Discussion Paper No. 1012

TIME DISCOUNTING, AMBIGUITY AVERSION,AND PREFERENCES
FOR FUTURE ENVIRONMENTAL POLICIES:EVIDENCE
FROM DISCRETE CHOICE EXPERIMENTS
Kenjiro Hirata
Shinsuke Ikeda
Masako Ikefuji
Myong-Il Kang
Katsunori Yamada

September 2017

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Time discounting, ambiguity aversion, and preferences for futureenvironmental policies: Evidence from discrete choice experiments∗
Kenjiro Hirata†, Shinsuke Ikeda‡, Masako Ikefuji§,Myong-Il Kang¶ and Katsunori Yamadak
September 30, 2017

Abstract
Designing eﬃcient environmental policies requires knowledge about households’ preference
parameters for their intertemporal decisions. By conducting an original Internet-based survey
using Japanese participants (n=2,906) and a follow-up survey (n=1,407), we examine how
people evaluate pro-environmental policies depending on their individual attributes. The
discount rates for environmental outcomes are estimated by using a discrete choice experiment.
We show that participants’ discount rates in environmental policy choices are on average
negative and future-biased. Those who are more ambiguity-averse and patient for money
concerns, and anticipate more rapid increases in future temperatures are more willing toincur present-day tax burdens to ensure future environmental improvements. These results
are highly robust against alternative estimation models and stable when using the follow-up
survey data obtained 21 months later.

JEL Classiﬁcation Numbers: D91; Q51
Keywords: Time discounting; Ambiguity aversion; Environmental policy; Random utility
model; Discrete choice analysis∗

We are grateful for comments on earlier versions of the paper to Kenji Miyazaki and participants at the 2015
Spring Meeting of Japanese Economic Association, the Ritsumeikan Conference on Financial Economics and theAsset Pricing Workshop. This research is ﬁnancially supported by the Joint Usage/ Research Center Project of
ISER from the Ministry of Education, Culture, Sports, Science and Technology; the Grants-in-Aid for ScientiﬁcResearch (B No. 17H02499) from the Japan Society for the Promotion of Science; and Grant-in-Aid for Challenging
Exploratory Research from JSPS (No. 26590030).
†
Faculty of Economics, Kobe International University. E-mail: khirata@kobe-kiu.ac.jp‡
Correspondence: Institute of Social and Economic Research, Osaka University.
E-mail: ikeda@iser.osaka-u.ac.jp§
Faculty of Humanities and Social Sciences, University of Tsukuba. E-mail: ikefuji.masako.gn@u.tsukuba.ac.jp¶
Department of Business Administration, Korea University. E-mail: myong il econ@yahoo.co.jpkFaculty of Economics, Kindai University. E-mail: kyamada@kindai.ac.jp1

1

Introduction

In the face of rapid climate change and global warming, a consensus seems to have been reachedthat governments must adopt policies to preserve the environment. These environmental policies
have an aspect of intertemporal choice for taxpayers, who stand to gain future environmental
improvements at the cost of a present-day tax burden. Hence, designing eﬃcient environmental
policies inevitably requires knowledge about households’ preference parameters related to theirintertemporal decisions.
This study examines how people evaluate the future environment. In particular, we investigate
how the imputed time preference over the future environment is associated with individual attributes that aﬀect the intertemporal tradeoﬀs in people’s environmental decision-making. The
attributes on which we focus are (i) the time preferences in the domain of monetary/ﬁnancial
outcomes, (ii) attitudes toward risk and ambiguity, (iii) subjective views on pro-environmental
activities, and (iv) the anticipation of future environmental deterioration. As discussed in theliterature (e.g., Hardisty and Weber, 2009; Richards and Green, 2015), it is important bothfor researchers and policy makers to know whether the features of the time preferences in theenvironment domain are well captured by those in the monetary domain (point (i)), which areusually taken into account when formulating environmental policies. For point (ii), as the future conditions of the environment are not deterministic, but risky and/or ambiguous (Fox andTversky, 1995; Eismont and Welsch, 1996), the intertemporal tradeoﬀs in environment-related
decision-making could depend on the degrees of risk and ambiguity aversion. Finally, subjective
views and the anticipation of future environmental deterioration (points (iii) and (iv)) may aﬀect
preferences for pro-environmental policies with delayed eﬀects. In particular, those who anticipate the environment becoming rapidly scarcer in the future will prefer environmental policies
that take eﬀects with longer delays.
Methodologically, we conduct an original Internet-based survey using Japanese participants(n=2,906) and a follow-up survey (n=1,407), where the cohort proﬁle mirrors that of the national
statistics. With those data, we estimate the discount rates for environmental outcomes by using
a discrete choice experiment approach a la Viscusi et al. (2008) and Meyer (2013) as well as elicit
other preference parameters such as the discount rates for monetary outcomes and degrees of risk
and ambiguity aversion by using choice-titration procedures (Scholten and Read, 2006; Rodzon
et al., 2011).
Our main ﬁndings can be summarized as follows. First, in contrast to the ﬁndings in theeconomics literature, participants’ discount rates in environmental policy choices are on average
negative and future-biased: they ceteris paribus prefer delayed environmental improvements to2

immediate ones. This is the case irrespective of the fact that the same participants do discountfuture monetary outcomes at normal positive rates, preferring present rewards to future ones.
Second, the time preferences in the environment domain are shown to be positively correlatedwith the time preferences in the monetary domain: those who value the future environment more
highly tend to be more patient in terms of monetary outcomes and hence of standard consumption
goods. This ﬁnding implies that diﬀerences in individual discount rates in the monetary domain
partially explain those in pro-environmental attitudes. However, from our ﬁrst ﬁnding, it is notstraightforwardly justiﬁable, unlike what Hardisty and Weber (2009) stress, to apply discountrates in the monetary domain to environment-related policy. For example, although people prefera present money amount to the same amount in the future because of positive discount rates,
they are willing to pay present-day tax payments for future environmental improvements withnegative discount rates. Policymakers can thus exploit this patient nature of taxpayers in theenvironmental domain when designing pro-environmental policies.
Third, ambiguity aversion plays a signiﬁcant role in generating participants’ preferences for
future environmental policies: a more ambiguity-averse participant is more patient in waiting
for future environmental improvements. By contrast, his or her risk aversion does not aﬀect hisor her patience in waiting for future environmental improvements. These ﬁndings are consistent
with the fact that the true probability distributions of future environmental conditions are usually
unknown to policymakers and consumers (Fox and Tversky, 1995).
These ﬁndings are highly robust against alternative estimation models (e.g., mixed logit models) and stable in another dataset taken from our follow-up survey conducted a year later. Furthermore, the follow-up survey detects our fourth ﬁnding: people who anticipate more rapid
increases in future temperatures can wait for future environmental improvements more patiently.
A policy implication follows from this: people would take a greater tax burden today for a
pro-environmental policy with a long delay, even though they have positive time preferences formonetary concerns, if the policy reduces future environmental uncertainty and contributes tomitigating rising temperatures.
Our study is closely related to Viscusi et al. (2008) and Meyer (2013), both of which conduct
discrete choice experiments by using Internet surveys to elicit time preferences in environmentrelated decision-making. Viscusi et al. (2008) develop a novel estimation method with whichto elicit discount rates, detecting positive and present-biased time preferences for environmental
policy choices. However, they do not examine how these estimated time preferences are associatedwith other individual preferences such as discount rates for monetary and ambiguity concerns or
the anticipation of future environmental improvements. By following their method that allows
3

