Improving the Regional Science and Technology Innovation Ability under the Background of Data Element Marketization: Insights from the Yunnan Province

Abstract

Under the background of the marketization of data elements, enhancing regional scientific and technological innovation capabilities has become the main driving force for urban sustainable development. It contributes to the sustainable development of the region. Based on the data of the Yunnan statistical yearbook, we built a regional science and technology innovation index system from three dimensions of science and technology innovation input, output, and benefit. Firstly, the entropy method is used to obtain the weight of each index. Secondly, TOPSIS is used to evaluate the 36 index data of each prefecture. Finally, the scores and rankings of the scientific and technological innovation ability of each prefecture are obtained. The research can effectively find the advantages and disadvantages of scientific and technological innovation in the Yunnan Province, provide data support and decision support for regional economic development, and play an important role in promoting the level of technological innovation in modern industries. At the same time, research projects can help various regions allocate various technological innovation resources reasonably, enhance their original innovation capabilities, lead the development of strategic emerging industries and future industries, and accelerate the formation of new quality productivity.

1. Introduction

Regional technological innovation can measure the level of technological development, management, and innovation capability of a region. Evaluating the technological innovation capability of a region can reveal the strength of its technological innovation capability and identify the shortcomings of technological innovation in each region. This can provide data support and decision-making support for enhancing regional economic development, thereby promoting the construction of scientific and technological innovation capabilities and promoting major scientific and technological breakthroughs in the region. At the same time, it helps to form a regional scientific and technological innovation community, promoting an efficient and coordinated development of scientific and technological innovation entities. The evaluation of regional scientific and technological innovation has important practical significance for the open sharing and efficient allocation of scientific and technological resources, as well as for improving the level of technological innovation in modern industries [1].

The Yunnan Province is located in the southwest of China, with relatively backward economic development. Its pillar industries include green energy, biomedicine, plateau characteristic agriculture, tourism culture, and tobacco. The Yunnan Province has always attached great importance to technological innovation, and has continuously increased its investment in science and technology. Taking 2023 as an example, the budget for the Yunnan Province’s science and technology plan projects reached CNY 3.4 billion, which is twice that of 2018. The research and development funds of the whole society in the province reached CNY 31.353 billion, ranking 19th in the country. The R&D investment intensity is 1.08%, ranking 23rd in the country. The significant increase in financial and technological investment has provided strong support for the Yunnan Province to deeply implement the innovation-driven development strategy, and accelerate the construction of an innovative Yunnan.

Although fiscal investment continues to increase, there are still problems such as regional input/output imbalance and lagging technological innovation. When technology investment reaches a certain level, it often faces development bottlenecks in multiple aspects, such as industrial structure, factor support, and innovation drive. How to break through the bottleneck of regional development and comprehensively shape the driving mechanism of technological innovation growth, in order to achieve a high-quality, sustainable, and prosperous development of technological innovation, is a common and key issue faced by various regions in the development process [2].

Regional innovation is a key driving force for regional development, an important force for a country and nation to move forward, and an important driving force for social progress [3]. There is a significant gap in regional development in China, with the eastern region maintaining the strongest level of innovation capability and the central and western regions being relatively weaker. However, overall, innovation has become the primary driving force for regional economic development. Regional innovation is the source of sustainable development of a regional economy and the core driving force for achieving surpassing growth in the region.

Establishing a regional innovation evaluation mechanism can objectively reflect the strength of regional innovation, and promote the rapid and coordinated development of a regional economy [4,5]. This paper studies various factors that affect regional scientific and technological innovation in the Yunnan Province from an empirical analysis perspective, establishes an indicator system for evaluating scientific and technological innovation capabilities, and ranks and analyzes the scientific and technological innovation capabilities of various prefectures in the Yunnan Province. The moisture value method used in this article can objectively set the weight of indicators, while the TOPSIS method can objectively obtain the ranking of each prefecture. The proposed theory and method are progressiveness.

The remainder of this paper is organized as follows: Section 2 presents related works. Evaluation indicators for the regional scientific and technological innovation capability are given in Section 3. Section 4 presents the methods. Data sources and an empirical analysis are described in Section 5. Section 6 presents a conclusion and recommendations.

