![The overall distribution of manufacturing enterprises in China’s three major urban clusters in 2015 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:NUM{E}_{all2015}$$\end{document})](https://www.researchgate.net/publication/389519621/figure/fig3/AS:11431281313305280@1741060857067/The-overall-distribution-of-manufacturing-enterprises-in-Chinas-three-major-urban_Q320.jpg)
![The overall distribution of manufacturing enterprises in China’s three major urban clusters in 2019 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:NUM{E}_{all2019}$$\end{document})](https://www.researchgate.net/publication/389519621/figure/fig4/AS:11431281313305281@1741060857552/The-overall-distribution-of-manufacturing-enterprises-in-Chinas-three-major-urban_Q320.jpg)
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An analysis of spatial changes in
the manufacturing industry in
china’s three major urban clusters
from 2015 to 2019 using POI data
Chenxi Jin1, Chenjing Fan1,2, Yiwen Gong1, Xinran Huang3, Shiqi Li1, Runhan Liu1,
Chunwei Guo1 & Yuxin Liu1
China’s manufacturing industry has been ranked as the most valuable in the world from 2010 to
2021. However, high-resolution manufacturing datasets are lacking, and this has precluded study
of the survival rates and spatial changes in the manufacturing industry in China. Here, we analyzed
spatial patterns of the manufacturing industry using point-of-interest (POI) data and a machine
learning classication algorithm based on the Naive Bayes classier. Using 2,780,266 POI data
points in 2015 and 3,426,501 POI data points in 2019 covering the three major urban clusters in
China, we classied the manufacturing industry data into seven categories: textile and garment
(TC), mechatronics and equipment (ME), wood furniture (WF), agricultural and sideline product food
processing (AF), metallurgical chemical industry and resource rough processing (MC), pharmaceutical
manufacturing (PM), and papermaking culture (PP). The evolution of the manufacturing industry at
the scale of 451 districts and counties in the urban clusters and the factors driving new entrants in the
manufacturing industry were studied. The main conclusions were as follows. (1) Between 2015 and
2019, manufacturing activities in the three major urban clusters were highly concentrated in provincial
capitals, municipalities under direct control of the central government, and their neighboring districts
and counties with favorable economic conditions; incremental growth was concentrated in the core
cities. (2) The survival rate of enterprises in the Beijing-Tianjin-Hebei urban cluster was relatively
high, whereas that in the Pearl River Delta urban cluster was low. Enterprises in the PM industry had
a relatively high survival rate, whereas those in the ME industry had a relatively low survival rate. (3)
Analysis of the factors driving new entrants in the manufacturing industry indicates that the industrial
foundation is the core factor aecting the entry of new manufacturing enterprises. Land transfer
policies and high population density promote the development of the manufacturing industry, and
regions with high per capita GDP and more research institutions tend to inhibit the development of the
manufacturing industry. Further regressions showed that the eects of the proportion of the secondary
industry in GDP, the number of development zones, and the number of research institutions on the
dierent urban clusters varied. This paper provides strategic guidance for the future development of
China’s manufacturing industry, which will help the government and planning departments optimize
the layout of the manufacturing industry, promote the development of the regional economy, and
enhance the sustainability of the manufacturing industry.
Manufacturing has been a key driver of global economic growth since the Industrial Revolution thanks to
increases in labor productivity. According to the data of the World Bank, in 2019, the global manufacturing
industry contributed 16.6% to global GDP or $14.01 trillion, which was 1.15 times the 2015 level ( h t t p s : / / d
a t a . w o r l d b a n k . o r g . c n / ) 1
. High-income countries have consolidated their economic dominance, and this has
promoted the formation of the global value chain through sustained innovation and technological advances in the
manufacturing industry. For middle- and low-income countries, the manufacturing industry is the cornerstone
of national strength and necessary for remaining competitive. In developing countries such as China, the rise of
the manufacturing industry has promoted industrial improvements and globalization, and this ensures that its
1College of Landscape Architecture, Nanjing Forestry University, Nanjing, China. 2Jinpu Research Institute, Nanjing
Forestry University, Nanjing, China. 3Society Hub, Urban Governance and Design Thrust, The Hong Kong University
of Science and Technology (Guangzhou), Guangzhou, China. email: fancj@njfu.edu.cn
OPEN
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economy will maintain a leading position globally. In 2019, the contribution of China’s manufacturing industry
to GDP was around 27%, with a total output value reaching 26.9 trillion RMB. However, as industrialization
in China advances, the manufacturing industry faces external global challenges, such as slowing economic
growth, trade protectionism, and technological revolutions; in recent years, the manufacturing industry has
developed amidst various twists and turns2. In addition, the decline in the manufacturing sector’s share of
GDP and imbalances in the industrial structure within the country have alerted the Chinese government to the
diminishing demographic dividend, the aging population, and rising costs; this has driven the manufacturing
sector towards a crucial transition point, and upgrading of this sector will be required to achieve signicant
improvements.
e development of the manufacturing industry has been characterized by distinct geographical features.
