Air Pollution and Attendance in the Chinese Super League: Environmental Economics and the Demand for Sport

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Air Pollution and Attendance in the Chinese Super League:
Environmental Economics and the Demand for Sport
Nicholas M. Watanabe and Grace Yan
University of South Carolina
Brian P. Soebbing
University of Alberta
Wantong Fu
The University of Mississippi
Although numerous discussions have taken place on the environmental policies and practices of sport organizations, there have been
very limited examinations of sport consumer behaviors in direct response to a polluted environment. To address this gap, this research
examines air pollution and attendance at soccer matches of the Chinese Super League, where deteriorating air quality in recent years
presents everyday challenges for urban activities. By employing actual air quality data gathered from various locations across China,
this study conducts a regression analysis to examine factors that impacted Chinese Super League match attendance from 2014 to
2016. The estimated results suggest that consumers did not change their consumption habits despite the presence of air pollution.
They yield critical managerial implications that need to be considered by consumers, sport organizations, and the government.
Keywords:environment, air quality index, China, attendance, soccer
Environmental issues present intricate connections with the
operations and socioeconomic consequences of sport (Babiak &
Trendafilova, 2011;Thibault, 2009). In the sport literature, a
lineage of studies have taken opportunities to explore such con-
nections by examining the environmental management perfor-
mance of sport organizations, environmental activities in sport
stakeholder relationships, and environmental tracking of event
operations, among others (Mallen, 2017;McCullough, Pfahl, &
Nguyen, 2016). Meanwhile, a number of scholars point out that to
advance the knowledge of sport and environment, more diversity is
needed in regard to the subject, theoretical framework, and meth-
odologies (Mallen, 2017). For instance, in the discussions of sport
consumers and the environment, the extant investigations primarily
focus on consumers’perceptions of sport organizations’environ-
mental policies and practices (Inoue & Kent, 2012a). Any further
attempt toward understanding consumers’actual responses toward
actual environment degradation and change is lacking. In other
words, a critical examination of the relationship between sport
market demand and the environment is necessary, considering
that certain categories of environmental degradation (e.g., air pollu-
tion) can impose health risks and costs to individual consumers, thus
affecting their willingness to attend matches and causing disruptions
to market order and sport organizations’ability to accrue resources
(King & Pearce, 2010). Indeed, considering that the market
functions to connect sport organizations and stakeholders, consumer
interest in response to polluted environments can play a powerful
role in influencing the decision making and environmental perfor-
mance of sport organizations.
The present study focuses on air pollution and attendance
at soccer matches in China’s Chinese Super League (CSL), where
deteriorating air quality in recent years presents everyday challenges
for urban activities (Ebenstein, Fan, Greenstone, He, & Zhou,
2017). In places like Beijing, for example, the average daily air
pollution between 2014 and 2016 was at the borderline of being
“unhealthy”(according to air quality index [AQI]), with more
than 40 days experiencing “severely polluted air”(Ebenstein
et al., 2017). In December 2016, Beijing was forced to close all
of its airports and highways due to the thick haze, where visibility
fell below 300 m at some points (Bacon, 2016). While air pollution
used to be heavily concentrated in north China, it pervasively
expanded across the nation (McLeod, Pu, & Newman, 2018).
Even on days when air pollution was not at its most severe degree,
there often existed dense smog blanketing the sky of many cities
(McCann, 2016). The air pollutants typically include sulfur dioxide,
carbon monoxide, carbon dioxide, nitrogen dioxide, and particulate
matter (PM
2.5
), which are complexly connected to health problems,
in particular, respiratory and cardiovascular disorders (Ebenstein
et al., 2017). Furthermore, studies pointed out even short-term air
pollution, such as the pollution that fans are exposed to during the
course of a soccer match, can be related to health hazards and may
contribute to accumulative effects in the long run (Lelieveld, Evans,
Fnais, Giannadaki, & Pozzer, 2015). Therefore, to fully investigate
the relationship between sport attendance and air pollution, the pres-
ent study constructs an attendance model analyzing CSL matches
from 2014 to 2016 through the theoretical lens of economic
Watanabe and Yan are with the Department of Sport and Entertainment Manage-
ment, University of South Carolina, Columbia, SC, USA. Soebbing is with the
Faculty of Physical Education and Recreation, University of Alberta, Edmonton,
Alberta, Canada. Fu is with the Department of Health, Exercise Science, and
Recreation Management, the University of Mississippi, University, MS, USA.
Watanabe (nmwatana@mailbox.sc.edu) is the corresponding author.
1
Journal of Sport Management, (Ahead of Print)
https://doi.org/10.1123/jsm.2018-0214
© 2019 Human Kinetics, Inc. ORIGINAL RESEARCH

demand, which specifically incorporates daily air pollution data
from metropolitan areas across China.
In so doing, this research seeks to make multiple contributions to
the literature from theoretical, methodological, and empirical perspec-
tives. Theoretically, the employment of demand theory provides an
economic lens that is rarely considered in previous examinations of
sport and the environment. That is, with the understanding that the
current investigations are frequently rooted in a few frameworks—
such as sustainability theory, institutional theory, and social exchange
theory (Mallen, 2017)—an economic approach serves to enrich the
theoretical discussions of sport and the environment. In so doing, it
emphasizes economic activities—in the form of sport fans’delibera-
tive response to pollution—as a vital component in the market process
and mechanisms that can potentially influence sport organizations’
decision making. In terms of methodological contribution, this study
takes an innovative approach by incorporating daily air quality data
collected from environmental websites that documented and moni-
tored air pollution. Such a method addresses a relative lack of actual,
real-time environmental measures to investigate sport and the envi-
ronment (Wicker, 2018b). Considering that previous examinations
primarily utilized surveys to estimate consumer perceptions, the
present research extends the analytic lens closer to sport consumers’
actual behaviors in relation to polluted conditions.
Finally, this research provides important managerial implica-
tions. Although a number of studies have emphasized the growth of
the sport marketplace in China (Liu, Zhang, & Desbordes, 2017;
Watanabe & Soebbing, 2017), the paradoxical environmental cost
and consequences are relatively unknown; these consequences are
detrimental to people’s health conditions as a result of the nation’s
fast-developing economy (McLeod et al., 2018). There is also the
observation that the very act of fans traveling to games may increase
environmental pollution (Collins, Flynn, Munday, & Roberts,
2007). Therefore, attending sport events in large numbers can
create conditions that may lead to deteriorated health conditions.
An investigation of the changes in sport consumer interest suggests
that sport enterprises take account of the impact of environmental
changes in managing market objectives and demands. More criti-
cally, considering that previous literature is largely underpinned by
the conception that economic growth and progress on environmen-
tal issues are compatible and can be mutually reinforcing (Thibault,
2009;Wilson, 2012), this study brings attention to the need of
identifying alternative market solutions and the necessity to nego-
tiate market activities of sport attendance in polluted environment
for ethical concerns. Above all, an observation of market reactions
to pollution sheds light on power relations that surround interac-
tions between sport business, government, and consumers, suggest-
ing that sport organizations need to assume more influential roles to
articulate environmental interests and push for reforms. With this in
mind, the following research question is proposed:
RQ: How does air pollution affect attendance at CSL matches?
