Available via license: CC BY 4.0
Content may be subject to copyright.
sustainability
Article
Bohemian Cultural Scenes and Creative Development of
Chinese Cities: An Analysis of 65 Cities Using Cultural
Amenity Data
Jun Wu 1,* , Hao Zheng 1, Tong Wang 2and Terry Nichols Clark 2
Citation: Wu, J.; Zheng, H.; Wang, T.;
Clark, T.N. Bohemian Cultural Scenes
and Creative Development of
Chinese Cities: An Analysis of 65
Cities Using Cultural Amenity Data.
Sustainability 2021,13, 5260. https://
doi.org/10.3390/su13095260
Academic Editors: Clemente J.
Navarro Yáñez, María
Jesús Rodríguez-García and
Miguel Amado
Received: 24 March 2021
Accepted: 29 April 2021
Published: 8 May 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Department of Sociology, Beijing Administration Institute, Beijing 100044, China; beiwenshu@126.com
2Department of Sociology, University of Chicago, Chicago, IL 60637, USA; wangt18@uchicago.edu (T.W.);
tnclark@uchicago.edu (T.N.C.)
*Correspondence: 18600102719@163.com; Tel.: +86-186-0010-2719
Abstract:
There has been a cultural turn in urban development, as an increasing number of scholars
are stressing the importance of culture in urban research and policy agendas. Specifically, the bo-
hemian cultural scene could drive an integral cultural policy approach between the cultural scenes
city and the creative city approach. Based on amenities data from 65 major Chinese cities, this paper
investigates the relationship between bohemian cultural scenes and creative development of Chi-
nese cities as well as regional differences using tree-based model, ordinary least squares (OLS) and
truncated regression, and provides conceptual and quantitative support for a bohemian cultural
scenes policy. Factor analysis suggests the bohemian cultural scene in Chinese cities consists of
two dimensions: self-expression and charisma. According to regression results, bohemian scenes
significantly promote urban creative development; specifically, charisma has a stronger impact on
urban creativity than self-expression. There are also significant regional differences: northern and
eastern cities should focus on the development of the charismatic dimension, creative subjects should
adjust away from prudent industriousness and practice dynamic experimentalism; whereas southern
cities should focus on the self-expressive dimension, and continue to promote tolerance, inclusivity
and expressive practice. Finally, the bohemian scenes policy demands an integral policy approach
sensitive to the existing socioeconomic context: policymakers should incorporate specific amenities
into existing qualities of local spaces and cultural consumption to stimulate creative development.
Keywords: bohemian; creative city; cultural scenes; integral urban policies; urban cultural policies
1. Introduction
China experienced exponential growth in urban development in the past few decades,
as its urbanization rate increased from 17.9% in 1978 to 60.6% in 2019. As the key driver
of urban development gradually shifts from industrial production to creativity and inno-
vation [
1
–
4
], China’s urban policy is facing new challenges. In China and more generally
in East Asia, urban innovation is based on local competition led by the government [
5
],
which is not only related to the creativity of local industries and talents, but is also closely
related to the government’s development strategy and cultural planning [
6
]. Household
registration policies have played a key role in attracting creative talents in Chinese cities.
Specifically, the household registration policies of Shanghai, Shenzhen and Xi’an are rela-
tively loose compared with cities of the same level, effectively attracting creative talents,
whereas Beijing’s relatively conservative policies have curbed the influx of creative talents
to a certain extent [
7
,
8
]. On the other hand, the government has employed policies to
support small businesses through public services and credit guarantees, which enhances
local creative development. However, in the new era of global urban development, could
traditional developmental and redistribution policies that rely largely on the allocation
of production factors support the next phase of urban creative development? There has
Sustainability 2021,13, 5260. https://doi.org/10.3390/su13095260 https://www.mdpi.com/journal/sustainability
Sustainability 2021,13, 5260 2 of 20
been a cultural turn in urban development. As cities transition from the industrial to the
post-industrial stage, traditional urban policies are increasingly limited in stimulating ur-
ban creative development, whereas cultural and consumption practices, once peripheral to
urban growth, are brought to the center of urban research and policy agendas [
1
,
9
,
10
]. The
economic growth policy paradigm lacking “quality of life” perspectives, such as cultural
creativity and consumption, is no longer sufficient to fully support the transformation
and development of China’s metropolises. Edward Gleaser, Jianing Zhang and Lixun Li,
and Qun Wang and Yelan Dong and others have emphasized a policy approach centered
on social tolerance [
10
–
12
]. According to Gleaser, creative talents are more likely to be
concentrated in cities with a greater level of tolerance. In high-density cities, face-to-face
interpersonal communication and multicultural exchange tend to generate creative pro-
cesses and human progress [
10
]. Similarly in China, Zhang and Li find that tolerance
and inclusiveness in the Pearl River Delta region have a positive impact on the spatial
distribution of the highly educated [
11
]; Wang and Dong find that with the gradual in-
crease in local consumption capacity, a flexible and tolerant living environment plays a
significant role in promoting the agglomeration of the creative class in Anhui Province [
12
].
In addition to urban infrastructures, such as high-quality universities and research and
development institutions, a tolerant social atmosphere has been proven to promote urban
creative development [13–16]. However, a policy perspective centered around promoting
tolerance also has certain limitations, since it is not a systematic and explicitly oriented
integral policy agenda that incorporates urban spaces, as well as residents and production
and consumption activities therein.
Some scholars have put forth a more systematic urban policy approach. Silver and
Clark argue that in the context of globalization, individualization, the rise of the middle
class and post-industrialization, the cultural scene has become a crucial factor in urban
policymaking [
17
]. Navarro and Clark argue that “cultural policy” is transversal, in that
it is amenable to finding coalition from both sides of the classical “pro-growth”–“social
redistribution” divide in urban policy [
18
]. More specifically, three paradigms of local
cultural policy are introduced: the creative city, the city as a cultural scene and educational
city. In particular, the bohemian scene is a multi-dimensional cultural scene, driving a
cultural policy approach between the creative city and cultural scene city. In a bohemian
scene, people are more likely to challenge conventions and express themselves equally.
With the development of the new economy, the bohemian scene has become a laboratory
for creative styles and a magnet for creative workers [
17
]. People can easily form social
networks, generate unconventional ideas and foster new projects and new companies,
along with other drivers of economic growth. The agglomeration of creative talents will, in
turn, increase cultural consumption and bohemian lifestyles and ideas, which further pro-
motes economic growth, and establishes a virtuous cycle between creativity and economic
growth. For instance, the successes of creative development in Chicago’s Wicker Park or
Toronto’s Queen Street West both benefited from an integral policy perspective around
the bohemian scene [
17
]. Do bohemian scenes similarly promote creative development of
metropolises in China?
Although Jun Wu, Ning Wang, Bo Chen, Ji Qi and others [
18
–
22
] have introduced
scenes theory and the bohemian scene as a research framework into China, there has been
little to no empirical research to verify the relationship between bohemianism and urban
creativity. The purpose of this paper is to verify the driving effect of bohemian scenes on the
creative development of cities in different regions of China, and to provide conceptual and
quantitative support for the formulation of a bohemian cultural scene policy approach. The
main focus of this paper is not to give specific policy recommendations, but to summarize
how to incorporate the concept and data of bohemian scene into policymaking, so that
policymakers could adjust measures according to varying local contexts; and, moreover,
to encourage more mayors and administrators to consider the importance of scenes and
cultural policy [
17
]. This paper is organized in the following manner: Section 2examines
the state of the field pertinent to the current study, specifically literature on urban policy,
Sustainability 2021,13, 5260 3 of 20
urban creativity, cultural scenes and bohemianism. Section 3explains the research design
of this study, including key variables, sample selection as well as methodology. Section 4
presents main research results from tree-based method and regression models. Section 5
presents policy implications of research results and perspectives for future research.
2. Review of Literature
The cultural scenes policy approach drives urban creative development by fostering a
cultural context attractive to creative talents and enterprises. Traditional economic develop-
ment and educational policies are both limited in promoting urban creative development
and innovation. Thus, an integral culture-oriented urban policy approach is required. The
aggregation of urban creative projects not only depends on granting preferential subsidies,
but also hinges on the particular cultural context of the city [
23
,
24
], which is mainly deter-
mined by “soft” urban cultural facilities, such as “city buzz”, cultural amenities and social
tolerance and diversity [
25
–
27
]. These components of urban cultural context are shown to
play a decisive role in attracting creative talents and promoting innovation [28,29].