Metropolitan areas and urban clusters have become important spatial carriers and incubators for the development
of the manufacturing industry. e spatial features of agglomerations, the survival rates of enterprises, and
the drivers of development in these areas have long been the focus of research on the development of the
manufacturing industry. First, the manufacturing industry tends to form industrial clusters within specic
regions. is clustering eect poses signicant economic advantages by promoting competition and cooperation
among enterprises, which not only reduces transportation and production costs for companies but also enhances
production eciency through shared resources and technological innovation3. Second, the dynamics of the
“birth,” survival, and death of manufacturing enterprises are important factors inuencing the spatial patterns
of the manufacturing industry. e entry and exit of enterprises can promote the optimization and upgrading of
the industrial structure and have a signicant impact on urban development patterns. Furthermore, the factors
driving the development of the manufacturing industry are closely related to local economic environmental
conditions. e existing manufacturing industry base has a signicant eect on the development of the
manufacturing industry, and this strong foundation has promoted improvements in manufacturing output and
eciency. Land transfer policies promote the development of the manufacturing industry by aecting the land
supply, and this plays an important role in the implementation of manufacturing industry projects. e level
of economic development, such as per capita GDP, is usually associated with higher consumption and stronger
market demand4,5. However, some studies indicate that consumer demand for the service industry will increase,
thus reducing the demand for manufactured products and thus aecting the development of the manufacturing
industry6. Higher contributions of secondary industries to GDP are associated with greater relative importance
and investment in the manufacturing industry7. However, over-reliance on the secondary sector may increase
environmental stress and resource consumption, and this has the potential to limit the development of the
manufacturing industry, especially developmental transitions8. e establishment of development zones has a
positive eect on the innovation and productivity of manufacturing enterprises and can signicantly improve
their overall performance9, but an excess of development zones may lead to excessive competition in the regional
economy and the dispersion of resources, which would hinder the entry of businesses10. Collaboration between
universities and industry can facilitate the transfer of technology and technological innovation, which could
promote the development of the manufacturing industry11. A high abundance of research institutions in an
area can drive the upgrading of the industrial structure, especially for innovation industries, but traditional
manufacturing industries are relatively absent in these areas12. High population densities provide an abundant
labor force and robust market demand, which promotes the development of the manufacturing industry13 In
addition, manufacturing enterprises in dierent urban clusters can benet from dierent policies, agglomeration
eects, and improved infrastructure, and this can result in dierent agglomeration eects14. Consideration of all
these factors is required to attain a more comprehensive understanding of the complexity and diversity of spatial
variation in the manufacturing industry. Few studies have examined the factors driving spatial variation in the
manufacturing industry, and these studies have mainly employed theoretical methods.
e rapid development of China’s manufacturing industry in recent years is closely intertwined with the
coordinated development of urban areas, but there are still many outstanding issues requiring research
attention. First, spatial variation in the manufacturing industry has not yet been claried. Previous studies of the
distributional patterns of the manufacturing industry at the national, urban cluster, and city levels in China have
been conducted15,16. ese studies have revealed a multi-polarization trend in China’s manufacturing industry,
with the major growth poles comprising multiple, urban clusters such as the Yangtze River Delta (YRD), the
Pearl River Delta (PRD), and Beijing-Tianjin-Hebei (BTH) urban clusters17. However, there are signicant
dierences in the development of manufacturing industries in these regions, and few in-depth analyses of the
micro-spatial distribution of dierent industries have been conducted. Second, the survival rates of enterprises
in the manufacturing industry have not been determined. A large number of traditional manufacturing
industries comprise the lower end of the industrial chain and rely on the dividend of cheap labor; small and
medium-sized enterprises are vulnerable to external pressures and uctuations in the global economy, which
makes them prone to closure and decreases their survival rate18. According to the “2013 Global Market Annual
Inventory Survey Report,” the average lifespan of manufacturing enterprises in the PRD urban cluster and YRD
urban cluster is only 11.1 years, with most enterprises surviving for less than ve years19. However, the survival
rates of specic manufacturing industries have not yet been evaluated. In addition, the development of China’s
manufacturing industry is complex and diverse, and the exact factors driving its development remain unclear.
