase its support for green innovation in the YREB, especially in the less developed areas such as the central and western regions. By promoting the development of technological innovation, we can develop more technologies that are beneficial to the ecological environment and promote the coordinated development of green innovation system.(5)Environmental regulation (ER) has significant positive effect on the green innovation efficiency of the YREB. For every 1% increase in the level of environmental regulation, the green innovation efficiency will increase by an average of 0.005%, which means that the more stringent the environmental regulation is, the stronger the environmental pollution cost constraints enterprises bear, so that they have the motivation to pay attention to the production of clean, ecological, and recycling, and the enterprises that take the lead in technological innovation have the first mover advantage in pollution control. It is helpful for enterprises to seize market share and gain competitive advantage, and it is also helpful for enterprises to improve their green innovation performance. The coefficient of the spatial lag term of environmental regulation is −0.003, and through the 1% significance test, it shows that the environmental regulation of the neighboring areas has negative spatial spillover benefits to the green innovation efficiency of the region. This may be due to the deterrence effect of environmental regulations on enterprises, which forces enterprises to increase investment in environmental governance, so that the corresponding low-tech pollution links are transferred to other areas with relatively low environmental standards, leading to the “pollution shelter” effect and inhibiting green innovation.

4.2.3. Spatial Spillover Effects of Green Innovation Efficiency

Based on the SDM model, this paper analyzes the direct, indirect, and total effects of various influencing factors, among which the total effect represents the average impact of influencing factors on green innovation, while the direct effect and indirect effect represent the decomposition of the total effect, which, respectively, represents the impact of influencing factors on the region and adjacent regions. Table 9 are the results.

The indirect effect of the level of economic development is negative, which shows that, in general, the level of economic development is not conducive to the spatial spillover of green innovation efficiency in the period under investigation, especially in the provinces with the higher level of economic development, the lower the spatial spillover effects of green innovation efficiency, which highlights that the more developed provinces pay more attention to “protect” the efficiency of green innovation in their own provinces in the YREB. Under the requirements of national green development, the economically developed regions in the YREB make use of their own advantages in capital, and the economically underdeveloped regions are eager to improve the local economic level and transfer some high energy consumption, high pollution, and high emission industries to the economically underdeveloped provinces, which to some extent causes the green innovation efficiency to show negative spatial spillover effects.

The total effect, direct effect, and indirect effect of industrial structure are all positive. The optimization of industrial structure is conducive to the transformation of development mode, reduction of energy consumption, and environmental pollution, so as to improve the efficiency of green innovation.

FDI in this region has no significant impact on the green innovation efficiency of surrounding areas in the YREB. It shows that there are corresponding regional technical barriers in the process of promoting the efficiency of green innovation in all regions of YREB. On the one hand, they are unwilling to cooperate and share the technical innovation; on the other hand, they are easy to transfer the cost of environmental pollution to the surrounding areas.

The direct effect regression coefficient is positive under the significance of 5%, and the indirect effect regression coefficient fails to pass the significance test. Government support is a strong backing to enhance the capacity of green innovation, especially along with the transformation and upgrading driven by green innovation in the YREB, and the government has given strong support in building basic innovation platform and increasing investment in innovation and R&D. The guiding effect of green innovation policy is significant, laying a solid foundation for the high-quality development of the YREB.

The direct effect of environmental regulation on the spatial spillover of green innovation efficiency is positive and passes the significance test; the indirect effect and the total effect on the productivity of green innovation pass the significance test of 5% but has a negative impact on the change in green innovation efficiency. This shows that, on the one hand, environmental regulation has a positive role in promoting the efficiency of green innovation in our province, but at present, it has not fundamentally changed the level of green development in China, so it cannot significantly improve the efficiency of green innovation in China. On the other hand, when China’s green innovation capacity is insufficient, the imbalance of the intensity of interprovincial environmental regulation is likely to lead the environmental pollution industry in the provinces with high intensity of regulation to enter the provinces with low intensity of regulation in the YREB.

5. Conclusions and Discussion

5.1. Conclusions

This study used super-SBM model to consider undesirable outputs, measuring the green innovation efficiency in YREB from 2008 to 2017. Since green innovation efficiency has spatial spillover effects, therefore, a spatial econometric model SDM model is applied to analyze the influencing factors of green innovation efficiency. The green innovation efficiency empirical results indicate the green innovation efficiency is developing slowly, and the green innovation of the eastern part of YREB is significantly better than that of the lower reaches in the west. From the spatial autocorrelation result, it shows that there is a significant spatial autocorrelation of green innovation efficiency in YREB regions. From the spatial econometric of SDM analysis, indicating that the level of economic development, foreign direct investment to the outside world and environmental pollution control has positive effects on the green economic efficiency of the YREB, while the proportion of the secondary industry has negative effects. The green economic efficiency of the YREB has a significant spatial correlation. The provinces with high level of economic development and environmental pollution control have a significant positive role in promoting the green economic efficiency of the neighboring provinces. The provinces with high proportion of the secondary industry and high government support have a negative inhibitory effect on the green economic efficiency of the neighboring provinces.

