Spatiotemporal Evolution and the Influencing Factors of China’s High-Tech Industry GDP Using a Geographical Detector

Abstract

With the rapid advancement of global technology, high-tech industries have become key drivers for the economic growth of many nations and regions. This study delves into the spatiotemporal dynamics and determinants influencing China’s high-tech sector from 2007 to 2021. The key findings include the following: (1) Nationally, the high-tech sector has been a cornerstone for China’s GDP growth over the preceding 15 years. The expansion rate of the high-tech domain consistently outpaces the broader economy. In particular, since 2015, the percentage of high-tech industries’ GDP has surged to approximately 42%. (2) At the provincial level, the spatial representation of the high-tech sector’s GDP predominantly leans towards the east and the south, revealing pronounced spatial autocorrelation. Nevertheless, the demarcations between east and west and between north and south are progressively diminishing. (3) Regarding influential determinants, R&D internal expenditure, operating revenue, and industry agglomeration have been instrumental in spearheading innovation and bolstering growth within the high-tech realm. These insights are invaluable for comprehending the evolutional nuances of China’s high-tech industry and devising pertinent policy measures.

1. Introduction

Since the economic reforms, China has experienced significant growth. However, this growth did not occur in isolation, but rather against the backdrop of global macroeconomic conditions, including the expansion of monetary policies by developed economies and rising global policy uncertainty. After the 2008 global financial crisis, central banks in the United States, Europe, and other developed economies implemented unprecedented quantitative easing policies. These policies maintained low global interest rates, significantly influencing global capital flows and investment decisions, especially impacting emerging economies like China [1,2]. Additionally, international political–economic turbulence, including uncertainties in trade policies and geopolitical conflicts, has introduced unpredictability to long-term investment and innovation activities for businesses [3].

Moreover, with the acceleration of technological innovation and industrial upgrading, industries with high added value, knowledge intensity, and strong innovation capabilities have rapidly developed globally, becoming a major driving force for economic growth in various countries and regions. These changes have not only reshaped the global economic structure but also provided new opportunities and challenges for China’s economic development. In this context, the Chinese economy must transition from traditional low-cost expansion and extensive growth models to adapt to long-term sustainable development goals. Addressing internal challenges such as structural imbalances, uneven regional development, and homogeneous competition is imperative as the economy enters a new normal phase. Accelerating the transformation of growth models, focusing on quality and efficiency, is now an urgent need for China’s socioeconomic development [4].

Sustainable development, which aims to meet the needs of the present without compromising the ability of future generations to meet their own needs [5], places high-tech industries, characterized by low consumption and pollution, at the core of national sustainable development strategies. The high-tech industry, as a new model of economic development, fundamentally differs from traditional industrial economies. This difference aligns the industry’s theoretical support, development goals, and driving forces closely with sustainable development strategies.

In August 1988, China implemented the National High-Tech Industrialization Development Program, known as the Torch Program, marking a significant emphasis on the development of high-tech industries. Since 2007, the “China Torch Statistical Yearbook” [6], published by the Ministry of Science and Technology, has started detailing the specific gross production values of high-tech industries across regions, reflecting significant progress in this field. Current research on the development of high-tech industries primarily focuses on four areas:

• Comprehensive Assessment Indices of High-Tech Industries: There is no unified standard system for evaluating high-tech industries. Early studies like Rogers provided preliminary frameworks. He W. [7] emphasized the necessity of a comprehensive assessment of high-tech zones using factor analysis. Tang R. [8] established a comprehensive evaluation system for provincial high-tech industries from the dimensions of input, output, and development potential. Later, scholars refined the evaluation of high-tech industries. For instance, Fang Y. [9] constructed an evaluation system for the innovation capabilities of high-tech zones, and Dai Z. [10] analyzed the factors affecting the technological innovation capacity of high-tech industries.
• Spatial Development Analysis of High-Tech Industries: Spatial analysis plays a significant role in high-tech industry research. Scholars have compared the spatial patterns of high-tech industries across different regions of China [11,12] and explored the driving factors and growth processes of high-tech industries [13,14,15,16]. Yu Y. [16] used spatial lag and spatial error models to reveal the significant impacts of different factors on innovation outputs. Wu Y. [17] applied GIS and exploratory spatial analysis methods to analyze the spatial differences and dynamic imbalances in China’s regional scientific and technological layouts. Wang X. [18] discovered the characteristics of innovation capacity enhancement from the southeast coast to central and southwestern regions via spatial autocorrelation analysis from 2010 to 2023. Tu W. [19] used data envelopment analysis (DEA) and spatial econometric models to measure the innovation efficiency of high-tech enterprises in China and their influencing factors.
• Regional Economics: Regional economics also plays a critical role in high-tech industry research. Chen H. [20] explored the impact mechanisms of geographical and cognitive proximity on innovation in high-tech zones, finding an “S-shaped” variation in geographical proximity’s impact on innovation performance. Li L. [21] and others, using the DEA-Malmquist model and ESDA analysis methods, empirically analyzed the spatiotemporal evolution and inter-provincial differences in the development of China’s high-tech industries. They highlighted the importance of policies, infrastructure, and agglomeration effects in regional high-tech industry development.
• Sustainable Development of High-Tech Industries: Yang Q. [22] used the Delphi method to predict and quantify the sustainable development index of high-tech industries, analyzing the relationship between the index and the current development level of high-tech industries. Liu L. [23], using a grey dynamic evaluation model, evaluated China’s high-tech industry’s sustainable development capability, finding a high correlation between development level and high-tech industry. Xu J. [24] established a system dynamics model for sustainable development in high-tech zones, offering policy suggestions for the sustainable development of high-tech industries in Shenzhen.
• In addition to these areas, the research methods used in this article, i.e., spatial autocorrelation and geographical detectors, have also played a key role in industrial economics. Spatial autocorrelation and geographical detectors, as two spatial models, are often used together, covering a wide range of research fields, such as population aging, tourism geography, agricultural modernization, and soil heavy metal pollution [25,26,27,28]. Researchers first use spatial autocorrelation to assess whether the distribution of spatial variables exhibits clustering characteristics and then apply geographical detectors to examine the spatial differentiation of geographical phenomena and unveil the underlying driving forces. The combined use of these two approaches provides a powerful analytical framework for interpreting and understanding complex spatial phenomena, holding significant value in advancing spatial science research.