Here, urban context is closely related to the concept of urban “cultural scene”, which
captures distinct lifestyles and cultures in urban spaces. It is an integral concept that
combines culture, technology, talented individuals, consumption and so on, endowing
esthetic significance to a place. A scene consists of a diverse combination of amenities
and activities [
17
]. Through cultural and artistic consumption, cultural scenes turn cities
into creative highlands, and, in turn, hatch creative products, consumption activities and
creative ideas [
30
]. Specifically, the “city as a cultural scene” is considered one of the three
main approaches of local cultural policy by Navarro and Clark [
18
]. The cultural scenes
policy approach centers on cultural consumption: it focuses on consumers and formulates
policies by responding to the demands of cultural lifestyle and consumption of distinct
social groups. It is important to note that the cultural scenes approach as a “transversal
policy” effectively bridges the classical divide between “pro-growth” and “social redis-
tribution” in local urban policies, breaking the competitive relationship between the two
policy advocates and forming an integral policy agenda. Specifically, on the one hand, a
scene composed of cultural amenities and activities induces regional economic growth
by increasing consumption and employment, which meets the purpose of the “economic
growth” approach; on the other hand, diversified cultural consumption opportunities
promote social integration of various groups of people by encouraging cultural participa-
tion of the public, which meets the goal of the “social redistribution” and social welfare
approach [
18
]. Thus, cultural scenes can be used to guide an integral policy approach that
centers on the cultural participation of congregations of creative individuals and the impact
of cultural consumption and leisure on urban creativity and innovation [31].
The bohemian scene is a new driving force of urban creative development. “Bohemian
culture” originated in 19th century Paris and refers to “an artistic, unconventional, frugal,
nomadic, and anti-establishment lifestyle” [
32
]. When cities enter the post-industrial
stage, there is an expansion of new industries oriented toward creativity, service and
consumption. Different from production and manufacturing workers of the industrial
age, the practitioners of these new industries tend to embrace bohemianism, due to its
emphasis on individuality, freedom, tolerance and diversity. Thus, bohemian culture has
been associated with urban creative development. In addition, bohemian culture requires
timely response to various new situations, and encourages people to make the best use
of their talents and succeed through individuality [
17
]. However, bohemian culture does
not unilaterally imply pursuing transgression or idiosyncrasy, for example, in Toronto, the
scenes with the strongest bohemian characteristics are often directly related to values of
rationalism and pragmatism [
17
]. Therefore, bohemianism also involves an attitude of
self-development of individuals with a strong sense of mission in their work, and who seek
to integrate life and work [
33
]. As a result, bohemianism gives rise to a new culture and
lifestyle that integrate traditionalism and rationalism, display artistic creativity and are
comfortable with the rapid advances in fashion and technology [1].
Sustainability 2021,13, 5260 4 of 20
The bohemian scene approach of cultural policy is an integral strategy that incorpo-
rates production and consumption factors, drawing from both the city as a “cultural scene”
and the creative city approach. Specifically, the creative city approach represents a produc-
tion and development-oriented strategy that takes culture as an instrument of economic
growth. Its core is to attract cultural and creative talents and enterprises, construct cultural
and creative industries and stimulate employment and economic growth [
16
]. On the other
hand, the city as a cultural scene approach centers on cultural consumption, promoting
amenities and activities that enhance cultural participation of diverse social groups as well
as generating economic growth. The bohemian cultural scene approach draws from both,
emphasizing creative development and innovation as well as cultural consumption. It
seeks to attract creative elements, such as artists, young professionals, entrepreneurs and
startup firms via vibrant and unconventional bohemian cultural scenes. The concentration
of these creative elements strengthens the level of bohemianism and the effect of the scene,
further driving incoming and local participants to engage in cultural consumption with
creative characteristics. This creates a spillover cultural effect that attracts and inspires
more creative talents and enterprises [
34
,
35
], stimulating local economic regeneration and
development incrementally [
36
,
37
]. Lloyd documented how Chicago’s Wicker Park, as a
center of artistic and cultural production, gathered a large number of young bohemian
artists and enterprises, providing human capital crucial for the region’s economic revival as
well as driving cultural and creative consumption [
35
]. The bohemian scene also gives rise
to a lively, stimulating and creative lifestyle through street activities, cafe culture, music,
etc. [10].
It can be seen that the bohemian scene integrates land, technology, capital, human
capital, local esthetics and cultural consumption, and that creative subjects are attracted
to cities with a vibrant bohemian scene. First of all, creative talents prefer local street
culture composed of small commercial amenities, such as coffee shops, street artists,
galleries and pubs. They are consumers and participants of the scene, but are its producers
simultaneously. The bohemian scene encourages self-expression and unconventional
ideas, enabling individuals to creatively participate in both consumption and production
activities. Second, creative talents and enterprises require spaces for social gathering
and brainstorming. In the bohemian scene, a diverse set of amenities, such as cafes and
bookstores, provide spaces to find like-minded people and exchange ideas [1].
Therefore, this paper poses the Hypothesis H1 that bohemian scene significantly
increases urban creative development. Specifically:
Hypothesis 1a (H1a).
Bohemian scene as a whole significantly increases urban creative develop-
ment score.
Hypothesis 1b (H1b).
Subdimensions of bohemian scene significantly increase urban creative
development score.
The bohemian scene is operationalized into bohemian amenities. Florida once pro-
posed a bohemian index to measure a city’s acceptance of bohemianism. The bohemian
index uses census data to count the number of writers, designers, artists and other groups,
that is, it operationalizes bohemian scenes through the amount of human capital within a
city [
1
]. Compared with the footloose nature of human capital, amenities are the founda-
tional components of bohemian scenes, as operationalization through human capital does
not account for the qualities of urban spaces and the activities within, or the experiential,
personalized and fragmented forms of production and consumption that increasingly take
place in the post-industrial city. This kind of post-industrial production and consumption
is inseparable from the amenities within an urban space. Urban creativity research needs to
pay attention to the creative context of the city, combining creative culture, creative urban
space and creative people. The bohemian scene combines the diverse creative talents and
cultures in the city, as well as the popular bohemian facilities in the city where they live.
Therefore, this study uses bohemian amenities as a measure of bohemian scenes. Based on
Sustainability 2021,13, 5260 5 of 20
the above point of view, Silver and Clark operationalize the bohemian scene into amenities
such as comedy theaters, rap restaurants and karaoke halls, based on data from Canada
and the US [
17
]. A similar measure of bohemianism is adopted by Jeong and Joeng and
Patterson [
32
,
38
]. However, there is no empirical research on bohemian scenes to date
in China. Thus, in order to construct the Chinese bohemian scene, we need to draw on
Silver and Clark as well as adjusting according to cultural and consumption activities in
the Chinese context.
3. Methods and Design
3.1. Sample City Selection
Cities were selected based on population size and economic aggregate, using data
from the 2018 China Statistical Yearbook and China Urban Statistical Yearbook. The first
step was to select cites according to the permanent urban population. To a certain ex-
tent, the population of a city reflects the result of the flow of labor factors under market
mechanisms [
39
]. In addition, the main administrative unit of Chinese cities—“wide area
city”—includes not only urban areas but also vast suburban counties. Thus, in order to
be consistent with domestic and international research designs, this paper selected cities
with a permanent urban area population of more than 1 million. The second step was
to select cities according to the economic aggregate. The development of creative cities
requires geographic scale and industrial foundation, and not all cities are suitable for it [
6
].
The development of innovation and creativity in a city requires a certain economic scale
and level of development. Therefore, this paper selected cities with a GDP of more than
100 billion yuan. Based on these two aspects, 65 cities were eventually selected. The names
and geographic locations of these cities are shown in Table 1.
Table 1. Names and geographic locations of sample cities.
