# High-Tech Industrial Agglomeration and Urban Innovation in China’s Yangtze River Delta Urban Agglomeration: From the Perspective of Industrial Structure Optimization and Industrial Attributes

Authors: Dan Xu; Bo Yu; Lina Liang
Journal: Complexity
 (2022)

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## Abstract

What is the interplay of high-tech industrial agglomeration and urban innovation? How does high-tech industrial agglomeration affect urban innovation? What are the heterogeneous effects of high-tech industry agglomeration on urban innovation in different conditions? To answer these questions, this paper analyzes the interrelationship between high-tech industry agglomeration and urban innovation based on panel data of China’s Yangtze River Delta urban agglomeration from 2010 to 2019. We discuss the influence mechanism of high-tech industrial agglomeration on urban innovation by exploring the mediating effect of industrial structure optimization and the threshold effect of industrial attributes. The heterogeneous impact of high-tech industry agglomeration on urban innovation is also been further studied. We find that (1) the interaction relationship between high-tech industry agglomeration and urban innovation output is positive. (2) The advancement of industrial structure plays a positive intermediary role between high-tech industrial agglomeration and urban innovation output, while the rationalization of industrial structure shows a suppressing effect. (3) There are different threshold effects between capital intensity and technology intensity. The influence of high-tech agglomeration on urban innovation is positive only when the capital intensity exceeds 1.125. However, the influence is always positive in different levels of technology intensity, significantly. When the technology intensity is higher than 9.012E − 06, the degree and significance of this positive impact would decrease. (4) There are heterogeneous impacts of high-tech industry agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration in different time stages, urban innovation development stages, and urban circles.


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## Body

## 1. Introduction

Core technologies are reshaping the world’s competitive landscape. Innovation is crucial and prerequisite for countries to gain sustainable competitive advantage [1] and growth [2]. In the process of promoting national innovation and development, many countries prioritize the development of high-tech industries and the improvement of technological competitiveness as important strategies in their national development strategy [3]. The activities of high-tech industries are vital force for elevating regional innovation level. High-tech industry is knowledge—and technology-intensive industry, which tends to be more congregate than other industries [4]. Compared with traditional industries, high-tech industries aggregated in some areas are more likely to motivate innovation due to the agglomeration of innovation elements and activities.Currently, China’s economy is shifting from high-speed growth to high-quality growth. Innovation plays a crucial role in China to achieve economic transformation. High-tech industries are the most dynamic growth point for regional innovation of China. Urban agglomerations have become the main spatial form for the development of innovation and high-tech industries in China. High-tech industrial agglomeration is conducive to boosting China’s technological progress, promoting industrial structure upgrading and realizing economic transformation. However, there is a considerable gap between high-tech industries in China and those in developed countries. There are still problems in some cities, such as low development level and unreasonable agglomeration of high-tech industries. These problems impede China’s economic transformation and its development as an innovative country inevitably.In this context, the study of relationship between high-tech industrial agglomeration and urban innovation is of great importance for China’s urban agglomerations to use high-tech industries to promote innovation capability and gain sustainable competitive advantage.Yangtze River Delta urban agglomeration is one of the world-class urban clusters in China. It is a typical high-tech industry intensive area and innovation highland in China. In this paper, 26 cities in China’s Yangtze River Delta urban agglomeration are selected as objects. Firstly, the interaction effect of high-tech industrial agglomeration and urban innovation is analyzed. Then, the influence mechanism of high-tech industrial agglomeration and urban innovation from the mediating effect of industrial structure optimization and the threshold effect of industrial attributes is also explored. Finally, we examine the heterogeneous effects of high-tech industry agglomeration on urban innovation. This paper aims to enrich the existing theory of agglomeration and regional innovation. Moreover, it provides theoretical support and practical inspiration for China’s high-tech industrial agglomeration to effectively release multiple positive effects, promote urban innovation capabilities, and develop innovative country. The paper is organized as follows. Section2 sorts literature about industrial agglomeration, innovation, industrial structure optimization, industrial attributes, and their connections. Section 3 describes the methods, empirical models, variable selection, and data source. The results and discussions of empirical tests are shown in Section 4, and Section 5 concludes.


## 2. Literature Review

### 2.1. Industrial Agglomeration and Innovation

Agglomeration theory was first proposed by Alfred Marshall in his Principles of Economics, which addresses the phenomenon of similar firms locating in geographic proximity to one another [5]. Important works by scholars such as Krugman [6], Porter [7], and other scholars have further advanced and helped proliferate the study of industrial agglomeration and industry clusters among academic circles. Early research focused on exploring the emergence and development of industrial cluster [8–10] and the implications of industrial agglomeration for regional economic growth [11, 12] and competitiveness [7]. It is widely believed that industrial agglomeration could promote economic growth.Innovation has always attracted widespread attention from scholars. Studies regarding geographic economies stress the determinants of regional variance in innovation. Agglomeration externalities have been considered as a driver of innovation among the regional variances [13]. Marshall pointed out that specialized agglomeration could cause MAR spillover and thus affect the efficiency of technology innovation [5]. Since researchers have explored the effects of agglomeration on firm innovation [14]. Baptista and Swann found manufacturing agglomeration could motivate firm innovation [15]. The firms in industrial cluster are more likely to have higher innovation output than other noncluster enterprises [16]. Similarly, Beaudry and Breschi found industrial agglomeration has a significant positive effect on innovation performance [17]. Turkina et al. examined the relationship between regional industrial agglomeration and firm innovation performance under interactionist lenses [18]. At the same time, Dalum et al. argued that industrial agglomeration could reduce the costs of innovative activities and then promote regional innovation growth [19]. Boix and Galletto found Marshallian industrial districts of Spanish generated more innovation products than national average, and it means that specialization agglomerations of manufacturing have a positive impact on regional innovation output [16]. Industrial agglomeration provides regions with considerable specialized resources and then drives the process of regional innovation [20]. Silvestre and Dalcol took oil and gas industry in Brazil as an example, analyzed the effect of industrial agglomeration on regional innovation, and indicated that industrial agglomeration had a positive impact on innovative activities of region [21]. VDC and Garcia pointed that the advantages of local industrial agglomeration were important for a higher performance of regional innovation [22]. Lee and Sohn confirmed that agglomeration externalities had a positive association with exploitative innovation of regions [13]. Related studies have proved that agglomeration of similar industry should drive the creation of innovative start-ups and enhance regional innovation level accordingly [23]. Industrial agglomeration has been seen a precondition for regional innovation and start-up activity [24]. However, Audretsch and Feldman argued that American specialized industry agglomeration had a negative effect on regional innovation output [25].There is also literature related to the influence of industrial agglomeration on regional innovation in China. Liu et al. explored the impact of industrial agglomeration on regional innovation in China. They revealed that high-tech industrial agglomeration affected positively on regional innovation within the province [26]. Zhang investigated the effect of agglomeration on product innovation based on China’s firm-level data and concluded that industrial agglomeration contributes to product innovation [27]. Xie et al. demonstrated a positive effect between high-tech industrial agglomeration and innovation in China’s science park [28]. Jin et al. found that industrial clusters strengthened regional innovation and improved regional competitiveness by reducing production costs [29]. Zhang et al. found high-tech industrial agglomeration promoted regional innovation performance significantly in China [3]. But there are some different points. Based on provincial panel data, Xie and Wu argued that industrial agglomeration could promote innovation efficiency in a reasonable range, and when the level of agglomeration exceeded the range, further industrial agglomeration would reduce the innovation efficiency [30]. Jiang and Gao selected creative industrial agglomeration in Shanghai and Shenzhen as samples, and explored the effect of creative industrial agglomeration on enterprises’ innovation performance. The results indicated that the relationship between them presents an inverted U-shape [31]. Xiong and Li found that there was a nonlinear relationship between high-tech industry agglomeration and regional technological innovation within the provincial scope of China [32].


### 2.2. Industrial Agglomeration, Innovation, and Industrial Structure Optimization

Industrial structure optimization is the essential requirement of speeding up China’s high-quality development and innovation-driven transformation. Scholars discussed how to promote the economic effect of industrial agglomeration from the perspective of industrial structure. Industrial agglomeration and structural optimization often go hand in hand, showing a synergistic effect on economic development [33]. On the one hand, from the direct impact of industrial agglomeration on industrial structure optimization, Wu and Yang found that industrial agglomeration could improve the efficiency of manufacturing industry, and then promoted the optimization of industrial structure effectively in China [34]. Specialized agglomeration of service industry would promote the advancement of industrial structure but hinder the rationalization of industrial structure [35]. Scholars also focused on the effect of industrial structure upon industrial agglomeration. Li et al. revealed that regional industrial structure played an important role in localization agglomeration, and pointed out highly concentrated industrial structure might weaken local industrial agglomeration [36]. On the other hand, some scholars explored the relationship between industrial structure optimization and national innovation development. Industrial structure would attract new firms and promote regional innovation level [37]. Capello et al. verified industrial structure played a mediating role between the presence of cities in European NUTS2 regions and its innovation performance [38]. Li and Nie used data at provincial level in China and indicated that the advancement of industrial structure had a positive effect on regional innovation [39]. Ning et al. found that specialized industrial structures provided a vibrant environment for local innovation by absorbing FDI spillovers in China [40]. Zhao found that industrial structure optimization would improve innovative efficiency within the province in China, and there was a “spiraling relationship” between them [41]. Innocenti et al. pointed that variety of the local industrial structure increased the regional innovation capacity by influencing the structure of local knowledge networks [42]. Lao et al. demonstrated that industrial structure enhanced the level of cities’ innovation in China [43].


### 2.3. Industrial Agglomeration, Innovation, and Industrial Attributes

The exertion of industrial agglomeration effects is dependent on industrial attributes [44, 45]. Technology intensity and capital intensity are two important features in the industrial production process. They are associated with innovation systems and industrial growth [46]. In existing literature, Xiao et al. considered technology intensity was widely suggested to be linked with competition strategies of firms in China [47]. Ugur et al. argued that the relationship between technology intensity and firm survival followed an inverted U pattern [48]. Liang and Goetz suggested the actual economic impacts of industrial agglomeration were closely related to technology intensity [49].In summary, firstly, the literature explored the influence of industrial agglomeration on urban innovation from multiple perspectives and proved there was correlativity between them. However, the conclusions of empirical studies are still indecisive in different objects and backgrounds. Above all, little empirical evidence has so far been published to confirm a clear interrelationship between high-tech industry agglomeration and urban innovation. Secondly, existing studies have mostly discussed the relationship between industrial agglomeration, industrial structure, and regional innovation, but failed to clarify the mechanism of industrial structure optimization in the effect of high-tech industrial agglomeration on urban innovation. Thirdly, present literature lacks of the role of industrial attributes including technology intensity and capital intensity playing in the effects of high-tech industry agglomeration on urban innovation. Finally, heterogeneous effects of high-tech industry agglomeration on urban innovation have been generally ignored. These points have been barriers to the exertion of innovation effect of high-tech industry agglomeration.In this paper, we employed the city-level panel data of the China’s Yangtze River Delta urban agglomeration from 2010 to 2019 to analyze the interrelationship between high-tech industrial agglomeration and urban innovation. Specifically, we took industrial structure optimization into the research framework, divided industrial structure optimization into the advancement of industrial structure and rationalization of industrial structure rationality, and studied the mechanism of high-tech industry agglomeration—industrial structure optimization—urban innovation output. After that, we examined whether the technology intensity and capital intensity have threshold effect between high-tech industry agglomeration and urban innovation. In addition, we studied heterogeneous effects of high-tech industry agglomeration on urban innovation in different time stages, urban innovation development stages, and urban circles. This study seeks to fill gaps of the present studies and provides theoretical basis and empirical experience for high-tech industry agglomeration promoting innovative level in China more effectively.