Under the impetus of policies such as “Made in China 2025” [29] and the “13th Five-Year Plan” [30], the high-tech industry has been steadily rising in prominence within China’s economy, highlighting the importance of real economic and societal development. An in-depth study of the high-tech industry is crucial to transition from a model reliant on extensive growth to improved production efficiency. Since August 1988, with the implementation of China’s National High-tech Industrialization Development Plan, commonly known as the Torch Program, there has been significant emphasis on developing high-tech industries. Starting in 2007, the “China Torch Statistical Yearbook” [31] began to detail the specific total production value and related data of high-tech industries in various regions, reflecting major progress in this field. High-tech industries, distinct from traditional high-tech industries, focus more on developing emerging forms of industries [32]. These industries typically possess higher innovation potential and more significantly contribute to sustainable development.

The concepts of “high-tech industry” and traditional “high-tech industry” are often confused in existing research but, in reality, they have essential differences. High-tech encompasses not only “high technology” but also “new technology” [33]. According to the “Management Measures for the Recognition of High-Tech Enterprises” issued by China in 2016 (Guo Ke Fa Huo No. 32), the high-tech fields currently prioritized by China include electronic information, biotechnology and new medicine, aerospace, new materials, high-tech services, new energy and energy saving, resources and environment, advanced manufacturing, and automation technologies.

Existing research often shows ambiguity in the concept of the high-tech industry, with few studies based on data from the “China Torch Statistical Yearbook” deeply discussing the spatiotemporal evolutionary patterns and change laws over a long time scale. Based on this, this paper employs spatial analysis techniques and statistical analysis methods to thoroughly understand the evolutionary characteristics and influencing factors of China’s high-tech industry GDP. Examining quantifiable indicators to find pathways for the development of high-tech industries is crucial to avoiding regional developmental imbalances and achieving mutual interaction between science, technology, and sustainable development. This study also integrates national policies and regional characteristics to provide valuable references for policymakers and academics, offering theoretical support and policy suggestions for promoting the sustainable development of China’s economy.

2. Data and Methods

2.1. Data Sources

(1)

China Torch Statistical Yearbook:

The GDP data of the high-tech industries for the years 2007–2021, spanning the entire nation as well as individual provinces, autonomous regions, and municipalities directly under the central government, are sourced from the “China Torch Statistical Yearbook” published by the Torch High Technology Industry Development Center under The Ministry of Science and Technology.

(2)

National Statistical Yearbook [34]:

The requisite indicator data for this study are obtained from the “National Statistical Yearbook”, annually published by the National Bureau of Statistics of China. This yearbook compiles extensive statistical data concerning the yearly economic and social facets of the nation, provinces, autonomous regions, and municipalities directly under the central government. Additionally, it encompasses pivotal historical and principal statistical data from the last two decades, positioning it as China’s most comprehensive and authoritative statistical yearbook.

(3)

The data used in this paper do not include data from Hong Kong, Macao, and Taiwan.

2.2. Research Methods

(1)

Spatial Autocorrelation: Spatial autocorrelation is bifurcated into global and local autocorrelation. Global autocorrelation acts as an indicator to discern the spatial distribution traits of a particular element or phenomenon throughout the designated research area [35,36]. The computation formula is as follows:

$$I=\frac{n{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}(x_i-\overline{x})(x_j-\overline{x})}}}{{\displaystyle {\sum }_{i=1}^n{(x_i-\overline{x})}^2{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}}}}}.$$

$I$ represents the global autocorrelation coefficient. The greater its value, the more aggregated a particular attribute value is across the entire region. $x_i$ denotes the attribute observation value of the $i^{th}$ region. $n$ is the sample size. $w_{ij}$ is a specific element in the adjacent spatial weight matrix. In this study, when region $i$ is adjacent to region $j$, $w_{ij}$ is 1; otherwise, it is 0. The assumption of global autocorrelation is that space is homogeneous, implying the presence of a trend permeating the entire region. While this can reveal the overall dependency of the matter, it overlooks possible local instabilities. Hence, local spatial autocorrelation methods are introduced to unveil the autocorrelation of local regional units in adjacent spaces. Typically, the Moran scatter plot and the significance level of local Moran’s I in the LISA graph are used for representation. The LISA clustering graph can further elucidate the contribution of each unit to the global spatial autocorrelation in the spatial clustering pattern. Its computation formula is as follows:

$$I_i=\frac{n(x_i-\overline{x}){\displaystyle {\sum }_{j=1}^n{w}_{ij}(x_j-\overline{x})}}{{\displaystyle {\sum }_{i=1}^n{(x_i-\overline{x})}^2}}.$$

