# 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

Authors: Ning Yang; Tao Lv; Hua Cui
Journal: International Journal of Energy Research
 (2023)

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## Abstract

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 scientific 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.


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## Body

## 1. 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 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: Section2 discusses the literature review of this study and introduces the relevant research theories, research methods, and models. Section 3 describes the current situation analysis. The current situation of STI, HE, and CE systems in China from 2020 to 2021 is introduced to provide the original data for model research. The methods of this study is explained in Section 4. The index system is designed, and the coupled coordination model is constructed. Entropy weight method is used to calculate the indicator weights of three systems. Coupling coordination model is used to calculate the degree of coupling coordination of the three systems. Gray correlation analysis method is used to calculate the gray correlations of STI, HE, and CE system indicators and coupling coordination degreeD. The empirical analysis of this study is presented in Section 5. The actual data of the three systems from 2010 to 2020 are used to test the model. Section 6 discloses the conclusions and recommendations of the study, summarizes the research results, and puts forward the three countermeasure suggestions.


## 2. 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 financial support can reduce clean energy costs. Lin and Zhu [10] used a linear regression model to research clean energy. Xiao et al. [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.


## 3. 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.Table 1
2010-2020 science and technology innovation development.
YearNational investment in R&DIntensity of R&D expenditureState financial allocation for science and technologyForeign fund expenditure of R&DNational R&D funding external expenditureTotal number of R&D employeesR&D personnel full-time equivalentPatent for inventionPublished scientific and technical papersNumber of R&D projects of scientific research institutionsNumber of high technology R&D institutionsX1X2X3X4X5X6X7X8X9X10X1120107062.61.734196.792.1445.43542244255.38814825135110670503696201186871.794797116.2494.344017578288.29960513172113709673623201210298.41.915600.1100.4584.214617120324.681255138217105793433674201311846.626184.9105.9607.225018218353.281313000207688850693651201413015.62.026454.5107.6656.355351472371.061302687233228914653677201514169.882.067005.8105.2719.375482528375.881718192359316995593650201615676.752.17760.7103.2888.095830741387.8117537634042081009253611201717606.132.128383.6113.311196213627403.3618364344201441124723547201819677.932.149518.271.41425.616571372438.1424474604321471178713306201922143.582.2310717.423.911634.267129256480.125916074528041256423217202024393.12.41009590.071963.337552986523.4536392685301271300893109Increase rate245.4%38.7%140.5%-2.2%340.8%113.2%105.0%346.6%292.4%94.0%-15.9%The data were collected fromChina Statistical Yearbook on Science and Technology, China Statistical Yearbook, from periods 2010 to 2020.We calculated the increase rate.By analyzing data from Table1, 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.Figure 1
2010-2020 investment in scientific research.Figure 2
2010-2020 changes in the number of R&D employees, patents granted, and research projects.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.


### 3.2. Analysis of the Development of Higher Education

As we all know, human resources are the most valuable resources. 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.Table 2
2010-2020 higher education development.
YearNumber of faculty and staff in colleges and universitiesNumber of full-time faculty in higher educationNumber of external faculty in higher educationNumber of higher education schoolsNumber of books in the collectionNumber of teaching computersTeaching and research instruments and equipment assetsEducation expensesY1Y2Y3Y4Y5Y6Y7Y82010227184914068084139704356194250.58592623923233238.651650270652011227385114335794192884347203309.1829728825828161.671956184712012231998414796854315174424214857.7898746229442981.762386929362013235267915305124421764420225394.17972043732830625.182865530522014238864415660484623904411234848.421041000636494627.173036471822015242061816028114779734457243335.911106781540485282.473280646092016244790316271824988234486252753.221173388645003240.123612919272017248440316572385218634528260505.131233691749812055.733888838502018252557116946615388704541268588.031290552655134919.214256200692019260279317607865628004568277439.191331148660746398.744614299802020270118318519335936624618288008.561385131466648919.29501781166Increase rate18.90%31.64%43.41%6.01%48.27%133.73%186.87%204.06%The data were collected fromEducational Statistics Yearbook of China, from periods 2010 to 2020.We calculated the increase rate.By collating and analyzing the data from Table2, 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.Figure 3
2010-2020 number of full-time teachers and external teachers in colleges and universities.Figure 4
2010-2020 expenditure of research equipment assets and educational expenditure.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 low-carbon 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].


### 3.3. Analysis of the 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, based on reference of relevant literature, we assumed the data of solar energy, wind energy, nuclear energy, and other energy sources as the data source for studying clean energy.From periods 2010 to 2021, the scale, speed, and effect of CE development in China are remarkable. The specific data are shown in Table3.Table 3
2010-2020 clean energy development.
YearClean energy power generationInstalled capacity of clean energyPrimary energy productionProportion of clean energy to total energy consumptionProportion of clean energy to total energy productionInstalled nuclear power capacityNuclear power generationInstalled wind power capacityWind power generation capacityInstalled thermal power capacityThermal power generationZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z1120108406.72567231212513.414.51082738.82958446.27096733319.320118232291433179861313.7125787446237327683438975201210654.83268733028914.515.31257973.96142959.88196838928.12013121313875135878415.516.214661116.1765214128700942470.1201413529.64463736186616.918.220081325.496571560.89323242686.5201514868.35200936219317.819.327171707.9130751857.710055442841.9201616344.15906034595419.121.933642132.9147472370.710609444370.7201717431.76740135886720.522.837752480.7163252972.311049547546201818921.27558537885922.123.644662943.6184273659.711440850963.2201920588.28201139731723.324.648743483.5209154060.311895752201.5202021879.395538407295.224.325.649893662.5281654664.712462453302.5202124032107945418292.1725.526.45326407532871655812973956655Increase rate185.87%320.48%34.01%90.30%82.07%392.24%451.57%1011.26%1369.74%82.82%70.04%The data were collectedChina Energy Statistics Yearbook, China Statistics Yearbook, China Energy Big Data Report, from periods 2010 to 2020. We calculated the increase rate.By analyzing data from Table3, 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.Figure 5
2010-2021 clean energy, nuclear power, wind power, and thermal power generation.Figure 6
2010-2021 clean energy nuclear and wind and thermal power installed capacity.


