Social Design Engineering Series

SDES-2020-2

Is climate change induced by humans? The impact of the
gap in perceptions on cooperation

Jinichi Hirose
Kochi University

Koji Kotani
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology

Yoshinori Nakagawa
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology

21st May, 2020

School of Economics and Management
Research Institute for Future Design
Kochi University of Technology
KUT-SDE working papers are preliminary research documents published by the School of Economics and Management jointly with the ResearchCenter for Social Design Engineering at Kochi University of Technology. To facilitate prompt distribution, they have not been formally reviewed
and edited. They are circulated in order to stimulate discussion and critical comment and may be revised. The views and interpretations expressedin these papers are those of the author(s). It is expected that most working papers will be published in some other form.

Is climate change induced by humans? The impact of the
gap in perceptions on cooperation
Junichi Hirose*,† Koji Kotani†,‡,§,¶,|| Yoshinori Nakagawa†May 13, 2020

Abstract
Climate change is a serious problem that requires people’s cooperation to solve, and it has
been reported that there have been gaps in perceptions about the cause. However, little is known
about what makes people perceive that climate change is human-induced, nature-induced or induced by some other factor and the linkage between perception and cooperation. We analyze thedeterminants of human-induced perception and the impact of the gap in perceptions on cooperative behaviors toward climate change by conducting a survey experiment with a climate donation
game with 400 Japanese subjects. First, the analysis identifies the importance of people’s scientific literacy in explaining the perception gaps in that those with high scientific literacy tend tohave the perception of human-induced climate change. Second, people are identified as being
cooperative toward climate change, as they have a prosocial value orientation, high scientific literacy and the perception of human-induced climate change, demonstrating two important roles ofscientific literacy as not only a direct determinant but also an indirect one, through a mediator of
people’s perceptions. Overall, the results suggest that scientific literacy shall be a key to enhancing cooperation toward climate change by promoting the perception of human-induced climatechange.

Keywords: Human-induced climate change; scientific literacy; climate donation game

* Multidisciplinary Science Cluster, Collaborative Community Studies Unit, Kochi University†

School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology§
Urban Institute, Kyusyu University¶
College of Business, Rikkyo University||
Corresponding author, E-mail: kojikotani757@gmail.com‡

1

Contents
Nomenclature

2

1

Introduction

3

2

Materials and methods

5

3

Results and discussion

94Conclusions

18

5

References

20

List of Figures

24

List of Tables

28

Nomenclature
CDG Climate donation game
GCF Green climate fund
JPY

Japanese yen

NISTEP National Institute of Science and Technology Policy of JapanSEM Structural equation modeling
SVO Social value orientation
WTP Willingness to pay

2

1

1

Introduction

2

Climate change is a serious problem that requires people’s cooperation to solve (Pacheco et al.,3

2014, Bang et al., 2015). Unfortunately, people around the world seem to have failed in cooperating4

and coordinating their efforts on this issue, although humans are known as unusually cooperative

5

species compared with other species (Boyd and Richerson, 2009, Tattersall, 2011). There have been

6

several types of research to analyze how people become cooperative regarding climate change. These

7

studies establish that correct perception and/or knowledge of climate change are positively associated8with cooperative attitudes, whereas a wide variety of gaps in such perceptions exist (Rand et al., 2009,9

Tobler et al., 2011, Fischer and Charnley, 2012, Islam et al., 2016). Despite its importance, few studies

10

have examined how such gaps in perception are related to knowledge and to other factors and how

11

the relationship influences cooperative behaviors. Given this state of affairs, this research addresses

12people’s perception gaps with a focus on the cause of climate change, knowledge, and cooperative

13

behaviors within a single framework.

14

Past studies have examined people’s perceptions of the cause of climate change (Bray, 2010, Cook15

et al., 2013, Carlton et al., 2015). By and large, there are two ideas about the cause of climate change.

16

One is that climate change is human-induced, in that climate change can be considered to be caused

17

by human activities, such as burning fossil fuels, cutting down forests and farming livestock (Karl18

and Trenberth, 2003, Koneswaran and Nierenberg, 2008, Doran and Zimmerman, 2009, Solomon

19

et al., 2009, Bechtel and Scheve, 2013, Höök and Tang, 2013). The other idea is that climate change

20

is nature-induced, in that climate change can be considered a part of natural climate cycles. It will21

continue to be so, being exemplified by many events in the Earth’s history, such as changes in solar

22

output, Earth’s orbit and volcanic eruptions (Karl and Trenberth, 2003, Solomon et al., 2009, Council

23

et al., 2011). A group of former studies show that scientists have largely accepted the idea of human-

24

induced climate change (Karl and Trenberth, 2003, Hegerl et al., 2007, Anderegg et al., 2010, Council

25

et al., 2011, Lehtonen et al., 2019). Leiserowitz et al. (2010) report that approximately 97 % of26

publications by climate scientists advocate human-induced climate change, while only half of the27

American public believe in human-induced climate change (Doran and Zimmerman, 2009, Anderegg3

28

et al., 2010, Carlton et al., 2015).

29

Shealy et al. (2016) and Shealy (2018) find that civil engineering students in America who do not

30

believe in human-induced climate change are less likely or never desire to take jobs associated with31

addressing climate change in their careers. Saleh Safi et al. (2012) examine the relationships among32

the vulnerability, beliefs and risk perception of human-induced climate change in rural Nevada. They

33

report that climate-change-specific beliefs, particularly whether people believe in the human-induced34

causes of climate change and/or whether they connect the locally observed impacts to climate change,

35

are the most prominent determinants of risk perception. The idea of human-induced climate change36

remains a public controversy despite the consensus among climate scientists (Bray, 2010, Cook et al.,37

2013, Tol, 2014, Carlton et al., 2015). Aside from this controversy, it is likely that the actual perception

38

and attitudes toward climate change will be affected by the extent to which people believe in human-

39

induced climate change.

