Research on the Improvement Path of Regional Green Technology Innovation Efficiency in China Based on fsQCA Method
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School of Management, Dalian Polytechnic University, Dalian 116034, China
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School of Economics and Management, Shihezi University, Shihezi 832003, China
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Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 3190; https://doi.org/10.3390/su15043190
Submission received: 19 November 2022
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Revised: 29 January 2023
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Accepted: 2 February 2023
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Published: 9 February 2023
(This article belongs to the Section Environmental Sustainability and Applications)
Abstract
:Improvements in green technology innovation efficiency is the core factor to promote to shape new advantages in resource-saving and environmental friendliness under the new pattern of double-cycle development. It is also the main driving force needed to establish a high-quality development model of an efficient and sustainable economy. Taking 30 provinces of China as case samples, this paper establishes the appraisal system of green technology innovation efficiency. The first step is using the three-stage DEA model to measure green technology innovation efficiency. Then, according to the configuration perspective, the paper uses fuzzy set qualitative comparative analysis (fsQCA) to explore multiple paths for promoting green technology innovation efficiency. The findings are as follows: (1) A single factor of environmental support or technology supply cannot effectively stimulate the improvement of green technology innovation efficiency. Therefore, the impacting factors must be matched to jointly improve green technology innovation efficiency. (2) There are three configuration paths for high green technology innovation efficiency. Namely, they are the driven by economic environment and environmental regulation type; the driven by industrial structure and supply of finance type; and the driven by industrial structure, supply of finance, and supply of manpower type. (3) The paths to generate non-high green technology innovation efficiency can be summarized as one. The shortage of human resources and a poor economic environment are the main reasons for the inhibition of improvements in green technology innovation efficiency; additionally, the configuration of high and non-high green technology innovation efficiency is asymmetrical. On the one hand, our results are helpful for the study of the efficiency of regional green technology innovation at the provincial level. On the other hand, the results also provide practical solutions and a theoretical basis for provinces to promote regional green technology innovation efficiency under the new economic normal.
1. Introduction
At present, China is at an important stage of sustainable development. Leading economic and social development through green technology innovation is becoming an efficient way to deepen economic structural reform. In the context of the new development pattern, the improvement of green technology innovation efficiency is the key to the transformation from high-speed economic growth to high-quality economic growth, and it is also the main driving force available to achieve the high-quality development of China’s economy. Therefore, continuously improving green technology innovation efficiency can effectively promote the construction of China’s open economy and society. Green technology innovation can help China realize industrial transformation and upgrading, strengthen innovation and development, and improve ecological and environmental protection. It will help China to integrate into the global green industrial and technology innovation chains, and will drive China’s domestic economic cycle by bringing a driving force and strategic support to China’s social development. All of these will enhance the domestic and international double cycle.
Green technology innovation efficiency refers to the overall level of green technology innovation input and output [1]. Green technology innovation is an important way to promote the formation of economic globalization. Green technology innovation efficiency is a driving force that effectively promotes the development of the international and domestic cycles [2], and is a significant influencing factor for the high-quality development of China’s society [3]. A series of studies have been carried out on how to evaluate green technology innovation efficiency. Based on previous studies, in response to differences in research perspectives on green technology innovation efficiency, the establishment of an evaluation system of green technology innovation efficiency can be summarized into the following three aspects. Firstly, the perspective of the innovation subject is considered, which includes participants and undertakers in the process of green technology innovation. Yin et al. (2020) mainly analyzed green technology innovation in the manufacturing industry using correlation analysis and exploratory factor analysis to analyze green innovation in the manufacturing industry with multi-agent cooperation in detail [4]. Tian et al. (2019) built a DEA model to explore green technology innovation efficiency in high-tech industries [5]. Secondly, green technology innovation efficiency is divided into different dimensions for evaluation. Liang et al. (2019) established the evaluation of green technology innovation efficiency in the development phase and the accomplishment transformation stage [6]. Huang et al. (2020) measured input indicators from the three levels of personnel, capital, and energy input, and measured output indicators from the two levels of expected output and non-expected output [7]. Thirdly, the external environment is examined from multiple perspectives to assess green technology innovation efficiency. Wei et al. (2020) utilized the kernel density function to analyze the dynamic change trend of industrial green technology innovation, and evaluated green industry technology innovation in a recent ten-year period in China from three levels [8]. Liu et al. (2021) explored green technology innovation from multiple environmental regulation levels [9].
To sum up, although scholars have carried out multi-angle and in-depth studies on the influencing factors of green technology innovation, how to improve green technology innovation efficiency under the combined effects of multiple factors remains to be explored. The innovation points of this study are mainly reflected in the following two aspects: Firstly, most of the existing research methods adopted a single qualitative or quantitative method to analyze from a single influencing factor or the interaction between influencing factors. Therefore, this paper uses fsQCA to explore from all sides “qualitative cases” and “quantitative variables”, which is beneficial for the exploration of the way to promote green technology innovation efficiency. Secondly, although existing studies have confirmed that understanding the combined effects of multiple factors is the key to affecting green technology innovation efficiency, few studies have found the relationships between multiple antecedent variables and outcome variables based on configuration dimensions. This paper discusses how to coordinate and improve green technology innovation efficiency from the two aspects of green environment support and green technology supply based on a multi-factor configuration dimension.