## 2.1. Industrial Agglomeration and Innovation

Agglomeration theory was first proposed by Alfred Marshall in his Principles of Economics, which addresses the phenomenon of similar firms locating in geographic proximity to one another [5]. Important works by scholars such as Krugman [6], Porter [7], and other scholars have further advanced and helped proliferate the study of industrial agglomeration and industry clusters among academic circles. Early research focused on exploring the emergence and development of industrial cluster [8–10] and the implications of industrial agglomeration for regional economic growth [11, 12] and competitiveness [7]. It is widely believed that industrial agglomeration could promote economic growth.Innovation has always attracted widespread attention from scholars. Studies regarding geographic economies stress the determinants of regional variance in innovation. Agglomeration externalities have been considered as a driver of innovation among the regional variances [13]. Marshall pointed out that specialized agglomeration could cause MAR spillover and thus affect the efficiency of technology innovation [5]. Since researchers have explored the effects of agglomeration on firm innovation [14]. Baptista and Swann found manufacturing agglomeration could motivate firm innovation [15]. The firms in industrial cluster are more likely to have higher innovation output than other noncluster enterprises [16]. Similarly, Beaudry and Breschi found industrial agglomeration has a significant positive effect on innovation performance [17]. Turkina et al. examined the relationship between regional industrial agglomeration and firm innovation performance under interactionist lenses [18]. At the same time, Dalum et al. argued that industrial agglomeration could reduce the costs of innovative activities and then promote regional innovation growth [19]. Boix and Galletto found Marshallian industrial districts of Spanish generated more innovation products than national average, and it means that specialization agglomerations of manufacturing have a positive impact on regional innovation output [16]. Industrial agglomeration provides regions with considerable specialized resources and then drives the process of regional innovation [20]. Silvestre and Dalcol took oil and gas industry in Brazil as an example, analyzed the effect of industrial agglomeration on regional innovation, and indicated that industrial agglomeration had a positive impact on innovative activities of region [21]. VDC and Garcia pointed that the advantages of local industrial agglomeration were important for a higher performance of regional innovation [22]. Lee and Sohn confirmed that agglomeration externalities had a positive association with exploitative innovation of regions [13]. Related studies have proved that agglomeration of similar industry should drive the creation of innovative start-ups and enhance regional innovation level accordingly [23]. Industrial agglomeration has been seen a precondition for regional innovation and start-up activity [24]. However, Audretsch and Feldman argued that American specialized industry agglomeration had a negative effect on regional innovation output [25].There is also literature related to the influence of industrial agglomeration on regional innovation in China. Liu et al. explored the impact of industrial agglomeration on regional innovation in China. They revealed that high-tech industrial agglomeration affected positively on regional innovation within the province [26]. Zhang investigated the effect of agglomeration on product innovation based on China’s firm-level data and concluded that industrial agglomeration contributes to product innovation [27]. Xie et al. demonstrated a positive effect between high-tech industrial agglomeration and innovation in China’s science park [28]. Jin et al. found that industrial clusters strengthened regional innovation and improved regional competitiveness by reducing production costs [29]. Zhang et al. found high-tech industrial agglomeration promoted regional innovation performance significantly in China [3]. But there are some different points. Based on provincial panel data, Xie and Wu argued that industrial agglomeration could promote innovation efficiency in a reasonable range, and when the level of agglomeration exceeded the range, further industrial agglomeration would reduce the innovation efficiency [30]. Jiang and Gao selected creative industrial agglomeration in Shanghai and Shenzhen as samples, and explored the effect of creative industrial agglomeration on enterprises’ innovation performance. The results indicated that the relationship between them presents an inverted U-shape [31]. Xiong and Li found that there was a nonlinear relationship between high-tech industry agglomeration and regional technological innovation within the provincial scope of China [32].


## 2.2. Industrial Agglomeration, Innovation, and Industrial Structure Optimization

Industrial structure optimization is the essential requirement of speeding up China’s high-quality development and innovation-driven transformation. Scholars discussed how to promote the economic effect of industrial agglomeration from the perspective of industrial structure. Industrial agglomeration and structural optimization often go hand in hand, showing a synergistic effect on economic development [33]. On the one hand, from the direct impact of industrial agglomeration on industrial structure optimization, Wu and Yang found that industrial agglomeration could improve the efficiency of manufacturing industry, and then promoted the optimization of industrial structure effectively in China [34]. Specialized agglomeration of service industry would promote the advancement of industrial structure but hinder the rationalization of industrial structure [35]. Scholars also focused on the effect of industrial structure upon industrial agglomeration. Li et al. revealed that regional industrial structure played an important role in localization agglomeration, and pointed out highly concentrated industrial structure might weaken local industrial agglomeration [36]. On the other hand, some scholars explored the relationship between industrial structure optimization and national innovation development. Industrial structure would attract new firms and promote regional innovation level [37]. Capello et al. verified industrial structure played a mediating role between the presence of cities in European NUTS2 regions and its innovation performance [38]. Li and Nie used data at provincial level in China and indicated that the advancement of industrial structure had a positive effect on regional innovation [39]. Ning et al. found that specialized industrial structures provided a vibrant environment for local innovation by absorbing FDI spillovers in China [40]. Zhao found that industrial structure optimization would improve innovative efficiency within the province in China, and there was a “spiraling relationship” between them [41]. Innocenti et al. pointed that variety of the local industrial structure increased the regional innovation capacity by influencing the structure of local knowledge networks [42]. Lao et al. demonstrated that industrial structure enhanced the level of cities’ innovation in China [43].


## 2.3. Industrial Agglomeration, Innovation, and Industrial Attributes

The exertion of industrial agglomeration effects is dependent on industrial attributes [44, 45]. Technology intensity and capital intensity are two important features in the industrial production process. They are associated with innovation systems and industrial growth [46]. In existing literature, Xiao et al. considered technology intensity was widely suggested to be linked with competition strategies of firms in China [47]. Ugur et al. argued that the relationship between technology intensity and firm survival followed an inverted U pattern [48]. Liang and Goetz suggested the actual economic impacts of industrial agglomeration were closely related to technology intensity [49].In summary, firstly, the literature explored the influence of industrial agglomeration on urban innovation from multiple perspectives and proved there was correlativity between them. However, the conclusions of empirical studies are still indecisive in different objects and backgrounds. Above all, little empirical evidence has so far been published to confirm a clear interrelationship between high-tech industry agglomeration and urban innovation. Secondly, existing studies have mostly discussed the relationship between industrial agglomeration, industrial structure, and regional innovation, but failed to clarify the mechanism of industrial structure optimization in the effect of high-tech industrial agglomeration on urban innovation. Thirdly, present literature lacks of the role of industrial attributes including technology intensity and capital intensity playing in the effects of high-tech industry agglomeration on urban innovation. Finally, heterogeneous effects of high-tech industry agglomeration on urban innovation have been generally ignored. These points have been barriers to the exertion of innovation effect of high-tech industry agglomeration.In this paper, we employed the city-level panel data of the China’s Yangtze River Delta urban agglomeration from 2010 to 2019 to analyze the interrelationship between high-tech industrial agglomeration and urban innovation. Specifically, we took industrial structure optimization into the research framework, divided industrial structure optimization into the advancement of industrial structure and rationalization of industrial structure rationality, and studied the mechanism of high-tech industry agglomeration—industrial structure optimization—urban innovation output. After that, we examined whether the technology intensity and capital intensity have threshold effect between high-tech industry agglomeration and urban innovation. In addition, we studied heterogeneous effects of high-tech industry agglomeration on urban innovation in different time stages, urban innovation development stages, and urban circles. This study seeks to fill gaps of the present studies and provides theoretical basis and empirical experience for high-tech industry agglomeration promoting innovative level in China more effectively.


## 3. Methodology

### 3.1. Models

The nature of innovation is knowledge production in which innovative production factors and conditions are put into innovative production system. Griliches first proposed the knowledge production function [50]. Jaffe further improved the function and considered innovative production inputs including R&D labor and R&D capital [51]. The improved knowledge production function is as follows:(1)Y=fK,L.This study takes high-tech industry agglomeration and control variables into account in the innovative production function and establishes expanded region innovation production function following Cobb–Douglas as follows:(2)RINNit=A×AGGLOitα1×RDLitα2×RDKitα3×Zitβ×εit,where i and t, respectively, refer to the city and the year, A represents constant, αi (i=1,2,3) and β is elastic coefficient, and ε is the stochastic error term. RINN refers to urban innovation output, AGGLO represents high-tech industry agglomeration of cities, RDL and RDK separately refer to investment of R&D labor and R&D capital, and Z stands for the control variables including economic development level of cities (PGDP), urban opening level (OPEN), urban education level (EDU), urban infrastructure level (INF), urban financial level (FINA), and government scale (GOV).We take the natural log of each variable. The basic empirical model becomes(3)lnRINNit=A+α1lnAGGLOit+α2lnRDLit+α3lnRDKit+βZit+μi+εit.Firstly, we test the interaction effect of high-tech industry agglomeration on urban innovation output based on empirical model as follows:(4)lnAGGLOit=A+γ1lnRINNit+γ2lnRDLit+γ3lnRDKit+δZit+μi+εit.Then, we take industrial structure optimization as mediating variable and aim to test whether the influence mechanism of high-tech industry agglomeration to urban innovation output is realized by industrial structure optimization. We adopt the principles of the mediating effect testing method [52, 53] to establish the test model of mediating effect:(5)lnRINNit=A+α1lnAGGLOit+α2lnRDLit+α3lnRDKit+βZit+μi+εit,(6)lnSTRUit=B+β1lnAGGLOit+β2Zit+μi+εit,(7)lnRINNit=C+γ1lnAGGLOit+γ2lnSTRUit+γ3lnRDLit+γ4lnRDKit+γ5Zit+μi+εit,where STRU represents the industrial structure optimization including the advancement of industrial structure (STRUH) and the rationalization of industrial structure (STRUR).Finally, this study adopts panel data threshold model proposed by Hansen [54] and takes technology intensity and capital intensity as the threshold variables to establish the following panel threshold model based on model (1):(8)lnRINNOit=A+δ1lnAGGLOitIlnINTENSITYit≤γ+δ2lnAGGLOitIlnINTENSITYit>γ+δ3Zit+εit,where INTENSITY includes technology intensity (TI) and capital intensity (CI).


### 3.2. Variables and Data Sources

#### 3.2.1. Dependent Variable

Urban innovation output (RINN): Patent is an important embodiment of regional innovation output [55], and it could reflect the regional innovation relatively truly [56]. We consider that the number of patent applications is less affected by human factors and policy factors, and urban patent applications do not have the time lag involved in patent authorization. Therefore, we select the quantity of urban patent applications to present urban innovation output in this paper.


#### 3.2.2. Independent Variable

High-tech industry agglomeration (AGGLO): The key to urban innovation depends on high-tech industry agglomeration. This paper uses the locational entropy to measure the agglomeration level of high-tech industries [57]. The calculation formula is as follows:(9)AGGLOitH=xitH/xitxtH/xt,where H refers to high-tech industry, xitH/xit refers to the proportion of the high-tech industry output of i city in t period, and xtH/xt is the proportion of the high-tech industry output of the county in t period.


#### 3.2.3. Mediating Variable

Industrial structure optimization (STRU): Urban high-tech industry agglomeration and industrial structure optimization are both important economic variables of urban innovation. High-tech industry agglomeration should affect urban industrial structure, and industrial structure optimization should have an impact on urban innovation output. Therefore, industrial structure optimization is an important variable between high-tech industry agglomeration and urban innovation output. This paper adopts the advancement of industrial structure (STRUH) and the rationalization of industrial structure (STRUR) to reflect the industrial structure optimization. The calculation formulas are as follows:(10)STRUHit=∑m=13YitmYit∗m,m=1,2,3,where Yitm/Yit represents the proportion of three industries to the local GDP of i city in t period. m=1,2,3, respectively, refers to primary industry, secondary industry, tertiary industry.We use re-defined Theil index [58] to describe the industrial structure rationality:(11)STRURitm=∑m=13YitmYitlnYitm/LitmYit/Lit,where Litm is employment of three industries of i city in t period. Lit is number of local employees in t period. Theil index retains the economic meaning of structural deviation degree. If STRURit is equal to 0, it indicates that the industrial structure has maintained an equilibrium state. If STRURit is not equal to 0, it indicates that the industrial structure has deviated from the equilibrium state. So, STRURitmis not equal to 0, it indicates that the industrial structure has deviated from the equilibrium state.


#### 3.2.4. Threshold Variables

Technology intensity (TI) and capital intensity (CI): The industrial attributes of urban include technology intensity and capital intensity playing a crucial role in the effects of high-tech industry agglomeration on urban innovation output. Technology intensity (TI) consists of R&D intensity and R&D human capital intensity. R&D intensity is measured by ratio of R&D expenditure to main business income. R&D human capital intensity is measured by ratio of R&D researchers to the total employed population of the cities. Capital intensity (CI) is ratio of actual net value of fixed assets to the number of employees. (This paper takes 2009 as the base period and deflates nominal net value of fixed assets based on price indices of investment in fixed assets. R&D expenditure’s deflator bases on price indices of proportion of national expenditure on personnel and labor services to expenditure on equipment. PGDP’s deflator bases on CPI. Actual GDP is based on GDP deflator.)


#### 3.2.5. Control Variables

Investment of R&D labor (RDL) is measured by number R&D researchers, and investment of R&D capital (RDK) is measured by actual internal R&D expenditure. Economic development level of cities (PGDP) is measured by actual urban per capita GDP. Urban opening level (OPEN) is measured by ratio of actual total exports and imports to actual GDP. Urban education level (EDU) is measured by ratio of expenditure on education to GDP. Urban infrastructure level (INF) is measured by the number of buses per 10,000 people. Urban financial level (FINA) is measured by ratio of year-end deposit and loan balance of financial institutions to GDP. Government scale (GOVS) is measured by ratio of government public financial expenditure to GDP.