Although several studies have examined the factors driving variation in the distribution of the manufacturing
industry, the conclusions drawn by these are oen inconsistent20,21.
e spatial distribution and survival rates of enterprises in China’s rapidly growing manufacturing industry
have not yet been determined. Although previous studies have been conducted at national and regional scales, a
more in-depth analysis of the spatial characteristics and distribution patterns of specic industries is still lacking,
and no studies have examined diversity in the life cycles of manufacturing enterprises and regional disparities.
Here, we processed POI data covering three major urban clusters in China using a machine learning classication
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algorithm based on the Naive Bayes classier to classify manufacturing industry enterprises into seven types.
We then investigated spatial patterns in the manufacturing industry, its evolution, and the factors driving new
entrants into each type of industry. Our ndings provide new insights into the geographical adaptability and life
cycles of manufacturing enterprises and will aid the formulation of manufacturing development policies.
Methods
In this paper, we conducted an in-depth study of the spatial evolution of the manufacturing industry in China’s
three major urban clusters—BTH, YRD, and PRD—using micro-level POI data and analyzed the factors driving
spatial changes in the manufacturing industry. First, we used a machine learning classication algorithm based
on the Naive Bayes classier to classify the 31 manufacturing industries into seven types according to the
national economic industry classication standard; we then constructed the manufacturing industry training
samples for the seven manufacturing categories. Second, POI data of national company enterprise types were
collected and cleaned; the spatial distribution and spatial changes in the seven major manufacturing industries
in the three urban clusters from 2015 to 2019 were then mapped. Finally, the survival rate of each manufacturing
type in the dierent urban clusters was calculated, and an analysis of the factors driving variation in the number
of new entrants in each type of manufacturing industry at the county level was conducted. e ow chart is
shown in Fig.1.
Overview of the study area
Our analysis in this paper was conducted using data from the BTH, YRD, and PRD urban clusters in China,
which covered 451 districts and counties. e three major urban clusters occur in the northern, eastern, and
southeastern coastal regions of China and play a central role in China’s manufacturing layout. ey are the
Fig. 1. Flow chart
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main powerhouses of China’s economic growth and important nodes in the global manufacturing value chain.
In 2023, the total GDP of the YRD, PRD, and BTH was 30.5, 11, and 10.4 trillion yuan, which is far greater than
that of the Central Plains, Chengdu-Chongqing, the Shandong Peninsula, and other urban clusters. From 2015
to 2019, the “Outline of Coordinated Development of the Beijing-Tianjin-Hebei Region,” the “Outline of the
Integrated Regional Development of the Yangtze River Delta,” and the “Pearl River Delta Region Reform and
Development Planning Guideline” were formulated for these regions (Fig.2). erefore, the construction of
these three major urban clusters is a major national strategy for China.
e three major urban clusters, which are leaders in China’s manufacturing sector, contain the country’s
advanced manufacturing clusters, occupy 6.1% of the country’s land, harbor 25% of the population, and produce
39.6% of China’s GDP. Specically, the BTH urban cluster, with Beijing and Tianjin comprising the major cities,
has transitioned to high-end manufacturing and technological innovation on the basis of its policy and resource
advantages, but the problem associated with an imbalance in regional development has been noted. is region
occupies 2.3% of China’s land, harbors 8% of China’s population, and produces 8.6% of China’s GDP. e YRD
urban cluster, which includes Shanghai, Nanjing, and Hangzhou, has become the world’s leading manufacturing
center due to its economic strength and well-developed industrial structure; this area occupies 2.2% of China’s
land, harbors 12% of China’s population, and produces 23.9% of China’s GDP. e PRD urban cluster, with
Guangzhou-Foshan and Shenzhen-Dongguan comprising the major urban centers, has close economic ties with
Hong Kong and Macao. Building a world-class advanced manufacturing center has long been a major goal, but
low- and medium-end manufacturing enterprises still dominate; this region occupies 0.57% of China’s land
mass, harbors 0.46% of China’s population, and produces 7.1% of China’s GDP (National Bureau of Statistics
data, http://www.stats.gov.cn/).
Data from 2015 to 2019 were used because the “13th Five-Year Plan” period is a key stage in the transition of
China’s manufacturing industry from a traditional mode to an innovative mode; it is also a key period in which
the spatial pattern of China’s manufacturing industry has been upgraded. is is also one of the periods during
which China’s economy has grown the fastest, and the development of the manufacturing industry has made
a signicant contribution to economic growth during this period. Data from subsequent years, which were
aected by lockdowns and economic closures due to the COVID-19 pandemic, were not analyzed because they
are likely of low scientic value for our study. Data from 2015 to 2019 would likely provide clearer insights into
Fig. 2. Location and scope of the three major urban clusters in China.
