A Study on the Coupling and Coordination Relationship of Science and Technology Innovation, Higher Education, and Clean Energy Based on the Entropy Weight and Gray Correlation Analysis Method

. Clean energy is the core element of China ’ s energy low-carbon transition strategy, and its development degree is closely link with science and technology innovation and higher education. Based on studying relevant research and analyzing data and information, we selected 11 primary indicators, such as investment in education, transformation of scienti ﬁ c research results, and clean energy development, and 30 secondary indicators, such as R&D investment intensity, number of patents granted for inventions, clean energy power generation, clean energy installed capacity, and clean energy consumption share, to construct an indicator evaluation system. Based on the actual data of three systems from periods 2010 to 2020, we try to use entropy weight method to calculate index weights, use coupled coordination model to calculate coupling coordination degree of three systems, and use gray correlation analysis method to in-depth analysis interaction relationships between three systems. Based on the research, we try to propose 3 countermeasures and recommendations. We hope that the research results of this paper will provide a new research method and ideas for related studies and make positive contributions to the high-quality development of China ’ s economy and the realization of the energy transition strategy.


Introduction
Energy is an important material basis and driving force for the progress of human civilization and is a matter of national planning and national security.In 8 November 2012, the report of the 18th Party Congress has proposed that China's energy green low-carbon transformation has made important progress, the installed capacity of renewable energy exceeds 1 billion kilowatts, and clean energy consumption ratio is from 14.5% to 25.5% [1].China has now built a diversified clean energy system with natural gas and renewable energy such as hydro energy and wind energy.The independent energy security capacity has always been maintained at more than 80%, with an average annual growth rate of 3% of energy consumption to support an average economic growth of 6.6% per year; the energy consumption intensity has dropped by 26.4% [2].China has explored an ecological priority, green, and low-carbon Chinese style energy development road [2].Energy revolution is known as the third revolution in human history, and energy has an extremely important significance in survival and development of the country.The widespread use of clean energy is the focus of energy revolution.Due to the difficulty of development, transportation, storage, and high level of technology required, the development of clean energy (abbreviated as CE) has encountered many difficulties, such as technological shortcomings, large reliance on foreign countries for key components and core materials, difficulties in transforming green and low-carbon technological achievements, and lack of green and low-carbon professionals [3].
The report of the 20th Party Congress focuses on the implementation of the energy transformation strategy and The data were collected from China Statistical Yearbook on Science and Technology, China Statistical Yearbook, from periods 2010 to 2020.We calculated the increase rate.
2 International Journal of Energy Research the construction of a new energy system.This means that energy development will play a more important role in the future economic and social development of China.Building a clean, low-carbon, safety, and efficient energy system is a major strategic goal for China, and scientific and technological innovation (abbreviated as STI) and higher education (abbreviated as HE) are the keys to achieving this goal.
In view of the background, we identified CE as the core element of energy transformation, explored interaction mechanism, and influencing factors among STI, HE, and CE.Based on studying relevant research and analyzing data and information, we selected 11 primary indicators and 30 secondary indicators from three systems to construct the evaluation index system.Based on the actual data from periods 2010 to 2020, we used entropy weight method to calculate index weights, coupled coordination model to calculate coupling coordination degree of three systems, and gray correlation analysis method to in-depth analysis interaction relationships between them.At last, we proposed three countermeasures and recommendations.We hope that the research results of this paper will provide a new research method and ideas for related studies and make positive contributions to the high-quality development of China's economy and the realization of the energy transition strategy.
The structure of the paper is as follows: Section 2 discusses the literature review of this study and introduces the relevant research theories, research methods, and models.

