Spatial Effects of the Land Supply Scale of Different Industrial Sectors on High-Quality Development in the Yangtze River Economic Belt

Abstract

The industrial land supply impacts regional high-quality development, with various impacts across sectors. Considering China’s Yangtze River Economic Belt (YREB), this paper uses entropy weighting, spatial analysis, and the spatial Durbin model for spatiotemporal and regional analysis of the high-quality development level (HDL) and its spatial correlation with the industrial land supply. (1) The annual average HDL in all cities of the YREB increases, the regional HDL is spatially unbalanced and decreases from downstream–midstream–upstream, and HDL overlaps with economic development spatiotemporally. (2) The increase in high-tech industrial land supply promotes local HDL, and the raw material industrial land supply promotes HDL more indirectly than directly. (3) The land supply of the industrial supporting service, processing, food and light textile, and raw material industries has significant indirect effects. Processing has the strongest positive spillover effect, and food and light textile has a significant negative spillover effect. The HDL equilibrium in the YREB increased from 2010 to 2019, and the clustering of the processing, industrial supporting service, and food and light textile industries aggravated the spatial imbalance. (4) The regional structure and layout of the industrial land supply should be optimized to promote the HDL of the YREB.

1. Introduction

Over the past 40 years of China’s reform and opening up, the national economy has grown at a high rate, with rapid expansion in quantity and scale. However, the situation of China’s economic operation has been changing in recent years, with the traditional development model showing a series of problems, such as serious environmental pollution, insufficient regional coordinated development, and insufficient potential for sustainable development, with downward pressure internally and a severe and complex external environment, and the pursuit of high-quality development has become increasingly urgent. In this regard, the 19th Party Congress report pointed out that the current Chinese economy has entered a stage of high-quality development. During the 14th Five-Year Plan period, China formed a development model with supply-side structural reform as the main line, resource factor allocation optimization as the center, and the real economy as the focus. In the new era where speed shift, power shift, and structural adjustment converge, more analysis of development issues from the supply-side perspective is needed. The Opinions of the State Council of the Central Committee of the Communist Party of China on Promoting High-quality Development emphasize the need to adhere to the new development concept and promote the high-quality development of the industrial sector, mainly in the manufacturing sector, to drive and lead the overall high-quality development. Since the land policy has been involved in macrocontrol, the supply scale of industrial land, as a carrier of industrial economic development, has expanded sharply with the acceleration of urbanization and has the highest proportion (In 2019, national: 19.43%, YREB: 20.20%) among all production land [1]. From 1990 to 2020, the total scale of construction land in China grew by 375%, and the proportion of industrial land supply in the total supply of state-owned construction land has long remained above 20% [2]. Therefore, the study of the scale of industrial land supply is key to understanding China’s economic restructuring, government relations, urban and rural spatial structure optimization, and other development issues [3].

The classical school of economics, represented by Adam Smith and David Ricardo, proposed that under the law of diminishing marginal returns, the total amount of land that remains constant is the key factor affecting economic growth, and recent studies on the relationship between land supply and demand and economic development have focused on the interaction mechanism [4], policy influence [5], and so on. High-quality development is a further transition of economic development, which requires richer development indicators. Research on high-quality development has increased sharply in recent years. Most related studies adopt a quantitative perspective and establish an evaluation index system, and some measure the level of high-quality development by a single index, such as green total factor productivity (GTFP) [6], total factor productivity (TFP) [7], and green technology innovation (GTI) [8]. However, a single indicator cannot reflect the multidimensional characteristics of quality comprehensively, so the new development concept based on “innovation, coordination, greenness, openness, and sharing” is commonly taken as the guideline to build evaluation index systems to describe high-quality development [9,10,11]. Therefore, the establishment of a multidimensional evaluation system has become the mainstream method in current research. In addition, the impact on high-quality development of single factors, such as import technology sophistication [12], environmental forcing mechanisms [13], and new economic momentum [14], is beginning to receive attention, but there has been little exploration of the supply and demand side of the land. Industrial land, as an important part of land supply, is the root of industrial economic growth and an important factor affecting high-quality development. The current research on industrial land supply, which mainly focuses on the dimensions of industrial land supply tenure [15], price [16], and scale [17], helps explore spatial patterns [18], influencing factors [19], and efficiency improvement [20] and has laid a solid foundation for the analysis of the effect of industrial land supply. However, the impact on high-quality development is not considered enough. Moreover, in the process of socioeconomic development, land not only has the attributes of “resources”, “capital”, and “assets” as factors of production but also has the spatial attribute of “place” for “performing production functions”, and land supply has an impact on the growth of land in the focal city and neighboring cities, with a certain spatial spillover to economic development [21]. These studies offer results supporting the spatial impact of land supply, but the impact mechanism has not yet been explored in depth, and the different impacts across types of land supply have not been explored enough. In addition, most studies have been conducted at the national or intracity scale, and few have been conducted at the regional scale [22,23].

Given the above, this paper takes the YREB as the study area, providing case support for the study of regional development scale; combines the current Chinese context, constructs a comprehensive multidimensional evaluation index system based on the analysis of the connotation of high-quality development, and evaluates local and regional HDL more comprehensively; explores the relationship between land supply and industrial economic development on local and regional high-quality development, uses the spatial Durbin model to analyze the spatial effects of different types of industrial land supply, and constructs a theoretical framework of the influence mechanism by combining empirical research; and provides a reference for optimizing industrial layout, promoting industrial transformation and upgrading, improving land supply behavior, and promoting urban and regional high-quality development.

2. Study Design

2.1. Concept Explanation

High-quality development has a profound connotation and practical direction; its connotation is mainly reflected in five dimensions: innovation, coordination, greenness, openness, and sharing [24]. Among them, innovation is the first driving force of development, covering all aspects of socioeconomic development and determining the speed, effectiveness, and sustainability of development. Coordination is the endogenous characteristic of development, emphasizing the balance of development, and this balance needs to maintain the optimal combination of all elements in the socioeconomic system, which is comprehensively reflected through the dual coordination of space and structure. Greenness is the universal form of development, emphasizing the sustainability of development, and is a breakthrough in development effectiveness. Openness is the necessary path of development, emphasizing the inclusiveness and interactivity of development, focusing on interlocal, inter-regional, and international exchange and cooperation as well as the soundness of market mechanisms to achieve higher allocation efficiency through cross-border flow of factors. Sharing is the fundamental purpose of development, emphasizing the people as the center, bringing benefits and well-being to the majority of urban and rural residents through more equitable and equal development, and realizing the positive interaction of all elements of the economy and society. Therefore, the new development concept of innovation, coordination, greenness, openness, and sharing is a new requirement in the era of high-quality development and an important evaluation criterion for whether high-quality development can be achieved.

2.2. Study Area

The YREB (Figure 1) runs through eastern, central, and western China, spanning the Chengdu–Chongqing urban agglomeration, the middle reaches of the Yangtze River, and the Yangtze River Delta urban agglomeration. It carries 42.8% of the population and 42.2% of the total economic volume with a land area of 21% of the country and has become the most promising region for China’s economic development [25,26]. China’s 13th Five-Year Plan identifies the YREB strategy as one of the three major regional development strategies in China. The YREB is the most concentrated region for industrial development in China, and its industrial land supply area accounted for 51.7% of the total industrial supply area in China in 2019, with 47% of China’s petrochemical industry, 53% of nonferrous metal smelting, and 42% of the tanning industry located along the Yangtze River [27]. The YREB is China’s most important manufacturing cradle, gathering more than 40% of the country’s total industrial output. Since the industrial development of the YREB occupies an essential position in China, it is important to study the industrial land supply in this region, which will help optimize its industrial pattern and industrial structure from the supply side and is significant for the healthy development of China’s industrial economy. In addition, the YREB is a strategic highland for China to achieve high-quality development and plays a coordinating and radiation-driven role in regional and national high-quality development. Therefore, scientific assessment of its HDL is conducive to the study of regional development differences, development shortcomings, and development evolution trends and is of great significance for the comprehensive promotion of high-quality development of the YREB.