From the perspective of configuration, combining the resource-based view and the institution-based view, this paper uses the fsQCA method to probe the driving path of green technology innovation efficiency of 30 provinces and regions in China. The purpose is to explore the linkage mechanism between green technology innovation efficiency and regional economic growth. This paper aims to clarify how to improve green technology innovation efficiency in the domestic big cycle, and to provide theoretical support and practical guidance for promoting high-quality economic development in China.
2. Theoretical Model Construction
2.1. Green Environment Support
(1) Economic environment and green technology innovation efficiency. Due to the double externality of green technology innovation, including the negative externalities of knowledge spillover and the positive externalities of environmental protection [10] is important. Negative externality means that the innovation income from high R&D investment will also benefit other enterprises. Positive externality means that the social benefits generated by environmental pollution control are greater than the private benefits [11]. When the economic environment is good, enterprises will be more socially responsible, and a higher GDP can provide a good innovation environment for the smooth development of green technology innovation. At the same time, a good economic environment is beneficial for solving the problem of capital scarcity and reducing the high risk in the innovation process, stimulating the activity of enterprises to participate in green technology innovation. To sum up, the innovation subject will improve the green technology innovation efficiency according to the favorability of the regional economic environment. There is a correlation between the economic environment and green technology innovation efficiency.
(2) Environmental regulation and green technology innovation efficiency. The Porter hypothesis states that appropriate environmental regulation will spur technology innovation [12]. With the continuous issuance of environmental protection laws and regulations by the government, companies with high emissions and high energy consumption will be forced to make green technology innovations to ease external pressure. Relevant studies have confirmed that regional preference for green technology innovation originates from internal choices, but is also subject to external constraints [13]. The government’s attention to the ecological environment will force innovation subjects to improve green technology innovation efficiency by improving technological and industrial chain processes and increasing R&D investment to achieve the purpose of energy conservation, emission reductions, and cleaner production. Therefore, the policy environment can contribute to green technology innovation efficiency.
(3) Industrial structure and green technology innovation efficiency. Many learned people believe that industrial structure also has a decisive impact on green technology innovation efficiency [14]. With the continuous upgrading of demands on the process of implementing green technology innovation, the processes of industrial structure upgrading, industrial upgrading, and product innovation can effectively solve the problems caused by negative externalities in the process of green technology innovation [15]. The province’s innovation vitality will be stimulated along with the optimization process of the tertiary industry. Through green technology, innovation will accelerate the industrial boom and bust cycle and contribute to the construction of a good industrial chain. This will stimulate the R&D vitality of innovation entities and further improve the efficiency of green technology innovation. Therefore, industrial structure helps to improve green technology innovation efficiency.
2.2. Green Technology Supply
(1) Supply of finance and green technology innovation efficiency. The high cost of environmental governance in the process of innovation will reduce the enthusiasm of innovation subjects for green technology research and development. Therefore, a steadying supply of finance can provide R&D with a guarantee for the improvement of green technology innovation efficiency. Researchers have explored how to drive the improvement of enterprises’ green technology innovation efficiency by increasing enterprises’ green R&D subsidies and technology-specific funds [16]. In other words, good R&D investment intensity and new product development investment are beneficial for enterprises to obtain technology progress and enhance their competitive advantage. Thus, it will help achieve the purpose of improving green technology innovation efficiency.
(2) Supply of manpower and green technology innovation efficiency. In neoclassical growth theory, it has been concluded that factor accumulation promotes regional technology innovation. R&D personnel functioning as core resources of enterprises is a key factor that determines the capital flow, strategic management, and output efficiency of enterprises. R&D personnel directly affect the efficiency improvement of innovation subjects. High-quality human capital is beneficial for provinces to help local enterprises adopt environmentally friendly innovation through product innovation, process improvement, technology construction, and other means. It will ultimately reduce pollutant emissions and elevate the improvement of green technology innovation efficiency.
(3) Supply of facilities and green technology innovation efficiency. Due to the instability of revenue, difficulties arise in innovation research and development. Because of high regulatory expenses, green technology innovation needs a good supply of facilities to improve green technology innovation efficiency. Providing a material basis of good facilities such as incubators and science and technology institutions can help innovation subjects deeply develop existing capital, break through existing constraints, and take full advantage of existing resources, instruments, and equipment. They will carry out innovative activities. Through the rapid and efficient innovation of existing knowledge and technology, green technology innovation efficiency can be greatly improved.
To sum up, green technology innovation is a complex process, and the improvement of green technology innovation efficiency is the result of the coordination and linkage of various factors. Based on this and previous studies, this paper uses fsQCA for a configuration analysis to explore the improvement path of green technology innovation efficiency from the two levels of green environment support and green technology supply. Referring to Behera [17], Zhu [18], and Peng [19] et al., green environment support is divided into three aspects, including economic environment, environmental regulation, and industrial structure. Referring to Bilal [20], Tang [21], and Yu [22] et al., green technology supply is divided into three aspects, including supply of finance, supply of manpower, and supply of facilities. The theoretical model is shown in Figure 1.