2. Related Works

Technological innovation is the main driving force for social progress and national development. Regional technological innovation is closely linked to sustainable development and is a key driving force for promoting regional sustainable development. Through technological and industrial innovation, regional innovation can cultivate new economic growth points and enhance economic competitiveness; through innovative means such as green technology and clean energy, environmental issues in regional development can be effectively addressed, achieving coordinated economic and environmental development; through institutional innovation and public service innovation, the quality of life of residents can be improved and regional sustainable development can be enhanced. Many scholars have conducted comprehensive research on technological innovation and sustainable development. Meramweliotakis [6,7] proposed the grand theory to explore the relationship between history, knowledge, and sustainable development. Mensah [8] conducted a literature review on multiple factors that affect sustainable development. Ding [9] elaborated on the relationship between the digital economy, technological innovation, and high-quality economic development. Wang [10] elaborated on the impact of tourism on China’s sustainable development.

The technological innovation capability reflects the economic vitality and development potential of a region. Evaluating regional scientific and technological innovation capabilities can better identify the current status of scientific and technological innovation, identify deficiencies in regional scientific and technological innovation capabilities, and help optimize the allocation of scientific and technological resources and improve efficiency [11,12]. Many experts have conducted research on technological innovation capabilities, focusing mainly on three aspects: (1) evaluation methods for technological innovation capabilities, (2) evaluation of research objects, and (3) evaluation indicators.

2.1. Evaluation Methods for Technological Innovation Capability

At present, there are various evaluation methods in the academic community for regional scientific and technological innovation capabilities, mainly including principal component analysis, cluster analysis, factor analysis, TOPSIS analysis, analytic hierarchy process, grey relational analysis, data envelopment analysis, fuzzy mathematics, and RBF neural network. Bawuerjiang et al. used the principal component analysis and cluster analysis methods to analyze the regional scientific and technological innovation capabilities of 30 provinces and cities in mainland China, and obtained the strength of the scientific and technological innovation capabilities in each region [13]. Xiong et al. used the factor analysis and TOPSIS analysis methods to compare and analyze the technological innovation capabilities of various provinces in the Pan Pearl River Delta from both horizontal and vertical perspectives [14]. Wu et al. used the analytic hierarchy process to evaluate the innovation capability of the six central provinces in China, and obtained scores and rankings for the innovation capability of each province [15]. Zhu et al. constructed a regional scientific and technological innovation evaluation index system, and used the grey correlation analysis method to conduct a horizontal comparative analysis of the scientific and technological innovation capabilities of Guangxi and 29 provinces in China [16]. Kou et al. used the data envelopment analysis method to evaluate the innovation capability of science and technology parks in the Beijing Tianjin Hebei, Yangtze River Delta, and Pearl River Delta regions [17]. Pang et al. used fuzzy mathematical methods to analyze the ranking and position of the Hebei Province’s scientific and technological innovation capability in the surrounding eight provinces [18]. Jia et al. used the RBP neural network method to evaluate the scientific and technological innovation capabilities of 16 counties in the Henan Province [19]. Musa [20] elaborated on the competitiveness of technological innovation capability from the perspective of knowledge sharing. AL Khat [21] analyzed the prerequisites for technological innovation from a dynamic perspective. Eriksson et al. [22] elaborated on the innovation of B2B industrial companies from a data-driven perspective. Da Silva [23], Chakravarty [24], Chatterjee [25], Bloem [26], and others have also studied technological innovation capabilities using different methods. From the above literature, it can be seen that there are various methods for evaluating technological innovation capabilities, but many of them are subjective and there are relatively few objective evaluation methods.

2.2. Research Objects for Evaluating Technological Innovation Capabilities

At present, the academic research on scientific and technological innovation capabilities mainly focuses on vertical or horizontal comparisons of 31 provinces in China. There are also studies comparing and analyzing the scientific and technological innovation capabilities of China and other countries around the world, in order to obtain scores and rankings of scientific and technological innovation capabilities in each region, analyze the strength of scientific and technological innovation capabilities in each region, and propose effective strategies. Shi et al. evaluated and analyzed the scientific and technological innovation activities of universities in six central provinces (Shanxi, Henan, Hubei, Hunan, Anhui, and Jiangxi) [27]. Asta et al. conducted a comparative analysis of the technological innovation capabilities of the cultural industry in 29 provinces in China (excluding Hong Kong, Macao, and Taiwan) [28]. Liang et al. conducted a comparative analysis of the scientific and technological innovation capabilities of agriculture in 31 provinces in China, and classified them according to regions with strong agricultural scientific and technological innovation capabilities, medium regions, and cross regions [29]. Wu et al. evaluated and analyzed the changes in the technological innovation efficiency index and spatiotemporal differences between China and 10 countries around the world (the United States, Japan, Germany, the United Kingdom, France, Canada, Italy, Russia, South Korea, and India) [30]. Sang et al. conducted a comparative analysis of the scientific and technological innovation capabilities of Yunnan universities from 2008 to 2017 [31]. Cao et al. evaluated and analyzed the technological innovation capabilities of 16 cities in the Anhui Province [32]. From the above literature, it can be seen that research on the evaluation of technological innovation capabilities has been carried out around the world. How to evaluate the technological innovation capabilities of various places, optimize the capabilities of various elements of technological innovation, improve technological innovation capabilities, and provide suggestions for local sustainable development has become the top priority for scholars and local governments.