In this formula, $x_i$ represents the attribute value, $\overline{x}$ is the mean of $x_i$, and $w_{ij}$ is a specific element in the adjacent spatial weight matrix between research objects $i$ and $j$. In the LISA clustering graph, “H-H” denotes the regions where both the region and its adjacent area have relatively high attribute values, “L-L” represents regions where both the region and its neighboring area have relatively low attribute values, “H-L” indicates that the region itself has a high attribute value while its neighboring area has a low value, and “L-H” signifies that the region itself has a low attribute value, but its adjacent area has a high value.

2.3. Geodetector

A Geodetector is a widely used model in recent years for detecting spatial heterogeneity and identifying and revealing interactions among multiple factors. It consists of four components: factor, interaction, risk, and ecological detectors [37]. After clarifying the spatiotemporal differentiation characteristics of the high-tech industry GDP, this study, drawing on the indicator evaluation criteria of certain scholars [38,39], uses end-of-year employment, R&D internal expenditure, operating revenue, number of enterprises included in statistics, total fixed investment in society, industry agglomeration degree, per capita GDP, road density, and foreign investment level as detecting factors. The results of the indicator determination are shown in Table 1. The Geodetector’s factor and interaction detectors are employed to quantitatively attribute the spatiotemporal changes in China’s high-tech industry GDP. The factor detector functions as follows: if there exists an independent variable $X$ that impacts a dependent variable $Y$, then the spatial distribution of the independent variable and the spatial distribution of the dependent variable should converge. Its computation formula is as follows:

$$q=1-\frac{{\displaystyle \sum _{h=1}^L{N}_h{\sigma }_h^2}}{N{\sigma }^2}=1-\frac{SSW}{SST}.$$

(1)

$$SSW={\displaystyle \sum _{h=1}^L{N}_h}{\sigma }_h^2, SST=N{\sigma }^2$$

(2)

In Equation (1), $q$ represents the detection power of the detecting factor $X$ on attribute $Y$, $q$ ∈ [0, 1]. A higher q-value indicates a stronger explanatory power of $X$ on $Y$, and vice versa. ${\sigma }_h^2$ and ${\sigma }^2$, respectively, represent the variance of $Y$ in layer $h$ and the entire region. $L$ signifies the number of detecting partitions. $N$ and $N_h$, respectively, represent the number of units in the entire region and layer. In Equation (2), SSW and SST are the sums of variance within layers and total regional variance, respectively.

Interaction detection primarily involves calculating the values of independent variables $X$ and $X_2$ for dependent variable $Y$ and their respective $q$ values, as well as the $q$ values post-interaction with $X_1$ and $X_2$. Then, this is compared with the $q$ values from single factors and the $q$ values after the interaction of two factors to determine the type and direction of the interaction. The types of interaction detection, as shown in Table 2, are constituted with the following expressions:

• Non-linear reduction: $q(X_1\cap X_2)<\min (q(X_1), q(X_2))$, the interaction between factors $X$ and $Y$, results in a non-linear reduction. In other words, their combined effect is less than the sum of their individual effects.
• Single-factor non-linear reduction: $\min (q(X_1), q(X_2))<q(X_1\cap X_2)<\max (q(X_1), q(X_2))$. One factor experiences a non-linear reduction in its influence, positively affecting the weaker factor but negatively impacting the stronger one.
• Bi-factor enhancement: $q(X_1\cap X_2)>\max (q(X_1), q(X_2))$. The interaction between two factors enhances both, where the combined effect on the dependent variable is stronger than the individual explanatory power of each factor.
• Independent: $q(X_1\cap X_2)=q(X_1)+q(X_2)$. Factors $X$ and $Y$ are independent of each other, indicating no interaction or influence between them.
• Non-linear enhancement: $q(X_1\cap X_2)<q(X_1)+q(X_2)$. The interaction between factors $X$ and $Y$ leads to a non-linear enhancement, where their combined explanatory power is greater than the simple sum of their individual powers.

Table 1. Driving factors of high-tech GDP.

Index	Type	Driving Factor
R&D Internal Expenditure	Innovation	$X_1$
Industry Agglomeration		$X_2$
Practitioner		$X_3$
Number of Integrated Enterprises		$X_4$
Total Investment in Fixed Assets	economic development	$X_5$
Foreign Investment Operating Revenue		$X_6$
Road Density		$X_7$
Foreign Investment		$X_8$
Per Capita GDP	infrastructure	$X_9$

Type is a primary indicator, and index is a secondary indicator under type. The “Practitioner” refers to in-service personnel of high-tech enterprises.

Table 1. Driving factors of high-tech GDP.