According to data availability, the sample of this paper includes 108 cities at the prefecture level and above (excluding minority autonomous states) in the YREB; the upstream region includes cities under the jurisdiction of Sichuan, Chongqing, Yunnan, and Guizhou Provinces; the midstream region includes cities under the jurisdiction of Hubei, Hunan, and Jiangxi Provinces; and the downstream region includes cities under the jurisdiction of Shanghai, Anhui, Suzhou, and Zhejiang Provinces.

2.3. Data Sources and Research Methods

2.3.1. Data Sources

In 2010, the former Ministry of Land and Resources issued a document requiring municipal- and county-level land departments to publish land supply announcements on the Land China website (www.landchina.com, accessed on 30 June 2022), including data such as electronic supervision number, project name, location, area, land use, and industry classification, which are currently widely used in various land studies [28]. Python was used to obtain supply result records, and after data cleaning, a total of 168,390 industrial land supply data observations were collected from 108 prefecture-level cities in the YREB from 2010 to 2019. The indicator system uses municipal panel data for these cities, and the raw data are obtained from provincial- and prefecture-level cities’ statistical yearbooks and statistical bulletins, the China City Statistical Yearbook (2011–2020), and the China City Construction Statistical Yearbook (2011–2020). Each indicator is measured using citywide data, and some missing data are filled by linear interpolation.

2.3.2. Classification of Industrial Sectors

According to the National Economic Industry and Code (GB/T 4754-2017), High Technology Industry (Manufacturing) Classification (2017), and existing research results [23,29,30], combined with the industrial development status, project types, and organization, elements, and technology structure of the YREB, the industry classification of industrial land supply results data obtained by the authors was divided into seven major categories (

Table 1. The industry classification of industrial land.

Classification of Industries after Division	Classification of Industries and Their Codes before Division
Extractive Industry	Mining industry (B)
Food and light textile industry	Agrifood processing industry (13); food manufacturing (14); wine, beverage, and refined tea manufacturing (15); tobacco products industry (16); textile industry (17); textile clothing and apparel industry (18); leather, fur, feathers, and their products and footwear industry (19); furniture manufacturing (21); paper and paper products industry (22); printing and recording media reproduction industry (23); education, culture industrial, aesthetic, sports, and recreational goods manufacturing (24); other manufacturing industries (41)
Raw material industry	Wood processing and wood, bamboo, rattan, palm, and grass products industry (20); petroleum, coal, and other fuel processing industry (25); chemical raw materials and chemical products manufacturing (26); chemical fiber manufacturing (28); rubber and plastic products industry (29); nonmetallic mineral products industry (30); ferrous metal smelting and rolling processing industry (31); nonferrous metal smelting and rolling processing industry (32); metal products industry (33)
Processing industry	General equipment manufacturing (34); special equipment manufacturing (35); automobile manufacturing (36); railroad, ship, aerospace, and other transportation equipment manufacturing (37); electrical machinery and equipment manufacturing (38); comprehensive utilization of waste resources (42); metal products, machinery, and equipment repair industry (43)
High-tech industry	Pharmaceutical manufacturing (27); aviation, spacecraft, and equipment manufacturing (374); computer, communications, and other electronic equipment manufacturing (39); instrumentation manufacturing (40); medical instruments and equipment manufacturing (358); cultural information chemicals manufacturing (2664); medical production with information chemicals manufacturing (2665)
Resources and energy supply industry	Electricity, heat, gas, and water production and supply industry (D)
Industrial support service	Telecommunications, radio and television, and satellite transmission services (63); public facilities management (78); scientific research and technical services (M); finance (J); construction (E), etc.

Note: The classification codes in brackets correspond to the National Economic Industries and Codes (GB/T 4754-2017).

). Among them, the industrial supporting service industry is defined as the guaranteed service industry that promotes the continuity of industrial production and technological progress, industrial upgrading, and efficiency improvement.

2.3.3. Evaluation Index System of HDL

Based on the connotation of high-quality development defined in 2.1, considering the Opinions of the Central Committee of the Communist Party of China State Council on Promoting High-Quality Development in the Central Region in the New Era, the Outline of the Yangtze River Delta Regional Integrated Development Plan, and relevant research [22,31,32], and following the principles of scientificity, systematization, and data accessibility, this paper sets 11 evaluation objectives and selects 34 evaluation indicators from five dimensions to construct a comprehensive measurement index system of the high-quality development level of the YREB (

Table 2. Comprehensive measurement index system of HDL.

Dimension	Evaluation Objective	Indicator	Unit	Property
Innovation	Innovation environment	Percentage of expenditure on education	%	+
		Percentage of expenditure on science and technology	%	+
		Number of college teachers per 10,000 people	person/10,000 people	+
	Innovation output	Digital Economy Index	-	+
		Number of college students per 10,000 people	person/10,000 people	+
		Number of patents granted per 10,000 people	pieces/10,000 people	+
Coordination	Urban–rural coordination	Primary industry value added as a proportion of GDP	%	-
		Urban–rural income balance	-	+
		Urbanization rate	%	+
	Industry coordination	Industrial Rationalization Index	-	+
		Industrial Advancement Index	-	+
		Tertiary industry value added as a proportion of GDP	%	+
Greenness	Green pressure	CO2 emissions per unit of GDP	million tons/million yuan	-
		SO2 pollution emissions per unit of industrial value added	t/million yuan	-
		Wastewater pollution emissions per unit of industrial value added	t/million yuan	-
		Soot emissions per unit of industrial value added	t/million yuan	-
		Energy consumption per unit of GDP	million tons of standard coal/million yuan	-
	Environmental governance	Green area per capita	km2/person	+
		Annual average PM2.5 concentration	μg/m3	-
		The domestic waste disposal rate	%	+
		Urban sewage treatment rate	%	+
Openness	Foreign trade	Foreign direct investment as a proportion of GDP	%	+
	Foreign investment utilization	Total imports and exports as a percentage of GDP	%	+
		Percentage of foreign-invested enterprises	%	+
	Opening environment	Marketization Index	-	+
Sharing	Public resources	The average density of the urban transportation network	km/km2	+
		Library collections per 10,000 people	books/10,000 people	+
		Number of medical beds per 10,000 people	beds/10,000 people	+
		Number of cinemas and theaters per 10,000 people	pcs/10,000 people	+
		Number of museums per 10,000 people	pcs/10,000 people	+
	Welfare for life	Basic pension insurance participation rate	%	+
		Medical insurance coverage rate	%	+
		Urban registered unemployment rate	%	-
		The average wage of employees	10,000 yuan/person	+

).