Western Cities Eastern Cities
Northern Cities
Baotou, Changchun, Daqing, Harbin,
Hohhot, Jilin, Lanzhou, Luoyang,
Taiyuan, Urumqi, Xi’an,
Yinchuan, Zhengzhou
Baoding, Beijing, Dalian, Handan,
Jinan, Jining, Linyi, Qingdao,
Shenyang, Shijiazhuang, Tangshan,
Tianjin Weifang, Xuzhou, Yantai, Zibo
Southern Cities
Changsha, Chengdu, Chongqing,
Guiyang, Hefei, Kunming, Liuzhou,
Nanchang, Nanning, Wuhan, Wuhu,
Xiangyang, Zhuzhou, Zunyi
Changzhou, Foshan, Fuzhou,
Guangzhou, Haikou, Hangzhou,
Huai’an, Huizhou, Nanjing, Nantong,
Ningbo, Quanzhou, Shanghai,
Shantou, Shaoxing, Shenzhen,
Suzhou, Wenzhou, Wuxi, Xiamen,
Yancheng, Yangzhou
3.2. Independent Variables
Control variables were traditional factors of urban creative development in China,
including GDP, the proportion of the tertiary industry in GDP, GDP per capita, the num-
ber of students enrolled in colleges and universities, southern/northern cities and east-
ern/western cities. The data for variables were obtained from China Statistical Yearbook
and China Urban Statistical Yearbook in 2018. In order to reduce heteroscedasticity, a
logarithmic transformation was performed on all continuous variables.
This paper measured the bohemian scene of a city using the number of cultural ameni-
ties that reflect bohemianism. Since bohemian scenes are consumption-oriented, and the
main source of consumption information in China is the Internet as well as social media,
this paper obtained amenities data from Dianping.com (http://www.dianping.com/, ac-
cessed on 30 November 2019), the most representative and comprehensive consumption
and comment-based social media platform. According to past studies [
17
] and the classifica-
tion of amenities in Dianping.com, the bohemian scene is operationalized into 27 bohemian
Sustainability 2021,13, 5260 6 of 20
cultural amenities, as shown in Table 2. These amenities are considered to reflect values of
bohemian scenes based on their descriptions and user comments on Dianping.com.
Table 2. Bohemian cultural amenities selected from Dianping.com.
No. Bohemian Cultural Amenities
1 Board games
2 Locked room escape
3 Bars
4 Live performances
5 Party house
6 Cafe
7 Video game halls
8 Cinemas
9 Graffiti
10 Rock climbing
11 DIY workshops
12 Tattoo parlors
13 Recording studios
14 Theaters
15 KTVs
16 Bookstores
17 Custom tailored clothes
18 Combative sports
19 Custom furniture
20 Galleries
21 Hair salons
22 Photo studios
23 Manicure and eyelash salons
24 Internet cafes
25 Medical cosmetology
26 Exhibitions
27 Floriculture
Factor analysis was conducted using the number of bohemian amenities (27 types)
in each city. The results indicate that the information extraction ratios contained in the
27 variables were all higher than 70%, and the Kaiser–Meyer–Olkin (KMO) measure of
sampling adequacy was 0.950. The approximate chi-square of the Bartlett’s test of sphericity
was 3101.055, which indicates the test result is significant, and the null hypothesis is rejected.
There is a strong correlation between the number of 27 types of bohemian cultural facilities.
Using the varimax-rotation method, the bohemian scene is composed of two principal
components after dimension reduction. The cumulative percentage of variance is 85.767%,
which indicates that these two principal components are sufficient to construct an urban
bohemian scene; the extraction sums of squared loadings variance of the first principal
component is 45.265%, the extraction sums of squared loadings variance of the second
principal component is 40.502%. Thus, we obtained the bohemian scene scores of 65 major
Chinese cities:
FBohemianSceneScore =Pvcr1∗ZF1+Pvcr2∗ZF2
∑k
i=1Pvcrk
k≤2 (1)
In Formula (1),
Pvcrk
stands for the variance contribution rate of common factor
k
,
ZFkstands for the standardized factor score of the sample on the common factor k
FBohemianSceneScore =45.265%∗ZF1+40.502%∗ZF2
45.265% +40.502% (2)
The scores of the amenities in the two principal components were examined. Under the
first principal component, “board game”, “escape from the locked room”, “bar”, “perfor-
Sustainability 2021,13, 5260 7 of 20
mance” scored higher. The activities involved in these amenities are considered to embody
qualities of “self-expression”—improvisation, responding to situations in unscripted ways
and bringing one’s own unique perspectives. Thus, the first principal component is named
“self-expression” [
17
]. Under the second principal component, amenities such as “book-
store”, “custom clothing”, “fighting”, “galleries” received higher scores. The activities
involved in these amenities are considered to embody qualities of “charisma”, as they
possess extraordinary spiritual qualities that attract and compel others to follow [
17
]. Thus,
the second principal component is named “charisma”.
The bohemian cultural scene in Chinese cities was operationalized into two dimen-
sions: “self-expression” and “charisma”. The standardized scores in these two dimensions
of each city were taken as the key independent variables of this study (see
Appendix A
).
The bohemian scene consisted of two dimensions, as a result of factor analysis using main-
stream Chinese cultural consumption amenities data, resonating with Silver and Clark,
“a scene is more Bohemian if it exhibits resistance to tradition, affirms individual self-
expression, eschews utilitarianism, values charisma” [
17
]. To a certain extent, this indicates
that bohemian scenes similarly exist in China, and that this research could contribute to
international comparative research.
3.3. Dependent Variables
The operationalization of dependent variables combined traditional technology and
creative talents. The dependent variable of this study was urban creative development/
innovation. Combining China’s developmental context and research feasibility, the creative
development level of a city can be operationalized into two dimensions—the number of
patents and creative talents. Accordingly, urban creative development is measured by
five variables: “number of patent applications”, “number of granted patents”, “cultural,
sports and entertainment industry”, “scientific research, technical services and geolog-
ical prospecting industry”, “information transmission, computer services and software
industry”.
“Urban creative development score” was used as the dependent variable in this
study. Exploratory factor analysis was performed on the above five variables to obtain
the city’s creative development score. The extraction ratios contained in the five variables
all exceeded 65%, so the extracted common factors can explain the variables relatively
well. A KMO measure of sampling adequacy was 0.747, which suggests the information
overlap between the variables is acceptable, and a satisfactory factor analysis model can
be obtained. The approximate chi-square of the Bartlett’s test of sphericity was 492.672,
which indicates the results are significant and the null hypothesis is rejected. The five
variables of urban creative development have a strong correlation. Using the varimax
rotation method, the five variables were transformed into one principal component, with a
cumulative percentage of variance of 76.72%. Thus, this principal component was sufficient
to construct an evaluation index for urban creative development. A function of urban
creative development can be constructed through weighting. According to regression,
the standardized scores of creative development level of 65 major cities in China can be
obtained to measure their respective creative development (see Appendix B).
ZFUrban Creative Development =
k
∑
i=1
(FLk∗Zlnxk)k≤5 (3)
In Formula (3),
FLk
stands for the loading of urban development level factor on
variable K,
Zlnxk
stands for the standardized score of the natural logarithm of the sample
in the mean value of the variable K.
Sustainability 2021,13, 5260 8 of 20
ZFUrban Creative Development
=0.939 ∗ZlnxInfo Transmission,Computer,Software +0.888
∗Zlnxscientific research,technical services and geological prospecting +0.887
∗Zlnxcultural,sports and entertainment +0.839 ∗Zlnxpatents granted +0.822
∗Zlnxpatent applications
(4)
4. Results
4.1. Creative Development of Major Chinese Cities
There are significant differences in the creative development of major cities in China.
Top-echelon cities, such as Beijing, have a strong leading position, while most other cities
lag. According to two-step cluster analysis and the Bayesian information criterion, the
creative development level of 65 major cities can be divided into three tiers. As shown in
Figure 1, the first echelon (high) includes 11 cities: Beijing, Shanghai, Chengdu, Shenzhen,
Guangzhou, Hangzhou, Nanjing, Tianjin, Xi’an, Chongqing, Wuhan, etc.; the second
echelon (middle) includes 22 cities: Zhengzhou, Jinan, Hefei, etc.; the third echelon includes
32 cities: Wenzhou, Yangzhou, Nantong etc.
Sustainability 2021, 13, x FOR PEER REVIEW 8 of 20
standardized scores of creative development level of 65 major cities in China can be ob-
tained to measure their respective creative development (see Appendix B).
