Dataset
of Economic Resilience and Industrial Evolution Path of Cities in the Yangtze
River Delta (2002-2016)
Qu, Y.1,2
1. Zhanjiang University of Science and Technology,
Zhanjiang 524094, China;
2. Institute of Marine Sustainable Development,
Liaoning Normal University, Dalian 116029, China
Abstract: According
to the data about GDP and export products and using 26 cities in the Yangtze
River Delta region as the research area, the author constructed a dataset of
economic resilience and industrial evolution path of cities in the Yangtze
River Delta region (2002–2016)
to reveal related temporal and spatial evolution characteristics. This dataset
includes economic resilience, the number of new industries of 2-digit HS codes
and dependence degree of urban industrial evolution path in the Yangtze River
Delta from 2002 to 2016. The dataset is archived in the .xlsx format with a size of 117 KB.
Keywords: economic resilience; industrial evolution; path dependence; Yangtze
River Delta
DOI: https://doi.org/10.3974/geodp.2023.03.09
CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2023.03.09
Dataset Availability Statement:
The
dataset supporting this paper was published and is accessible through the Digital
Journal of Global Change Data Repository at: https://doi.org/10.3974/geodb.2023.10.06.V1
or https://cstr.escience.org.cn/CSTR:20146.11.2023.10.06.V1.
1 Introduction
With
the development of globalization, the Black Swan and Gray Rhino events have
attracted more and more attention from scholars and policy makers. Especially
since the global financial crisis from 2007 to 2008, continuously improved
awareness of economic risk prevention has made economic resilience one of the
hot spots in the research field of economic development[1].
In the framework of evolutionary economic geography, economic resilience is an
important concept that facilitates our deep insights into the resistance,
recovery, relocation and renewal capabilities of economic systems in the face
of risks[2].
Existing studies
mostly focus on the measurement, temporal and spatial distribution and
influencing factors of economic resilience. Industrial structure is considered
to be one of the most important influencing factors[3].
Industrial evolution is closely related to local resource endowment and
development history, and the change in industrial structure can be analyzed
from the characteristics of industrial evolution path. Industrial evolution
path dependence refers to a strong correlation between old and new industries
in a region. The original technology, equipment and labor force provide
facilitate the generation and development of the new industry, but local
development is also potentially restricted by rigid institution and other
issues caused by too intimate relations[4]. Therefore,
exploring industrial evolution path dependence and its impact on economic
resilience is of great value for supporting existing local industries, guiding
the development of new industries, improving urban economic resilience, and
promoting high-quality economic development.
There are few
quantitative studies due to the lack of methods to calculate the industrial
evolution path dependence. The Yangtze River Delta, one of the most developed
regions in China, is typical and representative in the research on economic
resilience and industrial evolution path.
Therefore, the
author establishes the dataset of economic resilience and industrial evolution
path of cities in the Yangtze River Delta (2002–2016) using national economic
accounting data and export product data, which provides data support for
investigating urban industrial evolution path and its impact on economic
resilience of cities in the Yangtze River Delta.
2 Metadata of the Dataset
Table
1 summarizes the metadata of the Dataset of economic resilience and industrial
evolution path of cities in the Yangtze River Delta (2002-2016)[5], with dataset full and short names, authors, year, data format,
data size, data files, data publisher, and data sharing policy included.
3 Methods
3.1 Data Sources
According
to the Yangtze River Delta urban agglomeration development plan approved by
National Development and Reform Commission of China in 2016, the Yangtze River
Delta urban agglomeration includes a total of 26 cities, including a
municipality directly under the central government and some cities in three
provinces, with a total area of about 210,000 km2. We constructed
the dataset based on export product data of countries around the world and
cities in the Yangtze River Delta, as well as GDP data of China and cities in
the Yangtze River Delta from 2002 to 2016. The export product data of countries
around the world was derived from the UN Comtrade Database[7], the
export product data of cities in the Yangtze
River Delta from the China Customs Enterprise Database[8], Gross
domestic product (GDP) data of China and cities in the Yangtze River Delta from
the National Data of National Bureau of Statistics[9] and the China
Urban Statistical Yearbook[10].