(1) The development level of innovation is reflected in the regional innovation environment and innovation output. The innovation environment determines the sum of relationships formed by various factors in the spatial scope where innovation subjects are located, which has a profound impact on whether innovation factors can be gathered and innovation potential explored, while innovation output is the most important indicator directly reflecting the level of regional innovation. In this paper, the percentage of education expenditure, the percentage of science and technology expenditure, and the number of college teachers per 10,000 people are selected to measure the innovation environment. The Digital Economy Index (DEI), the number of college students per 10,000 people, and the number of patents granted per 10,000 people are selected to measure innovation output. Among them, the DEI represents a new type of economic activity involving digital technology and ICT, which has an important driving role in innovation development [33], and the calculation method refers to relevant research results [34].

(2) The development level of coordination is reflected in urban-rural coordination and industry coordination. Urban-rural coordination is the inevitable way for urban-rural relations to shift from separation to integration, and it is the key to realizing regional association and interaction and the rational allocation of resource factors [35]. Industry coordination is specifically manifested in the optimization and upgrading of industrial structure, which plays an important role in promoting the transformation of overcapacity and structural reform on the supply side. In this paper, the Industrial Rationalization Index, Industrial Advanced Index, and tertiary industry value added as a proportion of GDP are selected to reflect the coordination degree of industry, and the first two are calculated by referring to relevant research results [36]. Primary industry value added as a proportion of GDP, urban-rural income balance, and urbanization rate are selected to reflect the degree of urban-rural coordination. The urbanization rate measures the development of urbanization, and the urban-rural income balance is used to measure the income gap between urban and rural areas [37] and is calculated based on the Theil Index as follows:

$$I{B}_{it}=1-T_{it}=1-\left(T_{it}^c+T_{it}^r\right)=1-\frac{I_{it}^c}{I_{it}}ln\left(\frac{I_{it}^c}{I_{it}}/\frac{P_{it}^c}{P_{it}}\right)-\frac{I_{it}^r}{I}ln\left(\frac{I_{it}^r}{I}/\frac{P_{it}^r}{P_{it}}\right)$$

(1)

where $i$ and $t$ denote regions and years, respectively;$I{B}_{it}$ is the urban-rural income balance degree; $T_{it}$ is the Theil Index; $T_{it}^c$ and $T_{it}^r$ are the Theil indices of urban and rural population income, respectively; $I_{it}$ is the total income of the regional population; $I_{it}^c$ is the total income of the regional urban population; and $I_{it}^r$ is the total income of the regional rural population. $P_{it}$ is the total population of the region, $P_{it}^c$ is the total regional urban population, and $P_{it}^r$ is the total regional rural population. If the income coordination deviates more from the equilibrium state, the more $I{B}_{it}$ is skewed to 0, indicating a larger urban-rural income gap.

(3) The development level of greenness is reflected in the pressure on the ecological environment and the effectiveness of the local government’s governance. For the YREB, greenness is the most important development constraint indicator. Green pressure is an important factor to measure its development threshold, and the effectiveness of environmental governance is an important reference to assess the quality of regional green development [38]. In this paper, CO₂ emissions per unit of GDP, SO₂ pollution emissions per unit of industrial value added, wastewater pollution and soot emissions, and energy consumption per unit of GDP are selected to reflect green pressure, where energy consumption is calculated by summing up the total gas supply, total LPG supply, the total electricity consumption of the whole society, and the total heat supply based on the discounted standard coal coefficient. Green area per capita, annual average PM_2.5 concentration, domestic waste disposal rate, and urban sewage treatment rate are selected to measure environmental governance in four aspects.

(4) The development level of openness is reflected in foreign trade, foreign investment utilization, and the opening environment. Foreign trade is the precursor and foundation of opening up to the outside world, and the mutual integration and symbiosis of foreign investment and local economic components can support the building of a new system of China’s open economy, while a high level of opening up also relies on a strong market economy. In this paper, we choose the proportion of foreign direct investment to GDP to measure the level of foreign trade. Total imports and exports as a percentage of GDP and percentage of foreign-invested enterprises are selected to measure the level of foreign investment utilization. The Marketization Index is selected to reflect the level of the opening environment [39].

(5) The development level of sharing is reflected in public resources and welfare for life. Sharing is the ultimate goal of high-quality development and determines the essence of high-quality development [40]. In this paper, the average density of the urban transportation network is selected to reflect the level of infrastructure, the library collection per 10,000 people to reflect the sharing of educational resources, the number of medical beds per 10,000 people to reflect the sharing of medical resources, and the number of theaters and museums per 10,000 people to reflect the sharing of cultural resources. The above five indicators are used to measure the level of sharing of public resources. The basic pension insurance participation rate and basic medical insurance coverage rate are selected to reflect the level of social security, the urban registered unemployment rate to reflect the employment situation, and the average wage of employees to reflect the income situation; these four indicators are used to measure the level of welfare.

HDL is calculated by the entropy weight method, which is an objective weight calculation method and can effectively overcome the information superposition between indicators. Therefore, this paper uses this method to calculate the HDL to represent the high-quality development level of the city. The steps are as follows:

First, the data matrix was normalized using the extreme value method, and the formula was calculated as follows:

$$\mathrm{If} y_{ij} \mathrm{is} \mathrm{a} \mathrm{positive} \mathrm{indicator}: y_{ij}=\frac{x_j-x_{min}}{x_{max}-x_{min}}, \left(i=1,2,\dots ,m; j=1,2,\dots , n\right)$$

(2)

$$\mathrm{If} y_{ij} \mathrm{is} \mathrm{a} \mathrm{negative} \mathrm{indicator}: y_{ij}=\frac{x_{max}-x_j}{x_{max}-x_{min}}, \left(i=1,2,\dots ,m; j=1,2,\dots , n\right)$$

(3)

where $y_{ij}$ is the standardized value of the $j$th indicator of city $i$, and $x_{max}$ and $x_{min}$ are the maximum and minimum values of the $j$th indicator, respectively.

Next, the information entropy $E_j$ of indicator $j$ is found, which is calculated as follows:

$$E_j=-\left(\frac{1}{\ln z}\right){\displaystyle \sum }_{i=\mathrm{a}}^z{p}_{ij}\ln \left(p_{ij}\right)$$

(4)

$$p_{ij}=\frac{y_{ij}}{{\sum }_{i=1}^z{y}_{ij}}$$

(5)

where $E_j$ is the information entropy, $z$ is the number of cities in the study area, and $p_{ij}$ is the share of the $i$th city under the $j$th indicator in that indicator.

Next, the weight of indicator $j$ is determined. The information entropy of each indicator is $E_j$, and the weight $W_j$ is:

$$W_j=\frac{1-E_j}{{\sum }_{i=1}^z\left(1-E_j\right)}$$

(6)

Therefore, the HDL of the high-quality development level is:

$$HDL={\displaystyle \sum }_{i=1}^m{y}_{ij}{w}_j$$

(7)

2.3.4. Spatial Econometric Model

The main content of the study of spatial econometrics is the structural analysis of spatial autocorrelation and spatial heterogeneity in regression models dealing with cross-sectional and panel data. In recent years, spatial econometric models have been applied to many fields of the social sciences and are applicable to the problems explored in this study; their basic form is as follows:

$$\begin{array}{c}{Y}_{it}=\rho W{Y}_{it}+\beta X_{it}+\theta W{X}_{it}+{\mu }_i\\ {\mu }_i=\lambda W\mu +{\epsilon }_i\end{array}$$

(8)

where $i$ and $t$ denote the region and year, respectively; $Y_{it}$ is the dependent variable; $W$ denotes the spatial weight matrix; $X_{it}$ is the independent variable; $\beta$ denotes the explanatory variable regression coefficient; $\rho$ denotes the dependent variable spatial regression coefficient; $\theta$ denotes the independent variable spatial regression coefficient; and $\lambda$ denotes the spatial error regression coefficient. When $\rho$ ≠ 0 and $\theta$ = 0, Formula (8) is the spatial lag model (SLM); when $\lambda$ ≠ 0 and $\rho$ = 0, Formula (8) is the spatial error model (SEM); when $\rho$ ≠ 0 and $\theta$ ≠ 0 and $\lambda$ = 0, Formula (8) is the spatial Durbin model (SDM). In this paper, we set up the geographic neighborhood spatial weight matrix W1 based on the queen method and the economic distance spatial weight matrix W2. Formula (8) needs to be transformed into a suitable spatial econometric model based on the data structure, so model testing is needed. For the specific methodological steps, we refer to relevant research results [41].