us to elicit the environment-concerning time preferences directly from the utility parameters,
we show that our Japanese participants exhibit negative and future-biased time preferences incontrast to the ﬁndings in Viscusi et al. (2008).
By conducting a discrete choice experiment for residents in the Minnesota River basin, Meyer(2013) estimates both exponential and hyperbolic discounting models. Although he concludes
that the data are indicative of exponential discounting (p. 56, Meyer, 2013), his analysis showsthat the quasi-hyperbolic discounting model exhibits the best model ﬁt in terms of the simulated
log likelihood value among alternative models and that the point estimate of the present-biased
discount parameter (β) is greater than one (1.27); in other words, time preferences are futurebiased. Our study shows that our participants’ choices are consistent with this ﬁnding.
Hardisty and Weber (2009) and Richards and Green (2015) conduct lab experiments to examine
the diﬀerences in discounting for ﬁnancial and environmental outcomes. Hardisty and Weber(2009) report similar discounting in the two domains, whereas Richards and Green (2015) detectsigniﬁcantly lower discount rates for environmental concerns than those for ﬁnancial concerns.
Our study supports the latter ﬁnding, although our results are also consistent with Hardisty
and Weber (2009) in that the elicited discount rates for environmental concerns are positively
associated with those for monetary concerns. Furthermore, we examine the relationship between
the time preferences for environmental concerns and the other parameters mentioned above.
The remainder of this paper is organized as follows. Section 2 describes the data collected.
Section 3 presents the empirical model and econometric method used for the estimation. Section
4 reports the estimation results and discusses their implications. Section 5 checks the robustnessof our results by using a mixed logit model. Section 6 concludes.

2

Data

Our empirical research is based on our original nationwide Internet survey, the Internet Survey on
Environments and Behavior (ISEB 2014, hereafter), conducted between March 12 and 18, 2014,
and its follow-up survey (ISEBf 2015, hereafter), conducted on December 4–10, 2015. Nikkei
Research, a representative Japanese private research company, conducted the ISEB 2014 andthe ISEBf 2015, based on questionnaires that we originally prepared. For the ISEB 2014, theparticipants were 2906 Japanese people between the ages of 20 and 65, who had been enrolled
in the Nikkei Research Access Panel, composed of roughly 155,000 registrants. Cash vouchers of
JPY 500 are given as incentives by lottery to participants. The sample was selected by stratiﬁed
random sampling such that the age and sex distribution was as close as possible to that of theJapanese census. Table 1 presents the characteristics of the participants.
4

Insert Table 1.
As detailed in the next subsection, we included various questions in the ISEB 2014 to elicit
information about participants’ (i) preferences for future environmental policies, (ii) attitudes
toward the environment, (iii) preferences for time discounting and risk/ambiguity aversion on
monetary outcomes, and (iv) demographic and socioeconomic attributes. In particular, following
Viscusi et al. (2008), we designed hypothetical discrete choice questions for environmental policy
choices to examine participants’ preferences for environmental policies.
The ISEBf 2015 was conducted to check the stability of the elicited parameters and examine
how participants’ time preferences for the environment revealed from the choice questions wererelated to their anticipation of future environmental deterioration. Detailed information on thefollow-up survey is provided in Section 6.

2.1

Preferences for pro-environmental policies

By using data from the ISEB 2014 and ISEBf 2015, we apply a random utility model a la
McFadden (1974) to elicit participants’ time preferences for environmental improvements thattake eﬀect with certain delays. Discrete choice analysis has been widely used in environmental
economics to value environmental amenities or changes in environmental quality and natural
resources (e.g., Adamowicz et al., 1994).
Discrete choice analysis requires participants to rank diﬀerent policies with varying combinations of attribute levels. In our hypothetical questions, we specify alternative environmental
policies characterized by three attributes: (i) the expected rates of reduction in environmental
risk and pollution (5%, 10%, 15%, 20%, or 30%); (ii) annual costs in terms of tax payments perhousehold (JPY 3,100, JPY 3,600, JPY 4,100, JPY 4,600, or JPY 5,100); and (iii) the delay
before the environmental improvement takes eﬀect (zero (immediate eﬀectuation), 6 months, 1
year, 2 years, or 3 Years). As each of the three attributes above take ﬁve possible values, there
are 125 (= 5 × 5 × 5) potential variations of these alternatives. An orthogonal design (Orthoplan
in SPSS, 2014) is used to pick up 25 out of the 125 variations to make a vector of the alternatives.
We repeat this procedure three times to form the three-situation choice task used in the survey.
Fig. 1 presents a typical choice problem in the ISEB 2014 and ISEBf 2015, where a participant
is told to choose the most preferable policy from three alternatives (A to C). Each participant
replies to six such problems with diﬀerent alternative policies generated by the orthogonal design
procedure. Five sets of diﬀerent six choice problems are randomly assigned to participants.
Overall, we collect data on 17,436 decisions among the three policies.
Insert Fig. 1.
5

2.2

Individual characteristics

In addition to questions about policies for environmental improvements, participants are asked
various questions about their pro-environmental attitudes, money-based preferences for time,risk, and ambiguity, as well as economic, demographic, and social attributes such as sex, age,family, education, and income.
2.2.1

Environment-related behaviors

To examine the associations between participants’ imputed environmental time preferences andtheir actual environment-related attitudes and activities, we ask them 10 questions, Q1–Q10, on
environment-related behaviors in everyday life, as shown in Appendix A. Participants are asked
to reply to each by using a ﬁve-point scale from 1 (not at all, not the case) to 5 (applied perfectly).
Q1 asks whether a respondent participates in outdoor activities such as hiking, climbing, andﬁshing. We construct a binary indicator for active participation in outdoor activities, whichequals one if the chosen number is greater than 4 and zero otherwise. Q2–Q10 ask how proenvironmental a participant’s attitude is. The raw scores for each question are converted into
standardized values with a mean of zero and a standard deviation of one. The standardized
scores of Q2–Q10 are then averaged to form a composite score for each participant to quantify
his or her pro-environmental attitude.
2.2.2