#### 3.2.6. Data Source

This paper selected panel data from 2010 to 2019. And the samples include 26 cities in the Yangtze River Delta urban agglomeration. The dataset is sourced from the statistical yearbooks of Shanghai, Jiangsu Province, Zhejiang Province, and Anhui Province, China city statistical yearbook, China Urban Statistical Yearbooks, and statistical documents of science and technology bureaus among provinces and cities in Yangtze River Delta urban agglomeration. Variables’ descriptive statistics are shown in Table1.Table 1
Descriptive statistics for the variables.
VariableObs.MeanStd.MinMaxRINN26033052.13030771.950579.000173586.000AGGLO2603.1430.8621.2215.748STRUH26012.9695.4883.26130.665STRUR26046.821207.8431.8582911.055TI2605.98e − 51.037e − 41.38e − 68.722e − 4CI2605.6068.6060.71850.291RDL26036437.27036102.59072.000207945.000RDK26050.38461.2100.534304.359PGDP26054762.78023396.75014462.960109199.100OPEN2600.4830.3900.0502.116EDU2600.0270.0080.0140.051INF26013.0415.6942.53027.470FINA2603.2471.1821.2357.124GVOS2600.7430.2000.2961.107


## 3.1. Models

The nature of innovation is knowledge production in which innovative production factors and conditions are put into innovative production system. Griliches first proposed the knowledge production function [50]. Jaffe further improved the function and considered innovative production inputs including R&D labor and R&D capital [51]. The improved knowledge production function is as follows:(1)Y=fK,L.This study takes high-tech industry agglomeration and control variables into account in the innovative production function and establishes expanded region innovation production function following Cobb–Douglas as follows:(2)RINNit=A×AGGLOitα1×RDLitα2×RDKitα3×Zitβ×εit,where i and t, respectively, refer to the city and the year, A represents constant, αi (i=1,2,3) and β is elastic coefficient, and ε is the stochastic error term. RINN refers to urban innovation output, AGGLO represents high-tech industry agglomeration of cities, RDL and RDK separately refer to investment of R&D labor and R&D capital, and Z stands for the control variables including economic development level of cities (PGDP), urban opening level (OPEN), urban education level (EDU), urban infrastructure level (INF), urban financial level (FINA), and government scale (GOV).We take the natural log of each variable. The basic empirical model becomes(3)lnRINNit=A+α1lnAGGLOit+α2lnRDLit+α3lnRDKit+βZit+μi+εit.Firstly, we test the interaction effect of high-tech industry agglomeration on urban innovation output based on empirical model as follows:(4)lnAGGLOit=A+γ1lnRINNit+γ2lnRDLit+γ3lnRDKit+δZit+μi+εit.Then, we take industrial structure optimization as mediating variable and aim to test whether the influence mechanism of high-tech industry agglomeration to urban innovation output is realized by industrial structure optimization. We adopt the principles of the mediating effect testing method [52, 53] to establish the test model of mediating effect:(5)lnRINNit=A+α1lnAGGLOit+α2lnRDLit+α3lnRDKit+βZit+μi+εit,(6)lnSTRUit=B+β1lnAGGLOit+β2Zit+μi+εit,(7)lnRINNit=C+γ1lnAGGLOit+γ2lnSTRUit+γ3lnRDLit+γ4lnRDKit+γ5Zit+μi+εit,where STRU represents the industrial structure optimization including the advancement of industrial structure (STRUH) and the rationalization of industrial structure (STRUR).Finally, this study adopts panel data threshold model proposed by Hansen [54] and takes technology intensity and capital intensity as the threshold variables to establish the following panel threshold model based on model (1):(8)lnRINNOit=A+δ1lnAGGLOitIlnINTENSITYit≤γ+δ2lnAGGLOitIlnINTENSITYit>γ+δ3Zit+εit,where INTENSITY includes technology intensity (TI) and capital intensity (CI).


## 3.2. Variables and Data Sources

### 3.2.1. Dependent Variable

Urban innovation output (RINN): Patent is an important embodiment of regional innovation output [55], and it could reflect the regional innovation relatively truly [56]. We consider that the number of patent applications is less affected by human factors and policy factors, and urban patent applications do not have the time lag involved in patent authorization. Therefore, we select the quantity of urban patent applications to present urban innovation output in this paper.


### 3.2.2. Independent Variable

High-tech industry agglomeration (AGGLO): The key to urban innovation depends on high-tech industry agglomeration. This paper uses the locational entropy to measure the agglomeration level of high-tech industries [57]. The calculation formula is as follows:(9)AGGLOitH=xitH/xitxtH/xt,where H refers to high-tech industry, xitH/xit refers to the proportion of the high-tech industry output of i city in t period, and xtH/xt is the proportion of the high-tech industry output of the county in t period.


### 3.2.3. Mediating Variable

Industrial structure optimization (STRU): Urban high-tech industry agglomeration and industrial structure optimization are both important economic variables of urban innovation. High-tech industry agglomeration should affect urban industrial structure, and industrial structure optimization should have an impact on urban innovation output. Therefore, industrial structure optimization is an important variable between high-tech industry agglomeration and urban innovation output. This paper adopts the advancement of industrial structure (STRUH) and the rationalization of industrial structure (STRUR) to reflect the industrial structure optimization. The calculation formulas are as follows:(10)STRUHit=∑m=13YitmYit∗m,m=1,2,3,where Yitm/Yit represents the proportion of three industries to the local GDP of i city in t period. m=1,2,3, respectively, refers to primary industry, secondary industry, tertiary industry.We use re-defined Theil index [58] to describe the industrial structure rationality:(11)STRURitm=∑m=13YitmYitlnYitm/LitmYit/Lit,where Litm is employment of three industries of i city in t period. Lit is number of local employees in t period. Theil index retains the economic meaning of structural deviation degree. If STRURit is equal to 0, it indicates that the industrial structure has maintained an equilibrium state. If STRURit is not equal to 0, it indicates that the industrial structure has deviated from the equilibrium state. So, STRURitmis not equal to 0, it indicates that the industrial structure has deviated from the equilibrium state.


### 3.2.4. Threshold Variables

Technology intensity (TI) and capital intensity (CI): The industrial attributes of urban include technology intensity and capital intensity playing a crucial role in the effects of high-tech industry agglomeration on urban innovation output. Technology intensity (TI) consists of R&D intensity and R&D human capital intensity. R&D intensity is measured by ratio of R&D expenditure to main business income. R&D human capital intensity is measured by ratio of R&D researchers to the total employed population of the cities. Capital intensity (CI) is ratio of actual net value of fixed assets to the number of employees. (This paper takes 2009 as the base period and deflates nominal net value of fixed assets based on price indices of investment in fixed assets. R&D expenditure’s deflator bases on price indices of proportion of national expenditure on personnel and labor services to expenditure on equipment. PGDP’s deflator bases on CPI. Actual GDP is based on GDP deflator.)


### 3.2.5. Control Variables

Investment of R&D labor (RDL) is measured by number R&D researchers, and investment of R&D capital (RDK) is measured by actual internal R&D expenditure. Economic development level of cities (PGDP) is measured by actual urban per capita GDP. Urban opening level (OPEN) is measured by ratio of actual total exports and imports to actual GDP. Urban education level (EDU) is measured by ratio of expenditure on education to GDP. Urban infrastructure level (INF) is measured by the number of buses per 10,000 people. Urban financial level (FINA) is measured by ratio of year-end deposit and loan balance of financial institutions to GDP. Government scale (GOVS) is measured by ratio of government public financial expenditure to GDP.


### 3.2.6. Data Source

This paper selected panel data from 2010 to 2019. And the samples include 26 cities in the Yangtze River Delta urban agglomeration. The dataset is sourced from the statistical yearbooks of Shanghai, Jiangsu Province, Zhejiang Province, and Anhui Province, China city statistical yearbook, China Urban Statistical Yearbooks, and statistical documents of science and technology bureaus among provinces and cities in Yangtze River Delta urban agglomeration. Variables’ descriptive statistics are shown in Table1.Table 1
Descriptive statistics for the variables.
VariableObs.MeanStd.MinMaxRINN26033052.13030771.950579.000173586.000AGGLO2603.1430.8621.2215.748STRUH26012.9695.4883.26130.665STRUR26046.821207.8431.8582911.055TI2605.98e − 51.037e − 41.38e − 68.722e − 4CI2605.6068.6060.71850.291RDL26036437.27036102.59072.000207945.000RDK26050.38461.2100.534304.359PGDP26054762.78023396.75014462.960109199.100OPEN2600.4830.3900.0502.116EDU2600.0270.0080.0140.051INF26013.0415.6942.53027.470FINA2603.2471.1821.2357.124GVOS2600.7430.2000.2961.107


## 3.2.1. Dependent Variable

Urban innovation output (RINN): Patent is an important embodiment of regional innovation output [55], and it could reflect the regional innovation relatively truly [56]. We consider that the number of patent applications is less affected by human factors and policy factors, and urban patent applications do not have the time lag involved in patent authorization. Therefore, we select the quantity of urban patent applications to present urban innovation output in this paper.


## 3.2.2. Independent Variable

High-tech industry agglomeration (AGGLO): The key to urban innovation depends on high-tech industry agglomeration. This paper uses the locational entropy to measure the agglomeration level of high-tech industries [57]. The calculation formula is as follows:(9)AGGLOitH=xitH/xitxtH/xt,where H refers to high-tech industry, xitH/xit refers to the proportion of the high-tech industry output of i city in t period, and xtH/xt is the proportion of the high-tech industry output of the county in t period.


## 3.2.3. Mediating Variable

Industrial structure optimization (STRU): Urban high-tech industry agglomeration and industrial structure optimization are both important economic variables of urban innovation. High-tech industry agglomeration should affect urban industrial structure, and industrial structure optimization should have an impact on urban innovation output. Therefore, industrial structure optimization is an important variable between high-tech industry agglomeration and urban innovation output. This paper adopts the advancement of industrial structure (STRUH) and the rationalization of industrial structure (STRUR) to reflect the industrial structure optimization. The calculation formulas are as follows:(10)STRUHit=∑m=13YitmYit∗m,m=1,2,3,where Yitm/Yit represents the proportion of three industries to the local GDP of i city in t period. m=1,2,3, respectively, refers to primary industry, secondary industry, tertiary industry.We use re-defined Theil index [58] to describe the industrial structure rationality:(11)STRURitm=∑m=13YitmYitlnYitm/LitmYit/Lit,where Litm is employment of three industries of i city in t period. Lit is number of local employees in t period. Theil index retains the economic meaning of structural deviation degree. If STRURit is equal to 0, it indicates that the industrial structure has maintained an equilibrium state. If STRURit is not equal to 0, it indicates that the industrial structure has deviated from the equilibrium state. So, STRURitmis not equal to 0, it indicates that the industrial structure has deviated from the equilibrium state.


## 3.2.4. Threshold Variables

Technology intensity (TI) and capital intensity (CI): The industrial attributes of urban include technology intensity and capital intensity playing a crucial role in the effects of high-tech industry agglomeration on urban innovation output. Technology intensity (TI) consists of R&D intensity and R&D human capital intensity. R&D intensity is measured by ratio of R&D expenditure to main business income. R&D human capital intensity is measured by ratio of R&D researchers to the total employed population of the cities. Capital intensity (CI) is ratio of actual net value of fixed assets to the number of employees. (This paper takes 2009 as the base period and deflates nominal net value of fixed assets based on price indices of investment in fixed assets. R&D expenditure’s deflator bases on price indices of proportion of national expenditure on personnel and labor services to expenditure on equipment. PGDP’s deflator bases on CPI. Actual GDP is based on GDP deflator.)


## 3.2.5. Control Variables

Investment of R&D labor (RDL) is measured by number R&D researchers, and investment of R&D capital (RDK) is measured by actual internal R&D expenditure. Economic development level of cities (PGDP) is measured by actual urban per capita GDP. Urban opening level (OPEN) is measured by ratio of actual total exports and imports to actual GDP. Urban education level (EDU) is measured by ratio of expenditure on education to GDP. Urban infrastructure level (INF) is measured by the number of buses per 10,000 people. Urban financial level (FINA) is measured by ratio of year-end deposit and loan balance of financial institutions to GDP. Government scale (GOVS) is measured by ratio of government public financial expenditure to GDP.