3. Results and Analysis

3.1. HDL Estimation Results of the YREB

3.1.1. Regional Characteristics Analysis

The average value of HDL in the YREB increased from 0.154 to 0.198 from 2010 to 2019, with a cumulative increase of 28.924% and an average annual increase of 2.873%. The sample cities were further divided into three regional subsamples upstream, midstream, and downstream of the Yangtze River. The average values of HDL in the three regions showed an overall rising trend (Figure 2). By region, HDL rose the most in the upstream (cumulative increase of 46.639%), followed by the midstream (cumulative increase of 33.777%), while the downstream rose the least (cumulative increase of 18.854%). However, the baseline HDL in the downstream cities was significantly higher than that in the midstream and upstream, and although the growth rate was the lowest, it still formed a “downstream–midstream–upstream” ladder phenomenon.

In the space dimension, the only cities with high HDL in 2010 were Nanjing and Shanghai in the downstream, and the number of cities belonging to the downstream accounted for 80% of the cities with high HDL in 2019. This ratio is much higher than the upstream and midstream. The number of cities belonging to the upstream accounted for 38.028% of the cities with low HDL in 2010, and this ratio further increased to 52.941% in 2019 (

Table 3. HDL of cities in the YREB in 2010, 2015, and 2019.

Year	City	Low		Moderately Low		Medium		Moderately High		High		High HDL Cities
		<0.148		0.149–0.189		0.190–0.243		0.244–0.355		>0.356
		Quantity	%	Quantity	%	Quantity	%	Quantity	%	Quantity	%
2010	Upstream	27	38.028	1	10	2	25	1	5.882	0	0	Nanjing, Shanghai
	Midstream	30	42.254	2	20	1	12.500	3	17.647	0	0
	Downstream	14	19.718	7	70	5	62.500	13	76.471	2	100
2015	Upstream	24	44.444	3	13.636	1	10	3	15.789	0	0	Nanjing, Shanghai, Suzhou
	Midstream	24	44.444	8	36.364	1	10	3	15.789	0	0
	Downstream	6	11.111	11	50	8	80	13	68.421	3	100
2019	Upstream	18	52.941	7	22.581	3	14.286	3	17.647	0	0	Nanjing, Shanghai, Suzhou, Wuhan, Hangzhou
	Midstream	12	35.294	14	45.161	7	33.333	2	11.765	1	20
	Downstream	4	11.765	10	32.258	11	52.381	12	70.588	4	80

). Cities with high HDL growth in 2010–2019 are mainly located in the upstream (Dazhou, Baoshan, Bazhong, Qujing, Zhaotong, Lincang) and midstream (Yueyang, Jingzhou, Changde, Shaoyang, Huanggang), with growth rates higher than 50%; cities with growth rates lower than 10% are mainly located in the downstream (Shaoxing, Huzhou, Nantong).

In the time dimension, the HDL evolution curve of the YREB from 2010 to 2019 was fitted (Figure 3). The HDL of cities in the YREB increases year by year, and the proportion of cities with HDL at a low level drops abruptly, from 65.741% in 2010 to 31.481% in 2019; the proportion of cities at moderately low and medium levels grows faster, from 9.259% and 7.407% in 2010 to 28.704% and 19.444% in 2019, respectively; the proportion of high-level cities grew slowly, but showed a steady increase year by year, from 1.852% in 2010 to 4.630% in 2019. Overall, the HDL showed an evolution from a low to a moderately low level from 2010 to 2019.

3.1.2. Analysis of Spatiotemporal Pattern Characteristics

Three cross-sectional visualizations for 2010, 2015, and 2019 (Figure 4a–c) were selected to show the HDL evolution characteristics of the YREB, and the Jenks natural breakpoint method was used to cluster the average HDL values within the studied time series and draw local autocorrelation clusters as a way to reveal the spatial pattern of HDL (Figure 4d). To facilitate the comparison of the changes in indicator evaluation results between different years, the evaluation values at the end of the study period (2019) were used as the basis for grading. (1) The time evolution shows that most of the cities improved their HDL from 2010 to 2019 compared with the base period. (2) The spatial pattern shows that the pattern of HDL in the YREB overlaps with the pattern of economic development, and the regions with higher HDL overlap with the regions with higher economic development, indicating that economic output is one of the necessary conditions to promote high-quality development. The high value of HDL is concentrated in municipalities directly under the central government and provincial capitals, showing the typical “center–periphery” phenomenon, and the unevenness of regional development is more significant. The area of high HDL values in the YREB is always located downstream of the Yangtze River during the study period, and although the differences between the east, middle, and west have slowed down, polarization is prevalent, with the cities with high HDL in 2010, 2015, and 2019 being Nanjing (0.389, 0.425, and 0.505), and the cities with low HDL being Zhaotong (2010: 0.062, 2015: 0.084) and Lincang (2019: 0.103) in Yunnan Province. Among them, the regional differences in HDL are most obvious in the Yangtze River Delta urban agglomeration, with Nanjing’s average HDL reaching 0.434, while Fuyang’s is only 0.113, a serious polarization phenomenon. (3) Based on the average values of HDL used to generate the LISA map, it can be seen from the spatial clustering that the Lincang–Baoshan–Pu’er area and the Neijiang–Zigong–Luzhou–Yibin area upstream of the Yangtze River form significant regional LL clusters. HH clusters appear in the downstream Yangtze River Delta urban agglomeration, with Nanjing, Shanghai, Suzhou, and Hangzhou as the core areas with higher HDL and less spatial variability. HL outliers are found in the upstream cities of Chongqing and Kunming, which have higher HDL and lower levels in the surrounding areas, with significant gaps.

3.2. Spatial Econometric Model Analysis

3.2.1. Setting of Model Variables

In this paper, the HDL of the YREB during 2010–2019 is taken as the explanatory variable of the model, and the scale of supply of various types of industrial land in the YREB during that period is taken as the explanatory variable. Since the extractive industry and resource and energy supply industries are resource-intensive industries that depend on the distribution of natural resources, there is an obvious spatial directionality. Cities with no corresponding resource distribution over the period have zero supply of these two types of industrial land. Although the supply share is relatively small, there are more data gaps, which would cause bias in the model if included in the spatial econometric analysis, so both types of industries are excluded from the spatial econometric model section of this paper. Referring to existing studies [42,43], three control variables are introduced, namely, the economic development level, the investment level, and the financial level (

Table 4. Variable descriptions.