3.2 Research Method
3.2.1 Economic Resilience
According to the method
proposed by Martin[11], the economic resilience of cities was
calculated based on the GDP data of China and cities in the Yangtze River Delta
considering
Table
1 Metadata summary of the Dataset of
economic resilience and industrial evolution path of Cities in the Yangtze
River Delta (2002–2016)
|
Items
|
Description
|
|
Dataset full name
|
Dataset of
economic resilience and industrial evolution path of cities in the Yangtze
River Delta (2002-2016)
|
|
Dataset short
name
|
Res_EvolPath_YangtzeRiverDelta
|
|
Author
|
Qu, Y.
HOH-8736-2023, Zhanjiang University of Science and Technology, Institute of
Marine Sustainable Development, Liaoning Normal University, quyi1412@163.com
|
|
Geographical
region
|
Yangtze River
Delta in China
|
|
Year
|
2002‒2016
|
|
Data format
|
.xlsx
|
|
|
|
Data size
|
117 KB
|
|
|
|
Data files
|
(1) Economic
resilience of cities in the Yangtze River Delta from 2002 to 2016
(2) The number of
new industries of 2-digit HS codes of cities in the Yangtze River Delta from
2002 to 2016
(3) Industrial
evolution path dependence of cities in the Yangtze River Delta from 2002 to
2016
|
|
Foundations
|
National Natural
Science Foundation of China (41976207); Ministry of Education of P. R. China
(22JJD790029); Educational Department of Liaoning Province (LJKQZ2021090);
Zhanjiang University of Science and Technology (PPJHYLZY-202205)
|
|
Data publisher
|
Global Change Research Data Publishing & Repository,
http://www.geodoi.ac.cn
|
|
Address
|
No. 11A, Datun
Road, Chaoyang District, Beijing 100101, China
|
|
Data sharing
policy
|
(1) Data
are openly available and can be free downloaded via the Internet; (2) End
users are encouraged to use Data subject to citation; (3)
Users, who are by definition also value-added service providers, are welcome
to redistribute Data subject to written permission from the GCdataPR Editorial
Office and the issuance of a Data redistribution license; and (4)
If Data
are used to compile new datasets, the ‘ten per cent principal’ should be
followed such that Data records utilized should not
surpass 10% of the new dataset contents, while sources should be clearly
noted in suitable places in the new dataset [6]
|
|
Communication and searchable system
|
DOI, CSTR, Crossref, DCI, CSCD,
CNKI, SciEngine, WDS/ISC, GEOSS
|
short-term
shock and “slow burn”. The equation is expressed as:
(1)
where,
Resc,t and ΔEc,t
are the economic
resilience and the change in GDP of city c from t-year to (t+T)-year respectively.
is
GDP of city c in t-year.
is GDP’s gradient of whole country from t-year to (t+T)-year. According to the theory of industry life
cycle, an industry needs to experience a period of development from being
initially established to being relatively competitive. Referring to the
research of relevant scholars[12–14], we
define T=4 in the
dataset construction.
3.2.2 Industrial Evolution Path
We
employed the export product data of countries around the world and the export
product data of cities in the Yangtze River Delta for calculation. Figure 1
shows the developing process of industrial evolution path data. The steps are
presented as follows:
(1) We identified
the new and old industries according to the revealed comparative advantage
index (RCA) of the export products of cities in the Yangtze River Delta.
(2) We calculated
the RCA of all countries around the world and constructed the matrix of
industry correlation between any industry i and j.
(3) We calculated
the maximum proximity of new industry and old industry in each city.
(4) Monte Carlo
method was used to calculate the distribution of the counterfactual maximum
proximity in each city.
(5) We determined
whether the new industry is path-dependent one by one, and then calculated the
path dependence of each city.