Money-based discount rates

To explore the nature of the money-based discount rates of participants, our questionnaire contains two questions, Q18 and Q19, which consist of three sequential binary choices between small
rewards today and larger rewards a month later as well as between small rewards a year laterand larger rewards 13 months later, respectively. As detailed in Appendix B, in Q18, participants are ﬁrst asked to reply to Q-18-1 by choosing between two alternatives: (A) receiving
JPY 10,000 today or (B) receiving JPY 10,800 one month later. For Q-18-2, respondents choose
between (A) receiving JPY 10,000 today or (B) receiving JPY 13,000 later. For Q-18-3, thechoice is between(A) receiving JPY 10,000 today or (B) receiving JPY 10,150 later. In this way,
Q18 classiﬁes participants through the sequential three choices into eight classes according to
the degree of impatience for immediate intertemporal decisions. Similarly, the three sequential
questions of Q19 ask participants to choose between (A) receiving JPY 10,000 one year later or(B) receiving a larger amount of money one year and one month later, thereby classifying theminto eight classes according to impatience for long-term intertemporal decisions. By assumingthat the participant’s money-based discount factor obeys the quasi-hyperbolic discount model
6

(the β-δ model, Laibson, 1997), we elicit from the choices in Q18 and Q19 the parameters of thepresent bias (β) and the long-run discount factor (δ) for money.1 The imputed parameters fordiscounting are used to detect associations with the corresponding discount factors with respect
to environmental policies.
2.2.3

Risk aversion and ambiguity aversion

As the state of the future environment is uncertain, preferences for environmental policies are
expected to depend on participants’ aversive attitudes toward the risk and/or ambiguity of future
events. We measure participants’ degrees of money-based risk and ambiguity aversion by including two questions, Q20 and Q21, in our questionnaire, each composed of three sequential choices
as in Q18 and Q19. As detailed in Appendix C, these questions ask each participant whether
he or she would rather participate in a lottery that oﬀers a prize of JPY 10,000 for picking awinning ball from an urn or receive a smaller certain amount of money. In Q20, participants
know that the urn contains 10 winning balls and 10 losing balls, whereas in Q21, the compositionof the urn is not known. From the responses to Q20, we can stratify participants into eightclasses according to their degree of risk aversion. In the same way, form the responses to Q21,
participants are classiﬁed into eight groups. If a participant is ambiguity-averse, he or she will
participate in the lottery with smaller rewards in Q20 than he or she would in Q21. By taking
the diﬀerence between the class numbers of Q20 and Q21, we can classify participants into 15
categories according to their degree of ambiguity aversion.

3

Estimation of the discount rates for environmental outcomes

3.1

Random utility model

To analyze the data derived from our discrete choice experiments, we adopt the random utility
model developed by Viscusi et al. (2008). Following these authors, we assume that the utilityof environmental improvements is composed of three attributes: the cost of the environmental
improvements c , the eﬀect of the improvements e, and the time delay required before the improvements t. Formally, we specify the utility of alternative policy option i in choice set k forparticipant n, uikn , as follows:uikn = αcikn + λeikn + γtikn + θeikn tikn + εikn ,1(1)When eliciting money-based discount factors, we assume a linear utility function for simplicity. This may cause
a bias in their estimates (Andersen et al., 2008). However, as we shall show later, discount factors for environmental
concerns have no association with the degree of risk aversion and, hence, with the concavity of the utility function.
Therefore, the linearity assumption for the money-based discount factors does not aﬀect our result below that
discount factors for environmental concerns are associated with those for money.

7

where α, λ, and γ denote the marginal utilities associated with the cost, eﬀect, and time delayand εikn is the random error term. The interaction term θeikn tikn is added on the right-handside to capture how participants discount future environmental improvements. Given the random
utility speciﬁcation, the probability of participant n choosing alternative i from choice set k, pikn ,is determined bypikn = Prob(αcikn + λeikn + γtikn + θeikn tikn + εikn
> αcjkn + λejkn + γtjkn + θejkn tikn + εjkn ), for all j 6= i.

3.2

(2)

Conditional logit estimates

We ﬁrst estimate the parameters of the random utility model by using a conditional logit framework. Recall that the time delay variable tikn can take ﬁve values: 0, 6, 12, 24, and 36 months(denoted 0, 1, 2, 4, and 6, respectively). By letting Delay1, Delay2, Delay4, and Delay6 denotedummy variables that equal one if the time delay before the improvement in policy i is 6, 12, 24,
and 36 months, respectively, and zero otherwise, we rewrite Eq. (1) by replacing the time delay
variable in the interaction term with the four dummy variables as follows:uikn = αcikn + λeikn + γtikn+ξ1 eikn Delay1 + ξ2 eikn Delay2 + ξ4 eikn Delay4 + ξ6 eikn Delay6 + εikn .

(3)

From the estimated coeﬃcients of Eq. (3), we calculate the discount factors for environmental
improvements. Since these discount factors are deﬁned as the marginal utility of future environmental improvements divided by the marginal utility of present environmental improvements, thesemi-annual discount factor for the m month delayed environmental improvements δ̃m is givenby)6(
ξ m6 mδ̃m = 1 +, for m = 6, 12, 24, 36.
λ(4)Further, we interpret the discount factors in the quasi-hyperbolic (βδ) model proposed by
Laibson (1997) instead of the exponential function model. In the βδ model, the present bias
parameter β and long-run discount factor δ satisfy, respectively,δ̃6 = βδ,2δ̃12= βδ 2 ,4δ̃24= βδ 4 ,6δ̃36= βδ 6 .

8Therefore, β and δ for a certain period of time, such as from now to m months later, are calculatedby the following equations:
(
δm =
βm =

m

δ̃m6
δ̃6

) m1−1
6

,

(5)

δ̃6
, for m = 12, 24, 36.
δm(6)

We also investigate the inﬂuence of individual characteristics on environmental time discounting. To do so, we rewrite Eq. (3) by incorporating individual attributes into the interaction
terms in the equation as follows:uikn = αcikn + λeikn + γtikn + (ξ1 + ζ 01 xn )eikn Delay1+(ξ2 + ζ 02 xn )eikn Delay2 + (ξ4 + ζ 04 xn )eikn Delay4+(ξ6 + ζ 06 xn )eikn Delay6 + εikn ,(7)

where xn is a vector of the individual characteristics such as participants’ demographic andpreference attributes listed in Table 1.