Variable Type	Variables	Variable Description
Explained variables	HDL	According to the index system in Table 2, the entropy weighting method was applied for a comprehensive evaluation
Explanatory variables	HS	High-tech industrial land supply scale (ha)
	ISSS	Industrial supporting services land supply scale (ha)
	PS	Processing industrial land supply scale (ha)
	FLTS	Food and light textile industrial land supply scale (ha)
	RMS	Raw materials industrial land supply scale (ha)
Control variables	PGDP	GDP per capita (yuan/person)
	PFAI	Fixed asset investment per capita (yuan/person)
	PYLB	Year-end loan balance per capita (yuan/person)

). The multicollinearity test is conducted (

Table 5. Multicollinearity test.

Variables	VIF	1/VIF
HS	2.07	0.482629
ISSS	1.21	0.825546
PS	2.79	0.357982
FLTS	2.06	0.484901
RMS	2.35	0.426292
PGDP	4.02	0.248565
PFAI	2.38	0.421011
PYLB	2.56	0.389900
VIF mean value	2.43

), and the variance inflation factor results show that the VIF of each variable is less than 5, and there is no multicollinearity. Therefore, it is reasonable to construct the spatial econometric model.

3.2.2. Spatial Econometric Model Selection and Construction

In this paper, the module code in the Stata 16 software is used to perform the test, and the specific steps and code are referred to in the related literature [44]. The test procedure is as follows: First, the spatial autocorrelation test (

Table 6. Moran’s I for HDL with different spatial weight matrices.

Year	W1	W2
2010	0.443 ***	0.523 ***
2011	0.424 ***	0.519 ***
2012	0.435 ***	0.523 ***
2013	0.429 ***	0.511 ***
2014	0.404 ***	0.512 ***
2015	0.386 ***	0.493 ***
2016	0.406 ***	0.495 ***
2017	0.402 ***	0.474 ***
2018	0.419 ***	0.481 ***
2019	0.377 ***	0.466 ***

Note: *** p < 0.01.

) is conducted. HDL is the explanatory variable, and Moran’s I is significant and positive at the 1% level under two spatial weight matrices, indicating that there is significant spatial dependence and agglomeration of HDL in the YREB. Moran’s I under both W1 and W2 shows a fluctuating declining trend, indicating that the regional development is increasingly balanced. Second, the LM-lag, LM-error, and Hausman test results (

Table 7. Spatial econometric model test.

Matrix	LM-Lag	LM-Error	LR-Time	LR-Ind	Hausman	Wald-SLM	Wald-SEM	LR-SLM	LR-SEM
W1	5.529 **	191.036 ***	2239.80 ***	52.57 ***	224.24 ***	85.9 ***	77.58 ***	82.74 ***	76.25 ***
W2	39.412 ***	9.677 ***	2340.56 ***	65.66 ***	224.24 ***	58.10 ***	55.35 ***	56.59 ***	56.24 ***

Note: *** p < 0.01; ** p < 0.05.

) significantly reject the original hypothesis under W1 and W2, indicating that the fixed-effect model should be used. Third, the LR and Wald tests are both significant at the 1% level, indicating that the SDM cannot be transformed into the SLM or SEM. Furthermore, both the LR-time and LR-ind effect tests are significant at the 1% level, and the combination of log-likelihood and goodness-of-fit (R2) indicates that the results of choosing the temporal and spatial double-fixed (both) model fit best. Therefore, the SDM with temporal and spatial double-fixed should ultimately be chosen. The model is constructed under the base form of Formula (8), as in Formula (9):

$$\begin{gathered} HD{L}_{it}={\alpha }_0+\rho WHD{L}_{it}+{\beta }_{1}H{S}_{it}+{\beta }_{2}ISS{S}_{it}+{\beta }_{3}P{S}_{it}+{\beta }_{4}FLT{S}_{it}+{\beta }_{5}RM{S}_{it}+{\displaystyle \sum }_{j=1}^n{\beta }_j{X}_{it}^j+{\theta }_{1}WH{S}_{it} \\ +{\theta }_{2}WISS{S}_{it}+{\theta }_{3}WP{S}_{it}+{\theta }_{4}WFLT{S}_{it}+{\theta }_{5}WRM{S}_{it}+{\displaystyle \sum }_{j=1}^n{\theta }_{j}W{X}_{it}^j+{\epsilon }_{it} \end{gathered}$$

(9)

where ${\alpha }_0$ is the constant coefficient;$i$ and $t$ are regions and years, respectively; $j$ is the type of control variables; $HD{L}_{it}$ is the high-quality development level of each city in each year; $\rho$ is the spatial regression coefficient of the explained variables; $W$ is the spatial weight matrix; ${\beta }_1–{\beta }_6$ are the regression estimation coefficients of the explanatory variables; ${\theta }_1–{\theta }_6$ are the spatial regression estimation coefficients of the explanatory variables; $X_{it}$ is the control variables; ${\epsilon }_{it}$ is the random error term.

After establishing the spatial Durbin model Formula (9), the panel data and spatial weight matrix are loaded in the Stata 16 software, the “xsmle” code module is used and the temporal and spatial double-fixed (both) model is selected, and then the operations are performed, and the final model estimation results can be obtained.

3.2.3. Analysis of Model Estimation Results

• Point estimation results

The parameter estimation was performed under two types of spatial weight matrices, and the results are shown in

Table 8. Estimation results of SDM.

	W1			W2
	Time	Ind	Both	Time	Ind	Both
HS	0.0502 ***	0.00700 **	0.00649 **	0.0446 ***	0.00974 **	0.00983 **
ISSS	−0.0330	−0.00403	−0.00414	−0.00688	−0.00297	−0.00568
PS	−0.00109	−0.00651	−0.00529	−0.0348	−0.00140	−0.00003
FLTS	−0.0103	−0.00275	−0.000699	−0.0219	−0.00676	−0.00676
RMS	0.0248 *	0.00415	0.00328	0.0128 *	0.0168 ***	0.0114 *
PGDP	0.103 ***	0.09479 ***	0.0889 ***	0.138 ***	0.0554 ***	0.0574 ***
PFAI	0.0498 ***	0.03172 ***	0.0275 ***	0.0459 ***	0.0313 ***	0.0293 ***
PYLB	0.306 ***	0.06754 ***	0.0640 ***	0.317 ***	0.0949 ***	0.0998 ***
W × HS	−0.0201	−0.00815	−0.00651	−0.0510 **	−0.0129	−0.00857
W × ISSS	0.0549 ***	0.0186 **	0.0161 *	0.0366	0.0234 *	0.00217
W × PS	0.0144	0.04047 ***	0.0471 ***	0.121 ***	0.0114	0.00235
W × FLTS	−0.0303	−0.0236 ***	−0.0166 *	−0.00218	−0.000392	−0.00330
W × RMS	0.0367	0.03256 ***	0.0262 **	0.0960 **	0.00208	0.0342 **
W × PGDP	−0.00117	−0.00754	−0.0215	0.00757	0.0337 **	0.0928 ***
W × PFAI	−0.0121	−0.02238 *	−0.0608 ***	−0.00985	−0.0126	−0.0477 ***
W × PYLB	−0.136 ***	−0.00201	−0.00874	−0.101 ***	−0.0731 ***	−0.0825 ***
spatial rho	0.389 ***	0.215 ***	0.125 ***	0.0363 ***	0.119 ***	0.0103 ***
R2	0.7765	0.7503	0.8137	0.7581	0.7454	0.8289
Log-likelihood	2386.9993	3480.6153	3506.8984	2318.6786	3456.1281	3488.9599

Note: *** p < 0.01; ** p < 0.05; * p < 0.1.