40

Some researchers implement surveys on people’s perceptions and their cooperative attitudes to-41

ward climate problems proxied by their willingness to pay (WTP) (O’Connor et al., 1999, Akter and42

Bennett, 2011, Brechin and Bhandari, 2011, Islam et al., 2016). Brechin and Bhandari (2011) con-43

firm that people in some countries remain more concerned about general environmental problems

44

than global climate change through comparative national studies on the public perception of climate

45

change and its WTP. O’Connor et al. (1999) examine the relationship between people’s risk percep-

46

tions and their WTP for climate problems, reporting that an environmental belief is a strong predictor

47

of behavioral intentions for voluntary actions. Akter and Bennett (2011) examine Australian house-48

holds’ perceptions of climate change and their preferences for mitigation action, finding that people’s

49

willingness to take actions against climate problems at national and household levels is influenced by

50

their level of mass media exposure. Moreover, Islam et al. (2016) examine the relationship between

51

climatic perception and flood mitigation cooperation, suggesting that accurate climatic perception is52

key to increasing people’s cooperation in managing climate change.

53

These studies have demonstrated that people’s perception influences their cooperative attitudes

54

toward climate change. However, few works have addressed people’s perceptions of the cause of55

climate change along with their cooperative behaviors. Specifically, little is known about what makes4

56

people perceive that climate change is human-induced, nature-induced or induced by another factor

57

and the linkage between their perception and cooperative behaviors. To examine these issues, we

58

empirically analyze the determinants of people’s perception of human-induced climate change and59

the linkage to their cooperation toward climate change by conducting a survey experiment with a

60

climate donation game (CDG) with 400 Japanese subjects. In this survey, we measure and collect61

people’s scientific literacy, social preferences and actual cooperation toward climate change by the62

use of the CDG in addition to sociodemographic information. Social psychologists and economists

63

argue that scientific literacy and social preferences can be keys to influencing people’s cooperative

64

attitudes toward natural disasters and other social events (Van Lange et al., 2007, Bogaert et al., 2008,

65

Nakagawa, 2016, Mischkowski and Glöckner, 2016, Shahrier et al., 2016, Timilsina et al., 2019). With

66

these data, our research addresses the following two open questions. (1) What are the determinants of67

the human-induced perception of the cause of climate change? (2) How does the gap in perceptions

68of the cause of climate change, along with scientific literacy and social preferences, affect people’s

69

cooperative behaviors?

70

2

Materials and methods

71

The online survey experiment is conducted with 400 subjects in a web-based research organiza-

72

tion, Cross Marketing Inc. Subjects’ mean age is 49.61 years, with the standard deviation = 17.32

73

ranging between 20 and 89 years. The area the survey covers is divided into urban and nonurban areas

74

according to the population density of 500 people km−2 . If the population density in the residence area

75

where a subject lives is above or equal to 500 people km−2 , then it is urban; otherwise, it is nonurban.

76This survey collects a sample of 200 subjects in each of the urban and nonurban areas with informa-

77tion about (i) sociodemographic factors, such as age, gender, marital status, occupation, educational

78

background, family characteristics and household income, (ii) perceptions on the cause of climate

79

change, (iii) scientific literacy, (iv) cooperation for climate change and (v) social value orientation (as80a proxy for social preferences).

81[Figure 1 about here.]5

82

Subjects are asked which perception they have concerning the cause of climate change: human-

83

induced, nature-induced or induced by some other factor. Subjects read the explanatory notes 1 and 2,

84

each of which corresponds to the description of “human-induced climate change” and “nature-induced

85

climate change” associated with figures 1(a) and 1(b), respectively. After subjects understand these86

explanations, they are asked to choose one option that is the closest to their current perception among87

the five options. (1) “I choose the explanatory note of 1 of human-induced climate change,” (2) “I

88choose the explanatory note of 2 of nature-induced climate change,” (3) “Explanatory notes 1 and89

2 are somewhat persuasive, but I cannot choose which one to support,” (4) “None of explanatory

90

notes 1 and 2 are persuasive” and (5) “I cannot judge it because I do not or cannot understand the91

explanation.”

92

Explanatory note 1: Some studies on climate change suggest that the greenhouse gases and carbon

93

dioxide released by human production activities are changing the patterns and94

cycles of climate around the world, as described in figure 1(a). Currently, the95

challenges posed by climate change are well recognized. The greenhouse gases

96

and carbon dioxide released from various human activities have an adverse effect97

on societies.

98

Explanatory note 2: Human impacts on climate change may neither be significant nor relevant. In the99

long term of thousands or tens of thousands of years, it is said that the pattern100

and cycles of the climate are changing naturally, as demonstrated in figure 1(b).

101Some studies suggest that the cause of climate change cannot be verified as being

102

human-induced, claiming that human-induced climate change is exaggerated too

103much. It is appropriate to understand that climate change is a part of natural

104

cycles in the long-term dynamics of the earth.

105

Scientific literacy is measured by the NISTEP scientific literacy scale adopted from a national

106

questionnaire survey about people’s attitudes toward general science and technology (NISTEP, 2001).

107

The National Institute of Science and Technology Policy of Japan (NISTEP) has organized a scale

6

108

consisting of 15 questions regarding general scientific knowledge and literacy, and it has been em-

109

ployed in some recent field studies (Nakagawa, 2016, Jingchao et al., 2018). A subject is asked to110

answer “true,” “false” or “no idea” for each question, where either “true” or “false” is usually set to be111

a correct answer. When she chooses a correct answer for a question, she scores 1, and otherwise, she112scores 0. The answer “no idea” for each question is counted as 0. The scale is defined as the number

113

of questions for which a subject answers correctly, ranging from 0 to 15.