## 3.2.6. Data Source

This paper selected panel data from 2010 to 2019. And the samples include 26 cities in the Yangtze River Delta urban agglomeration. The dataset is sourced from the statistical yearbooks of Shanghai, Jiangsu Province, Zhejiang Province, and Anhui Province, China city statistical yearbook, China Urban Statistical Yearbooks, and statistical documents of science and technology bureaus among provinces and cities in Yangtze River Delta urban agglomeration. Variables’ descriptive statistics are shown in Table1.Table 1
Descriptive statistics for the variables.
VariableObs.MeanStd.MinMaxRINN26033052.13030771.950579.000173586.000AGGLO2603.1430.8621.2215.748STRUH26012.9695.4883.26130.665STRUR26046.821207.8431.8582911.055TI2605.98e − 51.037e − 41.38e − 68.722e − 4CI2605.6068.6060.71850.291RDL26036437.27036102.59072.000207945.000RDK26050.38461.2100.534304.359PGDP26054762.78023396.75014462.960109199.100OPEN2600.4830.3900.0502.116EDU2600.0270.0080.0140.051INF26013.0415.6942.53027.470FINA2603.2471.1821.2357.124GVOS2600.7430.2000.2961.107


## 4. Empirical Results and Discussion

Before the panel data regression analysis, we used unit root test and co-integration test to determine whether there were unit root and pseudo-regression problems. LLC, IPS, and Fisher test are used in this paper, and the results of unit root test are shown in Table2. The statistics of each variable are almost significant under the three methods, indicating that there is no unit root problem. Kao test was further used for co-integration test. The result showed statistic (−3.993, p≤0.01) is significant. It indicated that there was a long-term stable equilibrium relationship among the variables, which could be used for regression analysis.Table 2
Unit root test results.
VariableStatisticsLLC testIPS testFisher testln RINN−5.438∗∗∗−4.347∗∗∗150.859∗∗∗ln AGGLO−9.104∗∗∗−5.528∗∗∗143.516∗∗∗ln RDL−13.965∗∗∗−5.380∗∗∗233.758∗∗∗ln RDK−11.479∗∗∗−7.172∗∗∗24.831ln PGDP−20.235∗∗∗−16.648∗∗∗70.355∗∗ln OPEN−4.291∗∗∗−4.658∗∗∗137.703∗∗∗ln EDU−18.898∗∗∗−6.169∗∗∗146.545∗∗∗ln INF−7.875∗∗∗−5.581∗∗∗130.583∗∗∗ln FINA−4.562∗∗∗−3.095∗∗∗120.727∗∗∗ln GOVS−5.803∗∗∗−4.582∗∗∗96.999∗∗∗ln STRUH−7.867∗∗∗−1.812∗∗84.586∗∗∗ln STRUR−4.820∗∗∗−4.412∗∗∗113.170∗∗∗ln TI−6.387∗∗∗−5.228∗∗∗101.741∗∗∗ln CI−3.782∗∗∗−5.065∗∗∗65.590∗


### 4.1. Interaction Effect Test

We used F test and B-P test to determine whether mixed OLS model or fixed effect model, or mixed OLS model or random effect model was better separately. The F test (16.180,p≤0.01) and B-P test (213.970, p≤0.01) statistics are statistically significant, indicating that both fixed and random models are better than a mixed model. On this basis, we used Hausman test to determine the fixed effect and random effect. The Hausman test (−9.990) indicated that we should select random model to estimate basic model. According to Table 3, in model (1), the influence coefficient of high-tech industry agglomeration is positive and significant at the 1% level, indicating that high-tech industry agglomeration has a positive impact on the urban innovation output in China’s Yangtze River Delta urban agglomeration. The reasons for this finding could be explained from three aspects. Firstly, high-tech industry agglomeration would produce scale economy effect and consequently reduce innovation costs to increase regional innovation output [59]. Secondly, the agglomeration of high-tech industries strengthens the innovation cooperation and knowledge exchange among enterprises, and accelerates the knowledge and technology spillover effects among enterprises in the agglomeration area [60]. The knowledge and technology spillover of high-tech industry agglomeration enables enterprises to obtain knowledge, innovative information, and materials more easily, thus stimulating innovative activities and generating more innovative output. Thirdly, high-tech industry agglomeration could promote the agglomeration of innovative elements and resources, and enterprises could acquire resources and innovative elements at a low cost. At the same time, similar enterprises and related enterprises have closer connection with each other thanks to the high-tech industrial agglomeration in China’s Yangtze River Delta urban agglomeration. It reduces the cost of information communication and innovation cooperation among enterprises in the agglomeration areas. Thus, cost reduction encourages innovation and increases urban innovation output.Table 3
Interaction effect test results.
VariableModel (1)Model (2)ln RINNln AGGLOln AGGLO0.283∗∗ (2.460)ln RINN0.080∗∗ (2.360)ln RDL0.333∗∗∗ (6.350)0.105∗∗∗ (3.420)ln RDK0.487∗∗∗ (4.610)−0.164∗∗ (−2.540)ln PGDP0.022 (0.080)0.324∗∗ (2.120)ln OPEN−0.303∗∗∗ (−3.180)0.040 (0.730)ln EDU0.571∗∗∗ (3.380)0.053 (0.570)ln INF0.308∗∗∗ (3.330)0.038 (0.740)ln FINA0.742∗∗∗ (3.700)−0.324∗∗∗ (−2.830)ln GOVS0.490∗∗ (1.980)−0.173 (−1.270)_cons4.823∗ (1.910)−3.250 (−2.160)Within−R−sq0.6440.243Hausman−9.99010.590High-tech industry agglomeration and urban innovation could be interactive in theory. This paper tests whether there is an interaction between high-tech industry agglomeration and urban innovation output in Yangtze River Delta urban agglomeration and what kind of interaction there is. We further analyze the estimated results for the effect of urban innovation on high-tech industry agglomeration in model (2). The influence coefficient of urban innovation is significantly positive at the level of 5%, influence coefficient is 0.080, and it suggests that urban innovation positively affects high-tech industry agglomeration. The reasons for this result include the following aspects: the first reason is innovation input effect. The increase of urban innovation output encourages the government and enterprises in Yangtze River Delta urban agglomeration to invest in R&D and innovation. They could provide multi-party support for high-tech industry agglomeration and form innovative force to further promote the development of high-tech industry cluster. The second reason is “the pole effects.” According to the center-periphery theory, the spatial agglomeration and reorganization of various elements in the process of innovation could attract many enterprises and innovative resources to gather and improve the level of industrial agglomeration. The third reason is the economic effect. The improvement of urban innovation output has brought about the improvement of economic benefits. The profit-driven factors lead to the continuous inflow of production factors after the improvement of economic benefits, which form the agglomeration effect through recycling and accumulation, and promote the further development of high-tech industry agglomeration. In addition, urban innovation promotes high-tech industry agglomeration of Yangtze River Delta urban agglomeration through high-tech industry upgrading effect. High-tech industry upgrading enhances the added value of products and pushes industries up the value chain to the medium-high end. It could also generate new industrial clusters and promote the agglomeration of high-tech industries in China’s Yangtze River Delta urban agglomeration.Therefore, there is a positive interaction relationship between high-tech industrial agglomeration and urban innovation output in China’s Yangtze River Delta urban agglomeration.The results of control variables in model (1) show that all control variables have positive effects on urban innovation output expect the urban opening level.


### 4.2. High-Tech Industry Agglomeration, Industrial Structure, and Urban Innovation

The agglomeration of urban high-tech industries and the optimization of urban industrial structure are both important to urban innovation and development. We further analyzed the influence mechanism of high-tech industrial agglomeration on urban innovation output in China’s Yangtze River Delta urban agglomeration. Industrial structure optimization is used (including industrial structure advancement and rationality) as the mediator to explore the role of industrial structure optimization between high-tech industrial agglomeration and urban innovation output. Table4 reports the estimate results.Table 4
Mediating effect test results.
VariableIndustrial structure upgrading of citiesIndustrial structure rationality of citiesModel (3) ln STRUHModel (4) ln RINNModel (5) ln STRURModel (6) ln RINNln AGGLO0.198∗∗∗ (4.340)0.305∗∗∗ (2.730)−0.830∗∗∗ (−4.260)0.558∗∗∗ (4.930)ln STRUH0.712∗∗∗ (4.790)ln STRUR0.135∗∗∗ (3.810)Sobel/indirect effect0.141∗∗∗ (3.215)−0.112∗∗∗ (−2.839)Direct effect0.305∗∗∗ (2.734)0.558∗∗∗ (4.935)Control variableYesYesYesYesR-squared0.8360.8620.5570.858ln RDL−0.013 (−0.800)0.184∗∗∗ (4.770)0.454∗∗∗ (6.490)0.114∗∗∗ (2.680)ln RDK0.119∗∗∗ (4.820)0.611∗∗∗ (10.110)−0.349∗∗∗ (−3.320)0.743∗∗∗ (12.370)ln PGDP1.027∗∗∗ (16.880)−1.199∗∗∗ (−5.730)0.357 (1.370)−0.515∗∗∗ (−3.540)lnOPEN−0.181∗∗∗ (−6.300)−0.025 (−0.340)0.813∗∗∗ (6.640)−0.263∗∗∗ (−3.540)ln EDU0.021 (0.270)0.729∗∗∗ (3.950)0.129 (0.380)0.726∗∗∗ (3.880)ln INF0.123∗∗∗ (4.180)0.153∗∗ (2.150)0.115 (0.920)0.225∗∗∗ (3.210)ln FINA−0.122∗∗ (−2.140)0.439∗∗∗ (3.230)−0.179 (−0.740)0.376∗∗∗ (2.750)ln GOVS−0.613∗∗∗ (-6.430)1.190∗∗∗ (4.920)0.453 (1.110)0.693∗∗∗ (3.040)Regarding the advancement of industrial structure, the influence coefficient of high-tech industry agglomeration on industrial structure advancement is positive and significant at 1% level in model (3). At the same time, the influence coefficients of industrial structure advancement on urban innovation output and high-tech industry agglomeration on urban innovation output are both positive and significant at 1% level in model (4). The results indicate that the advancement of industrial structure is a partly mediator between high-tech industrial agglomeration and urban innovation output. The advancement of industrial structure refers to the upgrading degree that industrial structure transforms from low to high added value, high technology, high intensification, and high processing. Industrial structure advancement is regarded as an important bridge for high-tech industrial agglomeration to enhance urban innovation output in Yangtze River Delta urban agglomeration. The reasons are the following. Firstly, high-tech industrial agglomeration would promote knowledge spillover and intra-industry technological innovation, then play the scale economy effect, and significantly promote the industrial structure advancement. The comprehensive effect of high-tech industry agglomeration and industrial structure advancement stimulate R&D investment and increase regional labor productivity, and further stimulate innovation and enhance local innovation output. Secondly, high-tech industrial agglomeration realizes industrial structure advancement through improving the innovative capability of urban. High-tech industrial agglomeration increases the formal and informal interaction between enterprises in the agglomeration area, facilitates the formation of social network relations, accelerates the diffusion of knowledge, promotes the generation of new knowledge, and then promotes urban innovation level and establishes urban innovation environment. Urban innovation environment would promote the technological innovation of high-tech enterprises in agglomeration area and encourage the innovation and upgrade of products and the upgrade of industrial chains. The upgrade of industrial chains would drive the development of related industries and promote the efficient allocation of production factors. It would gradually realize the industrial structure advancement which evolves from low added value and low technology industry to high added value and high-tech industry. Thirdly, high-tech industry agglomeration initiates the spatial agglomeration of production factors. The agglomeration effect and the resource allocation effect generated by the flow of factors could improve the quality and quantity of all kinds of production factors in the agglomeration area. The optimization of resource allocation efficiency makes innovative factors shift from declining industries to emerging industries and from labor-intensive industries to technology-intensive industries, and promotes industrial structure advancement. In addition, high-tech industry agglomeration reduces the cost of knowledge exchange and information search among high-tech enterprises, gives full play to the synergistic effect among enterprises, and promotes knowledge spillover and R&D innovation. These results contribute to improving product added value and urban innovation level, further promoting the advancement of industrial structure.Regarding the mediating effect of industrial structure rationality, the influence coefficient of high-tech industry agglomeration on industrial structure rationality is negative and significant at 1% level in model (5). The influence coefficient is −0.830. It demonstrates that high-tech industrial agglomeration has a negative influence on industrial structure rationality in China’s Yangtze River Delta urban agglomeration. Industrial structure rationality refers to optimization degree of inter-industry coordination and correlation. On the one hand, we stress specialized agglomeration of high-tech industries which emphasizes the agglomeration of similar enterprises and related enterprises in the same industry in this paper. Therefore, high-tech industry agglomeration would also produce negative externalities which are called crowding effect. The crowding effect would cause a series of problems, such as excessive consumption of innovation resources, scarcity of production factors, congestion of transportation infrastructure, and rising prices of land and labor factors, and thus lead to excessive competition among enterprises. Excessive agglomeration reduces the efficiency of industrial production and innovation, hinders the benign development of high-tech industries, and is not conducive to the industrial structure rationality. On the other hand, this paper mainly emphasizes the influence of specialized agglomeration of high-tech industries on the industrial structure rationality, but the industrial structure rationality lays more emphasis on the coordination and correlation between industries. There are many economic and technological connections among different high-tech industries, forming diversified agglomeration of high-tech industries. Therefore, the industrial structure rationality of cities in the Yangtze River Delta urban agglomeration might come more from the diversified agglomeration of high-tech industries.Industrial structure advancement and industrial structure rationality both have significantly positive effects on urban innovation output in China’s Yangtze River Delta urban agglomeration. The first reason for this finding is that industrial structure optimization drives the expansion of the market. Increasing enterprises enter the market and intensify the market competition. Enterprises begin to carry out product and technological innovation to seize the market share. The continuous expansion of the market would bring about the expansion of demand which urges enterprises to improve their innovation ability and meet the new demand through product and technological innovation. In other words, industrial structure optimization leads to the expansion of demand and encourages enterprises to innovate to increase urban innovation output [61]. The expansion of the market and demand brought by industrial structure optimization enhance the specialization of high-tech enterprises and deepen the internal division of labor within industries. They also encourage enterprises to concentrate limited resources to carry out activities of product development and innovation, thus improving the urban innovation output. Secondly, industrial structure optimization would further guide the development of high-tech industries and upgrading of traditional industries, stimulate the investment of innovation funds, and improve the urban innovation abilities. Thirdly, industrial structure optimization promotes competition and cooperation between cities, drives regional independent innovation, and enhances urban innovation output. To be specific, cities would introduce high-tech industries to enhance innovative competitiveness and avoid the convergence of industrial structure. At the same time, cities would also carry out collaborative innovation and give play to comparative advantages to solve the common key technologies of industry and realize technological breakthrough, and then improve innovative level. Finally, industrial structure optimization could provide a high-quality policy environment for the development of high-tech industries. It could encourage industrial innovation and improve urban innovation output through implementing preferential policies for high-tech industries and providing R&D subsidies.In summary, the advancement of industrial structure plays a positive intermediary role between high-tech industrial agglomeration and urban innovation output in Yangtze River Delta urban agglomeration. The mediating effect size is 0.141, and the proportion of mediating effect to total effect is 0.317. However, high-tech industry agglomeration could promote urban innovation output, but weaken the rationalization of industrial structure in Yangtze River Delta urban agglomeration. In this case, the negative impact of high-tech industry agglomeration on industrial structure rationality suppresses the driving effect of high-tech industry agglomeration on urban innovation output. In other words, high-tech industry agglomeration suppresses its incentive to urban innovation output by reducing the industrial structure rationality, which shows a suppressing effect [62–64], and the absolute proportion of indirect effect to direct effect is 0.200.


