Empirical analysis of the factors of the green innovation performance of industrial enterprises based on DEMATEL method and factor analysis method

Abstract

This study investigated factors of the green innovation performance of industrial enterprises in 30 provinces and autonomous regions across China. This study used literature analysis and DEMATEL method to identify the factors of green innovation performance of industrial enterprises. A green innovation performance evaluation index system was then constructed, and a mathematical model was established using factor analysis method to empirically analyze the factors of green innovation performance of industrial enterprises across 30 provinces and autonomous regions in China from 2012 to 2016. The results show that green innovation strength, technological development level, energy consumption, and environmental protection are the three factors of green innovation performance. Firstly, the influence of innovation strength on comprehensive green innovation performance has the largest weight, meaning that it has the largest impact on comprehensive green innovation performance. Secondly, the influence of the level of scientific and technological development on the comprehensive green innovation performance is weaker. Thirdly, the main influencing factor is the level of energy consumption and environmental protection. Finally, based on the empirical analysis, it is suggested that industrial enterprises should increase investment in green innovation, developing science and technology, and improving ecological environment, so as to enhance the green innovation performance of industrial enterprises.

Keywords

Industrial enterprises green innovation performance influencing factor

1. Introduction

After entering the industrial society, highly developed industrial activities not only created a significant amount material wealth, but also allowed for the accumulation of said wealth. However, with the expansion of industrialization, urbanization, and economic globalization, the extensive development mode with economic development as the main goal consumes significant resources and causes significant damage to resources and the environment. As a result, existing wealth accumulation has come at a heavy environmental cost. Although scholars have been concerned about environmental protection since the beginning of this century, it is only over the last decade that the pursuit of economic benefits and the consideration of environmental protection have attracted attention and aroused extensive academic discussion. The exploitation of resources and pollution of the environment caused by economic development not only limit the long-term economic development ability of countries or regions, but also threaten the survival of the human rice. Global warming and the deterioration of the ecological environment have meant that people have begun to examine their own behavior, and people all over the world have begun to attach particular importance to ecological and environmental problems.

In China, since the implementation of reform and opening up more than 40 years ago, China’s economy has achieved sustained, high-speed development and made a number of achievements, rising to become the world’s second largest economic system. While Chinese people have achieved significant economic development, they have also paid a high environmental cost. The deterioration of the environment has caused people to consider environmental problems that have appeared as a result of economic development. Serious environmental pollution has triggered society to discuss positively the environmental protection in the process of economic development. For the current stage of China’s economic development, the development and contribution of industrial enterprises is still the main driving force, playing a significant role in promoting the rapid and sustainable growth of China’s economy. However, environmental degradation and resource shortage has further hindered the development of China’s industrial enterprises, thus hindering the healthy, sustainable development of the economy. The development of green innovation technology has become an important force in promoting China’s economic development. Therefore, it is urgent to develop green innovation technology. Furthermore, China’s economy is in an important transition period, and the traditional development model is no longer sustainable. The government attaches significant importance to “ecological civilization construction” and raises it to the height of the national development strategy. The Fifth Plenary Session of the 18th Central Committee of the Chinese Communist Party put forward the five development concepts of “innovation, coordination, green development, opening up and sharing”, and the report of the also clearly proposes to “promote green development”. In this context, green innovation has not only attracted the attention and development of the government, but has also received higher attention in the academic community. The research and analysis of green innovation performance has also become an important research field for many scholars.

China’s industrial development is currently in its middle stages. Industrial enterprises are the core industries of Chinese economic development and a major force in economic expansion. This study focused on eastern Chinese industrial enterprises as samples, conducting factor analysis on the green innovation performance of Chinese industrial enterprises and an empirical analysis of the factors affecting the size. The findings of this study will be advantageous in encouraging China’s industrial enterprises to implement effective green innovation and to take a new path to industrialization in China and offer the government viable recommendations for developing pertinent policies and processes. It is crucial to expedite the transformation of China’s economic growth and support the construction of ecological civilization in China.

On the other hand, Chinese and international researches on green innovation performance of industrial enterprise are still in their infancy. These studies have focused primarily on theoretical research, having not yet established a system on social production; an understanding of green innovation performance form dimensions has not yet been established, and the study of green innovation performance influence factors has been based solely on the economics of innovation and innovation-driving factors. The framework for the fundamental theory of analysis has been established, though the empirical analysis component is limited. In the current transformation of the mode of China’s economic development, promoting the construction of ecological civilization, the existing green innovation performance influence factors of the framework of analysis, the introduction of new variables, the expansion of the research framework, the exploration of new ideas, and the refinement of the existing green innovation performance analysis frame and theory system are of particular theoretical importance.

Based on research of green innovation performance of industrial enterprises in 30 provinces, cities and autonomous regions in China, this study analyzes factors of green innovation performance in terms of innovation input level, innovation output level, innovation process technology, and innovation environment factors. Firstly, the paper describes the concept, characteristics, evaluation indicators and relevant basic theories of green innovation performance and identifies the factors and indicators of green innovation performance using a literature analysis and the DEMATEL method. KMO and Bartlett validity tests were then carried out using SPSS 22.0, common factors were extracted and named for factors, factor scores were calculated, and empirical analysis results were obtained. Finally, relevant suggestions were provided based on the factors.

In the research and analysis of the factors of green innovation performance of Chinese industrial enterprises, the main methods are literature analysis, a combination of theory and empirical analysis, and quantitative and qualitative analysis.

(1) Literature analysis

The literature on green innovation performance, factors of green innovation, and industrial green innovation in China and abroad was sorted, and the relevant basic concepts and data needed in the research process were collected using existing conditions. This process aimed to gain the relevant theoretical knowledge of green innovation performance and factors, learn statistical analysis methods and modeling knowledge, establish a green innovation performance evaluation index system, and provide theoretical support to study the factors of green innovation performance of Chinese industrial enterprises.

(2) Combination of theoretical analysis and empirical analysis

This staged aimed to clarify the precise meaning of green innovation, innovation performance, and green innovation performance of industrial enterprises and select variables for theoretical analysis based on the characteristics of Chinese industrial enterprises. On the basis of calculating the comprehensive evaluation value of variables, an appropriate mathematical model was selected, the data analysis model was established, the empirical analysis was conducted, the conclusions were drawn, and feasible suggestions were put forward.

(3) Combination of qualitative analysis and quantitative analysis

Through the analysis of the green innovation performance of Chinese industrial enterprises, through the indicator system, qualitative analysis method was used measure the operation status and future development prospects of industrial enterprises. Combined with quantitative analysis, the relationship between the factors of green innovation performance can be more intuitively reflected, and the methods to improve green innovation performance may be formulated. The DEMATEL method is an analytical method that uses graphs and matrix tools to analyze the relationship between various elements or influencing factors. With the advice of experts and scholars from related fields, a direct influence matrix was created and calculated to obtain the degree of mutual influence between each influencing factor, as well as the degree of cause and centrality of influence. This study used DEMATEL method to empirically identify the factors of green innovation performance and carry out the cause and centrality of the mutual influence between each influencing factor of green innovation performance. Based on the empirical analysis results of DEMATEL method, this study used the factor analysis model to classify factors affecting the green innovation performance. Using factor analysis, the basic idea of “dimension” from the variables of the research question, the large, complex variables were integrated into one of the few public molecules to simplify the problem of complex processing and gain an understanding of the overall system. Factor analysis makes it easier to interpret factor variables by rotation, making names clearer.

2. Literature review and related theories

2.1 Concept of green innovation performance

The word “innovation” was first incorporated into the system as a basic category of economics by Austrian American economist Schumpeter, who states the following: “innovation is not limited to a specific field, and it is not necessarily directly related to technology. Including product innovation, process innovation, market innovation, organizational innovation, etc.” From this perspective, green innovation is a form of innovation theory, and its research and analysis cannot be separated from the empirical analysis of innovation theory [1, 2, 3, 4, 5, 6].

Green innovation refers is the first choice for enterprises to cope with increasingly stringent environmental laws and use resources to win market competitiveness and market share. With the rise of green innovation theory, more scholars have begun to pay attention to the field of green innovation and have argued green innovation pays more attention to ecological adjustment, puts forward new requirements for environmental management in the production process, and adds elements such as environmental leadership, environmental law enforcement and green organization identification into the green innovation process [7, 8, 9, 10, 11, 12]. This paper holds that corporate environmental ethical environment and corporate green organization identification may motivate employees to have enough pride and sense of belonging, fulfill their commitment to the organization, and master the knowledge and skills related to environmental protection and green innovation. Effective laws and regulations and social ethical concepts can encourage enterprises to build green databases, improve green innovation management systems, accelerate green culture construction, and enhance their green image.