2.3. Construction of Evaluation Index System for Technological Innovation Capability

A systematic and scientific evaluation index system is a very important part of the evaluation of scientific and technological innovation capabilities. Scholars in the academic community have established an evaluation index system from the aspects of scientific and technological innovation input, output, and benefits. Jia et al. evaluated the scientific and technological innovation capability of the Shandong Province based on four primary indicators, namely the ability to initiate, implement, transform, and support the environment, from the perspectives of human and financial resources, innovation platforms, knowledge output, economic output, economic benefits, ecological benefits, social benefits, educational resources, financial support, and social informatization [33]. Scholars have also evaluated the regional scientific and technological innovation capability of Hefei City, Anhui Province, based on five primary indicators of basic scientific and technological innovation capability, input capability, output capability, independent innovation capability, and efficiency capability, from the aspects of material and regional foundation, human and financial investment, scientific and technological achievements, technological innovation, economic development, social life, and environmental protection [34]. Cassia [35] evaluated the relationship between knowledge sharing and technological innovation capability. Fakhimi [36] studied the factors influencing the effectiveness of technological innovation from a technological catch-up perspective. Malik [37] analyzed the drivers for radical innovation in the context of archaeological uncertainty from the perspective of indicator systems. Jenatabadi [38] analyzed the factors that boost the technological capability of the Malaysian food manufacturing industry. From the above literature, it can be seen that the construction of technological innovation capability indicator systems varies in different regions, industries, and scopes. There is no completely unified indicator system. At the same time, the construction of the indicator system is relatively targeted and cannot comprehensively and objectively reflect the local technological innovation capability.

2.4. Summary

At present, scholars mainly use methods such as factor analysis, TOPSIS, and principal component analysis to study China’s provinces, regions, and cities around indicators such as technological innovation input, output, and benefits. However, the indicator system constructed has relatively low coverage, pertinence, and comprehensiveness, and there is little research on various states in the Yunnan Province, China. This paper uses the entropy method and TOPSIS method to compare and analyze the scientific and technological innovation capabilities of 16 regions in the Yunnan Province from three perspectives: scientific and technological innovation input, scientific and technological innovation output, and scientific and technological innovation benefits. The scores and rankings of scientific and technological innovation capabilities in each region are obtained. And, further, we propose effective strategies, which will help discover the current situation of the Yunnan Province’s scientific and technological innovation capabilities, and promote social and economic development.

3. Evaluation Indicators for Regional Scientific and Technological Innovation Capability

The regional technological innovation capability refers to the ability of technological innovation entities to utilize various technological innovation resources, carry out various technological innovation activities, and produce various technological innovation achievements, new products, and new technologies on the basis of technological innovation investment. It will ultimately achieve technological innovation benefits, promote regional economic development, strengthen environmental protection, and improve people’s quality of life. The main body of technological innovation includes universities, enterprises, research institutions, and governments. Technological innovation investment includes human, financial, and material resources. Technological innovation resources include information, knowledge, labor, and capital. Technological innovation activities include project research and development and product design. To evaluate the technological innovation capability of a region, a complete indicator system needs to be constructed.

Therefore, according to references [39,40], we follow the principles of systematicity, comprehensiveness, effectiveness, operability, and data availability to select indicators and construct a regional scientific and technological innovation evaluation system, which includes 3 primary indicators, 8 secondary indicators, and 36 tertiary indicators. The details are follows in

Table 1. An evaluation index system for regional scientific and technological innovation capability in the Yunnan Province.