114

Questions 1-13 pose scientific propositions such as (1) “the center of the Earth is very hot,” (2) “all115

radioactivity is humanmade,” (3) “the oxygen we breathe comes from plants,” (4) “it is the father’s

116

gene that decides whether the baby is a boy or a girl” and so on, each of which shall be answered by117

choosing “true,” “false” or “no idea.” Questions 14 and 15 are posed in a different manner. Question

118

14 is posed as “which travels faster, light or sound?” Each subject is asked to choose one of four an-

119

swers: “light,” “sound,” “the speeds are nearly the same” and “I have no idea.” Question 15 comprises

120

two subquestions, where the first subquestion is “does the Earth go around the sun, or does the sun

121

go around the Earth?” When a subject answers correctly in the first subquestion, the next subquestion122

is posed as “if the Earth goes around the Sun, how long does it take?” The NISTEP scientific scale123

is established as a reliable measurement to influence people’s behaviors and cooperative attitudes in124

disaster management and energy issues (Nakagawa, 2016, Jingchao et al., 2018).

125

We institute a climate donation game (CDG) to approximate the degree of people’s cooperation

126

toward climate change. This game is considered a variant of a dictator game in a two-player setting,127

where one person (the other person) is assigned to be a dictator (a receiver), and the dictator can

128

decide how to split a fixed amount of money between herself and the receiver (see, e.g., Bolton et al.,129

1998, Engel, 2011). In most cases, a dictator and a receiver play the game under an anonymous setting

130

so that each player never knows the identity of the other. The CDG is distinct from a typical dictator

131

game in two ways. First, each subject becomes a dictator, knowing who is a receiver. Second, the132

receiver is not a human but a well-known organization called the “Green Climate Fund” (GCF) in

133

Japan that runs a series of nonprofit activities to fight against climate change.

134

In the CDG, each subject is given 1000 JPY as an initial endowment and asked to distribute the135money between herself and GCF as she wishes. If she takes everything (nothing) for herself, then the7

136

money donated to GCF is 0 JPY (1000 JPY). If she takes 400 JPY for herself, then the money donated137

to GCF is 600 JPY. When we instruct subjects about the CDG, we are cautious about stating “how138

to split between yourself and GCF is totally up to you, and nobody can know how you split because

139

everything is recorded by an ID, not by your name.” Economists use the amount of money the dictator

140

gives to the receiver in dictator games as a good proxy of altruism, i.e., how much one person cares

141

about the generally unknown other (Diekmann, 2004, Bekkers, 2007, List, 2007, Andreoni et al.,1422017). In a similar fashion, we consider that the amount of money the dictator gives to GCF is a good143

proxy for how much one person cares about climate change, wanting to cooperate for its solution.

144

We use social value orientations (SVOs) in the triple-dominance game developed by Van Lange145

et al. (1997, 2007) to characterize subjects’ social preferences. It is known to be reliable and to reflect

146

a stable personality trait of how people evaluate interdependent outcomes for themselves and others in147social environments (Van Lange et al., 1997). This method categorizes individual value orientations

148

into four types—“competitive,” “individualistic,” “prosocial”, and “unidentified”—depending on their

149

choices in the questions. In one question, a subject chooses one option among three options, option

150(1): you get 480, and the other gets 80, option (2): you get 480, and the other gets 480, and option (3):151

you get 540, and the other gets 280. In this example, option (1) represents a competitive orientation

152

that maximizes the point gap between herself and the other (480−80 = 400). Option (2) is a prosocial

153

orientation that maximizes the joint outcome (480 + 480 = 960). Option (3) is an individualistic154

orientation that maximizes her own outcome of 540, being indifferent to the outcome of the other.

155

This SVO game contains nine questions, each of which consists of three options for herself and the156

other. In each question, one option among them corresponds to one of the following orientations,

157

i.e., “competitive,” “individualistic” and “prosocial.” Each subject is asked to choose one option as

158

the most preferred in each item, finally generating nine choices of options. Each subject is classified

159

as prosocial (individualistic or competitive) if she makes six or more choices of options with that160

orientation. Otherwise, she is categorized as “unidentified.”161

Our survey experiments have been conducted with real monetary payments in CDG and SVO

162

games. These are made for motivating subjects to seriously participate in the survey experiment, con-163

sidering their opportunity costs of time and their true revelation of social preferences and cooperative8

164

behaviors toward climate change, and one session took 40 to 60 minutes for each subject. In the165

CDG, subjects are informed that the amount of money they keep is theirs (subjects obtain 438 JPY

166≈ 3.98 USD on average in the CDG). In SVO games, subjects are informed that we randomly match

167

two subjects as a pair, and the more experimental points one subject gets from her own and partner’s

168

nine choices of options, the more real money she will earn with some exchange rate (20 points are169

converted to 1 JPY) (subjects obtain 226 JPY ≈ 2.05 USD on average in SVO games). In total, sub-170

jects are paid on average 769 JPY from the two games and surveys with a fixed participation fee of171

105 JPY.

172

3

Results and discussion

173

Tables 1 and 2 present the definitions of all variables used in the analysis and the summary statis-

174

tics of the variables for urban, nonurban and overall areas. The percentages of female subjects are

175

similar in urban and nonurban areas (38 % and 36 %, respectively). Subjects in urban and nonurban

176

areas possess a high school diploma as the median of education. The median household income in177

urban areas is 1 million JPY higher than that in nonurban areas. With respect to occupations, only

178

2 % of the subjects in nonurban areas are employed in agriculture, while all subjects in urban areas

179

report that they are salaried workers, such as company owners, office workers and civil servants.1 This180

implies that Japanese people depend on industries other than agriculture, even in nonurban areas. The

181

statistics of the sociodemographic information in table 2 are in accordance with our expectation; that

182

is, subjects in urban areas have higher education and household income than those in nonurban areas.