5.2. Discussion

According to the empirical results, this study put forward proposals to enhance green innovation efficiency.

First, the optimization and upgrading of industrial structure is promoted. Industrial structure has a significant negative inhibitory effect on the green innovation efficiency of the YREB, so it is necessary to speed up the pace of industrial structure adjustment and new industrialization. We will bring superiority into full play of industry and intelligence intensity in the YREB, vigorously implement innovation-driven development strategy, add to new momentum of reform, innovation, and development, subtract from the elimination of backward production capacity, and accelerate industrial transformation and upgrading. We will build a manufacturing innovation system, improve the ability to develop key systems and equipment, and foster and expand high technology industries, emerging sectors of strategic importance, equipment manufacturing, and other industries. We will optimize the layout of strategic emerging industries, accelerate the construction of regional characteristic industrial bases, give free rein to radiation driving and leading demonstration, and form a national strategic emerging industry development highland..

Second, the quality of opening up is improved. The technology spillover effects of FDI in the YREB are more than the environmental pollution effect. The introduction of FDI can improve green economic development level in the YREB, but improvement effect is not significant, so we should further improve the quality of opening up. We should further promote the improvement of the negative list of market access in the YREB, improve the project access mechanism, promote the formation of an institutionalized, standardized, green, transparent, and procedural system for foreign capital introduction system, vigorously introduce new green technologies and industries, attract environmentally friendly enterprises to settle down, give full play to the technology spillover effects of green foreign capital industries, and improve the green production of local enterprises to build a green ecological industrial chain and improve the level of green development and ecological quality.

Finally, we will intensify efforts to prevent and control environmental pollution. Environmental pollution control in the YREB has a positive role in improving green innovation efficiency, but it has not produced significant results, so the efforts of environmental pollution control need to be further strengthened. We should strengthen the joint prevention and control of environmental pollution; establish and improve the emergency response mechanism for cross department, cross region, and cross basin environmental emergencies; strictly control industrial pollution; dispose of urban sewage and garbage; control agricultural nonpoint source pollution; prevent ship and air pollution; strengthen the collaborative protection of ecological environment; establish a negative list management system; strengthen daily monitoring and supervision; and strictly implement the ecological environment. The system of responsibility investigation for environmental damage should be improved, the proportion of resource utilization rate, environmental pollution prevention and control, and quality evaluation system of economic and ecological development should be increased, and the performance evaluation system reflecting the requirements of ecological civilization should be improved.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Disclosure

Hangyuan Guo is the co-first author.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

This paper was supported by Wonkwang University in 2020.