Green innovation should not only improve the environmental benefits of natural environment, but also ensure the economic benefits of maximizing potential profits and the social benefits of improving human living standards. Under the principle of combining “economic benefits, environmental benefits and social benefits”, green innovative enterprises complete the transformation process from the traditional innovation mode to green innovation mode by comprehensively considering the economic benefit output, resource benefit output, and environmental benefit output in the green innovation link [13, 14, 15, 16, 17]. Green innovation can also be explained in terms of both goal orientation and process dimension. It is believed that green innovation aims to save resources and raw materials, reduce pollutant emission, and prevent ecological environment pollution [18, 19, 20, 21]. On the other hand, green innovation exists in the whole innovation system from concept development to commercialization of green products [22, 23, 24, 25]. Green innovation is the general term of a series of sub-processes that use modern technology to make green innovation of enterprise products and processes. Furthermore, green innovation means that enterprises should shoulder more social responsibilities while pursuing the maximization of economic profits. Modern science and technology should be adopted to improve green process innovation, and the concept of green innovation should be carried through all production links to reduce the negative impact caused by the production process.

The field of green innovation is a key research field for many Chinese and international scholars, though the literature on green innovation performance is limited. Green innovation performance mainly improves the comprehensive utilization efficiency of resources and energy through green technology innovation, so as to reduce the cost and material consumption of production and improve the production efficiency of enterprises. On the other hand, it is also reflected in ecological performance, that is, through the green innovation process can reduce the generation and emission of pollutants, reduce the damage to the ecological environment, and improve the quality of the ecological environment. The green innovation performance is mainly reflected in environmental and economic benefits, while the green innovation performance of industrial enterprises is reflected in the coordination with economic and environmental benefits, that is to say, while seeking the most economic benefits, the environmental benefits of enterprises should be improved [8, 9, 10].

Integrated predecessors’ research results, the green is the enterprise product innovation performance in such aspects as the environmental management and production process improvement and improve the method of a kind of green products and green production software, hardware, in the process of innovation, the enterprise can be in the process of product development and production for greening, creatively to reduce or reduce waste pollution arising from the production process, And improve the comprehensive utilization of resources. The green innovation performance of industrial enterprises refers to the impact of green innovation on the overall performance of enterprises in the production process.

2.2 Characteristics of green innovation performance

In recent years, most of the studies on green innovation performance in various industries (regions) in China have been conducted from the perspective of relative efficiency. It is apparent from existing literature in China that the main characteristics of green innovation performance are regional differences and performance stages.

(1) Regional differences

Based on recent research and analyses, the green innovation performance of various regions in China is gradually improving, though there are still many problems [11, 12, 13, 14, 15]. Firstly, there is a large gap in the efficiency of different regions, which shows a decreasing trend from east to west, that is, from the eastern coastal areas to the western inland areas. Jiangsu, Shanghai, Guangdong, and other economically developed regions, with their strong innovation scale, strong strength, and favorable environment for scientific and technological development, rank high in terms of innovation performance in China. Although the performance level of the central and western regions is lower than that of the eastern regions, according to the theory of latecomer advantage, for the relatively slow green innovation in the central and western regions, the advanced technology and scientific management experience of developed countries or the eastern regions should be learned and studied, and a reasonable spatial and temporal layout of innovation resources should be conducted to choose the correct path for green and innovative development. Secondly, the technical efficiency of some regions and industries is low. Energy consumption and the introduction of foreign technology are still the main development modes, which require improvements. Moreover, the industrial economic structure dominated by heavy industrial enterprises with high pollution and high energy consumption hinders the improvement of green innovation performance in some regions and industries. Therefore, enterprises should be guided to change from the “carbon reduction” green innovation development model to the “energy saving” development model.

(2) Stages of performance

The two stages of scientific and technological research and development and achievement transformation are not efficient, and the development of scientific and technological economic environment has not been coordinated. The eastern region has a strong scientific and technological force, a good market and innovation environment, and is significantly ahead of other regions in the transformation of scientific and technological achievements into market value. However, the level of scientific and technological research and development in the eastern region requires further improvements, and production factors should be properly invested to achieve the full flow and efficient allocation of innovation resources. On the contrary, in the western region, it is difficult for scientific and technological achievements to effectively promote regional economic growth and industrial structure optimization and upgrading because of the constraints of the market mechanism and other aspects. In the long term, the gap between industrial green innovation performance levels will gradually become smaller, which will aggravate polarization. As such, the overall level of enterprise green innovation performance must be improved.

2.3 Evaluation indicators of green innovation performance

The green innovation performance evaluation is based on a multi-level comprehensive evaluation index system. Further exploration of the technological innovation capability and greenness of the organization can determine problems and deficiencies in the process of organizational innovation more intuitively. In addition, appropriate innovation models and management policies may formulated on a macro level in order to achieve the rational allocation of resources and enhance the sustainable competitiveness of enterprises.

Based on an analysis of the existing Chinese domestic literature, the green innovation performance evaluation system is based on different perspectives and adopts different evaluation indicators. The main green innovation performance evaluation indicators include green innovation input, process, output, environment, and the overall development of EES [16, 17, 18]. Through the study and analysis of these five factors, the green innovation performance can be analyzed and evaluated.

(1) Evaluation index of green innovation input

The preparatory investment of green innovation in the early stages of the production of green products mainly includes the following: full-time equivalent of R&D personnel; internal expenditure of R&D funds; expenditure of new product development; expenditure of introducing foreign technology; expenditure of purchasing domestic technology; expenditure of technological transformation; and unit energy consumption of new products. For enterprises, the investment of funds will directly determine whether enterprises can conduct green innovation, and the expenditure of funds is an important material guarantee for enterprises to conduct R&D projects. Manpower is the core strength and support for project implementation, meaning that it is particularly important to invest in human capital. The introduction and training of high-tech talent is an important chain for enterprises to conduct green innovation activities. Moreover, for advanced green innovation technologies at home and abroad, enterprises can directly introduce technology and transform and integrate the introduced green innovation technologies to adapt to their own enterprises, which is also an important process of green innovation. In addition, the state and government encourage enterprises to actively implement green technology innovation and support the reduction of unit energy consumption of new products by means of fiscal policies and environmental regulatory policies. Therefore, human capital, the R&D expense of new products, technology introduction and environmental consumption often constitute important indicators in measuring green innovation input.

(2) Evaluation index of green innovation process

Green innovation covers the entire process of new product production, rather than acting a specific link of this process, covering the following: green innovation research and development; technological production process; green marketing means; and after-sales work. Research based on the innovation process has often examined the overall concept of green innovation and life cycle theory. The indicator system should cover all aspects of the green innovation process and promote the measurement of performance level from R&D, manufacturing, management, and marketing. The evaluation indexes consist of the growth rate of patent authorization, the proportion of new products developed in the total products, and the conversion rate of scientific and technological achievements to measure the performance level of green R&D, manufacturing and marketing of enterprises. The growth rate of the number of patents granted, the proportion of new products developed in total products, the number of new products produced, and the conversion rate of scientific and technological achievements to market achievements are indicators used to measure the green innovation process [16, 17, 18].

(3) Evaluation index of green innovation output

The benefits offered enterprises in the production process under the use of green innovation technology are mainly considered as the output value of new products developed and the number of patents authorized [20]. The number of patents granted and the operating profit of new products developed are the most commonly used indicators in measuring the output of green innovation, reflecting a firm’s ability to turn research into a commodity, the market coverage of new products, and the reflection effect. In addition to these positive indicators, negative indicators brought by the production process are also taken into account, such as waste water, waste gas, solid waste, etc., all of which are generated in the industrial production process. As the “three wastes” of industry are non-expected output factors, the innovation output is divided into positive and negative indicators in the research process. Research and analysis of green innovation output in an all-round and multi-level manner should ensure the rigor of research and analysis.

(4) Evaluation index of green innovation environment

The term “innovation environment” first appeared in the Regional Economic Research Institute represented by the European Research Group on Innovation Environment, emphasizing interactions between innovation subjects, collective efficiency, and innovation behavior. The green innovation environment refers to the use of green innovation technology in the process of innovation environment to make the production process cleaner and more environmentally friendly. In the research and analysis of green innovation environment, the government funds, the number of research and development institutions, the employees of research and development institutions and the internal expenditure of research and development institutions are the main three indicators. The number of R&D institutions, personnel and internal expenditure have a significant impact on the quality of R&D and are used to measure the green innovation environment of industrial enterprises.

(5) Evaluation index of EES overall development

EES refers to economy, environment, and society. The comprehensive development of EES emphasizes economic, environmental, and social performance. Economic performance is mainly measured by the quantity and output value of new products, the utilization rate of industrial “three wastes”, and the carbon emission rate to test the environmental performance, and social performance is measured in terms of social employment rate.

2.4 Theoretical framework of green innovation performance

As a large-scale, complex theoretical system, the green innovation system cannot be fundamentally supported by individual theories. Instead, it requires numerous research theories and multidisciplinary perspectives as a theoretical basis, including theories related to ecological civilization, innovation economics, and enterprise growth.