### 4.3. Threshold Effect of Technology Intensity and Capital Intensity

Some literature suggested that how urban innovation is affected by industrial agglomeration might depend on industrial level features [65, 66]. Among the industrial attributes that might influence industrial agglomeration and urban innovation, technology intensity and capital intensity attract attention. Therefore, based on the basic regression, this paper took industrial attribute variables (including technology intensity and capital intensity) as threshold variables to investigate the impact of high-tech industrial agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration. We tested the number, values, and significance of threshold. The results are shown in Tables 5 and 6. The single threshold effect of technology intensity and capital intensity is, respectively, significant at the level of 10% and 1%, while the double threshold and triple threshold fail the significance test. It indicates that we should employ single threshold model to investigate the threshold effect of technology intensity and capital intensity on the impact of high-tech industrial agglomeration on urban innovation output.Table 5
Threshold effect significance results of technology intensity.
Threshold modelFstatProbCritical valueThreshold95% confidence interval1%5%10%Single threshold21.830∗0.08045.87125.17319.352−11.671[−11.735, −11.646]Double threshold−0.7601.00023.30217.37614.967−11.671–11.080[−11.646, −11.473] [−12.154, −11.074]Triple threshold4.7300.54315.29911.1279.451−11.049[−11.059, −11.024]Table 6
Threshold effect significance results of capital intensity.
Threshold modelFstatProbCritical valueThreshold95% confidence interval1%5%10%Single threshold45.240∗∗∗0.00030.61525.56522.158−0.118[−0.255, −0.078]Double threshold14.0500.32391.47067.27743.618−0.118 2.246[−0.255, −0.078] [2.231, 2.272]Triple threshold9.0800.63098.96260.72545.2591.697[1.662, 1.702]We estimate and test the threshold value of single threshold model based on the threshold effect test. According to the estimated threshold values in Tables5 and 6, technology intensity and capital intensity have a threshold effect between high-tech industrial agglomeration and urban innovation output in the Yangtze River Delta urban agglomeration. The threshold values are, respectively, 9.012E − 06 and 1.125. Then, we estimate the threshold regression model according to the threshold values obtained. Table 7 reports the estimate results.Table 7
Threshold model estimate results.
VariableTechnology intensity model (7)Capital intensity model (8)CoefficienttCoefficienttln AGGLO(TI ≤ 9.012E − 06)0.684∗∗∗4.360ln AGGLO(TI > 9.012E − 06)0.205∗1.800ln AGGLO(CI ≤ 1.125)−3.803∗∗∗−5.900ln AGGLO(CI > 1.125)0.227∗∗2.100ln RDL0.161∗∗2.2900.214∗∗∗3.280ln RDK−5.751∗∗−2.460−3.345−1.500ln PGDP8.692∗∗∗3.6606.207∗∗∗2.770ln OPEN0.0940.6600.0140.010ln EDU0.347∗∗2.0000.425∗∗2.550ln INF0.253∗∗2.5400.190∗∗2.020ln FINA0.3261,2800.3401.380ln GOVS0.641∗∗2.3400.717∗∗∗2.780_cons−67.282∗∗∗−3.690−47.722∗∗∗−2.780F15.070∗∗∗20.390∗∗∗Within−R−sq0.6880.724Regarding the threshold effect of technology intensity, the estimation results of model (7) show that when the technology intensity is lower than 9.012E − 06, high-tech industry agglomeration has a significant positive impact on urban innovation output in Yangtze River Delta urban agglomeration. Under this threshold value of technology intensity, a 1% higher high-tech industry agglomeration boosts the coefficient of urban innovation output by 0.684%. The high-tech industry agglomeration in the Yangtze River Delta urban agglomeration also has a significant positive impact on the urban innovation output when the technology intensity is higher than 9.012E − 06. But the degree and significance of this positive impact decreased, a 1% higher high-tech industry agglomeration increases the coefficient of urban innovation output by 0.205%. The reasons are as follows: firstly, the higher the technology intensity of an industry indicates that the more capital and personnel are invested in R&D and technological innovation in the production process of an enterprise. Emerging and high-tech industries highly value the creation of new ideas and new technologies. The advancement, durability, and irreplaceability of technology are the core competitiveness of high-tech enterprises. Therefore, high-tech enterprises, especially those in agglomeration areas, have strong technological innovation willingness. Adequate inputs of R&D funds and scientific and technological talents stimulate the technological innovation willingness of high-tech enterprises in the agglomeration areas, thus enhancing the innovation performance of high-tech enterprises and improving the urban innovation output. At the same time, the high technology intensity could absorb more scientific and technological talents and form a good atmosphere for innovation. These results could further stimulate the investment in technological innovation and form a virtuous circle of innovation in high-tech industrial agglomeration areas. Therefore, the high level of technology intensity strengthens the positive impact of high-tech industrial agglomeration on urban innovation output. Secondly, the degree of technical complexity is relatively high in high-tech industry, and speed of technological upgrading is faster than that in other industries [64], so the formation of competitive advantage of high-tech industry is more dependent on technological innovation. For high-tech industries, the increase of technology intensity could provide support of R&D funds and personnel in various technological innovations in the early stage of high-tech industry agglomeration and the whole process of development. It could reduce the uncertainty of R&D innovation to a certain extent, improve the innovation efficiency of high-tech industries, and then enhance the urban innovation output. Thirdly, technology intensity directly reflects the level of technological contents in the production activities. The high level of technology intensity represents the high proportion of R&D funds and R&D personnel. It lays the foundation for high-tech enterprises in the agglomeration area to carry out technological innovation. The impetus and innovation space for technological innovation of high-tech industries is greater than other industries. The positive impact of high-tech industrial agglomeration on urban innovation output is also greater than others. However, technology intensity is not always better. When the R&D funds and talent reach saturation, excessive input would reduce the utilization efficiency and cause waste of resources, and would reduce the positive impact of high-tech industrial agglomeration on urban innovation output.Regarding the threshold effect of capital intensity, the estimation results of Model (7) show that high-tech industry agglomeration has a significant negative impact on urban innovation output when capital intensity is lower than 1.125. The influence of high-tech industry agglomeration on urban innovation output is positive when capital intensity overtakes threshold value. The reasons are the following: firstly, the high level of capital intensity represents the high ratio of capital consumption and labor consumption. Currently many industries, especially high-tech industries, have been transformed from labor intensive to capital intensive. High-tech industries with higher capital intensity especially concentrated industries tend to have lower costs and higher profits than other industries. These results encourage high-tech enterprises in the agglomeration areas to adopt innovation strategies and effectively integrate existing resources to obtain higher profits and sustained competitive advantages, and then improve efficiency of technological innovation and urban innovation output. Secondly, high-tech industries are essentially knowledge-intensive and technology-intensive industries. High-tech enterprises, especially in high-tech industrial agglomeration areas, have stronger demands for technological innovation. Technological innovation is an investment process highly dependent on capital. Capital is the driving force for technological innovation of high-tech industries. Capital plays a key role in the growth and development of high-tech enterprises in the agglomeration areas and provides multi-aspect support for technological innovation decision-making of high-tech enterprises. Therefore, the increasing capital intensity leads to high innovation motivation and strong innovation ability of high-tech enterprises in the agglomeration area, and the impact on the urban innovation output would also improve because of them. Thirdly, the success of technological innovation benefits from abundant and long-term capital support. The high level of capital intensity could provide powerful support for high-tech enterprises in the agglomeration areas. In addition, it could release a positive signal and provide a steady stream of talents for R&D and innovation activities through attracting R&D personnel with professional background and scientific and technological talents skilled in advanced technology. The high level of capital intensity is also conducive to support high-tech enterprises in the agglomeration areas to form independent R&D institutions or to cooperate with innovation subjects to form innovation institutions integrating industry, education, and research. That result would further promote urban innovation output and improve urban innovation level.


### 4.4. Heterogeneity Test and Further Discussion

The high-tech industrial agglomeration has a significantly positive impact on urban innovation output in China’s Yangtze River Delta urban agglomeration from the perspective of the whole sample. However, the heterogeneous impact in different conditions need to be further investigated. We discussed the spatiotemporal heterogeneity of the innovative effect of high-tech industrial agglomeration under different time stages, urban innovation development stages, and different urban circles. The results are shown in Table8.(1)
The positive promoting effect of high-tech industrial agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration from 2015 to 2019 is higher than that from 2010 to 2014. The elasticity coefficient of high-tech industrial agglomeration on urban innovation output increases from 0.270 to 0.395 in 2015–2019. As time goes on, the positive impact of high-tech industrial agglomeration on urban innovation output is constantly improving. It indicates that innovative high-tech industries gradually replace the industries which lack innovation and become an important force to support urban innovation and development. China could pay attention to the cultivation of high-tech industries, especially focusing on the agglomeration of high-tech industries; therefore, high-tech industries could fully release the positive effect of agglomeration and ensure urban innovation and development.(2)
The innovative development of cities is characterized by stages. We divide the stages of urban innovation development into higher stage (above the median) and lower stage (below the median) [67]. This paper explored the heterogeneity of the impact of high-tech industrial agglomeration on urban innovation output in different stages of innovation development in the Yangtze River Delta urban agglomeration by introducing dummy variables in the stage of urban innovation development.The urban innovation effect of high-tech industry agglomeration is different under different stages of urban innovation development. In the lower stage, a 1% higher high-tech industrial agglomeration increases the coefficient of urban innovation output by 0.433%. In the higher stage, a 1% higher high-tech industrial agglomeration increases the coefficient of urban innovation output by 0.538%. It could be found that under different stages of urban innovation development in the Yangtze River Delta urban agglomeration, the positive innovation effect of high-tech industrial agglomeration is prominent. However, in the higher stage, the innovation effect of high-tech industrial agglomeration is stronger, and the positive impact on urban innovation output is greater. The results show that the cities with higher innovation level are more conducive to the innovation effect of high-tech industrial agglomeration than cities with lower innovation. On the one hand, high urban innovation level could provide a good innovation environment for high-tech industrial agglomeration to give play to its innovation effect. On the other hand, high urban innovation level could attract increasing innovative elements and talent agglomeration. It could provide support for high-tech industrial innovation and accelerate the knowledge spillover among high-tech enterprises in the agglomeration area to drive their innovative production activities. The urban innovation output would further be enhanced. Therefore, in the process of high-tech industry agglomeration development, China’s urban agglomeration needs to pay attention to the improvement of the overall urban innovation environment and innovation level to further form a virtuous circle between high-tech industrial agglomeration and urban innovation output.(3)
The development of urban agglomeration in China is characterized by the expansion from the central city to the outer circle. With the acceleration of China’s urban agglomeration integration process, its internal transportation network has become increasingly developed, and the connection between cities in urban agglomerations has benefited to a large extent from the opening of high-speed rail. This paper divided urban circles (1 hour, 2 hours, 3 hours, and above 3 hours) based on the commuting time of high-speed rail between cities and central city (Shanghai) in the Yangtze River Delta urban agglomeration. We controlled related influencing factors and studied the spatial heterogeneity of innovation effect of high-tech industrial agglomeration from the perspective of spatial location.Table 8
Heterogeneity test results.
Variableln RINNTemporal (9)Stage (10)Spatial (11)1H2H3H>3Hln AGGLO0.270∗∗ (2.360)0.433∗∗∗ (2.740)0.615∗∗∗ (3.930)0.360∗ (2.440)0.032 (0.140)−0.546 (−0.880)ln AGGLO∗time0.125∗∗ (2.320)ln AGGLO∗stage0.105∗ (1.680)Within-R-sq0.6430.6440.7600.6290.7540.688Control variableYesYesYesYesYesYesAccording to the estimation results of model (11), with the expansion of the urban circles, the impact of high-tech industrial agglomeration on urban innovation output shows a decreasing trend. Within the one-hour circle, high-tech industrial agglomeration has the largest positive impact on urban innovation output. The influence coefficient is 0.615, and it is significant at the 1% level. In the two-hour circle, the innovation effect of high-tech industrial agglomeration decreases, and the influence coefficient decreases from 0.615 to 0.360, but it is still significant. In the three-hour circle and above the three-hour circle, the influence of high-tech industrial agglomeration on urban innovation output is no longer significant. There is even a negative influence in more than 3-hour circle. The reason is that the development of high-tech industries in cities close to the central city is affected by the radiation and spillover effect from the central city. These cities could play the spillover effect, scale effect, and industrial correlation effect of high-tech industrial agglomeration better than other cities. The radiation and spillover effect could also stimulate the innovation of enterprises and enhance the innovation ability and competitive advantage of industries. They could further alleviate the structural deceleration in the process of urban economic development and the unbalanced development in the Yangtze River Delta urban agglomeration and encourage the innovative growth of these cities. With the increase of commuting time and distance from the central city, the radiation driving effect of the central city is weakened, and the industrial association and innovation association between the central city and edge cities are also weakened. In addition, the edge cities have a low level of high-tech industry agglomeration and their economic development is relatively weak. Therefore, the positive impact of high-tech industry agglomeration on urban innovation output is gradually weakened with the expansion of the urban circles.