Dimension	Sub-Dimension	Indicators	Code	Attribution
Technological innovation investment	Human input	R&D personnel	X1	positive
		R&D personnel full-time equivalent	X2	positive
		Senior technical personnel	X3	positive
		Intermediate technical personnel	X4	positive
	Financial investment	R&D expenses (CNY 100 million)	X5	positive
		Local general public budget expenditure (CNY 100 million)	X6	positive
	Material investment	Public libraries	X7	positive
		Public Library Collection	X8	positive
		Cultural stations	X9	positive
		Museums	X10	positive
		Number of R&D institutions	X11	positive
Technological innovation output	Scientific and technological achievements	Number of R&D projects	X12	positive
		Number of patent applications	X13	positive
		Number of invention patents	X14	positive
		Number of valid invention patents	X15	positive
	New product	Number of new product projects	X16	positive
		New product output value (CNY 100 million)	X17	positive
		New product sales revenue (CNY 100 million)	X18	positive
Technological innovation benefits	Economic development	GDP (CNY 100 million)	X19	positive
		Per capita GDP (CNY/person)	X20	positive
		Average salary of employees (10,000 CNY/person)	X21	positive
		Total industrial output value (CNY 100 million)	X22	positive
		Total import and export volume (USD 100 million)	X23	positive
		Total revenue of tourism industry (CNY 100 million)	X24	positive
		Key service industry revenue (CNY 100 million)	X25	positive
	Environmental protection	Green coverage area (square kilometers)	X26	positive
		Urban sewage treatment rate (%)	X27	positive
		Disposal rate of general industrial solid waste (%)	X28	positive
		Industrial exhaust gas emissions (billion cubic meters)	X29	negative
		Days of air quality reaching or better than Level 2 (days)	X30	positive
		Air Quality Composite Index (mg/m3)	X31	negative
	Social life	Number of villages benefiting from tap water	X32	positive
		Number of villages connected to cable TV	X33	positive
		Number of villages with broadband access	X34	positive
		Rural electricity consumption (billion kilowatt hours)	X35	positive
		Private vehicle ownership (10,000 units)	X36	positive

.

4. Methods

4.1. Entropy Method

The entropy method can objectively reflect the information contained in the data. According to information theory knowledge, entropy is a measure of data uncertainty. The larger the amount of information, the smaller the entropy value; the smaller the amount of information, the greater the entropy value. By using the entropy method, the weights of each indicator in the object to be evaluated can be calculated, providing a basis for multi-objective comprehensive evaluation. The process of using the entropy method to calculate the weights of each indicator in this paper is as follows:

Step 1: In total, 16 prefectures in the Yunnan Province were selected as evaluation objects, each with 36 evaluation indicators. We constructed a numerical matrix of 16 × 36:

$$\mathrm{X}=\left[\begin{array}{l}{x}_{11} x_{12} ... x_{1m}\\ x_{21} x_{22} ... x_{2m}\\ ... ... ... ...\\ x_{n1} x_{n2} ... x_{nm}\end{array}\right]$$

(1)

In the formula, n represents the row number and m represents the column number.

Step 2: The standardization of indicators. The 29th and 31st columns in this article are negative indicators, we can use $x_i=\max -x_i$ to normalize the above two columns.

Step 3: calculate the feature ratio of the j-th indicator of the i-th evaluation object, which can be calculated using the following formula:

$$P_{ij}={\displaystyle \frac{v_{ij}}{{\displaystyle \sum _{i=1}^m{v}_{ij}}}}$$

(2)

Step 4: calculate the entropy value of the j-th indicator, and record this entropy value as $e_j$, then $e_j$ can be expressed as:

$$e_j=-{\displaystyle \frac{1}{\ln m}}{\displaystyle \sum _{i=1}^m{P}_{ij}\ln }(P_{ij})$$

(3)

Step 5: The coefficient of difference can reflect the degree of difference in the indicators. Define the coefficient of difference and denote this entropy value as $d_j$, then $d_j$ can be expressed as:

$$d_j=1-e_j,j=1,2,...,m$$

(4)

Step 6: based on the above steps, calculate the weights of each indicator and denote it as $w_j$, then $w_j$ can be expressed as:

$$w_j={\displaystyle \frac{d_j}{{\displaystyle \sum _{j=1}^m{d}_j}}}$$

(5)

At this point, the weights of each indicator among all indicators can be calculated. This paper takes each prefecture as the evaluation object, applies the entropy method to 36 indicators, calculates the weights of each indicator, and applies the obtained weights to the TOPSIS multi-objective comprehensive evaluation.