(1) Ecological civilization theory

Ecological civilization is a new form of human development. To respect and protect nature, ecological civilization complies with the objective law of coordinated development between human society and the natural environment, emphasizing that human consciousness and self-discipline are the main basis for harmonious coexistence between human and nature. With the deepening of industrialization, the disharmony between natural resources, natural ecological environment, and economic development is becoming increasingly obvious. Ecological civilization is put forward to alleviate and solve this contradiction, and its emergence is a result of the reflection on the problems in the process of industrialization and inevitable trend and result of economic and social progress. Ecological civilization has an extremely important strategic position and listed in China’s strategic system along with the economy, politics, culture and society, being complemented by the other four fields. While emphasizing “gold and silver mountains”, the country also needs “clear waters and green mountains”, so as to leave more space for future generations to live and develop.

(2) Theory of innovation economics

In 1912, “innovation” was defined for the first time, and scholars from the field of economics began to explain it. In the Economics of Industrial Innovation (1928), it is mentioned that innovative technologies are innovative products, processes, and services, and for the first time, transformed into products or service cycles that can become products or services through innovative marketing methods. An important concept of innovation economics is innovation externality, that is, the impact brought to enterprises in the process of innovation activities, including both positive and negative impacts. Based on the theory of innovation economics, it is necessary to understand that green innovation is a special form of innovation different from general innovation. The study of green innovation cannot be separated from innovation economics. Therefore, it is necessary to firmly grasp the direction of innovation economics, which is of particular use in the research and development of green innovation performance.

(3) Enterprise growth theory

In the discussion of management theory, the theories related to green innovation are resourced theory and innovation system theory. The basic resources of an enterprise are closely related to green innovation. In the book Enterprise Growth Theory, the concept of organizational internal growth theory is proposed, suggesting that the core competitiveness of an enterprise comes from its existing and controllable resources. On the basis of the internal growth theory, scholars have continued to study and develop the basic resources of enterprises, forming the current basic resources theoretical system, the core of which is that the tangible and intangible resources of enterprises can improve sustainable competitiveness, resulting in a difference of green innovation performance between enterprises.

The innovation system theory was proposed in the 1920s and expanded on at the end of the 20th century. A large number of Chinese and international scholars have studied and discussed this theory in terms of the following four aspects: government; enterprises; universities; and industries, finding a connection and interaction between them. The green innovation activities implemented by enterprises are a gradual process. For enterprises, the green innovation system framework theory offers a useful explanation and reference significance for the analysis of the research stage factors.

3. Identifying the factors of green innovation performance

The identification of the factors of green innovation performance system is key in improving green innovation performance. Firstly, this chapter determines the factors of green innovation performance and the impact of these factors on green innovation performance, classifying these factors according to their characteristics. The identification of influencing factors is conducive to the analysis of the degree of green innovation performance.

3.1 Process of identifying factors of the green innovation performance system

3.1.1 Identification of the factors based on literature analysis

Identification of the factors of green innovation performance system include the following five aspects: green innovation input; green innovation process; green innovation output; green innovation environment; and common innovation performance. This study, based on a review of existing Chinese studies, considers Chinese industrial enterprises to identify factors, as presented in Fig. 1.

Figure 1.

Process of identifying the factors of the green innovation performance system.

For the identification of the factors of green innovation performance system, literature collation and induction are usually adopted to identify the factors of green innovation performance system. Literature analysis is the most common, convenient method to study green innovation performance system. Firstly, innovation factors should be identified in combination with theories in existing research [21, 22, 23] (as shown in Table 1). Through this research method, factors affecting green innovation performance system should be identified.

Table 1

Identification of the factors of innovation

Author	Method	Result	Reference
Li Gang, Guo Peng	Dematel	The factors influencing the innovation in industry, university and research	[21]
		were identified
Xu Yingying, Zhuan	Grounded	Five types of low carbon technological innovation factors were identified	[22]
Liangqun, Lv Xishen	theory
Xu Yingying, Zhuan	Expert	Six types of factors of technological innovation were identified	[23]
Liangqun, Lv Xishen	interviews

Based on a literature review, existing Chinese studies have obtained the following findings. The main factors for green innovation performance system in industrial enterprises are green innovation input, green innovation process, green innovation output, green innovation environment, which included 15 indices: Internal expenditure of R&D funds B ${}_{1}$ ; new product development expenses B ${}_{2}$ ; expenditure on introducing foreign technology B ${}_{3}$ ; purchase of domestic technology expenditure B ${}_{4}$ ; expenditure for technical renovation B ${}_{5}$ ; growth rate of patent grants B ${}_{6}$ ; new product development quantity B ${}_{7}$ ; output value of new products B ${}_{8}$ ; number of patents granted B ${}_{9}$ ; total discharge of industrial wastewater B ${}_{10}$ ; total industrial exhaust emissions B ${}_{11}$ , industrial solid waste production B ${}_{12}$ ; number of research and development institutions B ${}_{13}$ ; employees of research and development institutions B ${}_{14}$ ; and internal expenditure of research and development organization funds B ${}_{15}$ (See Fig. 2).

Figure 2.

The factors of green innovation performance system.

3.1.2 Identification of the factors using the DEMATEL method

The DEMATEL method is an analytical method that uses graphs and matrix tools to analyze the relationship between various elements or influencing factors. With the advice of experts and scholars from related fields, a direct influence matrix was created and calculated to obtain the degree of mutual influence between each influencing factor, as well as the degree of cause and centrality of influence.

(1) Direct influence on matrix construction

The direct influence matrix was established and used to represent the influence degree. 0–4 was used to represent the strength of the influence degree between the factors, which respectively represented “no influence”, “slightly weak influence”, “weak influence”, “intensity influence”, and “strong influence”. If “yes”, no impact was displayed. The degree of mutual influence of different factors was obtained from scholars and enterprise managers in related fields, and the direct influence matrix is shown in Table 2.

Table 2
Direct influence matrix

	B ${}_{1}$	B ${}_{2}$	B ${}_{3}$	B ${}_{4}$	B ${}_{5}$	B ${}_{6}$	B ${}_{7}$	B ${}_{8}$	B ${}_{9}$	B ${}_{10}$	B ${}_{11}$	B ${}_{12}$	B ${}_{13}$	B ${}_{14}$	B ${}_{15}$
B ${}_{1}$	0	3	3	2	3	2	0	0	2	0	0	0	2	1	3
B ${}_{2}$	2	0	1	1	2	3	3	2	3	1	1	1	0	0	0
B ${}_{3}$	2	0	0	0	3	2	2	1	2	1	1	1	0	0	0
B ${}_{4}$	2	0	0	0	2	2	3	2	3	1	1	1	0	0	0
B ${}_{5}$	3	1	2	2	0	0	0	2	0	2	2	2	1	2	1
B ${}_{6}$	1	1	1	1	2	0	0	1	3	1	1	1	2	2	2
B ${}_{7}$	2	1	1	1	1	2	0	0	2	2	2	2	2	3	3
B ${}_{8}$	3	2	2	2	2	0	0	0	0	0	0	0	1	1	1
B ${}_{9}$	2	0	0	0	3	4	2	0	0	1	1	1	2	1	1
B ${}_{10}$	2	2	1	1	1	0	2	0	3	0	0	0	2	2	2
B ${}_{11}$	2	1	1	1	2	0	3	0	3	0	0	0	2	1	1
B ${}_{12}$	2	2	1	1	1	0	3	0	2	0	0	0	2	2	3
B ${}_{13}$	2	1	0	0	1	4	3	0	4	1	1	1	0	3	4
B ${}_{14}$	3	2	0	0	0	3	2	2	4	0	0	0	2	0	3
B ${}_{15}$	3	2	0	0	2	2	3	0	2	0	0	0	3	3	0

(2) Normalization

Equation (1) was used to normalize the data to obtain the direct influence matrix. The formula is as follows:

$\displaystyle\left\{\begin{array}[]{l}y_{ij}=\frac{x_{ij}}{x_{i}}\\ x_{i}=\max\left\{\sum_{j=1}^{n}x_{ij}\right.\\ \end{array}\right.$ (1)

Normalization results are shown in Table 3.