Literature Review
Sport and the Environment
In the realm of sport research, numerous discussions take place
on the interrelations between sport and the environment from
physical, institutional, marketing, and sociological perspectives
(McCullough et al., 2016). To begin with, a lineage of studies were
developed to investigate the environmental effect produced by
sport events, in terms of energy usage, the creation of waste, and the
depletion of natural resources, as well as the production of various
forms of pollution (Thibault, 2009). For instance, research exam-
ined the carbon footprint generated by spectators (e.g., Collins
et al., 2007), teams (e.g., Chard & Mallen, 2012), and participants
(e.g., Wicker, 2018a,2018b) traveling to and taking part in sport.
Not only did these studies reveal the harmful environmental damage
of sport consumption and participation activities, but they also
pointed out that, in some cases, sport activities could be part of a
vicious cycle causing further degradation to already fragile eco-
systems (McCullough et al., 2016;Wicker, 2018b).
Meanwhile, there was a wealth of research approaching sport
and the environment from the perspective of corporate social
responsibility (Mallen, 2017). These studies were commonly situ-
ated in the premise that additional sustainable practices would
strengthen the institutional legitimacy of sport organizations and
allow them to succeed in a marketplace that increasingly requires
sensitivity to the environmental concerns of consumers (Kellison &
Hong, 2015;Trendafilova & Babiak, 2013). Strategic efforts of
sport organizations in engaging environmental policies and prac-
tices across a variety of levels constituted a natural focus of these
studies (Babiak & Trendafilova, 2011;Inoue & Kent, 2012a,
2012b). Additionally, studies critically investigated organizational
structures of sport entities and varied effects of sustainability com-
munication (e.g., Casper, Pfahl, & McSherry, 2012), power imbal-
ances of stakeholder relations in approaching sport sustainability
(e.g., Kearins & Pavlovich, 2002), and the lack of participatory and
democratic decision making in the sport authority’s planning of
sustainability (e.g., Hayes & Horne, 2011), among others.
Furthermore, a third group of studies turned attention to the
question of how environmental change could affect sport operations
and activities, especially those operations and activities that were highly
dependent on the existence of certain natural resources (e.g., Fairley,
Ruhanen, & Lovegrove, 2015;Moen & Fredman, 2007;Phillips &
Turner, 2014). The study by Moen and Fredman (2007), for instance,
analyzed the impact of global warming on the availability of snow for
individuals to participate in winter sports. Consumers’willingness to
participate in winter sports in relation to environmental change also
presented a dimension for investigation (Dawson, Scott, & Havitz,
2013;Pickering, Castley, & Burtt, 2010). Compared to these explora-
tions, empirical examinations on degraded environmental conditions
and fans’behaviors in attending sport contests were rather limited.
To address this gap, attention is placed on sport consumer
behaviors in China, where the severity and geographic scope of
air pollution exists as a curious and compelling context. As stated
by many scholars, unprecedented pollution is present in most pro-
vinces, particularly affecting metropolitan populations (Lu et al.,
2015). For instance, in January 2013, a hazardous dense haze
covered 1.4 million km
2
of China and affected more than 800
million people (Xu, Chen, & Ye, 2013). Such information provides
relevant insight to the present study, considering that CSL teams
are located in major industrial metropolitan areas. Meanwhile, the
pollution mainly originates from industry- and traffic-related com-
bustion processes (Lu et al., 2015), reflecting China’s ongoing
socioeconomic struggles and paradoxes. Although a number of
policies and measures targeted at reducing pollution emissions
have been implemented, changing the development mode of “high
growth, high pollution”remains a critical economic, social, and
political challenge (Li & Zhang, 2014). The air pollutants gener-
ated from continued growth are linked to adverse health effects,
often as causes of cardiovascular disease, respiratory irritation, and
pulmonary dysfunction (Ebenstein et al., 2017;Lelieveld et al.,
2015). The government relied on methods including moving away
from burning coal for energy, restrictions on the number of cars, and
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shutting down factories to clean Beijing’s sky for staging the Olympic
Games (McLeod et al., 2018). The “Olympic Blue”was, however,
manufactured only during large international events, whereas the
domestic CSL games are scheduled to play from March to November,
regardless of polluted conditions. It, thus, presents both a choice and
cost to sport consumers—to forgo the opportunity of attending a
match or to choose to attend and potentially place their health at risk.
Demand for Attendance
The theoretical development of demand in sport can be traced to the
seminal works of Rottenberg (1956) and Neale (1964), who noted
the importance of fan interest for sport teams, leagues, and related
stakeholders. Based on these initial studies, recent publications
(Borland & Macdonald, 2003;Villar & Guerrero, 2009) further
explored factors relevant to the modeling of demand for attendance
at sporting events. More recently, Sanderson and Shaikh (2017)
outlined links betweenthe economics of sports and the environment.
Specifically, following Borland and Macdonald (2003), five catego-
ries of determinants are emphasized as being important in modeling
demand: economic factors, the quality of viewing, the quality of the
sporting content, supply capacity, and consumer preferences.
First off, the category of economic factors accounts for costs
that can be faced by consumers, potential complements or sub-
stitutes for the sport product, and macroeconomic factors, which
include gross domestic product, employment rate, and so forth.
Notably, empirical studies typically utilize variables such as ticket
price to capture the cost for consumers to attend sporting events
(Coates & Humphreys, 2007), as well as the size and purchasing
power of a local region to control for differences between markets.
Next, quality of viewing takes into account aspects such as
weather conditions (Ge, Humphreys, & Zhou, 2017), characteristics
of the facility in which a game is being played, and the timing of a
contest, among others (Coates & Humphreys, 2005). Specifically, the
quality of the sporting contest accounts for the strength of the teams
on the field and the relative level of parity between the teams (Villar &
Guerrero, 2009). It is important to note differences between the
absolute and relative quality of a sporting contest. Absolute quality
measures the total worth of the teams through variables such as the
number of star players or total market value of a roster. Relative
quality investigates differences between the teams in measuring
things, including the uncertainty of the outcome of the match through
using betting odds (Coates, Humphreys, & Zhou, 2014). Moving
along, the category of supply capacity takes into account that
attendance at sporting events is constrained to the total number of
seats in a stadium (Borland & Macdonald, 2003). Finally, consumer
preferences, noted by Schofield (1983)asresidual preferences,are
considered an essential characteristic of sport attendance demand.
They are often investigated through measuring factors, such as the
habitual nature of sport consumption (Lee & Smith, 2008), loyalty to
a team, and interest in specific athletes (Hansen & Gauthier, 1989).
Similar to the studies of attendance demand in other types of
sport (e.g., baseball, basketball, football), researchers focusing on
soccer suggested that determinants such as team strength, quality of
viewing, and market potential are necessary in modeling demand
(Bird, 1982;Dobson & Goddard, 2011). While soccer attendance
studies are recognized as particularly influential in understanding
the economics of sport leagues and consumer behaviors (Dobson &
Goddard, 2011), these inquiries mostly center on professional
leagues in Europe, as they were the organizations that had garnered
the highest viewership, economic power, and attention from con-
sumers around the world (Cox, 2018).