=(
∗
) ≤ 5 (3)
In Formula (3), stands for the loading of urban development level factor on var-
iable K, stands for the standardized score of the natural logarithm of the sample in
the mean value of the variable K.
= 0.939 ∗
,,
+0.888
∗
,
∗+0.887
∗
,
+0.839 ∗
+0.822
∗
(4)
4. Results
4.1. Creative Development of Major Chinese Cities
There are significant differences in the creative development of major cities in China.
Top-echelon cities, such as Beijing, have a strong leading position, while most other cities
lag. According to two-step cluster analysis and the Bayesian information criterion, the
creative development level of 65 major cities can be divided into three tiers. As shown in
Figure 1, the first echelon (high) includes 11 cities: Beijing, Shanghai, Chengdu, Shenzhen,
Guangzhou, Hangzhou, Nanjing, Tianjin, Xi’an, Chongqing, Wuhan, etc.; the second
echelon (middle) includes 22 cities: Zhengzhou, Jinan, Hefei, etc.; the third echelon in-
cludes 32 cities: Wenzhou, Yangzhou, Nantong etc.
Figure 1. Creative development level of major Chinese cities can be divided into three echelons based on cluster analysis.
Figure 1. Creative development level of major Chinese cities can be divided into three echelons based on cluster analysis.
4.2. Bohemian Scene as New Driver of Urban Creative Development
There is a linear and positive correlation between bohemian scenes and urban creative
development. As shown in Figure 2, for all the major cities in China, the bohemian scene
score is positively correlated with the level of urban creativity. Table 3and Figures 3–5
present correlations for specific dimensions (bohemian scene score, self-expression and
charisma) in different Chinese regions. First, the correlation between the bohemian scene
(bohemian score, self-expression and charisma) and urban creative development is stronger
in southern cities than northern cities. Second, the correlation between self-expression
Sustainability 2021,13, 5260 9 of 20
and urban creative development in eastern cities is stronger than that in western cities,
whereas the correlation between charisma and urban creative development in western
cities is higher than that in eastern cities. However, these are preliminary conclusions, and
the specific effects need to be verified using regression model.
Sustainability 2021, 13, x FOR PEER REVIEW 9 of 20
4.2. Bohemian Scene as New Driver of Urban Creative Development
There is a linear and positive correlation between bohemian scenes and urban crea-
tive development. As shown in Figure 2, for all the major cities in China, the bohemian
scene score is positively correlated with the level of urban creativity. Table 3 and Figures
3–5 present correlations for specific dimensions (bohemian scene score, self-expression
and charisma) in different Chinese regions. First, the correlation between the bohemian
scene (bohemian score, self-expression and charisma) and urban creative development is
stronger in southern cities than northern cities. Second, the correlation between self-ex-
pression and urban creative development in eastern cities is stronger than that in western
cities, whereas the correlation between charisma and urban creative development in west-
ern cities is higher than that in eastern cities. However, these are preliminary conclusions,
and the specific effects need to be verified using regression model.
Figure 2. Scatterplot of bohemian scene score and urban creative development level.
Table 3. Spearman correlation between the subdimensions of urban bohemian scenes and the level
of urban creativity in different regions.
Bohemian Scene
Score and Creative
Development
Self-Expression Di-
mension and Crea-
tive Development
Charisma Dimension
and Creative Develop-
ment
Eastern Cities 0.887 *** 0.866 *** 0.357 *
Western Cities 0.919 *** 0.693 *** 0.784 ***
Southern Cities 0.946 *** 0.762 *** 0.746 ***
Northern Cities 0.882 *** 0.625 *** 0.353
Note: * p < 0.05, *** p < 0.001.
Figure 2. Scatterplot of bohemian scene score and urban creative development level.
Table 3.
Spearman correlation between the subdimensions of urban bohemian scenes and the level
of urban creativity in different regions.
Bohemian Scene
Score and Creative
Development
Self-Expression
Dimension and
Creative Development
Charisma Dimension
and Creative
Development
Eastern Cities 0.887 *** 0.866 *** 0.357 *
Western Cities 0.919 *** 0.693 *** 0.784 ***
Southern Cities 0.946 *** 0.762 *** 0.746 ***
Northern Cities 0.882 *** 0.625 *** 0.353
Note: * p< 0.05, *** p< 0.001.
Sustainability 2021,13, 5260 10 of 20
Sustainability 2021, 13, x FOR PEER REVIEW 10 of 20
Figure 3. Scatterplot of bohemian scene score and urban creativity score in different regions.
Figure 4. Scatterplot of self-expression dimension and urban creativity score in different regions.
Figure 3. Scatterplot of bohemian scene score and urban creativity score in different regions.
Sustainability 2021, 13, x FOR PEER REVIEW 10 of 20
Figure 3. Scatterplot of bohemian scene score and urban creativity score in different regions.
Figure 4. Scatterplot of self-expression dimension and urban creativity score in different regions.
Figure 4. Scatterplot of self-expression dimension and urban creativity score in different regions.
Sustainability 2021,13, 5260 11 of 20
Sustainability 2021, 13, x FOR PEER REVIEW 11 of 20
Figure 5. Scatterplot of charisma dimension and urban creativity score in different regions.
Before regression analysis, tree-based model is used for initial exploration. In this
study, tree-based model of CRT growing algorithm is used for analysis. This analysis
method overcomes the omission of high-order terms and interaction terms of independent
variables. Moreover, the tree-based model algorithm is a non-parametric method without
too many restrictions on applicable conditions. The tree-based model of this study takes
the three ordinal variables of echelons of urban creative development as the dependent
variable. According to the previous clustering analysis results, echelons of urban creative
development are divided into three clusters: high, middle and low; maximum tree depth
is set to 5, minimum cases in parent node and minimum cases in child node are set to 1.
Based on Gini index (Formulas (5)–(7)), the tree model was used to predict the echelons
of urban creative development through growing and pruning. The final tree model in-
cludes the standardized (Z) bohemian scene score, GDP (in ten thousand Yuan), enroll-
ment in groups and universities, proportion of tertiary industry in GDP (%), per capital
GDP (yuan) and southern cities, as shown in Figure 6. There are 15 nodes, 8 terminal nodes
and tree depth of 5. The estimated risk of cross validation is 0.154, with a standard of error
of 0.045. The overall prediction accuracy of tree model is 98.5%—the prediction effect is
satisfactory. For any node t in the tree, the Gini index g (t) is calculated as follows:
()=
(
|)(|)
(5)
When split method (S) is used to divide the original node T into sub nodes, α and β,
the corresponding change in Gini index is:
(,)=
()
()
(6)
In Formula (6), and represent the proportion of the two sub nodes in which
the sample is split. The best splitting method is to maximize the change of Gini index. In
Formula (7), Ω is the set of all possible zero values.
Figure 5. Scatterplot of charisma dimension and urban creativity score in different regions.
Before regression analysis, tree-based model is used for initial exploration. In this
study, tree-based model of CRT growing algorithm is used for analysis. This analysis
method overcomes the omission of high-order terms and interaction terms of independent
variables. Moreover, the tree-based model algorithm is a non-parametric method without
too many restrictions on applicable conditions. The tree-based model of this study takes
the three ordinal variables of echelons of urban creative development as the dependent
variable. According to the previous clustering analysis results, echelons of urban creative
development are divided into three clusters: high, middle and low; maximum tree depth
is set to 5, minimum cases in parent node and minimum cases in child node are set to 1.
Based on Gini index (Formulas (5)–(7)), the tree model was used to predict the echelons of
urban creative development through growing and pruning. The final tree model includes
the standardized (Z) bohemian scene score, GDP (in ten thousand Yuan), enrollment in
groups and universities, proportion of tertiary industry in GDP (%), per capital GDP (yuan)
and southern cities, as shown in Figure 6. There are 15 nodes, 8 terminal nodes and tree
depth of 5. The estimated risk of cross validation is 0.154, with a standard of error of 0.045.
The overall prediction accuracy of tree model is 98.5%—the prediction effect is satisfactory.
