45

Cognitive Aversion of Mercury Scaled by Pairwise Comparison
Method with Thurstone’s Law of Comparative Judgement
Fumitake TAKAHASHI*
Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology
4259, Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan* E-mail: takahashi.f.af@m.titech.ac.jp

Abstract
The Minamata Convention on Mercury, which entered into force on Aug. 2017, requires mercury to be
recovered from society and its final disposal to be accomplished in environmentally safe ways. Because it isvery difficult to obtain public acceptance of mercury landfill disposal, this study focuses on emotional
perceptions of mercury. The author quantitatively evaluated aversions to mercury and other harmful matterssuch as radioactive waste or those perceived as hazardous using a pairwise comparison method withThurstone’s law of comparative judgement (sample size = 1030). The strongest aversion was found forradioactive waste, followed by mercury. Although gender and age usually affect risk perception to any hazard,
this study found that gender and age had no significant impact on mercury aversion at the 5 percent
significance level. When the participants had strong concerns about well-known hazards (radioactive waste,
dioxins, infectious medical waste and cadmium), they had a stronger aversion to mercury than those who
were concerned more with other matters like genetically modified foods or ultraviolet light. On the other
hand, the participants who had moderate or weak concerns about well-known hazards had even greater
mercury aversion than the group with strong concerns. No clear difference in mercury aversion was foundregardless of different strengths of concerns regarding waste recycling. Cognitive aversion to mercury is
associated with hazard perceptions and not affected by concerns regarding waste recycling.
Key words :	cognitive aversion, mercury, pairwise comparison, quantitative evaluation

1. Introduction
Mercury is a liquid metal under natural conditions
and has been used in many products like fluorescentlamps owing to its unique characteristics (UNEnvironment, 2019). Because mercury is highly toxic andits methylated species have bioaccumulative properties(Morel et al., 1998), international efforts have been madeto reduce mercury applications. In this context, theMinamata Convention on Mercury was accepted on Oct.
2013 and effectuated on Aug. 2017 (UN Environment,2019). It aims to protect human health and theenvironment from anthropogenic emissions and releasesof mercury, including its compounds. Mercury
applications are required to be reduced and/or substituted.
Therefore, mercury, which is now used by society andindustries, will need to be recovered for environmentallysafe control. This means that the final disposal ofrecovered mercury in environmentally safe ways will be
required in the near future. Mercury disposal in controlled
landfill sites might be a feasible option (Lee & Lee,2012). On the other hand, building public acceptance forconstruction of landfill sites for final mercury disposal is
Global Environmental Research
24/2020: 045-052
printed in Japan

Takahashi.indd

45

expected to be difficult. The major factors influencing
public acceptance are risk perception, benefit perception
and public attitude (Alhakami & Slovic, 1994; Barnett etal., 2007; Costa-Font et al., 2008; Purvis-Roberts et al.,2007; Schulte et al., 2004; Slovic et al., 1991). In
addition, trust, knowledge and individual differences may
also be greatly relevant (Gupta et al., 2012). Therefore,risk perceptions and attitudes toward mercury may be
important considerations for forming acceptance for theconstruction of mercury landfill sites. According toHirono’s survey on the history of mercury applications,mercury toxicity has been known since the ancient
Grecian era and many mercury poisonings have occurred,even before Minamata disease (Hirono, 2017). In risk
perception research by Slovic, 81 hazards were analyzed
focusing on 18 risk characteristics. These werecategorized based on two major risks: dread risk andunknown risk (Slovic, 1987). Mercury was grouped in
the high dread and high unknown risk categories alongwith cadmium and radioactive waste. On the other hand,risk perceptions can be affected by cultural and social
background as well as context (Kinoshita, 2002;
Kleinhesselink & Rosa, 1991). According to a study on©2020 AIRES

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F. TAKAHASHI

mercury risk perceptions in Japan, methylmercury was
perceived more strongly as a risk than other food-related
hazards like high fat content, synthetic colorants andgenetically modified materials (Niyama et al., 2011).
Structural equation modeling proposes that knowledge
and emotional appraisal of mercury hazards areassociated with mercury risk perception as well as
cognitive difficulty of avoiding mercury exposure. In
contrast to mercury risk perception, cognitive attitudes tomercury are still uncertain, in particular in terms of longterm storage or landfill disposal. Therefore, as a first step,
this study focused on emotional perceptions of mercury,in particular cognitive aversion. Cognitive aversion
toward mercury was quantitatively measured using apairwise comparison method. Personal characteristics like
concerns about well-known hazards such as radioactive
waste, dioxins and infectious medical waste and about
waste recycling were also focused on in terms of theirimpact on cognitive aversion toward mercury.

2. Survey and Analysis Methods
2.1 Matters Targeted in Aversion Evaluation
To quantitatively scale cognitive aversion toward
mercury, this study selected seven matters that also
seemed subject to public aversion like mercury forcomparison. These were radioactive waste, dioxins,
infectious medical waste, cadmium, ultraviolet light,
heavy noise, and genetically modified foods, listed inTable 1. Although genetically modified (GM) foods
might not be appropriate as a hazard source, they were
included owing to strong public concern about the safetyof GM foods (Frewer et al., 2004; Bawa & Anilakumar,2013).
2.2	Pairwise Comparison Method with Thurstone’s
Law of Comparative Judgement
This study quantitatively evaluated aversion to“hazardous” matters, listed in Table 1, using a pairwise
comparison method with Thurstone’s law of comparative
judgement. A questionnaire survey was employed, withpairs of matters presented to the survey participants in
random order. In this study, 45 pairs were shown to eachparticipant for binary choice. The participants selected
one matter to which they felt a stronger aversion. Thismethod is based on a psychological model proposed byThurstone (Thurstone, 1927). When two psychologicalstimuli are compared, they each invoke a “discriminal
process” on a psychological continuum. The judgement
between the two stimuli, which is a selection of the moreabhorrent matter in this case, is driven by the rank
produced by the discriminal process associated with eachstimulus (Cheng et al., 2013). Because psychologicalprocessing usually fluctuates, the discriminal process is
not fixed but subject to variation, called discriminal
dispersion. In Thurstone’s model, the probability
distribution of the discriminal process is described bynormal Gaussian distribution. The concept of this modelis shown in Fig. 1. When ZA and ZB are the aversions thatthe survey participants perceive to matter A and matter B,
respectively, the difference between the two aversions,(ZA − ZB) has the relationship of a cumulative normal
distribution with a selection ratio of matter A (or matter
B). When the aversion difference is larger, the selection
ratio will increase or decrease correspondingly. When the
two aversions are equal, no aversion difference is
expected, resulting in a balanced selection ratio of eachmatter (0.5). This study used case V of the initialcondition, in which the expected value (or mean) of theaversion difference was assumed to be zero and thevariances of the discriminal dispersions of the two matteraversions were equal. The variance was set at 1.0, which
is usual. As shown in Fig. 1, the selection ratio could beinversely transformed to the difference between two
matter aversions. The aversion to matter A could be
calculated from the average of all differences related tomatter A because of the initial condition of zero mean. In
this study, a larger score means greater aversion intensity.
2.3 Consistency of Respondence Data
Before the aversion was quantified, the consistencyof participant respondence was checked. In the case of

Table 1 Matters tested for aversion evaluation.

HazardMatterTakahashi.indd

46

SourceMercury

Neurotoxicity, like Minamata disease

Chang (1977)

Radioactive waste

Carcinogenicity

IARC (2001)

DioxinCarcinogenicity

IARC (1997)

Infectious medical waste Pathogenicity

Windfeld and Brooks (2015)

Cadmium

Neurotoxicity, carcinogenicity

IARC (1993)

Ultraviolet light

Skin damage, carcinogenicity

IARC (1992)

PM2.5

Positive to cardiovascular and lung cancer mortality

Laden et al. (2006)

Noise

Adverse effects on cognitive performance

Stansfeld et al. (2005)

Gene-modified foods

Concerns about toxins, allergens or genetic hazards

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Cognitive Aversion of Mercury Scaled by Pairwise Comparison Method
three matters, say A, B and C for example, in all there arethree possible pairs of two matters for pairwise
comparison (A-B, A-C, and C-B). When one participant
responds with aversion intensities in the order of A>B
and B>C, the aversion to matter A is assumed to begreater than that to matter C (A>C) in terms ofconsistency. If the response is C>A, the aversion order
will be circular (A>B>C>A), which is called a circular
triad. To evaluate respondence consistency, Kendall
proposed a coefficient of consistency (ξ), which is theratio of the number of circular triads in one participant’s
respondence data to the theoretically maximum numberof circular triads as described by Eq. 1 and 2 (Kendall,1985), with “K” being the number of matters, and “d,”the number of circular triads.

When K is even1
(K 3− 4K) − d
24
24d
ξ=
= 1− 3
K − 4K1
3(K − 4K)
24(Eq. 1)

1.0
0.9

Selection ratio (-) .

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
-3.0

-2.0

-1.0

0.0

0.0

1.0

2.0

Difference in psychological stimulus(aversion) (-)

3.0

Fig. 1 Relationship between difference in aversion and selection
ratio.

{

Household income

Personal income
100%

90%

90%

90%

80%

80%

80%

70%

70%
60%

One ore 70%
more 60%

50%

50%

50%

40%

40%

40%

40%

30%

30%

30%

30%

90%
80%

Kinki

70%
60%

Chubu

50%

Kanto

20%

60%

20%

TohokuHokaido

10%
0%

10%
0%

Children raised

MarriedSingle

20%
10%
0%

No

20%
10%
0%

Not answered
Unknown> 20 m
15 m -20 m
8 m - 12 m
6m-8m
4m-6m
2m-4m
<2m

(Eq. 4)

2.4 Web Questionnaire Surveys
Web questionnaire surveys were conducted in March2018 by QuickMill®, Macromill Inc., Japan. The numberof survey participants, sample size (N), was 1,030. The
participants were randomly selected and anonymous to
the author. On the other hand, each participant’s
respondence was traceable through a participant
identification number. Therefore, cross-tabulation
analysis was possible for detecting cognitive trends. The
participants were pretreated to obtain equal gender ratios
and an equal age distribution with 10-year age intervals(20s, 30s, 40s, 50s and 60s). Participant regionality mightyield biased data. For example, public concerns about
mercury and radioactive waste may be specifically large
in Kumamoto and Fukushima, respectively. To prevent
regionality-derived bias, participant pre-screening was
also used to harmonize participant regionality with real
regional population densities in Japan. The percentagesof participants from Kumamoto and Fukushima were
1.4% and 0.7%, respectively. Attributes and demographic
characteristics of the survey participants are shown in
Fig. 2.

100%

Civil status(Eq. 3)where df is degree of freedom, K is the number of thematters and d is the number of circular triads.

100%

KyushuShikoku
Chugoku}K(K− 1)(K− 2)
1
8
+ df
−d+
K− 4
24
2
K(K− 1)(K− 2)
df =(K − 4)2
χ2 =

100%

Region100%(Eq. 2)

When the respondence is more consistent, ξ reaches
1.0. In contrast, worse consistency of the respondence
causes ξ to approach 0. On the hypothesis that theparticipants give random selections in binary choices, thestatistic χ2, described by Eq. 3, follows χ2 distribution
with a degree of freedom described by Eq. 4 (Kendall,1985). Therefore, the randomness of the respondence canbe assessed by χ2 test.

When K is odd
1(K 3−K) − d
24
24d
ξ=
= 1− 3
K −K
1
3(K − K)
24

47

90%
80%
70%
60%
50%
40%

Occupation

Not answered
Unknown > 20 m
15 m -20 m
6 m - 8 m 8 m – 12 m
4m-6m
2m-4m

10%
0%

90%
80%
70%
60%

Freelance 50%
profession 40%
30%

30%
20%

100%

20%

<2m

Corporate
manager

10%0%

No jobOthersStudent
Part-time job
Homemaker
Self-employedCompany worker (Others)
Company worker (Technical jobs)
Company worker (Desk jobs)
Civil service

Fig. 2 Attributes and demographic properties of the survey respondents.

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F. TAKAHASHI

3. Results and Discussion

2.5	Aversion Score Adjustment for Comparisons and
95% Confidence Intervals
Not only the “hazardous” matters listed in Table 1
but also concerns about waste recycling were surveyed in
the questionnaire. This consisted of six questions asfollows: 1) Do you know the collection day of cans inyour residence area? 2) Do you know the correct rules forcan disposal? 3) Do you know the collection day for glass
in your residence area? 4) Do you know the correct rules
for glass disposal? 5) Do you wash cans before you
dispose of them? 6) Do you separate PET bottles fromother plastic wastes? Because the aversion scoresevaluated by Thurstone’s method are relative to eachother, they can vary depending on the initial condition ofvariance (1.0 in this study). To fairly compare theaversion scores among different gender and age groups
and different intensities of concerns about hazardous
wastes and waste recycling, the range of aversions was
adjusted to 2.0, with a maximum aversion score of 1.0
and minimum aversion score of −1.0. Owing to themethodology of Thurstone’s approach, it is impossible tocalculate 95% confidence intervals of aversion scores
directly using a single survey dataset. Therefore, the 95%
confidence interval of the selection ratio was first
calculated and then the aversion score ranges were
calculated. The 95% confidence intervals of the aversion
scores were also adjusted correspondingly to beharmonized with the score range adjustment.

(a)

0.80
0.60
0.40
0.20
0.00

3.2 Cognitive Aversions and Age/Gender Effects
Aversion scores of the matters tested are shown in
Fig. 4. The error bar indicates the 95% confidence
interval. The strongest aversion was found for radioactive
waste. Strong concerns about radioactive waste may be
the main contribution to this. Although the survey was
limited to university students, public attention to
everything “nuclear” has increased in Japan since the
Fukushima Daiichi nuclear accident in 2011 (Gallardo etal., 2014). Mercury had the second strongest aversion
followed by dioxins, infectious medical waste, PM2.5
and cadmium. Ultraviolet light and genetically modified
1.00

Relative frequency (-)

Cumulative frequency (-)

1.00

3.1 Respondence Data Quality
The consistency of the respondence data wasassessed by the coefficient of consistency, and thecumulative distribution of the coefficient of consistency
is shown in Fig. 3. Although there is no common standard
to certify sufficient consistency, it is regarded as sufficientwhen the coefficient of consistency is more than 0.8 (Van
Riel et al., 2016). More than 80% of the respondence data
exceeds 0.8 (see Fig. 3a). In addition, more than 90% of
the respondence data are regarded as non-random
selection by the χ2 test at the 5% significance level (seeFig. 3b). According to these results, it is concluded that
this study successfully collected respondence data with
sufficient quality.

0.80

(b)

0.60
0.40
0.20
0.00

0.00

0.20

0.40

0.60

0.80

1.00

Coefficient of consistency (-)

P value (-)

Fig. 3 Cumulative distribution of the coefficient of consistency (a) and P value frequency histogram (b).

NoiseUltraviolet light
GM foods

Infectious medical waste
Cadmium PM2.5
Dioxins Mercury-1.00 -0.80 -0.60 -0.40 -0.20

0.00

0.20

Aversion score (-)

0.40

0.60

0.80

Radioactive waste

1.00

Fig. 4 Aversion scores of tested matters (after range adjustment).

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Cognitive Aversion of Mercury Scaled by Pairwise Comparison Method
foods provoked similar aversions which were greatly lessthan that to cadmium. Noise pollution elicited the weakest
aversion among the matters tested. The 95% confidence
intervals of the aversion scores do not overlap between
radioactive waste and mercury, or between mercury anddioxins. This suggests that mercury elicits significantly
larger and smaller aversions than dioxins and radioactive
waste, respectively. On the other hand, dioxin aversion
should be regarded as comparable with that to infectious
medical waste owing to the large overlap of confidence
intervals. Publicly shared lessons on Minamata disease
may contribute to the strong aversion to mercury. For
example, in-depth news and documentaries about
Minamata disease, comprising straight news, school
education, public welfare announcements and othersources, have been broadcasted periodically throughout
the past 60 years (1956 –2015), appearing more frequently
in recent years (Kato et al., 2018). On the other hand, thestrong mercury aversion might also result fromenvironmental stigma (Colocousis, 2012). In fact, thestigmatization of contaminated or pseudo-contaminatedmatters has raised disgust (Rozin et al., 1985; Rozin etal., 1986; Kecinski et al., 2018) and this effect might bepersistent (Kecinski et al., 2018). A long-term
stigmatizing effect has been found in real estate asset
valuation even after remediation (McCluskey & Rausser,
2003). As shown in Fig. 5, gender produced no significant
differences in aversions. The paired t-test supports it atany significance level (P = 1.000) for aversion scores
both with and without range adjustment. According to thetwo-way ANOVA test, not only gender but also age hadno significant impact on mercury aversion at the 5%
significance level (P = 0.538 for gender and P = 0.618 forage in range-adjusted data). It differs from risk perception
to various hazards. Gender and age usually havesignificant or non-negligible impacts on risk perception(Siegrist et al., 2005). In terms of risk attitude, risk
avoidance is higher among females than males andincreases strongly with age (Dohmen et al., 2011). If
mercury aversion is associated directly with risk
avoidance, mercury aversion in females would be
expected to be stronger than that in males. As expected,
mercury aversion scores of the female group were 4%
higher than that of male group when aversion scoreswithout range adjustment were compared. On the other
hand, the mercury aversion score of the female group wasslightly lower than that of the male group for rangeadjusted data. This inconsistency is caused by a limitationof Thurstone’s model. As described above in Section 2.5,
any scores scaled by Thurstone’s method are relative toeach other and can vary depending on the initial conditionof the variance. To verify age and gender effects on
mercury aversion, use of different approaches isrecommended, like Scheffé’s method, which consists of amain effect, combination effect, order effect and error
terms based not on a psychophysical model but the multi-

Takahashi.indd

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49

Male (average)-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
Female (average)-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
Male (age group)
20’s-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00

30’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
40’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
50’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
60’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00

Female (age group)
20’s-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00

30’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
40’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
50’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
60’s

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
Aversion score (-)

Fig. 5 Aversion scores for matters tested, by gender and age group.

way ANOVA model (Scheffé, 1952; Inoue, 2012).
3.3	Effects of Concerns about Other Hazards andWaste Recycling
People who are concerned strongly about hazardous
matters like dioxins may have a stronger aversion to
mercury. According to the respondence data, theparticipants were categorized into three groups based on
the strength of their concerns regarding matters listed inTable 1, excluding mercury. In the case of dioxins, forexample, the number of selections of dioxins in all binary
choices was counted for each participant. When thenumber of dioxin selections was seven to eight, it was
categorized as a strong concern about dioxins. If thenumber of selections was four to six they werecategorized as moderate, and for zero to three, weaklevels of concern. In the cases of GM foods, ultraviolet
light and heavy noise, the categorization criteria werechanged to 6 –8 for strong concern, 3–5 for moderate

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F. TAKAHASHI

Mercury aversion score (-)

50

1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
-0.20
-0.40
-0.60
-0.80

Strong concern
Moderate concern
Weak concern

RaW

Dxn

IMW

Cd

PM2.5

UV

GMF

Noise

Fig. 6 Mercury aversion scores of groups with different concern intensity to tested matters (RaW: radioactive waste, Dxn:
dioxins, IMW: infectious medical waste, Cd: cadmium, PM2.3: -, UV: ultraviolet light, GMF: genetically modified
foods, Noise: heavy noise).

Takahashi.indd

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1.20

Mercury aversion score (-)

concern, and 0–2 for weak concern. The average mercury
aversions of different concern strength groups are shownin Fig. 6. When the participants had strong concerns
about well-known hazards, which were radioactive waste,
dioxins, infectious medical waste and cadmium, they had
a stronger aversion to mercury than those who wereconcerned more about other matters like GM foods,
ultraviolet light and heavy noise. However, the two-way
ANOVA test suggested that these differences were notsignificant at the 5% significance level (P = 0.762). On
the other hand, the participants who had moderate or
weak concerns about any of the tested hazards excluding
mercury had stronger mercury aversion than the groupwith strong concerns (see Fig. 6). This is regarded as asignificant difference according to a multiple comparisonby the Tukey-Kramer method (P = 0.00005). In contrast
to the initial expectations, cognitive aversion to mercury
might not be associated directly with concerns aboutother hazards.
The participants were also categorized into threegroups based on the strength of their concerns regarding
waste recycling. They were categorized based on thenumber of positive answers (yes) to the six questions
described above in Section 2.5. The categorization criteria
was 5 –6 for strong concern, 3–4 for moderate concern,and 0–2 for weak concern about waste recycling. Because
Cronbach’s alpha of the construct on waste recycling was
0.721, the internal consistency satisfied the acceptablelevel of the Nunnally criterion in terms of constructvalidity (Cronbach, 1951); Nunnally, 1978). Mercury
aversions of groups with different levels of concern about
waste recycling are shown in Fig. 7. No clear trend wasfound between mercury aversion and concern about waste
recycling. Aiming for sustailability, an educational
campaign for sustainable development has been emerging
in Japan (Nomura & Abe, 2009). In fact, theenvironmental education campaign has been effective at
raising awareness and concern about waste recycling andsustainable behaviors (Gallotti et al., 2012). In this social

1.00
0.80
0.60
0.40
0.20
0.00

StrongModerate

Weak

Fig. 7 Mercury aversions of groups with different levels ofconcern about waste recycling.

movement, the author expected that concerns encouraged
regarding waste recycling by environmental education
campaigns might collaterally mitigate the negative
perception of mercury and thus help the mercury disposal
problem be addressed more comprehensively. However,this study concluded that such a secondary effect on
mercury aversion mitigation was unlikely.
3.4 Limitations
In this study, the associations of gender, age andconcerns about the matters tested or waste recycling were
investigated with regard to mercury aversion. It should be
noted that the demographic properties tested are probably
insufficient for an in-depth analysis to reach any
generalized conclusions on mercury aversion. If mercury
aversion is, more or less, associated with awareness of
mercury toxicity, it should be considered that awarenessand knowledge of methylmercury would be significantly
linked to demographic characteristics like gender, race,
education, income, fish preparation experience and risk

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Cognitive Aversion of Mercury Scaled by Pairwise Comparison Method
perceptions, as reported by a survey in the US (Lando &Zhang, 2011). In addition, risk perception has strong links
to gender, age, education, income and race (Savage,1993). Not only demographic characteristics but also
instinctive emotion processing might need to beconsidered. Negative emotions, in particular disgust, areelicited by risk perception of pathogen transmission(Curtis et al., 2004). Disgust efficiently functions topromote avoidance of contaminants (Davey & Bond,2006) and/or hazardous substances (Rozin & Fallon,
1987). Disgust is often associated with the objective
presence of health threats. It motivates avoidance, andappears to be predominantly a health-promoting discreteemotion (Consedine & Moskowitz, 2007). Althoughmercury aversion was quantified in this study, it is only asemblance of observations of emotions. Mercury aversion
is produced through biological, personal andsociopsychological processing mechanisms.

4. Conclusions
This study quantified emotional perceptions of
mercury. Cognitive aversions to mercury and otherharmful matters like radioactive waste or those perceived
as hazardous were quantitatively evaluated using apairwise comparison method with Thurstone’s law ofcomparative judgement (sample size = 1,030). The
strongest aversion was found for radioactive waste,followed by mercury. Although gender and age usuallyaffect risk perception of any hazard, this study found thatgender and age had no significant impact on mercury
aversion at the 5% significance level. To verify theseresults, use of different approaches is recommended like
Scheffé’s method, which was designed based on themulti-way ANOVA model. When the participants hadstrong concerns about well-known hazards (radioactive
waste, dioxins, infectious medical waste and cadmium),
they had stronger aversion to mercury than those who
were concerned more about other matters like geneticallymodified foods and ultraviolet light. On the other hand,the participants who had moderate or weak concerns
about well-known hazards had stronger mercury aversion
than the group with strong concerns. No clear differencesin mercury aversion were found regardless of different
strength of concerns about waste recycling. Cognitive
aversion to mercury is associated with hazard perception
and not affected by concerns about waste recycling.
Acknowledgements
This research was financially supported byEnvironment Research and Technology Development
Funds (3K143002, 3-1701, and SII-6-1(2)(JPMEERF20S20602)) of the Ministry of theEnvironment, Japan. The author appreciates the support
greatly.

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Fumitake TAKAHASHI
Fumitake Takahashi is an associate professor in
the Department of Transdisciplinary Science andTechnology, School of Environment and Society,
Tokyo Institute of Technology. He received his
Ph. D. in Urban Engineering at the University ofTokyo. His current research fields include waste
management, waste recycling, environmental risk
assessment, socio-psychological analysis of waste recycling behaviors andrecycling tools.
(Received 10 February 2020, Accepted 8 June 2020)

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