References

Y. Yuan, R. Luo, and Y. Li, “Analysis of the development level and spatial evolution characteristics of ecological civilization in the Yangtze river economic belt,” Statistics and Decision, vol. 1, no. 20, pp. 98–101, 2016.
View at: Google Scholar
L. W. Lu, D. Y. Song, and X. F. Li, “Research on green efficiency of urban development in the Yangtze river economic belt,” China Population, Resources and Environment, vol. 26, no. 6, pp. 35–42, 2016.
View at: Google Scholar
Q. Luo, C. Miao, L. Sun, X. Meng, and M. Duan, “Efficiency evaluation of green technology innovation of China’s strategic emerging industries: an empirical analysis based on Malmquist-data envelopment analysis index,” Journal of Cleaner Production, vol. 238, Article ID 117782, 2019.
View at: Publisher Site | Google Scholar
C. Ghisetti, S. Mancinelli, M. Mazzanti, and M. Zoli, “Financial barriers and environmental innovations: evidence from EU manufacturing firms,” Climate Policy, vol. 17, no. sup1, pp. S131–S147, 2017.
View at: Publisher Site | Google Scholar
D. Li, M. Zheng, C. Cao, X. Chen, S. Ren, and M. Huang, “The impact of legitimacy pressure and corporate profitability on green innovation: evidence from China top 100,” Journal of Cleaner Production, vol. 141, pp. 41–49, 2017.
View at: Publisher Site | Google Scholar
M. Cao, J. Ukko, and T. Rantala, “Sustainability as a driver of green innovation investment and exploitation,” Journal of Cleaner Production, vol. 179, pp. 631–641, 2018.
View at: Publisher Site | Google Scholar
T. Bernauer, S. Engel, and D. Kammerer, “Explaining green innovation: ten years after porter’s win-win proposition: how to study the effects of regulation on corporate environmental innovation,” Politische Vierteljahresschrift, vol. 39, pp. 323–341, 2007.
View at: Google Scholar
C. Fussier and P. James, Book Review: Driving Eco-Innovation: A Breakthrough Discipline for Innovation and Sustainability, Pitman Pub, Wetherby, UK, 1996, [CrossRef].
R. Kemp and A. Arundel, 1998, Survey indicators for environmental innovation. IDEA (indicators and data for European analysis). Sub-project 2.2, Indicators on the importance of environmental goals. 26.
L. Ahlvik, P. Ekholm, K. Hyytiäinen, and H. Pitkänen, “An economic-ecological model to evaluate impacts of nutrient abatement in the Baltic Sea,” Environmental Modelling & Software, vol. 55, pp. 164–175, 2014.
View at: Publisher Site | Google Scholar
T. Heffels, R. McKenna, and W. Fichtner, “An ecological and economic assessment of absorption-enhanced-reforming (AER) biomass gasification,” Energy Conversion and Management, vol. 77, pp. 535–544, 2014.
View at: Publisher Site | Google Scholar
Y. Chen, C. Jayaprakash, and E. Irwin, “Threshold management in a coupled economic-ecological system,” Journal of Environmental Economics and Management, vol. 64, no. 3, pp. 442–455, 2012.
View at: Publisher Site | Google Scholar
Z. J. Feng and W. Chen, “Sources of technology and the total factor productivity growth of R & D innovation: based on China regional big medium-sized industrial enterprises,” Science of Science and Management of S & T, vol. 34, no. 3, pp. 33–41, 2013.
View at: Google Scholar
Y. Ren, C. K. Niu, T. Niu, and X. L. Yao, “Research on the green Innovation efficiency model and empirical analysis,” Management World, vol. 7, pp. 176-177, 2014.
View at: Google Scholar
Q. Yin and Y. Chen, “Study on the regional differences and causes of green innovation efficiency in China,” Jiangsu Social Sciences, vol. 18, no. 2, pp. 64–69, 2016, [CrossRef].
View at: Google Scholar
T. Charoenrat and C. Harvie, “The efficiency of SMEs in Thai manufacturing: a stochastic frontier analysis,” Economic Modelling, vol. 43, pp. 372–393, 2014.
View at: Publisher Site | Google Scholar
H. Li, J. Zhang, C. Wang, Y. Wang, and V. Coffey, “An evaluation of the impact of environmental regulation on the efficiency of technology innovation using the combined DEA model: a case study of Xi’an, China,” Sustainable Cities and Society, vol. 42, pp. 355–369, 2018.
View at: Publisher Site | Google Scholar
H. W. Lampe and D. Hilgers, “Trajectories of efficiency measurement: a bibliometric analysis of DEA and SFA,” European Journal of Operational Research, vol. 240, no. 1, pp. 1–21, 2015.
View at: Publisher Site | Google Scholar
C. Miao, D. Fang, L. Sun, Q. Luo, and Q. Yu, “Driving effect of technology innovation on energy utilization efficiency in strategic emerging industries,” Journal of Cleaner Production, vol. 170, pp. 1177–1184, 2018.
View at: Publisher Site | Google Scholar
L. Xiao, J. Gao, and S. Liu, “The change trend of green technology innovation efficiency in China based on spatial gradient—empirical analysis of provincial panel data,” Soft Science, vol. 31, pp. 63–68, 2017.