## 4.1. Interaction Effect Test

We used F test and B-P test to determine whether mixed OLS model or fixed effect model, or mixed OLS model or random effect model was better separately. The F test (16.180,p≤0.01) and B-P test (213.970, p≤0.01) statistics are statistically significant, indicating that both fixed and random models are better than a mixed model. On this basis, we used Hausman test to determine the fixed effect and random effect. The Hausman test (−9.990) indicated that we should select random model to estimate basic model. According to Table 3, in model (1), the influence coefficient of high-tech industry agglomeration is positive and significant at the 1% level, indicating that high-tech industry agglomeration has a positive impact on the urban innovation output in China’s Yangtze River Delta urban agglomeration. The reasons for this finding could be explained from three aspects. Firstly, high-tech industry agglomeration would produce scale economy effect and consequently reduce innovation costs to increase regional innovation output [59]. Secondly, the agglomeration of high-tech industries strengthens the innovation cooperation and knowledge exchange among enterprises, and accelerates the knowledge and technology spillover effects among enterprises in the agglomeration area [60]. The knowledge and technology spillover of high-tech industry agglomeration enables enterprises to obtain knowledge, innovative information, and materials more easily, thus stimulating innovative activities and generating more innovative output. Thirdly, high-tech industry agglomeration could promote the agglomeration of innovative elements and resources, and enterprises could acquire resources and innovative elements at a low cost. At the same time, similar enterprises and related enterprises have closer connection with each other thanks to the high-tech industrial agglomeration in China’s Yangtze River Delta urban agglomeration. It reduces the cost of information communication and innovation cooperation among enterprises in the agglomeration areas. Thus, cost reduction encourages innovation and increases urban innovation output.Table 3
Interaction effect test results.
VariableModel (1)Model (2)ln RINNln AGGLOln AGGLO0.283∗∗ (2.460)ln RINN0.080∗∗ (2.360)ln RDL0.333∗∗∗ (6.350)0.105∗∗∗ (3.420)ln RDK0.487∗∗∗ (4.610)−0.164∗∗ (−2.540)ln PGDP0.022 (0.080)0.324∗∗ (2.120)ln OPEN−0.303∗∗∗ (−3.180)0.040 (0.730)ln EDU0.571∗∗∗ (3.380)0.053 (0.570)ln INF0.308∗∗∗ (3.330)0.038 (0.740)ln FINA0.742∗∗∗ (3.700)−0.324∗∗∗ (−2.830)ln GOVS0.490∗∗ (1.980)−0.173 (−1.270)_cons4.823∗ (1.910)−3.250 (−2.160)Within−R−sq0.6440.243Hausman−9.99010.590High-tech industry agglomeration and urban innovation could be interactive in theory. This paper tests whether there is an interaction between high-tech industry agglomeration and urban innovation output in Yangtze River Delta urban agglomeration and what kind of interaction there is. We further analyze the estimated results for the effect of urban innovation on high-tech industry agglomeration in model (2). The influence coefficient of urban innovation is significantly positive at the level of 5%, influence coefficient is 0.080, and it suggests that urban innovation positively affects high-tech industry agglomeration. The reasons for this result include the following aspects: the first reason is innovation input effect. The increase of urban innovation output encourages the government and enterprises in Yangtze River Delta urban agglomeration to invest in R&D and innovation. They could provide multi-party support for high-tech industry agglomeration and form innovative force to further promote the development of high-tech industry cluster. The second reason is “the pole effects.” According to the center-periphery theory, the spatial agglomeration and reorganization of various elements in the process of innovation could attract many enterprises and innovative resources to gather and improve the level of industrial agglomeration. The third reason is the economic effect. The improvement of urban innovation output has brought about the improvement of economic benefits. The profit-driven factors lead to the continuous inflow of production factors after the improvement of economic benefits, which form the agglomeration effect through recycling and accumulation, and promote the further development of high-tech industry agglomeration. In addition, urban innovation promotes high-tech industry agglomeration of Yangtze River Delta urban agglomeration through high-tech industry upgrading effect. High-tech industry upgrading enhances the added value of products and pushes industries up the value chain to the medium-high end. It could also generate new industrial clusters and promote the agglomeration of high-tech industries in China’s Yangtze River Delta urban agglomeration.Therefore, there is a positive interaction relationship between high-tech industrial agglomeration and urban innovation output in China’s Yangtze River Delta urban agglomeration.The results of control variables in model (1) show that all control variables have positive effects on urban innovation output expect the urban opening level.


## 4.2. High-Tech Industry Agglomeration, Industrial Structure, and Urban Innovation

The agglomeration of urban high-tech industries and the optimization of urban industrial structure are both important to urban innovation and development. We further analyzed the influence mechanism of high-tech industrial agglomeration on urban innovation output in China’s Yangtze River Delta urban agglomeration. Industrial structure optimization is used (including industrial structure advancement and rationality) as the mediator to explore the role of industrial structure optimization between high-tech industrial agglomeration and urban innovation output. Table4 reports the estimate results.Table 4
Mediating effect test results.
VariableIndustrial structure upgrading of citiesIndustrial structure rationality of citiesModel (3) ln STRUHModel (4) ln RINNModel (5) ln STRURModel (6) ln RINNln AGGLO0.198∗∗∗ (4.340)0.305∗∗∗ (2.730)−0.830∗∗∗ (−4.260)0.558∗∗∗ (4.930)ln STRUH0.712∗∗∗ (4.790)ln STRUR0.135∗∗∗ (3.810)Sobel/indirect effect0.141∗∗∗ (3.215)−0.112∗∗∗ (−2.839)Direct effect0.305∗∗∗ (2.734)0.558∗∗∗ (4.935)Control variableYesYesYesYesR-squared0.8360.8620.5570.858ln RDL−0.013 (−0.800)0.184∗∗∗ (4.770)0.454∗∗∗ (6.490)0.114∗∗∗ (2.680)ln RDK0.119∗∗∗ (4.820)0.611∗∗∗ (10.110)−0.349∗∗∗ (−3.320)0.743∗∗∗ (12.370)ln PGDP1.027∗∗∗ (16.880)−1.199∗∗∗ (−5.730)0.357 (1.370)−0.515∗∗∗ (−3.540)lnOPEN−0.181∗∗∗ (−6.300)−0.025 (−0.340)0.813∗∗∗ (6.640)−0.263∗∗∗ (−3.540)ln EDU0.021 (0.270)0.729∗∗∗ (3.950)0.129 (0.380)0.726∗∗∗ (3.880)ln INF0.123∗∗∗ (4.180)0.153∗∗ (2.150)0.115 (0.920)0.225∗∗∗ (3.210)ln FINA−0.122∗∗ (−2.140)0.439∗∗∗ (3.230)−0.179 (−0.740)0.376∗∗∗ (2.750)ln GOVS−0.613∗∗∗ (-6.430)1.190∗∗∗ (4.920)0.453 (1.110)0.693∗∗∗ (3.040)Regarding the advancement of industrial structure, the influence coefficient of high-tech industry agglomeration on industrial structure advancement is positive and significant at 1% level in model (3). At the same time, the influence coefficients of industrial structure advancement on urban innovation output and high-tech industry agglomeration on urban innovation output are both positive and significant at 1% level in model (4). The results indicate that the advancement of industrial structure is a partly mediator between high-tech industrial agglomeration and urban innovation output. The advancement of industrial structure refers to the upgrading degree that industrial structure transforms from low to high added value, high technology, high intensification, and high processing. Industrial structure advancement is regarded as an important bridge for high-tech industrial agglomeration to enhance urban innovation output in Yangtze River Delta urban agglomeration. The reasons are the following. Firstly, high-tech industrial agglomeration would promote knowledge spillover and intra-industry technological innovation, then play the scale economy effect, and significantly promote the industrial structure advancement. The comprehensive effect of high-tech industry agglomeration and industrial structure advancement stimulate R&D investment and increase regional labor productivity, and further stimulate innovation and enhance local innovation output. Secondly, high-tech industrial agglomeration realizes industrial structure advancement through improving the innovative capability of urban. High-tech industrial agglomeration increases the formal and informal interaction between enterprises in the agglomeration area, facilitates the formation of social network relations, accelerates the diffusion of knowledge, promotes the generation of new knowledge, and then promotes urban innovation level and establishes urban innovation environment. Urban innovation environment would promote the technological innovation of high-tech enterprises in agglomeration area and encourage the innovation and upgrade of products and the upgrade of industrial chains. The upgrade of industrial chains would drive the development of related industries and promote the efficient allocation of production factors. It would gradually realize the industrial structure advancement which evolves from low added value and low technology industry to high added value and high-tech industry. Thirdly, high-tech industry agglomeration initiates the spatial agglomeration of production factors. The agglomeration effect and the resource allocation effect generated by the flow of factors could improve the quality and quantity of all kinds of production factors in the agglomeration area. The optimization of resource allocation efficiency makes innovative factors shift from declining industries to emerging industries and from labor-intensive industries to technology-intensive industries, and promotes industrial structure advancement. In addition, high-tech industry agglomeration reduces the cost of knowledge exchange and information search among high-tech enterprises, gives full play to the synergistic effect among enterprises, and promotes knowledge spillover and R&D innovation. These results contribute to improving product added value and urban innovation level, further promoting the advancement of industrial structure.Regarding the mediating effect of industrial structure rationality, the influence coefficient of high-tech industry agglomeration on industrial structure rationality is negative and significant at 1% level in model (5). The influence coefficient is −0.830. It demonstrates that high-tech industrial agglomeration has a negative influence on industrial structure rationality in China’s Yangtze River Delta urban agglomeration. Industrial structure rationality refers to optimization degree of inter-industry coordination and correlation. On the one hand, we stress specialized agglomeration of high-tech industries which emphasizes the agglomeration of similar enterprises and related enterprises in the same industry in this paper. Therefore, high-tech industry agglomeration would also produce negative externalities which are called crowding effect. The crowding effect would cause a series of problems, such as excessive consumption of innovation resources, scarcity of production factors, congestion of transportation infrastructure, and rising prices of land and labor factors, and thus lead to excessive competition among enterprises. Excessive agglomeration reduces the efficiency of industrial production and innovation, hinders the benign development of high-tech industries, and is not conducive to the industrial structure rationality. On the other hand, this paper mainly emphasizes the influence of specialized agglomeration of high-tech industries on the industrial structure rationality, but the industrial structure rationality lays more emphasis on the coordination and correlation between industries. There are many economic and technological connections among different high-tech industries, forming diversified agglomeration of high-tech industries. Therefore, the industrial structure rationality of cities in the Yangtze River Delta urban agglomeration might come more from the diversified agglomeration of high-tech industries.Industrial structure advancement and industrial structure rationality both have significantly positive effects on urban innovation output in China’s Yangtze River Delta urban agglomeration. The first reason for this finding is that industrial structure optimization drives the expansion of the market. Increasing enterprises enter the market and intensify the market competition. Enterprises begin to carry out product and technological innovation to seize the market share. The continuous expansion of the market would bring about the expansion of demand which urges enterprises to improve their innovation ability and meet the new demand through product and technological innovation. In other words, industrial structure optimization leads to the expansion of demand and encourages enterprises to innovate to increase urban innovation output [61]. The expansion of the market and demand brought by industrial structure optimization enhance the specialization of high-tech enterprises and deepen the internal division of labor within industries. They also encourage enterprises to concentrate limited resources to carry out activities of product development and innovation, thus improving the urban innovation output. Secondly, industrial structure optimization would further guide the development of high-tech industries and upgrading of traditional industries, stimulate the investment of innovation funds, and improve the urban innovation abilities. Thirdly, industrial structure optimization promotes competition and cooperation between cities, drives regional independent innovation, and enhances urban innovation output. To be specific, cities would introduce high-tech industries to enhance innovative competitiveness and avoid the convergence of industrial structure. At the same time, cities would also carry out collaborative innovation and give play to comparative advantages to solve the common key technologies of industry and realize technological breakthrough, and then improve innovative level. Finally, industrial structure optimization could provide a high-quality policy environment for the development of high-tech industries. It could encourage industrial innovation and improve urban innovation output through implementing preferential policies for high-tech industries and providing R&D subsidies.In summary, the advancement of industrial structure plays a positive intermediary role between high-tech industrial agglomeration and urban innovation output in Yangtze River Delta urban agglomeration. The mediating effect size is 0.141, and the proportion of mediating effect to total effect is 0.317. However, high-tech industry agglomeration could promote urban innovation output, but weaken the rationalization of industrial structure in Yangtze River Delta urban agglomeration. In this case, the negative impact of high-tech industry agglomeration on industrial structure rationality suppresses the driving effect of high-tech industry agglomeration on urban innovation output. In other words, high-tech industry agglomeration suppresses its incentive to urban innovation output by reducing the industrial structure rationality, which shows a suppressing effect [62–64], and the absolute proportion of indirect effect to direct effect is 0.200.