## 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.Table 1
2010-2020 science and technology innovation development.
YearNational investment in R&DIntensity of R&D expenditureState financial allocation for science and technologyForeign fund expenditure of R&DNational R&D funding external expenditureTotal number of R&D employeesR&D personnel full-time equivalentPatent for inventionPublished scientific and technical papersNumber of R&D projects of scientific research institutionsNumber of high technology R&D institutionsX1X2X3X4X5X6X7X8X9X10X1120107062.61.734196.792.1445.43542244255.38814825135110670503696201186871.794797116.2494.344017578288.29960513172113709673623201210298.41.915600.1100.4584.214617120324.681255138217105793433674201311846.626184.9105.9607.225018218353.281313000207688850693651201413015.62.026454.5107.6656.355351472371.061302687233228914653677201514169.882.067005.8105.2719.375482528375.881718192359316995593650201615676.752.17760.7103.2888.095830741387.8117537634042081009253611201717606.132.128383.6113.311196213627403.3618364344201441124723547201819677.932.149518.271.41425.616571372438.1424474604321471178713306201922143.582.2310717.423.911634.267129256480.125916074528041256423217202024393.12.41009590.071963.337552986523.4536392685301271300893109Increase rate245.4%38.7%140.5%-2.2%340.8%113.2%105.0%346.6%292.4%94.0%-15.9%The data were collected fromChina Statistical Yearbook on Science and Technology, China Statistical Yearbook, from periods 2010 to 2020.We calculated the increase rate.By analyzing data from Table1, 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.Figure 1
2010-2020 investment in scientific research.Figure 2
2010-2020 changes in the number of R&D employees, patents granted, and research projects.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.


## 3.2. Analysis of the Development of Higher Education

As we all know, human resources are the most valuable resources. 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.Table 2
2010-2020 higher education development.
YearNumber of faculty and staff in colleges and universitiesNumber of full-time faculty in higher educationNumber of external faculty in higher educationNumber of higher education schoolsNumber of books in the collectionNumber of teaching computersTeaching and research instruments and equipment assetsEducation expensesY1Y2Y3Y4Y5Y6Y7Y82010227184914068084139704356194250.58592623923233238.651650270652011227385114335794192884347203309.1829728825828161.671956184712012231998414796854315174424214857.7898746229442981.762386929362013235267915305124421764420225394.17972043732830625.182865530522014238864415660484623904411234848.421041000636494627.173036471822015242061816028114779734457243335.911106781540485282.473280646092016244790316271824988234486252753.221173388645003240.123612919272017248440316572385218634528260505.131233691749812055.733888838502018252557116946615388704541268588.031290552655134919.214256200692019260279317607865628004568277439.191331148660746398.744614299802020270118318519335936624618288008.561385131466648919.29501781166Increase rate18.90%31.64%43.41%6.01%48.27%133.73%186.87%204.06%The data were collected fromEducational Statistics Yearbook of China, from periods 2010 to 2020.We calculated the increase rate.By collating and analyzing the data from Table2, 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.Figure 3
2010-2020 number of full-time teachers and external teachers in colleges and universities.Figure 4
2010-2020 expenditure of research equipment assets and educational expenditure.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 low-carbon 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].


## 3.3. Analysis of the 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, based on reference of relevant literature, we assumed the data of solar energy, wind energy, nuclear energy, and other energy sources as the data source for studying clean energy.From periods 2010 to 2021, the scale, speed, and effect of CE development in China are remarkable. The specific data are shown in Table3.Table 3
2010-2020 clean energy development.
YearClean energy power generationInstalled capacity of clean energyPrimary energy productionProportion of clean energy to total energy consumptionProportion of clean energy to total energy productionInstalled nuclear power capacityNuclear power generationInstalled wind power capacityWind power generation capacityInstalled thermal power capacityThermal power generationZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z1120108406.72567231212513.414.51082738.82958446.27096733319.320118232291433179861313.7125787446237327683438975201210654.83268733028914.515.31257973.96142959.88196838928.12013121313875135878415.516.214661116.1765214128700942470.1201413529.64463736186616.918.220081325.496571560.89323242686.5201514868.35200936219317.819.327171707.9130751857.710055442841.9201616344.15906034595419.121.933642132.9147472370.710609444370.7201717431.76740135886720.522.837752480.7163252972.311049547546201818921.27558537885922.123.644662943.6184273659.711440850963.2201920588.28201139731723.324.648743483.5209154060.311895752201.5202021879.395538407295.224.325.649893662.5281654664.712462453302.5202124032107945418292.1725.526.45326407532871655812973956655Increase rate185.87%320.48%34.01%90.30%82.07%392.24%451.57%1011.26%1369.74%82.82%70.04%The data were collectedChina Energy Statistics Yearbook, China Statistics Yearbook, China Energy Big Data Report, from periods 2010 to 2020. We calculated the increase rate.By analyzing data from Table3, 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.Figure 5
2010-2021 clean energy, nuclear power, wind power, and thermal power generation.Figure 6
2010-2021 clean energy nuclear and wind and thermal power installed capacity.