3. Research Design and Variable Selection
3.1. Research Methods
(1) Evaluation of green technology innovation efficiency
Data Envelope Analysis (DEA) can deal with the problems of multiple outputs, and can evaluate decision-making units more objectively without giving weight to input and output. When the three-stage DEA model is used, the calculation results can reflect the evolution of rules and characteristics of different decision-making units. The three-stage DEA model is used to calculate green technology innovation efficiency as the result variable. Due to the mature development of the three-stage DEA model, this paper will not repeat its calculation formula.
(2) Construction of the improvement path of green technology innovation efficiency
Qualitative comparative analysis (QCA) is a comprehensive method to study “qualitative cases” and “quantitative variables”. Several antecedent conditions in the case are compared to explore the complex causal relationship between the outcome variable and each antecedent condition [23]. QCA was chosen because the results of the interaction between each antecedent condition not only include “yes” and “no”, but also include detailed membership scores. This paper adopts the fsQCA method to explore how to influence green technology innovation efficiency from the two aspects of environment support and technology supply.
3.2. Data Sources
Based on previous studies, we considered the availability of data and the need to create an evaluation system for provincial green technology innovation efficiency. This paper designated the antecedents affecting provincial green technology innovation efficiency as green environment support and green technology supply (first-level evaluation index), and created nine evaluation indexes (third-level evaluation indexes) of six levels (second-level evaluation indexes). Green technology innovation efficiency (first-level evaluation index) is divided into eight evaluation indexes (third-level evaluation indexes) of input and output (second-level evaluation indexes). The index data are selected from the 2021 statistical yearbook of 30 provinces and the official data published by the statistics bureau of each province in 2021.
3.3. Data Collection
Based on the relatively complete evaluation indicators in the 2021 China Regional Science and Technology Innovation Evaluation Report and the research of several scholars, the variables in the paper are evaluated as follows:
① Economic environment: The economic environment is a basic support for improving green technology innovation efficiency. The paper uses GDP to measure the economic environment.
② Environmental regulation: Due to the high risk and uncertainty of green technology innovation activities, innovation subjects are required to actively identify environmental protection, laws and regulations, and other factors. Environmental regulation is an important factor affecting the improvement of green technology innovation efficiency. The text collection method is adopted to search relevant laws and regulations in Peking University Talisman for comprehensive measurement.
③ Industrial structure: A good industrial structure is beneficial for elevating green technology innovation efficiency. A high structural optimization rate will stimulate green innovation vitality. The industrial structure is one of the direct influences on the improvement of green technology innovation efficiency, which is comprehensively measured by the ratio of the output value of the secondary industry to the output value of the tertiary industry.
④ Supply of finance: Financial resources can help enterprises to identify the surrounding environment and have the confidence to improve green technology innovation efficiency through technology reconstruction, market construction, resource utilization, and other ways. The paper uses R&D expenditure intensity and new product development expenditure to measure this variable.
⑤ Supply of manpower: Because it is the basic condition for improving green technology innovation efficiency, this variable is crucial to achieving the aims of increasing the investment of human resources in the process of green research and development, reducing the weak link in the process of green technology innovation, and improving green technology innovation efficiency. Innovative talents have an important impact on the improvement of green technology innovation efficiency, which is comprehensively measured by the number of R&D personnel and general undergraduate students.
⑥ Supply of facilities: The supply of facilities is one of the direct influencing factors on the improvement of green technology innovation efficiency. A perfect supply of facilities can help innovation subjects achieve green standards in production, conversion, consumption, and other links. The total number of incubators and the number of science and technology institutions are selected to comprehensively measure this.
⑦ Green technology innovation efficiency: Green technology innovation efficiency is the internal driving force to prevent ecological damage and improve the resource utilization rate. It helps enterprises to expand economic profits and improve technological production efficiency. It is comprehensively measured by input and output. Input indicators include internal R&D expenditure, proportion of investment in environmental pollution control, number of people working in education, and energy consumption per 10,000 yuan of GDP. Output indicators include per capita emissions of major pollutants, comprehensive utilization of industrial solid waste, sales: amount of technology market, and sales: number of new products.
The indicators are shown in Table 1.
4. Empirical Analysis
4.1. The Measurement of Green Technology Innovation Efficiency
The paper uses the correlation coefficient evaluation method to select the evaluation indexes of green technology innovation efficiency, using SPSS 23.0 software [24]. The results are shown in Table 2. According to Table 2, there are positive correlations between input and output variables, which meet the requirements of three-stage DEA.
Deap 2.1 software is used to measure the data of each province with input orientation, and the calculation results are shown in Table 3 [25]. In Table 3, the overall situation of green technology innovation efficiency in China is shown to be relatively good in 2020. However, there are great differences between different regions in green technology innovation efficiency. Among the 30 provinces in China, 24 regions (such as Beijing, Hebei, and Shanxi) have an efficiency value of 1, whereas the remaining 6 regions have an efficiency value of less than 1. Considering that the first-stage DEA model includes the influence of geographical environment and random factors on green technology innovation efficiency, the current efficiency value fails to truly reflect the actual situation of each province, so further analysis is needed.