183

Additionally, in Japan, there exists a difference between urban and nonurban areas with respect to184

basic sociodemographic factors; however, the difference is not so large.
[Table 1 about here.]

185

186

We report the summary statistics of subjects’ SVOs, focusing on the percentages of prosocial

187

subjects in urban, nonurban, and overall areas (see the “SVO (prosocial)” row in table 2). While 58 %1

In nonurban areas, 173 out of 200 subjects report that they receive a regular salary in the same way as urban subjects.

9

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of subjects overall are prosocial, 56 % (60 %) of urban (rural) subjects are prosocial. This result is

189

in sharp contrast to those of similar studies in developing nations showing that the percentages of

190

prosocial subjects between urban and rural areas are quite different, and the percentage of prosocial

191

subjects in rural areas is higher than that in urban areas (Shahrier et al., 2016, 2017, Timilsina et al.,192

2017, 2019).2 Our result can be interpreted as indicating that the degree of prosociality among people

193

is not different between urban and nonurban areas in Japan compared to other developing countries.

194[Table 2 about here.]

195

Table 2 shows the summary statistics of subjects’ scientific literacy in urban, nonurban and overall

196

areas. We compute the Cronbach’s alpha of this scale to be 0.76, illustrating that the scientific literacy

197

scale possesses acceptable internal consistency in our sample. The median score of scientific literacy

198

is 9 points in both urban and nonurban areas, while the average scores of scientific literacy are 8.53 and199

8.24 points, respectively. This implies that scientific literacy between urban and nonurban subjects

200

is not much different; however, scientific literacy in urban subjects is slightly higher than that in201

nonurban subjects. This result is in line with the expectation because education levels are almost the202same between the two areas. With respect to people’s perception of the causes of climate change

203(see the “Perception of climate change” row in table 2), 30 % (33 %) of urban (nonurban) subjects

204

answer that climate change is caused by human-induced factors. Conversely, 12 % (14 %) of urban

205(nonurban) subjects answer that climate change is caused by nature-induced factors, implying that,206

interestingly, 59 % (53 %) of urban (nonurban) subjects answer that the cause of climate change cannot

207

be judged to be human-induced or nature-induced. Regarding the results of the climate donation game

208

(CDG) (see the last row of “Donation” in table 2), the donation (JPY) by urban subjects (mean =209455.53, median = 500) is generally higher than that by nonurban subjects (mean = 419.90, median =210400), while the overall average donation is 437.71 (median = 500).
[Table 3 about here.]211

2

As mentioned earlier, Japanese people depend on some industries other than agriculture, even in nonurban areas,
receiving a regular salary for office work. This may be one of the reasons why the difference in prosociality between
urban and nonurban areas is not found in our study.

10

212

Table 3 demonstrates that there is some relationship between subjects’ perceptions of the causes of213

climate change and their donations (JPY) to climate change. With respect to those with the perception

214of human-induced climate change, the average (median) donations are 590.25 (500) by urban subjects

215

and 525.00 (500) by nonurban subjects, respectively, as shown in the “Human-induced” row of table 3.

216

With respect to those with the perception of nature-induced climate change, the average (median)217

donations are 535.22 (500) by urban subjects and 272.50 (100.00) by nonurban subjects. Finally,218

with respect to those with “Other,” the average (median) donations are 370.78 (300) by urban subjects

219

and 393.39 (300.00) by nonurban subjects. Overall, it appears that subjects who perceive climate

220

change as being human-induced tend to donate more to the prevention of climate change than those221

who perceive climate change to be nature-induced or induced by another factor, irrespective of urban

222

and nonurban areas. We can also confirm the same tendency from the “Overall” column of table 3,

223

depending on whether subjects perceive human-induced climate change.
[Figure 2 about here.]

224

225

Figure 2 shows a series of boxplots to represent whether there is a distributional difference of226

donations among those with perceptions of human-induced, nature-induced, and other climate change.

227

Figures 2(a) and 2(b) present that the distribution of donations by subjects with the perception of228

human-induced climate change is in higher positions than those by subjects with the perception of229

nature-induced and other climate change. Moreover, figure 2(c) shows that there is little difference

230

between those with the perception of nature-induced and those with the perception of other climate

231

change. To statistically check whether the distribution of donations differs by subjects’ perceptions on

232

the cause of climate change, we run Mann-Whitney tests with the following three pairs of donations

233

by those with different perceptions: (a) human-induced vs. nature-induced, (b) human-induced vs.

234

other, (c) nature-induced vs. other and (d) nature-induced+other vs. human-induced.3 For each pair, a

235

null hypothesis is that the distribution of donations by those with one perception is the same as that by236

those with the other perception. The Mann-Whitney tests show that pairs (a), (b) and (d) statistically237

reject the null hypotheses (Z = −2.667, p < 0.01, Z = −4.106, p < 0.01 and Z = 4.31, p < 0.01),
3

For pair (d), we have combined the observations of donations from “nature-induced” and “other” climate changes as“nature-induced + other. ”

11

238

while pair (c) does not (Z = 0.946, p = −0.068). This implies that whether subjects have human-239

induced perceptions can truly matter in determining their cooperative behaviors regarding climate

240

change.

241

Given the qualitative statistical results associated with perceptions and cooperation, we now seek

242

to quantitatively characterize what makes one person possess the perception of human-induced cli-243

mate change. To this end, we run the logit regression by taking the perception of climate change as a244

dependent variable (human-induced = 1, and 0 otherwise) and scientific literacy, prosociality and ba-

245

sic sociodemographic factors as independent variables (see table 1 for the definitions of the variables).