View at: Google Scholar
E. G. Carayannis, E. Grigoroudis, and Y. Goletsis, “A multilevel and multistage efficiency evaluation of innovation systems: a multiobjective DEA approach,” Expert Systems with Applications, vol. 62, pp. 63–80, 2016.
View at: Publisher Site | Google Scholar
P. Samut and R. Cafri, “Analysis of the efficiency determinants of health systems in OECD countries by DEA and panel tobit,” Social Indicators Research, vol. 129, no. 1, pp. 113–132, 2016.
View at: Publisher Site | Google Scholar
C. Lafarga and J. Balderrama, “Efficiency of Mexico’s regional innovation systems: an evaluation applying data envelopment analysis (DEA),” African Journal of Science Technology Innovation & Development, vol. 7, pp. 36–44, 2015.
View at: Publisher Site | Google Scholar
Y. Ren and C. Wang, “Research on the regional difference and spatial effect of green innovation efficiency of industrial enterprises in China,” Revista Ibérica de Sistemase Tecnologias de Informação, vol. 10, pp. 373–384, 2016.
View at: Google Scholar
J.-L. Du, Y. Liu, and W.-X. Diao, “Assessing regional differences in green innovation efficiency of industrial enterprises in China,” International Journal of Environmental Research and Public Health, vol. 16, no. 6, p. 940, 2019.
View at: Publisher Site | Google Scholar
M. Buesa, J. Heijs, M. Martínez Pellitero, and T. Baumert, “Regional systems of innovation and the knowledge production function: the Spanish case,” Technovation, vol. 26, no. 4, pp. 463–472, 2006.
View at: Publisher Site | Google Scholar
R. Färe, S. Grosskopf, and G. Whittaker, “Network DEA II,” in Data Envelopment Analysis. International Series in Operations Research & Management Science, W. Cook and J. Zhu, Eds., vol. 208, pp. 307–327, Springer, Berlin, Germany, 2014.
View at: Google Scholar
J. Guan and K. Zuo, “A cross-country comparison of innovation efficiency,” Scientometrics, vol. 100, no. 2, pp. 541–575, 2014.
View at: Publisher Site | Google Scholar
X. Yu, Z. Li, R. Chi, and M. Shi, “Technological innovation efficiency of different regions in China: status quo and causes,” Studies In Science of Science, vol. 23, pp. 258–264, 2005.
View at: Google Scholar
H. Wang, S. Wang, Z. Miao, and X. Li, “Heterogeneity threshold effect of R & D investment on green innovation efficiency based on Chinese high-tech industries,” Science Research Management, vol. 37, pp. 63–71, 2016.
View at: Google Scholar
S. Yu, X. Li, and Z. Peng, “Environmental regulation model and green innovation efficiency of the Yangtze river economic belt,” Jianghai Academic Journal, vol. 3, pp. 209–214, 2017.
View at: Google Scholar
S. Yang, T. Wu, and Z. Li, “Study on the spatial-temporal differences and influencing factors of green innovation efficiency in the Yangtze river economic belt,” MACROECONOMICS, vol. 6, pp. 107–132, 2018.
View at: Google Scholar
X. Gong, M. Li, and H. Zhang, “Has OFDI promoted the industrial enterprises’ green innovation efficiency in China——evidence based on agglomeration economic effect,” Journal of International Trade, vol. 11, pp. 127–137, 2017.
View at: Google Scholar
W. D. Cook and L. M. Seiford, “Data envelopment analysis (DEA)-Thirty years on,” European Journal of Operational Research, vol. 192, no. 1, pp. 1–17, 2009.
View at: Publisher Site | Google Scholar
H. Li, K. Fang, W. Yang, D. Wang, and X. Hong, “Regional environmental efficiency evaluation in China: analysis based on the super-SBM model with undesirable outputs,” Mathematical and Computer Modelling, vol. 58, no. 5-6, pp. 1018–1031, 2013.
View at: Publisher Site | Google Scholar
J. Zhang, W. Zeng, J. Wang, F. Yang, and H. Jiang, “Regional low-carbon economy efficiency in China: analysis based on the super-SBM model with CO₂ emissions,” Journal of Cleaner Production, vol. 163, pp. 202–211, 2017.
View at: Publisher Site | Google Scholar
K. Tone, “A slacks-based measure of efficiency in data envelopment analysis,” European Journal of Operational Research, vol. 130, no. 3, pp. 498–509, 2001.
View at: Publisher Site | Google Scholar
K. Tone, “A slacks-based measure of super-efficiency in data envelopment analysis,” European Journal of Operational Research, vol. 143, no. 1, pp. 32–41, 2002.
View at: Publisher Site | Google Scholar
F. Jin and L. Lee, “On the bootstrap for Moran’s I test for spatial dependence,” Journal of Economics, vol. 184, pp. 295–314, 2015.
View at: Publisher Site | Google Scholar
S. J. Rey, “Spatial empirics for economic growth and convergence,” Geographical Analysis, vol. 33, no. 3, pp. 195–214, 2001.
View at: Publisher Site | Google Scholar