Frontier 4.1 is software specifically designed to complete stochastic frontier analysis. It can estimate the stochastic frontier cost model and stochastic frontier production model using the stochastic frontier cost model with the maximum likelihood method [26].
Frontier 4.1 and Deap 2.1 software are used to replace the original data with the adjusted input values of the second-stage SFA. The paper then analyzes green technology innovation efficiency again. The results of the third stage are shown in Table 4.
In Table 4, after adjusting, the innovation efficiency of most provinces has changed significantly. And the number of provinces with effective DEA has decreased from 24 to 5, indicating that there are phenomena of apparent exaggerating effect in most provinces, under the influence of environmental factors. Given the unbalanced green technology innovation efficiency in different regions, it is necessary to rationalize the influencing factors of green technology innovation in different regions to narrow the development gap among provinces. The result of the third stage is taken as the result variable to further measure the effective path to drive green technology innovation efficiency in each province.
4.2. Data Calibration
The direct calibration method is adopted to calibrate the variable into three anchor points [27]. The direct calibration method assigns the set subordination score to a given case, which can more scientifically and effectively avoid subjective weighting and reduce subjective errors. The results are shown in Table 5.
4.3. Configuration Analysis
The comprehensive efficiency value is taken as the result variable and fsQCA is used for configuration analysis. A single conditional necessity test is performed, as shown in Table 6. According to Table 6, when the outcome variable is high and non-high green technology innovation efficiency, none of the six antecedent conditions’ consistency levels are higher than 0.9. Therefore, the effect of a single antecedent condition on green technology innovation efficiency is not strong. It is essential to further study the configuration of high and non-high green technology innovation efficiency.
When fsQCA 3.0 software is used for exploration, the consistency threshold is set at 0.8 and the case threshold is set at 1 to obtain the configuration that generates high green technology innovation efficiency. It will be defined as the core condition when a condition occurs in both the intermediate solution and the simple solution, according to the study by Park [26]. If it is defined as a core condition, it means that it has a significant impact on the efficiency of green technology innovation. On the condition that the condition variable appears in the intermediate solution but does not appear in the simple solution, it is defined as an auxiliary condition, which means that it plays an auxiliary role in the efficiency of green technology innovation. If the auxiliary condition is regarded as to whether or not it exists, it indicates whether it is a highly efficient green technology innovation. In Table 7, “•” means that the existence of an auxiliary condition, “⊗” indicates the lack of an auxiliary condition, “●” means that the existence of a core condition, “⊗” indicates the lack of a core condition, and whitespace means that the condition is in a dispensable state. When reporting, you can combine the configurations with the same core conditions into the same type [26].
(1) Path analysis of high green technology innovation efficiency
① Driven by economic environment and environmental regulation type
The configuration H1 (including H1a, H1b, and H1c) confirms that economic environment and environmental regulation are core conditions, which can generate high efficiency in green technology innovation. The configuration H1a shows that economic environment and environmental regulation as core conditions, with industrial structure, supply of manpower, and supply of facilities as auxiliary conditions, can lead to the high efficiency of green technology innovation. The configuration H1b shows that economic environment and environmental regulation as core conditions, with supply of facilities as an auxiliary condition and the absences of industrial structure, supply of finance, and supply of manpower as auxiliary conditions can lead to the high efficiency of green technology innovation. The configuration H1c shows that economic environment and environmental regulation as core conditions, with supply of finance and supply of manpower as auxiliary conditions and the absences of industrial structure and supply of facilities as auxiliary conditions can lead to the high efficiency of green technology innovation. The configuration H1 (including H1a, H1b, and H1c) confirms that provinces can benefit from the “helping hand” of the government, and the government directly influences green technology innovation efficiency. The provinces with configuration H1 include Jiangsu, Zhejiang, Shandong, Anhui, Guangdong, Hebei, Hubei, Sichuan, Fujian, Henan, Shaanxi, Shanghai, and Hunan. Most of these provinces are in the eastern region, which has obvious advantages in general, sound policies and systems, and a sound economic environment. For example, Jiangsu strives to achieve high-quality development through innovation, actively introduces green technology, improves its environmental management system under the premise of continuous optimization of economic development, and actively encourages the public to participate in green technology innovation in the form of competition. Additionally, it constantly explores innovation in forms of intellectual property protection mechanisms. Jiangsu is the first national green development demonstration zone. Shanghai has clearly promoted the high-quality, intelligent, and green development of the industrial chain. Shanghai has also realized the transformation and upgrading of the industrial chain and has formed the overall high-quality development advantage under the impetus of both system and economy. Guangdong is actively implementing plans for green technology innovation, aiming to solve prominent problems in resources, environment, and ecology. It is forming a new situation of green development featuring integrated research and development, application and promotion, and industrial development.