Table 3

The result of normalization

	B ${}_{1}$	B ${}_{2}$	B ${}_{3}$	B ${}_{4}$	B ${}_{5}$	B ${}_{6}$	B ${}_{7}$	B ${}_{8}$	B ${}_{9}$	B ${}_{10}$	B ${}_{11}$	B ${}_{12}$	B ${}_{13}$	B ${}_{14}$	B ${}_{15}$
B ${}_{1}$	0.000	0.120	0.120	0.080	0.120	0.080	0.000	0.000	0.080	0.000	0.000	0.000	0.080	0.040	0.120
B ${}_{2}$	0.080	0.000	0.040	0.040	0.080	0.120	0.120	0.080	0.120	0.040	0.040	0.040	0.000	0.000	0.000
B ${}_{3}$	0.080	0.000	0.000	0.000	0.120	0.080	0.080	0.040	0.080	0.040	0.040	0.040	0.000	0.000	0.000
B ${}_{4}$	0.080	0.000	0.000	0.000	0.080	0.080	0.120	0.080	0.120	0.040	0.040	0.040	0.000	0.000	0.000
B ${}_{5}$	0.120	0.040	0.080	0.080	0.000	0.000	0.000	0.080	0.000	0.080	0.080	0.080	0.040	0.080	0.040
B ${}_{6}$	0.040	0.040	0.040	0.040	0.080	0.000	0.000	0.040	0.120	0.040	0.040	0.040	0.080	0.080	0.080
B ${}_{7}$	0.080	0.040	0.040	0.040	0.040	0.080	0.000	0.000	0.080	0.080	0.080	0.080	0.080	0.120	0.120
B ${}_{8}$	0.120	0.080	0.080	0.080	0.080	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.040	0.040	0.040
B ${}_{9}$	0.080	0.000	0.000	0.000	0.120	0.160	0.080	0.000	0.000	0.040	0.040	0.040	0.080	0.040	0.040
B ${}_{10}$	0.080	0.080	0.040	0.040	0.040	0.000	0.080	0.000	0.120	0.000	0.000	0.000	0.080	0.080	0.080
B ${}_{11}$	0.080	0.040	0.040	0.040	0.080	0.000	0.120	0.000	0.120	0.000	0.000	0.000	0.080	0.040	0.040
B ${}_{12}$	0.080	0.080	0.040	0.040	0.040	0.000	0.120	0.000	0.080	0.000	0.000	0.000	0.080	0.080	0.120
B ${}_{13}$	0.080	0.040	0.000	0.000	0.040	0.160	0.120	0.000	0.160	0.040	0.040	0.040	0.000	0.120	0.160
B ${}_{14}$	0.120	0.080	0.000	0.000	0.000	0.120	0.080	0.080	0.160	0.000	0.000	0.000	0.080	0.000	0.120
B ${}_{15}$	0.120	0.080	0.000	0.000	0.080	0.080	0.120	0.000	0.080	0.000	0.000	0.000	0.120	0.120	0.000

(3) Composite influence matrix

The comprehensive influence matrix was created with Eq. (2) (see Table 4), where $E$ is the identity matrix in order to study the influence relationship between factors:

$\displaystyle T=Y(E-Y)^{-1}$ (2)

Table 4

Composite influence matrix

	B ${}_{1}$	B ${}_{2}$	B ${}_{3}$	B ${}_{4}$	B ${}_{5}$	B ${}_{6}$	B ${}_{7}$	B ${}_{8}$	B ${}_{9}$	B ${}_{10}$	B ${}_{11}$	B ${}_{12}$	B ${}_{13}$	B ${}_{14}$	B ${}_{15}$
B ${}_{1}$	0.000	0.120	0.120	0.080	0.120	0.080	0.000	0.000	0.080	0.000	0.000	0.000	0.080	0.040	0.120
B ${}_{2}$	0.080	0.000	0.040	0.040	0.080	0.120	0.120	0.080	0.120	0.040	0.040	0.040	0.000	0.000	0.000
B ${}_{3}$	0.080	0.000	0.000	0.000	0.120	0.080	0.080	0.040	0.080	0.040	0.040	0.040	0.000	0.000	0.000
B ${}_{4}$	0.080	0.000	0.000	0.000	0.080	0.080	0.120	0.080	0.120	0.040	0.040	0.040	0.000	0.000	0.000
B ${}_{5}$	0.120	0.040	0.080	0.080	0.000	0.000	0.000	0.080	0.000	0.080	0.080	0.080	0.040	0.080	0.040
B ${}_{6}$	0.040	0.040	0.040	0.040	0.080	0.000	0.000	0.040	0.120	0.040	0.040	0.040	0.080	0.080	0.080
B ${}_{7}$	0.080	0.040	0.040	0.040	0.040	0.080	0.000	0.000	0.080	0.080	0.080	0.080	0.080	0.120	0.120
B ${}_{8}$	0.120	0.080	0.080	0.080	0.080	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.040	0.040	0.040
B ${}_{9}$	0.080	0.000	0.000	0.000	0.120	0.160	0.080	0.000	0.000	0.040	0.040	0.040	0.080	0.040	0.040
B ${}_{10}$	0.080	0.080	0.040	0.040	0.040	0.000	0.080	0.000	0.120	0.000	0.000	0.000	0.080	0.080	0.080
B ${}_{11}$	0.080	0.040	0.040	0.040	0.080	0.000	0.120	0.000	0.120	0.000	0.000	0.000	0.080	0.040	0.040
B ${}_{12}$	0.080	0.080	0.040	0.040	0.040	0.000	0.120	0.000	0.080	0.000	0.000	0.000	0.080	0.080	0.120
B ${}_{13}$	0.080	0.040	0.000	0.000	0.040	0.160	0.120	0.000	0.160	0.040	0.040	0.040	0.000	0.120	0.160
B ${}_{14}$	0.120	0.080	0.000	0.000	0.000	0.120	0.080	0.080	0.160	0.000	0.000	0.000	0.080	0.000	0.120
B ${}_{15}$	0.120	0.080	0.000	0.000	0.080	0.080	0.120	0.000	0.080	0.000	0.000	0.000	0.120	0.120	0.000

(4) Comprehensive influence calculation

The influence degree K, influence degree L, centrality M, cause degree N, and comprehensive influence degree Z of related factors were calculated using the comprehensive influence matrix. The formula is as follows:

$\displaystyle\left\{\begin{array}[]{l}K_{i}=\sum_{j=1}^{n}t_{ij},(1\leqslant i% \leqslant n,1\leqslant j\leqslant n)\\ L_{j}=\sum_{i=1}^{n}t_{ij},(1\leqslant i\leqslant n,1\leqslant j\leqslant n)\\ M=K_{i}+L_{j},(1\leqslant i\leqslant n,1\leqslant j\leqslant n)\\ N=k_{i}-L_{j},(1\leqslant i\leqslant n,1\leqslant j\leqslant n)\\ Z_{i}=\frac{m_{i}w_{i}}{\sum_{j=1}^{n}m_{j}w_{j}},(i=1,\ldots,n);(w_{i}\textit% { in the weight from experts})\\ \end{array}\right.$ (3)

Using the formula, the comprehensive influence degree was calculated (Table 5).

3.2 Identification results

Based on a review of existing Chinese studies, this study identified factors of green innovation performance system, finding the following. The main factors for green innovation performance system in industrial enterprises are green innovation input, green innovation process, green innovation output and green innovation environment, which included 15 indices.

According to the analysis using the DEMATEL method, the following conclusions may be drawn. Among the influencing indicators of the green innovation performance system, the internal expenditure of R&D funds, the growth rate of patent granted, and the expenditure for technical renovation have a more significant influence, particularly in terms of green innovation performance system factors. However, the impact degree of industrial “three wastes” is relatively small as it has a limited influence on the influencing factors of the green innovation performance system. The three indicators of green innovation environment rank high, whereas the five indicators of green innovation output rank low. Therefore, the influence of green innovation environment is relatively strong, and the influence of green innovation output is weak.

4. Empirical analysis

4.1 Data collection

This paper studies the factors of the green innovation performance of Chinese industrial enterprises. In order to achieve the research purpose and ensure the reliability of the analysis results, data were collected

Table 5
Comprehensive influence level

	B ${}_{1}$	B ${}_{2}$	B ${}_{3}$	B ${}_{4}$	B ${}_{5}$	B ${}_{6}$	B ${}_{7}$	B ${}_{8}$	B ${}_{9}$	B ${}_{10}$	B ${}_{11}$	B ${}_{12}$	B ${}_{13}$	B ${}_{14}$	B ${}_{15}$
W	0.085	0.083	0.075	0.077	0.091	0.090	0.082	0.071	0.064	0.021	0.021	0.021	0.074	0.072	0.073
K	3.927	3.696	2.826	3.191	3.649	3.597	4.602	2.620	3.558	3.550	3.368	3.794	4.870	4.062	4.055
L	5.639	3.419	2.495	2.230	4.765	5.075	4.474	1.872	6.005	2.076	2.076	2.076	4.206	4.196	4.760
M	9.566	7.115	5.321	5.421	8.414	8.672	9.076	4.491	9.563	5.627	5.444	5.871	9.076	8.258	8.815
N	$-$ 1.712	0.277	0.330	0.961	$-$ 1.116	$-$ 1.479	0.128	0.748	$-$ 2.447	1.474	1.292	1.718	0.664	$-$ 0.134	$-$ 0.705
Z	0.106	0.077	0.052	0.054	0.099	0.101	0.097	0.041	0.079	0.015	0.015	0.016	0.087	0.077	0.083
Rank	1	9	11	10	3	2	4	12	7	14	15	13	5	8	6

from China Statistical Yearbook (2013–2017) and China Statistical Yearbook of Science and Technology (2013–2017). Among the factors, the internal expenditure of R&D funds, the expenditure of new product development, the output value of new products, the number of patents authorized, the total amount of industrial wastewater discharged, the total amount of industrial waste gas discharged, and the production volume of industrial solid waste are from the China Statistical Yearbook (2013–2017). The data of other variables are from the Science and Technology of China Statistical Yearbook (2013–2017).