## 4. Methods

The paper uses data fromChina 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.


### 4.1. 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 Table4.Table 4
Coupled model evaluation index system.
SubsystemFirst indexSecondary indexIndicatorsUnitDirection of actionScientific and technological innovationInvestment situationNational investment in R&DX1Billion yuan+Intensity of R&D expenditureX2%+State financial allocation for science and technologyX3Billion yuan+Foreign funds in national expenditure of R&D fundsX4Billion yuan+National R&D funding external expenditureX5Billion yuan+Talent situationTotal number of R&D employeesX6Person+R&D personnel full-time equivalentX7Million person/years+Scientific research resultsPatent for inventionX8Pieces+Published scientific and technical papersX9Million articles+R&D investmentNumber of R&D projects (topics) of scientific research institutionsX10Item+Number of high technology R&D institutionsX11Individual+Higher educationHuman resource inputNumber of faculty and staff in colleges and universitiesY1Person+Number of full-time faculty in higher educationY2Person+Number of external faculty in higher educationY3Person+Physical resource inputNumber of higher education schools (institutions)Y4Individual+Number of books in the collectionY5Million copies+Number of teaching computersY6Unit+Financial resource inputTeaching and research instruments and equipment assetsY7Million yuan+Education expensesY8Million yuan+Clean energyCurrent development statusClean energy power generationZ1Billion kWh+Installed capacity of clean energyZ2Million kw+Primary energy productionZ3Million tons of standard coal+Market scaleProportion of clean energy to total energy consumptionZ4%+Proportion of clean energy to total energy productionZ5%+Development of clean energy industryInstalled nuclear power capacityZ6Million kw+Nuclear power generationZ7Billion kwh+Installed wind power capacityZ8Million kw+Wind power generation capacityZ9Billion kwh+Traditional energy developmentInstalled thermal power capacityZ10Million kw—Thermal power generationZ11Billion kwh—We assumed thatX1~X11 as the indicators of STI subsystem, Y1~Y8 as the indicators of HE subsystem, and Z1~Z11 as the indicators of CE subsystem. We supposed that M representing numbers of index and N representing year, forming as original data matrix.


### 4.2. 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.


#### 4.2.1. 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].
(1)Xij′=xij−minxijmaxxij−minxij+0.001,(2)Xij′=maxxij−xijmaxxij−minxij+0.001.


#### 4.2.2. 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].Calculate contribution of thej year under the indicator Pij.
(3)Pij=xij′∑j=1nxij′.Calculate total contributionEi of all years to the index xi.
(4)Ei=−k∑j=1npijlnpij,k=1lnn.n refers to the number of years, 0≦Ei≦1.Calculate index weights of each subsystemWi.
(5)wi=1−Ei∑i=1m1−Ei.The values of the constructedWi weighting vectors lead to the weighting coefficients of STI, HE, and CE subsystems.
(6)w=wi⋯wmT.


#### 4.2.3. 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
(7)Fx=∑i=1m∂i×Xij,(8)Gy=∑i=1mβi×Yij,(9)Qz=∑i=1mγi×Zij.


### 4.3. Coupled Coordination Model

In coupled coordination model,C represents coupling degree between three systems with values between [0, 1] [23]. Calculation procedure is shown in equation (10). System coupling degree values and corresponding coupling levels are shown in Table 5.
(10)C=FxGyQzFx+Gy+Qz2.Table 5
System coupling degree values and corresponding coupling levels.
GradeCoupling degree valueCoupling level of the system1C=0Disorder2C∈0~0.3Low level3C∈0.3~0.5In antagonistic phase4C∈0.5~0.8In break-in phase5C∈0.8~1High level of coupling6C=1In completely ordered stateCoupling degreeC 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].
(11)D=C×T,(12)T=αFx+βGy+γQz.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.Table 6
Evaluation criteria of system coupling coordination level.
Coupling coordination valueDCoupling coordination levelD∈0~0.09Extreme disorderD∈0.1~0.19Severe disordersD∈0.20~0.29Moderate disorderD∈0.30~0.39Mild disordersD∈0.40~0.49On the verge of disorderD∈0.5~0.59Barely coordinatedD∈0.6~0.69Primary coordinationD∈0.7~0.79Intermediate coordinationD∈0.8~0.89Good coordinationD∈0.9~1.00Quality coordination


### 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(13)Xi⋯Xn=x11⋯x1n⋮⋮xm1⋯xmnStep 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.
(14)xi′k=xik1/m∑k=1mxik,Xi′⋯Xn′=x11′⋯x1n′⋮⋮xm1′⋯xmn′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]. x0k−xikk=1⋯,m,i=1⋯n, n is the number of evaluated objectsStep 4: calculate the number of gray correlation coefficients. Calculation process is shown in(15)ξik=mini=1mink=1x0k−xik+ρmaxi=1maxk=1x0k−xikx0k−xik+ρmaxi=1maxk=1x0k−xikIn 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].