4.2. TOPSIS Method

Technique for order preference by similarity to ideal solution (TOPSIS) is a comprehensive evaluation method that can accurately reflect various evaluation objects using raw data, and has been unanimously recognized by industry scholars. This paper uses the TOPSIS method to evaluate the three primary indicators of each prefecture in the Yunnan Province, and obtains the comprehensive scores of each prefecture, thereby obtaining the comprehensive scientific and technological innovation ability scores of each prefecture in the Yunnan Province, providing an important reference for government decision making and resource allocation.

In this paper, two negative indicators were used. In order to facilitate data processing, we will normalize the negative indicators using the following formula:

$${\tilde{x}}_i=\max -x_i$$

(6)

To eliminate the influence of different data metric dimensions, the matrix can be standardized. The method of standardization is to divide each element by the sum of squares of all the elements in the column.

$$z_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{{\displaystyle \sum _{i=1}^n{x}_{ij}^2}}}}$$

(7)

After all the elements are processed, a standardized matrix Z is formed.

$$Z=\left[\begin{array}{l}{z}_{11} z_{12} ... z_{1m}\\ z_{21} z_{22} ... z_{2m}\\ ... ... ... ...\\ z_{n1} z_{n2} ... z_{nm}\end{array}\right]$$

(8)

Take the maximum number from each column to form the optimal solution vector in the ideal state, denoted as $z^{+}$, then $z^{+}$ can be expressed as:

$$z^{+}=\left[z_1^{+},z_2^{+},...,z_m^{+}\right]=\left[max\left\{z_{11},z_{21},...,z_{n1}\right\},max\left\{z_{12},z_{22},...,z_{n2}\right\},...,max\left\{z_{1m},z_{2m},...,z_{nm}\right\}\right]$$

(9)

Take the smallest number from each column to form the worst-case solution vector in the ideal state, denoted as $z^{-}$, then $z^{-}$ can be expressed as:

$$z^{-}=\left[z_1^{-},z_2^{-},...,z_m^{-}\right]=\left[min\left\{z_{11},z_{21},...,z_{n1}\right\},min\left\{z_{12},z_{22},...,z_{n2}\right\},...,min\left\{z_{1m},z_{2m},...,z_{nm}\right\}\right]$$

(10)

In this paper, column j represents the indicators to be evaluated, and row i represents the specific prefecture. For each prefecture to be evaluated, we calculate the distance between each column and the optimal solution, as well as the distance between the worst solution, and set the weights for each column using the entropy method. The results are denoted as $d_i^{+}$ and $d_i^{-}$. Then, $d_i^{+}$ and $d_i^{-}$ can be expressed as:

$$d_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^n{(z_j^{+}-z_{ij})}^2}}$$

(11)

$$d_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^n{(z_j^{-}-z_{ij})}^2}}$$

(12)

Define the comprehensive rating for each prefecture as $S_i$, then $S_i$ can be defined as:

$$S_i={\displaystyle \frac{d_i^{-}}{d_i^{+}+d_i^{-}}}$$

(13)

At this point, we can obtain the comprehensive evaluation score for each prefecture. From the equation, it can be seen that $0\le S_i\le 1$. The TOPSIS method comprehensively considers the optimal solution distance and the worst solution distance. When $d_i^{+}$ is smaller, it indicates that the distance between the comprehensive score and the optimal solution is smaller, and $S_i$ is larger; when $d_i^{-}$ is smaller, the distance between the comprehensive score and the worst solution is smaller, and $S_i$ is smaller. Therefore, TOPSIS can perfectly achieve a comprehensive evaluation of the various prefectures.

5. Data Sources and Empirical Analysis

5.1. Data Sources

Based on the regional scientific and technological innovation evaluation index system constructed in the previous text, this paper takes 16 prefectures in the Yunnan Province as the research objects and obtains data from the Yunnan Statistical Yearbook 2019. Use the entropy method to weigh the indicator system, and then use the TOPSIS method to evaluate the technological innovation capabilities of each region.

5.2. Weight Analysis of Evaluation Indicators for Regional Scientific and Technological Innovation Capability Based on Entropy Method

Indicator weights are used to measure the importance of each indicator in the evaluation indicator system. The weight of the indicators can be divided into a subjective weighting method and an objective weighting method. This article uses the entropy method in the objective weighting method, and the larger the weight of the indicators obtained, the greater their influence and contribution to regional scientific and technological innovation capabilities. According to

Table 2. The weight and ranking of the evaluation index system for regional scientific and technological innovation capability in the Yunnan Province.