② Driven by industrial structure and supply of finance type
Configuration H2 proves that the high efficiency of green technology innovation can be generated with a good industrial structure and enough supply of finance as core conditions. The auxiliary conditions of configuration H2 are the existence of a supply of facilities, lack of industrial structure, lack of supply of finance, and lack of supply of manpower. The double externalities of green technology innovation may lead to market failures, such as information asymmetry, high risk coefficients, and imbalance between supply and demand, resulting in low autonomy and enthusiasm of enterprises for green technology innovation. An effective supply of finance and a good industrial structure will effectively alleviate problems such as insufficient investment and financing and severe overcapacity. In other words, the stimulation of green technology innovation needs to integrate industrial structure and supply of finance to eliminate the double externalities. The typical province is Beijing. Beijing has demonstrated the path of promoting green technology innovation in a favorable industrial structure by relying on the economic environment, infrastructure supply, innovation input, and industrial organization optimization. Compared to regions with lower income levels such as the central and western regions, Beijing and other economically developed regions actively attract foreign investment, and comprehensively implement the measures of “stabilizing foreign trade and foreign investment”. With the support of fiscal supply, the development level of green innovation in Beijing is constantly improving. At the same time, through strengthening the linkage of departments, increasing guidance, improving research and development funds, and other measures, green innovation to achieve better development is stimulated.
③ Driven by industrial structure, supply of finance, and supply of manpower type
Configuration H3 indicates that industrial structure, supply of finance, and supply of manpower as core conditions, with the absence of economic environment, environmental regulation, and supply of facilities as auxiliary conditions can lead to high efficiency in green technology innovation. Through the integration of resources, provinces can transform resources into their unique competitive advantages. Government’s direct or indirect auxiliary actions can provide a research and development environment, research and development funds, personnel, and foreign investment to activate green technology innovation. The typical province is Liaoning. Liaoning vigorously develops scientific and technological innovation in energy conservation and emission reduction, and carries out industry research in collaboration with enterprises, universities, and scientific research institutes. In this way Liaoning implements the national general requirements of ecological protection and scientific and technological innovation, strengthens the construction of independent innovation systems, and stimulates provincial innovation by strengthening departmental linkage, improving guidance, increasing R&D funds, and other measures.
(2) Path analysis of non-high green technology innovation efficiency
In this paper, fsQCA 3.0 software is also used to test the configuration paths that generate non-high green technology innovation efficiency. Three configurations that generate non-high green technology innovation efficiency with the same core conditions are present. After a second-order equivalent configuration is formed, there is a total of one path, as shown in Table 7. NH1 shows that the absence of a high economic environment and supply of manpower as core conditions will lead to non-high green technology innovation efficiency. The typical provinces are Xinjiang, Qinghai, Gansu, Jilin, Ningxia, Yunnan, Hainan, Guizhou, Guangxi, Shanxi, and Heilongjiang. In this paper, it is found that configuration HN1 is characterized by a low economic environment and insufficient supply of manpower. Regardless of other conditions, a low economic environment and insufficient supply of manpower will lead to non-high green technology innovation efficiency.
(3) Robustness test
Since some configurations of a qualitative comparative analysis may have only a small number of observed samples, it is necessary to determine the samples not observed by counterfactual analysis. Therefore, the robustness of fsQCA results is questioned. To effectively solve this problem, scholars put forward two methods to test robustness. One is to add other valid influencing factors, and test whether these influencing factors have great effects on the configuration results, and whether the research results are consistent with the hypothesis. Second, the non-high influencing factors are selected for configuration analysis to check whether they are consistent with the high influencing factors configuration analysis. In this paper, the first method is adopted to test the robustness of the antecedent configuration of high green technology innovation efficiency. Firstly, the case number threshold is raised from one to two, and the configuration is basically the same. Secondly, the consistency of PRI is increased from 0.80 to 0.85, and the configuration is basically consistent, which confirms the test results have high robustness.
5. Conclusion and Enlightenment
5.1. Conclusions
In this paper, 30 provinces, municipalities, and autonomous regions in China are taken as research cases, and fsQCA is used to analyze the multiple concurrent causal relationships and multiple paths of green technology innovation efficiency. The results show that: (1) There are three ways to effectively activate high green technology innovation efficiency, which can be summarized as the driven by economic environment and environmental regulation type, the driven by industrial structure and supply of finance type, and the driven by industrial structure, supply of finance, and supply of manpower type. The results confirm that the reasons leading to high green technology innovation efficiency are diverse and complex. (2) There is only one way to generate non-high green technology innovation efficiency. It appears mostly in the Midwest. It is mainly characterized by a shortage of human resources and a bad economic environment that inhibit the improvement of green technology innovation efficiency. (3) The path to generate non-high green technology innovation efficiency is not the contrary of the configuration to generate high green technology innovation efficiency, which verifies the asymmetry of the empirical results of the qualitative comparative analysis.
5.2. Theoretical Significance
In combination with conditions specific to China, multiple concurrent factors are discussed based on the theory of configurations that improve green technology innovation efficiency. Secondly, on the premise of finding out a variety of ways to promote the efficiency of green technology innovation, this paper reveals the root causes that produce and hinder the efficiency of green technology innovation. Thirdly, by introducing the QCA method into the study of green technology innovation efficiency, the application field of the QCA method is expanded.