Literature Review
Researches on the interrelationship between STI, HE, and CE systems in domestic and international academia are mainly reflected in the following: the mainstream view in China equates clean energy with renewable energy sources, i.e., those are continuously renewable and can be recycled for multiple uses, mainly including solar, wind, hydro, and biomass energy [4].According to He et al. [5], in contrast to fossil fuels, renewable energy is limitless and does not emit any pollution while in use.As a result, they are viewed as viable alternatives to fossil fuels.Renewable energy, however, has drawbacks that restrict its application.First of all, they are not available everywhere and are only offered in a few locations.Second, some of these energy sources like wind and solar are erratic and cannot be relied upon to supply a consistent energy source.As a result, rational renewable energy planning is required to handle these problems.Irandoust [6] argued that there is a unidirectional causal relationship between technological innovation and clean energy, but the results of the study could not confirm any association between clean energy and economic growth.Tabrizian [7] argued that the development, promotion, and implementation of clean energy should be given high attention, where the technology is the best solution for energy transition.Suli and Fenfen [8] argued that long-term covariance existed between science and technology innovation, standardization, and new energy development, which has a significant promoting effect.Chen and Lei [9] concluded that technological innovation has a great impact on countries with relatively high greenhouse gas emissions, and    [11] proposed a national energy science and technology system concept that integrates clean and efficient utilization of fossil energy, clean energy scale application, and low carbonation multienergy strategy, but none of them paid attention to the promotion of science and technology innovation to energy transition strategy.However, none of them have focused on the adequacy of S&T innovation for energy transition strategy, especially the research on the extent of S&T innovation to support clean energy development is almost blank.Among the studies on the impact of higher education hierarchy on technological progress, most empirical studies have shown that higher education hierarchy has a significant contribution to technological progress [12].
Reyes-Mercado and Rajagopa [13] showed that the compatibility and complexity of technological innovation affect the choice of energy sources by the public, but in most cases, the public tends to choose the lowest cost energy source and does not care about the type of energy source.Mikhaylov et al. [14] used MF-X-DMA method and regression analysis to determine the correlations and weights of indicators.
Using the k-means method, 18 characteristic days of the year were calculated and these were used to simplify the annual data in order to save computational costs [15].In summary, there is no research on the coupled and coordinated relationship among the three systems of STI, HE, and CE, and combined use of entropy weight method, gray correlation analysis, and coupled coordination model for indicator research is also relatively novelty.Based on studying relevant research results and analyzing data, firstly, we select 11 primary indicators, such as investment in education, transformation of scientific research results, and clean energy development, and 30 secondary indicators, such as R&D investment intensity, number of patents granted, clean energy power generation, clean energy installed capacity, and clean energy consumption ratio, to construct an indicator evaluation system.Secondly, we use the entropy value method, coupling coordination model, and gray correlation analysis method to deeply study the interaction relationship among STI, HE, and CE.Finally, we try to use the actual data of three systems in China from 2010 to 2020 to empirically study.On this basis, three countermeasure suggestions are proposed, such as improving the collaborative working mechanism among energy, science and technology, and education sectors; stimulating the vitality of talent innovation in the energy field; and improving the quality of talent training in the green and low-carbon direction.The research results can effectively analyze whether science and technology innovation and higher education really promote clean energy development and whether they are sufficient or not and then provide a basis for the adjustment and prognosis of China's energy transition.