## 4.3. Threshold Effect of Technology Intensity and Capital Intensity

Some literature suggested that how urban innovation is affected by industrial agglomeration might depend on industrial level features [65, 66]. Among the industrial attributes that might influence industrial agglomeration and urban innovation, technology intensity and capital intensity attract attention. Therefore, based on the basic regression, this paper took industrial attribute variables (including technology intensity and capital intensity) as threshold variables to investigate the impact of high-tech industrial agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration. We tested the number, values, and significance of threshold. The results are shown in Tables 5 and 6. The single threshold effect of technology intensity and capital intensity is, respectively, significant at the level of 10% and 1%, while the double threshold and triple threshold fail the significance test. It indicates that we should employ single threshold model to investigate the threshold effect of technology intensity and capital intensity on the impact of high-tech industrial agglomeration on urban innovation output.Table 5
Threshold effect significance results of technology intensity.
Threshold modelFstatProbCritical valueThreshold95% confidence interval1%5%10%Single threshold21.830∗0.08045.87125.17319.352−11.671[−11.735, −11.646]Double threshold−0.7601.00023.30217.37614.967−11.671–11.080[−11.646, −11.473] [−12.154, −11.074]Triple threshold4.7300.54315.29911.1279.451−11.049[−11.059, −11.024]Table 6
Threshold effect significance results of capital intensity.
Threshold modelFstatProbCritical valueThreshold95% confidence interval1%5%10%Single threshold45.240∗∗∗0.00030.61525.56522.158−0.118[−0.255, −0.078]Double threshold14.0500.32391.47067.27743.618−0.118 2.246[−0.255, −0.078] [2.231, 2.272]Triple threshold9.0800.63098.96260.72545.2591.697[1.662, 1.702]We estimate and test the threshold value of single threshold model based on the threshold effect test. According to the estimated threshold values in Tables5 and 6, technology intensity and capital intensity have a threshold effect between high-tech industrial agglomeration and urban innovation output in the Yangtze River Delta urban agglomeration. The threshold values are, respectively, 9.012E − 06 and 1.125. Then, we estimate the threshold regression model according to the threshold values obtained. Table 7 reports the estimate results.Table 7
Threshold model estimate results.
VariableTechnology intensity model (7)Capital intensity model (8)CoefficienttCoefficienttln AGGLO(TI ≤ 9.012E − 06)0.684∗∗∗4.360ln AGGLO(TI > 9.012E − 06)0.205∗1.800ln AGGLO(CI ≤ 1.125)−3.803∗∗∗−5.900ln AGGLO(CI > 1.125)0.227∗∗2.100ln RDL0.161∗∗2.2900.214∗∗∗3.280ln RDK−5.751∗∗−2.460−3.345−1.500ln PGDP8.692∗∗∗3.6606.207∗∗∗2.770ln OPEN0.0940.6600.0140.010ln EDU0.347∗∗2.0000.425∗∗2.550ln INF0.253∗∗2.5400.190∗∗2.020ln FINA0.3261,2800.3401.380ln GOVS0.641∗∗2.3400.717∗∗∗2.780_cons−67.282∗∗∗−3.690−47.722∗∗∗−2.780F15.070∗∗∗20.390∗∗∗Within−R−sq0.6880.724Regarding the threshold effect of technology intensity, the estimation results of model (7) show that when the technology intensity is lower than 9.012E − 06, high-tech industry agglomeration has a significant positive impact on urban innovation output in Yangtze River Delta urban agglomeration. Under this threshold value of technology intensity, a 1% higher high-tech industry agglomeration boosts the coefficient of urban innovation output by 0.684%. The high-tech industry agglomeration in the Yangtze River Delta urban agglomeration also has a significant positive impact on the urban innovation output when the technology intensity is higher than 9.012E − 06. But the degree and significance of this positive impact decreased, a 1% higher high-tech industry agglomeration increases the coefficient of urban innovation output by 0.205%. The reasons are as follows: firstly, the higher the technology intensity of an industry indicates that the more capital and personnel are invested in R&D and technological innovation in the production process of an enterprise. Emerging and high-tech industries highly value the creation of new ideas and new technologies. The advancement, durability, and irreplaceability of technology are the core competitiveness of high-tech enterprises. Therefore, high-tech enterprises, especially those in agglomeration areas, have strong technological innovation willingness. Adequate inputs of R&D funds and scientific and technological talents stimulate the technological innovation willingness of high-tech enterprises in the agglomeration areas, thus enhancing the innovation performance of high-tech enterprises and improving the urban innovation output. At the same time, the high technology intensity could absorb more scientific and technological talents and form a good atmosphere for innovation. These results could further stimulate the investment in technological innovation and form a virtuous circle of innovation in high-tech industrial agglomeration areas. Therefore, the high level of technology intensity strengthens the positive impact of high-tech industrial agglomeration on urban innovation output. Secondly, the degree of technical complexity is relatively high in high-tech industry, and speed of technological upgrading is faster than that in other industries [64], so the formation of competitive advantage of high-tech industry is more dependent on technological innovation. For high-tech industries, the increase of technology intensity could provide support of R&D funds and personnel in various technological innovations in the early stage of high-tech industry agglomeration and the whole process of development. It could reduce the uncertainty of R&D innovation to a certain extent, improve the innovation efficiency of high-tech industries, and then enhance the urban innovation output. Thirdly, technology intensity directly reflects the level of technological contents in the production activities. The high level of technology intensity represents the high proportion of R&D funds and R&D personnel. It lays the foundation for high-tech enterprises in the agglomeration area to carry out technological innovation. The impetus and innovation space for technological innovation of high-tech industries is greater than other industries. The positive impact of high-tech industrial agglomeration on urban innovation output is also greater than others. However, technology intensity is not always better. When the R&D funds and talent reach saturation, excessive input would reduce the utilization efficiency and cause waste of resources, and would reduce the positive impact of high-tech industrial agglomeration on urban innovation output.Regarding the threshold effect of capital intensity, the estimation results of Model (7) show that high-tech industry agglomeration has a significant negative impact on urban innovation output when capital intensity is lower than 1.125. The influence of high-tech industry agglomeration on urban innovation output is positive when capital intensity overtakes threshold value. The reasons are the following: firstly, the high level of capital intensity represents the high ratio of capital consumption and labor consumption. Currently many industries, especially high-tech industries, have been transformed from labor intensive to capital intensive. High-tech industries with higher capital intensity especially concentrated industries tend to have lower costs and higher profits than other industries. These results encourage high-tech enterprises in the agglomeration areas to adopt innovation strategies and effectively integrate existing resources to obtain higher profits and sustained competitive advantages, and then improve efficiency of technological innovation and urban innovation output. Secondly, high-tech industries are essentially knowledge-intensive and technology-intensive industries. High-tech enterprises, especially in high-tech industrial agglomeration areas, have stronger demands for technological innovation. Technological innovation is an investment process highly dependent on capital. Capital is the driving force for technological innovation of high-tech industries. Capital plays a key role in the growth and development of high-tech enterprises in the agglomeration areas and provides multi-aspect support for technological innovation decision-making of high-tech enterprises. Therefore, the increasing capital intensity leads to high innovation motivation and strong innovation ability of high-tech enterprises in the agglomeration area, and the impact on the urban innovation output would also improve because of them. Thirdly, the success of technological innovation benefits from abundant and long-term capital support. The high level of capital intensity could provide powerful support for high-tech enterprises in the agglomeration areas. In addition, it could release a positive signal and provide a steady stream of talents for R&D and innovation activities through attracting R&D personnel with professional background and scientific and technological talents skilled in advanced technology. The high level of capital intensity is also conducive to support high-tech enterprises in the agglomeration areas to form independent R&D institutions or to cooperate with innovation subjects to form innovation institutions integrating industry, education, and research. That result would further promote urban innovation output and improve urban innovation level.


## 4.4. Heterogeneity Test and Further Discussion

The high-tech industrial agglomeration has a significantly positive impact on urban innovation output in China’s Yangtze River Delta urban agglomeration from the perspective of the whole sample. However, the heterogeneous impact in different conditions need to be further investigated. We discussed the spatiotemporal heterogeneity of the innovative effect of high-tech industrial agglomeration under different time stages, urban innovation development stages, and different urban circles. The results are shown in Table8.(1)
The positive promoting effect of high-tech industrial agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration from 2015 to 2019 is higher than that from 2010 to 2014. The elasticity coefficient of high-tech industrial agglomeration on urban innovation output increases from 0.270 to 0.395 in 2015–2019. As time goes on, the positive impact of high-tech industrial agglomeration on urban innovation output is constantly improving. It indicates that innovative high-tech industries gradually replace the industries which lack innovation and become an important force to support urban innovation and development. China could pay attention to the cultivation of high-tech industries, especially focusing on the agglomeration of high-tech industries; therefore, high-tech industries could fully release the positive effect of agglomeration and ensure urban innovation and development.(2)
The innovative development of cities is characterized by stages. We divide the stages of urban innovation development into higher stage (above the median) and lower stage (below the median) [67]. This paper explored the heterogeneity of the impact of high-tech industrial agglomeration on urban innovation output in different stages of innovation development in the Yangtze River Delta urban agglomeration by introducing dummy variables in the stage of urban innovation development.The urban innovation effect of high-tech industry agglomeration is different under different stages of urban innovation development. In the lower stage, a 1% higher high-tech industrial agglomeration increases the coefficient of urban innovation output by 0.433%. In the higher stage, a 1% higher high-tech industrial agglomeration increases the coefficient of urban innovation output by 0.538%. It could be found that under different stages of urban innovation development in the Yangtze River Delta urban agglomeration, the positive innovation effect of high-tech industrial agglomeration is prominent. However, in the higher stage, the innovation effect of high-tech industrial agglomeration is stronger, and the positive impact on urban innovation output is greater. The results show that the cities with higher innovation level are more conducive to the innovation effect of high-tech industrial agglomeration than cities with lower innovation. On the one hand, high urban innovation level could provide a good innovation environment for high-tech industrial agglomeration to give play to its innovation effect. On the other hand, high urban innovation level could attract increasing innovative elements and talent agglomeration. It could provide support for high-tech industrial innovation and accelerate the knowledge spillover among high-tech enterprises in the agglomeration area to drive their innovative production activities. The urban innovation output would further be enhanced. Therefore, in the process of high-tech industry agglomeration development, China’s urban agglomeration needs to pay attention to the improvement of the overall urban innovation environment and innovation level to further form a virtuous circle between high-tech industrial agglomeration and urban innovation output.(3)
The development of urban agglomeration in China is characterized by the expansion from the central city to the outer circle. With the acceleration of China’s urban agglomeration integration process, its internal transportation network has become increasingly developed, and the connection between cities in urban agglomerations has benefited to a large extent from the opening of high-speed rail. This paper divided urban circles (1 hour, 2 hours, 3 hours, and above 3 hours) based on the commuting time of high-speed rail between cities and central city (Shanghai) in the Yangtze River Delta urban agglomeration. We controlled related influencing factors and studied the spatial heterogeneity of innovation effect of high-tech industrial agglomeration from the perspective of spatial location.Table 8
Heterogeneity test results.
Variableln RINNTemporal (9)Stage (10)Spatial (11)1H2H3H>3Hln AGGLO0.270∗∗ (2.360)0.433∗∗∗ (2.740)0.615∗∗∗ (3.930)0.360∗ (2.440)0.032 (0.140)−0.546 (−0.880)ln AGGLO∗time0.125∗∗ (2.320)ln AGGLO∗stage0.105∗ (1.680)Within-R-sq0.6430.6440.7600.6290.7540.688Control variableYesYesYesYesYesYesAccording to the estimation results of model (11), with the expansion of the urban circles, the impact of high-tech industrial agglomeration on urban innovation output shows a decreasing trend. Within the one-hour circle, high-tech industrial agglomeration has the largest positive impact on urban innovation output. The influence coefficient is 0.615, and it is significant at the 1% level. In the two-hour circle, the innovation effect of high-tech industrial agglomeration decreases, and the influence coefficient decreases from 0.615 to 0.360, but it is still significant. In the three-hour circle and above the three-hour circle, the influence of high-tech industrial agglomeration on urban innovation output is no longer significant. There is even a negative influence in more than 3-hour circle. The reason is that the development of high-tech industries in cities close to the central city is affected by the radiation and spillover effect from the central city. These cities could play the spillover effect, scale effect, and industrial correlation effect of high-tech industrial agglomeration better than other cities. The radiation and spillover effect could also stimulate the innovation of enterprises and enhance the innovation ability and competitive advantage of industries. They could further alleviate the structural deceleration in the process of urban economic development and the unbalanced development in the Yangtze River Delta urban agglomeration and encourage the innovative growth of these cities. With the increase of commuting time and distance from the central city, the radiation driving effect of the central city is weakened, and the industrial association and innovation association between the central city and edge cities are also weakened. In addition, the edge cities have a low level of high-tech industry agglomeration and their economic development is relatively weak. Therefore, the positive impact of high-tech industry agglomeration on urban innovation output is gradually weakened with the expansion of the urban circles.