For any node t in the tree, the Gini index g(t) is calculated as follows:
g(t)=∑
j6=i
p(j|t)p(i|t)(5)
When split method (S) is used to divide the original node T into sub nodes,
α
and
β
,
the corresponding change in Gini index is:
Φ(s,t)=g(t)−pαg(tα)−pβgtβ(6)
Sustainability 2021,13, 5260 12 of 20
In Formula (6),
pα
and
pβ
represent the proportion of the two sub nodes in which
the sample is split. The best splitting method is to maximize the change of Gini index. In
Formula (7), Ωis the set of all possible zero values.
ϕ(s∗,t)=max
seΩ Φ(s,t)(7)
Sustainability 2021, 13, x FOR PEER REVIEW 12 of 20
(∗,)=max
(,) (7)
Figure 6. Tree-based model results affecting echelons of urban creative development.
After running a tree model with CRT growth algorithm, the importance of the factors
affecting the development of urban creativity is obtained, as indicated by the results of
the normal Gini index. As shown in Figure 7, the ranking of the importance of the factors
affecting the development of urban creativity from high to low is: the total urban GDP, Z
bohemian scene, enrollment in colleges and universities, proportion of tertiary industry
in GDP (%), per capita GDP (yuan) and southern cities.
Figure 6. Tree-based model results affecting echelons of urban creative development.
After running a tree model with CRT growth algorithm, the importance of the factors
affecting the development of urban creativity is obtained, as indicated by the results of
the normal Gini index. As shown in Figure 7, the ranking of the importance of the factors
affecting the development of urban creativity from high to low is: the total urban GDP, Z
Sustainability 2021,13, 5260 13 of 20
bohemian scene, enrollment in colleges and universities, proportion of tertiary industry in
GDP (%), per capita GDP (yuan) and southern cities.
Results from the tree-based model indicate that the bohemian scene of the city has
an important impact on the level of creative development, ranking second among the
independent variables. Only 6.1% of the cities with a Z bohemian scene score higher than
0.094 have a low level of urban creative development, while 93.8% of the cities with a
Z bohemian scene score of less than or equal to
−
0.094 have low level of urban creative
development. For cities with Z bohemian scene higher than
−
0.094, if their GDP is higher
than RMB 10,000 trillion, then 100% of them have a high level of creative development,
while for cities with a Z bohemian scene score of less than or equal to RMB 10,000 trillion,
only 4.3% of them are at high creative development level. After a preliminary investigation,
whether a city is located in eastern China is not an important factor affecting urban creative
development, but whether it is located in southern China is an important factor affecting
urban creative development. This shows that different regions may have different effects on
the development of urban creativity, which cannot be explained in detail by this tree-based
model. It needs to be further explored through multiple regression models.
Sustainability 2021, 13, x FOR PEER REVIEW 13 of 20
Results from the tree-based model indicate that the bohemian scene of the city has an
important impact on the level of creative development, ranking second among the inde-
pendent variables. Only 6.1% of the cities with a Z bohemian scene score higher than 0.094
have a low level of urban creative development, while 93.8% of the cities with a Z bohe-
mian scene score of less than or equal to −0.094 have low level of urban creative develop-
ment. For cities with Z bohemian scene higher than −0.094, if their GDP is higher than
RMB 10,000 trillion, then 100% of them have a high level of creative development, while
for cities with a Z bohemian scene score of less than or equal to RMB 10,000 trillion, only
4.3% of them are at high creative development level. After a preliminary investigation,
whether a city is located in eastern China is not an important factor affecting urban crea-
tive development, but whether it is located in southern China is an important factor af-
fecting urban creative development. This shows that different regions may have different
effects on the development of urban creativity, which cannot be explained in detail by this
tree-based model. It needs to be further explored through multiple regression models.
Figure 7. Importance of independent variables measured by Gini index.
The continuous variable “Z creative development” is used as a dependent variable
in regression models for further investigation. Regression results are presented in Table
4. While controlling for other variables, bohemian scenes promote the development of
urban creativity. In Model 1, which only includes traditional policy factors, GDP (ln), the
proportion of tertiary industry in GDP (ln) and the number of students enrolled in colleges
and universities (ln) have a significant (sig < 0.05) and positive effect on urban creativity,
controlling for other variables. This suggests that the classical economic and educational
urban policy could promote the development of urban creativity. Model 2 adds the bohe-
mian scenes score. Controlling for traditional variables, bohemian scene scores signifi-
cantly (sig < 0.05) enhance the creative development of China’s major cities; GDP and the
proportion of tertiary industry in GDP still have significant effect on the creative devel-
opment of major Chinese cities; however, the number of students enrolled in colleges and
universities no longer significantly impacts the level of creative development. In model 2,
after a collinearity test, the vif values of each variable are less than 10, indicating there is
GDP (trillion yuan)
Z Bohemian scene score
Enrollment in colleges and universities(one million)
Proportion of tertiary industry in GDP(%)
Southern cities
Importance
0.5%0.4%0.3%0.2%0.1%0.0%
Normalized Importance
100806040200
Growing Method:CRT
Dependent Variable:Echelons of urban creative development
Figure 7. Importance of independent variables measured by Gini index.
The continuous variable “Z creative development” is used as a dependent variable
in regression models for further investigation. Regression results are presented in Table 4.
While controlling for other variables, bohemian scenes promote the development of ur-
ban creativity. In Model 1, which only includes traditional policy factors, GDP (ln), the
proportion of tertiary industry in GDP (ln) and the number of students enrolled in col-
leges and universities (ln) have a significant (sig < 0.05) and positive effect on urban
creativity, controlling for other variables. This suggests that the classical economic and
educational urban policy could promote the development of urban creativity. Model 2 adds
the bohemian scenes score. Controlling for traditional variables, bohemian scene scores
significantly (
sig < 0.05
) enhance the creative development of China’s major cities; GDP
and the proportion of tertiary industry in GDP still have significant effect on the creative
development of major Chinese cities; however, the number of students enrolled in colleges
and universities no longer significantly impacts the level of creative development. In model
2, after a collinearity test, the vif values of each variable are less than 10, indicating there is
no collinearity issue in this model. The heteroscedasticity (hettest) test yields a chi
2
value
of 2.65 (prob > chi
2
= 0.1033 < 0.05). Therefore, the null hypothesis is rejected, and there is
no issue of heteroscedasticity in this model.
Sustainability 2021,13, 5260 14 of 20
In order to account for the differences in the impact of the two dimensions of bohemian
scene on the creative development of major cities, Model 3 adds the two dimensions of
bohemian scene. Controlling for other variables, self-expression, charisma, GDP and the
proportion of tertiary industry in GDP all significantly (sig < 0.05) promote urban creativity.
For all major Chinese cities, the charisma dimension has a stronger effect in promoting
urban creativity than the self-expression dimension; the traditional growth-oriented urban
policy is the cornerstone of promoting the construction of bohemian scene, while education
does not significantly promote urban creativity. Since only major Chinese cities are selected
into the sample and analyzed, there is a certain level of truncation. Therefore, for Model 4,
a truncated regression is applied to the variables of Model 3, generating similar results as
Model 3. Therefore, the above conclusions are relatively robust.
In order to account for the differences in the influence of the bohemian scene and
its dimensions among different regions of China, Model 5 and Model 6 consider the
regional differences between cities in southern and northern China. Model 5 only includes
major cities in southern China. The results indicate that self-expression, charisma and
regional GDP (ln) significantly (sig < 0.05) promote the creative development of cities in
southern China; further, self-expression is found to have a stronger impact on the creative
development of southern cities than charisma. Similarly, Model 6 includes major cities in
northern China, and finds that charisma and regional GDP significantly (sig < 0.05) promote
the creative development of major cities in northern China, but the effect of self-expression
is not significant.
Model 7 and Model 8 consider the regional differences between eastern and western
cities in China. Model 7 only includes the eastern coastal cities of China. Since the
heteroscedasticity (hettest) test of Model 7 is significant (sig < 0.05), Model 7 adopts robust
OLS regression. The results of Model 7 indicate, controlling for other variables, that
charisma, regional GDP (ln), the proportion of tertiary industry in GDP (ln), being located
at the southeast coast of China, all significantly promote urban creative development
(sig < 0.05), while the effect of the self-expression dimension is not significant. Model 8
only includes cities in western China, and finds that both self-expression and charisma
significantly promote (sig < 0.05) the creative development of cities, while the effect of
regional GDP (ln) is not significant.