, where $\mathrm{time}$ and $\mathrm{ind}$ denote temporal-fixed and spatial-fixed, respectively, as robustness tests. The results show the consistent direction of the influence factors. The coefficients of the spatial lag term $spatial rho$ of HDL under W1 and W2 are 0.125 and 0.0103, respectively, and significant at the 1% level, indicating that the explanatory variable HDL has a spatial spillover effect on itself; that is, the increase in local HDL has a significant spatial spillover effect on neighboring places, and there is a spatial lag. Lesage argues that point estimates reflecting spatial spillover effects are subject to estimation bias, the effect of the independent variable on the dependent variable is difficult to reflect by a single regression coefficient, and the formulation of this effect is more complex; therefore, the spatial effect needs to be decomposed into a direct effect, an indirect effect, and a total effect using partial differencing methods [45]. The direct effect is the effect of the explanatory variables on the local urban area. The direct effect is the impact of the explanatory variable on the local high-quality development of the city, the indirect effect is the impact of the explanatory variable on the high-quality development of the surrounding areas of the city, and the total effect indicates the impact of the explanatory variable on the overall high-quality development of the whole region.

• Partial differential estimation results

Under the two types of spatial weight matrices, the total effect of the land supply scale of different industrial sectors on high-quality development was decomposed into direct and indirect effects (

Table 9. Results of spatial spillover partial differential estimation.

	W1			W2
	Direct Effect	Indirect Effect	Total Effect	Direct Effect	Indirect Effect	Total Effect
HS	0.00650 **	−0.00550	0.00100 **	0.0100 **	−0.00762	0.00239 **
ISSS	−0.00382	0.0178 *	0.0140	−0.00588	0.00158	−0.0043
PS	−0.00330	0.0512 ***	0.0479 ***	−0.00049	0.00327	0.00278
FLTS	−0.00139	−0.0181 *	−0.0195 *	−0.00693	−0.00406	−0.00286
RMS	0.00402	0.0289 **	0.0329 **	0.0113 *	0.0355 **	0.0468 *
PGDP	0.0893 ***	−0.0105	0.0788 ***	0.0580 ***	0.0941 ***	0.152 ***
PFAI	0.0256 ***	−0.0646 ***	−0.0390	0.0294 ***	−0.0480 ***	−0.0187
PYLB	0.0637 ***	−0.000193	0.0635 ***	0.0994 ***	−0.0816 ***	0.0178

Note: *** p < 0.01; ** p < 0.05; * p < 0.1.

).

The direct impact on local high-quality development is reflected in the following aspects: (1) The direct effect of the impact of high-tech industrial land supply on high-quality development under W1 is estimated to be 0.00650 and significant at the 5% level, indicating that the increase in the scale of high-tech industrial land supply can significantly enhance the local HDL, and the direction of W2 is consistent and significant, confirming the robustness of the results. This may be attributed to the high-tech industry’s combination of advanced technology, advanced management systems, and innovation capabilities; its better innovation environment promotes the output of innovative products and its coexistence of capital-saving and material energy-enhancing capital deepening that promotes total productivity gains through non-neutral technological progress [46]. The increase in the proportion of high-tech industries will enhance regional land use intensification, improve energy structure, and reduce environmental pressure, thus promoting regional innovation and green development, which is important for the implementation of the “Yangtze River Protection” strategy. (2) The direct effect of raw material industrial land supply on HDL under W2 is estimated to be 0.0113 and significant at the 10% level, indicating that the increase in the scale of raw material industrial land supply can enhance the HDL. This effect may be due to the nature of the industry itself. For example, the metal products industry is a typical labor-intensive industry requiring certain skills and usually provides a series of pre-employment training for workers to teach relevant knowledge and skills, which laterally improves the quality of the local labor force [47]. (3) Among the control variables, the direct effect estimates of all three are positive and significant at the 1% level under both types of spatial weight matrices, indicating that the improvement in economic development level, investment level, and financial level can significantly contribute to the improvement in local HDL.

The spatial spillover effect on the impact of high-quality development is reflected in the following aspects: (1) The estimated results under both W1 and W2 show that the estimated coefficient of the indirect effect of high-tech industrial land supply is negative and does not pass the significance test, indicating that there is a negative spillover of high-tech industrial land supply to high-quality development, but its effect is not significant. This may be because the high-tech industry is in a period of rapid development and needs to absorb a large number of production factors, such as capital and manpower, from other industries and regions, resulting in the exploitation of resources for industrial development and urban development in other regions. The total effect is estimated to be 0.00100 and significant at the 5% level, indicating that the contribution of high-tech industrial land supply to HDL is mainly through direct effects rather than spatial spillover effects. (2) The indirect effect of industrial supporting service land supply on HDL under W1 is estimated to be 0.0178 and significant at the 10% level, indicating that industrial supporting service land supply forms a significant positive spatial spillover to high-quality development. This may be because with the development of “advanced manufacturing services” and the integration of secondary and tertiary industries, the matching degree of land supply for industrial supporting services and the spare land for industrial development in major industrial parks is increasing, resulting in the formation of logistics parks with a certain degree of concentration in or around industrial parks, enhancing the degree of regional industrial coordination and open communication. In addition, it provides a large number of jobs and public resources and promotes the construction of regional infrastructure, which is in line with the current development mode of the midstream Yangtze River of relying on infrastructure to pull investment and maintain growth. In the process of industrial-scale agglomeration effect and regional integrated development in the midstream area, the positive influence of the industrial supporting service industry on the adjacent areas is gradually manifested. (3) The indirect effect of processing industrial land supply on HDL under W1 is estimated to be 0.0512 and significant at the 1% level, indicating that processing industrial land supply forms a significant positive spatial spillover on high-quality development. The processing manufacturing industry is still the core support for the development of the YREB, and cities that have long been supplying land mainly for the processing industry have been gathering in the lower reaches of the Yangtze River, forming large industrial clusters and extending to form a more complete processing industry circular economic chain through interenterprise exchanges and cooperation. Together with the strong demand for transportation conditions, this has prompted the accelerated coverage of modern transportation networks, such as regional airports, high-speed railways, and highways, which has strengthened the mobility of inter-regional logistics, people, and information flows, prompting factors to flow into spatially adjacent areas through the trickle-down effect and enhancing overall regional economic efficiency. The total effect is 0.0479 and significant at the 1% level, indicating that the contribution of processing industrial land supply to HDL is mainly reflected through the spatial spillover effect. (4) The indirect effect of food and light textile land supply on HDL under W1 is estimated to be −0.0181 and significant at the 10% level, indicating that food and light textile land supply forms a significant negative spatial spillover on high-quality development. This is probably because the food and light textile industry is a typical labor-intensive industry that relies on low-cost land and labor. At the early stage of industrialization, the supply pattern of the food and light textile industry in the middle and upper reaches of the Yangtze River was “scattered overall and concentrated locally”, and the limited land resources were occupied by industrial enterprises with low efficiency, low output, and low value added. In the process of industrialization, the food and light textile industry, which is suitable for the initial development stage, has lost its strong driving effect, but it still absorbs the labor resources and occupies the space of other industries in the neighboring areas, which has slowed the high-quality development of the neighboring areas to a certain degree. The total effect is −0.0195 and significant at the 10% level, indicating that the negative effect of food and light textile industrial land supply on HDL is reflected through the spatial spillover effect. (5) The indirect effect of the impact of raw material industrial land supply on HDL under W1 is estimated to be 0.0289 and significant at the 5% level, indicating that raw material industrial land supply forms a significant positive spatial spillover on high-quality development. This may be due to the strong market orientation of the raw material industry in the YREB [29]. For example, the processing industry of organic chemical raw materials is clustered in the coastal areas, while the concentrated production of phosphate fertilizer in Yunnan and Guizhou has generated a huge market demand for sulfuric acid, so the processing of inorganic chemical raw materials is clustered in this area. After the formation of spatial agglomeration, the demand for market size, market capacity, and openness level in the supply area grows significantly, which has an impact on the neighboring regions through the transforming ability, mobility, and freedom of the market role. The same characteristics are presented under W2. (6) Among the control variables, there is a significant negative spatial spillover effect of investment level; that is, the higher the level of local investment is, the more it inhibits the level of high-quality development of neighboring cities, but its total effect is not significant under both types of matrices. Similarly, there is a significant negative spillover of financial level under W2.