Analysis of the Development Situation of STI, HE, and CE
3.1.Analysis of the Development of STI.China has become the world's largest energy producer, consumer, and carbon emitter for many years."The 14th Five-Year Plan for Science and Technology Innovation" [16] in the energy sector clearly states that China has made significant breakthroughs in high percentage renewable energy system technology, oil and gas safety supply technology, nuclear power technology, and fossil energy utilization technology.Among them, wind turbines, photovoltaic cell production, and installed capacity, generation of nuclear power technology ranked the first in the world.In addition, the STI activities in the fields of energy infrastructure intelligence, big data analysis, multienergy complimentary, energy storage and electric vehicle applications, intelligent energy use, and value-added services are very active [16].The specific data are shown in Table 1.
By analyzing data from Table 1, we can see that, from 2010 to 2020, the scale of investment in scientific research in China has grown significantly.The national R&D expenditure on science and technology increased to 245.4%, intensity of R&D expenditure grew from 1.73% to 2.4%, national financial allocation for science and technology increased to 140.5%, the number of R&D personnel increased to 113.23%, full-time equivalent of R&D personnel increased to 105%, number of patents granted increased to 346.6%, number of R&D projects increased to 94.02%, number of scientific and technological achievements registered increased to 81.73%, and all of these are shown in Figures 1 and 2.
With the promotion of the "double carbon" target, the strategic position of science and education innovation in China's energy development has been further strengthened and relevant policies have been improved.In 2018, General Secretary Xi JinPing proposed at the national two sessions that development is the first priority, talent is the first resource, and innovation is the first driving force [17].The realization of China's strategic goal of energy transformation requires the strong support of human resources.The specific data are shown in Table 2.
By collating and analyzing the data from Table 2, we can see that from 2010 to 2020, number of higher education school teachers in China increased only to 18.9%, number of higher education schools increased only to 6.01%, number of books in the collection increased to 48.27%, number of teaching computers increased to 133.73%, assets of teaching and research instruments and equipment increased to 186.87%, expenditure on education expenses increased to 204.06%, and all of these are shown in Figures 3 and 4.
During more than ten years, China has invested manpower, material, and financial resources in the field of higher education, enacted many favorable policy documents.However, along with the implementation of the "double carbon" policy, the demand for green low-carbon talents from government departments and green low-carbon-related industries in China has exploded.According to the data selected from China Petroleum and Chemical Industry Federation show that from periods 2021 to 2025, China needs 550,000 to 1 million "dual carbon" talents [17].The "2022 Employment Trends of Mid-and High-End Talents Report" released by Hire.com shows that the largest increase in new jobs in the first quarter of 2022 is in the field of carbon neutral, with an increase of 408.26% [17].Although China has formulated number of policies to promote the training of "dual carbon" talents, there are still problems such as severe shortage of dual carbon talent supply and demand, lack of high-level talents, and inadequate talent training system.The realization of the strategic transformation of energy needs the important guarantee of human resources.The notice of the Ministry of Education on the issuance of the "Work Plan for Strengthening the Construction of the Higher Education Talent Cultivation System for Carbon Dumping and Carbon Neutrality," i.e., Education High Letter (2022) No. 3 [18], clearly states that the green lowcarbon concept will be incorporated into the education and teaching system.In this way, we gradually move away from national support and policy subsidies to form the core support for energy transformation [18].3.

Analysis of the
By analyzing data from Table 3, we can see that from 2010 to 2021, CE power generation increased from 8406.7 Bkh to 24032 Bkh, increased to 185.87%, installed capacity of CE increased to 320.48%, proportion of CE in total energy consumption increased to 90.30%, installed capacity and power generation of nuclear energy increased to 392.24% and 451.57%, and installed capacity and power generation of wind energy increased to 1011.26% and 1369.74%.We can see that the development of clean energy is very rapid.It is inseparable from the country's high concern for ecological and environmental protection, sustainable development, and low-carbon energy transition issues.Conversely, the installed capacity and power generation of traditional thermal energy only increased to 82.82% and 70.04% in almost ten years; all of these are shown in Figures 5 and 6.We can see that the development of CE has made significant progress, and China's energy strategy transformation has achieved remarkable results.

Methods
The paper uses data from China Statistical Yearbook on Science and Technology, China Statistical Yearbook, Educational Statistics Yearbook of China, China Energy Statistics Yearbook, and China Energy Big Data Report, regarding the main indicators, from periods 2010 to 2020.
Energy is the material basis for the survival and development of the country; STI and HE are the keys to achieve a low-carbon energy transition [19].In view of the background, the study of the interaction mechanism between STI, HE, and CE is more helpful to analyze the problems arising in China's energy development and then put forward opinions and countermeasures suitable for the implementation of energy transition strategy.

Establish Index Evaluation System
. In order to deeply analyze the complex interactions among three systems, we try to select 11 primary indicators and 30 secondary indicators to construct the coupled model evaluation index system.We assumed that the development of STI subsystem is evaluated by 11 indicators such as national investment in R&D and number of high technology R&D institutions; development of HE subsystem is evaluated by 8 indicators such as number of faculty and staff in colleges and universities and teaching and research instruments and equipment assets; development of CE subsystem is evaluated by 11 indicators such as clean energy power generation and thermal power generation.
The specific contents of the model index are shown in Table 4.
We assumed that X 1 ~X11 as the indicators of STI subsystem, Y 1 ~Y8 as the indicators of HE subsystem, and Z 1 ~Z11 as the indicators of CE subsystem.We supposed that M representing numbers of index and N representing year, forming as original data matrix.