Table 4. Regression analysis results of factors influencing urban creativity.
Model 1
(OLS)
Model 2
(OLS)
Model 3
(OLS)
Model 4
(Truncated Regression)
BSEBSEBSEBSE
Z Bohemian scene score 0.577 *** 0.144
Z Self-expression
dimension 0.236 *** 0.100 0.232 * 0.145
Z Charisma dimension 0.290 *** 0.070 0.291 *** 0.067
ln GDP 0.920 *** 0.078 0.580 *** 0.109 0.608 *** 0.112 0.608 *** 0.109
ln proportion of tertiary
industry in GDP 0.864 ** 0.250 0.677 ** 0.227 0.868 ** 0.289 0.890 ** 0.280
ln enrollment in colleges
and universities 0.186 ** 0.065 0.042 0.068 0.048 0.069 0.059 0.067
ln per capita GDP −0.144 0.140 0.006 0.130 −0.001 0.130 −0.002 0.130
Southern cities 0.176 * 0.083 0.122 0.075 0.151 0.079 0.156 * 0.078
Eastern cities 0.062 0.091 0.005 0.082 −0.053 0.989 −0.041 0.095
Constant −20.277 *** 1.553 −23.493 *** 2.184 −14.727 *** 2.469 −14.946 *** 2.385
N 65 65 65 65
F 101.53 *** 111.99 *** 98.38 ***
chi2833.20 ***
R20.913 0.932 0.934
Adj-R20.904 0.924 0.924
Sustainability 2021,13, 5260 15 of 20
Table 4. Cont.
Model 5
(Southern Cities OLS)
Model 6
(Northern Cities OLS)
Model 7
(Eastern Cities OLS)
Model 8
(Western Cities OLS)
BSEBSEBSEBSE
Z Bohemian scene score
Z Self-expression
dimension 0.318 * 0.129 0.194 0.153 0.180 0.093 0.436 * 0.180
Z Charisma dimension 0.292 ** 0.094 0.297 * 0.108 0.220 ** 0.070 0.477 ** 0.128
ln GDP 0.453 ** 0.140 0.824 *** 0.179 0.700 *** 0.126 0.023 0.243
ln proportion of tertiary
industry in GDP 0.374 0.485 0.741 0.363 1.154 ** 0.405 −0.508 0.567
ln enrollment in colleges
and universities 0.061 0.085 0.089 0.131 0.021 0.080 0.299 0.144
ln per capita GDP 0.144 0.173 −0.262 0.216 −0.089 0.122 0.202 0.243
Southern cities 0.219 * 0.119 −0.169 0.141
Eastern cities 0.137 0.157 0.062 0.141
Constant −11.848 ** 3.579 −15.334 *** 3.456 −16.217 *** 2.667 −4.332 5.391
N 36 65 29 38
F 69.88 *** 98.38 *** 56.04 *** 120.58 ***
chi2
R20.946 0.934 0.949 0.961
Adj-R20.932 0.924 0.932
Note. * p< 0.05, ** p< 0.01, *** p< 0.001.
5. Conclusions and Discussion
The bohemian scene is a special type of cultural scene that could drive an integral
cultural policy approach between the city as a “cultural scene” and the creative city
approach [
18
]. It is a new driver for urban cultural and creative development. Through an
empirical analysis of 65 major Chinese cities, this study finds that the bohemian cultural
scene of Chinese cities is mainly composed of two dimensions, “self-expression” and
“charisma”. The bohemian scene is found to significantly promote the development of
urban creativity, which suggests that China’s urban development policies need to be
changed: First, a developmental policy approach oriented at technological innovation is no
longer sufficient facing a new stage of urban development; instead, an integral cultural
policy approach oriented toward creative service industries and creative consumption is
increasingly required. Second, urban policies need to heed the cultural context attractive to
creative talents and creative enterprises.
At the current stage, economic growth is still the basis of bohemian scenes policy in
China. For all 65 major cities in China, with bohemian scenes variables included, GDP still
significantly promotes the development of urban creativity. Therefore, growth-oriented
policies should be a basic component of the bohemian scene policy approach. However,
higher education does not significantly promote the development of urban creativity when
bohemian scenes variables are included, which suggests that educational policies are less
effective at promoting urban creativity than bohemian scenes policy. From the empiri-
cal results, as a whole, bohemian scenes significantly promote creative development in
China’s major cities; specifically, the charisma dimension has a stronger impact on urban
creativity than self-expression. In addition, there are significant regional differences, as
different dimensions of bohemian scenes have different effects on creativity development
in cities of different Chinese regions. For northern cities, charisma significantly promotes
the development of urban creativity, while the effect of self-expression is not significant;
for southern cities, the self-expression dimension has a stronger effect in promoting the
development of urban creativity than charisma; for eastern coastal cities, only the charisma
dimension significantly promotes the development of urban creativity; for western cities,
both self-expression and charisma significantly promote the development of urban cre-
Sustainability 2021,13, 5260 16 of 20
ativity, but charisma has a stronger impact. Therefore, the results suggest that cities in
northern and eastern China should focus on developing the charisma dimension of bo-
hemian scenes, encouraging creative subjects to loosen their prudent industriousness and
practice dynamic experimentalism to break the “iron cage” of creativity; while southern
cities should focus on developing the dimension of self-expression, continue to promote
tolerance of people from all walks of life, engage in expressive practices, so as to strive for
an esthetic life [
17
]. For western cities, the two dimensions of bohemian scene should be
promoted simultaneously.
Development of bohemian cultural scenes demands an integral policy approach.
Firstly, the bohemian scene approach includes both production and cultural consump-
tion aspects. There is an agglomeration effect in the production and consumption of
creative subjects: they express values through various activities, and further realize cre-
ative consumption and reproduction. The lifestyle and values embedded in the bohemian
scene enables urban development in the new stage: creative subjects not only possess
a strong sense of individuality, but are also professionally driven, willing to pursue a
sense of achievement in transforming their creativity into reality. In addition, even though
bohemian scenes could promote economic growth by encouraging cultural creative con-
sumption and employment, the implementation of bohemian scenes policy requires a
certain level of economic foundation. Judging from the experience of the world’s creative
hubs, the government’s strategic promotion is the most effective means to develop creative
industries [
40
]. Granted, cultural and esthetic intervention cannot replace the material
foundation of the city, “If commuting into a city is a lengthy torment, then companies will
head for the suburbs, no matter how many cool museums the city has” [
10
]. A city cannot
achieve creative development entirely through the promotion of bohemian culture. Instead,
promotion of the bohemian scene needs to be part of an integral urban policy approach,
with the active participation of local actors and citizens as well as increased investment in
infrastructure from the government.
Secondly, the implementation of bohemian scenes policy needs to adapt to local
conditions, combining local economic foundation, cultural context and the involvement
of multiple social actors, and must take creativity and innovation as the goal of urban
development, to eventually realize “a better life” for Chinese cities. At present, there are sig-
nificant variations among the creative development of 65 major Chinese cities. The specific
dimensions of bohemian cultural scenes have different effects on creative development in
different regions, so urban policies need to be guided according to local conditions. China’s
southwest city of Chengdu has put forward the concept of “cultural scenes city-making”,
and achieved successes in promoting creative development. Considering the vast regional
differences and cultural policy experiences of advanced cities, the policies of major cities in
China should follow the following principles: first, the policy decision of any city needs to
be based on the existing comparative advantages. For example, Chengdu combines the
concept of cultural scenes with its strategic position as a hub city of the “one belt, one road”
supply chain to promote creative applications for the “international supply-chain service
scenes” [41].
Second, the cultivation of cultural scenes often requires consistent collaboration from
the public, private and the third sector, to create an environment conducive to the growth
of bohemian amenities and services, and, in turn, to foster cultural consumption with
self-expressive and charismatic characteristics [
9
,
42
]. More importantly, it is necessary
to organically incorporate new amenities into the existing qualities of space and local
patterns of cultural consumption and avoid the “one size fits all” approach. The bohemian
cultural scenes approach requires a flexible and inclusive policy approach, widely soliciting
the opinions of entrepreneurs, civic leaders, citizens and other multiple social subjects to
ascertain the existing cultural and esthetic characteristics and political economy of different
regions of the city [
17
]. For instance, Chengdu created the Future Urban Scenes Laboratory
that grants funding and policy support for selected enterprises; applicants are evaluated
Sustainability 2021,13, 5260 17 of 20
based on user experience, whether new products and services are generated and whether
new consumption demands are satisfied [43].
