ArticlePDF Available

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

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 &
Trendalova, 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 consumersperceptions of sport organizationsenviron-
mental policies and practices (Inoue & Kent, 2012a). Any further
attempt toward understanding consumersactual 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 organizationsability 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 inuencing the decision making and environmental perfor-
mance of sport organizations.
The present study focuses on air pollution and attendance
at soccer matches in Chinas 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
), 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 ( is the corresponding author.
Journal of Sport Management, (Ahead of Print)
© 2019 Human Kinetics, Inc. ORIGINAL RESEARCH
demand, which specically 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 activitiesin the form of sport fansdelibera-
tive response to pollutionas a vital component in the market process
and mechanisms that can potentially inuence 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 peoples health conditions as a result of the nations
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 inuential 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;Trendalova & 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 & Trendalova, 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 authoritys 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. Consumerswillingness 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 fansbehaviors 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
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 trafc-related com-
bustion processes (Lu et al., 2015), reecting Chinas 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 pollutionremains 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
(Ahead of Print)
2Watanabe et al.
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
shutting down factories to clean Beijings sky for staging the Olympic
Games (McLeod et al., 2018). The Olympic Bluewas, 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 consumersto 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.
Specically, following Borland and Macdonald (2003), ve 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). Specically, the
quality of the sporting contest accounts for the strength of the teams
on the eld 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 Schoeld (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 specic 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 inuential 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 ve 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
rst appeared in Birds(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 19621963 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 ndings 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.
Following prior theoretical examinations of attendance demand in
sport, a function was developed to examine the research question.
Specically, 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,
(Ahead of Print)
Chinese Soccer and Air Pollution 3
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
Tcontrols for the effects of the timing and scheduling of each match,
Srepresents stadium characteristics, Wsignies the weather at the time
of each match, and nally, 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.
The Chinese government rst 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 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)
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
Scientic Rating Air quality rating based on scientic scale
Scientic Rating AdjSeven = daily Scientic Rating average Scientic Rating for the previous 7 days
Scientic Rating AdjThirty = daily Scientic Rating average Scientic Rating for the previous 30 days
Scientic Rating AdjYear = daily Scientic Rating average Scientic 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)
(Ahead of Print)
4Watanabe et al.
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
Moving to the independent variables, team performance was
measured by utilizing the lagged win percentage for both the home
(HomeWPCT) and away (AwayWPCT) teams.
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 Yearbookpub-
lished online. In the yearbook, the population and income level
were reported for the built-up areain each city, which included
the city as well as the suburban region surrounding it. Meanwhile,
income was reported in Chinese yuan and adjusted for ination
to 2017 levels.
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)orofcial national holidays (Holiday). Variables
accounting for the months in which CSL matches were played
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 stadiumsage(StadiumAge
), 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. Specically, 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
specically 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 ne PM
and PM
) that is present within a cubic meter of air, which
is then scaled between 0 and 500 (Ebenstein et al., 2017).
Specically, 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.
governments 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 Chinas 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 scientic rating
system (Scientic 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 Scientic
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
scientic rating (Scientic Rating AdjSeven,Scientic Rating
AdjThirty, and Scientic Rating AdjYear). For the adjusted pollu-
tion variables, a zero indicated that the pollution level had no
difference between the current days 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.
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-Wilkstestwasinsignicant (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 nd any evidence of endogeneity
or multicollinearity, the BreuschPagan test was statistically signi-
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 SEsarealsobenecial
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 xed and random effects. To do so,
a Hausman test was calculated to examine whether the coef-
cients between the two models were systematically different
(Gujarati, 2003). The results of the test were statistically signicant
(Ahead of Print)
Chinese Soccer and Air Pollution 5
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
(Prob >chi2 = 0.03), meaning that xed effects were suitable for
estimating the model. Therefore, the nal results for this study were
estimated using a panel regression with xed effects and clustered
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
was the team effect, iindexed teams, j
represented weeks, trepresented seasons, and μ
was the error term
for the equation.
The results for all 12 models are listed in Tables 35. Table 3
contains the results from the models, using the AQI metric. Table 4
displays the results for Ministry Rating, whereas Table 5reports the
ndings for the regressions estimated with the Scientic Rating
variables. The R
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 signicance 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 insignicant in
relation to attendance. This nding 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 insignicant, indicating that fans did not
change their patterns of attending games based on the adjusted
pollution for each city,either. The sameinsignicance 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 scientic rating system as well as the three adjusted variables also
returned insignicant results. Overall, the ndings repeatedly con-
rmed 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
signicant at the 1% level, indicating that the quality of both teams in
amatchhaveasignicant inuence 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 ndings suggest
that both the quality of the on-eld 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 insignicant in all models. This nding indicates that
CSL attendance is not inuenced 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
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
1.01 31.00 122 193
Air Quality Index
1.80 35.18 124 226
Air Quality Index
9.89 36.28 93.19 221
Ministry Rating 2.19 0.828 1 5
Ministry Rating
0.028 0.645 2 2.43
Ministry Rating
0.039 0.729 2.2 2.87
Ministry Rating
0.175 0.754 1.95 3.12
Scientic Rating 2.19 0.834 1 6
Scientic Rating
0.028 0.651 2 2.86
Scientic Rating
0.039 0.734 2.33 3.33
Scientic Rating
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
(Ahead of Print)
6Watanabe et al.