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major patterns in the development of the manufacturing industry, as well as the spatial distribution and survival
rates of enterprises in the manufacturing industry.
Classication of manufacturing industries using POI semantic classication
We constructed the dataset of the distribution of the number of manufacturing industries in China in each of
the seven manufacturing industry types using POI data; we used the Industrial Enterprise Database (IED) to
obtain accurate training sample data, and the map POIs were classied and identied using machine learning
methods. e seven types of manufacturing industries included textile and garment (TC), mechatronics and
equipment (ME), wood furniture (WF), agricultural and sideline product food processing (AF), metallurgical
chemical industry and resource rough processing (MC), pharmaceutical manufacturing (PM), and papermaking
culture (PP). e classication criteria were based on those described in two previous studies16,22, and the main
industrial types of various development zones are described in the 2018 version of the “Catalog of Audited
Chinese Development Zones.” Industrial types with higher frequencies of occurrence were extracted, and
similar industrial types were combined into larger categories. e specic steps of the classication process are
described below.
e rst step was data collection. Nearly every company, enterprise, rm, site, or facility in China (even those
not registered with the Industry and Commerce Department) has a specic location on government-approved
web maps. Our data were obtained from the Amap website (https://www.amap.com/) through web scraping.
Because some factories or rms do not appear on the map aer they have gone bankrupt or changed addresses,
and remote branch factories are shown on the map, these data have higher precision than questionnaire and
statistical data. We collected data using the services provided by Amap’s API and divided them into batches,
which involved using web crawlers to collect the POI names and locations of rms across China from January
2nd to 20th, 2015, and from December 20th to 30th, 2019. To reduce potential interference from the original
data, preprocessing was carried out as follows:
“Scenery,” “shopping,” “catering,” “road name,” and other types of built-in classications from the map
unrelated to rms were removed, and only the rm and its corresponding latitude and longitude were retained
to increase the classication speed.
By identifying and matching word items, points with the same place name but dierent marks within 1km
around the POI points (e.g., East Entrance, West Gate) were deleted to solve the problem associated with
counting the same POI. e nal map POI dataset contained rm names and spatial information (approximately
5.24million in 2015 and 7.35million in 2019).
e second step was the construction of the machine learning keyword library using the Bayesian method.
e simple Bayesian classier is eective for the data of this study. On the one hand, it can eciently use the
explicit and regularized features of business names to complete machine learning, which avoids unnecessarily
complex modeling; on the other hand, the interpretability of its parameters enhances the transparency and
credibility of the research results. ese characteristics fully reect the scientic and rational nature of model
selection. Based on the classication criteria, some of the screened POI data were classied, and under-sampling
or over-sampling of the samples was avoided. e mapping relationships between POI names and specic
categories were determined, and machine learning training samples were constructed. Aer tokenizing the
training samples for learning, keyword weights of the learning samples were calculated based on the Bayesian
method, and a keyword-weight library was constructed using machine learning methods. We used IED names
to construct a “Name-Manufacturing Industry Type” machine learning sample database. Based on the naming
conventions of Chinese enterprises, there is a connection between the name of a facility and its manufacturing
type. erefore, a database of name-manufacturing samples or a keyword dataset can be constructed for machine
learning studies of manufacturing classication.
e third step was to classify the POI information based on the Naive Bayes algorithm. All screened POI data
were classied using the machine learning-based Naive Bayes classier. e specic category of the POI data
was determined according to the Naive Bayes algorithm, matched with other elds, and output to a database
containing elds such as POI name, classication information, latitude, and longitude.
In the fourth step, a high spatial resolution grid map of the manufacturing industry in 2015 and 2019, as well
as the subdistrict county dataset
Eim2015
,
Eim2019
, was plotted based on the clean data. Here, ‘i’ represents
statistical units, and ‘m’ represents types of manufacturing industries (TC, ME, WF, AF, MC, PM, and PP). Aer
obtaining the above data, we examined the quality of these data, and our detailed algorithms for data processing
and accuracy tests are available in a previous study22. e data are provided in gshare ( h t t p s : / / g s h a r e . c o m / a r t
i c l e s / d a t a s e t / C h i n a _ s _ G r i d d e d _ M a n u f a c t u r i n g _ D a t a s e t / 1 9 8 0 8 4 0 7).