4

Results

4.1

Time discounting and future bias revealed from preferences for environmental policies

The ﬁrst column of Table 2 summarizes the estimation results of the conditional logit model (3).
Rows (1)–(3) of the ﬁrst column show that a lower cost, a higher improvement eﬀect, and a shorter
delay of the environmental policy lead to higher utility, which is intuitively understandable. Our
novel ﬁnding here is that, as the last four rows of the table indicate, the marginal utilities of the
improvement eﬀect are greater for delayed ones: our participants prefer delayed environmental
improvements to immediate environmental improvements.
Insert Table 2.
More explicitly, by using the estimates of the interaction eﬀects, we can obtain from Eq. (4)the imputed discount factors for the environmental improvements. The second column of Panel
A of Table 3 shows the results, indicating that the imputed discount factors are greater thanunity with a decreasing trend over the examined time period of delay. For example, the point
estimate of δm for 0–0.5 year is 1.36, which is higher than that for 0–3 years, 1.07. The associated
95% conﬁdence intervals indicate that all the point estimates for δm are greater than unity.
Insert Table 3.
9Based on the βδ speciﬁcation, we compute the associated long-run discount factor δ and present
bias parameter β by using Eqs. (5) and (6). The fourth and ﬁfth columns of Panel A of Table
3 show that both the parameters are greater than unity. In particular, for any horizon, thepoint estimates of the environmental beta are larger than 1.3. These results imply that participants’ time preferences with respect to environmental improvements are on average negativeand future-biased. Although the results seem to contradict the existing empirical evidence thattime preferences are positive and present-biased (e.g., Viscusi et al., 2008), our participants consistently display positive and present-biased time preferences for monetary rewards, as shownin Table 1. The negative and future-biased time preferences, revealed in Panel A of Table 3,
might thus be considered to be speciﬁc to environmental concerns in our experiments. Even for
those impatient and present-biased for monetary rewards, negative and future-biased time preferences are detected during intertemporal decisions on environmental improvements. Furthermore,
we show in the next section that the imputed time preferences for environmental concerns are
positively associated with those for monetary concerns.

4.2

Individual attributes and environmental time discounting

This subsection presents the estimation results of Eq. (7). To examine the association between
the individual attributes and environmental time discounting, we focus on how the interactions
between environmental improvements and each delay dummy correlate with the individual preferences and other characteristics. A positive (negative) correlation between them implies thatenvironmental discount factor δm positively (negatively) depends on the attribute variable.
Two ﬁndings in Table 4 are noteworthy. First, as can be seen from rows (5), (13), (21), and(29) of the table, the interaction between environmental improvements and each delay dummy
has a signiﬁcantly positive correlation with the pecuniary discount factor, where for diﬀerent
delay periods, the estimated coeﬃcients are stable in magnitude (0.032–0.036) and signiﬁcancelevel (p-values < 0.05). This ﬁnding indicates that participants more patient in waiting for
future monetary rewards wait for future environmental improvements more patiently. Thus, thediscount rates for monetary outcomes, which economics researchers usually estimate as a proxyof the degree of impatience, would indeed be at least partially useful in environmental issues.
Insert Table 4.
Second, all the interactions between delayed environmental improvements and ambiguity aversion are signiﬁcantly positive (see rows (7), (15), (23), and (31) of Table 4), whereas those between
delayed environmental improvements and risk aversion are all insigniﬁcant (see rows (6), (14),(22), and (30) of Table 4). Thus, the level of the discount factor for environmental concerns is pos10

itively correlated with the degree of ambiguity aversion, but not associated with risk aversion: amore ambiguity-averse person is more patient in waiting for future environmental improvements.
This result is reasonable if more distant future environmental improvements are more ambiguousin the Knightian sense. In other words, as its probability distribution is unknown, participantsprefer future environmental improvements that can compensate for the greater ambiguity.2
Table 4 also shows the associations between the environmental discount factors and other individual attributes. First, as seen from rows (8), (16), (24), and (31), the discount factor forenvironmental concerns is signiﬁcantly higher for university graduates than for non-graduates.
Second, respondents who more frequently participate in outdoor activities are less patient in waiting for future environmental improvements (see row (9) of Table 4). This tendency is consistent
with the ﬁnding in Viscusi et al. (2008) that recreational users of water bodies tend to evaluate
immediate water improvements highly. Third, on the contrary, rows (10) and (18) show that theinteractions of delayed environmental improvements and pro-environmental attitudes are signiﬁcantly positive, which indicates that participants with stronger pro-environmental attitudes are
more patient in waiting for future environmental improvements.

5

Mixed logit estimation

To check the robustness of our results, we re-estimate the random utility model (3) by using amixed logit speciﬁcation, which allows for heterogeneous utility parameters for individual participants (αn , γn , ξ1n , ..., ξ6n ). Formally, we estimate
uikn = αn cikn + λeikn + γn tikn + ξ1n eikn Delay1+ξ2n eikn Delay2 + ξ4n eikn Delay4 + ξ6n eikn Delay6 + εikn ,(8)instead of Eq. (3).
The last column of Table 2 summarizes the estimation results for Eq. (8), in a comparable way
to the conditional logit case (the ﬁrst column of Table 2). The estimates and standard errorsof the utility parameters here represent the mean magnitudes of the heterogeneous parametersand extent of the variation in the sample, respectively. The estimated coeﬃcients are similarin signs and signiﬁcance levels to those in the case of the conditional logit model: participantsprefer environmental policies that are less costly, more eﬀective, and less delayed (rows (1)–(3)of the last column); they also prefer environmental policies with delayed eﬀects to those taking
2

In the theoretical literature, how ambiguity aversion aﬀects the optimal timing of environmental policies
is controversial (e.g., Asano and Shibata, 2014). However, no research considers the case that environmental
ambiguity is delay-dependent.

11

eﬀect immediately (rows (4)–(7)). As in the conditional logit results, again, all the estimates are
signiﬁcant at the 1% level.
By substituting the estimated individual participants’ utility parameters into Eq. (4), we
can calculate their discount factors. To check the robustness of the correlations between the
discount factor for future environmental improvements and each individual attribute, we regress
the estimated individuals’ discount factors on their individual attributes by adopting the following
ordinary least squares (OLS) model:δ̃mn = ζ 0 xn + εn .

(9)Panel A of Table 5 summarizes the results for the environmental discount factors in the shortest
horizon δ̃36n (the 0–3 years setting).3 The table indicates that, with minor exceptions, theestimated associations between the environmental discount factors and individual attributes arehighly consistent with those in Table 4. In particular, as in the conditional logit estimation,
the environmental discount factor for either horizon is positively associated with the pecuniary
discount factor and ambiguity aversion at the 1% level and has no signiﬁcant association withrisk aversion.
Insert Table 5.