5.3. Practical Significance
In order to improve the unbalanced development of provincial and urban areas, the government should strengthen the policy support system for innovation efficiency, guide the rational allocation of resources, improve the construction level of green technology instruments and facilities, use the market and relevant policies to guide capital and technology to drive the efficiency of green development, enhance the efficiency of regional green technology innovation, and implement the high-quality modernization and construction of ecological protections in the provinces. To be specific, provinces in the eastern region should strengthen regional learning efficiency, giving full play to geographical advantages of neighboring regions. That will increase the possibility of industrial cooperation, improve their initiative for independent research and development, enhance their green technology innovation efficiency, and accelerate regional industrial transformation and upgrading. It can also steady economic improvement and form a good market linkage mechanism. The provinces in the central and western regions can actively develop and cultivate characteristic technology industry clusters according to their advantages, and form a new pattern of “great protection, great opening, and high quality”, to realize the special and professional development of the regional technology industry, and then build a specialized cluster region, promote the regional technology industry clusters, and actively integrate into the construction of “The Belt and Road” to form regional synergy effects. The three provinces and regions in northeast China are close enough in geographical location and are similar in natural endowment and environment, but have great differences in development. Therefore, each regional center can be selected to give full play to its innovation agglomeration effect. At the same time, the business exchanges and industrial cooperation of the three provinces and regions can be strengthened to promote the economies and trade levels of the regions with weak development. Regional industrial mutual assistance and cooperation will encourage local universities, research institutes and other non-profit institutions to participate in green technology innovation activities. That will help each region realize the strategy of diversified industrial development and build a sound high-quality economic and social development engine with high efficiency in global resource allocation, technological innovation, and collaborative development through opening-up.
5.4. Limitations and Prospects
Firstly, the research object of this paper is the provinces, but the impacts of subdivisions of region and industry on the research conclusions is not considered. Future research can select specific industries to discuss. Secondly, based on the perspective of configuration, this paper studies the antecedent variables of green technology innovation efficiency. However, because it is limited by the number of antecedent variables, the influence of other factors on the efficiency of green technology innovation needs to be further studied. Finally, future studies can be carried out by combining with dynamic QCA to study configuration evolution and explore effective ways to drive the development of provincial green technology innovation under the new normal.
Author Contributions
Conceptualization, X.Q. (Xiaoyu Qu); methodology, X.Q. (Xutian Qin); formal analysis, X.Q. (Xiaoyu Qu) and H.H.; investigation, X.Q. (Xutian Qin); resources, X.Q. (Xutian Qin); project administration, X.Q. (Xutian Qin) and H.H.; funding acquisition, X.Q. (Xiaoyu Qu). All authors have read and agreed to the published version of the manuscript.
Funding
The research was funded by Social Sciences Planning Fund of Liaoning Province (project number: L22CJY005), Federation of Social Sciences Project of Liaoning Province (project number:2022lslwtkt005) (project number: 2023lslybkt-009), Science and Technology Innovation Fund of Dalian City (project number: 2022JJ13FG102), Federation of Social Sciences Project of Dalian City (project number: 2022dlskzd304), and Academy of Social Sciences Project of Dalian City (project number: 2022dlsky120).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Theoretical model.
Table 1.
Indicator system.
| First Level Indicators | Second Level Indicators | Third Level Indicators | Unit |
|---|---|---|---|
| Environment Support | Economic Environment | Gross domestic product | Yuan |
| Environmental Regulation | Number of environmental policies | Item | |
| Industrial Structure | Output value of the secondary industry/output value of the tertiary industry | % | |
| Technology Supply | Supply of Finance | Intensity of R&D expenditure | % |
| Expenditure on new product development | Ten thousand yuan | ||
| Supply of Manpower | Number of R&D personnel | People | |
| Number of general undergraduate students | People | ||
| Supply of Facilities | Number of incubators | Item | |
| Number of science and technology organizations | Item | ||
| Green technology innovation efficiency | Input | Internal R&D expenditure | Ten thousand yuan |
| Proportion of investment in environmental pollution control | % | ||
| Number of people working in education | People | ||
| Energy consumption per 10,000 yuan of GDP | Tons of standard coal/ten thousand yuan | ||
| Output | Per capita emissions of major pollutants | Ten thousand tons | |
| Comprehensive utilization of industrial solid waste | Ten thousand tons | ||
| Sales: amount of technology market | Ten thousand yuan | ||
| Sales: number of new products | Ten thousand yuan |
Table 2.