Analysis of the evolution of spatial changes in the seven types of manufacturing industries in
the three major urban clusters
We mainly focused on the analysis of spatial patterns of the manufacturing industry in three major urban clusters,
especially the overall distribution of dierent types of manufacturing industries over a ve-year period. We
focused on incremental changes in manufacturing enterprises, the survival rates of manufacturing enterprises,
and the number of new entrants.
Increments of manufacturing enterprises
e term “increment of manufacturing enterprises” refers to the dierence between the total number of
enterprises in 2019 and the total number of enterprises in 2015. e calculation formula was as follows:
∆ NUMi=NUMEim2019 −NUMEim2015
(1)
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where
∆ NUMim
is the increment of the total number of type m manufacturing enterprises within unit i (grid/
administrative area), which reects the net change in the quantity of enterprises in a region and reveals the
growth or decline of the regional economy. Positive increments indicate an increase in the number of enterprises
and a possible increase in economic activity, whereas negative increments indicate a decrease in the number of
enterprises and a possible general economic downturn or outward migration of enterprises.
Number of surviving manufacturing enterprises and their survival rate
e number of surviving manufacturing enterprises (S) refers to manufacturing enterprises that existed in 2015
and that were still in existence in 2019. e following calculation formula was used to determine S:
Sim
={
e
|
eim
∈(
E
im2015
,E
im2019
)
and location
(
e
im2015)=
location
(
e
im2019)}
(2)
e set of surviving enterprises
Sim
(S) is an individual enterprise
eim
of type m that exists at the same location
in both 2015 and 2019 within unit i (grid/administrative area).
e survival rate (perS) refers to the proportion of surviving enterprises in the total number of enterprises in
2015. e calculation formula is as follows:
perS
im =
S
im
Eim2015
×100%
(3)
is indicator measures the survival capability of the enterprises over a certain period, which reects the stability
of enterprises in the region and the sustainability of the business environment. A higher survival rate indicates
that the enterprises are more viable in the region and may benet from a favorable business environment and
policy support.
Number of new entrants in the manufacturing industry
e number of new entrants in the manufacturing industry refers to the number of enterprises that did not exist
in 2015 but existed in 2019. e calculation formula is as follows:
Nim =Eim2019 −Sim
(4)
Nim
is the number of type m enterprises in unit i (grid\administrative area) that did not exist in 2015 but existed
in 2019. is indicator specically quanties the absolute number of new entrants. By comparing the number
of new entrants in dierent regions, it is possible to identify the regions that tend to attract new enterprises. In
contrast to the incremental enterprise indicator
∆ NUMi
, analyzing the number of new entrants can provide
insights into the source of manufacturing data discrepancies between the two points in time; this can help
identify the key factors that motivate new enterprises to select a particular region.
Analysis of the factors underlying the arrival of new entrants in the manufacturing industry in the three major
urban clusters
Analysis of the factors driving observed patterns has always been a subject of interest for scholars both within
and outside of China. In the early stages of studies of the factors underlying changes in land use, geographical
factors were thought to be the primary factors leading to changes in land use patterns23,24. e set of factors
examined in the current study are more diverse and include land transfer policies25, population26, GDP per
capita output27, number of development zones28, general tertiary institutions29, and many other factors. We also
used related research to study the pattern of new entrants in China’s manufacturing industry in three major
urban clusters at the county scale in ArcGIS and explored the mechanism underlying the spatial and temporal
patterns of the manufacturing industry. We analyzed the factors aecting new entrants in the manufacturing
industry by constructing a regression model. e model can be expressed as follows:
Nm=β0+β1X1m+β2X2m+β3X3m+β4X4+β5X5+β6X6+β7X7+β8X8+ϵ
(5)
In the equation, for manufacturing industry
m
within the three major urban clusters in the study area,
Nm
is
the dependent variable, indicating the number of new entrants in the manufacturing industry
m
within each
district or county from 2015 to 2019.
β0
is the constant term;
ϵi
is the error term.
X1
,
X2
,…,
X8
are factors
that potentially aect the development of manufacturing industries in the three major urban clusters at the
county level, which include the industrial base, the proportion of above-standard enterprises, the proportion of
high-tech enterprises, land transfer policies, per capita GDP, the proportion of secondary industries in GDP, the
number of development zones, and the conditions of general tertiary institutions (Table1).