Index	Type	Driving Factor
R&D Internal Expenditure	Innovation	$X_1$
Industry Agglomeration		$X_2$
Practitioner		$X_3$
Number of Integrated Enterprises		$X_4$
Total Investment in Fixed Assets	economic development	$X_5$
Foreign Investment Operating Revenue		$X_6$
Road Density		$X_7$
Foreign Investment		$X_8$
Per Capita GDP	infrastructure	$X_9$

Type is a primary indicator, and index is a secondary indicator under type. The “Practitioner” refers to in-service personnel of high-tech enterprises.

Table 2. Interaction type of two factors.

Interaction Type	q Value Relationship
Non-linear reduction	$q(X_1\cap X_2)<\min (q(X_1), q(X_2))$
Single-factor non-linear reduction	$\min (q(X_1), q(X_2))<q(X_1\cap X_2)<\max (q(X_1), q(X_2))$
Bi-factor enhancement	$q(X_1\cap X_2)>\max (q(X_1), q(X_2))$
Independent	$q(X_1\cap X_2)=q(X_1)+q(X_2)$
Non-linear enhancement	$q(X_1\cap X_2)<q(X_1)+q(X_2)$

$q$ represents the influence of driving factors on high-tech GDP; $X_1, X_2$ denote any of two driving factors.

Table 2. Interaction type of two factors.

Interaction Type	q Value Relationship
Non-linear reduction	$q(X_1\cap X_2)<\min (q(X_1), q(X_2))$
Single-factor non-linear reduction	$\min (q(X_1), q(X_2))<q(X_1\cap X_2)<\max (q(X_1), q(X_2))$
Bi-factor enhancement	$q(X_1\cap X_2)>\max (q(X_1), q(X_2))$
Independent	$q(X_1\cap X_2)=q(X_1)+q(X_2)$
Non-linear enhancement	$q(X_1\cap X_2)<q(X_1)+q(X_2)$

$q$ represents the influence of driving factors on high-tech GDP; $X_1, X_2$ denote any of two driving factors.

3. Time Series Analysis

3.1. Rapid Growth of High-Tech Industry with Significant Fluctuations

From Figure 1 and Figure 2 and

Table 3. Summary of China’s total GDP and high-tech GDP from 2007 to 2021.

Years	High-Tech GDP (Billion Yuan)	High-Tech GDP Growth Rate	Total GDP (Billion Yuan)	Total GDP Growth Rate
2007	95,911.5	33.5%	270,092.3	0.18
2008	96,546.2	0.7%	319,244.6	0.09
2009	93,319.1	−3.4%	348,517.7	0.18
2010	119,022	27.5%	412,119.3	0.18
2011	140,338.9	17.9%	487,940.2	0.10
2012	152,235.3	8.5%	538,580	0.10
2013	175,106.4	15.0%	592,963.2	0.09
2014	211,335.9	20.7%	643,563.1	0.07
2015	189,757.5	−10.2%	688,858.2	0.08
2016	212,268.8	11.9%	746,395.1	0.11
2017	243,898	14.9%	832,035.9	0.10
2018	288,706.3	18.4%	919,281.1	0.07
2019	324,137.4	12.3%	986,515.2	0.03
2020	367,111.6	13.3%	1,015,986.2	0.13
2021	478,489.1	30.3%	1,149,237	0.18

, it is evident that during the 15-year period from 2007 to 2021, China’s total GDP and the high-tech industry’s GDP experienced remarkable growth. The total GDP grew from CNY 2.70092 trillion to CNY 11.49237 trillion, a 324% increase. Concurrently, the high-tech industry’s GDP surged from CNY 959.115 billion to CNY 4.78489 trillion, a growth of 399%. This indicates that the growth rate of China’s high-tech industry outpaced the overall economy. The proportion of high-tech GDP relative to the national GDP also consistently rose. Using the three-year moving average index [40], Figure 3 provides an in-depth analysis of the growth rate of the high-tech industry’s GDP. The figure shows an ascending trend in growth rate from 2007 to 2021, highlighting the progressively increasing role of China’s high-tech industry in the national economy and its accelerating development rate. However, this growth was not strictly linear and exhibited fluctuations, with several periods of increase and decrease. For instance, the rate dropped from 17.08% in 2007–2009 to 10.28% in 2008–2010 and then climbed back to 14.04% in 2010–2012, followed by another decline in 2016–2018. While there were short-term decelerations in certain years, the linear trend of the three-year moving average index consistently moved upwards. This emphasizes that China’s high-tech industry remains a sector with significant growth potential but with considerable volatility. From 2007 to 2021, China’s total GDP and high-tech industry GDP both significantly increased, with the high-tech sector growing faster than the overall economy. This growth was influenced by global macroeconomic trends, particularly the global financial crisis. Central banks in developed economies, notably the US and Europe, implemented unprecedented quantitative easing, impacting global capital flows and investment decisions [41]. The expansive monetary policies, especially the US Federal Reserve’s balance sheet expansion, significantly affected emerging economies like China, indirectly influencing the pace and nature of its high-tech industry growth.

3.2. Provincial High-Tech Industries’ Share of National GDP Increases Annually

From Figure 4, the proportion of high-tech industry GDP in various regions of China from 2007 to 2021 can be summarized in two key points:

(1)

General upward trend: This likely reflects the significant outcomes of China’s active efforts in promoting high-tech industry development and implementing its technological innovation strategies.