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
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 signicant,
whereas its square term is negative and signicant. Curiously, these
results contradicted ndings 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-specic variables, average tem-
perature, wind speed, clear skies, and snow are all insignicant 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 nd positive and
signicant 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 gamesthat 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 1Air
Quality Index
Model 2Weekly
adjusted Air
Quality Index
Model 3Monthly
adjusted Air
Quality Index
Model 4Yearly
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
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
.5842 .5837 .5838 .5837
*p<.10. **p<.05. ***p<.01.
(Ahead of Print)
Chinese Soccer and Air Pollution 7
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
for each CSL match were analyzed instead, considering that these
measures also indicated quality of games (Coates et al., 2014).
Specically, 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 nal
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 signicant 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.
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 nal 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 insignicant (p= .44), thus further
Table 4 Rating MEP Regression Results
Model 5Rating
Model 6Weekly
adjusted rating MEP
Model 7Monthly
adjusted rating MEP
Model 8Yearly
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
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
.5837 .5838 .5837 .5837
Note. MEP = Ministry of Environmental Protection.
*p<.10. **p<.05. ***p<.01.
(Ahead of Print)
8Watanabe et al.
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
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 nding, 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 Chinas 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
Table 5 Scientic Rating Regression Results
Model 9Scientific
Model 10Weekly
Scientific Rating
Model 11Monthly
Scientific Rating
Model 12Yearly
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
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
Scientic Rating 88.34 380 ––––––
Scientic Rating AdjSeven ––225 467 ––––
Scientic Rating AdjThirty ––––121 426 ––
Scientic 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
.5837 .5838 .5837 .5838
*p<.10. **p<.05. ***p<.01.
(Ahead of Print)
Chinese Soccer and Air Pollution 9
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
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 retrot
involves putting roofs on current open-air facilities, along with
proper systems to have air conditioning and ltration to reduce
pollutants. However, these are costly endeavors (estimates of at
least $200 million USD
) for teams, and cities may not want to
contribute to these costs. As the California wildres in November
2018 illustrate, newer indoor state-of-the-art arenas are not neces-
sarily able to keep pollution from entering a facility.
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 fanshabitual consumption of CSL games
rst needs to be understood in relation to larger sociopolitical
forces surrounding Chinas economic growth and concomitant
environmental crisis. Although Chinas 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
Chinas 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 Chinas political sphere presents signicant
challenges to Chinas 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
reects the essential philosophy of corporate social responsibility.
The recent guideline issued by the Korea Football Association can be
illuminating, as it species that if the level of ne 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
Scientic Rating .025 .074 .067 .040
Scientic Rating AdjSeven .030 .080 .074 .020
Scientic Rating AdjThirty .015 .079 .063 .032
Scientic 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.
*Signicance at p<.05.
(Ahead of Print)
10 Watanabe et al.
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
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. Specic
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 Chinas 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 nal note, there are a few limitations within this study
that must be addressed. The rst issue which may exist is in the
accuracy of the pollution data that are reported for each day on
the MEPs 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
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
(Ahead of Print)
Chinese Soccer and Air Pollution 11
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
While these measures of absolute team quality were used, uncertainty of
outcome was also tested in the models, but caused no signicant change in
the results.
In 2017, the average exchange rate was US$1 and was approximately
equal to 6.7 yuan or US$0.14 per yuan.
and PM
refer to the size of air particles, with PM
matter with a diameter of 2.5 μm (microns) or less, and PM
matter with a diameter of 10 μm or less.
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
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
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.
In such conditions, there are such high levels of pollutants in the air; it
becomes difcult even for healthy adults to breathe. Visibility also greatly
decreases, as the particles in the air create a haze, making it difcult to see
over relatively short distances.
Babiak, K., & Trendalova, S. (2011). CSR and environmental responsi-
bility: Motives and pressures to adopt green management practices.
Corporate Social Responsibility and Environmental Management,
18,1124. doi:10.1002/csr.229
Bacon, P. (2016, December 19). China chokes on smog so bad that planes
cant land. USA Today. Retrieved from
Baimbridge, M., Cameron, S., & Dawson, P. (1996). Satellite television
and the demand for football: A whole new ball game? Scottish
Journal of Political Economy, 43, 317333. doi:10.1111/j.1467-
Bird, P.J. (1982). The demand for league football. Applied Economics, 14,
637649. doi:10.1080/00036848200000038
Borland, J., & MacDonald, R. (2003). Demand for sport. Oxford Review of
Economic Policy, 19, 478502. doi:10.1093/oxrep/19.4.478
Buraimo, B., Tena, J.D., & de la Piedra, J.D. (2018). Attendance demand in
a developing football market: The case of the Peruvian rst division.
European Sport Management Quarterly, 18, 671686. doi:10.1080/
Casper, J., Pfahl, M., & McSherry, M. (2012). Athletics department
awareness and action regarding the environment: A study of NCAA
athletics department sustainability practices. Journal of Sport Manage-
ment, 26,1129. doi:10.1123/jsm.26.1.11
Chard, C., & Mallen, C. (2012). Examining the linkages between auto-
mobile use and carbon impacts of community-based ice hockey.
Sport Management Review, 15, 476484. doi:10.1016/j.smr.2012.
Chen, S. (2013, March 29). Most Chinese cities hiding vital pollution
data from public: Mainland government not sharing big polluters
names or amounts of pollutants released. South China Morning Post.
Retrieved from
Coates, D., & Humphreys, B.R. (2005). Novelty effects of new facilities on
attendance at professional sporting events. Contemporary Economic
Policy, 23, 436455. doi:10.1093/cep/byi033
Coates, D., & Humphreys, B.R. (2007). Ticket prices, concessions and
attendance at professional sporting events. International Journal of
Sport Finance, 2, 161170.
Coates, D., Humphreys, B.R., & Zhou, L. (2014). Reference-dependent
preferences, loss aversion, and live game attendance. Economic
Inquiry, 52, 959973. doi:10.1111/ecin.12061
Collins, A., Flynn, A., Munday, M., & Roberts, A. (2007). Assessing
the environmental consequences of major sporting events: The
2003/04 FA Cup Final. Urban Studies, 44, 457476. doi:10.1080/
Cox, A. (2018). Spectator demand, uncertainty of results, and public
interest: Evidence from the English Premier League. Journal of
Sports Economics, 19,330. doi:10.1177/1527002515619655
Dawson, J., Scott, D., & Havitz, M. (2013). Skier demand and behavioural
adaptation to climate change in the US Northeast. Leisure/Loisir, 37,
127143. doi:10.1080/14927713.2013.805037
Dobson, S., & Goddard, J. (2011). The economics of football. Cambridge,
United Kingdom: Cambridge University Press.
Ebenstein, A., Fan, M., Greenstone, M., He, G., & Zhou, M. (2017). New
evidence on the impact of sustained exposure to air pollution on life
expectancy from Chinas Huai River Policy. Proceedings of the
National Academy of Sciences, 114, 1038410389. doi:10.1073/
Fairley, S., Ruhanen, L., & Lovegrove, H. (2015). On frozen ponds: The
impact of climate change on hosting pond hockey tournaments. Sport
Management Review, 18, 618626. doi:10.1016/j.smr.2015.03.001
Feddersen, A., & Rott, A. (2011). Determinants of demand for televised
live football: Features of the German national football team. Journal
of Sports Economics, 12, 352369. doi:10.1177/1527002511404783
García, J., & Rodríguez, P. (2002). The determinants of football match
attendance revisited: Empirical evidence from the Spanish football
league. Journal of Sports Economics, 3,1838.
Gasparetto, T., Barajas, A., & Fernandez-Jardon, C.M. (2018). Brand team
and distribution of wealth in Brazilian state championships. Sport,
Business, Management: An International Journal, 8,214. doi:10.
Ge, Q., Humphreys, B.R., & Zhou, K. (2017). Are fair weather fans
affected by weather? Rainfall, habit formation, and live game atten-
dance (Working Paper No. 17-24). Morgantown, WV: West Virginia
Department of Economics. Retrieved from
Gujarati, D.N. (2003). Basic econometrics (4th ed.). New York, NY:
Hansen, H., & Gauthier, R. (1989). Factors affecting attendance at
professional sport events. Journal of Sport Management, 3,1532.
Harvard Dataverse. (2018). China AQI PM25s Archive Dataverse.
Harvard University. Retrieved from
(Ahead of Print)
12 Watanabe et al.
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
Hayes, G., & Horne, J. (2011). Sustainable development, shock and awe?
London 2012 and civil society. Sociology, 45, 749764. doi:10.1177/
Huang, H., & Humphreys, B. R. (2012). Sports participation and happi-
ness: Evidence from US microdata. Journal of Economic Psychology,
33, 776793. doi:10.1016/j.joep.2012.02.007
Inoue, Y., & Kent, A. (2012a). Investigating the role of corporate
credibility in corporate social marketing: A case study of environ-
mental initiatives by professional sport organizations. Sport Manage-
ment Review, 15, 330344. doi:10.1016/j.smr.2011.12.002
Inoue, Y., & Kent, A. (2012b). Sport teams as promoters of pro-
environmental behavior: An empirical study. Journal of Sport
Management, 26, 417432. doi:10.1123/jsm.26.5.417
Jang, H., & Lee, Y.H. (2015). Outcome uncertainty, governance structure,
and attendance: A study of the Korean professional football league.
In Y.H. Lee & R. Fort (Eds.), The sports business in the Pacic
Rim (pp. 5981). New York, NY: Springer International Publishing.
Jewell, R.T. (2017). The effect of marquee players on sports demand: The
case of US Major League Soccer. Journal of Sports Economics, 18,
239252. doi:10.1177/1527002514567922
Kearins, K., & Pavlovich, K. (2002). The role of stakeholders in Sydneys
green games. Corporate Social Responsibility and Environmental
Management, 9, 157169. doi:10.1002/csr.19
Kellison, T.B., & Hong, S. (2015). The adoption and diffusion of
pro-environmental stadium design. European Sport Management
Quarterly, 15, 249269. doi:10.1080/16184742.2014.995690
Khare, A., & Inman, J.J. (2006). Habitual behavior in American eating
patterns: The role of meal occasions. Journal of Consumer Research,
32, 567575. doi:10.1086/500487
King, B., & Pearce, N. (2010). The contentiousness of markets:
Politics, social movements, and institutional change in markets.
Annual Review of Sociology, 36, 249267. doi:10.1146/annurev.
Lee, Y.H., & Smith, T.G. (2008). Why are Americans addicted to baseball?
An empirical analysis of fandom in Korea and the United States.
Contemporary Economic Policy, 26,3248. doi:10.1111/j.1465-
Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015).
The contribution of outdoor air pollution sources to premature
mortality on a global scale. Nature, 525(7569), 367371. PubMed
ID: 26381985 doi:10.1038/nature15371
Li, F., Liu, Y., Lü, J., Liang, L., & Harmer, P. (2015). Ambient air
pollution in China poses a multifaceted health threat to out-
door physical activity. Journal of Epidemiology & Community
Health, 69, 201204. PubMed ID: 24970766 doi:10.1136/jech-
Li, J., Moul, C.C., & Zhang, W. (2017). Hoping grey goes green: Air
pollutions impact on consumer automobile choices. Marketing
Letters, 28(2), 267279. doi:10.1007/s11002-016-9405-2
Li, M., & Zhang, L. (2014). Haze in China: Current and future challenges.
Environmental Pollution, 189,8586. PubMed ID: 24637256
Li, P., & Jourdan, A. (2017, July 25). Game on: Suning leads Chinas$2
billion soccer rights frenzy. Reuters. Retrieved from https://www.
Liu, D., Zhang, J.J., & Desbordes, M. (2017). Sport business in China:
Current state and prospect. International Journal of Sports Marketing
and Sponsorship, 18,210. doi:10.1108/IJSMS-12-2016-0086
Lu, F., Xu, D., Cheng, Y., Dong, S., Guo, C., Jiang, X., & Zheng, X.
(2015). Systematic review and meta-analysis of the adverse health
effects of ambient PM
and PM
pollution in the Chinese
population. Environmental Research, 136, 196204. PubMed ID:
25460637 doi:10.1016/j.envres.2014.06.029
Mallen, C. (2017). Robustness of the sport and environmental sustainabil-
ity literature and where to go from here. In B.P. McCullough & T.B.
Kellison (Eds.), Routledge handbook of sport and the environment
(pp. 1135). New York, NY: Routledge.
Matus, K., Nam, K.M., Selin, N.E., Lamsal, L.N., Reilly, J.M., & Paltsev, S.
(2012). Health damages from air pollution in China. Global Environ-
mental Change, 22,5566. doi:10.1016/j.gloenvcha.2011.08.006
McCann, E. (2016, December 16). Life in China, Smothered by Smog. The
New York Times. Retrieved from
McCullough, B.P., Pfahl, M.E., & Nguyen, S.N. (2016). The green
waves of environmental sustainability in sport. Sport in Society,
19, 10401065. doi:10.1080/17430437.2015.1096251
McLeod, C.M., Pu, H., & Newman, J.I. (2018). Blue skies over Beijing:
Olympics, environments, and the Peoples Republic of China.
Sociology of Sport Journal, 35,2938. doi:10.1123/ssj.2016-0149
Moen, J., & Fredman, P. (2007). Effects of climate change on alpine skiing
in Sweden. Journal of Sustainable Tourism, 15, 418437. doi:10.
Moore, J.W. (2015). Capitalism in the web of life: Ecology and the
accumulation of capital. New York, NY: Verso Books.
Neale, W.C. (1964). The peculiar economics of professional sports. The
Quarterly Journal of Economics, 78,114. doi:10.2307/1880543
Phillips, P., & Turner, P. (2014). Water management in sport. Sport
Management Review, 17, 376389. doi:10.1016/j.smr.2013.08.002
Pickering, C., Castley, J., & Burtt, M. (2010). Skiing less often in a warmer
world: Attitudes of tourists to climate change in an Australian ski
resort. Geographical Research, 48, 137147. doi:10.1111/j.1745-
Roh, J. (2018, April 17). Soccer-KFA issue guidelines to address pollution
concerns. Reuters. Retrieved from
Rottenberg, S. (1956). The baseball playerslabor market. Journal of
Political Economy, 64, 242258. doi:10.1086/257790
Sanderson, A.R., & Shaikh, S.L. (2017). Economics, sports, and the environ-
ment. In B.P. McCullough & T.B. Kellison (Eds.), Routledge handbook
of sport and the environment (pp. 3653). New York, NY: Routledge.
Schoeld, J. A. (1983). Performance and attendance at professional team
sports. Journal of Sport Behavior, 6, 196206.
Stock, J.H., & Watson, M.W. (2008). Heteroskedasticity-robust standard
errors for xed effects panel data regression. Econometrica, 76,
155174. doi:10.1111/j.0012-9682.2008.00821.x
Sung, H., & Mills, B.M. (2018). Estimation of game-level attendance in
Major League Soccer: Outcome uncertainty and absolute quality
considerations. Sport Management Review, 21, 519532. doi:10.
Thibault, L. (2009). Globalization of sport: An inconvenient truth. Journal
of Sport Management, 23,120. doi:10.1123/jsm.23.1.1
Tilt, B., & Xiao, Q. (2010). Media coverage of environmental pollution in
the Peoples Republic of China: Responsibility, cover-up and state
control. Media, Culture & Society, 32, 225245. doi:10.1177/
Trendalova, S., & Babiak, K. (2013). Understanding strategic corporate
environmental responsibility in professional sport. International
Journal of Sport Management and Marketing, 13,126. doi:10.
Villar, J.G., & Guerrero, P.R. (2009). Sports attendance: A survey of the
literature 19732007. Rivista di Diritto ed Economia dello Sport, 5,
(Ahead of Print)
Chinese Soccer and Air Pollution 13
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
Watanabe, N., Wicker, P., & Yan, G. (2017). Weather conditions, travel
distance, rest, and running performance: The 2014 FIFA world cup
and implications for the future. Journal of Sport Management, 31,
2743. doi:10.1123/jsm.2016-0077
Watanabe, N.M. (2012). Japanese professional soccer attendance and the
effects of regions, competitive balance, and rival franchises. Interna-
tional Journal of Sport Finance, 7, 309323.
Watanabe, N.M., & Soebbing, B. (2017). Chinese Super League:
Attendance, pricing, and team performance. Sport, Business and
Management: An International Journal, 7, 157174. doi:10.1108/
Watanabe, N.M., & Soebbing, B.P. (2015). Ticket price behavior and
attendance demand in Chinese professional soccer. In Y.H. Lee &
R. Fort (Eds.), The sports business in the Pacic Rim (pp. 139157).
New York, NY: Springer International Publishing.
Wicker, P. (2018a). The carbon footprint of active sport participants. Sport
Management Review. Advanced online publication. doi:10.1016/j.
Wicker, P. (2018b). The carbon footprint of active sport tourists: An
empirical analysis of skiers and boarders. Journal of Sport & Tour-
ism, 22, 151171. doi:10.1080/14775085.2017.1313706
Wilson, B. (2012). Growth and nature: Reections on sport, carbon
neutrality, and ecological modernization. In D. L. Andrews &
M. Silk (Eds.), Sport and neo-liberalism (pp. 90108). Philadelphia,
PA: Temple University Press.
Xu, P., Chen, Y., & Ye, X. (2013). Haze, air pollution, and health in China.
The Lancet, 382(9910), 2067. PubMed ID: 24360386 doi:10.1016/
Yu, L., Newman, J., Xue, H., & Pu, H. (2017). The transition game:
Toward a cultural economy of football in post-socialist China.
International Review for the Sociology of Sport. Advance online
publication. doi:10.1177/1012690217740114
Zhang, A., Zhong, L., Xu, Y., Wang, H., & Dang, L. (2015). Tourists
perception of haze pollution and the potential impacts on travel:
Reshaping the features of tourism seasonality in Beijing, China.
Sustainability, 7(3), 23972414. doi:10.3390/su7032397
(Ahead of Print)
14 Watanabe et al.
Downloaded by UNIV OF ALBERTA LIBRARY on 03/11/19
... Against this backdrop, sport scholars have recently incorporated actual environmental measures within their research. The focus of these studies has predominantly been on understanding how environmental conditions, such as air pollution, impact the behaviors and decision making of sport consumers and participants [9]. For instance, researchers found that Major League Baseball (MLB) umpires made more errors in their judgments when there were higher levels of air pollution in the local area where the game was played [10]. ...
... Against this backdrop, a new lineage emerging within sport ecology [1] has utilized public databases of air pollution to provide a more precise accounting of the relationship between sport and the environment. Watanabe et al. [9] considered whether pollution levels in major Chinese cities had an impact on consumer interest in attending professional soccer matches. Despite being one of the most heavily polluted regions in the world, this study found no evidence of pollution reducing sport consumption. ...
... Two additions are made to the model to control for factors that are not included within Locke's model. First, we include a dummy variable for games played in domed stadiums (Dome), as previous studies have noted for the potential for domes to impact air pollution levels [9]. Because domes may use additional systems, such as air conditioning, it is possible that they could produce higher levels of certain types of emissions. ...
Full-text available
(1) Background: Prior research has found that large-scale sporting events may potentially impose negative consequences on the environment, thus impeding the sustainability goals of the sport industry. Along these lines, the current study extends the literature by examining the impact that National Football League (NFL) games have on local-area air pollution. (2) Methods: Air Quality Index (AQI) data measuring six major forms of air pollution were gathered from air monitors positioned close to NFL stadiums and matched with the number of attendees at games. From this, multiple regression analysis was utilized to estimate whether the number of fans was related to changes in air pollution. (3) Results: The regression models found that Ozone and Nitrogen Dioxide levels increased as more individuals attended NFL games. Additional robustness checks and falsification tests suggest that the average NFL event results in an approximately two-percent increase in Ozone levels. (4) Conclusions: The findings from this study contribute to the literature by providing evidence that highly attended sporting events increase pollution levels in the areas near stadiums. Thus, governments and sport organizations should consider low-emission methods to get fans to travel to games in order to reduce their environmental impact.
... Most research into attendance at sports events initially focused on sport in the United States, such as baseball (Baade & Tiehen, 1990), American football (Doyle et al., 1980) or hockey (Jones, 1984). Yet over the last few decades, interest has spread to other areas such as Europe (Baimbridge et al., 1995;Carmichael et al., 1999), Australia (Borland & Lye, 1992) or Asia (Watanabe et al., 2019). ...
This study breaks new ground by examining determinants of away fan travel in a professional football league. Using data from two consecutive seasons of the Spanish league, a gravity model is estimated based on a three-dimensional panel to discuss the effects of a number of factors that influence the flows of away fans. Results evidence the importance of factors such as distance between locations, the day or time the match is played, and uncertainty concerning the visiting team’s current form. Joint interpretation of all these factors by the clubs and by those organising the competition may help to reshape their management strategies, which would result in increased flows of away supporters and an increase in sports clubs’ revenues from this concept.
... Nevertheless, the impacts of outdoor air quality, specifically in areas with greater air pollution, remain a largely understudied area of concern for athlete performance and overall health. Although declining air quality has not significantly discouraged fan attendance at elite athletic events [29,30], there appears to be adverse impacts on athletes when competing in areas with poorer outdoor air quality, as indicated by reductions in high-intensity performance [31]. These studies, in combination with others that have directly shown causative declines in physical health (e.g., [6,7,16,17]), collectively indicate that air quality has impacts on athletic performance, specifically on athletes competing at elite levels where minute differences in physical capacity can determine the outcome of a competition. ...
Full-text available
Air quality is a growing environmental concern that has implications for human physical and mental health. While air pollution has been linked to cognitive disease progression and declines in overall health, the impacts of air quality on athletic performance have not been extensively investigated. Much of the previous research focused on endurance sports indicates that air quality negatively impacts athletic performance; however, the effects of air quality on non-endurance elite team performance remains largely unknown. The purpose of this study was to examine the impact of air quality on errors committed by Major League Baseball (MLB) teams, interceptions thrown by quarterbacks in the National Football League (NFL), and overall quarterback performance in the NFL. Linear regression analysis was used to determine the impact of the median air quality index (AQI) of counties with MLB and NFL teams on errors, interceptions, and overall quarterback performance of players on those MLB and NFL teams. AQI was a significant positive predictor of errors and interceptions, indicating increased errors and interceptions with decreased air quality. Similarly, quarterback performance was significantly reduced for quarterbacks from teams in counties with worse air quality. These findings suggest that air quality has a significant impact on performance in the MLB and NFL, indicating impairments in physical and cognitive performance in professional athletes when competing in areas with poorer air quality. Hence, it is likely that air quality impacts athletic performance in numerous sports that have not yet been investigated.
... Altering spectating behaviours to be better prepared for air pollution and extreme heat/rainfall (Watanabe et al., 2019). 2022). ...
Full-text available
Association football is popular and influential globally. Interest in how football relates to climate change, and the climate policy required for football, is growing. Clubs, players and fans increasingly call for action to reduce football’s impact on the climate, and for plans to adapt to climate impacts on football. However, well-intentioned actions must be underpinned by robust evidence. This synthesis reviews research at the interface of football and climate change. After summarizing the main climate actions identified for fans, players, clubs and organizing bodies, the review looks in-depth at four areas: impacts of football on climate; impacts of climate on football; football as a driver for pro-climate actions; and the relationship between football and carbon-intensive industries. The review then outlines research gaps for an evidence-driven response to climate change in football: adaptation across different geographical contexts; understanding what climate change means for community-level football; understanding how carbon-intensive industries relate to sense of place identity in football under a just transition; developing principles for phasing-out fossil fuel financing; and considering how climate change relates to women’s football. Key policy insights • Football is a forum for galvanizing societal action in support of climate policy. However, football also contributes to, and is impacted by, climate change, and hence requires policy support under a changing climate; • Reducing transportation emissions, especially flying, is a key climate policy requirement for football. Institutional policy, with government support, may enable more efficient scheduling and use of surface transport; • Institutional policies, and public health policies, should develop standards and guidelines for football under extreme heat. Football also ought to be integrated within local, regional and national climate adaptation policy to ensure climate resilience; • Clubs and players can lead by example on climate-positive actions, and energize wider action through fan bases. Alignment of initiatives with national or international climate policy may raise public awareness of climate polices and targets; • Institutional policies for clubs, tournaments and associations should regulate fossil fuel financing. Football also offers an avenue to understand relations between local identity and carbon-intensive industries, and thus to identify socio-cultural factors for regional just transition policies.
... The sport organizations primarily incorporate two approaches to the environmental impact of sport, a direct approach and an educational one. The direct approach includes reducing the carbon footprint of sporting events and encouraging more sustainable operations (Watanabe et al., 2019). The educational approach includes promoting more sustainable activities by fans, such as using alternative transportation means to travel to games and different consumerism patterns at events and use the public platform to encourage an overall environmental agenda (Orr & Inoue, 2019;Martins et al., 2022 McCullough & Trail, in press;Trail & McCullough, 2020. ...
Sport organizations are engaging in more advanced environmental sustainability efforts to reduce their environmental impacts. In particular, sport organizations and venues can align their environmental goals with the surrounding city’s environmental objectives. In this study, we examine the relationship between the city and the various sport venues given the city’s agenda to reduce pollution by encouraging the use of public transport. Through ticket purchase data and GIS analysis, we examine how the city capacity helps sport venues fulfill their environmental goals to reduce the environmental impact from fan transportation. We identified the geographical fragmentation of sport fans and found that most fans live beyond five miles of a light rail station. This distance decay from the station makes fans reliant on another form of transportation. Researchers and practitioners can use this approach to leverage internal data and GIS analyses to understand the influence of geographical segmentation of their fans and the influence of this distribution on the environmental impact of fans. This analysis also provides the foundation for future researchers to further examine transportation behaviors of sport fans and the effect of distance decay from transportation stations on sustainable transportation choices.
... Reade et al. [11] studied attendance demand for the Belarusian Premier League, a football league that kept playing despite the COVID-19 pandemic, finding that football fans reacted negatively to the threat of COVID-19 but a gradual habituation effect arose. Another study, with a slightly different focus, investigating the behavioral response of sport consumers to a polluted environment concluded that the consumption habits of football fans do not change despite the presence of air pollution [12]. In view of these mixed results, in order to elaborate on the impact of external shocks to health threats on spectator behavior, it would be useful to observe consumer's psychological response, i.e., the preferences and intention of sport consumers as a precursor to attendance. ...
Full-text available
In the current investigation, we assess the effect of COVID-19 on intention-based spectator demand for professional sports in Japan captured by eight, monthly repeated cross-sectional national surveys from May to December 2020 (n = 20,121). We regress spectator demand on individual (e.g., gender), prefecture-wave (e.g., COVID-19 infection status), and prefecture-level factors (i.e., with or without quality professional teams). The results of multilevel logistic regression demonstrate that individual (i.e., male, younger, full-time employment, and with children status) and prefecture-level team factors (i.e., with teams) were associated with intention-based spectator demand. Nevertheless, COVID-19-related factors were found to be unrelated to spectator demand. The findings imply that sports fans are likely to return to the stadium once behavioral restrictions are lifted. The current research provided further evidence that individual factors and team quality serve as influential antecedents of spectator demand in the context of the COVID-19 epidemic.
... Therefore, considering RQ1 "Are members aware of the SI programs implemented by the PSO?", results from this study show that the majority of the PSO members were not aware. Since PSOs are credible in passing messages related to SI [14,52], these results confirm that, with regard to SI planning, communication and execution, there is still a long way to go [13,15]. ...
Full-text available
This study explores the importance of sustainable initiatives (SI) in sport for the stakeholders of a professional sports organization (PSO) after three months of absence of the public at the stadiums due to the pandemic situation. Two topics—diversity and inclusion (DIVIN) and the attraction and retention of human capital (ARHC)—were considered and analyzed. A third factor—the distance of residence of the members and the PSO—was considered as an element of the possible relationship between the awareness of the SI and the assessment of the topics in question. A total of 5694 PSO members took an online survey. Through the description of the data, the results show that being aware of the SI performed is a crucial factor for the success of the SI. Distance positively influences SI awareness. The topics considered are rated most positively by members with awareness of the SI, with a higher rating for the topic with the highest external visibility.
The Chinese Super League (CSL) is the top tier football league in China and over the past 10 years has expanded rapidly in terms of popularity, brand strength, and name recognition. Despite the CSL’s rapid development, limited empirical research has been done to examine the league and what drives demand in attending the games. Thus, in order to gain a better understanding of CSL and identify unique patterns of demand for professional sports leagues in China, this study used data from the 2017–2019 CSL regular seasons to examine the relationship between various stadium determinants and game attendance. Moreover, the nested structure found in attendance data was considered, thus adopting multilevel modeling (MLM) to analyze game level and home team level variables to predict stadium attendance in the CSL. Our findings reveal that 12 variables significantly influenced attendance, with variables related to home teams showing more relevance to explaining stadium attendance than game level variables.
Purpose This study sought to determine if environmental barriers (i.e. air pollution, temperature and precipitation) affect outdoor (i.e. soccer and baseball) and indoor (i.e. basketball) professional sport attendance in South Korea. Design/methodology/approach By including actual air quality, temperature and precipitation data collected from each place where the sporting events take place, this study conducted a regression analysis to examine factors that influenced outdoor and indoor sport attendance. Findings In outdoor sports, the estimated results suggested that soccer and baseball attendance were not affected by air pollution. Indoor sport consumers did not change their consumption behaviors in attending sports despite the presence of air pollution. In addition, there was mixed evidence on the effect of weather-related variables on attendance. Average temperature had a positive effect on baseball (outdoor) and basketball (indoor) sport attendance, indicating that the warmer the temperature, the more likely those fans were to attend the games. Average precipitation was negatively associated with outdoor (soccer) sport spectators. Originality/value The present study contributes to the sport environment literature by examining the impact of environmental barriers on spectators' behaviors in the context of outdoor and indoor professional sports.
Full-text available
Attention by sport management researchers and practitioners toward the societal externalities of professional sport franchises and venues has increased recently. This study asserts that while sport organizations are very active in this regard, there remain several issues that have not received much attention in the sport management literature nor by sport organizations themselves. Criminal activity, or the perception of criminal activity, at and near sport venues is one of these issues. The negative binominal regression analysis of police stops in Minneapolis revealed that police stops were greater within a quarter and half a mile of Minneapolis professional sport venues on event days. Furthermore, during nonevent days, the venues can be urban “dead spaces” and the design of venues in urban areas should address the internal and external amenities of the sport venues and the potential increase in crime and police-related activity on days with and without events.
Full-text available
Research question: Most of the empirical research on football demand has focused on leagues in developed countries while those in developing countries have received comparatively limited attention. In the absence of more specific analyses of leagues in developing countries, an implicit assumption is that the demand for football across different economies is homogenous. However, such an assumption may lead to inappropriate policies. Therefore, decision-making and policies aimed at attendance and pricing in developing countries should be substantiated with empirical evidence reflecting their settings. Research methods: This paper models match level attendance demand and price in the first tier of the Peruvian football league from 2012 to 2016 inclusive. Using a sample of 1719 matches, two-stage least-squares estimation with instrumental variables is used. Results and findings: We find that attendance in the Peruvian football league is driven by market size, distance between teams and recent performance while local rivalry and price do not exert significant impacts. More importantly, attendance exhibits properties of an inferior good given its relationship with levels of poverty and associated correlation with income. Implications: The implications are that demand models for football in developing and developed economies generate different results as the impacts of some exogenous variables on attendance offer different outcomes. For example, the impact of price and income differ from prior expectations indicating that different approaches are necessary to managing football in developing countries including distinct policies on stadium accommodation.
Full-text available
Despite its continued growth, there are doubts about the sustainability of demand for Major League Soccer, which has a strong focus on superstar externalities through its designated player rules. Yet there is relatively limited research directly focusing on classical determinants of demand for league attendance. The authors set out to establish an estimate of the relative importance of relative quality - outcome uncertainty - and absolute quality in game attendance. They find that fans behave in ways more consistent with the loss aversion hypothesis than the uncertainty of outcome hypothesis, with considerable interest in both home and away team absolute quality. © 2017 Sport Management Association of Australia and New Zealand.
Michael Lewis’s best-selling book, Moneyball, demonstrated the efforts of Oakland A’s General Manager, Billy Beane, to create a successful baseball team in spite of its location in a small market. Previous studies have argued that the salary returns to the neglected skill of on base percentage (OBP) should rise once the Oakland A’s hitters demonstrated proficiency with this skill. Our key result is that after Moneyball was published in 2003, hitter salaries for free agents signing new contracts were not more closely related to OBP. Consistent with efficiency, we find no long-term change in valuation in OBP. In contrast, we do find evidence of a rise in salary returns to productivity in the form of bases per hit (‘power hitting’) but this again is consistent with efficient market adjustment. In sum, it appears the labor market for hitters in baseball was efficient both before and after the appearance of Moneyball.