β1
,
β2
,…,
β8
is the
one-to-one regression parameter, and the direction and magnitude of this parameter describe the eect of this
factor on the entry of manufacturing enterprises. In addition to regression analyses for the overall full samples,
we conducted regression analyses for the number of new entrants and the factors inuencing them in each of
the three urban clusters.
Results
Overall spatial pattern of the manufacturing industry in the three major urban clusters in
2015 and 2019
Using the POI manufacturing classication results and the methods described in the Methods section, we
mapped the spatial distribution of manufacturing industries in the three major urban clusters at a 0.01°×0.01°
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scale for 2015 and 2019 (Figs.3, 4 and 5). Although the overall distribution of the manufacturing industry
remained the same, the manufacturing industry in the PRD region has experienced the most rapid growth, and
economic activities are particularly concentrated in the core areas of the urban cluster. However, the overall
distribution of the manufacturing industry is uneven, and the BTH and PRD regions are densely populated in
the south and sparsely populated in the north; the distribution of the manufacturing industry is well-balanced
in the YRD region. e number of enterprises in various types of manufacturing industries in the three urban
clusters in 2015 and 2019 is shown in Fig.6, and the spatial distribution and incremental changes in each type of
manufacturing industry are shown in Appendix Fig.1 to Appendix Fig.21.
Tab le 1. Sources of data on the factors driving spatial variation in manufacturing industries and their
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Analysis of the survival rates of seven types of manufacturing industries in the three major
urban clusters from 2015 to 2019
Following the method outlined in Sect.2.3.2, we calculated the survival rates for enterprises in the seven types of
manufacturing industries in the BTH, YRD, and PRD urban clusters based on the classication results (TC, ME,
WF, AF, MC, PM, and PP) in 2015 and 2019. e survival rate of manufacturing enterprises was higher in the
BTH region than in the other two regions, especially in the TC, PM, and PP industries. In contrast, the survival
rate of manufacturing enterprises was lowest in the PRD region, especially in the AF industry (Fig.7; Table2).
Fig. 4. e overall distribution of manufacturing enterprises in China’s three major urban clusters in 2019
(
NUMEall2019
)
Fig. 3. e overall distribution of manufacturing enterprises in China’s three major urban clusters in 2015
(
NUMEall2015
)
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Fig. 6. Number of enterprises in dierent manufacturing industries in the three major urban clusters in 2015
and 2019.
Fig. 5. Incremental changes in manufacturing enterprises in BTH, YRD, and PRD from 2015 to 2019
(ΔNUM).
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Research on the factors underlying the new entrants in the manufacturing industry at the
district and county level in the three major urban clusters
Patterns of new entrants in the manufacturing industry
According to the method described in Sect.2.3.3, we determined the spatial patterns of dierent types of new
entrants in the manufacturing industry at the district and county level. e results are shown in Figs.8, 9, 10,
11, 12, 13 and 14.
Analysis of the factors associated with new entrants in the manufacturing industry
Descriptive statistics for each variable are provided in Appendix Table2. e Appendix Tables4, 5 and 6 are
descriptive statistics of BTH, YRD and PRD variables respectively. In the regression model, the p-values of all
variables were less than 0.01, indicating that the model had a high predictive power. All models exhibited low
multicollinearity, with variance ination factors (VIFs) less than 1.5. e results indicate that the industrial base
has a signicant eect on the new entrants in the manufac turing industries (Appendix Table3). Appendix Tables7
and 8, and 9 describe the regression analysis results (non-standardized coecient) of the spatial dierence drivers
of dierent types of rms in BTH, YRD, and PRD, respectively. Figure15 shows the standardized coecients
of the signicant eects. Figure15a shows the standardized coecients of the signicant eects for all districts
and counties, as well as the standardized coecients calculated separately for the BTH(Fig.15b), YRD(Fig.15c),
and PRD(Fig.15d).
Discussion
Analysis of the spatial patterns of the manufacturing industry and the survival rates of
manufacturing enterprises
We found that the spatial distribution of the manufacturing industry was uneven; manufacturing enterprises
were mainly concentrated in provincial capitals, as well as municipalities under direct control of the central
government and the periphery (Figs.3 and 4). ere was a signicant increase in the number of manufacturing
enterprises in the urban clusters, but there was a decrease in the total number of manufacturing enterprises
in the central urban areas of Beijing, Shanghai, and Shenzhen (Fig.5). is change is related to the policy of
Total survival rate TC ME WF AF MC PM PP
BTH 52.61% 57.17% 51.16% 54.51% 57.71% 53.79% 59.25% 53.88%
YRD 52.36% 55.48% 52.98% 52.14% 50.34% 52.86% 56.82% 53.58%
PRD 48.87% 53.01% 48.78% 48.41% 45.80% 49.39% 52.02% 52.13%
Tab le 2. Survival rates for enterprises in the seven manufacturing types at the district and county level in the
three urban clusters from 2015 to 2019 (S %).