More recently, scholars have engaged in analyses of soccer
attendance demand in a range of global contexts, including Brazil
(Gasparetto, Barajas, & Fernandez-Jardon, 2018), Peru (Buraimo,
Tena, & de la Piedra, 2018), the United States (Jewell, 2017;Sung
& Mills, 2018), Japan (Watanabe, 2012), South Korea (Jang & Lee,
2015), and China (Watanabe & Soebbing, 2017). Among them all,
the fast speed at which the CSL developed and expanded in recent
seasons is worth noting in the global soccer scene. According
to Yu, Newman, Xue, and Pu (2017), CSL revenue grew from
US$17.53 million in 2012 to US$223 million in 2016, while many
teams annually spent in excess of US$25 million to attract inter-
national players to enhance consumer interest. In 2016, league
attendance for all 240 matches was 5,789,135, marking it as one of
the top five most attended leagues in the world (Yu et al., 2017).
The feverish growth of the CSL market further increases the
necessity of considering fan attendance and air pollution.
Furthermore, of particular relevance to the study is the deter-
minant of environmental conditions in considering sport attendance.
In the literature, environmental conditions are mostly linked to
weather factors, which are explained in relation to the quality of
viewing and consumer preferences for attending sport contests. The
consideration of weather as a potential determinant of sport demand
first appeared in Bird’s(1982) examination of the Football League in
England. He used a dummy variable to take into account exception-
ally bad winter weather that caused disruptions to the scheduling of
matches in the 1962–1963 season. Following Bird (1982), studies
explored the dimension of weather by utilizing dummy variables to
consider the presence of certain types of weather (e.g., sunny, rain),
as well as cardinal measurements to investigate factors including
temperature and wind speed (e.g., Baimbridge, Cameron, &
Dawson, 1996;Feddersen & Rott, 2011). For example, in their
analysis of viewership of the German national team, Feddersen and
Rott (2011) included exact measures of temperature, wind speed,
and the amount of rain, while also utilizing a dummy variable to
account for the presence of being sunlight during the game. In
general, these studies revealed mixed results in regard to temperature
and rain in relation to soccer attendance (Bird, 1982;Feddersen &
Rott, 2011;García & Rodríguez, 2002).
In addition to weather conditions, how pollution may consti-
tute an important environmental factor in determining sport atten-
dance is rarely discussed. Related studies, however, systematically
disclosed that urban air pollution is associated with reduced
participation of outdoor physical activities (Li, Liu, Lü, Liang,
& Harmer, 2015;Matus et al., 2012), tourism activities (Zhang,
Zhong, Xu, Wang, & Dang, 2015), and purchasing behaviors
(Li, Moul, & Zhang, 2017). These established findings also urge
scholars and practitioners to consider examining the relationship
between air pollution and sport attendance, considering that such
investigations can further illuminate the relationships of environ-
ment, consumer behaviors, and social norm.
Methods
Estimation
Following prior theoretical examinations of attendance demand in
sport, a function was developed to examine the research question.
Specifically, the function took the form of
Attendance =fðQijk,Mijk ,Tijk ,Sijk ,Wijk ,Pijk Þ:(1)
In this function, Qis a vector of team performance in a match,
Maccounts for the market potential of the city each team plays in,
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Tcontrols for the effects of the timing and scheduling of each match,
Srepresents stadium characteristics, Wsignifies the weather at the time
of each match, and finally, Pis an indicator for the pollution level in
each city. Due to the panel nature of the data set, i,j,andkindex the
home team, match, and season, respectively, considering the repeating
observations of teams playing throughout a season.
Data
The Chinese government first made daily air quality data in 2014.
For this study, data were collected at the match level for the 2014,
2015, and 2016 CSL seasons. The dependent variable, match
attendance, was collected for 718 out of 720 available matches
played over these three seasons. Two matches were removed from
the data set because these games were played in empty stadiums as
punishment for teams violating league rules. The attendance
numbers come from three sources: the box scores on the CSL web-
site, the transfermarkt.de website, and media reports. Afterward,
each observation was cross-checked to ensure accuracy (Table 1).
As revealed from the summary statistics in Table 2, there was an
average of 21,725 spectators per game, or about 15.5 million total
attendees over the 718 matches.
Table 1 Variable Descriptions
Variable Measure
Attendance Number of attendees at a match
HomeWPCT Win percent of home team
AwayWPCT Win percent of away team
Population Population of city
Income Average income of residents in the city
Rivalry Match played against rivals (1 = yes)
Weekend Match held on weekdays (1 = yes)
Holiday Match held on holidays (1 = yes)
March Match held in March (1 = yes)
April Match held in April (1 = yes)
May Match held in May (1 = yes)
June Match held in June (1 = yes)
July Match held in July (1 = yes)
August Match held in August (1 = yes)
September Match held in September (1 = yes)
October Match held in October (1 = yes)
November Match held in November (1 = yes). Reference group.
StadiumAge Age of stadium (in years)
StadiumAge
2
Stadium age squared
Capacity Number of seats in stadium
AvgTemp Average temperature on a match day (in degrees Celsius)
WindSpeed Wind speed on a match day (in meters per second)
Clear Clear skies on a match day (1 = yes)
Rain Presence of rain on a match day (1 = yes)
Snow Presence of snow on a match day (1 = yes)
Air Quality Index Air Quality Index for the city on a match day
Air Quality Index AdjSeven = daily Air Quality Index −average Air Quality Index for the previous 7 days
Air Quality Index AdjThirty = daily Air Quality Index −average Air Quality Index for the previous 30 days
Air Quality Index AdjYear = daily Air Quality Index −average Air Quality Index for the previous 365 days
Ministry Rating Air quality rating released by the Ministry of Environmental Protection
Ministry Rating AdjSeven = daily Ministry Rating −average Ministry Rating for the previous 7 days
Ministry Rating AdjThirty = daily Ministry Rating −average Ministry Rating for the previous 30 days
Ministry Rating AdjYear = daily Ministry Rating −average Ministry Rating for the previous 365 days
Scientific Rating Air quality rating based on scientific scale
Scientific Rating AdjSeven = daily Scientific Rating −average Scientific Rating for the previous 7 days
Scientific Rating AdjThirty = daily Scientific Rating −average Scientific Rating for the previous 30 days
Scientific Rating AdjYear = daily Scientific Rating −average Scientific Rating for the previous 365 days
2014 Match is held in the year 2014 (yes = 1). Reference group.
2015 Match is held in the year 2015 (yes = 1)
2016 Match is held in the year 2016 (yes = 1)
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Moving to the independent variables, team performance was
measured by utilizing the lagged win percentage for both the home
(HomeWPCT) and away (AwayWPCT) teams.
1
These variables
were included, considering prior work looking at CSL attendance
(Watanabe & Soebbing, 2017), to show that the quality of both the
home and away team were important in determining attendance.