## 4.1. 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 Table4.Table 4
Coupled model evaluation index system.
SubsystemFirst indexSecondary indexIndicatorsUnitDirection of actionScientific and technological innovationInvestment situationNational investment in R&DX1Billion yuan+Intensity of R&D expenditureX2%+State financial allocation for science and technologyX3Billion yuan+Foreign funds in national expenditure of R&D fundsX4Billion yuan+National R&D funding external expenditureX5Billion yuan+Talent situationTotal number of R&D employeesX6Person+R&D personnel full-time equivalentX7Million person/years+Scientific research resultsPatent for inventionX8Pieces+Published scientific and technical papersX9Million articles+R&D investmentNumber of R&D projects (topics) of scientific research institutionsX10Item+Number of high technology R&D institutionsX11Individual+Higher educationHuman resource inputNumber of faculty and staff in colleges and universitiesY1Person+Number of full-time faculty in higher educationY2Person+Number of external faculty in higher educationY3Person+Physical resource inputNumber of higher education schools (institutions)Y4Individual+Number of books in the collectionY5Million copies+Number of teaching computersY6Unit+Financial resource inputTeaching and research instruments and equipment assetsY7Million yuan+Education expensesY8Million yuan+Clean energyCurrent development statusClean energy power generationZ1Billion kWh+Installed capacity of clean energyZ2Million kw+Primary energy productionZ3Million tons of standard coal+Market scaleProportion of clean energy to total energy consumptionZ4%+Proportion of clean energy to total energy productionZ5%+Development of clean energy industryInstalled nuclear power capacityZ6Million kw+Nuclear power generationZ7Billion kwh+Installed wind power capacityZ8Million kw+Wind power generation capacityZ9Billion kwh+Traditional energy developmentInstalled thermal power capacityZ10Million kw—Thermal power generationZ11Billion kwh—We assumed thatX1~X11 as the indicators of STI subsystem, Y1~Y8 as the indicators of HE subsystem, and Z1~Z11 as the indicators of CE subsystem. We supposed that M representing numbers of index and N representing year, forming as original data matrix.


## 4.2. 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.


### 4.2.1. 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].
(1)Xij′=xij−minxijmaxxij−minxij+0.001,(2)Xij′=maxxij−xijmaxxij−minxij+0.001.


### 4.2.2. 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].Calculate contribution of thej year under the indicator Pij.
(3)Pij=xij′∑j=1nxij′.Calculate total contributionEi of all years to the index xi.
(4)Ei=−k∑j=1npijlnpij,k=1lnn.n refers to the number of years, 0≦Ei≦1.Calculate index weights of each subsystemWi.
(5)wi=1−Ei∑i=1m1−Ei.The values of the constructedWi weighting vectors lead to the weighting coefficients of STI, HE, and CE subsystems.
(6)w=wi⋯wmT.


### 4.2.3. 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
(7)Fx=∑i=1m∂i×Xij,(8)Gy=∑i=1mβi×Yij,(9)Qz=∑i=1mγi×Zij.


## 4.2.1. 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].
(1)Xij′=xij−minxijmaxxij−minxij+0.001,(2)Xij′=maxxij−xijmaxxij−minxij+0.001.


## 4.2.2. 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].Calculate contribution of thej year under the indicator Pij.
(3)Pij=xij′∑j=1nxij′.Calculate total contributionEi of all years to the index xi.
(4)Ei=−k∑j=1npijlnpij,k=1lnn.n refers to the number of years, 0≦Ei≦1.Calculate index weights of each subsystemWi.
(5)wi=1−Ei∑i=1m1−Ei.The values of the constructedWi weighting vectors lead to the weighting coefficients of STI, HE, and CE subsystems.
(6)w=wi⋯wmT.


## 4.2.3. 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
(7)Fx=∑i=1m∂i×Xij,(8)Gy=∑i=1mβi×Yij,(9)Qz=∑i=1mγi×Zij.


## 4.3. Coupled Coordination Model

In coupled coordination model,C represents coupling degree between three systems with values between [0, 1] [23]. Calculation procedure is shown in equation (10). System coupling degree values and corresponding coupling levels are shown in Table 5.
(10)C=FxGyQzFx+Gy+Qz2.Table 5
System coupling degree values and corresponding coupling levels.
GradeCoupling degree valueCoupling level of the system1C=0Disorder2C∈0~0.3Low level3C∈0.3~0.5In antagonistic phase4C∈0.5~0.8In break-in phase5C∈0.8~1High level of coupling6C=1In completely ordered stateCoupling degreeC 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].
(11)D=C×T,(12)T=αFx+βGy+γQz.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.Table 6
Evaluation criteria of system coupling coordination level.
Coupling coordination valueDCoupling coordination levelD∈0~0.09Extreme disorderD∈0.1~0.19Severe disordersD∈0.20~0.29Moderate disorderD∈0.30~0.39Mild disordersD∈0.40~0.49On the verge of disorderD∈0.5~0.59Barely coordinatedD∈0.6~0.69Primary coordinationD∈0.7~0.79Intermediate coordinationD∈0.8~0.89Good coordinationD∈0.9~1.00Quality coordination


## 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(13)Xi⋯Xn=x11⋯x1n⋮⋮xm1⋯xmnStep 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.
(14)xi′k=xik1/m∑k=1mxik,Xi′⋯Xn′=x11′⋯x1n′⋮⋮xm1′⋯xmn′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]. x0k−xikk=1⋯,m,i=1⋯n, n is the number of evaluated objectsStep 4: calculate the number of gray correlation coefficients. Calculation process is shown in(15)ξik=mini=1mink=1x0k−xik+ρmaxi=1maxk=1x0k−xikx0k−xik+ρmaxi=1maxk=1x0k−xikIn 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].