Entropy Weight Method.
The entropy weight method was proposed by information scientist Shannon, and its core is to calculate index weights by studying the dispersion of indicator data and calculating the entropy value of indicators [20,21].Entropy weight method is an effective way to calculate the weights of comprehensive indicators.

Step 1: Normalization of Positive and Negative Data.
Since the indicators of the three subsystems have differences in scale and order of magnitude and different indicators have different units and different properties, the first step is normalization of positive and negative data.The indicators have two directions of action: positive and negative.The positive standardization uses equation (1) and the negative standardization uses equation (2) [20,21].
Step 2: Calculate Indicator Weights.The main principle of entropy method is that the more weight the indicator value has, the more important the indicator is [22].12 International Journal of Energy Research Calculate contribution of the j year under the indicator P ij .
Calculate total contribution E i of all years to the index x i .
n refers to the number of years, 0 Calculate index weights of each subsystem W i .
The values of the constructed W i weighting vectors lead to the weighting coefficients of STI, HE, and CE subsystems.
Step 3: Calculate Comprehensive Evaluation Index.
Calculate comprehensive evaluation index of STI, HE, and CE systems.The standardized values of indicators are multiplied with the corresponding indicator weight coefficients and then summed up to obtain the comprehensive evaluation value of STI, HE, and CE systems from 2010 to 2020 [23].The calculation process is shown in .Calculation procedure is shown in equation (10).System coupling degree values and corresponding coupling levels are shown in Table 5.
Coupling degree C only reflects the degree of mutual influence between systems.We used the coupling coordination degree D to study the degree of coupling coordination between STI, HE, and CE systems [24].
In equations ( 11) and ( 12), D represents coupling coordination degree, T represents comprehensive coordination index between subsystems, and α, β, and γ represents coefficients to be determined [24].Since there are some differences in the degree of mutual promotion among STI, HE, and CE systems, we tasked into account the relevant research results and experts' experience; α, β, and γ are assigned as 0.3, 0.2, and 0.5, respectively [25].The coupling coordination degree D is divided into 10 grades as shown in Table 6.We calculated the results by SPSS.
13 International Journal of Energy Research 4.4.Gray Correlation Analysis Method.Gray system theory (GST) is a concept of information interval analysis, which was founded by Professor Deng Julong.Gray correlation analysis method can be used to research degree of correlation between factors.In the process of system development, the trend of two factors changes with consistency, i.e., the degree of synchronous change is higher; on the contrary, it is lower [26].
Step 1: construct data sequences matrix Step 2: dimensionless processing (averaging and priming) for the data.Using mean value method to dimensionless the index data, the calculation process is shown in equation (14).The dimensionless data series form a new matrix.
Step 3: calculate the absolute difference between the index series (comparison series) of each evaluated object and the corresponding element of the reference series [27].jx 0 ðkÞ − x i ðkÞj k = 1 ⋯ , m, i = 1 ⋯ n, n is the number of evaluated objects    International Journal of Energy Research Step 4: calculate the number of gray correlation coefficients.Calculation process is shown in In equation (15), ρ is a discrimination coefficient within the value (0,1).If ρ is smaller, the difference between the correlation coefficient is greater, the ability to distinguish is stronger, and the value of ρ is taken as 0.5 [28,29].

Empirical Analysis
We select data about STI, HE, and CE systems from periods 2010 to 2020 for empirical analysis.We calculated coupling degree C and coordination degree D of STI, HE, and CE systems from periods 2010 to 2020 by using formulas (10)- (12).According to calculation results, we can grade the coupling coordination degree of STI, HE, and CE systems into 8 levels.The specific results are shown in Table 11.
By analyzing data and charts from Table 11 and Figure 7, we can see that, from 2010 to 2015, coupling coordination degree D between STI, HE, and CE systems has grown from 0.1000 to 0.6560, and the level of coupling coordination has changed from severe disorders to primary coordination, which indicated that the state and society do not pay enough attention to energy transition, environmental protection, and STI in this period.From 2016 to2020, the degree of C and D between STI, HE, and CE systems has increased significantly; coupling coordination degree D between STI, HE, and CE systems has grown from 0.7204 to 0.9950; and the level of coupling coordination has changed from intermediate coordination to quality coordination; especially in 2019 and 2020, degree of D is high-quality coordination.Because during this time, the state had issued   We calculated the results by SPSS.
15 International Journal of Energy Research International Journal of Energy Research series of policies to ecological protection, sustainable development, and energy which achieved good results.The proportion of CE consumption such as natural gas, hydro energy, wind energy, solar energy, and nuclear energy has been increasing progressively.At the same time, the state has been increased investment in higher education, talent training, and scientific research institutions, especially in the professional settings and human training programs, with special emphasis on the addition of dual carbon majors related to energy transition strategies, as well as the cultivation of scientific and creative capabilities, which all further promote the rapid development of clean energy.