Dimension	Weight	Rank	Sub-Dimension	Weight	Rank	Indicators	Weight	Rank
Technological innovation investment	34.19%	1	Human input	19.51%	2	X1	0.0535	4
						X2	0.0371	14
						X3	0.0566	2
						X4	0.0479	7
			Financial investment	5.43%	8	X5	0.0374	13
						X6	0.0168	22
			Material investment	9.25%	5	X7	0.0119	27
						X8	0.0135	25
						X9	0.0105	33
						X10	0.0246	18
						X11	0.0320	15
Technological innovation output	31.69%	3	Scientific and technological achievements	19.33%	3	X12	0.0391	12
						X13	0.0501	5
						X14	0.0552	3
						X15	0.0488	6
			New product	12.36%	4	X16	0.0431	8
						X17	0.0398	10
						X18	0.0407	9
Technological innovation benefits	34.12%	2	Economic development	20.47%	1	X19	0.0232	19
						X20	0.0118	28
						X21	0.0115	30
						X22	0.0277	16
						X23	0.0398	11
						X24	0.0144	23
						X25	0.0763	1
			Environmental protection	7.25%	6	X26	0.0181	20
						X27	0.0031	35
						X28	0.0139	24
						X29	0.0262	17
						X30	0.0031	36
						X31	0.0079	34
			Social life	6.40%	7	X32	0.0118	29
						X33	0.0132	26
						X34	0.0106	32
						X35	0.0173	21
						X36	0.0112	31

, by comparing the primary indicators, the impact of scientific and technological innovation investment on regional scientific and technological innovation capacity is the greatest, at 34.19%. By comparing the secondary indicators, it is found that economic development, human resources investment, and scientific and technological achievements have a significant impact on regional scientific and technological innovation capabilities, at 20.47%, 19.51%, and 19.33%, respectively. By comparing the tertiary indicators, the top 10 are key service industry revenue (0.0763), senior technical personnel (0.0566), number of invention patents (0.0552), R&D personnel (0.0535), number of patent applications (0.0501), number of effective invention patents (0.0488), mid-level technical personnel (0.0479), number of new product projects (0.0431), new product sales revenue (0.0407), and new product output value (0.0398).

Overall, the key service industry has a significant impact on economic development in terms of revenue, total import and export volume, total industrial output value, and gross domestic product. Senior technical personnel and R&D personnel have a significant impact on manpower investment. The number of invention patents has a significant impact on technological achievements. The number of new product projects has a significant impact on new products. The number of R&D institutions has a significant impact on material investment. Industrial exhaust emissions have a significant impact on environmental protection. The rural electricity consumption has a significant impact on social life. The R&D budget has a significant impact on financial investment. The investment in human resources has a significant impact on technological innovation investment. Economic development has the greatest impact on the benefits of technological innovation. Technology achievements have the greatest impact on the output of technological innovation.

In summary, investment in scientific and technological innovation plays a crucial role in regional scientific and technological innovation, with corresponding secondary indicators of human resources investment and tertiary indicators of senior technical personnel and R&D personnel ranking high. It is evident that in order to improve regional scientific and technological innovation capabilities, it is necessary to attach importance to and strengthen investment in scientific and technological personnel, especially senior technical personnel and R&D personnel, who play a very important role. The benefits of technological innovation also have a significant impact on regional technological innovation, with corresponding secondary indicators of economic development and tertiary indicators of key service industry revenue ranking the highest. It is evident that in order to improve regional technological innovation capabilities, economic development must be emphasized and strengthened, especially the development and utilization of key service industries. The impact of technological innovation output on regional technological innovation capability is relatively weak, with corresponding secondary indicators of technological achievements and tertiary indicators ranking higher in the number of invention patents. It is evident that in order to improve regional technological innovation capability, it is necessary to attach importance to and increase technological achievements, especially the number of invention patents.

5.3. Analysis of Comprehensive Evaluation Results of Regional Science and Technology Innovation Capability Based on TOPSIS Method

Based on the weights of various indicators obtained by the entropy method, we used the TOPSIS comprehensive evaluation method to analyze and organize the data of 36 tertiary indicators in each prefecture, and we obtained the scores and corresponding rankings of the primary indicators of the Yunnan Province’s regional scientific and technological innovation capacity, as shown in

Table 3. Scores and rankings of the evaluation index system for regional scientific and technological innovation capability in the Yunnan Province.