## 5. Conclusions and Managerial Implications

### 5.1. Main Conclusions

This paper discussed the interaction relationship between high-tech industrial agglomeration and urban innovation output based on panel data from 26 cities in China’s Yangtze River Delta urban agglomeration from 2010–2019. We also incorporated high-tech industrial agglomeration, industrial structure optimization, and urban innovation into a research framework to systematically explore the influence mechanism of high-tech industrial agglomeration on urban innovation output. At the same time, this paper introduced industrial attribute variables and investigated the threshold effects of technology intensity and capital intensity between high-tech industrial agglomeration and urban innovation output. In addition, a further in-depth analysis of the heterogeneous impact of high-tech industrial agglomeration on urban innovation output was performed. The empirical conclusions are as follows:(1)
There is a significant positive interaction relationship between high-tech industrial agglomeration and urban innovation output in China’s Yangtze River Delta urban agglomeration. This finding indicates that high-tech industrial agglomeration is main source of urban innovation. The development of high-tech industrial agglomeration could not be separated from urban innovation and should receive substantial attention.(2)
The optimization of urban industrial structure plays an important role between high-tech industrial agglomeration and urban innovation output in Yangtze River Delta urban agglomeration. Specifically, the advancement of industrial structure plays a positive intermediary role between high-tech industrial agglomeration and urban innovation output. The rationalization of industrial structure has suppressing effect in the influence of high-tech industrial agglomeration on urban innovation output. High-tech industrial agglomeration suppresses its incentive to urban innovation output by reducing the industrial structure rationality.(3)
The influence of high-tech industrial agglomeration on urban innovation output increases with technology intensity. When the technology intensity is higher than 9.012E − 06, high-tech industrial agglomeration could still improve the innovative output of cities; however, the degree and significance of this positive impact decrease. However, only when capital intensity is higher than 1.125, it could significantly stress the influence of high-tech industrial agglomeration on urban innovation output.(4)
There are heterogeneous impacts of high-tech industrial agglomeration on urban innovation output in different time stages, urban innovation development stages, and urban circles. From 2015 to 2019, the positive promoting effect of high-tech industrial agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration is higher than that in the last five years; compared with the lower stage of urban innovation development, the innovation effect of high-tech industrial agglomeration is stronger, and the positive impact on urban innovation output is greater than the higher stage of urban innovation development; when the city is closer to the central city, the influence of high-tech industrial agglomeration on urban innovation output is stronger, especially in the one-hour circle.


### 5.2. Theoretical Contributions

This paper makes theoretical contributions from four aspects:First, China’s Yangtze River Delta urban agglomeration is taken as an example; this paper analyzes the interaction effect between high-tech industrial agglomerations. It extends existing research in this area, combining agglomeration theory with innovation theory effectively and enriching related theories. In addition, there is still no consensus on the influence of high-tech industrial agglomeration on urban innovation. And there is literature about the impact of urban innovation on high-tech industrial agglomeration. Therefore, this article focuses on the interaction effect and greatly enriches the research on industrial agglomeration and urban innovation.Second, this paper puts high-tech industrial agglomeration, industrial structure optimization, and urban innovation into a new unified frame and empirically analyzes the mediating effect of industrial structure optimization. The influence mechanism of high-tech industrial agglomeration on urban innovation is fully investigated. The results show that industrial structure advancement and rationality, respectively, play a positive intermediary and a negative suppressing effect role between high-tech industrial agglomeration and urban innovation output. These results expand the boundary of agglomeration theory, industrial structure theory, and urban innovation theory, thus further enriching present literature.Third, industrial attributes have a crucial impact on the exertion of innovation effect of high-tech industrial agglomeration. So, the complex influence mechanism could not be fully understood if discarding industrial attribute variables. Therefore, this paper introduces the industrial attribute variables, investigates the threshold effects of technology intensity and capital intensity, and reveals the complex law that drives the urban innovation output. From an industrial attribute perspective, the research is enriching on the influence of high-tech industrial agglomeration on urban innovation.Finally, this paper discusses and compares the heterogeneity influence of high-tech industrial agglomeration on urban innovation output based on the spatiotemporal factors. The results indicate that the effect of spatiotemporal heterogeneities existed. These results provide theoretical instructions for conditions under which high-tech industrial agglomeration could realize the maximum innovation effect.


### 5.3. Management Implications

The findings of this paper provide reference and policy implications for improving the innovation level in urban agglomeration.First, high-tech industrial agglomeration is an effective way to improve the innovation level of cities. So, we should pay more attention to the development of high-tech industries and vigorously cultivate high-tech industrial clusters that fit the cities’ characteristic and development. We should also actively promote and give full play to the positive innovation effects formed by high-tech industrial agglomeration. Moreover, we should accelerate the agglomeration of innovative elements, deepen the specialized division of labor and cooperation, and strengthen the construction of supporting facilities in agglomeration areas. Finally, the agglomeration development of high-tech industries and improving the innovative level of cities would be realized.Second, in Yangtze River Delta urban agglomeration, the industrial structure of cities plays a mediating role between high-tech industrial agglomeration and urban innovation output. When formulating regional policies, it is crucial to combine the industrial structure and the location advantages to scientifically and macroscopically distribute the high-tech industries. In terms of industrial attributes, capital intensity has a positive influence on the innovation effect of high-tech industrial agglomeration; therefore, capitals might be invested to provide necessary guarantee for bringing about the innovation effect of high-tech industry agglomeration. At the same time, technology intensity also plays an important role between high-tech industrial agglomeration and urban innovation output. Therefore, the enterprises in agglomeration areas and government should attract more R&D funds and talents and put them into the agglomeration and innovation of high-tech industries to provide high-tech industry agglomeration and urban innovation with basis.Finally, attention might be paid to the heterogeneous influence of high-tech industrial agglomeration on urban innovation. On the one hand, we need focus on the innovative level of cities and optimize the agglomeration environment of high-tech industries through improving the innovative level of cities. On the other hand, the urban close to central city has a stronger innovation effect of high-tech industrial agglomeration than others. The central cities and urban innovation center could lead the improvement of the innovation level in surrounding cities and drive the innovation of surrounding late-developing cities by the strength of the urban circles. Therefore, it is feasible to focus on radiation and spillover effects of central cities to strengthen their communication and knowledge sharing with surrounding cities, especially fringe cities, and enable a point-to-point radiation.


## 5.1. Main Conclusions

This paper discussed the interaction relationship between high-tech industrial agglomeration and urban innovation output based on panel data from 26 cities in China’s Yangtze River Delta urban agglomeration from 2010–2019. We also incorporated high-tech industrial agglomeration, industrial structure optimization, and urban innovation into a research framework to systematically explore the influence mechanism of high-tech industrial agglomeration on urban innovation output. At the same time, this paper introduced industrial attribute variables and investigated the threshold effects of technology intensity and capital intensity between high-tech industrial agglomeration and urban innovation output. In addition, a further in-depth analysis of the heterogeneous impact of high-tech industrial agglomeration on urban innovation output was performed. The empirical conclusions are as follows:(1)
There is a significant positive interaction relationship between high-tech industrial agglomeration and urban innovation output in China’s Yangtze River Delta urban agglomeration. This finding indicates that high-tech industrial agglomeration is main source of urban innovation. The development of high-tech industrial agglomeration could not be separated from urban innovation and should receive substantial attention.(2)
The optimization of urban industrial structure plays an important role between high-tech industrial agglomeration and urban innovation output in Yangtze River Delta urban agglomeration. Specifically, the advancement of industrial structure plays a positive intermediary role between high-tech industrial agglomeration and urban innovation output. The rationalization of industrial structure has suppressing effect in the influence of high-tech industrial agglomeration on urban innovation output. High-tech industrial agglomeration suppresses its incentive to urban innovation output by reducing the industrial structure rationality.(3)
The influence of high-tech industrial agglomeration on urban innovation output increases with technology intensity. When the technology intensity is higher than 9.012E − 06, high-tech industrial agglomeration could still improve the innovative output of cities; however, the degree and significance of this positive impact decrease. However, only when capital intensity is higher than 1.125, it could significantly stress the influence of high-tech industrial agglomeration on urban innovation output.(4)
There are heterogeneous impacts of high-tech industrial agglomeration on urban innovation output in different time stages, urban innovation development stages, and urban circles. From 2015 to 2019, the positive promoting effect of high-tech industrial agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration is higher than that in the last five years; compared with the lower stage of urban innovation development, the innovation effect of high-tech industrial agglomeration is stronger, and the positive impact on urban innovation output is greater than the higher stage of urban innovation development; when the city is closer to the central city, the influence of high-tech industrial agglomeration on urban innovation output is stronger, especially in the one-hour circle.


## 5.2. Theoretical Contributions

This paper makes theoretical contributions from four aspects:First, China’s Yangtze River Delta urban agglomeration is taken as an example; this paper analyzes the interaction effect between high-tech industrial agglomerations. It extends existing research in this area, combining agglomeration theory with innovation theory effectively and enriching related theories. In addition, there is still no consensus on the influence of high-tech industrial agglomeration on urban innovation. And there is literature about the impact of urban innovation on high-tech industrial agglomeration. Therefore, this article focuses on the interaction effect and greatly enriches the research on industrial agglomeration and urban innovation.Second, this paper puts high-tech industrial agglomeration, industrial structure optimization, and urban innovation into a new unified frame and empirically analyzes the mediating effect of industrial structure optimization. The influence mechanism of high-tech industrial agglomeration on urban innovation is fully investigated. The results show that industrial structure advancement and rationality, respectively, play a positive intermediary and a negative suppressing effect role between high-tech industrial agglomeration and urban innovation output. These results expand the boundary of agglomeration theory, industrial structure theory, and urban innovation theory, thus further enriching present literature.Third, industrial attributes have a crucial impact on the exertion of innovation effect of high-tech industrial agglomeration. So, the complex influence mechanism could not be fully understood if discarding industrial attribute variables. Therefore, this paper introduces the industrial attribute variables, investigates the threshold effects of technology intensity and capital intensity, and reveals the complex law that drives the urban innovation output. From an industrial attribute perspective, the research is enriching on the influence of high-tech industrial agglomeration on urban innovation.Finally, this paper discusses and compares the heterogeneity influence of high-tech industrial agglomeration on urban innovation output based on the spatiotemporal factors. The results indicate that the effect of spatiotemporal heterogeneities existed. These results provide theoretical instructions for conditions under which high-tech industrial agglomeration could realize the maximum innovation effect.


## 5.3. Management Implications

The findings of this paper provide reference and policy implications for improving the innovation level in urban agglomeration.First, high-tech industrial agglomeration is an effective way to improve the innovation level of cities. So, we should pay more attention to the development of high-tech industries and vigorously cultivate high-tech industrial clusters that fit the cities’ characteristic and development. We should also actively promote and give full play to the positive innovation effects formed by high-tech industrial agglomeration. Moreover, we should accelerate the agglomeration of innovative elements, deepen the specialized division of labor and cooperation, and strengthen the construction of supporting facilities in agglomeration areas. Finally, the agglomeration development of high-tech industries and improving the innovative level of cities would be realized.Second, in Yangtze River Delta urban agglomeration, the industrial structure of cities plays a mediating role between high-tech industrial agglomeration and urban innovation output. When formulating regional policies, it is crucial to combine the industrial structure and the location advantages to scientifically and macroscopically distribute the high-tech industries. In terms of industrial attributes, capital intensity has a positive influence on the innovation effect of high-tech industrial agglomeration; therefore, capitals might be invested to provide necessary guarantee for bringing about the innovation effect of high-tech industry agglomeration. At the same time, technology intensity also plays an important role between high-tech industrial agglomeration and urban innovation output. Therefore, the enterprises in agglomeration areas and government should attract more R&D funds and talents and put them into the agglomeration and innovation of high-tech industries to provide high-tech industry agglomeration and urban innovation with basis.Finally, attention might be paid to the heterogeneous influence of high-tech industrial agglomeration on urban innovation. On the one hand, we need focus on the innovative level of cities and optimize the agglomeration environment of high-tech industries through improving the innovative level of cities. On the other hand, the urban close to central city has a stronger innovation effect of high-tech industrial agglomeration than others. The central cities and urban innovation center could lead the improvement of the innovation level in surrounding cities and drive the innovation of surrounding late-developing cities by the strength of the urban circles. Therefore, it is feasible to focus on radiation and spillover effects of central cities to strengthen their communication and knowledge sharing with surrounding cities, especially fringe cities, and enable a point-to-point radiation.



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*Source: 2555182-2022-05-20.xml*