## 5. Empirical Analysis

We select data about STI, HE, and CE systems from periods 2010 to 2020 for empirical analysis.


### 5.1. Stage 1: Collation of Evaluation Index Data

The original values of indicatorsX1‐X11 for STI system, Y1‐Y8 for HE system, and Z1‐Z11 for CE system are shown in Tables 1–3.


### 5.2. Stage 2: Calculate Indicator Weights and Comprehensive Evaluation Values

We used equations (1)–(5) to calculate indicator weights and used equations (7)–(9) to calculate comprehensive evaluation values. Calculation results are shown in Tables 7–10.Table 7
2010-2020 STI system indicator weights.
Entropy methodItemIndicatorInformation entropy valueInformation utility valueWeight (%)National investment in R&DX10.8910.1099.426Intensity of R&D expenditureX20.9120.0887.585State financial allocation for science and technologyX30.8930.1079.208Foreign funds in national expenditure of R&D fundsX40.9540.0463.935National R&D funding external expenditureX50.910.097.751Total number of R&D employeesX60.8110.18916.337R&D personnel full-time equivalentX70.9110.0897.719Patent for inventionX80.8590.14112.168Published scientific and technical papersX90.8840.11610.031Number of R&D projects of scientific research institutionsX100.8880.1129.656Number of high technology R&D institutionsX110.9280.0726.182We calculated the results by SPSS.Table 8
2010-2020 HE system indicator weights.
Entropy methodItemIndicatorInformation entropy valueInformation utility valueWeight (%)Number of faculty and staff in colleges and universitiesY10.850.1516.399Number of full-time faculty in higher educationY20.8880.11212.255Number of external faculty in higher educationY30.8510.14916.222Number of higher education schools (institutions)Y40.8810.11912.927Number of books in the collectionY50.9020.09810.675Number of teaching computersY60.9350.0657.044Teaching and research instruments and equipment assetsY70.8740.12613.756Education expensesY80.9020.09810.722We calculated the results by SPSS.Table 9
2010-2020 CE system indicator weights.
Entropy methodItemIndicatorInformation entropy valueInformation utility valueWeight (%)Clean energy power generationZ10.8770.1238.88Installed capacity of clean energyZ20.8620.1389.959Primary energy productionZ30.8960.1047.487Proportion of clean energy/total energy consumptionZ40.8680.1329.498Proportion of clean energy/total energy productionZ50.8760.1248.925Installed nuclear power capacityZ60.830.1712.195Nuclear power generationZ70.8370.16311.746Installed wind power capacityZ80.8730.1279.109Wind power generation capacityZ90.8670.1339.573Installed thermal power capacityZ100.90.17.188Thermal power generationZ110.9240.0765.439We calculated the results by SPSS.Table 10
2010-2020 comprehensive evaluation value of STI, HE, and CE systems.
YearComprehensive evaluation value of science and technology innovation systemComprehensive evaluation value of higher education systemComprehensive evaluation value of clean energy system20100.09100.00440.010620110. 16330.06240.056320120.26430. 18490. 132820130.31840.26350.233020140.36690.33500.324520150.45470.42920.426620160.51640.52050.530420170.59760.62180.638720180.67140.71210.773120190.75100.83720.887220200.91820.99990.9999We calculated the results by SPSS.


### 5.3. Stage 3: Calculate Degree of Coupling and Coordination

We calculated coupling degreeC 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.Table 11
Coupled coordination model calculation results.
YearCoupling degreeCCoordination indexTCoupling coordination degreeDCoordination levelLevel of coupling coordination20101.00000.01000.10002Severe disorders20110.97410.07270.26603Moderate disorder20120.97920.17800.41755On the verge of disorder20130.99670.25830.50746Barely coordinated20140.99980.33110.57536Barely coordinated20150.99980.43040.65607Primary coordination20161.00000.51900.72048Intermediate coordination20170.99990.62000.78748Intermediate coordination20180.99920.72330.85019Good coordination20190.99910.83340.912510Quality coordination20201.00000.99000.995010Quality coordinationWe calculated the results by SPSS.By analyzing data and charts from Table11 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 series of policies to ecological protection, sustainable development, and energy transition 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.Figure 7
2010-2020 data of indicators related to coupling coordination degree.