246

The logit regressions assume a logit form of the following distribution function:

Prob(yi = 1) =exp(Xi β)
1 + exp(Xi β)

(1)

247

where yi is a binary dependent variable, Xi is a vector of independent variables, and β is a vector of

248

unknown parameters. With this distributional assumption, the maximum likelihood methods estimate249

the unknown parameters of β, enabling the identification of the marginal probability of one person

250

possessing the perception of human-induced climate change when the independent variable increases

251

by one unit (holding other independent variables fixed). Therefore, the estimation of the logit re-252

gression answers the open question (1) posed in the introduction: “What are the determinants of the253

human-induced perception on the cause of climate change?”

254

Table 4 reports the marginal probabilities and the respective standard errors of the independent

255

variables on the perception of human-induced climate change along with the statistical significance.

256

Model 1 of table 4 contains only scientific literacy as an independent variable, and next, we gradually257

add age, gender, prosociality and other factors as independent variables in models 2 to 4, building

258

upon model 1. We first find that scientific literacy is statistically significant, with a positive sign at259

the 1 % level in a robust manner, irrespective of the models. The estimated marginal probabilities of260

scientific literacy on the perception of human-induced climate change range between 4.4 % and 3.9 %261

in models 1 to 4, implying that a subject is likely to have the perception of human-induced climate

262

change by 4.4 %∼3.9 % when her scientific literacy increases by one point.

12

263

Second, the age and gender dummy exhibit 1 % and 5 % statistical significance with positive264

signs in every model, respectively, while SVO is statistically significant only at the 10 % level, with265

positive signs in models 3 and 4. For instance, in model 4, a subject is likely to have the perception

266

of human-induced climate change by 0.3 % (8.3 %) when their age (gender) increases by one year (is267female). Similarly, a subject tends to have the perception of human-induced climate change by 6.6 %268

when their SVO is prosocial. The other independent variables, such as marital status, education,

269

household income, area dummy and family type, are identified to be statistically insignificant, as

270

shown in model 4 of table 4. We have confirmed that the aforementioned main results qualitatively

271

remain the same after trying various specifications of the models other than models 1 to 4, such272

as including age squared or interaction terms among the variables. Overall, scientific literacy, age,273

gender and prosociality are established as the main determinants that are statistically and practically274

significant of the likelihood for a subject having the perception of human-induced climate change.

275[Table 4 about here.]276

We now seek to identify the determinants of people’s cooperative behaviors toward the prevention277

of climate change by estimating the marginal effect of an independent variable on a donation in278

the CDG. To this end, tobit regression is applied because it is established to be appropriate when279

a considerable portion of observations for a dependent variable is found to be zero in the sample,

280

and our donation data include 106 zero observations in a sample size of 400 (Wooldridge, 2010,281

2016). In the regression, the donation to the prevention of climate change in the CDG is taken as a282

dependent variable, whereas the perceptions of climate change, scientific literacy, prosociality, and283

basic sociodemographic factors are taken as independent variables (see table 1 for the definitions of284

the variables). A specification of tobit regressions is given asyi∗ = β0 + Xi β1 + Si β2 + Pi β3 + Oi β4 + εi

(2)

285

where subscript i represents a subject ID from 0 to 400, yi∗ is a latent dependent variable of donations286

in CDG satisfying yi = max(0, yi∗ ), yi is an observed actual donation, Xi is a dummy variable of
13

287

the perception of human-induced climate change, Si is a scientific literacy scale, Pi is a dummy

288

variable of prosociality, and Oi is a vector of sociodemographic variables at individual and household

289

levels such as age, gender, marital status, household income, education and housing areas. The β0

290

is an intercept, βj s for j = 1, . . . , 4 are the unknown parameters associated with Xi , Si , Pi , Oi to be291

estimated, and εi is a normally distributed error term.

292

While the latent variable yi∗ is assumed to be normally distributed, the observed donation yi does293

not follow the same assumptions. yi is assumed to be equal to yi∗ when yi∗ > 0; otherwise, yi = 0.

294

With the distributional assumptions for the actual donations in CDG and the associated latent variable,

295

a tobit regression identifies the estimates of β0 , β1 , β2 , β3 and β4 via the maximum likelihood method,296

enabling us to calculate a marginal effect of an independent variable on the donation in the CDG.

297

Specifically, the marginal effect is interpreted to be a change in the donation when one independent

298

variable increases by one unit while holding other variables fixed. Therefore, the estimation of the299

tobit regression for the marginal effects associated with βj s should be able to answer the open question300(2) posed in the introduction: “How does the perception gap on the cause of climate change along301

with scientific literacy and social preferences affect people’s cooperative behaviors?”

302

Table 5 reports the marginal effects and their respective standard errors of independent variables

303

on subjects’ donations by the CDG along with the statistical significance. Model 1 of table 5 con-304

tains only the perception of human-induced climate change as an independent variable, and next,305

we gradually add scientific literacy, prosociality, age and other factors as independent variables in

306

models 2 to 4, building upon model 1. We first find that the perception of human-induced climate

307

change is statistically significant, with a positive sign at the 1 % level in a robust manner, irrespective308

of the models. The estimated marginal effects of the perception of human-induced climate change309

on their donations range between 235.016 JPY and 136.400 JPY in models 1 to 4, implying that a

310

subject with the perception of human-induced climate change is likely to make more donations by

311

235.016 JPY ∼ 136.400 JPY than that with other perceptions.

312

Second, scientific literacy has a positive effect on donations at the 1 % significance level in mod-313

els 2 and 3 but at the 10 % significance level in model 4. The estimated marginal effects of scientific314

literacy in models 2 to 4 suggest that a subject is likely to increase her donation by 24.101 JPY ∼
14