6

Follow-up survey

Section 4 showed that respondents were on average willing to incur higher costs for environmental
policies with longer delays. In other words, our subjects have negative discount rates for futureenvironmental goods. After making these ﬁndings, we conducted an Internet-based follow-up
survey using the participants in the ﬁrst survey for two purposes. First, we check the stability ofthe elicited preference parameters. In particular, since our ﬁnding of a negative discount rate forthe environment is novel in the literature, checking if the ﬁnding is robust is important for thevalidity of our study. Second, we address a mechanism that may account for the negative discountrate: we hypothesize that respondents’ future preferences for environmental policy choices are
related to their expectations of future environmental deterioration. Speciﬁcally, people who
anticipate more rapid deterioration in the natural environment prefer policies with more delayed
pro-environmental eﬀects because of consumption smoothing.
For these purposes, the follow-up survey was constructed in two sections: (i) three consecutive
questions (AQ1–AQ3) asking participants about their views on future changes in the global envi3

When estimating the environmental discount factor for the 0–0.5 year setting, the environmental discountfactor for the 0.5–3 year setting is added to the list of control variables to control for the possible cofounding eﬀect.
The results do not change substantially even when we exclude it from the controls.

12ronment (to be shown Figs. 3–5 below) and (ii) discrete choice questions on environmental policy
choices as in the ﬁrst survey. Regarding the discrete choice questions on environmental policy,
we distributed the same ﬁve sets of questions as in the ﬁrst survey randomly to participants,
unconditional on the sets of questions they responded to in the ﬁrst survey. The follow-up survey
was again conducted by Nikkei Research, Inc. and run from December 4–10, 2015. The total
number of respondents was 1407 (response rate 48.4%).

6.1

Stability of the elicited discount rates for the future environment

We ﬁrst check the stability of the elicited discount factors for environmental improvements between the two surveys, which were 21 months apart. By using the data derived from the follow-up
survey, we re-estimate Eq. (3). Panel B of Table 3 shows the imputed discount factors on the
future environment from the follow-up survey data. They reveal the same tendencies as those in
the ﬁrst survey. Most importantly, the re-elicited discount factors retain the same novel featuresof negative discount rates and future bias reported in the ﬁrst survey. We also show that thevalues of these imputed discount factors, δ and β, do not diﬀer signiﬁcantly from those in theﬁrst study. Hence, we conclude that the results of our discrete choice experiments are robust. By
using the follow-up survey data, we further re-elicit individual participants’ discount factors forthe environment in the same way as in Section 5. 2 shows the jitter scatterplots of individual participants’ environmental discount factors estimated from the ﬁrst survey data compared with thefollow-up survey data. Both for the shortest horizon (0–0.5 year) and for the longest horizon (0–3
years), we ﬁnd signiﬁcantly positive correlations between the discount factors elicited in the twosurveys (ρ = 0.3342, p-value < 0.0001 for the 0–0.5 year setting and ρ = 0.3122, p-value < 0.0001
for the 0–3 years setting).4
Insert Fig. 2.

6.2

Mechanism of negative time discounting for the future environment

To test our hypothesis that respondents who expect faster environmental deterioration prefer todefer environmental improvements to the far future for consumption smoothing, we use the dataon respondents’ views about future environmental changes. The three questions, AQ1–AQ3, areshown below. Each of these subjective evaluations is additionally incorporated into Eq. (9) to
see if our hypothesis is supported.
4

We also estimate the same regression models as in Table 5 for individual respondents’ environmental discountfactors estimated from the ﬁrst survey data, but with the inclusion of the imputed environmental discount factors
from the follow-up survey data on the right-hand side. The coeﬃcients of the imputed discount factors from thefollow-up survey data are signiﬁcantly positive in both horizons (not tabulated). The signs and signiﬁcance levelsof the other coeﬃcients are also similar to those in Table 5.

13Insert Figs. 3–5.
From AQ1, we ﬁnd that the higher people expect future global temperatures to be, the morethey want to postpone future environmental improvements. The regression results are shown in
Table 6 for some speciﬁcations. This ﬁnding supports our hypothesis (see row (8) in Table 6).
Insert Table 6.
Regarding the variables on natural resource depletion (AQ2) and CO2 (AQ3), however, we
did not ﬁnd stable supporting results for our hypothesis. In some regressions, the R2 values are
smaller than those taken from the regression with the global warming variable, while the signsof the coeﬃcients of the expectation of future environmental improvements are not as expected(results available upon request).
We speculate that we did not obtain consistent results since our subjects are more concerned
about global temperatures than the increase in CO2 or decrease in natural resources: governments
globally seem to be well aware of the need to reduce CO2 emissions and ensure the eco-friendly
use of natural resources, while abnormal weather such as heat and drought is conspicuous in the
media.
To summarize the results from the follow-up survey, negative and future-biased time discounting for the future environment is robust for our sample of Japanese subjects. We ﬁnd partly
supporting evidence that this is due to the motivation of consumption smoothing under the anticipated increased scarcity of future environmental resources. Another possible mechanism froma theoretical viewpoint is the increasing-returns-to-scale nature of environmental goods, which isleft unanswered in our study.

7

Conclusions

In this study, we showed that negative time preferences are detected in environmental policy
choices: participants prefer delayed environmental improvements to immediate ones. This futurebiased preference for environment choices is stronger for participants who are more patient formonetary concerns; have stronger preferences for ambiguity aversion; and anticipate a steeper
upward trend in future temperatures. The presented ﬁndings are highly robust against alternative
estimation models and compared with data derived from a follow-up survey conducted 21 months
later.
A policy implication follows from the results: even if people have positive time preferences formonetary concerns, they are willing to incur present-day tax burdens to improve the future environment, particularly when this reduces environmental uncertainty and contributes to mitigating14

rising temperatures. Our ﬁndings are partly consistent with the ﬁndings of Richards and Green(2015), who show signiﬁcantly lower discount rates for environmental concerns than those forﬁnancial concerns. However, our ﬁndings are in contrast to those of Viscusi et al. (2008) wheretime preferences are positive and present-biased. A possible clue for these contrasting ﬁndings
can be found in the study of Richards and Green (2015).
Richards and Green (2015) distinguish not only between short-horizon environmental goods
and long-horizon environmental goods but also between private-beneﬁt environmental goods andpublic-beneﬁt environmental goods. These distinctions enable them to ﬁnd that discount rates
for long-horizon environmental goods with a public beneﬁt are signiﬁcantly lower than for shorthorizon environmental goods with a private beneﬁt. Their ﬁnding may be useful in understanding
why our results diﬀer from those of Viscusi et al. (2008). Concerning the settings of the environmental policy choice questions, there are several important diﬀerences between our study andthat of Viscusi et al. (2008). For instance, Viscusi et al. (2008) administer hypothetical environmental policy choices with respect to improvements in the water quality of lakes and rivers.
These improvements are hypothesized to persist for ﬁve years. On the contrary, in our environmental policy choice questions, the environmental improvements are deﬁned as a reduction inenvironmental pollution and the risk of natural disaster, which are associated more strongly witha public beneﬁt than improvements in the water quality of lakes and rivers.5 In addition, ourquestions state that after a policy takes eﬀect, it continues for 10 years, far longer than the settingof Viscusi et al. (2008). The diﬀerences in these settings may partially explain the contrasting
results.6

5

In the experiments of Richards and Green (2015), improvements to park space and greenhouse gas abatement
are used as examples of short-horizon environmental goods with a private beneﬁt and long-horizon environmental
goods with a public beneﬁt, respectively.
6
In Richards and Green (2015), investments in greenhouse gas abatement are discounted at a near-zero (0.005%)
rate.