### 5.4. 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 Tables12 and 13.Table 12
Number of gray correlation.
YearNational investment in R&DIntensity of R&D expenditureState financial allocation for science and technologyForeign funds expenditure of R&DNational R&D funding external expenditureTotal number of R&D employeesR&D personnel full-time equivalentPatent for inventionPublished scientific and technical papersNumber of R&D projects of scientific research institutionsNumber of high technology R&D institutionsX1X2X3X4X5X6X7X8X9X10X1120100.6650.4730.6000.4270.6700.5650.5480.6760.7070.5410.40920110.8050.5810.7350.4310.8800.6800.6550.8530.8610.6790.50720120.9830.7050.8780.6080.9030.8030.7810.9600.9910.8250.62720130.9540.7990.9690.6650.7660.8890.8580.8780.7730.9350.74220140.9090.9190.9220.7390.7140.9590.9420.7530.7441.0000.84820150.8420.9180.8510.9110.6630.8860.8870.8610.9320.9270.95920160.8380.8140.8490.9060.7190.8360.8020.7690.8860.8150.81220170.8620.7200.8220.9050.8520.7930.7370.7140.9690.8250.69620180.9090.6500.8850.5000.8490.7560.7280.9920.9350.7760.58320191.0000.6130.9730.3340.7310.7550.7350.9600.8850.7550.52020200.9730.5830.7220.4860.5840.7050.7180.5900.9650.6810.457We calculated the results by SPSS.Table 13
Gray correlation ranking of STI indicators and coupling coordination degree.
Relevance resultsEvaluation itemsIndicatorDegree of relevanceRanksNational investment in R&DX10.8861Published scientific and technical papersX90.8772State financial allocation for science and technologyX30.8373Patent for inventionX80.8194Number of R&D projects (topics) of scientific research institutionsX100.7965Total number of R&D employeesX60.7846R&D personnel full-time equivalentX70.7637National R&D funding external expenditureX50.7578Intensity of R&D expenditureX20.7079Number of high technology R&D institutionsX110.65110Foreign funds in national expenditure of R&D fundsX40.62811We calculated the results by SPSS.Figure8 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 X1 (correlation: 0.886), published scientific and technological papers X9 (correlation: 0.877), state financial allocation for science and technology X3 (correlation: 0.837), and patent for invention (correlation: 0.819).Figure 8
Correlation coefficient chart of STI indicators andD.Using the same method, we calculated gray correlations of HE system and coupling coordination. Calculation results are shown in Tables14 and 15.Table 14
Number of gray correlation of HE indicators and coupling coordination degree.
YearNumber of faculty and staff in colleges and universitiesNumber of full-time faculty in higher educationNumber of external faculty in higher educationNumber of higher education schoolsNumber of books in the collectionNumber of teaching computersTeaching and research instruments and equipment assetsEducation expensesY1Y2Y3Y4Y5Y6Y7Y820100.3590.3760.3860.3460.4030.5320.5320.57020110.4650.4850.5060.4450.5180.5670.7210.74920120.6190.6460.6870.5870.6840.7490.9930.94320130.7680.7820.8600.7370.8200.8690.9320.94720140.9290.9341.0000.9160.9510.9600.8860.99320150.8880.9020.8630.8950.9090.9540.8230.87520160.7430.7570.7670.7410.7970.8710.8270.86720170.6380.6520.6900.6310.6960.7850.8350.82420180.5650.5820.6220.5500.6260.7200.8770.83820190.5170.5380.5780.4890.5710.6500.9380.85020200.4640.4900.5270.4290.5090.5760.9500.829We calculated the results by SPSS.Table 15
Gray correlation ranking of HE indicators andD.
Relevance resultsEvaluation itemsIndicatorDegree of relevanceRanksTeaching and research instruments and equipment assetsY70.8471Education expensesY80.8442Number of teaching computersY60.7483Number of books in the collectionY50.6814Number of external faculty in higher educationY30.6815Number of full-time faculty in higher educationY20.6496Number of faculty and staff in colleges and universitiesY10.6327Number of higher education schools (institutions)Y40.6158We calculated the results by SPSS.Figure9 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 Y7 (correlation: 0.847), and education expenses Y8 (correlation: 0.844).Figure 9
Correlation coefficient chart of HE indicators andD.Using the same method, we calculated gray correlations of CE system and coupling coordination. Calculation results are shown in Tables16 and 17.Table 16
Number of gray correlation of CE indicators and coupling coordination degree.
YearClean energy power generationInstalled capacity of clean energyPrimary energy productionProportion of clean energy to total energy consumptionProportion of clean energy to total energy productionInstalled nuclear power capacityNuclear power generationInstalled wind power capacityWind power generation capacityInstalled thermal power capacityThermal power generationZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z1120100.4700.5410.3360.3850.3840.6230.6250.8490.9120.3920.37920110.7460.7840.4400.5610.5740.9760.9600.8320.7770.5090.44520120.8990.8220.5930.7540.7790.6070.6720.6410.5920.7020.64220130.9970.7610.6660.9320.9960.5410.5910.6090.6520.8620.72820140.9550.7570.8220.9930.9950.6160.5890.6590.6040.9720.92720150.8620.7630.8830.8100.8240.7740.6600.8720.6050.8960.79220160.8520.8050.6450.7540.8810.9620.8350.9270.7660.8000.68620170.7860.8920.5700.7070.7630.8770.9970.9590.8870.7000.64020180.7851.0000.5320.6880.6760.6510.7330.8980.5890.6240.61320190.8050.9570.4950.6440.6170.6040.5390.7320.5220.5700.54520200.7280.7340.4320.5640.5400.7170.5750.3910.4360.5080.46820210.4700.5410.3360.3850.3840.6230.6250.8490.9120.3920.379We calculated the results by SPSS.Table 17
Gray correlation ranking of CE indicators andD.
Relevance resultsEvaluation itemsIndicatorDegree of relevanceRanksClean energy power generationZ10.8081Installed capacity of clean energyZ20.8012Installed wind power capacityZ80.7613Proportion of clean energy to total energy productionZ50.734Installed nuclear power capacityZ60.7235Proportion of clean energy to total energy consumptionZ40.7086Nuclear power generationZ70.7077Installed thermal power capacityZ100.6858Wind power generation capacityZ90.6689Thermal power generationZ110.62410Primary energy productionZ30.58311We calculated the results by SPSS.Figure10 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 can conclude that indicators with correlations more than 0.8 are clean energy power generation Z1 (correlation: 0.808) and installed capacity of clean energy Z2 (correlation: 0.801).Figure 10
Correlation coefficient chart of CE indicators andD.