In summary, the level of high-quality development in the YREB is directly and positively influenced by the scale of the supply of land for high-tech industries, as well as the spatial spillover of the scale of the supply of land for industrial supporting services, processing industries, food and light textile industries, and raw material industries. The degree of influence under spatial and economic relationships is somewhat different in terms of significance, but the direction is consistent. The difference in impact originates from the attributes, positioning, and development of the different types of industries carried by the land itself. The long-term large-scale clustering of processing industrial land supply in the downstream junction of Anhui, Jiangsu, and Zhejiang; the initial clustering of industrial supporting service land supply in the midstream of Hubei and Hunan; and the clustering of food and textile land supply in the upstream junction of Sichuan, Yunnan, and Guizhou are important reasons for the spatial gradient differences in the level of high-quality development in the YREB.

3.3. Mechanism of the Impact of the Industrial Land Supply Scale on High-Quality Development

Industrial land is the mapping of the industrial economy on the supply side, and the scale of supply is the reflection of the result of land supply in terms of quantity. The high-quality development of the industrial economy is an important part, key foundation, and important guarantee for the high-quality development of a region or city. The former has a direct impact on the latter, and this impact is achieved mainly through the role of the five dimensions in the connotation of quality development. (1) The industrial economy is the validation site for commercialization and industrialization of innovation results, and industry is the sector with the highest investment in R&D and the most active in science and technology innovation. The value transformation of funds, talents, and hardware facilities of innovation activities depends on industrial development. Most innovation activities depend directly or indirectly on the help of industry. High-quality development of the industrial economy will energize local innovation activities. (2) The industrial economy has a long industrial chain, strong driving force, strong technology diffusion, and irreplaceable role in the coordinated development of the region. Combining local resource endowment, social environment, and economic base to introduce industries that meet local development needs is the current practical experience of promoting social and economic development in many places in China. (3) The energy structure of the industrial economy and its pressure on ecological output are the main factors affecting local green development. For a long time, the industrial economy has shown the development characteristics of high energy consumption and high input, and while growing at a high speed, it is also outputting pressure on the ecological environment; the energy structure of the traditional industrial sector is dominated by fossil fuels and other high-carbon energy sources, which will produce a large number of industrial waste pollutants, causing a stagnant effect on green sustainable development. Therefore, the degree of low carbonization and low pollution of the industrial economy and the degree of green transformation and upgrading of industrial structure will affect the level of local high-quality development. (4) The industrial economy has a key supporting role for international trade and investment and is currently an important area that attracts foreign investment. The development of many large manufacturing companies has led to the internationalization of the region. In addition, many industrial sectors have a strong market orientation, which can have an impact on the development of local markets. (5) Industry is the most important material production sector, providing the material basis for social and economic activities, industrial consumer goods for the material and cultural life of the population, manufactured goods for infrastructure construction, and a guarantee for people to live and work in peace and happiness. In summary, the impact of the front end on the back end originates from the supply side, represented by the supply of industrial land, mediated by the development of the industrial economy at the middle end, and is transmitted to the demand side of high-quality development through the impact on the five dimensions mentioned above. This process directly or indirectly promotes the flow and diffusion of factors.

Based on the empirical evidence in this paper, the theoretical framework of the impact mechanism of the industrial land supply scale on high-quality development is further constructed (Figure 5). In this framework, the impact mechanism is clarified, and the spatial spillover effect is considered. The level of high-quality development of a regional system is constituted by the multiple “local” and “neighborhood” states of development contained within it. The impact of industrial land supply on high-quality development is essentially a transmission effect mediated by the high-quality development of the industrial economy, which is divided into the direct and indirect transmission, and the impact differs according to different industrial sectors.

Direct transmission is dominated by local effects. The industrial land supply can regulate, guide, and allocate the development of the industrial economy. The scale of supply reflects the allocation of land resources in terms of quantity, and this allocation influences the degree of development of the five dimensions of innovation, coordination, greenness, openness, and sharing in cities through the conduction effect of the industrial economy, which in turn affects the level of regional high-quality development.

Indirect transmission is dominated by spillover effects. This spillover effect mechanism can be divided into two aspects: (1) Industrial land supply can form the spatial structure of industrial development and influence the spatial distribution state and spatial combination form of industrial economic activities. Industrial economic activities are distributed in the form of points, the land supply determines the place where the points occur, and the scale of supply determines the level of the points, based on which linear activities, such as transportation, communication, and energy supply, are connected to form many “point-line” industrial economic hub systems, which eventually constitute the regional network spatial structure, resulting in the outflow of local factors through the network space, thus affecting the development of the entire region. (2) Industrial land supply determines the intensity of spatial agglomeration and diffusion of industrial economic development. Industrial agglomeration leads to a form of spatial organization based on a deepening division of labor, and the concentration of several industrial enterprises in a certain geographical area promotes the formation of specialized industrial clusters and causes spatial reorganization. The transmission effect generated by agglomeration has a multifaceted impact on the transformation of the center of gravity of the urban industrial spatial layout and the overall development of the urban economy.

In addition, under the development competition driven by political promotion and the regulation of market mechanisms, the land supply behavior of the local government has an impact on the industrial land supply of neighboring cities, thus affecting the industrial economic development of neighboring areas. Moreover, there is spatial spillover from HDL itself, and cities with higher quality development levels drive the development of surrounding cities.

4. Discussion

The level of high-quality development in the YREB is generally on an upward trend, but the regional nonequilibrium is obvious. The HDL in downstream cities, such as Nanjing, Shanghai, Suzhou, and Hangzhou, is significantly higher than that in the middle and upper reaches, and the polarization phenomenon exists steadily within the region, but this regional gap is gradually decreasing. Zeng et al. [48] used TFP as the research object and obtained similar results. This paper also finds that there is a significant positive spatial spillover of urban high-quality development itself, which makes a connection between local HDL and neighboring HDL, validates the association, and completes the theoretical framework. A similar conclusion was reached in a study by Zhang et al. [49].