Third, cities need to maintain and build upon their own identities and characteristics in
the process of creative development. Jacobs argues that excessively imitating and copying
the success of other cities will lead to the collapse of diversity and authenticity [
44
]. A
city’s excessive branding and marketing makes consumers feel inauthentic, which will
lead to the commercialization of bohemian scenes and stifling of creativity; it will also
lead to the insistence on imitation, and homogenization of urban structures, lifestyles and
cultures [
45
,
46
]. This is contrary to bohemianism, which emphasizes self-identity and
self-expression. Imitation from other cities will only achieve temporary results. Chengdu
incorporated its historical “Tianfu culture” to create a multicultural consumption and
immersion scene with the theme of “international style and old Chengdu flavor”: from
gourmet food and leisure to rural tourism, from fashion to traditional art, Chengdu’s
cultural tourism projects explore modern and creative expressions of Chengdu’s profound
Tianfu heritage to create a multistructure amenities system, providing young creative
individuals with a more fashionable cultural consumption experience, and promoting
production and consumption in the scene [47].
Fourth, the city should aim for practicality and moderate success, instead of a tem-
porary sensation [
10
]. Intensive capital and scale economy will hinder the development
of new companies [
48
]. “Big city diseases” and “big company disease” will become the
“iron cage” of creativity. Especially in the post-industrial era, cities should seek small and
practical projects rather than gamble on the future of cities with the expensive dice of high
investment. The real purpose of investing in cultural amenities is not to develop any spe-
cific industry, such as tourism, but to stimulate urban creativity [
1
]. Therefore, regardless
of the specific measures taken to develop bohemian scenes, policymakers should stay true
to the original intention of attracting creative talents and promoting the development of
urban creativity.
Finally, we suggest three aspects for future research. First, the unit of analysis could
be refined to the level of community or neighborhood. Due to increasing urbanization,
migration and globalization, cities are growing in both size and diversity. Different commu-
nities in the city may present distinct combinations of amenities, social groups and cultures.
Therefore, the analysis of cultural scenes should focus on the community rather than the
city level. Due to current limited access to community-level data in China, this study
focuses on the city level, which is a relatively macro level unit. Second, future research
could extend its analysis to 15 scenes subdimensions [
17
], and other ideal-typical scenes
like the bohemian scene. Specifically, to investigate the cultural and esthetic characteristics
of a scene and its impact on urban development; in addition, international comparative
research could contribute to the localization of scenes dimensions in line with China’s
special national conditions and shed important light on the different effects of similar scene
dimensions in China and the West. Third, future research could consider using scenes
theory as a methodology to study a wider range of urban issues and related policy options,
including but not limited to attracting tourists and improving the city’s soft power through
scenes creation, or investigating the relationship between scenes and urban inequality and
even urban risk and security.
Author Contributions:
Conceptualization, J.W.; methodology, J.W. and H.Z.; software, H.Z.; val-
idation, H.Z. and T.W.; formal analysis, J.W., H.Z. and T.W.; investigation, H.Z.; resources, J.W.;
data curation, H.Z.; writing—original draft preparation, J.W., H.Z. and T.W.; writing—review and
editing, T.W. and T.N.C.; visualization, H.Z.; supervision, J.W. and T.N.C.; project administration, J.W.;
funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Social Science Fund of China, “Research on the
Characteristics and Development Trend of Central City Population Aggregation from the Perspective
of Circle Structure Theory”, grant number 20ARK001.
Institutional Review Board Statement: Not applicable.
Sustainability 2021,13, 5260 18 of 20
Informed Consent Statement: Not applicable.
Data Availability Statement:
Data are available from the corresponding author upon reasonable
request.
Acknowledgments:
The authors would like to thank Daniel Silver, Cary Wu, Ning Wang, Ji Qi,
Bo Chen, Deting Yin, Yuping Hu, Xuemei Wang, Licheng Ying, Linlin Diao, Yongli Jiao, Ling
Huang, Xiaohua Zhong, Bichun Zhang. This article is supported by Beijing Population and Society
Development Research Center and Beijing Population Institute.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Variables Factor Variance Self-Expression Dimension Charisma Dimension
Board games 0.932 0.897 0.357
Locked room escape 0.889 0.868 0.368
Bars 0.825 0.857 0.302
Live performances 0.940 0.842 0.480
Party house 0.830 0.797 0.443
Cafe 0.844 0.782 0.481
Video game halls 0.870 0.779 0.514
Cinemas 0.923 0.768 0.577
Graffiti 0.856 0.750 0.542
Rock climbing 0.874 0.745 0.566
DIY workshops 0.925 0.717 0.640
Tattoo parlors 0.729 0.714 0.638
Recording studios 0.820 0.709 0.564
Theaters 0.773 0.681 0.556
KTVs 0.729 0.625 0.581
Bookstores 0.895 0.204 0.924
Custom tailored clothes 0.847 0.396 0.831
Combative sports 0.931 0.528 0.807
Custom furniture 0.850 0.489 0.781
Galleries 0.826 0.472 0.777
Hair salons 0.903 0.573 0.758
Photo studios 0.844 0.543 0.741
Manicure and eyelash salons 0.958 0.679 0.705
Internet cafes 0.852 0.600 0.702
Medical cosmetology 0.740 0.509 0.693
Exhibitions 0.822 0.587 0.691
Floriculture 0.741 0.540 0.670
KMO measure of sampling adequacy 0.950
The approximate chi-square of the Bartlett’s test
of sphericity 3101.055 ***
Cumulative percentage of variance 85.767
*** p< 0.001.
Appendix B
Variables Factor Variance Creative Development Factor
Employment in information transmission, computer services and
software industry 0.882 0.939
Employment in scientific research, technical services and geological
prospecting industry 0.788 0.888
Employment in culture, sports and entertainment 0.787 0.887
Number of patents granted 0.703 0.839
Number of patents applied 0.676 0.822
KMO measure of sampling adequacy 0.747
The approximate chi-square of the Bartlett’s test of sphericity 492.672 ***
Cumulative percentage of variance 76.72
*** p< 0.001.
Sustainability 2021,13, 5260 19 of 20
References
1.
Florida, R.L. The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life; Basic Books:
New York, NY, USA, 2006; ISBN 978-0-465-02476-6.
2.
You, J.-S.; Lu, C.; Zheng, H.-A.; Chen, Z. Analysis on the Construction Mode of Innovative Cities–Taking Shanghai and Shenzhen
as Examples. China Soft Sci. 2011,7, 82–92. (In Chinese)
3.
Huang, R.; Liang, Q.-J.; Lv, L.-C. The relationship between Urban Population Structure and Innovation: An Empirical Analysis
Based on Chinese Cities. Urban Dev. Res. 2014,9, 84–91. (In Chinese)
4.
Fang, C.-L.; Ma, H.-T.; Wang, Z.-B.; Li, G.-D. Comprehensive evaluation and spatial pattern differentiation of China’s innovative
city construction. Acta Geogr. Sin. 2014,4. (In Chinese) [CrossRef]
5.
He, S.-J.; Wang, J. State-led creative/cultural city making and its contestations in East Asia: A multi-scalar analysis of the
entrepreneurial state and the creative class. Geoforum 2019,106, 305–309. [CrossRef]
6. Li, M.-C. Creative Cities and the Rise of Creative Industries in the UK. J. Public Adm. 2008,4, 93–100, 127. (In Chinese)
7.
Feng, S.-L.; Tan, Y.; Huang, N.; Gong, L.-T. Heterogeneity of matching degree between professional and employment industry
in the perspective of registered residence system-Based on the analysis of employment data of 2008–2014 graduates of Peking
University. Econ. Sci. 2017,5, 113–128. (In Chinese) [CrossRef]
8.