Next, market characteristics were represented by population
(Population), per capita income (Income), and rivalry matches
(Rivalry). Population and income data were collected from the
National Bureau of Statistics’“China Statistical Yearbook”pub-
lished online. In the yearbook, the population and income level
were reported for the “built-up area”in each city, which included
the city as well as the suburban region surrounding it. Meanwhile,
income was reported in Chinese yuan and adjusted for inflation
to 2017 levels.
2
Additionally, the Rivalry dummy variable was
created to measure the presence of games between local teams and
traditional rivals.
In considering the timing of matches, dummy variables were
employed to control for whether matches were played on weekends
(Weekend)orofficial national holidays (Holiday). Variables
accounting for the months in which CSL matches were played
(March,April,May,June,July,August,September,October,
and November) were also included. Next, as previous literature
revealed that attendance demand was impacted by the character-
istics of a stadium (Borland & Macdonald, 2003;Watanabe &
Soebbing, 2017), variables measuring the age of stadiums in years
(StadiumAge), the square of the stadium’sage(StadiumAge
2
), and
the total capacity (Capacity) for each stadium that a match was
played in were included.
For weather-related factors, data were collected from the
Chinese Meteorological Administration. Specifically, the study
gathered daily average temperature (AvgTemp) in degrees Celsius
and wind speed (WindSpeed) in meters per second. Moreover,
dummy variables were created to consider if there was the presence
of a clear sky (Clear), rain (Rain), or snow (Snow). Finally, to
specifically account for the pollution levels in each city, data
for three environmental variables were generated from the website
of the Chinese Ministry of Environmental Protection (MEP).
To control for the actual level of pollutants in the air, the daily
average AQI data for each city were gathered. AQI is calculated by
measuring the concentration of pollution in the form of fine PM
(PM
2.5
and PM
103
) that is present within a cubic meter of air, which
is then scaled between 0 and 500 (Ebenstein et al., 2017).
4
Specifically, the pollutants measured in the AQI metric are pollu-
tants that are regulated by the Clean Air Act, as set out by the U.S.
government’s Environmental Protection Agency. As such, the AQI
measure represented the average amount of pollution in a day, with
increasing numbers representing higher concentrations of pollution
in the air. To check the reliability of the MEP data, it was also cross-
checked against a larger database of hourly pollution data for cities
across China, gathered and published by Harvard University
(Harvard Dataverse, 2018).
In addition to the actual air quality, the rating system generated
by the MEP (Ministry Rating) was gathered to analyze whether fans
were responsive to ratings that were published via the media.
Notably, the Ministry Rating ranged from 1 to 5, with 5represent-
ing the most hazardous of air quality conditions. The data were
displayed daily in China’s major media channels and phone apps to
provide the public with the forecasted level of air pollution for the
day. Finally, data were collected for the daily scientific rating
system (Scientific Rating), which presented the international stan-
dard for warning the public about air quality. The measure is a scale
from 1 to 6, with 6being the worst level of air pollution. Altogether,
data were collected from these different sources to ensure the
validity of actual pollution levels.
The present study also took into consideration the possibility
that individuals can become acclimatized after being exposed to air
pollution for some time. For instance, in a week that is heavily
polluted, a soccer fan may choose to go to a game on a day when
it is relatively less polluted and more visible, despite facing high
levels of pollution. As such, AQI,Ministry Rating, and Scientific
Rating were all adjusted using the weekly, monthly, and yearly
averages for each city. This adjustment was done by subtracting the
average measure of pollution from the base version of the variable,
as represented in the following equation:
Adjusted pollution =pollution −average pollution:(2)
Subsequently, three additional variables were generated to repre-
sent the adjusted level for each pollution metric, including the AQI
(Air Quality Index AdjSeven,Air Quality Index AdjThirty, and Air
Quality Index AdjYear), media rating (Ministry Rating AdjSeven,
Ministry Rating AdjThirty, and Ministry Rating AdjYear), and the
scientific rating (Scientific Rating AdjSeven,Scientific Rating
AdjThirty, and Scientific Rating AdjYear). For the adjusted pollu-
tion variables, a zero indicated that the pollution level had no
difference between the current day’s pollution and the average
pollution. Furthermore, a negative value meant a lower pollution
level for the day of the match than average, whereas a positive
value indicated a higher pollution level than average for that city.
Finally, a dummy variable was included for each season to account
for any difference that may exist between each season. Based on
these, 12 regression models were estimated in this study.
5
To be
sure, each model interchanged the abovementioned pollution
variables, while keeping all of the other variables constant, to
estimate whether consumers were responsive to the pollution level
provided by different metrics.
Modeling and Econometrics
Before estimating the results for the model, the dependent variable of
Attendance was analyzed to examine whether it was normally
distributed (Gujarati, 2003). Along these lines, Attendance was
plotted out in a histogram in STATA15 (StataCorp LLC, College
Station, TX) and was found to be normally distributed. Additionally,
the results from a Shapiro-Wilk’stestwasinsignificant (p=.34),
indicating that the null hypothesis that the data were normally
distributed could not be rejected. Following this check, the data were
also tested for heteroscedasticity, endogeneity, and multicollinearity.
Although the statistical tests did not find any evidence of endogeneity
or multicollinearity, the Breusch–Pagan test was statistically signifi-
cant (p<.001), suggesting the presence of heteroscedasticity. When
there is heteroscedasticity, one potential correction can come through
using SEs clustered by team, as they are heteroscedastic consistent
(Stock & Watson, 2008). Team clustered SEsarealsobeneficial
econometrically because they account for the possibility that SEscan
be correlated over time for individual teams (Huang & Humphreys,
2012). As such, the models in this research utilized clustered SEs
by team.
Finally, considering the panel nature of the data set, models
were estimated using both fixed and random effects. To do so,
a Hausman test was calculated to examine whether the coeffi-
cients between the two models were systematically different
(Gujarati, 2003). The results of the test were statistically significant
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(Prob >chi2 = 0.03), meaning that fixed effects were suitable for
estimating the model. Therefore, the final results for this study were
estimated using a panel regression with fixed effects and clustered
SEs.
6
Taken collectively, the following equation was developed to
calculate the results:
Attendanceit =θjþβ1HomeWPCTijt þβ2AwayWPCTijt
þβ3Populationijt þβ4Incomeijt þβ5Derbyijt
þβ6Weekendijt þβ7Holidayijt þXβmMonths
þβ8StadiumAgeijt þβ9StadiumAgeSqijt þβ10Capacityijt
þβ11AvgTempijt þβ12 Windijt þβ13 Clearijt
þβ14Rainijt þβ15 Snowijt þβ16Pollutionijt þμijt :(3)
In this expanded model, θ
j
was the team effect, iindexed teams, j
represented weeks, trepresented seasons, and μ
ijt
was the error term
for the equation.
Results
The results for all 12 models are listed in Tables 3–5. Table 3
contains the results from the models, using the AQI metric. Table 4
displays the results for Ministry Rating, whereas Table 5reports the
findings for the regressions estimated with the Scientific Rating
variables. The R
2
values for the models range from .5837 to .5842,
indicating that the respective models explain about 58% of the
variation in the dependent variable.
In terms of the results, all of the models have returned similar
results and significance levels for most independent variables,
suggesting robustness of the models. First, it is revealed that the
base level of air pollution as measured by the AQI is insignificant in
relation to attendance. This finding means that CSL fans were not
sensitive tothe level of pollution in the air. As previously mentioned,
models were run utilizing variables adjusted for the average pollu-
tion level in each city. However, the weekly, monthly, and yearly
adjusted variables are all insignificant, indicating that fans did not
change their patterns of attending games based on the adjusted
pollution for each city,either. The sameinsignificance is found in the
models using the Ministry Rating metric, suggesting Chinese soccer
fans were not responsive to the level of pollution revealed by the
rating systems published by the media. Finally, four models using
the scientific rating system as well as the three adjusted variables also
returned insignificant results. Overall, the findings repeatedly con-
firmed a lack of reaction to polluted air from CSL fans.
Second, focusing on the team performance and market variables,
the home team and away team win percentages are both positive and
significant at the 1% level, indicating that the quality of both teams in
amatchhaveasignificant influence on fan attendance. Likewise,
having a higher population and income level in a city is positively
associated with increased attendance at matches. At the same time,
results for the Rivalry variable reveal that matches between rivals
have a slight positive impact on consumer interest, with such games
having about 1,000 more attendees. From this, the findings suggest
that both the quality of the on-field product and market potential are
important determinants of demand for Chinese soccer.
Third, moving to the variables controlling for the timing of a
match, the Weekend,Holiday, and month dummy variables are
statistically insignificant in all models. This finding indicates that
CSL attendance is not influenced by the timing of matches. These
results run counter to previous research of CSL attendance
(Watanabe & Soebbing, 2017), which estimated higher fan interest
during certain months of the year. As these two studies utilize
Table 2 Summary Statistics
Variable Mean SD Min Max
Attendance 21,725 11,875 1,368 53,526
HomeWPCT 0.462 0.194 0 1
AwayWPCT 0.448 0.195 0 1
Population 1,008 593 213 3,372
Income 94,947 95,499 42,896 544,441
Rivalry 0.102 0.466 0 10
Weekend 0.770 0.421 0 1
Holiday 0.075 0.264 0 1
March 0.095 0.293 0 1
April 0.156 0.363 0 1
May 0.157 0.364 0 1
June 0.099 0.299 0 1
July 0.144 0.351 0 1
August 0.123 0.328 0 1
September 0.105 0.306 0 1
October 0.111 0.315 0 1
November 0.011 0.105 0 1
StadiumAge 21.34 18.89 2 67
StadiumAge
2
812 1,361 4 4,489
Capacity 43,771 14,971 18,000 66,161
AvgTemp 20.98 6.82 −3 34.50
WindSpeed 2.83 0.907 0 5
Clear 0.237 0.426 0 1
Rain 0.371 0.486 0 2
Snow 0.003 0.053 0 1
Air Quality Index 85.45 40.38 23 344
Air Quality Index
AdjSeven
−1.01 31.00 −122 193
Air Quality Index
AdjThirty
−1.80 35.18 −124 226
Air Quality Index
AdjYear
−9.89 36.28 −93.19 221
Ministry Rating 2.19 0.828 1 5
Ministry Rating
AdjSeven
−0.028 0.645 −2 2.43
Ministry Rating
AdjThirty
−0.039 0.729 −2.2 2.87
Ministry Rating
AdjYear
−0.175 0.754 −1.95 3.12
Scientific Rating 2.19 0.834 1 6
Scientific Rating
AdjSeven
−0.028 0.651 −2 2.86
Scientific Rating
AdjThirty
−0.039 0.734 −2.33 3.33
Scientific Rating
AdjYear
−0.184 0.759 −2.01 3.14
2014 0.334 0.472 0 1
2015 0.332 0.471 0 1
2016 0.334 0.472 0 1
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different timeframes, the difference in results may be due to
changes in the scheduling of CSL matches. Fourth, in regard to
stadium characteristics, stadium age is positive and significant,
whereas its square term is negative and significant. Curiously, these
results contradicted findings from most studies of North American
professional sport demand, where newer stadiums tended to have
higher attendance numbers (Coates & Humphreys, 2005). How-
ever, they echo previous studies on CSL demand (Watanabe &
Soebbing, 2015,2017), which discovered increasing attendance
associated with stadiums aging, but this effect decreased over time.
Additionally, capacity displays a positive impact on attendance,
which is an expected result, as stadiums with a larger supply of
seats are able to admit more consumers for matches. Fifth, in
examining the results for weather-specific variables, average tem-
perature, wind speed, clear skies, and snow are all insignificant in
relation to attendance. On the other hand, rain is estimated to have a
negative relationship with attendance, which decreases attendance
on average by about 2,400 people. Finally, the dummy variables
accounting for differences between seasons find positive and
significant results for 2015 and 2016, suggesting an upward
attendance trend in the CSL.
In order to perform further robustness checks on the results
from this research, an additional analysis was conducted on the
pollution data for this study. To begin with, one potential concern
that existed was a relationship between the air pollution and the
quality of the games—that high levels of air pollution could affect
player performance and thus impact fan interest. Ideally, to conduct
such an inquiry, performance data, such as running distance, shots
taken, or attacking opportunities, would be utilized, as seen in
previous studies that examined environmental conditions and the
quality of soccer matches (e.g., Watanabe, Wicker, & Yan, 2017).
Within our search, however, no such data for the CSL during the
sample period covered could be located in any public forum
(Chinese or English). Therefore, betting odds and total goals scored
Table 3 Air Quality Index Regression Results
Model 1—Air
Quality Index
Model 2—Weekly
adjusted Air
Quality Index
Model 3—Monthly
adjusted Air
Quality Index
Model 4—Yearly
adjusted Air
Quality Index
Variable Coefficient SE Coefficient SE Coefficient SE Coefficient SE
HomeWPCT 14,647*** 1,944 14,622*** 1,944 14,649*** 1,946 14,677*** 1,943
AwayWPCT 10,968*** 1,599 10,907*** 1,598 10,946*** 1,601 10,888*** 1,597
Population 3.43*** 0.541 3.41*** 0.540 3.42*** 0.541 3.40*** 0.540
Income 0.008* 0.004 0.008** 0.004 0.008** 0.004 0.007** 0.004
Rivalry 1,089* 636 1,090* 635 1,088* 636 1,093* 635
Weekend 1,026 727 1,003 727 1,019 727 1,008 726
Holiday −806 1,156 −828 1,155 −817 1,157 −865 1,156
March 1,279 3,002 1,629 3,008 1,374 3,015 1,744 3,012
April 689 2,957 895 2,961 740 2,961 908 2,957
May 969 3,016 1,100 3,017 1,001 3,019 1,055 3,013
June 1,199 3,191 1,294 3,191 1,225 3,193 1,245 3,188
July 1,172 3,163 1,272 3,165 1,189 3,166 1,165 3,161
August 1,252 3,142 1,360 3,145 1,258 3,143 1,209 3,140
September −1,365 3,079 −1,196 3,086 −1,338 3,089 −1,324 3,077
October −1,159 3,007 −948 3,014 −1,109 3,020 −958 3,007
StadiumAge 757*** 92.30 758*** 92.29 757*** 92.32 755*** 92.25
StadiumAge
2
−9.38*** 1.26 −9.40*** 1.26 −9.38*** 1.26 −9.35*** 1.26
Capacity 0.354** 0.023 0.355*** 0.023 0.355*** 0.023 0.355*** 0.023
AvgTemp −85.25 71.05 −76.66 71.33 −82.80 72.16 −70.30 72.11
WindSpeed −230 362 −238 361 −235 361 −230 361
Clear 116 780 149 780 125 780 157 780
Rain −2,015*** 697 −2,087*** 687 −2,046*** 690 −2,112*** 688
Snow 5,404 5,683 5,698 5,673 5,513 5,683 5,942 5,686
Air Quality Index 1.48 7.88 ––––––
Air Quality Index AdjSeven ––−6.89 9.87 ––––
Air Quality Index AdjThirty ––––−0.59 8.88 ––
Air Quality Index AdjYear ––––––−8.05 8.82
2015 2,218*** 774 2,268*** 774 2,230*** 774 2,290*** 775
2016 3,754*** 774 3,763*** 773 3,746*** 772 3,789*** 774
Constant −14,777*** 3,667 −14,955*** 3,661 −14,725*** 3,667 −15,065*** 3,663
R
2
.5842 .5837 .5838 .5837
*p<.10. **p<.05. ***p<.01.
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for each CSL match were analyzed instead, considering that these
measures also indicated quality of games (Coates et al., 2014).
Specifically, correlations were run between the outcome uncer-
tainty of each match as measured through betting odds, total goals
scored, and all of the pollution metrics included in the previous
models. The correlation matrix (Table 6) displayed no large
correlations between any of the game performance metrics (out-
come uncertainty or total goals scored) and pollution. As a final
step, we estimated several regressions with the team performance
metrics as the dependent variable, and pollution, team quality,
weather, and timing, as well as other factors, included in the model.
In all of these regressions, no significant relationship between team
performance and pollution was found. As such, these results
highlighted the lack of effect from pollution on both the quality
of CSL matches and attendance.
Finally, whether individual instances of pollution hitting
extreme levels could impact attendance at individual games
presented an interesting question. To consider this factor, the
matches with the highest and lowest pollution levels for each
CSL franchise that played in each season from 2014 to 2016
were analyzed (Table 7). It was found that many teams, such as
Beijing Guoan, Changchun Yatai, Shandong Luneng, and Tianjin
Teda, all had higher attendance on days where the highest levels
of pollution were experienced, in comparison to days with the
lowest level of pollution. Although there were certainly other
factors at play, such as team quality, rivalries, and opponents,
many of these games took place in polluted conditions, with
AQI near 300 and the MEP rating at 5.
7
Conversely, a few other
teams (e.g., Hebei CFFC, Hangzhou Greentown, Shanghai SIPG)
experienced higher attendance when the pollution levels were
lower. Thus, in the final level of analysis, a ttest was calculated
to determine if there was a statistical difference in attendance for
the days with the highest and lowest levels of pollution for each
team in the CSL. The result was insignificant (p= .44), thus further
Table 4 Rating MEP Regression Results
Model 5—Rating
MEP
Model 6—Weekly
adjusted rating MEP
Model 7—Monthly
adjusted rating MEP
Model 8—Yearly
adjusted rating MEP
Variables Coefficient SE Coefficient SE Coefficient SE Coefficient SE
HomeWPCT 14,651*** 1,944 14,634*** 1,944 14,679*** 1,946 14,654*** 1,944
AwayWPCT 10,969*** 1,598 10,924*** 1,598 10,982*** 1,600 10,926*** 1,598
Population 3.43*** 0.541 3.41*** 0.540 3.43*** 0.542 3.41*** 0.541
Income 0.008** 0.004 0.008** 0.003 0.008** 0.004 0.007** 0.004
Rivalry 1,088* 636 1,090* 636 1,087* 636 1,092* 636
Weekend 1,026 727 1,011 726 1,027 727 1,016 726
Holiday −804 1,156 −824 1,155 −792 1,158 −838 1,157
March 1,279 2,996 1,495 2,997 1,242 3,004 1,497 3,004
April 690 2,955 809 2,956 675 2,957 792 2,956
May 969 3,016 1,041 3,016 953 3,017 1,015 3,015
June 1,199 3,191 1,262 3,191 1,189 3,191 1,230 3,190
July 1,180 3,163 1,211 3,163 1,160 3,164 1,160 3,163
August 1,270 3,143 1,267 3,141 1,255 3,142 1,201 3,144
September −1,358 3,079 −1,285 3,082 −1,403 3,083 −1,356 3,078
October −1,153 3,005 −1,043 3,008 −1,198 3,013 −1,076 3,006
StadiumAge 756*** 92.31 759*** 92.38 756*** 92.38 757*** 92.30
StadiumAge
2
−9.38*** 1.26 −9.40*** 1.26 −9.37*** 1.26 −9.38*** 1.26
Capacity 0.354*** 0.023 0.355*** 0.023 0.355*** 0.023 0.355*** 0.023
AvgTemp −85.78 71.19 −78.62 71.39 −87.94 72.15 −77.78 72.11
WindSpeed −229 362 −238 361 −233 361 −234 361
Clear 117 780 135 779 119 779 133 779
Rain −2,010*** 697 −2,077*** 688 −2,012*** 691 −2,078*** 690
Snow 5,389 5,683 5,638 5,675 5,367 5,682 5,684 5,687
Ministry Rating 84.81 383 ––––––
Ministry Rating AdjSeven ––−231 471 ––––
Ministry Rating AdjThirty ––––120 429 ––
Ministry Rating AdjYear ––––––−174 423
2015 2,221*** 773 2,251*** 774 2,213*** 774 2,251*** 775
2016 3,757*** 774 3,755*** 773 3,746*** 772 3,760*** 773
Constant −14,835*** 3,696 −14,830*** 3,653 −14,604*** 3,659 −14,836*** 3,659
R
2
.5837 .5838 .5837 .5837
Note. MEP = Ministry of Environmental Protection.
*p<.10. **p<.05. ***p<.01.
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reinforcing the fact that pollution did not play an important role in
CSL match attendance.
Discussion and Conclusions
While environmental conditions such as weather have long been
theorized as a determinant for sport attendance (Borland &
Macdonald, 2003), limited economic investigations have explored
the relationship between the degradation of environmental condi-
tions and sport consumer interest (Sanderson & Shaikh, 2017). The
result that CSL fans were not affected by air pollution is rather
surprising, as it contradicts the premise largely surrounding sport
and the environment studies, that a degraded environment would
cast a negative impact on sport attendance and participation (Chard
& Mallen, 2012;McCullough et al., 2016). The ensuing discussion
seeks to explain our finding, followed by deliberations on ethical
concerns and managerial implications.
To interpret the result, one must consider a scenario where
CSL fans developed a habitual consumption pattern (Lee & Smith,
2008). The concept of habitual consumption is based on the
observation that individuals do not necessarily have the time
and resources to constantly perceive, evaluate, and act with respect
to every aspect of life (Khare & Inman, 2006). Therefore, they
make consumption decisions largely based on habitual patterns
(Lee & Smith, 2008). In this case, it is possible that China’s sport
fans may have been conditioned to air pollution as a part of
everyday life for a long time, thus, forgoing health-related ratio-
nales and warnings when viewing CSL games. The likelihood that
a large number of CSL fans were relatively young, and therefore,
less vigilant about the issues of air pollution and health must also be
recognized.
Table 5 Scientific Rating Regression Results
Model 9—Scientific
Rating
Model 10—Weekly
adjusted
Scientific Rating
Model 11—Monthly
adjusted
Scientific Rating
Model 12—Yearly
adjusted
Scientific Rating
Variables Coefficient SE Coefficient SE Coefficient SE Coefficient SE
HomeWPCT 14,651*** 1,944 14,634*** 1,944 14,681*** 1,946 14,653*** 1,944
AwayWPCT 10,970*** 1,598 10,924*** 1,598 10,983*** 1,600 10,921*** 1,598
Population 3.43*** 0.541 3.41*** 0.540 3.43*** 0.542 3.41*** 0.541
Income 0.008** 0.004 0.008** 0.004 0.008** 0.004 0.007* 0.004
Rivalry 1,088* 636 1,089* 636 1,087* 636 1,092* 636
Weekend 1,026 727 1,012 726 1,026 727 1,016 726
Holiday −804 1,156 −824 1,155 −792 1,158 −842 1,157
March 1,276 2,996 1,494 2,997 1,240 3,004 1,522 3,004
April 689 2,955 810 2,957 673 2,957 802 2,956
May 968 3,016 1,042 3,016 951 3,017 1,017 3,014
June 1,198 3,191 1,263 3,191 1,188 3,191 1,229 3,190
July 1,180 3,163 1,213 3,163 1,159 3,164 1,154 3,163
August 1,270 3,143 1,269 3,142 1,254 3,142 1,191 3,144
September −1,358 3,079 −1,284 3,082 −1,405 3,084 −1,358 3,078
October −1,155 3,005 −1,041 3,008 −1,202 3,014 −1,065 3,006
StadiumAge 756*** 92.31 759*** 92.38 756*** 92.38 757*** 92.29
StadiumAge
2
−9.38*** 1.26 −9.40*** 1.26 −9.37*** 1.26 −9.38*** 1.26
Capacity 0.354*** 0.023 0.355*** 0.023 0.355*** 0.023 0.355*** 0.023
AvgTemp −85.85 71.18 −78.66 71.41 −88.05 72.19 −76.64 72.16
WindSpeed −229 362 −238 361 −233 361 −233 361
Clear 117 780 134 779 120 779 133 779
Rain −2,008*** 697 −2,077*** 688 −2,012*** 691 −2,084*** 690
Snow 5,385 5,682 5,636 5,675 5,366 5,682 5,717 5,687
Scientific Rating 88.34 380 ––––––
Scientific Rating AdjSeven ––−225 467 ––––
Scientific Rating AdjThirty ––––121 426 ––
Scientific Rating AdjYear ––––––−202 420
2014 ––––––––
2015 2,220*** 773 2,252*** 774 2,212*** 774 2,255*** 775
2016 3,757*** 774 3,756*** 773 3,746*** 773 3,763*** 773
Constant −14,840*** 3,695 −14,833*** 3,654 −14,600*** 3,659 −14,863*** 3,659
R
2
.5837 .5838 .5837 .5838
*p<.10. **p<.05. ***p<.01.
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Meanwhile, the potential health damage that can result from
such habitual consumption cannot be understated. According to
Ebenstein et al. (2017), air pollution costs some people in certain
areas of China 3 years of life. If no appropriate measure is estab-
lished, air pollution can take away 3.7 billion total years of life
based on the current population levels. In particular, for vulnerable
populations of soccer fans such as those with asthma or other health
conditions, attending CSL games may facilitate growing disparities
of health among different groups of sport consumers. One possi-
bility to avoid air pollution would be to consider the con-
struction or transformation of current stadiums to domed facilities
to try and reduce contact with air pollution. The simple retrofit
involves putting roofs on current open-air facilities, along with
proper systems to have air conditioning and filtration to reduce
pollutants. However, these are costly endeavors (estimates of at
least $200 million USD
8
) for teams, and cities may not want to
contribute to these costs. As the California wildfires in November
2018 illustrate, newer indoor state-of-the-art arenas are not neces-
sarily able to keep pollution from entering a facility.
9
Therefore, it is necessary and critical for sport managers,
scholars, and a wide range of stakeholders to initiate systematic
efforts toward developing habit change among sport consumers.
To do so, the Chinese fans’habitual consumption of CSL games
first needs to be understood in relation to larger sociopolitical
forces surrounding China’s economic growth and concomitant
environmental crisis. Although China’s political discourse and
civil life are certainly concerned with pollution and its mounting
impact on health, there is also a fear that too much disclosure can
generate antagonism toward the government and destabilize
China’s economic growth (Tilt & Xiao, 2010). As revealed by
a journalist from the South China Morning Post, most Chinese
cities hid vital pollution data from the public, which, instead,
emphasized the need for socioeconomic life to continue to
move forward (Chen, 2013). As such, a preoccupation with
economic priority in China’s political sphere presents significant
challenges to China’s sport organizations to fully engage in
environmental reform.
Precisely because there lacks governmental forces that alert
and protect civic and consumption activities from environmental
degradation, it is critical for sport organizations to take an active
role that negotiates power relations that underlie interactions
between sport industry, government, consumers, and citizens.
As noted by Moore (2015), “humans make environments and
environments make humans —and human organization”(p. 3).
In the case of the CSL, establishing league-wide policies and
measures that take air pollution and consumer health into consid-
eration is vital for the functioning of the CSL, as well as the support
from wider publics.
First, from the perspective of behaviors of market participants,
the likelihood that the uncritical and convenient following of the
current market routines can lead to problematic consequences in the
future needs to be comprehended. When there is growing awareness
and activism toward the environment and health in sport, the CSL
market can become vulnerable and disrupted (King & Pearce, 2010).
As previously discussed, there are already debates on participating
in outdoor physical activities in polluted air (Li et al., 2015). Even
when consumer awareness does surge and force CSL teams to make
changes through economic mechanisms, such awareness can be
unevenly distributed among consumers based on factors such as age,
gender, and region, thus making the results of health protection and
environmental change ambivalent.
Second, while there is perhaps no immediate economic incen-
tive, the league also needs to take into consideration that sustainable
behaviors require measures beyond merely market centric decisions
(Inoue & Kent, 2012b). That is, to embrace a relational ontology by
self-positioning the league in an intricate web across the mounting
pollution problems, sport business, and a healthy future for humanity
reflects the essential philosophy of corporate social responsibility.