315

13.506 JPY when her scientific literacy increases by one point. The prosociality also exhibits 5 %316

statistical significance with a positive sign in models 3 and 4, implying that a subject with prosocial

317

orientation tends to make more donations by 102.215 JPY ∼ 104.477 JPY than that with other orien-

318

tations. Similarly, in model 4, a subject is likely to make more donations by 4.935 JPY (93.457 JPY)

319

when her age (marriage) increases by one year (is experienced). The other independent variables,

320

such as the gender dummy, household income, education and area dummy, are identified to be sta-

321

tistically insignificant, as shown in model 4 of table 5. Here, we have again confirmed that the main322

results in the tobit regressions qualitatively remain the same, irrespective of the various specifications

323

of models other than models 1 to 4, such as including additional terms among the variables. Overall,324

the perception of human-induced climate change, scientific literacy, prosociality and age are estab-325

lished as the main determinants that are statistically and practically significant in cooperation with the326

prevention of climate change.
[Table 5 about here.]

327

328

Based on the aforementioned results, there appear to exist some “path” relationships in the fol-329

lowing three pairs: (1) scientific literacy → the perception of human-induced climate change, (2)330

scientific literacy → donations in the CDG and (3) the perception of human-induced climate change331→ donations in the CDG. Examining the existence of the three paths is interpreted to test that the per-332

ception of human-induced climate change is a mediator between donations in the CDG and scientific333

literacy as graphically conceptualized in figure 3.4 To statistically address whether the perception of334

human-induced climate change is a mediator or not, structural equation modeling (SEM) is employed335

by checking the paths among the three variables together with the direct and indirect effects of scien-

336

tific literacy, following the procedures in Gunzler et al. (2013, 2014) and Venturini and Mehmetoglu

337(2019). The SEM analysis computes a beta weight as a standard coefficient, β, along with the associ-338

ated statistical significance for each path. This analysis enables us to establish that scientific literacy339

and perception of human-induced climate change are important determinants for people’s coopera4Mediation is established as a concept to describe a causal chain in which the first variable, X, (scientific literacy), affects a second variable, M , (the perception of human-induced climate change) that affects a third variable of the outcome,Y , (donations in the CDG), where the second variable is called a “mediator” (Baron and Kenny, 1986, Newsom, 2018).

15

340

tion through not only their direct but also indirect effects, which is another robustness check for the341

regression results.

342

We first analyze the two direct effects from scientific literacy to donations in the CDG (path A

343

in figure 3) and from the perception of human-induced climate change to donations in CDG (path344

C in figure 3) by SEM analysis. The results successfully show the existence of the path A with345

(β = 0.144, p < 0.001) and that of path C (β = 0.165, p < 0.001), meaning that both the perception346

of human-induced climate change and scientific literacy have direct effects on donations in the CDG.

347

Next, we analyze the direct effect from scientific literacy to the perception of human-induced climate

348

change (path B in figure 3) and an indirect effect from scientific literacy to donations via the per-349

ceptions of human-induced climate change (path C̄ in figure 3). The SEM analysis demonstrates the350

significance of path B (β = 0.275, p < 0.001) as well as path C̄ (β = 0.045, p < 0.01). Overall, the351

SEM analysis establishes that scientific literacy and the perception of human-induced climate change352

directly and indirectly affect donations in the CDG, where the perception of human-induced climate353

change is a mediator between scientific literacy and donations.
[Figure 3 about here.]

354

355

We are now ready to summarize the answers to the two open questions posed at the end of the356

introduction section based on our statistical analyses. The first question is “what are the determinants

357

of the human-induced perception of the cause of climate change?” Our answer to the question is that358

scientific literacy, age, gender and prosociality are the main determinants regarding whether people359

possess the perception of human-induced climate change. In particular, scientific literacy is of utmost

360

importance due to the magnitude and significance of the regression and SEM analyses. The second

361

question is “how does the gap in perceptions on the cause of climate change along with social prefer-362

ences and scientific literacy affect people’s cooperative behaviors?” Our answer to the question is that363

the perception of human-induced climate change, scientific literacy, prosociality and age positively

364

affect people’s cooperative behaviors toward climate change, demonstrating the importance of pos-365

sessing the perception of human-induced climate change and high scientific literacy for cooperation366

with climate change.
16

367

Some of the literature has reported that prosociality, age and gender matter for people’s perception368

and behaviors toward climate change, which is in line with our findings (Bord and O’Connor, 1997,

369

O’Connor et al., 1999, Meyer and Liebe, 2010, Gatersleben et al., 2014, Kline et al., 2018, MacManus,

3702018). Specifically, the literature shows that people are likely to possess correct perceptions and to371

cooperate for climate change as they are prosocial, aged and female. Here, a majority of people372

agree that age and gender shall be considered almost impractical to change, while some may wonder

373

whether or not social preferences of prosociality change over time. The literature appears to reach a374

consensus that people’s social preferences remain the same once they are fixed in their young ages.

375

Brosig-Koch et al. (2011) studied people’s cooperation between East and West Germany 20 years

376

after reunification by performing a solidarity game, demonstrating that their social preferences had

377

remained unchanged over 20 years. Thus, prosociality, age and gender are considered exogenous and378

impossible to change by policy interventions or education in the short run.

379

American Association for the Advancement of Science (2016) and Wigginton et al. (2016) report380

that further urbanization will have taken place and 65 % ∼ 75 % of the world population are predicted381

to concentrate in urban cities in Asia and Africa. Although technology and education will be making

382

progress along with such urbanization processes in the world, there remains an important question of383

how people’s perception and cooperative behaviors toward climate change evolve over time. The lit-384

erature claims that people tend to be proself, individualistic, and less cooperative with social problems

385

when societies transition from rural to urban (Schwartz, 2007, Shahrier et al., 2016, Timilsina et al.,386

2017, 2019).5 If this is the case, our results suggest that people are unlikely to have the perception

387

of human-induced climate change, being less cooperative with climate change and having negative388

impacts on future generations.