Prefecture	Technological Innovation Investment		Technological Innovation Output		Technological Innovation Benefits		Innovation Capability
	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Kunming	0.3860	1	0.4652	1	0.1668	1	1.0180	1
Qujing	0.0797	3	0.1330	3	0.0793	2	0.2920	2
Yuxi	0.0563	5	0.1366	2	0.0656	5	0.2585	3
Baoshan	0.0469	7	0.0230	8	0.0525	11	0.1224	7
Zhaotong	0.0392	8	0.0129	10	0.0541	10	0.1062	11
Lijiang	0.0197	14	0.0084	12	0.0470	12	0.0751	13
Puer	0.0376	10	0.0153	9	0.0580	7	0.1109	10
Lincang	0.0259	12	0.0074	13	0.0441	14	0.0774	12
Chuxiong	0.0390	9	0.0415	5	0.0555	8	0.1360	6
Honghe	0.0814	2	0.0793	4	0.0733	3	0.2340	4
Wenshan	0.0333	11	0.0256	7	0.0607	6	0.1196	9
Xishuangbanna	0.0657	4	0.0091	11	0.0461	13	0.1209	8
Dali	0.0504	6	0.0357	6	0.0708	4	0.1569	5
Dehong	0.0257	13	0.0046	14	0.0421	15	0.0724	14
Nujiang	0.0064	16	0.0022	15	0.0295	16	0.0381	16
Diqing	0.0067	15	0.0002	16	0.0548	9	0.0617	15

.

Table 3 shows the scores and rankings of 16 prefectures in the Yunnan Province in terms of scientific and technological innovation investment, scientific and technological innovation output, scientific and technological innovation benefits, and comprehensive scientific and technological innovation capabilities.

From Table 3, it can be seen that although the rankings of each prefecture in the single primary indicator are different, the comprehensive score and ranking of scientific and technological innovation ability, to a certain extent, reflect the regional scientific and technological innovation ability of the Yunnan Province. From comprehensive data, Kunming’s technological innovation capability far exceeds that of other prefectures. As the capital of the Yunnan Province, Kunming’s scores for scientific and technological innovation investment, scientific and technological innovation output, scientific and technological innovation benefits, and comprehensive scientific and technological innovation capabilities are 0.3860, 0.4652, 0.1668, and 1.0180, respectively. The corresponding values for the 15 prefectures except Kunming are 0.6139, 0.5348, 0.8334, and 1.9821, respectively. Among these four indicators, Kunming’s proportion in the entire Yunnan Province is 38.6%, 46.5%, 16.7%, and 33.9%, respectively. It accounts for more than one/three of the three indicators of scientific and technological innovation input, scientific and technological innovation output, and comprehensive innovation ability. Specifically, the proportion of scientific and technological innovation output reaches 46.5%, almost accounting for half of the Yunnan Province, indicating that Kunming, as the provincial capital city, is in a leading position in scientific and technological innovation in the Yunnan Province. This is mainly because Kunming is leading in attracting talents, transportation hubs, and commercial agglomeration, and has obvious advantages in evaluating multiple tertiary indicators such as key service industry revenue, per capita GDP, R&D personnel investment, and invention patents. At the same time, in 2023, the First Financial Weekly selected 15 new first tier cities, and Kunming was successfully selected, indicating the enormous development potential of Kunming.

From the perspective of comprehensive scientific and technological innovation ability scores, the comprehensive scientific and technological innovation ability scores of the Qujing, Yuxi, and Honghe prefectures are 0.2920, 0.2585, and 0.2340, respectively, with scores above 0.2, ranking 2–4. Although its score is far lower than Kunming, it is also better than other prefectures. Qujing ranks second in terms of technological innovation efficiency and third in terms of technological innovation input and output; Yuxi has performed excellently in technology innovation output, ranking second and fifth in technology innovation investment and benefits; the Honghe prefecture has performed well in technology innovation investment, ranking second, fourth in technology innovation output, and third in technology innovation benefits. Qujing, Yuxi, and Honghe constitute the second tier of scientific and technological innovation in the Yunnan Province. This is mainly because Qujing, Yuxi, and Honghe are located in the central Yunnan region, with good economic development, a large population base, and high government investment. At the same time, they have obvious advantages in three indicators such as R&D funding, R&D personnel full-time equivalent, and GDP. Specifically, Yuxi’s per capita GDP has reached 62,641 CNY/person, almost catching up with Kunming.