15

Appendix A

Q1–Q10Please select the appropriate number to indicate the extent to which each of thefollowing 10 statements represents you. Read each statement carefully and then click
the appropriate number rated on a ﬁve-point scale from 1 (not at all, not the case)to 5 (applies perfectly) as it relates to you. Choose the number that most closely
matches your answer.
Q1. I often spend time on outdoor activities such as hiking, climbing, and ﬁshing.
Q2. I don’t know precisely what my monthly electricity costs are.
Q3. I always bring my own reusable cup or a water bottle when I go somewhere. I
avoid disposable cups.
Q4. In the supermarket, I take a shopping bag with me instead of using disposable
plastic bags.
Q5. I am not willing to dispose waste properly at home, that is to separate waste
according to the rules in my local community.
Q6. I often participate in environmental conservation activities such as cleaning areasof arid land, cleaning beaches or waterways, or protecting rare animals, such as
the Crested Ibis (Toki) Restoration Program.
Q7. I choose an energy-saving type when I purchase electrical appliances such as an
air-conditioner or a refrigerator.
Q8. I don’t think of habitat conservation as a cause close to me.
Q9. I buy reﬁllable shampoo and detergent bottles.
Q10. I prefer environmentally friendly products for my daily needs.

16Appendix B

Q18
Insert Fig. A1.

Appendix C

Q20
Insert Fig. A2.

17

References
Adamowicz, W., J. Louviere and M. Williams (1994), “Combining Revealed and Stated Preference Methods for Valuing Environmental Amenities,” Journal of Environmental
Economics and Management, Vol. 26, Issue 3, pp. 271-292.
Andersen, S., G. W. Harrison, M. I. Lau and E. E. Rutström (2008), “Eliciting Risk and Time
Preferences,” Econometrica, Vol. 76, Issue 3, pp. 583-618.
Asano, T. and A. Shibata (2014), “Natural Capital Investment under Knightian Uncertainty,”
Environment and Development Economics, Vol. 19, Issue 5, pp. 529-547.
Eismont, O. and H. Welsch (1996), “Optimal Greenhouse Gas Emissions under Various Assessments of Climate Change Ambiguity,” Environmental and Resource Economics, Vol.
8, pp. 129-140.
Fox, C. R. and A. Tversky (1995), “Ambiguity Aversion and Comparative Ignorance,” Quarterly
Journal of Economics, Vol. 110, Issue 3, pp. 586-603.
Hardisty, D. J. and E. U. Weber (2009), “Discounting Future Green: Money Versus the Environment,” Journal of Experimental Psychology: General, Vol. 138, pp. 329-430.
Laibson, D. (2008), “Golden Eggs and Hyperbolic Discounting,” The Quarterly Journal of Economics, Vol. 112, No. 2, pp. 443-477.
McFadden, M. (1974), “The Measurement of Urban Travel Demand,” Journal of Public Economics, Vol. 3, Issue 4, pp. 303-328.
Meyer, A. J (2013), “Intertemporal Valuation of River Restoration,” Environmental and Resource Economics, Vol. 54, Issue 1, pp. 41-61.
Richards, T. J. and G. P. Green (2015), “Environmental Choices and Hyperbolic Discounting:
An Experimental Analysis,” Environmental and Resource Economics, Vol. 62, pp.
83-103.
Rodzon, K., M. S. Berry and A. L. Odum (2011), “Within-Subject Comparison of Degree of
Delay Discounting Using Titrating and Fixed Sequence Procedures,” Behavioural
Processes, Vol. 86, No. 1, pp. 164-167.
Scholten, M. and D. Read (2006), “Discounting by Intervals: A Generalized Model of Intertemporal Choice,” Management Science, Vol. 52, No. 9, pp. 1424-1436.
18

Viscusi, W. K., J. Huber and J. Bell (2008), “Estimating Discount Rates for Environmental
Quality from Utility-based Choice Experiments,” Journal of Risk and Uncertainty,
Vol. 37, Issue 2-3, pp. 199-220.

19

Table 1: Descriptive statistics
Variable

Obs.

Mean

Std. dev.

Min

Max

Participation in Outdoor Activities

2906

0.2646

0.44120.00001.0000Pro-environmental Attitude2906

0.00000.5221-2.1861

1.4595Male

2906

0.48180.49980.00001.0000Age

290645.361712.6862

20.0000

65.0000

Marriage2906

0.64870.47750.00001.0000Having Children

2906

0.54200.49830.00001.0000

University Graduate2906

0.51030.50000.00001.0000Household Income(in JPY million)

2890

6.1576

4.16890.000025.0000

Pecuniary Discount Factor

2906

0.87570.16220.50001.0000Present Bias for Money2906

0.9726

0.15930.50002.0000Long-run Discount Factor for Money2906

0.90690.13730.50001.0000Absolute Risk Aversion(x1000)2906

0.08810.0848-0.0471

0.1960

Ambiguity Aversion

2906

1.1401

1.9424-7.0000

7.000020

21

Improvement Effect

Delay

Delay 0.5 Year x Improvement

Delay 1 Year x Improvement

Delay 2 Years x Improvement

Delay 3 Years x Improvement

(2)

(3)

(4)

(5)

(6)

(7)

0.02100.02000.01420.0142-0.1661

0.0398-1.1337(0.0036) ***(0.0027) ***(0.0019) ***(0.0017) ***(0.0098) ***(0.0026) ***(0.0249) ***Conditional Logit
Coefficient (Std. Error)

0.03200.06080.03250.0396-0.3323

0.0533-2.3235(0.0079) ***(0.0059) ***(0.0037) ***(0.0031) ***(0.0233) ***(0.0032) ***(0.0658) ***Mixed Logit
Coefficient (Std. Error)

Note: The dependent variable is coded 1 for a chosen policy, and 0 for the other policies in a given policy choice set. *** Statisticalsignificance at the 1% level. ** Statistical significance at the 5% level. * Statistical significance at the 10% level.

Cost

(1)

Variable

Table 2: Conditional logit and mixed logit estimates of policy choices

22

1.1644

1.1072

1.07310-1 year

0-2 years

0-3 years

1.12181.09120-1 year

0-2 years(1.0255, 1.0986)(1.0464, 1.1360)(1.0506, 1.1931)(1.1243, 1.4181)

Note: The 95% confidence intervals are in parentheses.