4.2 Measurement

In order to make original data easy to operate, the method of coding simplification was used to process the data, and the text information in the data was transformed.

This study refers to related references and domestic scholars research results about the green innovation performance evaluation index system, based on the comprehensive, reasonable and scientific principles, have chosen green innovation input, process of green innovation, green innovation output, and green innovation environment from four aspects of 15 indices of green innovation performance evaluation index system of influencing factors, and simplify the index layer. Under the green innovation input index, the internal expenditure of R&D expenditure, the expenditure of new product development, the expenditure of introducing foreign technology, the expenditure of purchasing domestic technology, and the expenditure of technological transformation are named X ${}_{1}$ , X ${}_{2}$ , X ${}_{3}$ , X ${}_{4}$ , and X ${}_{5}$ , respectively. Under the green innovation process index, the growth rate of the number of patents granted and the number of new products developed are named X ${}_{6}$ and X ${}_{7}$ , respectively. Under the green innovation output index, the output value of new products, the number of patents granted, the total amount of industrial wastewater discharged, the total amount of industrial waste gas discharged, and the production volume of industrial solid waste are named X ${}_{8}$ , X ${}_{9}$ , X ${}_{10}$ , X ${}_{11}$ , and X ${}_{12}$ , respectively. The number of research and development institutions under the green innovation environment indicator, the staff of research and development institutions, and the internal expenditure of research and development institutions are named X ${}_{13}$ , X ${}_{14}$ , and X ${}_{15}$ , respectively. After data coding, the coding and its corresponding data were input into SPSS22.0 for data processing (See Table 6).

Relevant data from 2012 to 2016 were used in this paper. Due to the lack of data in Tibet, the research objects mainly covered the 30 provinces and autonomous regions in mainland China to ensure that the research and analysis were comprehensive.

Table 6
Name of evaluation index of the factors of green innovation performance

Target layer	Rule layer	Index layer	Code
Green innovation	Green innovation input CX ${}_{1}$	Internal expenditure of R&D funds	X ${}_{1}$
performance system		New product development expenses	X ${}_{2}$
		Expenditure on introducing foreign technology	X ${}_{3}$
		Purchase of domestic technology expenditure	X ${}_{4}$
		Expenditure for technical renovation	X ${}_{5}$
	Green innovation process CX ${}_{2}$	Growth rate of patent grants	X ${}_{6}$
		New product development quantity	X ${}_{7}$
	Green innovation output CX ${}_{3}$	Output value of new products	X ${}_{8}$
		Number of patents granted	X ${}_{9}$
		Total discharge of industrial wastewater	X ${}_{10}$
		Total industrial exhaust emissions	X ${}_{11}$
		Industrial solid waste production	X ${}_{12}$
	Green innovation environment CX ${}_{4}$	Number of research and development institutions	X ${}_{13}$
		Employees of research and development institutions	X ${}_{14}$
		Internal expenditure of research and development	X ${}_{15}$
		organization funds

4.3 Empirical analysis method selection

This study used the factor analysis model to classify factors affecting the performance of green innovation. Using factor analysis, the basic idea of “dimension” from the variables of the research question, the large, complex variables were integrated into one of the few public molecules to simplify the problem of complex processing and gain an understanding of the overall system. Factor analysis makes it easier to interpret factor variables by rotation, making names clearer. According to the data obtained from the China Statistical Yearbook and China Statistical Yearbook of Science and Technology (2013–2017), the relevant data of 30 provinces and autonomous regions were obtained through statistical analysis, and the factors affecting green innovation performance were evaluated with factor analysis. Due to the large number of selected indicators, the complexity of the analysis increased, and the factor analysis method was used. Using factor analysis method, it was possible to find the main factors affecting the green innovation performance and the importance of these factors, which were used to enhance and improve the green innovation performance of Chinese industrial enterprises.

4.4 Empirical analysis process based on factor analysis

4.4.1 Factor analysis model construction

Based on the evaluation index system, the general form of factor coefficient matrix was constructed:

$\displaystyle\left\{\begin{array}[]{c}X_{1}=\alpha_{11}F_{1}+\alpha_{12}F_{2}+% \ldots+\alpha_{1m}F_{m}+\varepsilon_{1}\\ X_{2}=\alpha_{21}F_{1}+\alpha_{22}F_{2}+\ldots+\alpha_{2m}F_{m}+\varepsilon_{2% }\\ X_{3}=\alpha_{31}F_{1}+\alpha_{32}F_{2}+\ldots+\alpha_{3m}F_{m}+\varepsilon_{3% }\\ \ldots\\ X_{p}=\alpha_{p1}F_{1}+\alpha_{p2}F_{2}+\ldots+\alpha_{pm}F_{m}+\varepsilon_{p% }\\ \end{array}\right.$ (4)

The matrix form is as follows:

$\displaystyle\left[\begin{array}[]{c}X_{1}\\ X_{2}\\ X_{3}\\ \ldots\\ X_{p}\\ \end{array}\right]=\left[\begin{array}[]{c}a_{11}\ldots a_{1m}\\ \ldots\\ a_{p1}\ldots a_{pm}\\ \end{array}\right]\left[\begin{array}[]{c}F_{1}\\ F_{2}\\ F_{3}\\ \ldots\\ F_{m}\\ \end{array}\right]+\left[\begin{array}[]{c}\varepsilon_{1}\\ \varepsilon_{2}\\ \varepsilon_{3}\\ \ldots\\ \varepsilon_{p}\\ \end{array}\right]$ (5)

Where $X=(X_{1},X_{2}\ldots X_{p})$ is the observable random vector, $a_{ij}$ is the factor loading, $F_{i}$ is called the common factor of, and $\varepsilon_{j}$ is called the special factor. In factor analysis, according to the principle of retaining original information as much as possible, $(q<m)$ were selected as the main factors analysis research problem. Each principal factor was represented by a linear combination of variables, and the value of each principal factor was calculated using the observed values of the variables, the model of which is as follows:

$\displaystyle F_{1}=b_{i1}x_{1}+b_{i2}x_{2}+\ldots+b_{im}x_{m},(i=1,2,3\ldots q)$ (6)

Where $F_{i}$ is the score of the $i$ th factor, and the proportion of variance contribution rate of each principal factor in the cumulative contribution rate was taken as the weight.

The weighted sum and the composite score were calculated by taking the proportion of the variance contribution rate to the cumulative contribution rate of each major factor as the weight:

$\displaystyle\left\{\begin{array}[]{c}\partial_{1}=\frac{\lambda_{1}}{\lambda_% {1}+\lambda_{2}+\ldots+\lambda_{i}}\\ \partial_{2}=\frac{\lambda_{2}}{\lambda_{1}+\lambda_{2}+\ldots+\lambda_{i}}\\ \ldots\\ \partial_{i}=\frac{\lambda_{i}}{\lambda_{1}+\lambda_{2}+\ldots+\lambda_{i}}\\ \end{array}\right.$ (7) $\displaystyle F=(\lambda_{1}F_{1}+\lambda_{2}F_{2}+\ldots+\lambda_{i}F_{i})/(% \lambda_{1}+\lambda_{2}+\ldots\lambda_{i})$ (8)

Where $\lambda_{i}$ is the variance contribution rate of each principal factor.

4.4.2 Test of fitness

Due to the lack of complete data in Tibet, the data of 30 provinces and autonomous regions in China except Tibet were used, taken from the China Statistical Yearbook and China Statistical Yearbook of Science and Technology (2013–2017), with high credibility. The validity test aimed to determine whether the data could complete the analysis of the research purpose. SPSS22.0 was used to conduct KMO and Bartlett tests on the collected data to draw validity analysis results (Table 7).

Table 7
The validity analysis results according to KMO and Bartlett test

Kaiser-Mryer-Olkin of Sampling Adequacy	.703
Bartlett’s Test of Approx. Chi-Square	783.320
Sphericity df	105
Sig.	.000

According to data analysis, the KMO value was 0.703, the Bartlett value was 783.320, and the corresponding significance level was 0.000, less than 0.001. Thus, the data had good validity, which proved that it could be analyzed with a factor analysis.

4.4.3 Extraction of common factors

Principal component extraction method is the most common extraction method for factor extraction and synthesizes the original variables into several factors. Table 8 is the results of the factor analysis on the total variance of the original variables.

Table 8
Results of total variance explained

Component	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	8.388	55.920	55.920	8.388	55.920	55.920	8.028	53.518	53.518
2	2.720	18.130	74.051	2.720	18.130	74.051	2.841	18.937	72.455
3	11.846	12.306	86.357	1.846	12.306	86.357	2.085	13.901	86.357
4	0.651	4.338	90.694
5	0.547	3.648	94.343
6	0.326	2.175	96.517
7	0.199	1.327	97.845
8	0.111	0.739	98.584
9	0.097	0.649	99.233
10	0.055	0.369	99.602
11	0.032	0.215	99.816
12	0.020	0.132	99.948
13	0.005	0.035	99.983
14	0.002	0.014	99.997
15	0.001	0.003	100.000

Extraction Method: Principal Component Analysis.