Fig. 7. Survival rates for enterprises in seven manufacturing industry types at the district and county level in
the three urban clusters from 2015 to 2019.
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shiing industries from central cities to peripheral areas promoted by the three major urban clusters during
the 13th Five-Year Plan period, which led to the outward relocation of manufacturing industries from rst-
tier large cities to the suburbs30. Enterprises in the manufacturing sector in the BTH region were mainly
concentrated in Beijing, and the survival rate of most industries was higher in the BTH region than in the YRD
and PRD. is might stem from the dominance of state-owned enterprises (SOEs) in this region, as intervention
by local governments provides SOEs with various advantages, including resources and policy support31. e
manufacturing industry in the YRD region is mainly concentrated in Shanghai and its neighboring areas of
Suzhou, Wuxi, and Changzhou, and state-owned enterprises and private enterprises co-exist in this region; these
are regulated by the local government through administrative means and market mechanisms, and the enterprises
can obtain more resources and opportunities via competition. e manufacturing industry in the PRD region
is mainly concentrated in Shenzhen as well as in the vicinity of Guangzhou. e survival rate of manufacturing
enterprises was lowest in the PRD region, which might be attributed to the high-cost competitive environment
and the greater market volatility in the region. Private enterprises are dominant in the PRD region, and direct
Fig. 9. Patterns of the new entrants in the ME industry from 2015 to 2019.
Fig. 8. Patterns of the new entrants in the TC industry from 2015 to 2019.
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intervention by the local governments and ocials is reduced, which gives enterprises greater autonomy but also
makes them more vulnerable to the eects of market volatility and cost pressures31.
Study of the factors aecting changes in new entrants in the manufacturing industry in the three major urban
clusters from 2015 to 2019
From 2015 to 2019, the development of manufacturing in urban clusters was aected by various factors. Overall,
manufacturing industries were more prevalent in areas with a strong manufacturing base, more industrial
land grants, greater population density, and the PRD region. Conversely, manufacturing industries were less
prevalent in areas with more research institutions and more economic development. e size of each industry
in 2015 had a signicant positive eect on manufacturing, which indicates that the larger initial size of the
industry is associated with stronger future development, and the scale eect of existing industries aects the
attractiveness of new enterprises and the expansion of existing enterprises32. In contrast, the proportion of
above-standard enterprises and the proportion of high-tech enterprises did not have a signicant eect on the
Fig. 11. Patterns of new entrants in the AF industry from 2015 to 2019.
Fig. 10. Patterns of new entrants in the WF industry from 2015 to 2019.
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entry of new manufacturing industries. e number of land grants from 2015 to 2019 also had a signicant
eect on the introduction of manufacturing industries; there was a signicant positive eect of land grants
on the WF and AF industries, and this was associated with the increased supply of land33. However, GDP per
capita had a signicant negative eect on the AF industry, which reects a shi in the consumption structure
of higher GDP per capita areas towards services and higher-end products, and this has reduced the demand
for traditional processing industries34. e number of development zones has a signicant negative eect on
the TC and WF industries, as areas with development zones are generally more conducive to the development
of manufacturing enterprises. Manufacturing parks are not required for the integrated management of the TC
and WF industries35. e number of general tertiary institutions had a signicant negative eect on the TC,
ME, and WF industries, implying that development of the technological and service industries was promoted
in areas with a high concentration of tertiary institutions in the three urban clusters36. Population density has
a signicant positive eect on the TC, ME, AF, and PM industries, suggesting that areas of high population
density can provide more market demand and labor resources, which can promote the development of these
Fig. 13. Patterns of new entrants in the PM industry from 2015 to 2019.
Fig. 12. Patterns of new entrants in the MC industry from 2015 to 2019.
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industries26. ese factors have shaped the evolution of new entrants in the manufacturing industry within the
three major urban clusters from 2015 to 2019.