1.0621

1.27120-3 years(1.0469, 1.0992)(1.0746, 1.1397)(1.1101, 1.2186)(1.2413, 1.4733)(2) Discount Factor

0-0.5 year

(1) Period

Panel B: Follow-up survey

1.3573(2) Discount Factor

0-0.5 year

(1) Period

Panel A: First survey(-0.0908, -0.0454)(-0.1233, -0.0702)(-0.1812, -0.1012)(-0.3262, -0.2003)-0.0584-0.0836-0.1086-0.2133(-0.0908, -0.0260)(-0.1212, -0.0459)(-0.1653, -0.0520)(-0.3043, -0.1224)(3) Rate of Time Preference-0.0681-0.0968-0.1412-0.2633(3) Rate of Time Preference

1.02461.03710.9901---1.02381.03450.9988

---

---

---

(0.9939, 1.0552)(0.9997, 1.0744)(0.8988, 1.0813)(4) Delta(1.0032, 1.0445)(1.0100, 1.0589)(0.9361, 1.0616)(4) Delta

1.24071.22581.2839---1.32581.31211.3589---

Table 3: 95% Conﬁdence intervals for the discount factors, rates of time preferences, δ, and β

---

---

(1.1000, 1.3814)(1.0807, 1.3708)(1.0708, 1.4970)(5) Beta(1.2154, 1.4361)(1.1995, 1.4247)(1.1992, 1.5186)(5) Beta

Table 4: Conditional logit estimates with the individual characteristics interactions
Variable

Coefficient (Std. Error)

(1)
(2)
(3)

Cost
Improvement EffectDelay-1.13630.0405-0.1643(0.0251) ***(0.0026) ***(0.0098) ***

(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)

Delay 0.5 Year x Improvementx Pecuniary Discount Factor
x Risk Aversion
x Ambiguity Aversion
x University Graduatex Participation in Outdoor Activities
x Pro-environmental Attitude
x Male0.00300.03565.30120.00520.0141-0.0075
0.01570.0089(0.0048)(0.0121) ***(21.4790)(0.0010) ***(0.0033) ***(0.0037) **(0.0031) ***(0.0034) ***

(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)

Delay 1 Year x Improvementx Pecuniary Discount Factor
x Risk Aversion
x Ambiguity Aversion
x University Graduatex Participation in Outdoor Activities
x Pro-environmental Attitude
x Male0.01140.0349-39.5604
0.00310.00690.00170.0049-0.0035(0.0047) **(0.0120) ***(20.4438)(0.0009) ***(0.0031) **(0.0035)(0.0029) *(0.0032)

(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)

Delay 2 Years x Improvementx Pecuniary Discount Factor
x Risk Aversion
x Ambiguity Aversion
x University Graduatex Participation in Outdoor Activities
x Pro-environmental Attitude
x Male0.01430.03246.39060.00370.0065-0.0065
0.00550.0064(0.0060) **(0.0149) **(25.8372)(0.0011) ***(0.0039) *(0.0043)(0.0036)(0.0041)

(28)
(29)
(30)
(31)
(32)
(33)
(34)
(35)

Delay 3 Years x Improvementx Pecuniary Discount Factor
x Risk Aversion
x Ambiguity Aversion
x University Graduatex Participation in Outdoor Activities
x Pro-environmental Attitude
x Male0.01880.03469.00950.00260.00650.0008-0.0025
0.0030(0.0062) ***(0.0150) **(24.7594)(0.0011) **(0.0037) *(0.0042)(0.0035)(0.0039)(36)

Control Variables
Interacted with Delay x Improvement

Yes

Note: The dependent variable is coded 1 for a chosen policy, and 0 for the other policies in a given
policy choice set. Control (interaction) variables are not shown. Control (interaction) variables
include age, marriage, child, income, pecuniary present bias parameter. *** Statistical significance atthe 1% level. ** Statistical significance at the 5% level. * Statistical significance at the 10% level.

23

24

Pecuniary Discount Factor
Risk Aversion
Ambiguity Aversion
University Graduate
Participation in outdoor Activities
Pro-environmental Attitude
Male
Constant
Yes

0.5393-269.7636
0.06200.1755-0.09100.1861
0.0586
1.0414(0.1297) ***(231.3039)(0.0101) ***(0.0356) ***(0.0388) **(0.0348) ***(0.0366)(0.1886) ***Yes

1.0838-752.2856
0.14180.43320.02750.62020.2653-0.2300(0.3449) ***(614.9455)(0.0269) ***(0.0947) ***(0.1031)(0.0925) ***(0.0974) ***(0.5015) ***0-3years
Coefficient (Std. Error)

Note: The dependent variable is individuals' environmental discount factor estimated from the mixed logit model. Control variables are
not shown. Control variables include age, marriage, child, income, pecuniary present bias parameter. In the case where the dependent
variable is the environmental discount factor (0-0.5year), the environmental discount factor (0.5-3year) is added to the control variables.
*** Statistical significance at the 1% level. ** Statistical significance at the 5% level. * Statistical significance at the 10% level.

(9) Control Variables

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)

Variable

0-0.5year
Coefficient (Std. Error)

Table 5: OLS regression on individual characteristics using the mixed logit estimates

25(0.2667) ***0.9408(0.7122) ***-0.5801
Yes(0.4883) **(866.6482)(0.0374) ***(0.1362) ***(0.1466)(0.1349) ***(0.1407) **(0.7213) **0.9901-703.0850
0.12350.4527-0.12820.57930.30381.4791Model1
Coefficient (Std. Error)

Yes(0.1828) ***(324.5667)(0.0140) ***(0.0510) ***(0.0549) **(0.0505) ***(0.0527)(0.2700) ***0.581588.8067
0.06350.2026-0.12510.15470.0586
0.8931Model1
Coefficient (Std. Error)(0.1749) **(310.0381)(0.0134) ***(0.0488) ***(0.0524) *(0.0482) ***(0.0502)(0.2577) ***(0.0221) ***(0.2556) ***Yes

0.6633-88.41200.10830.3484-0.0806
0.45630.23940.83910.2992-0.9079(0.4689)(832.3989)(0.0359) ***(0.1308) ***(0.1406)(0.1298) ***(0.1350) *(0.6937)(0.0268) ***(0.6832) ***Model2
Coefficient (Std. Error)

Yes

0.4035315.2736
0.05260.1614-0.09120.12790.05650.75440.26220.6280Model2
Coefficient (Std. Error)

Note: The dependent variable is the mixed logit estimate of individuals' environmental discount factor from the first survey. Control variables are not shown.
Control variables include age, marriage, child, income, pecuniary present bias parameter. In the case where the dependent variable is the environmental
discount factor (0-0.5year), the environmental discount factor (0.5-3year) is added to the control variables.
*** Statistical significance at the 1% level. ** Statistical significance at the 5% level. * Statistical significance at the 10% level.