The contribution rate of the principal component shown in Table 8 represents the amount of information of the original indicator reflected by the principal component, and the cumulative contribution rate refers to the amount of information of the original indicator reflected by the corresponding principal component. It can be seen in Table 8 that the eigenvalues of the first three principal components are 8.388, 2.720, and 1.846, all exceeding the eigenvalue 1. In addition, the cumulative contribution rate of the first three principal components reaches 86.357%, that is, the first three principal components reflect over 86.357% of the information of the original index. On the whole, there is little difference in the information of the original variables, and the effect of factor analysis is relatively ideal, which fully reflects the green innovation performance ability of industrial enterprises in 30 provinces and autonomous regions. Therefore, the above 15 indicators may be integrated into three principal components according to the above analysis, namely F ${}_{1}$ , F ${}_{2}$ , and F ${}_{3}$ .

Figure 3.

Screen plot.

As can be seen in Fig. 3, the first three eigenvalues showing steep slope state play a significant role and make the largest contribution to the explanatory variables. The eigenvalue slope of the fourth and subsequent factors tends to be gentle, and their contribution to the explanation of the original variables is small, making it feasible to extract the three principal factors.

4.4.4 The naming of common factors

In the process of factor analysis, factor naming is particularly important and involves renaming the extracted factor with a new comprehensive name. In order to make the interpretation of principal factors more convenient, the loading matrix of principal factors was rotated by the method of maximum rotation of variance.

Table 9
Rotated component matrix

	Component
	1	2	3
Internal expenditure of R&D funds	0.972	0.092	0.112
New product development expenses	0.980	0.108	0.041
Expenditure on introducing foreign technology	0.727	0.313	$-$ 0.281
Purchase of domestic technology expenditure	0.894	0.101	$-$ 0.138
Expenditure for technical renovation	0.830	0.041	0.240
Growth rate of patent grants	$-$ 0.575	$-$ 0.258	$-$ 0.209
New product development quantity	0.955	0.085	$-$ 0.007
Output value of new products	0.985	0.065	0.013
Number of patents granted	0.982	0.086	0.033
Total discharge of industrial wastewater	0.892	0.084	0.226
Total industrial exhaust emissions	0.251	$-$ 0.066	0.925
Industrial solid waste production	$-$ 0.079	$-$ 0.050	0.939
Number of research and development institutions	0.225	0.880	0.177
Employees of research and development institutions	0.111	0.956	$-$ 0.097
Internal expenditure of research and development organization funds	0.044	0.961	$-$ 0.207

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations.

As can be seen in Table 9, internal expenditure of R&D funds, output value of new products, new product development quantity, growth rate of patent grants, number of patents granted, expenditure on introducing foreign technology, purchase of domestic technology expenditure, expenditure for technical renovation, expenditure for absorbing and digesting imported technologies, and total discharge of industrial wastewater have strong explanatory ability. Most of these variables reflect the performance of regional innovation input and innovation output, which is the strength factor. Number of research and development institutions, employees of research and development institutions, and internal expenditure of research and development organization fund reflect the region’s emphasis on scientific research and are called science and technology factors. Industrial waste gas and solid waste are the environmental impacts of innovative production and are named energy consumption factors.

4.4.5 Calculate factor scores

Based on the main factor variance contribution rate, cumulative variance contribution rate, and factor score coefficient matrix, the influencing factors evaluation model of green innovation performance of industrial enterprises was constructed.

Table 10
Component score coefficient matrix

	Component
	1	2	3
Internal expenditure of R&D funds	0.124	$-$ 0.024	0.018
New product development expenses	0.127	$-$ 0.023	$-$ 0.017
Expenditure on introducing foreign technology	0.092	0.054	$-$ 0.153
Purchase of domestic technology expenditure	0.124	$-$ 0.031	$-$ 0.103
Expenditure for technical renovation	0.102	$-$ 0.026	0.085
Growth rate of patent grants	$-$ 0.053	$-$ 0.074	$-$ 0.095
New product development quantity	0.127	$-$ 0.034	$-$ 0.041
Output value of new products	0.132	$-$ 0.042	$-$ 0.034
Number of patents granted	0.129	$-$ 0.032	$-$ 0.023
Total discharge of industrial wastewater	0.108	$-$ 0.014	0.078
Total industrial exhaust emissions	$-$ 0.003	0.017	0.446
Industrial solid waste production	$-$ 0.050	0.046	0.469
Number of research and development institutions	$-$ 0.038	0.339	0.135
Employees of research and development institutions	$-$ 0.046	0.359	0.008
Internal expenditure of research and development organization funds	$-$ 0.051	0.358	$-$ 0.043

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores.

Table 10 presents the factor score coefficient matrix. The factor score can be obtained by combining Eq. (9), which is as follows:

$\displaystyle\left\{\begin{array}[]{l}F_{1}=0.124X_{1}+0.127X_{2}+0.092X_{3}+0% .124X_{4}+0.102X_{5}-0.053X_{6}+0.127X_{7}\\ \qquad\,{}+0.132X_{8}+0.129X_{9}+0.108X_{10}-0.003X_{11}-0.050X_{12}-0.038X_{1% 3}-0.046X_{14}\\ \qquad\,{}-0.510X_{15}\\ F_{2}=-0.024X_{1}-0.023X_{2}+0.056X_{3}-0.031X_{4}-0.023X_{5}-0.074X_{6}-0.034% X_{7}\\ \qquad\,{}-0.042X_{8}-0.032X_{9}-0.014X_{10}+0.017X_{11}+0.046X_{12}+0.339X_{1% 3}\\ \qquad\,{}+0.359X_{14}+0.358X_{15}\\ F_{3}=0.018X_{1}-0.017X_{2}-0.153X_{3}-0.103X_{4}+0.085X_{5}-0.095X_{6}-0.041X% _{7}\\ \qquad\,{}-0.034X_{8}-0.023X_{9}+0.078X_{10}+0.446X_{11}+0.469X_{12}+0.135X_{1% 3}+0.008X_{14}\\ \qquad\,{}-0.043X_{15}\end{array}\right.$ (9)

According to Table 8, the variance contribution rates of the three common factors are 53.518%, 18.937% and 13.902%, respectively. Furthermore, the cumulative variance contribution rate is 86.357%. According to Eq. (10), the weight was calculated, and the process of which is as follows:

$\displaystyle\left\{\begin{array}[]{l}\sigma_{1}=\frac{\lambda_{1}}{\lambda_{1% }+\lambda_{2}+\lambda_{3}}=0.6197\\ \sigma_{2}=\frac{\lambda_{2}}{\lambda_{1}+\lambda_{2}+\lambda_{3}}=0.2193\\ \sigma_{3}=\frac{\lambda_{3}}{\lambda_{1}+\lambda_{2}+\lambda_{3}}=0.1610\\ \end{array}\right.$ (10)

Therefore, according to Eqs (4) and (5), the formula for calculating regional score is as follows:

$\displaystyle F=0.6197X_{1}+0.2193X_{2}+0.1610X_{3}$ (11)

Based on this mathematical model, the comprehensive scores of factors affecting the green innovation performance of industrial enterprises in 30 provinces and autonomous regions were calculated, as shown in Table 11.