(2)

Significant variations in growth speed and level among regions: To understand this phenomenon in depth, it can be examined from the perspectives of three major areas: the eastern, central, and western regions.

• Eastern Region: Comprises provinces like Zhejiang, Jiangsu, Fujian, Guangdong, Hainan, and Shandong and cities like Beijing, Shanghai, and Tianjin. Notably, Zhejiang and Jiangsu’s high-tech GDP proportions have steadily increased over the past 15 years. Shanghai and Jiangsu particularly experienced significant growth. Guangdong’s proportion showed minor fluctuations between 2012 and 2017 but predominantly trended upwards. In contrast, Fujian saw an uptick from 2007 to 2012 but a decline over the subsequent decade. Overall, with its advanced economic foundation and vast technological resources, the eastern region has consistently led in high-tech industry development.
• Central Region: Mainly includes provinces like Henan, Hubei, Hunan, and Anhui. These provinces generally witnessed an increase in their high-tech GDP proportions from 2007 to 2012. Over the subsequent decade, Hunan and Hubei largely maintained stable proportions, Henan’s increased, and Anhui’s remained stable from 2012 to 2017 and ascended from 2017 to 2020. Despite the central region trailing the eastern region in economic advancements, the growth momentum of its high-tech industry has been steadily strengthening.
• Western Region: Constituted by provinces such as Inner Mongolia, Ningxia, Qinghai, Gansu, and Xinjiang. These provinces maintained stable high-tech GDP proportions from 2007 to 2012. Between 2012 and 2017, Inner Mongolia and Gansu observed a rise, which stabilized from 2017 to 2020. In contrast, Ningxia, Qinghai, and Xinjiang maintained stability from 2007 to 2012 but saw consistent growth in the subsequent decade. Though the western region started its economic endeavors later than the eastern and central regions, under the “Western Development Strategy”, its high-tech industry’s proportion has been steadily increasing.

Figure 4. Proportion of high-tech GDP by province from 2007 to 2021.

Figure 4. Proportion of high-tech GDP by province from 2007 to 2021.

4. Spatial Pattern Analysis

4.1. Overall Growth with a Predominance in the East and South

As shown in Figure 5, from 2007 to 2021, the high-tech industry GDP in China’s provinces generally demonstrated a growth trend. However, this growth has not been even. In distribution, the east outperforms the west, and the south outperforms the north. This distribution reveals the characteristics of China’s high-tech GDP industrial development: rapid but uneven. To better understand the specific spatial distribution patterns of various provinces in China, this article will analyze according to the years shown in Figure 5:

In 2007, the most pronounced high GDP areas were primarily confined to the eastern coastal areas, such as Guangdong, Jiangsu, Shanghai, and Zhejiang. These regions displayed a significant lead in the development of high-tech industry. However, as time progressed, this geographic distribution began to change.

In 2015, the eastern coastal regions still maintained their leadership position in high-tech industry GDP. At the same time, inland areas, such as Hubei, Jiangxi, and Anhui, began to show growth momentum in their high-tech industry GDP. This might indicate that technology and capital were gradually spreading from the coast to the inland.

In 2021, the high-tech industry GDP in the eastern coastal areas further strengthened. However, some provinces in the central and western regions, such as Chongqing, Sichuan, and Hunan, rapidly developed their high-tech industries, reflecting the gradual rise in inland areas in the development of the high-tech industry. In 2007, western regions like Xinjiang, Gansu, and Qinghai lagged behind the eastern regions in high-tech industry development. However, by 2021, these areas began to show significant growth trends. This indicates that over time, the gap in high-tech industry development across the country is slowly narrowing, consistent with the previous analysis of the proportion of the high-tech industry.

4.2. Spatial Autocorrelation Shows Significance, but Correlation Is Decreasing

(1)

Global Autocorrelation:

The spatial autocorrelation of the national high-tech industry GDP can be quantified using the Moran’s I index. This index ranges between −1 and 1. A positive value indicates that similar values tend to cluster in space, while a negative value implies that dissimilar values tend to cluster. When the p-value is less than 0.05, its Moran’s I index is considered significant, indicating that the observed spatial autocorrelation exceeds the level of random distribution.

Analyzing China’s high-tech GDP from 2007 to 2021 using the Moran’s I index and summarizing it in Table 4, we find that the Moran’s I index for the national high-tech industry has always remained positive. Except for 2017, the p-value was less than 0.05 every year. Statistically, this indicates a high degree of significance in spatial autocorrelation during this period, suggesting that neighboring regions’ high-tech GDP has a clustering tendency in space. To better understand the spatial agglomeration trend of the high-tech industry, the specific changes in Moran’s I index from 2007 to 2021 are given as follows:

• From 2007 to 2009, the Moran index rose from 0.169 to 0.293, indicating that the spatial relationship between the high-tech GDP of various regions became closer during this period.
• From 2010 to 2016, the Moran index decreased from 0.172 to 0.163, a change of only 0.009, indicating little change in the spatial relationship among regions and a more stable spatial distribution.

Table 4. Moran’s I index for China’s high-tech GDP in 2007–2021.