Stage 4:
Calculate Gray Correlation.We used gray correlation analysis method to calculate the gray correlations of STI, HE, and CE systems and coupling coordination.Calculation results are shown in Tables 12 and 13.
Figure 8 shows the variation of the gray correlation coefficients between 11 indicators of STI system and coupling coordination degree D. We can analyze data from Table 13; indicators with degree of relevance more than 0.8 are national R&D investment X 1 (correlation: 0.886), published scientific and technological papers X 9 (correlation: 0.877), state financial allocation for science and technology X 3 (correlation: 0.837), and patent for invention (correlation: 0.819).
Using the same method, we calculated gray correlations of HE system and coupling coordination.Calculation results are shown in Tables 14 and 15.
Figure 9 shows the variation of the gray correlation coefficients between eight indicators of HE system and coupling coordination degree D. By analyzing data in Table 15, we can conclude that indicators with correlations more than 0.8 are teaching and research instruments and equipment assets Y 7 (correlation: 0.847), and education expenses Y 8 (correlation: 0.844).
Using the same method, we calculated gray correlations of CE system and coupling coordination.Calculation results are shown in Tables 16 and 17.
Figure 10 shows the variation of the gray correlation coefficients between 11 indicators of CE system and coupling coordination degree D. By analyzing data in Table 17, we International Journal of Energy Research can conclude that indicators with correlations more than 0.8 are clean energy power Z 1 (correlation: 0.808) and installed capacity of clean energy Z 2 (correlation: 0.801).

Countermeasures and Suggestions
Based on previous study, it shows that the coupling coordination between STI, HE, and CE systems has gradually increased, but there is still much room for expansion.The coupling coordination level between STI, HE, and CE systems has developed from severe dissonance in 2010, through moderate dissonance, near dissonance, barely coordinated, to high-quality coordination in 2020, reflecting development trend of three systems from extremely unrelated to fully related, and the coupling coordination benefits are increasing, indicating that the interaction and influence between the three are strengthening and more innovation and higher education on the development of hydrogen energy to produce a positive role in promoting the gradual increase.However, we should still be sober to see that although China has built and has the world's largest clean energy industries such as hydro, photovoltaic, and wind power, the role of science and technology innovation in promoting clean energy is not sufficient, and most of them are still at the level of mere technology, lacking in the promotion and application of specific practical aspects.In addition, the problem of lack of human resources in the field of energy transition still needs to be solved.
We selected 11 primary indicators and 30 secondary indicators to construct evaluation system.Based on the actual data of three systems from periods 2010 to 2020, we tried to use entropy weight method to calculate index weights, use coupled coordination model to calculate coupling coordination degree of three systems, and use gray correlation analysis method to in-depth analysis interaction relationships between three systems.Based on the research, we try to propose 3 countermeasures and recommendations: (1) Improve collaborative working mechanism of energy, science and technology, and education sectors.The realization of the strategic goals of energy transformation requires the collaboration and full participation of multiple sectors across the country and the establishment and improvement of a collaborative working mechanism with multisector participation, clear objectives, and reasonable division of labor to promote the work.Take advantage of the new national system; support the leading implementation units to unite with relevant enterprises, scientific research institutions, and universities to support energy development needs and major engineering construction; establish cross-disciplinary and interdisciplinary innovation consortia; and form a synergistic attack force (2) Stimulate the innovative vitality of talents in the field of energy.Improve the market-oriented mechanism of energy technology innovation; build a technology innovation system with enterprises as the main body, market-oriented, and deep integration of industry, academia, research, and application.Improve the evaluation system of talents in the energy field, improve the income distribution system, loosen the ties for researchers in all aspects, and continuously stimulate the innovative activities of talents in the energy field (3) Improve the quality of training talents in the direction of green and low-carbon.China has a large shortage of green low-carbon science and technology talents and needs to accelerate the cultivation of technical leaders and application-oriented talents who can adapt to the requirements of green development.First, based on the key core technology of green low-carbon needs, relying on relevant national key laboratories, national engineering research centers, provincial and ministerial key laboratories, and engineering centers, cultivate green lowcarbon technology leaders and innovation teams.Secondly, in the key fields of green and lowcarbon development such as energy, construction, manufacturing, and transportation, we promote universities to create new majors or upgrade existing majors to cultivate multidisciplinary green and low-carbon professionals.Third, strengthen international exchange and cooperation on advanced green low-carbon technologies, enhance the innovation capacity and level of green low-carbon talents through cooperative research and personnel exchange visits, build an international green lowcarbon talent team, and contribute China's scientific and technological strength to global green lowcarbon development
Number of faculty and staff in colleges and universitiesY1Number of full-time faculty in higher educationY2 Number of external faculty in higher