Table 11

Evaluation ranking of the factors of green innovation performance

Province	Strength F ${}_{1}$		Technology F ${}_{2}$		Consumption F ${}_{3}$		Comprehension F
	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Beijing	$-$ 0.6611	25	4.6059	1	0.9479	6	0.4478	6
Tianjin	$-$ 0.1186	12	$-$ 0.5523	23	1.1353	3	$-$ 0.3774	19
Hebei	$-$ 0.3337	14	$-$ 0.1910	14	$-$ 2.5681	30	0.1648	8
Shanxi	$-$ 0.6112	20	0.2189	6	$-$ 1.9329	29	$-$ 0.0195	14
Inner Mongolia	$-$ 0.6415	21	$-$ 0.2539	16	$-$ 1.3051	26	$-$ 0.2431	16
Liaoning	$-$ 0.1083	11	0.3590	5	$-$ 1.5828	27	0.2664	7
Jilin	$-$ 0.4475	16	$-$ 0.1953	15	0.4234	12	$-$ 0.4067	21
Heilongjiang	$-$ 0.5703	19	0.1786	8	0.1771	16	$-$ 0.2857	17
Shanghai	0.9541	5	0.8698	3	1.5977	1	0.5248	5
Jiangsu	2.9853	1	0.1535	10	$-$ 0.3002	23	1.9120	1
Zhejiang	1.5989	3	$-$ 0.5900	26	0.5167	10	0.7783	4
Anhui	0.1230	8	$-$ 0.1818	13	$-$ 0.1996	22	0.0685	12
Fujian	0.1478	7	$-$ 0.5544	24	0.7493	7	$-$ 0.1506	15
Jiangxi	$-$ 0.4792	17	$-$ 0.4481	22	0.2501	14	$-$ 0.4355	23
Shandong	1.5793	4	0.1919	7	$-$ 1.6181	28	1.2813	3
Henan	0.0065	10	$-$ 0.0596	12	$-$ 1.0016	25	0.1522	9
Hubei	0.0353	9	0.1536	9	0.1651	17	0.0290	13
Hunan	0.2392	6	$-$ 0.2821	18	$-$ 0.0400	19	0.0928	11
Guangdong	2.7830	2	$-$ 0.0249	11	0.5226	9	1.6354	2
Guangxi	$-$ 0.4944	18	$-$ 0.3260	19	0.2614	13	$-$ 0.4200	22
Hainan	$-$ 0.6793	28	$-$ 0.7931	28	1.2588	2	$-$ 0.8019	30
Chongqing	$-$ 0.1405	13	$-$ 0.7035	27	1.0216	5	$-$ 0.4058	20
Sichuan	$-$ 0.3471	15	1.2031	2	$-$ 0.3025	24	0.0974	10
Guizhou	$-$ 0.6472	23	$-$ 0.5788	25	0.1906	15	$-$ 0.5587	26
Yunnan	$-$ 0.6617	26	$-$ 0.2601	17	$-$ 0.1432	21	$-$ 0.4440	24
Shaanxi	$-$ 0.6726	27	0.5766	4	0.1500	18	$-$ 0.3145	18
Gansu	$-$ 0.6508	24	$-$ 0.3457	20	0.4979	11	$-$ 0.5593	27
Qinghai	$-$ 0.7959	30	$-$ 0.8233	29	0.5375	8	$-$ 0.7603	28
Ningxia	$-$ 0.6431	22	$-$ 0.9911	30	1.0386	4	$-$ 0.7775	29
Xinjiang	$-$ 0.7276	29	$-$ 0.3556	21	$-$ 0.1197	20	$-$ 0.5096	25

4.5 Empirical analysis results

As can be seen in Table 11, in terms of comprehensive ability, Jiangsu ranks first among all provinces and autonomous regions in China with a score of 1.9120, while Guangdong and Shandong rank the second and third, with scores of 1.6354 and 1.2813, respectively, indicating that these three areas have strong green comprehensive innovation ability. Furthermore, these regions’ green innovation performance is higher than that of other regions. In terms of the influencing factors, Jiangsu ranked first with a score of 2.9853, followed by Guangdong, Zhejiang, Shandong, and Shanghai, with scores of 2.7830, 1.5989, 1.5793 and 0.9541, respectively. The provinces and autonomous regions that ranked highest in the strength factor score are all in the eastern region, which has a superior geographical location, convenient transportation, and rapid economic development, indicating that the eastern provinces and autonomous regions have an advantage in green innovation due to their strong economic strength. In factors of science and technology, Beijing, Sichuan, Shanghai achieved scores of 4.4065, 1.2031, and 0.8698, respectively making the top three.

Beijing has a number of with unique advantages, making it significantly ahead of other regions. Th city is the political, cultural, and economic center, has a good development of science and technology environment, and government support for the development of science and technology. Shanghai is an international metropolis, with the development of economy, culture, and environment in the forefront, and talents gather, making significant contributions to the development of science and technology, meaning that their development is relatively high. In terms of “Western development” and “One Belt and One Road”, Sichuan seized development opportunities and constantly improved its scientific research level, making the region a leader in China.

The final factor is energy consumption. The three regions with the highest scores are Shanghai, Hainan and Tianjin, with scores of 1.5977, 1.2588, and 1.1353, respectively. Shanghai and Tianjin, as international cities, have a clean environment, reduced energy consumption, developed tertiary and high-tech industry, and do little damage to the ecological environment. In addition, Shanghai and Tianjin have achieved good results under the national ecological civilization construction policy. As a tourist resort, Hainan has a favorable geographical location, relatively late development, small industrial scale, no damage to the ecological environment, and effective ecological environment protection, allowing it to rank higher in the influencing factors of energy consumption and environmental protection.

Based on this analysis, the following conclusion can be drawn. The most important factor affecting green innovation performance is innovation strength, followed by technological development and energy consumption level.

(1) Factors of innovation strength

Innovation strength has the most significant impact on green innovation performance, accounting for the largest weight. In terms of innovation strength, the regions also rank high, and their comprehensive green innovation ability often ranks in the front, such as in Jiangsu, Guangdong, Zhejiang, etc. The size of innovation strength will directly affect the level of green innovation performance. The size of innovation strength is mainly reflected in economic strength, and the investment of funds directly determines innovation strength.

(2) Scientific and technological factors

The development status of science and technology has a direct impact on the green innovation performance of industrial enterprises, such as in Beijing, Sichuan, etc. The high level of science and technology may directly promote the improvement of green innovation technology of industrial enterprises, improve the comprehensive green innovation ability, and improve the overall green innovation performance of industrial enterprises.

(3) Energy consumption level factor

By virtue of unique geographical location and enhanced ecological environment, Shanghai and other regions have improved their overall green innovation capacity and significantly improved their green innovation performance. Therefore, energy consumption is also particularly important for green innovation performance.

5. Suggestions for improving the green innovation performance of industrial enterprises

Based on the empirical analysis results of the influencing factors of the green innovation performance of industrial enterprises across 30 regions of China, the following sections provide recommendations to improve the green innovation performance of industrial enterprises.

5.1 Increasing investment in innovation funds, enhancing green innovation strength

Green innovation strength is the main component of the green innovation performance of industrial enterprises. The improvement of this has a significant impact on green innovation performance in general. It is necessary to increase funding for green innovation. In the research and analysis, it is found that the overall strength of green innovation performance of industrial enterprises in developed areas is stronger than that of industrial enterprises in economically developing areas. Economic strength is the basis, and the investment of funds is conducive to accelerating the development of technology and continuous improvement, as well as strengthening the improvement of innovation strength. Moreover, it is necessary to accelerate the investment and research and development of new products. The continuous emergence of new products is the embodiment of innovation strength. The investment and research and development of new products have a significant effect on the improvement of green innovation performance of industrial enterprises. The government may policy and financial support to encourage green and innovative enterprises, providing enterprises sufficient development space and development power and enhancing the economic strength of enterprises. Enterprises should increase their input in talent introduction costs and green innovation costs. The introduction of talent significantly improves the level of green innovation performance, and investment in green innovation links can make the improvement of comprehensive green innovation level more secure.

5.2 Accelerating the development of science and technology, raising the level of green innovation

Improving science and technology has a significant impact on improving green innovation performance. Accelerating the development of science and technology is the internal requirement and inevitable path of green innovation. To enhance the strength of science and technology development, the government, society and enterprises should be unified, so as to achieve the enhanced, faster development of science and technology. The government should increase investment in educational institutions and scientific research institutions, cultivate talent advantageous for the development of science and technology. Enterprises should optimize the environment for scientific and technological development, strengthen the construction of basic conditions for innovation, focus on becoming the forefront of the leading scientific and technological development, accelerate the construction of scientific and technological innovation bases and platforms, and enhance sustainable scientific and technological innovation. To create an ideal space for the development of science and technology, society has accelerated the pace of scientific research, established a complete system of scientific research institutions, and increased funding for scientific research institutions. To combine colleges and universities with society, colleges and universities should pay attention to the cultivation of talent and train scientific researchers with high technology, high level, and high morality. The enterprise should establish an ideal learning encouragement mechanism, stimulate awareness of the technical learning of the enterprise staff, improve their own skills, and enhance the scientific research level of the enterprise. Furthermore, in the allocation of resources, the enterprise should be reasonable, using the research and development of new products and the introduction of technology to provide support.

5.3 Strengthening ecological environment protection, creating green and innovative environment

From the perspective of the three influencing factors on green innovation performance, ecological environments also occupy a large proportion; increasing the quality of ecological environment is also an important way of improving the quality of green innovation. To enhance the quality of the ecological environment, the government, society, and individuals should focus on three aspects. For the government, it is necessary to improve and perfect the legal system of environmental protection, make clear regulations, strictly monitor the emission of enterprises, and crack down on unqualified enterprises. It is necessary to effectively prevent the risks of ecological environment, improve the level of environmental management, and speed up the construction of an ecological environment protection team. On the societal level, enterprises should increase energy conservation and emission reduction efforts, introduce environmental protection and energy saving equipment, improve the efficiency of energy and resource utilization, and vigorously reduce emissions of industrial wastes. Furthermore, reasonable rules and regulations should be established to encourage employees with low carbon and environmental protection. On the individual level, it is necessary to establish green environmental awareness, establish green consumption awareness, develop a good lifestyle and habits, reduce or refuse to save energy and protect the environment.