389

Scientific literacy is known to be increased by education or cultural learning at any age, while390

urban city life is reported to detach people from having hands-on experience, knowledge and learn-391

ing about nature (NISTEP, 2001, Nakagawa, 2016, Jingchao et al., 2018). Given the findings in our
5

In our study, prosociality does not differ between urban and nonurban Japanese people. We conjecture that this may
be due to the fact that Japanese nonurban life is urbanized and depends on industries other than agriculture in comparison
with developing countries, as demonstrated by our data. For example, Shahrier et al. (2016, 2017), Timilsina et al. (2017)
and Timilsina et al. (2019) study people between urban and rural areas that are quite different from one another withrespect to industries and practices.

17

392

research, how to enhance scientific literacy shall be a key for giving positive influences on not only393

the perceptions of human-induced climate change but also actual cooperative behaviors, especially394

when societies are further urbanized. Akter and Bennett (2011) and Sun and Han (2018) present

395that people are likely to have risk perception and willingness to take actions toward climate change

396

when they are educated or exposed to mass media. While the results are quite plausible, they can be

397

reinterpreted. We conjecture that scientific literacy works as a substitute or complement of hands-on

398

experience, mass-media exposure, education, knowledge and learning about nature even in urban-

399

ization processes. In this sense, scientific literacy may be more important than years of schooling400

for linking people with the perception of human-induced climate change and cooperative behaviors.

401

Thus, it is vital for climate scientists to prepare and explain some evidence, facts and the associated402

programs for not only enhancing people’s scientific literacy but also convincing people to recognize

403that climate change is genuinely human-induced, as argued in Lehtonen et al. (2019).

404

4Conclusions

405

This research has explored the determinants of the perception of human-induced climate change

406

and the impact of the perception gaps on cooperative behaviors toward climate change by conducting

407

a survey experiment with a climate donation game for 400 Japanese subjects. The results suggest408

two main findings. First, the analysis finds the importance of people’s scientific literacy to explain409

the perception gaps in that those with high scientific literacy tend to have the perception of human-

410

induced climate change. Second, people are identified as cooperative toward climate change, as

411

they have prosocial value orientation, high scientific literacy and the perception of human-induced

412

climate change, demonstrating that scientific literacy plays two important roles as not only a direct

413

determinant but also an indirect determinant through a mediator of people’s perceptions of human-414

induced climate change. The results imply that the enhancement of scientific literacy among people

415

shall be a key for giving positive influences on not only the perceptions of human-induced climate

416

change but also actual cooperative behaviors to climate change.

417

We note some limitations of our study and directions for future research. Our survey experiment
18

418

was conducted in one country (Japan), which is culturally homogeneous and relatively urbanized,

419

even in nonurban areas, compared to the rest of the world. To generalize our findings, the same types

420

of empirical studies should be conducted in other countries, especially developing countries that are421

in contrast with Japan in several aspects and are more vulnerable to climate change than developed422

countries. In addition, our findings are established only by empirical and quantitative research meth-423

ods. Future research should employ a qualitative approach, such as individual interviews, to clarify424

the detailed processes of how a personal perception of human-induced climate change actually “influ-

425

ences” her cooperative behaviors, as suggested in Corbin and Strauss (2014), contributing to further426

policy implications. These caveats notwithstanding, we believe that this study is the first step toward427

understanding the importance of having the perception of human-induced climate change along with428

the associated determinants, hoping that further studies will ensue for further identification of how to429

enhance cooperation toward climate change for its resolution.

19

5

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List of Figures
1
2
3

Graphical explanations for human-induced and nature-induced climate change . . . . 25
Boxplots of donations among human-induced, nature-induced and other climate change 26
The mediating effects among scientific literacy, people’s perceptions and donations . 27

24

Figure 1: Graphical explanations for human-induced and nature-induced climate change(a) Human-induced climate change(b) Nature-induced climate change

25

Figure 2: Boxplots of donations among human-induced, nature-induced and other climate change(a) Nature-induced vs. Human-induced(b) Other vs. Human-induced(c) Nature-induced vs. Other(d) Nature-induced + Other vs. Human-induced

26

Figure 3: The mediating effects among scientific literacy, people’s perceptions and donations

27

List of Tables
1
2
3
4
5

Variable definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary statistics of the variables . . . . . . . . . . . . . . . . . . . . . . . . . .
Donations and subjects’ perception of the cause of climate change across urban,
nonurban and overall areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Marginal probability of independent variables in logistic regression for the perceptionof human-induced climate change . . . . . . . . . . . . . . . . . . . . . . . . . . .
Marginal effects of independent variables in the tobit regression for donations to theprevention of climate change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