Y. Xiong, D. Bingham, W. J. Braun, and X. J. Hu, “Moran’s I statistic-based nonparametric test with spatio-temporal observations,” Journal of Nonparametric Statistics, vol. 31, no. 1, pp. 244–267, 2019.
View at: Publisher Site | Google Scholar
T. Zhang and G. Lin, “On Moran’s I coefficient under heterogeneity,” Computational Statistics & Data Analysis, vol. 95, pp. 83–94, 2016.
View at: Publisher Site | Google Scholar
H. D. Liu, “The inside, outside, and space spillover effects of regional innovation,” Science Research Management, vol. 34, no. 1, pp. 28–36, 2013.
View at: Google Scholar
J. P. Elhorst, “Dynamic spatial panels: models, methods, and inferences,” Journal of Geographical Systems, vol. 14, no. 1, pp. 5–28, 2012.
View at: Publisher Site | Google Scholar
J. P. LeSage and R. K. Pace, Introduction to Spatial Econometrics, CRC Press, Boca Raton, FL, USA, 2009.
K. R. Zuo and J. C. Gong, “Exploring the change and influence factors of R & D efficiency at province-level of China,” Science of Science and Management of S & T, vol. 37, no. 4, pp. 79–88, 2016.
View at: Google Scholar
H. Chen, H. Lin, and W. Zou, “Research on the regional differences and influencing factors of the innovation efficiency of China’s high-tech industries: based on a shared inputs two-stage network DEA,” Sustainability, vol. 12, no. 8, p. 3284, 2020.
View at: Publisher Site | Google Scholar
K. Rennings, “Redefining innovation-eco-innovation research and the contribution from ecological economics,” Ecological Economics, vol. 32, no. 2, pp. 319–332, 2000.
View at: Publisher Site | Google Scholar
M. E. Porter and C. Van Der Linde, “Green and competitive: ending the stalemate,” Harvard Business Review, vol. 73, no. 5, pp. 120–134, 1995.
View at: Google Scholar
J. Horbach, C. Rammer, and K. Rennings, “Determinants of eco-innovations by type of environmental impact-the role of regulatory push/pull, technology push and market pull,” Ecological Economics, vol. 78, pp. 112–122, 2012.
View at: Publisher Site | Google Scholar
E. Kesidou and P. Demirel, “On the drivers of eco-innovations: empirical evidence from the UK,” Research Policy, vol. 41, no. 5, pp. 862–870, 2012.
View at: Publisher Site | Google Scholar
K. Rennings and C. Rammer, “The impact of regulation-driven environmental innovation on innovation success and firm performance,” Industry & Innovation, vol. 18, no. 3, pp. 255–283, 2011.
View at: Publisher Site | Google Scholar
B. R. Copeland and M. S. Taylor, “North-South trade and the environment,” The Quarterly Journal of Economics, vol. 109, no. 3, pp. 755–787, 1994.
View at: Publisher Site | Google Scholar
J. X. Zhang, N. Cai, J. S. Mao, and C. Yang, “Independent innovation, technology introduction and green growth of industry in China: an empirical research based on industry heterogeneity,” Studies in Science of Science, vol. 33, no. 2, pp. 185–194, 2015.
View at: Google Scholar
M. H. Kim and N. Adilov, “The lesser of two evils: an empirical investigation of foreign direct investment-pollution tradeoff,” Applied Economics, vol. 44, no. 20, pp. 2597–2606, 2012.
View at: Publisher Site | Google Scholar
K. X. Bi, C. J. Yang, and P. Huang, “Research on the impact of FDI on the green process innovation of Chinese manufacturing industries: an empirical analysis based on the panel data,” China Soft Science, vol. 20, no. 9, pp. 172–180, 2011.
View at: Google Scholar
J. Horbach, “Determinants of environmental innovation-new evidence from German panel data sources,” Research Policy, vol. 37, no. 1, pp. 163–173, 2008.
View at: Publisher Site | Google Scholar
M. C. Cuerva, Á. Triguero-Cano, and D. Córcoles, “Drivers of green and non-green innovation: empirical evidence in low-tech SMEs,” Journal of Cleaner Production, vol. 68, pp. 104–113, 2014.
View at: Publisher Site | Google Scholar
L. Luo and S. Liang, “Study on the efficiency and regional disparity of green technology innovation in China’s industrial companies,” Chinese Journal of Population Resources and Environment, vol. 14, no. 4, pp. 262–270, 2017.
View at: Publisher Site | Google Scholar
P. C. Zhu, D. H. Liu, and X. H. Huang, “An evaluation of science and technology innovation efficiency of cities from the dynamic perspective by taking 9 prefecture-level cities in Fujian Province as an example,” Science Research Management, vol. 38, no. 6, pp. 43–50, 2017.
View at: Google Scholar
R. Y. Long, H. Z. Ouyang, and H. Y. Guo, “Super-slack-based measuring data envelopment analysis on the spatial-temporal patterns of logistics ecological efficiency using global Malmquist index model,” Environmental Technology & Innovation, vol. 18, Article ID 100770, 2020.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2020 Renyan Long et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies