- Email: [email protected]
Evaluating source separation of plastic waste using conjoint analysis
Available online at www.sciencedirect.com
Waste Management 28 (2008) 2393–2402 www.elsevier.com/locate/wasman
Evaluating source separation of plastic waste using conjoint analysis Jun Nakatani *, Toshiya Aramaki, Keisuke Hanaki Department of Urban Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Accepted 25 November 2007 Available online 18 January 2008
Abstract Using conjoint analysis, we estimated the willingness to pay (WTP) of households for source separation of plastic waste and the improvement of related environmental impacts, the residents’ loss of life expectancy (LLE), the landfill capacity, and the CO2 emissions. Unreliable respondents were identified and removed from the sample based on their answers to follow-up questions. It was found that the utility associated with reducing LLE and with the landfill capacity were both well expressed by logarithmic functions, but that residents were indifferent to the level of CO2 emissions even though they approved of CO2 reduction. In addition, residents derived utility from the act of separating plastic waste, irrespective of its environmental impacts; that is, they were willing to practice the separation of plastic waste at home in anticipation of its ‘‘invisible effects”, such as the improvement of citizens’ attitudes toward solid waste issues. 2007 Elsevier Ltd. All rights reserved.
1. Introduction In recent years, the necessity for a transparent decisionmaking process has become recognized in the field of municipal solid waste (MSW) management. One of the tools used by decision-makers is cost-benefit analysis (CBA), which makes it possible to specify an alternative that maximizes the net social benefit on the basis of ‘‘potential Pareto efficiency” (see, e.g., Boardman et al., 2001). A high-quality decision-making process should address the interrelationships among environmental, social, and economic aspects. From this perspective, the social costs and benefits of the environmental, social, and economic impacts of decisions should be accounted for within the framework of CBA. Decision-making related to MSW management is often strongly related to global and site-specific environmental impacts, as well as to the amenity for residents. Separate collection of plastic waste, which has recently been implemented nationwide in Japan under the country’s Contain* Corresponding author. Present address: Department of Chemical System Engineering, School of Engineering, The University of Tokyo, Japan. Tel./fax: +81 3 5841 6876. E-mail address: [email protected] (J. Nakatani).
0956-053X/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2007.11.010
ers and Packaging Recycling Law, can be linked with various kinds of environmental improvements and impacts, and requires the participation of residents in the form of source separation (i.e., separation of plastic waste from other domestic wastes at the source of these wastes). In the framework of CBA, the benefit derived from the implementation of a specific plan is assumed to be based on the stakeholders’ willingness to pay (WTP). Therefore, in order to apply CBA to the decision-making process for MSW management, it is desirable to evaluate the decision in monetary terms based on the WTP of stakeholders. Several methods for estimating WTP for an environmental good have been developed, and numerous researchers have adopted conjoint analysis because it allows separate estimation of WTPs for the individual attributes of a multi-attribute good. The objective of the present study was to estimate the marginal WTPs (MWTPs) of residents for source separation of plastic waste, and the related environmental improvements that resulted from this decision, using conjoint analysis within the CBA framework. To perform this analysis, we chose a case study of the MSW management system of Kawasaki City (Kanagawa Prefecture, Japan), and considered the local residents’ loss of life expectancy (LLE), the landfill capacity of Kawasaki City, and CO2
2394
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
emissions to be environmental impacts associated with source separation of plastic waste. In our analysis, we considered the existence of respondents with dominant preferences and the identification of the utility function to be important factors, and the preferences of residents related to separate waste collection and the above-mentioned environmental impacts were characterized. 2. Socioeconomic evaluation 2.1. Socioeconomic evaluation of waste management Several authors have estimated the WTP of residents for waste management options using the contingent valuation (CV) method or the contingent ranking (CR) method. Lake et al. (1996) undertook a CV survey in the village of South Norfolk, UK, and estimated the WTP for maintaining a recycling system that was already operational. The benefit of the curbside recycling system was evaluated and compared with its expected cost. Tiller et al. (1997) applied CV to estimate the household WTP for a dropoff recycling option in a rural and suburban area of Tennessee, USA. Caplan et al. (2002) used the CR approach to estimate household WTP for various curbside services that enable the separation of green waste and recyclable materials from other solid wastes in Utah, USA. In each of these studies, the implementation of a specific MSW management option was presented to respondents and their responses were analyzed. Positive WTPs for the implementation of waste separation or recycling were observed in those studies, possibly because respondents expected some beneficial effects (e.g., decreased environmental impacts) of the plans. However, those studies did not account for the factors that made respondents willing to pay for those options. Most residents were probably unaware of the extent of environmental improvement that would result from the implementation of a given waste management option, and it is thus doubtful whether the resulting WTPs were meaningful in the framework of CBA. In particular, several plastic waste management options exist in practice (e.g., mechanical recycling, feedstock recycling, energy recovery, landfilling), and their environmental impacts (e.g., human health damage due to the emission of air pollutants, decreases in landfill capacity, emissions of greenhouse gases) generally differ. Therefore, in order to properly evaluate the social costs and benefits of various MSW management options, it is necessary to elicit the preferences of residents for the various environmental impacts associated with these options. Conjoint analysis has been used for the evaluation of multi-attribute goods, and it is therefore also applicable to evaluating the various environmental impacts associated with MSW management options. Sakata (2007) elicited the preferences of residents for different forms of waste management using conjoint analysis, and evaluated the monetary value of recycling rate, dioxin emissions, and the number of waste separations. However, the recycling rate
is generally less likely to fully represent the environmental impacts, since different forms of recycling will have different degrees and types of environmental impacts. In addition, the dioxin problem might not necessarily illustrate the full spectrum of environmental impacts associated with MSW management. Therefore, the economic evaluation of the recycling rate itself, and probably that of dioxin emissions, may be inappropriate in the framework of CBA. To date, no other studies seem to have fully evaluated the environmental impacts associated with waste management. 2.2. Conjoint analysis In the field of environmental economics, CV has traditionally been used to evaluate environmental goods. An individual’s WTP can be elicited using CV, but the CV method cannot distinguish the individual values of the attributes of a multi-attribute object. Therefore, many researchers have recently used conjoint analysis to independently estimate an individual’s MWTP for each attribute. In addition, conjoint analysis has advantages over CV, such as built-in tests of scope sensitivity (Hanley et al., 1998). Conjoint analysis takes several different forms: full-profile rating, the CR method, choice experiments, and pair-wise choice experiments (Washida, 1999; Ohno, 2000). Choice experiments and pair-wise choice experiments are being used with increasing frequency. The advantage of pair-wise choice experiments over choice experiments is that a large number of attributes can be evaluated with a relatively light burden on the respondents because attributes which have the same level between the two alternatives in a choice set do not have to be presented to respondents. In the present study, we used pair-wise choice experiments. Briefly, individuals were asked which of two alternatives they preferred using a rating scale with the lowest value for one alternative (referred to as ‘‘the left side”) and the highest value for the other alternative (referred to as ‘‘the right side”). For example, a rating scale might range from 1 to 5, where 1 indicates a strong preference for the left-side alternative, 5 indicates a strong preference for the right-side alternative. Each alternative consists of multiple attributes. If one of the attributes can be assigned a monetary price, then it is possible to calculate the MWTPs of respondents for each attribute (i.e., the marginal rates of substitution between each attribute and the monetary attribute) based on their responses. 2.3. Dominant preferences In recent years, some researchers have discussed the issue of dominant preferences in the context of choice experiments (e.g., Cairns and van der Pol, 2004). A dominant preference, often called ‘‘non-trading behavior”, is a special case of a ‘‘lexicographic preference” (Scott, 2002). According to the theory of a lexicographic ordering, a deci-
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
sion-maker first considers the best alternatives with respect to the first-ranked (most important) attribute, and then, paying attention exclusively to these alternatives, considers the best of these alternatives with respect to the secondranked attribute, and so on until all attributes have been considered. Thus, a respondent with a dominant preference always chooses the alternative that optimizes one particular attribute. The difference between a dominant preference and a lexicographic preference is that an individual with a dominant preference is willing to trade other attributes when the levels of the dominant (first-ranked) attribute are equal. Clearly, this may lead to problems in pair-wise choice experiments. There is, as yet, no general agreement as to how to deal with responses that are consistent with dominant preferences, and the appropriate treatment of these responses depends on why individuals were not willing to trade (van der Pol and Cairns, 2001; Cairns and van der Pol, 2004). Several researchers who have studied CV and choice experiments have discussed the influence of lexicographic choices on estimates of research parameters (e.g., Spash and Hanley, 1995; Hanley and Milne, 1996; Carlsson and Martinsson, 2001; Rizzi and Ortuzar, 2003; Rouwendal and Blaeij, 2004). Lexicographic choices in stated-preference methods can be explained in three ways: (i) as being a simplified way (a rule of thumb) of answering questions, (ii) as indicating that the levels of attributes are not sufficiently differentiated to allow such respondents to make any trade-offs, or (iii) as genuinely lexicographic preferences. These three explanations are also valid for dominant preferences (Cairns and van der Pol, 2004). Individuals whose responses are consistent with explanations (ii) or (iii) might be regarded as merely having strong preferences. On the other hand, responses of type (i) would be the explanation for decision-makers who use decision heuristics to simplify their choices. According to Scott (2002), factors that influence the existence of dominant preferences include an individual’s past experiences as well as the complexity of the conjoint questions. It is likely that past experiences create a stereotyped bias in favor of a specific attribute. In addition, ‘‘yea-saying”, which is defined as the tendency of some respondents to agree with an interviewer’s request regardless of their true views in the context of CV (Mitchell and Carson, 1989), can obviously lead to type (i) dominance responses. Such responses should be excluded from the analysis in order to gain a more accurate evaluation. 3. Survey design 3.1. Study subjects Kawasaki City is an industrial city and a residential suburb of Tokyo located on the southern side of the Tokyo metropolitan area. Its population was 1,307,089 as of 1 February 2005. Overall environmental quality in the city was once regarded as poor. In particular, ‘‘Kawasaki Asth-
2395
ma” was a major public health issue during the 1960s. Household wastes in Kawasaki City are sorted at the source into combustibles, recyclable wastes (i.e., aluminum and steel cans, glass bottles, PET bottles, metals, and batteries), and bulky waste. Plastic waste is collected along with the combustibles, and combustible waste is incinerated at four municipal facilities. Incineration ash is then disposed of at the Ukishima landfill site on the city’s waterfront, which is the city’s only landfill site. An environmental impact assessment study of Kawasaki’s MSW management system (Nakatani et al., 2007) indicated that the following impact categories were related to the implementation of separate collection of plastic waste: emissions of greenhouse gases (leading to global warming), fossil fuel consumption (leading to the depletion of fossil resources), damage to human health due to exposure to air pollutants, and a shortage of landfill space. Preliminary surveys of resident attitudes toward separate waste collection in Kawasaki City and Tokyo (Nakatani et al., 2005) found that citizens recognized CO2 emissions, exposure to air pollutants, and landfill capacity as environmental impacts related to this policy. Based on these impact categories, we therefore used conjoint analysis in the present study to evaluate three factors: the loss of life expectancy (LLE) due to exposure to air pollutants, used to represent the damage to human health; the number of years remaining before the landfill capacity was used up; and CO2 emissions, which account for most of the emissions of greenhouse gases. 3.2. Profiles and choice sets Each alternative in a choice set was characterized using five attributes: loss of life expectancy (LLE), the landfill capacity, the amount of CO2 emissions, whether or not source separation of plastic waste was implemented, and the additional household payment. Although the use of designated trash bags is the most common means of funding waste collection in Japan, it was considered that using the price of the trash bags in the profiles could not allow respondents to estimate their expenses. Therefore, we used fixed monthly payments per household as a monetary attribute in the profiles. The levels for each of these five attributes are presented in Table 1. The values of environmental impacts were based on their actual values in 2001 in Kawasaki City (Nakatani et al., 2007). The amounts of the additional payments were determined from the results of pilot surveys conducted before the present study, and bore no relationship to the actual costs of implementing particular MSW management options such as separate waste collection, but respondents were asked to assume that the additional payment would be used exclusively for MSW management. We selected 25 profiles from 640 possible combinations of the attribute levels using the orthogonal main effect design tool provided by the SPSS Conjoint software. In addition, we established a status quo alternative in which
2396
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
provides an example of the description of a choice set. Respondents chose a value for each question to represent their choice between the left-side alternative and the rightside alternative. In the questionnaire, the instruction ‘‘consider the LLE for all individuals in your household” was given. Then the estimated MWTP for the reduction of LLE was defined as the value of avoiding loss of life in the respondent’s household. Ten types of questionnaires were created, and four pairs (conjoint questions) were included in each questionnaire.
Table 1 Levels of the five attributes in the profiles
Level 1 Level 2 Level 3 Level 4 Level 5
Loss of life expectancya (min/ person)
Landfill capacity (yr)
Collection of plastic waste
45
10
Mixed
30
20
15 5 –
CO2 emissions (1000 t/ yr)
Additional payment per household (yen/mo)b 0
100
Separate
200
80
30
–
500
50
40
–
1000
10
–
–
2000
3.3. Outline of the survey
–
Before asking a respondent to fill in the questionnaire, we explained the environmental impacts associated with the MSW management in Kawasaki City and its current status as follows: the LLE due to exposure to air pollutants was approximately 30 min per person, Kawasaki City had approximately 30 yr of landfill capacity remaining, and annual CO2 emissions were approximately 80,000 t. In addition, we asked respondents to assume that separate waste collection or an additional payment would not necessarily improve the environmental impacts in the conjoint questions in order to avoid the stereotyped bias for them. We then presented the questions concerning their acceptance of source separation of plastic waste and an additional payment for MSW management, followed by the four conjoint questions. Immediately after the conjoint questions, we presented two follow-up questions (described in Section 4.2) that would allow us to eliminate unreliable responses. We randomly selected 1000 residents of Kawasaki City from the telephone directory. The survey was delivered by
Note: Bold letters indicate the present environmental impacts that were explained to respondents based on an environmental impact assessment study of Kawasaki’s MSW management system (Nakatani et al., 2007). a Calculated on the assumption that the term of exposure to air pollutants is 30 years. b At the time we collected our data, 1 USD corresponded to around 105 yen.
all of the attributes were set at the current level. We excluded nine of these profiles that were obviously unrealistic, leaving only 17 profiles, for fear that respondents would be biased against the conjoint questions. We designed 20 pairs of profiles so that only three attributelevel differences were presented to the respondents at a time, in order to lighten their decision burden. The respondents were instructed to assume that the other two attributes had the same levels between the left-side and right-side alternatives, and therefore, that the differences between the two alternatives equaled zero. Fig. 1
Which option would you prefer ? Select one choice. * Consider the loss of life expectancy for all individuals in your household.
Option 1
CO2 emissions
100000 t/year
0.37% of the total in Kawasaki City
Strongly Option 2
15 minutes /person
Option 2
Loss of life expectancy
No Difference
this case …
Option 1
Impacts in
Separate
Strongly Option 1
Collection of plastics waste
Option 2 Collection of plastic waste Impacts in
Mixed this case …
Loss of life expectancy
30 minutes /person
CO2 emissions
80000 t/year
0.30% of the total in Kawasaki City
> In both options, additional household payment are not imposed. > In both options, the remaining years of landfill capacity is 30 years.
Fig. 1. An example of a conjoint question.
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402 Table 2 Respondents’ acceptance of source separation of plastic waste and of an additional payment for MSW management
Agree even if there are no environmental improvements Agree if there are enough environmental improvements Disagree even if there are enough environmental improvements No answer Total
Source separation of plastic waste
Additional household payment
95 (21.9%)
74 (17.1%)
322 (74.2%)
262 (60.4%)
8 (1.8%)
98 (22.6%)
9 (2.1%) 434 (100.0%)
0 (0.0%) 434 (100.0%)
postal mail accompanied by gifts (ballpoint pens) on 24 March 2005, and we had received 531 completed questionnaires by 27 April (a 53.1% response rate). Individuals who did not report the number of persons in their household were regarded as ineffective respondents and were eliminated from the data set. In addition, 56 questionnaires were protest responses that we judged inappropriate for the analysis. As a result, 1652 of a total of 2124 conjoint questions in the completed questionnaires (77.8%) were used for the analysis. The answers to the questions on source separation and the additional payment are presented in Table 2. On the whole, separate waste collection was found to be more acceptable than the additional payment. 4. Models and analysis 4.1. Models The rating for pairs of alternatives can be modeled using a random utility framework (Johnson and Desvousges, 1997; Kuriyama and Ishii, 2000). First, the individual utility function can be denoted as U ij ¼ V ij þ eij ;
ð1Þ
where Uij indicates the total utility derived from alternative j in the ith pair, Vij indicates the systematic component of utility, eij is a random (unobservable) component, and j = L or R denotes the left-side or right-side alternative, respectively, in each pair. The difference in utility between alternatives R and L, DU RL (=UiR UiL), is i ¼ V iR V iL þ ei ¼ DV RL þ ei ; DU RL i i
ð2Þ
where ei = eiR eiL is the error term that captures the effect of unobservable factors. When the utility is specified as a simple linear function of the attributes, Vij = b0 xij, the utility difference is DU RL ¼ b0 ðxiR xiL Þ þ ei ¼ b0 DxRL þ ei ; i i
ð3Þ
where xij is a vector representing the attributes of alternative j in the ith pair, and b is a vector representing the attribute parameters.
2397
is not observed directly. Instead, the discrete ratDU RL i ing data related to the utility difference, yi, is observed. Then, the probability that rating s (=1, 2, . . . , 5) is observed in the ith pair, Pr (yi = s ), is expressed as 6 as P is ¼ Pr as1 6 DU RL i ¼ Pr as1 DV RL 6 ei 6 as DV RL ; ð4Þ i i where the as values are threshold parameters that demarcate the ranges of the difference in utility corresponding to rating s and s + 1 (a0 = 1, a5 = 1). Assuming that the error term, ei, in Eq. (2) is distributed as a standard normal distribution, the probability is P is ¼ U as DV RL U as1 DV RL ; ð5Þ i i where r is the cumulative standard normal distribution function. This model is known as the ordered probit model, which was proposed by Johnson and Desvousges (1997). The attribute parameters and the threshold parameters are estimated using a maximum-likelihood model. The log-likelihood function is XX ln L ¼ d is ln P is ; ð6Þ i
s
where dis is a dummy variable for the rating in the ith pair (for rating s, dis = 1; for any other rating, dis = 0). The systematic component of the utility can be assumed, for example, to be X V ¼ b0 x ¼ bk xk þ bp xp ; ð7Þ k
where xk is the attribute of the profile, xp is the monetary attribute of the profile, and bk and bp are coefficients for xk and xp, respectively. Eq. (7) can then be differentiated to produce X @V @V dxk þ dxp ¼ dV : ð8Þ @x @x k p k When the utility is fixed at the present level (dV = 0) and all attributes other than x1 are also fixed at the present level (dxk = 0; "k6¼1) in Eq. (8), the MWTP for attribute x1 is described by dxp @V @V b MWTP1 ¼ ¼ ¼ 1: ð9Þ @x1 @xp dx1 bp 4.2. Unreliable respondents In Japan, separate waste collection is usually considered by residents to be a good practice because they believe it will improve the environment. Some respondents may have answered the conjoint questions exclusively on the basis of the belief (i.e., based on a stereotyped bias) that source separation of plastic waste should be implemented because it is necessary to improve the environmental impacts of waste management. Although such a belief may be partially accurate in the real world, respondents were asked to assume that the levels of environmental
2398
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
impacts were independent of whether waste collection was separate or mixed in the conjoint analysis. Those responses are consistent with explanation (i) for the dominant prefer-
ences that was described in Section 2.3, and therefore indicate that the responses were inappropriate for use in our analysis.