(11) Control Variables(1) Pecuniary Discount Factor(2) Risk Aversion(3) Ambiguity Aversion(4) University Graduate(5) Participation in outdoor Activities(6) Pro-environmental Attitude(7) Male(8) Expected Change of Temperature(9) Environmental Discount Factor (0-3year) of Follow-up Suvey(10) ConstantVariable

Panel B: Longest Horizon (0-3year Setting)(11) Control Variables(1) Pecuniary Discount Factor(2) Risk Aversion(3) Ambiguity Aversion(4) University Graduate(5) Participation in outdoor Activities(6) Pro-environmental Attitude(7) Male(8) Expected Change of Temperature(9) Environmental Discount Factor (0-0.5year) of Follow-up Suvey(10) ConstantVariable

Panel A: Shortest Horizon (0-0.5year Setting)

Table 6: OLS regression on individual characteristics and expectation of future temperature changes using the mixed logit estimates

Suppose that the government is planning an “Earth Is Our Friend” policy to improve theenvironment in your region.
The policy uniformly reduces the environmental risk from landslides and other natural
disasters, and the environmental burden of air, water, and soil pollution.
Residents must pay the total costs (around JPY 20–36 billion) in the form of a special
environmental tax over a period of 10 years from now onwards.
The environment-improving eﬀect of the policy continues for 10 years after it takes
eﬀect .
There are alternative policies, which diﬀer in (i) the rate of environmental improvement,(ii) the annual cost (tax) per household, and (iii) the delay before the environmental
improvement begins.
Please indicate which of the three policies below you prefer.

Policy A

Policy B

Policy C

the delay before theenvironmental
improvement begins

3 years

1 year

2 years

the rate ofenvironmental
improvement

20%

5%

10%

JPY 3,600
per year

JPY 4,600
per year

JPY 5,100
per year

the annual cost perhousehold

Which policy do you
most prefer?

Figure 1: Policy choices

26

0

1

2

3

4

−2

0

2

4

6

8
−1

−4

Environmental Discount Factor (0−3years) of First Survey

3
2
1
0
−1
−2

Environmental Discount Factor (0−0.5year) of Follow−up Survey

y = 0.9530 + 0.3268 x−6

4Panel By = 1.2125 + 0.2925 x−2

Environmental Discount Factor (0−0.5year) of First Survey

Panel A

−6

−4

−2

0

2

4

6

8

Environmental Discount Factor (0−3years) of Follow−up Survey

Figure 2: Jitter scatterplots of individuals’ environmental discount factors between the ﬁrst
survey and follow-up survey

27

The following ﬁgure indicates the trend of the moving average temperature in Tokyo,
which is a series of averages calculated by averaging the temperature over the past 50
years. According to the data, the moving average temperature has increased by 1.1◦ C(from 15.6◦ C to 16.1◦ C) over the past 35 years (1980–2014).
16.2

moving average temperature

16
15.8
15.6
15.4
15.2
15
14.8
1980

1985

1990

1995

2000

2005

2010

year

What would you predict the moving average temperature in Tokyo after 35 years to
be? From the following nine answers, please choose the one that comes closest to yourprediction.










decrease by 4◦ C
decrease by 2◦ C
decrease by 1.1◦ C
decrease by 0.5◦ C
no change
increase by 0.5◦ C
increase by 1.1◦ C
increase by 2◦ C
increase by 4◦ C

Figure 3: AQ1

28

The following graph presents the value of natural resource depletion in Japan estimatedby World Bank-published data gathered from around 200 countries and regions. Natural
resource depletion, which is the sum of energy depletion, mineral depletion, net forest
depletion, and carbon dioxide and local air pollution damage, is an indicator of environmental degradation developed by the World Bank. The data shown from 1970 to 2013
are reported in constant 2005 prices. Thus, you can compare the value in 1980 with thevalue in 2005 without worrying about the eﬀect of inﬂation.

improvement

environmental condition

deterioration

Natural resource depletion (100 billion US$)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1970

1975

1980

1985

1990 1995
year

2000

2005

2010

Please take a careful look at the graph below and answer the following question. Whatwould you predict natural resource depletion to be after 2015?






increase signiﬁcantly
increaseno change
decreasedecrease signiﬁcantly

Figure 4: AQ2

29

The solid line over the period between 1980 and 2011 shows the historical estimates.
The ﬁve lines show the projected changes in CO2 concentration over the period between
2012 and 2040 for each of the ﬁve emissions scenarios. This graph shows the projections
from our original scenarios using data published in the Fifth Assessment Report (Climate
Change 2013) WGI: The Physical Science Basis of IPCC.
500
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5

CO2 concentration (ppm)

480
460
440
420
400
380
360
340
the historical estimates

320
1970

1980

1990

2000

the projected changes

2010
year

2020

2030

2040

2050

From the following ﬁve scenarios, please choose the one that comes closest to your prediction.






Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5

Figure 5: AQ3

30

31

Q-18-1

Q-18-3(A) To get JPY 10,000 today.
(B) To get JPY 10,800 a month later.

Q-18-2

Q-18-2-1

Figure A1: Q18

Q-18-3-2(A) To get JPY 10,000 today.
(B) To get JPY 10,150 a month later.

Q-18-3-1

Q-18-2-2(A) To get JPY 10,000 today.
(B) To get JPY 13,000 a month later.

Q18 (Question on discount rates for immediate future)
Which one do you prefer? Choose the one you prefer.

(A) To get JPY 10,000 today.
(B) To get JPY 10,080 a month later.

(A) To get JPY 10,000 today.
(B) To get JPY 10,350 a month later.

(A) To get JPY 10,000 today.
(B) To get JPY 11,500 a month later.

(A) To get JPY 10,000 today.
(B) To get JPY 14,500 a month later.

32

Q-20-1

Q-20-3(A) To participate in the lottery.
(B) To get JPY 2,000.

Q-20-2

Figure A2: Q20

Q-20-3-2(A) To participate in the lottery.
(B) To get JPY 1,000.

Q-20-3-1

Q-20-2-2(A) To participate in the lottery.
(B) To get JPY 4,000.

Q-20-2-1(A) To participate in the lottery.
(B) To get JPY 10,200.

(A) To participate in the lottery.
(B) To get JPY 11,500.

(A) To participate in the lottery.
(B) To get JPY 3,000.

(A) To participate in the lottery.
(B) To get JPY 5,000.

Q20 (Question about a lottery that oﬀers a prize of JPY 10,000 when the composition of the urn is known)
Which one do you prefer? Choose the one you prefer.