Liu, X.-Y.; Jin, N. Reorientation of the urban policy of “fighting for talents”—An analysis focusing on young mobile talents. China
Youth Stud. 2019,9, 47–53. (In Chinese) [CrossRef]
9. Clark, T.N. (Ed.) The City as an Entertainment Machine; Lexington Books: Lanham, MD, USA, 2003; ISBN 978-0-76230-541-4.
10.
Glaeser, E.L. Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier and Happier; Penguin
Random House: London, UK, 2012; ISBN 9780143120544.
11.
Zhang, J.-N.; Li, L.-X. Evolution and Influencing Factors of Spatial Distribution Pattern of Talents in Pearl River Delta. J. Sun
Yat-sen Univ. 2020,2. (In Chinese) [CrossRef]
12.
Wang, Q.; Dong, Y.-L. Research on the Influencing Factors of Creative Class Agglomeration in the Yangtze River Delta. Mod. Econ.
2020,6. (In Chinese) [CrossRef]
13.
Basant, R.; Pankaj, C. Role of Educational and R&D Institutions in City Clusters: An Exploratory Study of Bangalore and Pune
Regions in India. World Dev. 2006,35. [CrossRef]
14.
Feser, E.; Renski, H.; Goldstein, H. Clusters and Economic Development Outcomes: An Analysis of the Link Between Clustering
and Industry Growth. Econ. Dev. Q. 2008,22, 324–344. [CrossRef]
15.
Jiang, Y.; Wu, K.-L. Research on the Model of Influencing Factors of Creative Industry Districts Based on Factor Analysis—Taking
Hangzhou’s Four Creative Industry Districts as Examples. Shanghai J. Econ. 2009,1. (In Chinese) [CrossRef]
16.
Florida, R.; Mellander, C.; Adler, P. Handbook of Creative Cities; Andersson, D.E., Mellander, C., Eds.; Elgar: Cheltenham, UK, 2011;
ISBN 978-0-85793-639-4.
17.
Silver, D.A.; Clark, T.N. Scenescapes: How Qualities of Place Shape Social Life; The University of Chicago Press: Chicago, IL, USA,
2016; ISBN 978-0-226-35685-3.
18.
Navarro, C.J.; Clark, T.N. Cultural Policy in European Cities: An Analysis from the Cultural Agenda of Mayors. Eur. Soc.
2012
,
14, 636–659. [CrossRef]
19. Wu, J. The frontier of Urban Sociology: A review of Scenes Theory. Sociol. Rev. China 2014,2, 90–95. (In Chinese)
20.
Wang, N. Local consumerism, urban amenities and industrial structure optimization—Industrial transformation and upgrading
from the perspective of consumption Sociology. Sociol. Study 2014,29, 24–48. (In Chinese) [CrossRef]
21.
Chen, B.; Wu, Y.-M.-R. Research on the development of urban creative community from the perspective of Scenes Theory. J.
Shenzhen Univ. 2017,34, 40–46. (In Chinese)
22.
Qi, J.; Qi, R. Urban cultural innovation from the perspective of Buzz Theory. Theory Mon.
2020
,10, 89–98. (In Chinese) [CrossRef]
23. Hutton, T. The New Economy of the inner city. Cities 2004,21, 21. [CrossRef]
24.
Drinkwater, B.; Platt, S. Urban development process and creative clustering: The film industry in Soho and Beyoglu. Urban Des.
Int. 2015,21. [CrossRef]
25.
Wenting, R.; Atzema, O.; Frenken, K. Urban Amenities and Agglomeration Economies? The Locational Behaviour and Economic
Success of Dutch Fashion Design Entrepreneurs. Urban Stud. 2011,48, 1333–1352. [CrossRef]
26.
Murphy, E.; Fox-Rogers, L.; Redmond, D. Location Decision Making of “Creative” Industries: The Media and Computer Game
Sectors in Dublin, Ireland: Location Decision Making of Creative Industries. Growth Chang. 2015,46, 97–113. [CrossRef]
27.
Falck, O.; Fritsch, M.; Heblich, S. The phantom of the opera: Cultural amenities, human capital, and regional economic growth.
Labour Econ. 2011,18, 755–766. [CrossRef]
28.
Haisch, T.; Klöpper, C. Location Choices of the Creative Class: Does Tolerance Make a Difference? J. Urban Aff.
2015
,37, 233–254.
[CrossRef]
29.
Rodríguez-Gulías, M.J.; Fernández-López, S.; Rodeiro-Pazos, D. Innovation in Cultural and Creative Industries Firms with an
Academic Origin (CCI-USOs): The Role of Regional Context. Technovation 2020,92, 102044. [CrossRef]
30.
Hutton, T.A. Spatiality, Built Form, and Creative Industry Development in the Inner City. Env. Plan A
2006
,38, 1819–1841.
[CrossRef]
31.
Wu, J. Scenes Theory: A New Perspective of using Cultural Factors to Promote Urban Development. Hunan Soc. Sci.
2017
,2,
175–182. (In Chinese)
Sustainability 2021,13, 5260 20 of 20
32. Jeong, H. The role of the arts and bohemia in sustainable transportation and commuting choices in Chicago, Paris, and Seoul. J.
Urban Aff. 2018, 1–26. [CrossRef]
33. Zhao, H.-X.; Zheng, X.-M. Research Status and Prospects of Work Mission. Bus. Manag. J. 2013,10. (In Chinese) [CrossRef]
34. Zukin, S. The Cultures of Cities; Blackwell: Malden, MA, USA, 1995; ISBN 978-1-557-86437-6.
35. Lloyd, R. Neo-Bohemia: Art and Commerce in the Postindustrial City; Routledge: New York, NY, USA, 2006; ISBN 9780415870979.
36.
Markusen, A. Urban development and the politics of a creative class: Evidence from a study of artists. Environ. Plan.
2006
,38,
1921–1940. [CrossRef]
37. Markusen, A. Creative cities: A 10-year research agenda. J. Urban Aff. 2014,36, 567–589. [CrossRef]
38.
Jeong, H.; Patterson, M. Starchitects in Bohemia: An Exploration of Cultural Cities from the ‘Top-Down’ and ‘Bottom-Up’. Urban
Aff. Rev. 2020. [CrossRef]
39.
Ye, X.-Q.; Chen, W. Research on the Comprehensive Attraction of Chinese Cities to Scientific and Technological Innovation
Talents–Construction and Demonstration of Evaluation Index System based on Amenities Theory. Stud. Sci. Sci.
2019
. (In
Chinese) [CrossRef]
40. Hu, X.-W. The Age of Creative Economy and New Opportunities for the City. Urban Probl. 2006,5, 21–27. (In Chinese)
41. Chengdu New Economy ‘Double Thousand’ Press Conference Successfully Held. Available online: http://gk.chengdu.gov.cn/
govInfo/detail.action?id=2793215&tn=2 (accessed on 1 December 2020). (In Chinese)
42.
Kloosterman, R.C. Cultural Amenities: Large and Small, Mainstream and Niche—A Conceptual Framework for Cultural Planning
in an Age of Austerity. Eur. Plan. Stud. 2014,22, 2510–2525. [CrossRef]
43.
Notice of Chengdu New Economic Development Committee and Chengdu Finance Bureau on Organizing and Applying
for Chengdu Future Scenes Laboratory. Available online: http://gk.chengdu.gov.cn/govInfo/detail.action?id=2725422&tn=2
(accessed on 6 January 2021). (In Chinese)
44. Jacobs, J. The Death and Life of Great American Cities; Random House: New York, NY, USA, 1961; ISBN 978-0-394-42159-9.
45. Jiang, H. Urban Spirit and the Shaping of Modern Urban Image. Commer. Times 2007,10, 2. (In Chinese)
46.
Tao, Q.-Q. Homogenization of Urban Space: Essence, Problems and Transcendence. Master’s Thesis, Suzhou University, Suzhou,
China, April 2016. (In Chinese)
47.
Chengdu’s Immersive Experience of New Scenes Drives the Upsurge of Culture and Tourism Consumption. Available online:
http://gk.chengdu.gov.cn/govInfo/detail.action?id=2840081&tn=2 (accessed on 27 January 2021). (In Chinese)
48. Audretsch, D.B.; Mahmood, T. Firm Selection and Industry Evolution: The Post-Entry Performance of New Firms. J. Evol. Econ.
1994,4, 243–260. [CrossRef]