The recent guideline issued by the Korea Football Association can be
illuminating, as it specifies that if the level of fine dust in the air
Table 6 Correlation Matrix for Team Performance and Pollution
HomeGoals AwayGoals TotalGoals Uncertainty
HomeGoals 1
AwayGoals .021 1
TotalGoals .754* .673* 1
Uncertainty .330* −.302* .046 1
Air Quality Index −.011 −.061 −.048 .047
Air Quality Index AdjSeven −.025 −.055 −.055 .033
Air Quality Index AdjThirty −.004 −.064 −.045 .037
Air Quality Index AdjYear .016 −.063 −.029 .065
Ministry Rating −.025 −.073 −.067 .039
Ministry Rating AdjSeven −.032 −.078 −.075 .017
Ministry Rating AdjThirty −.016 −.078 −.063 .031
Ministry Rating AdjYear −.001 −.077 −.051 .056
Scientific Rating −.025 −.074 −.067 .040
Scientific Rating AdjSeven −.030 −.080 −.074 .020
Scientific Rating AdjThirty −.015 −.079 −.063 .032
Scientific Rating AdjYear .001 −.078 −.051 .058
Note. Uncertainty is measured by the differential of the home team and away team win probabilities as measured
through betting odds.
*Significance at p<.05.
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exceeds 300 μg per cubic meter for more than two consecutive
hours, then soccer games can be canceled (Roh, 2018). While such
a measure means the markets of attendance and sponsorship will
be impacted, it assists sport consumers to develop their understand-
ing and willingness to engage in environmental reform. Based on
that, it would be prudent to work with environmental scientists and
health professionals to develop a similar policy for the CSL. Specific
real-time AQI measures need to be considered in the creation and
implementation of such a policy, as it takes into account the six
common air pollutants and can serve as a critical indicator
(Ebenstein et al., 2017). From there, these efforts can be transformed
into a wider part of the economic and political mechanisms that
affect China’s environmental restructuring in the long term.
Meanwhile, it is necessary for the league to consider identifying
alternative markets, shifting focus from live attendance to television
viewership and online streaming. The broadcasting rights of CSL,
which were traditionally dominated by China Central Television,
are now controlled by Suning Commerce Group, a corporation that
owns both the Jiangsu Suning (CSL) and Inter Milan (Li & Jourdan,
2017) football clubs. Additionally, the large amounts of investment
recently announced by Suning Sports and Alibaba to develop a digi-
tal broadcasting ecosystem (Li & Jourdan, 2017) seem to indicate
growing markets for both forms of viewership in China. In the
future, it is suggested that these segments of the market need to be
considered a priority by the CSL, especially as there may not be an
instant method to solve the complex problem of deteriorating air
quality in China.
On a final note, there are a few limitations within this study
that must be addressed. The first issue which may exist is in the
accuracy of the pollution data that are reported for each day on
the MEP’s website. Even though the measurements of AQI were
cross-checked using data collected from air pollution monitors set
up by research universities from around the world, it is possible that
inaccuracy still exists. Another potential limitation is the lack of
demographic data from those attending the CSL matches. That is,
as individuals of different demographic groups may respond to
pollution in different ways, it could be that certain populations
would exhibit different reactions than others when there are higher
pollution levels. However, because there is no such data available
on the entire composition of attendees at CSL matches, this
information could not be utilized for analysis. This limitation
suggests that future studies consider methods such as surveying
and interviewing individuals to enrich the understanding of pollu-
tion and sport attendance.
Table 7 Highest and Lowest Air Pollution Days and Attendance for CSL Teams
Date Home Visiting team Attendance
Air Quality
Index
Ministry
Rating
Scientific
Rating
October 17, 2015 Guoan RF 38,710 344 5 6
October 22, 2016 Guoan RF 33,145 38 1 1
October 25, 2015 Yatai SIPG 9,621 289 5 5
September 16, 2016 Yatai RF 7,463 31 1 1
September 25, 2016 RF Jianye 7,803 201 5 5
July 2, 2016 RF Shijiazhuang 8,955 32 1 1
April 1, 2016 Evergrande RF 46,993 129 3 3
May 15, 2015 Evergrande SIPG 48,637 35 1 1
March 15, 2014 Renhe Teda 18,011 99 2 2
November 2, 2014 Renhe Harbin 5,611 29 1 1
March 6, 2016 Greentown Yatai 11,273 217 5 5
October 23, 2016 Greentown Shenhua 12,685 42 1 1
April 12, 2014 Luneng Greentown 20,166 189 4 4
July 20, 2016 Luneng Greentown 16,372 37 1 1
March 5, 2016 Sainty Shandong 48,795 195 4 4
July 11, 2015 Sainty Shandong 23,654 29 1 1
October 25, 2015 Liaoning Shenhua 13,219 166 4 4
September 12, 2015 Liaoning Guoan 15,379 37 1 1
July 10, 2016 Hebei RF 15,623 103 3 3
April 2, 2016 Hebei Sainty 24,368 41 1 1
April 19, 2015 SIPG Lifan 18,008 155 4 4
October 22, 2016 SIPG Shandong 26,680 29 1 1
May 21, 2016 Liaoning Evergrande 32,478 130 3 3
October 23, 2016 Liaoning Yatai 11,023 49 1 1
March 23, 2014 Teda Luneng 17,080 218 5 5
August 13, 2014 Teda Sainty 15,768 48 1 1
March 22, 2015 Jianye Lifan 19,528 288 5 5
October 23, 2016 Jianye Shijiazhuang 15,533 52 2 2
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Notes
1
While these measures of absolute team quality were used, uncertainty of
outcome was also tested in the models, but caused no significant change in
the results.
2
In 2017, the average exchange rate was US$1 and was approximately
equal to 6.7 yuan or US$0.14 per yuan.
3
PM
2.5
and PM
10
refer to the size of air particles, with PM
2.5
representing
matter with a diameter of 2.5 μm (microns) or less, and PM
10
representing
matter with a diameter of 10 μm or less.
4
Theoretically, it is possible to have AQI values higher than 500; however,
these are often in instantaneous moments and do not often present
themselves over longer periods of time. As such, AQI is typicallymeasured
between 0 and 500 in most international rating systems developed by
scientists.
5
Additional models were tested using the average AQI measurements
for each type of pollution measure. The results from the models with
the average measures returned similar results to all other models in this
research.
6
As a robustness check, the results were also estimated using a random-
effects regression with clustered SEs and produced similar results. The
results for all of the pollution variables remained the same for all the
models we tested.
7
In such conditions, there are such high levels of pollutants in the air; it
becomes difficult even for healthy adults to breathe. Visibility also greatly
decreases, as the particles in the air create a haze, making it difficult to see
over relatively short distances.
8
https://www.crainsdetroit.com/article/20180729/news/667236/the-perils-
and-perks-of-adding-a-retractable-roof-to-ford-field
9
https://losangeles.cbslocal.com/2018/11/11/california-fire-lebron-lakers/
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