The specific
calculation formulas above can be found in the reference[1].
Figure 1 Technology roadmap of dataset development
4 Data Results and Validation
4.1 Data Composition
The
dataset consists of three sections, namely economic resilience, the number of
new industries of 2-digit HS codes and industrial evolution path dependence of
cities in the Yangtze River Delta from 2002 to 2016.
4.2 Data Products
(1)
Figure 2 shows the economic resilience of cities in Yangtze River Delta from
2002 to 2016, with an overall trend of “decline firstly, then rise, and finally
decline again”. Shanghai’s economic resilience was less than 0 except for the
periods of 2002–2006 and 2012–2016. The economic resilience of cities in
Jiangsu province (Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yangzhou,
Zhenjiang, Taizhou (Jiangsu), Yancheng) was mostly larger than 0, with
relatively consistent variation trend. The economic resilience of cities in
Zhejiang province (Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, Zhoushan,
Taizhou (Zhejiang) and Jinhua) was less than 0 in the research period, showing a
relatively obvious “depression”. There was
little difference among cities except Zhoushan. The economic resilience of
cities in Anhui province (Hefei, Chuzhou, Ma’anshan, Wuhu, Xuancheng, Tongling,
Chizhou and Anqing) was generally higher than that of other provinces. However,
the change amplitude between different cities in the same statistical year and
between different statistical years of the same city was larger than that of
other provinces.
(2) Table 2
summarizes the number of new industries in the Yangtze River Delta in the
research period. The number of new industries in Shanghai, Nanjing, Wuxi,
Changzhou, Suzhou and Taizhou (Jiangsu) declined steadily, and that in cities
in Zhejiang province fluctuated and declined except Zhoushan. Cities in Anhui
province respectively experienced a significant increase and decrease in the
number of new industries in the periods of 2007–2011 and 2012–2016 except Hefei
and Tongling.
Figure 2 Economic resilience of cities in the
Yangtze River Delta (2002‒2016)
Table
1 The list of new industries of cities in
the Yangtze River Delta (2002‒2016)
|
City
|
2002‒
2006
|
2003‒
2007
|
2004‒
2008
|
2005‒
2009
|
2006‒
2010
|
2007‒
2011
|
2008‒
2012
|
2009‒
2013
|
2010‒
2014
|
2011‒
2015
|
2012‒
2016
|
|
Shanghai
|
52
|
50
|
43
|
34
|
31
|
23
|
18
|
25
|
18
|
15
|
14
|
|
Nanjing
|
50
|
53
|
58
|
44
|
39
|
38
|
46
|
58
|
36
|
39
|
23
|
|
Wuxi
|
62
|
62
|
57
|
40
|
48
|
36
|
38
|
33
|
25
|
19
|
18
|
|
Changzhou
|
78
|
70
|
62
|
40
|
41
|
29
|
26
|
33
|
27
|
30
|
22
|
|
Suzhou
|
30
|
35
|
40
|
27
|
30
|
35
|
25
|
26
|
21
|
14
|
13
|
|
Nantong
|
65
|
57
|
47
|
38
|
39
|
37
|
46
|
69
|
69
|
105
|
75
|
|
Yangzhou
|
56
|
74
|
67
|
52
|
36
|
41
|
67
|
50
|
32
|
29
|
27
|
|
Zhenjiang
|
54
|
67
|
56
|
49
|
39
|
56
|
101
|
64
|
37
|
30
|
22
|
|
Taizhou (Jiangsu)
|
59
|
63
|
47
|
40
|
45
|
33
|
39
|
46
|
36
|
50
|
43
|
|
Yancheng
|
66
|
68
|
68
|
70
|
58
|
44
|
59
|
66
|
48
|
99
|
68
|
|
Hangzhou
|
55
|
52
|
48
|
45
|
39
|
33
|
22
|
26
|
28
|
29
|
39
|
|
Ningbo
|
46
|
55
|
53
|
35
|
32
|
23
|
24
|
19
|
29
|
20
|
13
|
|
Jiaxing
|
75
|
69
|
69
|
56
|
36
|
38
|
43
|
37
|
36
|
27
|
20
|
|