## 5.1. Stage 1: Collation of Evaluation Index Data

The original values of indicatorsX1‐X11 for STI system, Y1‐Y8 for HE system, and Z1‐Z11 for CE system are shown in Tables 1–3.


## 5.2. Stage 2: Calculate Indicator Weights and Comprehensive Evaluation Values

We used equations (1)–(5) to calculate indicator weights and used equations (7)–(9) to calculate comprehensive evaluation values. Calculation results are shown in Tables 7–10.Table 7
2010-2020 STI system indicator weights.
Entropy methodItemIndicatorInformation entropy valueInformation utility valueWeight (%)National investment in R&DX10.8910.1099.426Intensity of R&D expenditureX20.9120.0887.585State financial allocation for science and technologyX30.8930.1079.208Foreign funds in national expenditure of R&D fundsX40.9540.0463.935National R&D funding external expenditureX50.910.097.751Total number of R&D employeesX60.8110.18916.337R&D personnel full-time equivalentX70.9110.0897.719Patent for inventionX80.8590.14112.168Published scientific and technical papersX90.8840.11610.031Number of R&D projects of scientific research institutionsX100.8880.1129.656Number of high technology R&D institutionsX110.9280.0726.182We calculated the results by SPSS.Table 8
2010-2020 HE system indicator weights.
Entropy methodItemIndicatorInformation entropy valueInformation utility valueWeight (%)Number of faculty and staff in colleges and universitiesY10.850.1516.399Number of full-time faculty in higher educationY20.8880.11212.255Number of external faculty in higher educationY30.8510.14916.222Number of higher education schools (institutions)Y40.8810.11912.927Number of books in the collectionY50.9020.09810.675Number of teaching computersY60.9350.0657.044Teaching and research instruments and equipment assetsY70.8740.12613.756Education expensesY80.9020.09810.722We calculated the results by SPSS.Table 9
2010-2020 CE system indicator weights.
Entropy methodItemIndicatorInformation entropy valueInformation utility valueWeight (%)Clean energy power generationZ10.8770.1238.88Installed capacity of clean energyZ20.8620.1389.959Primary energy productionZ30.8960.1047.487Proportion of clean energy/total energy consumptionZ40.8680.1329.498Proportion of clean energy/total energy productionZ50.8760.1248.925Installed nuclear power capacityZ60.830.1712.195Nuclear power generationZ70.8370.16311.746Installed wind power capacityZ80.8730.1279.109Wind power generation capacityZ90.8670.1339.573Installed thermal power capacityZ100.90.17.188Thermal power generationZ110.9240.0765.439We calculated the results by SPSS.Table 10
2010-2020 comprehensive evaluation value of STI, HE, and CE systems.
YearComprehensive evaluation value of science and technology innovation systemComprehensive evaluation value of higher education systemComprehensive evaluation value of clean energy system20100.09100.00440.010620110. 16330.06240.056320120.26430. 18490. 132820130.31840.26350.233020140.36690.33500.324520150.45470.42920.426620160.51640.52050.530420170.59760.62180.638720180.67140.71210.773120190.75100.83720.887220200.91820.99990.9999We calculated the results by SPSS.


## 5.3. Stage 3: Calculate Degree of Coupling and Coordination

We calculated coupling degreeC 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.Table 11
Coupled coordination model calculation results.
YearCoupling degreeCCoordination indexTCoupling coordination degreeDCoordination levelLevel of coupling coordination20101.00000.01000.10002Severe disorders20110.97410.07270.26603Moderate disorder20120.97920.17800.41755On the verge of disorder20130.99670.25830.50746Barely coordinated20140.99980.33110.57536Barely coordinated20150.99980.43040.65607Primary coordination20161.00000.51900.72048Intermediate coordination20170.99990.62000.78748Intermediate coordination20180.99920.72330.85019Good coordination20190.99910.83340.912510Quality coordination20201.00000.99000.995010Quality coordinationWe calculated the results by SPSS.By analyzing data and charts from Table11 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 series of policies to ecological protection, sustainable development, and energy transition 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.Figure 7
2010-2020 data of indicators related to coupling coordination degree.