28

29
30
31
32
33

29

Definitions

Area is a dummy variable that takes 1 when the area is urban and 0 otherwise.
Age is defined as years of age.
Gender is a dummy variable that takes 1 when the subject is female, and 0 otherwise.
Education is an ordered categorical variable of 0, 1, 2, 3 and 4
where no scholastic education, junior high school, high school, undergraduate, graduate andhigher education are coded as 0, 1, 2, 3 and 4, respectively.
Household income
Household income per year in JPY. Categorical variable of 0 to 12 with an interval of 1 M,
However, 11 presents as earning 10 M < 15 M, and 12 represents as earning more than
15 M per year.
Marital status
Marital status is a dummy variable with categorical variables of 0 and 1
where marital status experienced, marital status
nonexperienced are coded as 0 and 1, respectively.
Family type
Family type is the categorical variable of 0 and 1
where family type, nuclear family or extended family, are coded as 0 and 1, respectively.
Occupation/agriculture Occupation/agriculture is defined as 1 if the respondent engages in agriculture or is employed.
SVO
The “SVO” represents a dummy variable taking 1 when the subject is prosocial
and 0 otherwise, based on SVO games.
Scientific literacy
This scale is defined as the number of questions for which subjects provided correct answers.
The theoretical range is from 0 to 15.
Perceptions
Perception of the cause of climate change is categorized as human-induced climate change,
nature-induced climate change or climate change induced by another factor (cannot say and no idea).
Human-induced
Perception of human-induced climate change represents a dummy variabletaking 1 when the subject chooses human-induced and 0 otherwise(nature-induced, cannot say, and no idea).
Donation
Donation is defined as a donation payment (range is between 0 and 1000 JPY)

Area
Age
Gender
Education

Variables

Table 1: Variable definitions

30
0.46
0.32
0.49
403.880.3 (0)0.12 (0)0.59 (0)455.53 (500)n = 200

17.72
0.49
0.70
3.1
0.47
0.3
0
0.50
3.36

49.4 (47)0.38 (0)2.71 (3)6.21 (6)0.69 (1)0.1 (0)0 (0)0.56 (1)8.53 (9)1 SD stands for standard deviation.

Subjects

Age
Gender (female)
Education
Household income
Marital status (experienced)
Family type (nuclear family)
Occupation (agriculture)
SVO (prosocial)
Scientific literacy
Perception of climate change
Human-induced
Nature-induced
Other
Donation

Mean (Median)

SD1

0
0
0
0

20
0
0
0
0
0
0
0
0

Min

1
1
1
1000

89
1
4
12
1
1
0
1
14

Max

0.33 (0)0.14 (0)0.53 (1)419.90 (400)

49.82 (49)0.36 (0)2.54 (3)5.52 (5)0.66 (1)0.12 (0)0.02 (0)0.60 (1)8.24 (9)Mean (Median)n = 200

0.47
0.35
0.5
381.09

16.96
0.48
0.67
2.97
0.48
0.33
0.12
0.49
2.95

SD

Nonurban areas

0
0
0
0

21
0
1
0
0
0
0
0
0

Min

Table 2: Summary statistics of the variables
Urban areas

1
1
1
1000

86
1
4
12
1
2
1
1
14

Max

0.32 (0)0.13 (0)0.56 (1)437.71 (500)49.61 (48)0.37 (0)2.65 (3)5.86 (5)0.67 (1)0.11 (0)0.01 (0)0.58 (1)8.39 (9)Mean (Median)n = 400

0.47
0.33
0.50
392.56

16.32
0.48
0.69
3.05
0.47
0.31
0.12
0.49
3.16

SD

Overall

0
0
0
0

20
0
0
0
0
0
0
0
0

Min

1
1
1
1000

89
1
4
12
1
1
1
1
14

Max

31

60
23
117

n
386.23444.23385.55

2 Proself includes individualists and competitors.

n = 200

590.25 (500)535.22 (500)
370.78 (300)SD1

Urban areas
Mean (Median)

1 SD stands for standard deviation.

Subjects

Human-induced
Nature-induced
Other

Perception
0
0
0

Min
1000
1000
1000

Max
66
28
106

n
365.88
326.7
389.2

SD

n = 200

525 (500)
272.50 (100)393.39 (300)

Mean (Median)Nonurban areas

0
0
0

Min
1000
1000
1000

Max

126
51
223

n

375.62402.36386.58SD

n = 400

556.07 (500)390.98 (300)
381.52 (300)

Mean (Median)

Overall

0
0
0

Min

Table 3: Donations and subjects’ perception of the cause of climate change across urban, nonurban and overall areas

1000
1000
1000

Max

32

0.044***(0.007)

Scientific literacy

2

10.039*** 0.039***(0.008)(0.008)0.004*** 0.004***(0.001)(0.001)
0.096**
0.100**(0.045)(0.045)0.069*(0.049)Marginal probability
Model 2 Model 3

0.040***(0.008)0.003**(0.001)0.083**(0.046)
0.066*(0.045)0.069(0.059)−0.040(0.045)0.007(0.008)−0.045(0.234)−0.004(0.070)Model 4*** significant at the 1 % level, ** significant at the 5 % level, and * significant at the 10 % level
The age squared variable is not added to these models because we judge this variable as less importantby a predicted probability logistic regression.

Family type (nuclear family = 1, extended family = 0)

Area dummy (urban = 1, nonurban = 0)

Household income

Education

Marital status (experienced = 1, nonexperienced = 0)

SVO (prosocial = 1, otherwise = 0)

Gender dummy (female = 1, male = 0 )AgeModel 1

Variable

Table 4: Marginal probability of independent variables in logistic regression for the perception of human-induced climate change

33

235.016***(54.566)

Model 1190.834***(56.004)
24.101***(8.490)Model 4183.948*** 136.4***(55.835)(55.317)22.996***13.506*(8.477)(8.881)102.251** 104.477**(51.441)(50.191)
4.935***(1.696)67.191(64.313)93.457*(64.313)6.037(8.611)−27.055(3.912)49.880(49.651)

Marginal effectModel 2Model 3*** significant at the 1 % level, ** significant at the 5 % level, and * significant at the 10 % level

Area dummy (urban = 1, nonurban = 0)

Education

Household income

Marital status (experienced = 1, nonexperienced = 0)

Gender dummy (female = 1, male =0)

Age

SVO (prosocial = 1, otherwise = 0)

Scientific literacy

Perception (Human-induced = 1, Otherwise = 0)

Variable

Table 5: Marginal effects of independent variables in the tobit regression for donations to the prevention of climate change