Correlation analysis of input and output variables.
| Variables | Internal R&D Expenditure | Proportion of Investment in Environmental Pollution Control | Number of People Working in Education | Energy Consumption Per 10,000 Yuan of GDP | Per Capita Emissions of Major Pollutants | Comprehensive Utilization of Industrial Solid Waste | Sales: Amount of Technology Market | Sales: Number of New Products |
|---|---|---|---|---|---|---|---|---|
| Internal R&D expenditure | 1.000 | |||||||
| Proportion of investment in environmental pollution control | 0.508 ** | 1.000 | ||||||
| Number of people working in education | 0.827 ** | 0.305 * | 1.000 | |||||
| Energy consumption per 10,000 yuan of GDP | 0.966 ** | 0.568 ** | 0.571 ** | 1.000 | ||||
| Per capita emissions of major pollutants | 0.874 ** | 0.506 ** | 0.241 | 0.811 ** | 1.000 | |||
| Comprehensive utilization of industrial solid waste | 0.903 ** | 0.383 * | 0.327 * | 0.964 ** | 0.882 ** | 1.000 | ||
| Sales: amount of technology market | 0.456 ** | 0.467 ** | 0.385 * | 0.897 ** | 0.579 ** | 0.598 ** | 1.000 | |
| Sales: number of new products | 0.768 ** | 0.587 ** | 0.857 ** | 0.698 ** | 0.696 ** | 0.784 ** | 0.879 ** | 1.000 |
Note: “*” indicates p < 0.1, “**” indicates p < 0.01.
Table 3.
Results of the first stage.
| Region | Crste | Vrste | Scale | Return to Scale |
|---|---|---|---|---|
| Beijing | 1.000 | 1.000 | 1.000 | - |
| Tianjin | 0.635 | 0.647 | 0.982 | irs |
| Hebei | 1.000 | 1.000 | 1.000 | - |
| Shanxi | 1.000 | 1.000 | 1.000 | - |
| Inner Mongolia | 1.000 | 1.000 | 1.000 | - |
| Liaoning | 1.000 | 1.000 | 1.000 | - |
| Jilin | 1.000 | 1.000 | 1.000 | - |
| Heilongjiang | 1.000 | 1.000 | 1.000 | - |
| Shanghai | 1.000 | 1.000 | 1.000 | - |
| Jiangsu | 1.000 | 1.000 | 1.000 | - |
| Zhejiang | 1.000 | 1.000 | 1.000 | - |
| Anhui | 0.681 | 0.689 | 0.988 | drs |
| Fujian | 1.000 | 1.000 | 1.000 | - |
| Jiangxi | 1.000 | 1.000 | 1.000 | - |
| Shandong | 0.943 | 1.000 | 0.943 | drs |
| Henan | 1.000 | 1.000 | 1.000 | - |
| Hubei | 1.000 | 1.000 | 1.000 | - |
| Hunan | 0.821 | 0.828 | 0.991 | drs |
| Guangdong | 1.000 | 1.000 | 1.000 | - |
| Guangxi | 1.000 | 1.000 | 1.000 | - |
| Hainan | 1.000 | 1.000 | 1.000 | - |
| Chongqing | 1.000 | 1.000 | 1.000 | - |
| Sichuan | 1.000 | 1.000 | 1.000 | - |
| Guizhou | 0.808 | 0.819 | 0.987 | irs |
| Yunnan | 1.000 | 1.000 | 1.000 | - |
| Shaanxi | 1.000 | 1.000 | 1.000 | - |
| Gansu | 1.000 | 1.000 | 1.000 | - |
| Qinghai | 1.000 | 1.000 | 1.000 | - |
| Ningxia | 0.23 | 0.545 | 0.422 | irs |
| Xinjiang | 1.000 | 1.000 | 1.000 | - |
| Mean value | 0.928 | 0.950 | 0.977 |
Note: crste, vrste, scale respectively indicate comprehensive technical efficiency, pure technical efficiency and scale efficiency; “drs”, “-” and “irs” respectively indicate diminishing, constant, and increasing returns to scale.
Table 4.
Results of the third stage.
| Region | Crste | Vrste | Scale | Return to Scale |
|---|---|---|---|---|
| Beijing | 1.000 | 1.000 | 1.000 | - |
| Tianjin | 1.000 | 1.000 | 1.000 | - |
| Hebei | 0.678 | 0.968 | 0.700 | drs |
| Shanxi | 0.202 | 0.377 | 0.536 | drs |
| Inner Mongolia | 0.399 | 0.632 | 0.632 | irs |
| Liaoning | 0.637 | 0.990 | 0.643 | drs |
| Jilin | 0.067 | 0.100 | 0.674 | drs |
| Heilongjiang | 0.727 | 0.985 | 0.738 | drs |
| Shanghai | 1.000 | 1.000 | 1.000 | - |
| Jiangsu | 0.068 | 0.100 | 0.678 | drs |
| Zhejiang | 1.000 | 1.000 | 1.000 | - |
| Anhui | 0.704 | 0.896 | 0.786 | drs |
| Fujian | 0.740 | 0.967 | 0.765 | drs |
| Jiangxi | 0.665 | 0.979 | 0.679 | drs |
| Shandong | 0.590 | 0.894 | 0.660 | drs |
| Henan | 0.086 | 0.100 | 0.861 | drs |
| Hubei | 0.816 | 1.00 | 0.816 | drs |
| Hunan | 1.000 | 1.000 | 1.000 | - |
| Guangdong | 0.659 | 0.844 | 0.781 | drs |
| Guangxi | 0.545 | 0.854 | 0.638 | drs |
| Hainan | 0.786 | 1.000 | 0.786 | drs |
| Chongqing | 0.861 | 1.000 | 0.861 | drs |
| Sichuan | 0.752 | 1.000 | 0.752 | drs |
| Guizhou | 0.289 | 0.438 | 0.659 | irs |
| Yunnan | 0.287 | 0.541 | 0.531 | drs |
| Shaanxi | 0.492 | 0.752 | 0.654 | drs |
| Gansu | 0.399 | 0.547 | 0.730 | irs |
| Qinghai | 0.763 | 1.000 | 0.763 | irs |
| Ningxia | 0.237 | 0.430 | 0.551 | irs |
| Xinjiang | 0.528 | 0.791 | 0.668 | drs |
| Mean value | 0.580 | 0.772 | 0.751 |
Table 5.