Variables	I	E(I)	sd(I)	z	p Value
2007	0.169	−0.033	0.107	1.889	0.029
2008	0.178	−0.033	0.109	1.949	0.026
2009	0.293	−0.033	0.113	2.880	0.002
2010	0.172	−0.033	0.108	1.905	0.028
2011	0.270	−0.033	0.110	2.767	0.003
2012	0.288	−0.033	0.110	2.918	0.002
2013	0.244	−0.033	0.110	2.519	0.006
2014	0.228	−0.033	0.110	2.370	0.009
2015	0.216	−0.033	0.109	2.282	0.011
2016	0.163	−0.033	0.105	1.874	0.030
2017	0.117	−0.033	0.102	1.480	0.069
2018	0.083	−0.033	0.100	1.159	0.123
2019	0.075	−0.033	0.102	1.067	0.143
2020	0.081	−0.033	0.104	1.098	0.136
2021	0.055	−0.033	0.107	0.823	0.205

Table 4. Moran’s I index for China’s high-tech GDP in 2007–2021.

Variables	I	E(I)	sd(I)	z	p Value
2007	0.169	−0.033	0.107	1.889	0.029
2008	0.178	−0.033	0.109	1.949	0.026
2009	0.293	−0.033	0.113	2.880	0.002
2010	0.172	−0.033	0.108	1.905	0.028
2011	0.270	−0.033	0.110	2.767	0.003
2012	0.288	−0.033	0.110	2.918	0.002
2013	0.244	−0.033	0.110	2.519	0.006
2014	0.228	−0.033	0.110	2.370	0.009
2015	0.216	−0.033	0.109	2.282	0.011
2016	0.163	−0.033	0.105	1.874	0.030
2017	0.117	−0.033	0.102	1.480	0.069
2018	0.083	−0.033	0.100	1.159	0.123
2019	0.075	−0.033	0.102	1.067	0.143
2020	0.081	−0.033	0.104	1.098	0.136
2021	0.055	−0.033	0.107	0.823	0.205

However, starting in 2017, the p-value increased annually, suggesting that the spatial autocorrelation of high-tech GDP might be declining and moving towards randomness.

(2)

Local Autocorrelation:

As shown in Figure 5, this article conducted a local spatial autocorrelation analysis to understand the agglomeration of high-tech industries among neighboring provinces in China. The specific analysis is as follows:

• From 2007 to 2016, the “high-high” areas, where the GDP of high-tech industries is high and the inter-regional correlation is strong, were mainly concentrated in coastal areas such as Jiangsu, Anhui, Zhejiang, and Shanghai. These results are consistent with Moran’s index during the same period, showing strong spatial autocorrelation. Meanwhile, the “low-low” areas, where the GDP of high-tech industries is low and the inter-regional correlation is also strong, were primarily located in western regions such as Inner Mongolia, Xinjiang, Gansu, and Sichuan. Additionally, Beijing frequently appeared in the “high-low” areas, possibly indicating a lower spatial correlation with other regions.
• However, starting in 2017, there was a noticeable change in the geographical distribution of the “high-high” areas: regions like Jiangsu and Zhejiang along the coast no longer appeared in the “high-high” category. In contrast, inland areas such as Jiangxi began to be featured in the “high-high” category, while Beijing and Shanghai consistently appeared in this category. During the same period, the geographical distribution of the “low-low” areas remained relatively stable, still mainly concentrated in Inner Mongolia, Xinjiang, Gansu, and Sichuan. Additionally, regions like Fujian and Jiangxi frequently appeared in the “low-high” areas, suggesting that although the GDP of high-tech industries in these regions is low, their spatial correlation with other high-GDP areas began to strengthen. Previously, the “high-high” areas were mainly concentrated in coastal regions such as Jiangsu and Zhejiang, where the high–tech industry GDP is high, and the inter-regional correlation is strong.
• Conclusion: The analysis reveals that the results of the two models are generally consistent. From 2007 to 2021, the more developed high-tech industries remained concentrated in the eastern coastal areas. However, starting in 2017, the agglomeration effect of the high-tech industry began to shift towards the central and western regions. While the high-tech industry in the western region is less developed than in the eastern region, it exhibits a significant agglomeration effect and strong interrelatedness (Figure 6).

5. Analysis of the Driving Factors of Temporal and Spatial Differences in High-Tech Industry

5.1. Factor Detection Analysis

Based on the factor detection model in the Geodetector, this study conducted an in-depth exploration and analysis of the key indicators for the provinces in 2007, 2015, and 2021. From

Table 5. Factor detection results.

Geodetector	2007		2015		2021
	q Statistic	p Value	q Statistic	p Value	q Statistic	p Value
R&D Internal Expenditure	0.8301	0.000	0.9095	0.000	0.7876	0.000
Industry Agglomeration	0.6013	0.0033	0.6074	0.0036	0.6073	0.0028
Practitioner	0.9460	0.000	0.9475	0.000	0.8436	0.000
Total Investment in Fixed Assets	0.6116	0.0132	0.5653	0.0244	0.6020	0.0035
Number of Integrated Enterprises	0.7902	0.000	0.7093	0.000	0.7618	0.0031
Foreign Investment Operating Revenue	0.0811	0.7158	0.1029	0.6515	0.1164	0.5951
Road Density	0.3856	0.0139	0.4548	0.0209	0.2511	0.1055
Foreign Investment	0.7977	0.000	0.8142	0.000	0.6986	0.000
Per Capita GDP	0.5598	0.0111	0.4326	0.0490	0.2581	0.2500

q statistic represents the explanatory power of the indicator. p-value represents the correlation with the target fit, and <0.05 represents a significant difference.