The comprehensive scientific and technological innovation ability scores of Dali, Chuxiong, Baoshan, Xishuangbanna, Wenshan, Pu’er, and Zhaotong are 0.1569, 0.1360, 0.1224, 0.1209, 0.1196, 0.1109, and 0.1062, respectively, with scores above 0.1, ranking 5–11, but there is a significant gap compared to Kunming, Qujing, Yuxi, and Honghe. The common characteristics of these seven prefectures are that they have a certain economic foundation, more investment in science and technology, and a good social foundation. Therefore, they have a good performance in terms of scientific and technological innovation output and benefits. These seven prefectures constitute the third tier of scientific and technological innovation in the Yunnan Province.

The comprehensive innovation ability scores of five prefectures, including Lincang, Lijiang, Dehong, Diqing, and Nujiang, are all below 0.1, ranking last in the Yunnan Province in terms of technology innovation input, technology innovation output, and technology innovation benefits. The common characteristics of these five prefectures are poor economic foundation, a small population, being located in the border areas of the Yunnan Province, inconvenient transportation, and weak talent attraction. These factors combined ultimately led to insufficient scientific and technological innovation capabilities in these five prefectures, making them the region with the most backward development of scientific and technological innovation capabilities in the Yunnan Province.

6. Conclusions and Recommendations

This paper takes 16 regions in the Yunnan Province as the research object, constructs an evaluation system for scientific and technological innovation capabilities, uses the entropy method to assign weights to regional scientific and technological innovation evaluation indicators, and then uses the TOPSIS method to evaluate the scientific and technological innovation capabilities of 16 states and cities, obtaining the scientific and technological innovation scores and rankings of each region. Empirical studies have shown that there is a significant gap in the technological innovation capabilities of the Yunnan Province. As the capital city of the Yunnan Province, Kunming ranks first in terms of investment, output, and benefits of technological innovation, followed by Qujing, Yuxi, and Honghe. However, compared with Kunming, the gap is still significant. Dehong, Diqing, and Nujiang, which rank lower, have weaker technological innovation capabilities, and have a larger gap compared to other regions. In order to improve the technological innovation capabilities of different regions in the Yunnan Province and narrow the gap, we propose the following suggestions:

(1)

Integrate and utilize advantageous resources to play a leading role. Kunming, as the capital of the Yunnan Province, has attracted a considerable amount of talent resources. The government has invested heavily in it, and enterprises and institutions have gathered, making it a leading force in scientific and technological innovation in the Yunnan Province. Therefore, Kunming should fully leverage its geographical advantages, increase scientific and technological output, and improve the efficiency of scientific and technological innovation.

(2)

Increase investment in regions with higher output/input to enhance the overall technological innovation capabilities. The investment in cities such as Yuxi and Chuxiong is relatively small, but the output capacity is strong and the output/input is high. Further increase investment in Yuxi and Chuxiong, provide support from various aspects such as manpower, financial resources, and material resources, summarize and draw on the scientific and technological innovation experience of Yuxi and Chuxiong, and establish typical scientific and technological innovation demonstration zones.

(3)

Strengthen support and assistance for areas with underdeveloped technological innovation capabilities, and provide policy incentives in terms of funding, material resources, and human resources to enhance the technological innovation capabilities of border areas. The science and technology investment scores in areas such as Diqing and Nujiang are all below 0.01, which is significantly lower, and the science and technology output is also lower. Therefore, it is necessary to increase attention and assistance to these areas, enhance science and technology output, promote economic development, and improve people’s living standards.

The theories and methods proposed in this article have good universality and can be effectively promoted from a single province to various provinces and regions across the country, and further extended to countries and regions around the world. This study’s difficulty and limitations lie in obtaining indicator data. The acquisition of indicator data requires the collection of a large amount of relevant data, and it needs to be accurate and authoritative, otherwise it will interfere with the results. The ability of technological innovation is closely related to sustainable development, and the evaluation of technological innovation is imperative. In future research, one method is to condense evaluation methods to make scientific and technological innovation evaluation more objective and accurate; the second is to further refine the indicator system to make the evaluation of technological innovation more targeted.