Among the supply of land for different industrial sectors in cities in the YREB, the supply of high-tech industrial land plays an important role in promoting high-quality development, and the cities with a high proportion of such land supply overlap highly with areas with high HDL values. However, the spatial spillover effect on the level of high-quality development is still not significant, and even a slight negative spillover occurs, which contradicts the findings of Wang et al. [50]. This may be because their study did not develop from the perspective of land supply but focused on macroeconomic factors; therefore, the indicators and data selected for measuring the variables differ from those in this study. Based on the aforementioned analysis, this paper concludes that the current “industry–university–research” system in China has not yet been fully established, and an industrial linkage between regions has not yet been formed. With the upgrading of industrial structure and the construction of a modern industrial system, the indirect effect of the high-tech industry will turn from negative to positive and from insignificant to significant in the future. In addition, there are structural nesting and relational nesting patterns between the current industrial supporting service industry and other industries, and the spatial spillover effect of its main existence is dependent on the industrial agglomeration of the processing industry. The research results of this paper show that the positive spatial spillover of the industrial supporting service industry is weakly significant, indicating that a mature integrated development model has not yet been formed. The research of Wang et al. [51] supports this view.

As a preliminary exploration of the impact of industrial land supply on high-quality development, this paper supports the further deepening of research in this area in the following aspects: (1) The YREB has already seen significant integration trends, and the spatial economic linkages among the cities within it are becoming increasingly significant, which will have a profound impact on the high-quality development of the region. In this context, the continued improvement in and introduction of new explanatory variables will be an important direction to deepen related research. (2) Since this paper is limited to a single study area, there are certain limitations in the research findings, and the significance and direction of the spatial effects of a land supply scale in different industrial industries in other study areas may be different from this paper, but the research ideas, modeling methods, and theoretical framework of the impact mechanism proposed in this paper have some universality. In the future, we can refer to the theoretical framework proposed in this paper, further examine the mechanism behind the role of land supply, examine its transmission path in depth, decompose the explanatory variables in a more detailed way, and include more studies of economic sample zones for cross-sectional comparison. (3) It is necessary to conduct more in-depth research on how to further strengthen the linkage of industrial land supply structure and high-quality development, specifically allocate the proportion of various types of industrial land, fully utilize the spatial effects of different types of industrial land, and promote the positive interaction between industrial structure transformation and upgrading and regional high-quality development.

5. Conclusions and Implications

5.1. Conclusions

This paper establishes a high-quality development level measurement and evaluation index system, integrates the use of multisource data to measure and analyze the HDL of 108 prefecture-level cities in the YREB from 2010–2019, explores the spatial effects of different categories of an industrial land supply scale on the impact of high-quality development through the spatial Durbin model, and constructs a theoretical analysis framework. The main conclusions are as follows:

(1) The spatiotemporal pattern of the prefecture-level city-scale HDL in the YREB overlaps highly with the pattern of economic development. The annual average HDL values for cities in the entire region show an overall increasing trend. In particular, upstream cities have the largest HDL growth, followed by midstream and downstream cities, but downstream cities have higher baseline levels. The annual mean HDL values show a gradient of “downstream–midstream–upstream” from high to low.

(2) The increase in the scale of supply of high-tech industrial land can effectively promote the level of local high-quality development and shows a significant positive direct effect under both the spatial adjacency matrix and the economic distance matrix. The scale of industrial land supply for raw materials shows a significant positive direct effect under the economic distance matrix, but the intensity is lower than that of the indirect effect.

(3) The scale of industrial land supply has a relatively significant spatial spillover effect on high-quality development, with differences stemming from the attributes of different industrial sectors themselves. The land supply scales of industrial supporting services, processing industries, food and light textile industries, and raw materials industries show significant indirect effects, with the strongest spillover effect of processing industries, and all of them are positive except that of food and light textile industries. The balance of high-quality development in the YREB increased from 2010–2019, and the clustering of land supply for processing industries, industrial supporting services, and food and light textile industries is an important reason for the spatial imbalance.

5.2. Implications

5.2.1. Adjust the Scale of Industrial Land Supply, and Optimize the Structure and Spatial Layout of Industrial Land Supply to Strengthen the Direct Driving Effect on Local High-Quality Development

First, the supply of land for high-tech industries needs to be strongly supported. In the current growth period of high-tech industries, planned and targeted land supply support by local governments will have important guidance and promotion effects from the supply side. It is important to promote the industrial supporting service industry to further adapt to the trend of servitization of advanced manufacturing industries and learn from Singapore’s Jurong Island Chemical Park and Germany’s Frankfurt–Hurst Industrial Park industrial service industry and processing manufacturing industry integration development model. We should maximize the positive spatial spillover effect of the industrial service industry, accelerate the construction of the processing industry corridor in the YREB, continue to promote the “innovative industrial cluster construction project”, and gradually form a spatial pattern with complementary functions and advantages through interregional cooperation and dialog. We should strengthen the scientific and technological innovation support for the food and light textile industry, upgrade the modernization level of the industrial chain, promote green and low-carbon transformation, coordinate the industrial ecology, and build a light industrial system with higher added value, stronger innovation power, and more sustainable development. The supply of industrial land for raw materials should be directed to further concentrate in downstream coastal areas, while currently scattered enterprises should be attracted to enter industrial parks, further reducing resource consumption and pollution emissions and enhancing the positive spillover effect.

5.2.2. Pay Attention to the Synergy and Balance of Development between Regions in the YREB and Establish a Coordinated Mechanism across the Region

It needs to focus on strengthening the leading role of downstream cities, breaking down administrative boundary barriers, insisting on integrated regional development, and ensuring the full flow of technology, capital, information, and other factors, sharing markets, human capital, infrastructure, and so on.

For example, Chongqing, Hubei, Shanghai, Zhejiang, and other regions in the YREB have many universities and research institutes, which have relatively sufficient educational and scientific research resources, and can build an “industry–university–research” system through regional cooperation. The interaction among enterprises, universities, and governments determines the level of the innovation ability of a region, and the combination of “industry–university–research” is highly localized [52,53], which requires the full play of the functions of local governments. First, local governments can jointly build an innovation management platform and explore the model of “enterprises as the leading position, research institutes as the main force, the government as the main support, cooperation and sharing among all regions”. Second, local governments need to cooperate to improve the market-demand-oriented R&D model, promote the transformation of scientific and technological achievements of research institutes in industrial enterprises, and at the same time promote the financial support of industrial enterprises for scientific and technological activities, forming a virtuous circle. Finally, local governments participating in the cooperation need to adjust the allocation of innovation resources in a targeted manner according to the endowments and comparative advantages of different regions themselves, strengthen the cooperation between downstream cities and midstream and upstream cities for industry–university–research–related projects, unblock the path of innovation factor circulation, and promote the joint development of research institutes, industrial chains, and supply chains.

5.2.3. Continuously Promote the Reform of the Land Supply System

To improve the match between industry and land, not only the scale of land supply should be considered, but also the design of the relevant system is very important. First, it is necessary to correct the land supply behavior of vicious competition between governments and change the official performance evaluation system of “GDP only”. The government needs to focus on the development quality and ecological environment of the YREB from the perspective of high-quality development, and strengthen the weighting of officials’ assessment indicators, such as environmental governance, scientific innovation, and people’s livelihood. At the same time, it is necessary to strengthen land development and supervision, establish a mechanism for evaluating the effectiveness of land supply, prohibit low-priced agreements on large-scale industrial land sales, improve the efficiency of land use, and guide the healthy development of land market. For example, the current industrial project “standard land” transfer system reform in Zhejiang, Shanghai, Chongqing, and other pilot projects can be more proactive in adjusting the local industrial structure, forcing the market allocation of resource factors, optimizing the business environment, and promoting industrial transformation and upgrading, and is an important way to promote high-quality development.