Our study also revealed that the eects of certain factors signicantly diered among urban clusters. First,
the proportion of secondary industry in GDP did not have a signicant eect on enterprise entry. However, in
the BTH, areas with a higher proportion of secondary industry in GDP signicantly attracted the entry of more
enterprises. is may be because, in the BTH, having a higher proportion of secondary industry in GDP indicates
a strong path dependency. As a result, new entrants in BTH in the future are more likely to be concentrated
within areas with a high proportion of secondary industry in GDP. Second, the number of development zones
had a negative eect on enterprise entry in the BTH and YRD, while it had a positive eect on enterprise entry
in the PRD. is may stem from the fact that the PRD development zones have become the main platform for
attracting manufacturing and foreign investment, thus facilitating the entry of new enterprises. In contrast, the
development zone policies in the BTH and YRD may have attracted more established enterprises and inhibited
the inux of new enterprises. erefore, when planning development zones in the future in the BTH and YRD,
increasing the number of development zones should not be the sole focus, as moderate adjustments to their policies
are needed to increase consideration of the quality of the development zones and their functional positioning.
In addition, areas with a greater concentration of research institutions have facilitated enterprise entry in the
YRD and PRD but negatively aected enterprise entry in the BTH. is may stem from the fact that the research
institutions in the BTH are highly concentrated in the main city of Beijing, which is suitable for the development
of high-technology and service industries but not conducive to the clustering of manufacturing industries. In
the YRD and the PRD, research institutions are more closely integrated with the manufacturing industry, which
promotes technological innovation and talent cultivation for the development of the manufacturing industry;
this in turn promotes the entry of a greater number of enterprises. erefore, collaborative innovation between
research institutions and the manufacturing industry should be strengthened in the BTH; technology transfer
and talent training should also be promoted.
Conclusion
We studied spatial patterns of the manufacturing industry based on POI data, which were classied using a
machine learning classication algorithm based on the Naive Bayes classier. We found that manufacturing
industries in the three urban clusters were highly concentrated in provincial capital cities, municipalities under
direct control of the central government and their neighboring districts, and counties with superior economic
conditions, and pronounced scale and spatial agglomeration eects were observed. Although the growth of
various enterprises was observed, growth was concentrated in the core cities. e survival rates of enterprises in
the BTH and PRD urban clusters were relatively high and low, respectively. e survival rates of enterprises in
the PM and ME industries were high and low, respectively. Analysis of the factors associated with new entrants
in the manufacturing industry indicates that the industrial foundation is the key factor aecting new entrants.
Land transfer policies and high population density promote the development of the manufacturing industry,
and manufacturing industry development is inhibited in regions with high per capita GDP and greater numbers
of research institutions. Further regressions showed that the eects of the proportion of the secondary industry
in GDP, the number of development zones, and the number of general tertiary institutions vary among urban
clusters.
Fig. 14. Patterns of new entrants in the PP industry from 2015 to 2019.
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Our ndings enhance our understanding of changes in the spatial patterns of the manufacturing industry, as
well as the factors driving them, using POI data, classication methods, and statistical model analysis, yet some
limitations require consideration. For example, our study mainly focused on three major urban clusters; thus, the
general patterns observed in these three urban clusters might not be applicable to other regions. Although the
predictive and explanatory power of the model was notable, the eects of changes in the external environment
(e.g., global epidemics and international trade conicts) on the manufacturing industry were not considered.
Fig. 15. Correlations (standardized coecients) of the factors associated with new entrants in the
manufacturing industry. (a) all (b) BTH (c) YRD (d) PRD
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In addition, our study mainly focused on the 13th Five-Year Plan period; thus, long-term changes were not
considered. In future studies, long time-series data should be used to explore patterns of change in the spatial
distribution of the manufacturing industry over long periods to assess the eectiveness of the implementation
of stage-by-stage policies. Lastly, a broader set of factors should be considered, including sudden public health
events and changes in the international economy, to improve the timeliness and adaptability of the model.
Increased research on changes in the internal structure of the manufacturing industry, variation in enterprise
size, and policy response mechanisms will help guide the sustainable development of the manufacturing industry.
Data availability
e datasets generated and/or analysed during the current study are available in the Figshare repository, h t t p s : /
/ d o i . o r g / 1 0 . 6 0 8 4 / m 9 . g s h a r e . 1 9 8 0 8 4 0 7 .
Received: 20 July 2024; Accepted: 12 February 2025
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Author contributions
C.F. designed the study and wrote the paper, C.J. did the data processing and wrote the paper, Y.G. e article
was translated, X.H. provided data for the article, S.L. drew Fig.1, R.L., C.G. and Y.L. Collected data. All authors
reviewed the manuscript.
Declarations
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at h t t p s : / / d o i . o r g / 1
0 . 1 0 3 8 / s 4 1 5 9 8 - 0 2 5 - 9 0 3 7 3 - w .
Correspondence and requests for materials should be addressed to C.F.
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