## 5.4. 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 Tables12 and 13.Table 12
Number of gray correlation.
YearNational investment in R&DIntensity of R&D expenditureState financial allocation for science and technologyForeign funds expenditure of R&DNational R&D funding external expenditureTotal number of R&D employeesR&D personnel full-time equivalentPatent for inventionPublished scientific and technical papersNumber of R&D projects of scientific research institutionsNumber of high technology R&D institutionsX1X2X3X4X5X6X7X8X9X10X1120100.6650.4730.6000.4270.6700.5650.5480.6760.7070.5410.40920110.8050.5810.7350.4310.8800.6800.6550.8530.8610.6790.50720120.9830.7050.8780.6080.9030.8030.7810.9600.9910.8250.62720130.9540.7990.9690.6650.7660.8890.8580.8780.7730.9350.74220140.9090.9190.9220.7390.7140.9590.9420.7530.7441.0000.84820150.8420.9180.8510.9110.6630.8860.8870.8610.9320.9270.95920160.8380.8140.8490.9060.7190.8360.8020.7690.8860.8150.81220170.8620.7200.8220.9050.8520.7930.7370.7140.9690.8250.69620180.9090.6500.8850.5000.8490.7560.7280.9920.9350.7760.58320191.0000.6130.9730.3340.7310.7550.7350.9600.8850.7550.52020200.9730.5830.7220.4860.5840.7050.7180.5900.9650.6810.457We calculated the results by SPSS.Table 13
Gray correlation ranking of STI indicators and coupling coordination degree.
Relevance resultsEvaluation itemsIndicatorDegree of relevanceRanksNational investment in R&DX10.8861Published scientific and technical papersX90.8772State financial allocation for science and technologyX30.8373Patent for inventionX80.8194Number of R&D projects (topics) of scientific research institutionsX100.7965Total number of R&D employeesX60.7846R&D personnel full-time equivalentX70.7637National R&D funding external expenditureX50.7578Intensity of R&D expenditureX20.7079Number of high technology R&D institutionsX110.65110Foreign funds in national expenditure of R&D fundsX40.62811We calculated the results by SPSS.Figure8 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 X1 (correlation: 0.886), published scientific and technological papers X9 (correlation: 0.877), state financial allocation for science and technology X3 (correlation: 0.837), and patent for invention (correlation: 0.819).Figure 8
Correlation coefficient chart of STI indicators andD.Using the same method, we calculated gray correlations of HE system and coupling coordination. Calculation results are shown in Tables14 and 15.Table 14
Number of gray correlation of HE indicators and coupling coordination degree.
YearNumber of faculty and staff in colleges and universitiesNumber of full-time faculty in higher educationNumber of external faculty in higher educationNumber of higher education schoolsNumber of books in the collectionNumber of teaching computersTeaching and research instruments and equipment assetsEducation expensesY1Y2Y3Y4Y5Y6Y7Y820100.3590.3760.3860.3460.4030.5320.5320.57020110.4650.4850.5060.4450.5180.5670.7210.74920120.6190.6460.6870.5870.6840.7490.9930.94320130.7680.7820.8600.7370.8200.8690.9320.94720140.9290.9341.0000.9160.9510.9600.8860.99320150.8880.9020.8630.8950.9090.9540.8230.87520160.7430.7570.7670.7410.7970.8710.8270.86720170.6380.6520.6900.6310.6960.7850.8350.82420180.5650.5820.6220.5500.6260.7200.8770.83820190.5170.5380.5780.4890.5710.6500.9380.85020200.4640.4900.5270.4290.5090.5760.9500.829We calculated the results by SPSS.Table 15
Gray correlation ranking of HE indicators andD.
Relevance resultsEvaluation itemsIndicatorDegree of relevanceRanksTeaching and research instruments and equipment assetsY70.8471Education expensesY80.8442Number of teaching computersY60.7483Number of books in the collectionY50.6814Number of external faculty in higher educationY30.6815Number of full-time faculty in higher educationY20.6496Number of faculty and staff in colleges and universitiesY10.6327Number of higher education schools (institutions)Y40.6158We calculated the results by SPSS.Figure9 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 Y7 (correlation: 0.847), and education expenses Y8 (correlation: 0.844).Figure 9
Correlation coefficient chart of HE indicators andD.Using the same method, we calculated gray correlations of CE system and coupling coordination. Calculation results are shown in Tables16 and 17.Table 16
Number of gray correlation of CE indicators and coupling coordination degree.
YearClean energy power generationInstalled capacity of clean energyPrimary energy productionProportion of clean energy to total energy consumptionProportion of clean energy to total energy productionInstalled nuclear power capacityNuclear power generationInstalled wind power capacityWind power generation capacityInstalled thermal power capacityThermal power generationZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z1120100.4700.5410.3360.3850.3840.6230.6250.8490.9120.3920.37920110.7460.7840.4400.5610.5740.9760.9600.8320.7770.5090.44520120.8990.8220.5930.7540.7790.6070.6720.6410.5920.7020.64220130.9970.7610.6660.9320.9960.5410.5910.6090.6520.8620.72820140.9550.7570.8220.9930.9950.6160.5890.6590.6040.9720.92720150.8620.7630.8830.8100.8240.7740.6600.8720.6050.8960.79220160.8520.8050.6450.7540.8810.9620.8350.9270.7660.8000.68620170.7860.8920.5700.7070.7630.8770.9970.9590.8870.7000.64020180.7851.0000.5320.6880.6760.6510.7330.8980.5890.6240.61320190.8050.9570.4950.6440.6170.6040.5390.7320.5220.5700.54520200.7280.7340.4320.5640.5400.7170.5750.3910.4360.5080.46820210.4700.5410.3360.3850.3840.6230.6250.8490.9120.3920.379We calculated the results by SPSS.Table 17
Gray correlation ranking of CE indicators andD.
Relevance resultsEvaluation itemsIndicatorDegree of relevanceRanksClean energy power generationZ10.8081Installed capacity of clean energyZ20.8012Installed wind power capacityZ80.7613Proportion of clean energy to total energy productionZ50.734Installed nuclear power capacityZ60.7235Proportion of clean energy to total energy consumptionZ40.7086Nuclear power generationZ70.7077Installed thermal power capacityZ100.6858Wind power generation capacityZ90.6689Thermal power generationZ110.62410Primary energy productionZ30.58311We calculated the results by SPSS.Figure10 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 can conclude that indicators with correlations more than 0.8 are clean energy power generation Z1 (correlation: 0.808) and installed capacity of clean energy Z2 (correlation: 0.801).Figure 10
Correlation coefficient chart of CE indicators andD.


## 6. 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 low-carbon technology leaders and innovation teams. Secondly, in the key fields of green and low-carbon 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 low-carbon talent team, and contribute China’s scientific and technological strength to global green low-carbon development



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*Source: 9122778-2023-03-03.xml*