Huzhou
|
58
|
50
|
61
|
36
|
38
|
37
|
27
|
26
|
24
|
27
|
29
|
|
Shaoxing
|
67
|
55
|
58
|
32
|
27
|
25
|
25
|
25
|
17
|
18
|
31
|
|
Zhoushan
|
21
|
16
|
13
|
8
|
9
|
5
|
3
|
4
|
5
|
44
|
134
|
|
Taizhou (Zhejiang)
|
33
|
39
|
40
|
41
|
24
|
20
|
18
|
16
|
10
|
8
|
13
|
|
Jinhua
|
76
|
59
|
46
|
35
|
28
|
27
|
45
|
50
|
54
|
60
|
20
|
|
Hefei
|
71
|
64
|
68
|
81
|
58
|
51
|
81
|
65
|
27
|
29
|
22
|
|
Chuzhou
|
52
|
50
|
46
|
33
|
33
|
40
|
121
|
165
|
152
|
130
|
61
|
|
Ma’anshan
|
23
|
21
|
29
|
63
|
62
|
81
|
111
|
176
|
93
|
114
|
82
|
|
Wuhu
|
47
|
42
|
43
|
47
|
37
|
50
|
75
|
109
|
135
|
153
|
52
|
|
Xuancheng
|
73
|
40
|
55
|
48
|
50
|
80
|
128
|
156
|
149
|
128
|
54
|
|
Tongling
|
13
|
30
|
34
|
25
|
34
|
17
|
53
|
19
|
9
|
21
|
99
|
|
Chizhou
|
21
|
34
|
36
|
38
|
39
|
35
|
92
|
99
|
43
|
28
|
26
|
|
Anqing
|
53
|
49
|
61
|
49
|
59
|
38
|
100
|
152
|
169
|
203
|
95
|
(3) Figure 3
shows the path dependence of cities in Yangtze River Delta from 2002 to 2016.
The path dependence value of almost all cities was higher than 0.5, indicating
that the industrial evolution of these cities was mainly characterized by path
dependence. At the same time, the average path dependence of each research
period fluctuated and declined, and the advantages from developing new
industries by relying on local old ones gradually weakened.
Figure 3 Industrial
evolution path dependence of cities in the Yangtze River Delta (2002‒2016)
5 Discussion and Conclusion
Urban
economic resilience mirrors the comprehensive ability of urban economic system
to cope with risks, and the evolution path of urban industry is considered to
be one of the factors affecting economic resilience. The constructed dataset
provides a data reference for analyzing the spatio-temporal distribution of
economic resilience and the characteristics of industrial evolution path of
cities in the Yangtze River Delta region, and exploring the impact of
industrial evolution path dependence on economic resilience.
Firstly, the
economic resilience of cities in the Yangtze River Delta exhibits a variation
trend of “decline firstly, then rise, and finally decline again” on the whole.
Secondly, the number of new industries in Shanghai, as well as most of the
cities in Jiangsu and Zhejiang provinces shows a downward trend, and that in
Anhui province fluctuates greatly. Finally, the industrial evolution of cities
in the Yangtze River Delta region is path-dependent, whose advantage, however,
tends to weaken.
The constructed
dataset reflects the city-level economic resilience and industrial evolution in
the Yangtze River Delta region, which provides data support for understanding
the evolution characteristics of economic resilience in various cities, helps
to scientifically predict the development trend of new industries and
reasonably guide the optimization of industrial structure. Based on this
dataset, future studies are advised to continue to excavate data such as
culture and social relations by using new technologies and new means, so as to
conduct a more in-depth and comprehensive exploration of factors affecting
economic resilience.
Conflicts
of Interest
The
authors declare no conflicts of interest.
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