Calibration anchor points of variables.
| Conditions and Results | The Calibration | ||
|---|---|---|---|
| Full Subordination (75%) | Point of Intersection (50%) | Full Non-Subordination (25%) | |
| Economic Environment | 0.376 | 0.208 | 0.102 |
| Environmental Regulation | 0.611 | 0.450 | 0.371 |
| Industrial structure | 0.448 | 0.208 | 0.088 |
| Supply of Finance | 0.251 | 0.181 | 0.063 |
| Supply of Manpower | 0.442 | 0.296 | 0.186 |
| Supply of Facilities | 0.134 | 0.076 | 0.031 |
| Green technology innovation efficiency | 0.786 | 0.659 | 0.399 |
Table 6.
Necessity test.
| Conditions | High Green Technology Innovation Efficiency | Non-High Green Technology Innovation Efficiency | ||
|---|---|---|---|---|
| Consistency | Coverage | Consistency | Coverage | |
| Economic Environment | 0.889 | 0.840 | 0.278 | 0.273 |
| 0.231 | 0.235 | 0.838 | 0.887 | |
| Environmental Regulation | 0.789 | 0.829 | 0.283 | 0.310 |
| 0.343 | 0.315 | 0.844 | 0.806 | |
| Industrial Structure | 0.790 | 0.737 | 0.340 | 0.331 |
| 0.283 | 0.291 | 0.729 | 0.783 | |
| Supply of Finance | 0.775 | 0.860 | 0.282 | 0.326 |
| 0.393 | 0.344 | 0.879 | 0.803 | |
| Supply of Manpower | 0.867 | 0.845 | 0.298 | 0.302 |
| 0.284 | 0.279 | 0.847 | 0.869 | |
| Supply of Facilities | 0.837 | 0.862 | 0.282 | 0.303 |
| 0.323 | 0.301 | 0.871 | 0.848 | |
Table 7.
Configuration of high and non-high green technology innovation efficiency.
| Solution | ||||||||
|---|---|---|---|---|---|---|---|---|
| H1 | H2 | H3 | NH1 | |||||
| H1a | H1b | H1c | NH1a | NH1b | NH1c | |||
| Economic Environment | ● | ● | ● | • | ⊗ | ⊗ | ⊗ | ⊗ |
| Industrial structure | • | ⊗ | ⊗ | ● | ● | ⊗ | ⊗ | |
| Environmental Regulation | ● | ● | ● | ⊗ | ⊗ | ⊗ | ⊗ | ⊗ |
| Supply of Finance | ⊗ | • | ● | ● | ⊗ | ⊗ | ||
| Supply of Manpower | • | ⊗ | • | ⊗ | ● | ⊗ | ⊗ | ⊗ |
| Supply of Facilities | • | • | ⊗ | • | ⊗ | ⊗ | ⊗ | |
| Consistency | 0.952 | 0.920 | 0.996 | 0.969 | 0.990 | 0.993 | 0.993 | 0.969 |
| Net coverage | 0.616 | 0.102 | 0.160 | 0.107 | 0.073 | 0.534 | 0.549 | 0.206 |
| Degree of coverage | 0.496 | 0.016 | 0.015 | 0.057 | 0.025 | 0.046 | 0.060 | 0.121 |
| Overall consistency | 0.947 | 0.980 | ||||||
| Overall coverage | 0.784 | 0.717 | ||||||
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Qu, X.; Qin, X.; Hu, H. Research on the Improvement Path of Regional Green Technology Innovation Efficiency in China Based on fsQCA Method. Sustainability 2023, 15, 3190. https://doi.org/10.3390/su15043190
AMA Style
Qu X, Qin X, Hu H. Research on the Improvement Path of Regional Green Technology Innovation Efficiency in China Based on fsQCA Method. Sustainability. 2023; 15(4):3190. https://doi.org/10.3390/su15043190
Chicago/Turabian StyleQu, Xiaoyu, Xutian Qin, and Haichen Hu. 2023. "Research on the Improvement Path of Regional Green Technology Innovation Efficiency in China Based on fsQCA Method" Sustainability 15, no. 4: 3190. https://doi.org/10.3390/su15043190
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