6. Conclusion

The empirical analysis of the influencing factors of the green innovation performance of industrial enterprises is mainly conducted using four factors: green input; green processes; green outputs; and green environment. Based on the relevant theories of green innovation performance, this study examined industrial enterprises in 30 regions of China as the research objects to study factors affecting the green innovation performance of Chinese industrial enterprises. Through the literature search, the influencing factors of green innovation performance of relevant industrial enterprises were determined, the factors were identified, the index system was constructed, and the mathematical model was established. Through empirical analysis, the influencing and decisive factors were found, and reasonable suggestions were given on the basis of empirical analysis conclusions. The specific research conclusions are as follows:

(1)

By means of literature analysis, the relevant theoretical basis of green innovation performance were discussed and explained, focusing on the connotation, characteristics, evaluation index and theoretical basis of green innovation performance. The paper argued that the green innovation performance of industrial enterprises refers to the impact of green innovation on their overall performance in the production process;

(2)

The relevant factors affecting the green innovation performance of industrial enterprises were identified. Firstly, existing literature was summarized, and relevant experts and scholars were interviewed to distinguish the relevant differences between the general innovation performance new factors and the four major factors of green innovation input, process, output, and environment. Then, using the DEMATEL method to identify the influence degree of the factors, the results of the process identified factors analysis, determined the final impact factors and the index factors, and found that R&D expenses within budget, the authorized number spending growth, and technical transformation had a significant influence on green innovation performance;

(3)

In the empirical analysis, the indicators of industrial enterprises in 30 regions were selected, and the factor analysis method was used to conduct an empirical analysis on the influencing factors of the green innovation performance of industrial enterprises. The analysis results show that the influence of innovation strength on comprehensive green innovation performance has the largest weight, meaning that it has the largest impact on comprehensive green innovation performance. The influence of the level of scientific and technological development on the comprehensive green innovation performance is weaker. Furthermore, the main influencing factor is the level of energy consumption and environmental protection.

(4)

Recommendations were made to improve the green innovation performance of industrial enterprises, that is, industrial enterprises should increase investment in green innovation funds, accelerate the development of science and technology, strengthen the protection of ecological environment, and create a green innovation environment.

Footnotes

Funding

This research is funded by 2021 scientific research project of department of education of Liaoning Province (LJKR0225, LJKR0224), National Natural Science Foundation of China (72001055), Research Base of Science and Technology Innovation Think Tank of Liaoning Province (Research Base of High Quality Development of Equipment Manufacturing Industry, NO. 09).

References

Arici

Uysal

. Leadership, green innovation, and green creativity: A systematic review. Serv Ind J. 2022; 42: 280-320.

Cui

Dai

Wang

Zhao

. Does environmental regulation induce green innovation? A panel study of chinese listed firms. Technol Forecast Soc Chang. 2022; 176: 121492.

Fan

Lian

Liu

Wang

. Can environmental regulation promote urban green innovation Efficiency? An empirical study based on Chinese cities. J Clean Prod. 2021; 287: 125060.

Fang

Razzaq

Mohsin

Irfan

. Spatial spillovers and threshold effects of internet development and entrepreneurship on green innovation efficiency in China. Technol Soc. 2022; 68: 101844.

Hao

. Corporate social responsibility (CSR) performance and green innovation: Evidence from China. Financ Res Lett. 2022; 48: 102889.

Chen

Zhang

. Senior management’s academic experience and corporate green innovation. Technol Forecast Soc Chang. 2021; 166: 120664.

Hsu

Quang-Thanh

Chien

Mohsin

. Evaluating green innovation and performance of financial development: Mediating concerns of environmental regulation. Environ Sci Pollut Res. 2021; 28: 57386-57397.

Irfan

Razzaq

Sharif

Yang

. Influence mechanism between green finance and green innovation: Exploring regional policy intervention effects in China. Technol Forecast Soc Chang. 2022; 182: 121882.

. Spatial effect of environmental regulation on green innovation efficiency: Evidence from prefectural-level cities in China. J Clean Prod. 2021; 286: 125032.

10.

Liu

Jiang

Gan

Zhang

. Can digital finance promote corporate green innovation? Environ Sci Pollut Res. 2022; 29: 35828-35840.

11.

Liu

Wang

. Environmental regulation and green innovation: Evidence from China’s new environmental protection law. J Clean Prod. 2021; 297: 126698.

12.

Jia

Zou

. Is institutional pressure the mother of green innovation? Examining the moderating effect of absorptive capacity. J Clean Prod. 2021; 278: 123957.

13.

Rehman

Kraus

Shah

Khanin

Mahto

. Analyzing the relationship between green innovation and environmental performance in large manufacturing firms. Technol Forecast Soc Chang. 2021; 163: 120481.

14.

Shahzad

Zafar

Appolloni

. Does the interaction between the knowledge management process and sustainable development practices boost corporate green innovation? Bus Strateg Environ. 2021; 30: 4206-4222.

15.

Sun

Razzaq

. Composite fiscal decentralisation and green innovation: Imperative strategy for institutional reforms and sustainable development in OECD countries. Sustain Dev. 2022; 30: 944-957.

16.

Takalo

Tooranloo

Parizi

. Green innovation: A systematic literature review. J Clean Prod. 2021; 279: 122474.

17.

. How does green innovation improve enterprises’ competitive advantage? The role of organizational learning. Sustain. Prod Consump. 2021; 26: 504-516.

18.

Wang

Khan

MAS

Anwar

Shahzad

Adu

Murad

. Green innovation practices and its impacts on environmental and organizational performance. Front Psychol. 2021; 11: 553625.

19.

Wang

Zhou

Huang

. Green credit policy, government behavior and green innovation quality of enterprises. J Clean Prod. 2022; 331: 129834.

20.

Xia

Gao

Wei

Ding

. Government subsidy and corporate green innovation – Does board governance play a role? Energy Policy. 2022; 161: 112720.

21.

Yang

Feng

Zhao

Chang

. The impacts of energy insecurity on green innovation: A multi-country study. Econ Anal Policy. 2022; 74: 139-154.

22.

Zhang

Chen

Zhao

. Demand for green finance: Resolving financing constraints on green innovation in China. Energy Policy. 2021; 153: 112255.

23.

Yuan

Cao

. Do corporate social responsibility practices contribute to green innovation? The mediating role of green dynamic capability. Technol Soc. 2022; 68: 101868.

24.

Zeng

Huang

. Industrial collaborative agglomeration, marketization, and green innovation: Evidence from China’s provincial panel data. J Clean Prod. 2021; 279: 123598.

25.

Zheng

Feng

Jang

Chang

. Terrorism and green innovation in renewable energy. Energy Econ. 2021; 104: 105695.

Empirical analysis of the factors of the green innovation performance of industrial enterprises based on DEMATEL method and factor analysis method

Abstract

Keywords

1. Introduction

(1) Literature analysis

(2) Combination of theoretical analysis and empirical analysis

(3) Combination of qualitative analysis and quantitative analysis

2. Literature review and related theories

2.1 Concept of green innovation performance

2.2 Characteristics of green innovation performance

(1) Regional differences

(2) Stages of performance

2.3 Evaluation indicators of green innovation performance

(1) Evaluation index of green innovation input

(2) Evaluation index of green innovation process

(3) Evaluation index of green innovation output

(4) Evaluation index of green innovation environment

(5) Evaluation index of EES overall development

2.4 Theoretical framework of green innovation performance

(1) Ecological civilization theory

(2) Theory of innovation economics

(3) Enterprise growth theory

3. Identifying the factors of green innovation performance

3.1 Process of identifying factors of the green innovation performance system

3.1.1 Identification of the factors based on literature analysis

(1) Direct influence on matrix construction

Table 2 Direct influence matrix

(2) Normalization

(3) Composite influence matrix

(4) Comprehensive influence calculation

4. Empirical analysis

4.1 Data collection

Table 5 Comprehensive influence level

Table 6 Name of evaluation index of the factors of green innovation performance

4.4 Empirical analysis process based on factor analysis

4.4.1 Factor analysis model construction

Table 7 The validity analysis results according to KMO and Bartlett test

Table 8 Results of total variance explained

Table 9 Rotated component matrix

Table 10 Component score coefficient matrix

(1) Factors of innovation strength

(2) Scientific and technological factors

(3) Energy consumption level factor

5. Suggestions for improving the green innovation performance of industrial enterprises

5.1 Increasing investment in innovation funds, enhancing green innovation strength

5.2 Accelerating the development of science and technology, raising the level of green innovation

5.3 Strengthening ecological environment protection, creating green and innovative environment

6. Conclusion

Footnotes

Funding

References

Table 2
Direct influence matrix

Table 5
Comprehensive influence level

Table 6
Name of evaluation index of the factors of green innovation performance

Table 7
The validity analysis results according to KMO and Bartlett test

Table 8
Results of total variance explained

Table 9
Rotated component matrix

Table 10
Component score coefficient matrix