, in 2007, Practitioner > R&D Internal Expenditure > Operating Revenue > Number of Integrated Enterprises > Total Investment in Fixed Assets > Industry Agglomeration > Per Capita GDP > Road Density > Foreign Investment. By 2021, the order shifted to Practitioner > Number of Integrated Enterprises > R&D Internal Expenditure > Operating Revenue > Total Investment in Fixed Assets > Industry Agglomeration > Road Density > Per Capita GDP > Foreign Investment. Notably, the correlation of indicators such as practitioner, operating revenue, R&D internal expenditure, and number of integrated enterprises with high-tech GDP remained high. The explanatory power of R&D internal expenditure ranked first, and the explanatory power of the number of integrated enterprises rose to second place in 2021.

During the statistical time frame, R&D internal expenditure, practitioner, and operating revenue all exhibited high q-values in relation to the high-tech industry GDP, and their p-values were all 0.000. This suggests that these three factors significantly influence the high-tech industry, with very stable statistical significance. Conversely, road density and per capita GDP have a smaller influence on the regional differences in high-tech GDP. The foreign investment p-value exceeded 0.05 for all three periods, indicating that this factor might not have a significant connection with the development of high-tech industries. However, there might be significant associations when it interacts with other factors.

Through the categorization and analysis of various indicators, it can be observed that indicators related to economic activities tend to show stronger correlations, followed by those related to technological innovation. The eastern and southern regions have more robust economic activities and more prominent technological innovation capabilities. This matches the high-correlation economic activity and technological innovation indicators in the model, leading to rapid growth in the high-tech industry GDP in these regions. This phenomenon echoes the research of Ellison and Glaeser, as well as Ellison, Glaeser, and Kerr [42,43], who explored the causes of industry agglomeration and the impact of economies of scale on industry development. Particularly in the technology sector, economies of scale and existing infrastructure likely support the rapid development of these regions. This explains why the economic activities in the eastern and southern regions are more prosperous and their technological innovation capabilities are more prominent, thereby driving the rapid growth of the high-tech industry GDP. In contrast, the western and central regions are relatively lagging due to their poor performance in these key indicators, limiting the development of their high-tech industry GDP. This trend suggests that the prosperity of economic activities and advancements in technological innovation are primary drivers of high-tech GDP growth to a certain extent. Furthermore, although the correlations of some indicators fluctuated over different years, they generally maintained a consistent trend across periods.

5.2. Interaction Detection Analysis

From Figure 7, in 2007, the highest interaction value was 0.974 between R&D internal expenditure and the number of practitioners at the end of the year. In contrast, the lowest value was 0.081 between foreign investment level and road density. In 2015, the highest interaction value was 0.985 between the number of practitioners at the end of the year and the number of integrated enterprises. The lowest was 0.103 between the foreign investment level and road density. In 2021, the interaction of R&D internal expenditure with all other indicators was generally high, all above 0.78, with the highest being 0.917 in interaction with industry agglomeration.

R&D internal expenditure showed a strong bi-factor enhancement relationship with all other indicators in 2007, 2015, and 2021. This indicates that R&D internal expenditure played a key reinforcing role in boosting the high-tech industry GDP and had a positive relationship with several other factors, collectively producing a positive impact on the high-tech industry GDP. In addition, a nonlinear enhancement relationship appeared between industry agglomeration and foreign investment level in 2015, suggesting that as industry agglomeration increases, its relationship with the foreign investment level strengthens. The foreign investment level showed a nonlinear weakening relationship with the number of practitioners at the end of the year, total social fixed investment, and the number of integrated enterprises across the three years. Road density and foreign investment level were the only combinations showing a unifactorial nonlinear weakening relationship, implying that the coexistence of industry agglomeration and foreign investment level would reduce the impact on high-tech industry GDP. Other combinations of indicators showed a bi-factor enhancement relationship.

From 2007 to 2021, the interaction between most indicators was a bi-factor enhancement relationship, especially indicators related to R&D internal expenditure. Moreover, as time progressed, the nonlinear relationship between foreign investment level and other indicators changed from nonlinear enhancement to nonlinear weakening. This suggests that the influential factor’s impact on the high-tech industry began to diminish. The positive stimulus is approaching saturation or its limit. Further improvement may no longer bring the same proportion of benefits or growth as before, indicating a decreasing dependency of the high-tech industry on foreign investment.

6. Conclusions and Recommendations

6.1. Limitations and Future Prospects

Although we achieved certain results in analyzing the influencing factors of the spatial differentiation of China’s high-tech industry, there are some limitations in this study. First, the research mainly focuses on the spatial differentiation of the high-tech industry, without assessing the developmental characteristics and influencing factors of China’s high-tech industry over time. This suggests that understanding the industry’s developmental trends and phase characteristics may not be sufficiently comprehensive. Second, despite efforts to collect and utilize various data indicators, due to limitations in data acquisition, these indicators may not be broad and in-depth enough. The current data may not fully capture all key factors driving spatial differentiation in the high-tech industry, especially those that could significantly impact provincial differences.