Figure 3 :
Figure 3: 2010-2020 number of full-time teachers and external teachers in colleges and universities.

Figure 4 :
Figure 4: 2010-2020 expenditure of research equipment assets and educational expenditure.
Development of Clean Energy.Academics usually divide clean energy into two categories, first is renewable energy, and second is low-pollution nonrenewable energy.The clean energy studied in this paper refers to the first category.Due to the limitation of data collection ability, 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Clean Energy Power GenerationZ1 Wind power generation capacityZ9 Nuclear power generationZ7 Thermal power generationZ11

Figure 6 :
Figure 6: 2010-2021 clean energy nuclear and wind and thermal power installed capacity.

Figure 7 :
Figure 7: 2010-2020 data of indicators related to coupling coordination degree.

. 3 .
Coupled Coordination Model.In coupled coordination model, C represents coupling degree between three systems with values between [0, 1] [23] State financial allocation for science and technology National R&D funding external expenditure R&D personnel full time equivalent Foreign funds expenditure of R&D Total number of R&D employees Patent for invention Published scientific and technical papers Number of high technology R&D institutions Number of R&D projects of scientific research institutions Intensity of R&D expenditure

Figure 8 :
Figure 8: Correlation coefficient chart of STI indicators and D.

5. 1 .
Stage 1: of Evaluation Index Data.The original values of indicators X 1 -X 11 for STI system, Y 1 -Y 8 for HE system, and Z 1 -Z 11 for CE system are shown in Tables 1-3.
and staff in colleges and universities Number of full-time faculty in higher education Number of external faculty in higher education Number of higher education schools Number of teaching computers Teaching and research instruments and equipment assets Education expenses Number of books in the collection

Figure 9 :
Figure 9: Correlation coefficient chart of HE indicators and D.

Table 2 :
2010-2020 higher education development.data were collected from Educational Statistics Yearbook of China, from periods 2010 to 2020.We calculated the increase rate. The
6he data were collected China Energy Statistics Yearbook, China Statistics Yearbook, China Energy Big Data Report, from periods 2010 to 2020.We calculated the increase rate.6InternationalJournal of Energy Research3.2.Analysis of the Development of Higher Education.As we all know, human resources are the most valuable resources.

Table 5 :
System coupling degree values and corresponding coupling levels.

Table 6 :
Evaluation criteria of system coupling coordination level.
We calculated the results by SPSS.
We calculated the results by SPSS.

Table 11 :
Coupled coordination model calculation results.
Year Coupling degree C Coordination index T Coupling coordination degree D Coordination level Level of coupling coordination

Table 10 :
2010-2020 comprehensive evaluation value of STI, HE, and CE systems.

Table 12 :
Number of gray correlation.

Table 13 :
Gray correlation ranking of STI indicators and coupling coordination degree.

Table 14 :
Number of gray correlation of HE indicators and coupling coordination degree.

Table 15 :
Gray correlation ranking of HE indicators and D.