Researchers examining carbon dioxide-equivalent emissions (carbon footprint) in sport have focused on sport events and, to a lesser extent, sport teams, but provided only average or aggregate values. The author takes the perspective of active sport participants and considers the heterogeneity of individual sport participation behavior. Using online surveys, adult active sport participants (n = 6537) in 20 different sports with main residence in Germany were asked to report their sport-travel behavior in 2015, including traveling in the context of regular (weekly) activity, sport competitions/tournaments, league games, day trips, and training camps/vacations. Annual carbon footprints were estimated using information about travel distances and transportation means. The results revealed an average annual carbon footprint of 844 kg of carbon dioxide-equivalent emissions, with individual sports producing more emissions than team/racket sports. Participants in nature sports had the highest emission levels. Regression analyses revealed that environmental consciousness significantly reduced carbon footprint in individual sports, but not in team/racket and nature sports, supporting the existence of an environmental value-action gap. Activity years, club membership, weekly exercise hours, performance level, and income were mainly positively associated with annual carbon footprint, while gender was insignificant. The findings have implications for policy makers and managers in sport associations and clubs. © 2018 Sport Management Association of Australia and New Zealand
Purpose-The purpose of this paper is to analyse the demand for tickets in the Brazilian State Championships focussing in the impact generated by the brand teams as well as the play-off matches in the demand for tickets and, consequently, in the match day revenues. Design/methodology/approach-An equations system by three-stage least square estimator is employed. The data set comprises 1,114 matches from Mineiro, Carioca and Paulista Championships over the seasons 2013-2015. Findings-All explanatory variables increase both attendance and match day revenues. However, the most important goal is the distribution of wealth found. The presence of brand teams in those championships provides a financial aid for smaller teams. Practical implications-The proposals from the mass media to exclude the brand teams and design those championships exclusively in play-off stages should not be implemented by the policymakers. On the contrary, rearranging the design of the competition with more matches between small teams and brand teams may help to all of them. Originality/value-The paper contributes to introduce the Brazilian State Championships in the sport economics literature as well as evidences the redistribution effect of wealth among clubs.
Following decades of significant economic and political reform, a once-closed China has emerged as the world’s fastest growing and arguably most interconnected political economic system. In the context of what has been termed a “post-socialist” transition, China’s sport system has similarly undergone rapid marketization (bringing in market actors and action). In this article, we examine the changing state and function of football (soccer) within this period of post-socialist transition. We provide a critical analysis of recent (c. 2010–2017) private and state-based initiatives to develop the commercial viability, international interconnectivity, and cultural significance of football (soccer). Drawing upon theories of cultural economy as developed by the globalization theorist Arjun Appadurai, we provide an historical and conceptual investigation of the strategic efforts to nationally imagine football culture as, and within, transitioning China. To do this, we examine how state actors and private intermediaries have leveraged increases in high-profile player transfers, domestic franchise valuations, investment in foreign teams, development of player academies, overall youth and adult participation, and expanded media rights agreements to simultaneously economize Chinese football culture and culturalize the logics of commercial sport and free market capitalism more generally. In so doing, we map the various “scapes” through which people, capital, images, technologies, and ideologies have been set aflow and thereby frame new imaginings of mass privatization, mediation, and consumerism for a national football consuming public.
Significance An estimated 4.5 billion people are currently exposed to particulate matter (PM) levels at least twice the concentration that the WHO considers safe. Existing evidence linking health to air pollution is largely based on populations exposed to only modest levels of PM and almost entirely composed of observational studies, which are likely to confound air pollution with other unobserved determinants of health. This study uses quasiexperimental variation in particulate matter smaller than 10 μm (PM 10 ) generated by an arbitrary Chinese policy to find that a 10-μg/m ³ increase in PM 10 reduces life expectancy by 0.64 years. The estimates imply that bringing all of China into compliance with its Class I standards for PM 10 would save 3.7 billion life-years.
During the 2008 Olympic Games, after years of environmental regulations, two months of short-term measures, and opportune weather, Beijing measured a record number of "blue sky days," at the same time reassuring international athletes and journalists the air was safe for competition and Beijing residents. We use this case to understand how environmental objectives are achieved in sport. Using Bruno Latour's object-oriented political ecology, we describe the events leading to, during, and after the Games. We argue environmental objectives are possible when environments are made public; this means environmental objects-such as skies and particulate matter-must be assembled and then articulated or, in other words, brought forward and made capable of speech.
This study estimated the annual carbon footprint of active sport tourists caused by snow-sport-related travel in the context of day trips, vacations, training courses, and competitions in 2015. Information about individual travel behaviour, sport profile, environmental consciousness, and socio-economic characteristics was collected using a nationwide online survey of adult skiers and boarders living in Germany (n = 523). The average annual carbon footprint of snow sport tourists was 431.6 kg of carbon dioxide equivalent emissions in 2015. Boarders had a higher carbon footprint than skiers. Regression analyses revealed that income and number of snow days had a significant positive effect on annual carbon footprint, while environmental consciousness was insignificant. This finding can be explained with the value–action gap and the low-cost hypothesis, suggesting that environmental attitudes were not associated with pro-environmental behaviour in terms of a lower carbon footprint because snow-sport-related travel was perceived as a high-cost situation by respondents. Segmenting respondents by snow-sport-related travel behaviour yielded two clusters, frequent travellers (56% boarders) and occasional riders (43% skiers), which differed with regard to annual carbon footprint, club membership, number of snow days, and performance level. This study contributes to the literature on active sport tourism and carbon footprinting.