For future research, the following directions could be considered for further exploration and expansion:

• Time Series Analysis: Future studies could pay more attention to the developmental characteristics of the high-tech industry over time, including industry growth trajectories, cyclical fluctuations, and long-term trends, as well as the driving factors behind these characteristics.

By obtaining more diverse and in-depth data, such as regional innovation capabilities, corporate R&D activities, policy support, and market demand, a more comprehensive analysis of factors influencing the development of the high-tech industry can be conducted.

2.

Detailed Analysis of Regional Characteristics: Focus on the unique characteristics of different provinces in the development of high-tech industries, including regional policy differences, resource allocation, and market potential, to reveal the heterogeneity in the development of high-tech industries between regions.

6.2. Policy Recommendations

China’s high-tech industry has achieved remarkable achievements in the past period, but continuous policy innovation and adjustment are essential to maintain its continued growth and stand out in global competition. Based on existing research and market observations, the following policy recommendations are proposed:

(1)

Strengthening Technology R&D and Innovation Capability:

From the time series analysis, it is evident that the development speed of the high-tech industry exceeds the overall economy, but with significant fluctuations. Additionally, the analysis of driving factors shows a strong correlation between R&D internal expenditure, year-end employment, and operating revenue with the GDP of the high-tech industry. Therefore, the government should increase scientific research investment, encourage enterprises to increase R&D spending, and provide policy rewards and tax incentives. Cultivating and attracting innovative talents, offering more scholarships and research funds, encouraging students and researchers to study high-tech industry, and attracting foreign talents to develop domestically are important. Strengthening cooperation between enterprises and research institutions, establishing industry–academia–research cooperation mechanisms, and promoting the commercialization and application of high-tech are crucial.

(2)

Enhancing Balanced Regional Development of High-Tech Industries:

The spatial pattern analysis shows that the development of China’s high-tech industry presents a more significant trend in the eastern than the western and southern than the northern regions, leading to rapid growth but regional imbalance overall. The current issues faced by China’s high-tech industry are as follows:

• Coastal Eastern Regions: Although these areas are leading in high-tech industry development, they also face the challenges of industrial over-concentration and increased resource and environmental carrying pressures. Therefore, solutions should focus on promoting industry diversification and improving resource efficiency, such as encouraging investment in green technologies and sustainable development projects through policies.
• Central Regions: The main challenge in these areas is to enhance innovation capabilities and attract high-tech investments. It is recommended that the government support local universities and research institutions to cooperate with enterprises by establishing innovation platforms and providing R&D funding to facilitate technology transfer and industrial upgrading.
• Western Regions: Challenges in these areas include insufficient infrastructure and talent shortages. Addressing these issues requires government investment in infrastructure development and providing education and career development opportunities to retain and attract talents.

To achieve balanced development of the high-tech industry nationwide, the following measures are suggested:

• Increase Investment in Western and Northern Regions: Central and local governments should increase financial investment in the high-tech industries of these regions, especially in infrastructure construction, educational resources, and technology R&D centers.
• Promote the Construction of High-Tech Industrial Parks: The establishment of high-tech industrial parks in the western and northern regions should be encouraged to attract domestic and foreign enterprise investments, providing tax incentives and other support measures to promote economic development and technological innovation in these areas.
• Facilitate Technology and Talent Exchange Between Eastern and Northwestern Regions: Special projects or funds should be established to support cooperation between enterprises and research institutions in developed coastal areas and western and northern regions, strengthen technology transfer and talent exchange, and jointly promote the development of high-tech industries.

These measures not only promote balanced economic development between regions but also contribute to the healthy and sustainable growth of the high-tech industry nationwide.

(3)

Enhancing Achievement Transformation and Developing Diversified Industries:

Technological innovation is the key driving force behind economic development and is crucial for industrial upgrading. Thus, it is necessary to increase R&D investments, particularly in new technologies, new industries, and new business models. This involves not only increasing government funding and private investment but also optimizing the innovation environment to stimulate more innovative activities.

Strengthening basic research is the cornerstone of innovation. Both the government and businesses should invest in fundamental scientific research to lay a solid theoretical and experimental foundation for innovation. At the same time, accelerating the transformation of scientific and technological achievements is essential. This requires establishing effective mechanisms to transfer the research results from laboratories to the market, promoting the deep integration of technological innovation with industries.

Promoting cooperation between enterprises and research institutions is significant for facilitating technology transfer and innovation. Establishing cooperation platforms, such as technology transfer offices, industrial innovation centers, or joint research projects, can more effectively share resources, knowledge, and technology and accelerate the application and industrialization of new technologies.

Additionally, optimizing the industrial structure and developing diversified industries are important aspects of enhancing economic resilience and promoting innovation. We should particularly focus on optimizing the structure of secondary and tertiary industries, reducing reliance on traditional industries, and actively developing modern service industries and high-tech industries. These measures can mitigate the risks associated with over-reliance on a single industry, promoting balanced and sustainable economic development across various regions.