Table 3 Attitudes of respondents toward the conjoint questions Main question: How did you answer the conjoint questions?
Number of responses 17 77 45 132 132 8 411
(1) Prioritized alternatives with mixed collection of plastic waste (2) Prioritized alternatives with source separation of plastic waste (3) Prioritized alternatives with the least additional household payment (4) Prioritized alternatives with the best improvement of the environmental impacts (5) Accounted for source separation of plastic waste, additional household payment, and the environmental impacts (6) Other Total Supplementary question: Why did you prioritize alternatives with source separation of plastic waste? (respondents were allowed to choose more than one answer) (1) Hope to improve the environmental impacts (2) Attitudes of citizens toward solid waste issues would be improved (3) Satisfaction would be derived from the act of separating plastic waste (4) Willing to obey current trends (5) Willing to obey any rule imposed by the city (6) No reason given (7) Other Total respondents = 77
Number of responses 60 26 7 16 12 0 2
Table 4 Models for the systematic component of utility Model 1
V ¼
3 P
bHLLEd HLLEd þ
d¼1
Model 2
V ¼
3 P d¼1
3 P
bLFd LFd þ bPW PW þ
d¼1
bHLLEd HLLEd þ
3 P
4 P
bPAYd PAYd þ
d¼1
bLFd LFd þ bPW PW þ bPAY PAY þ
d¼1
3 P
bCO2d CO2d
d¼1 3 P
bCO2d CO2d þ bMS MS
d¼1
Model 3
V = blnHLLE ln HLLE + bln LF ln LF + bPWPW + bPAYPAY + bCO20CO20 + bMSMS
HLLE1 HLLE2 HLLE3
Dummy variable (=n for 45 min of LLE per person, otherwise = 0) Dummy variable (=n for 15 min of LLE per person, otherwise = 0) Dummy variable (=n for 5 min of LLE per person, otherwise = 0)
HLLE
Loss of life expectancy in one’s household a[100 min/household]
LF1 LF2 LF3
Dummy variable (=1 for 10 remaining yr of landfill capacity; otherwise = 0) Dummy variable (=1 for 20 remaining yr of landfill capacity; otherwise = 0) Dummy variable (=1 for 40 remaining yr of landfill capacity; otherwise = 0)
LF
Remaining years of landfill capacity [100 yr]
PW PAY1 PAY2 PAY3 PAY4
Collection of plastic waste (separate = 1, mixed = 0) Dummy variable (=1 for 200 yen/mo of additional household payments, otherwise = 0) Dummy variable (=1 for 500 yen/mo of additional household payments, otherwise = 0) Dummy variable (=1 for 1000 yen/mo of additional household payments, otherwise = 0) Dummy variable (=1 for 2000 yen/mo of additional household payments, otherwise = 0)
PAY
Amount of additional household payment [1000 yen/mo]
CO21 CO22 CO23
Dummy variable (=1 for 100,000 t/yr of CO2 emissions, otherwise = 0) Dummy variable (=1 for 50,000 t/yr of CO2 emissions, otherwise = 0) Dummy variable (=1 for 10,000 t/yr of CO2 emissions, otherwise = 0)
CO20
Dummy variable (=1 for less than 80,000 t/yr of CO2 emissions, otherwise = 0)
MS
Dummy variable (=1 for positive additional payments, otherwise = 0)
Note: n denotes the number of persons in the household. a: Loss of life expectancy per person multiplied by n. At the time we collected our data, 1 USD corresponded to around 105 yen.
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
In this paper, as typical decision-makers following explanation (i), respondents who might answer the conjoint questions based on the stereotyped bias for source separation of plastic waste were identified and removed from the sample based on their responses to the two follow-up questions (a main one and a supplementary one; see Table 3). The main question made respondents select one of six possible answers, and those who prioritized separation of plastic waste were asked to answer a supplementary question, in which they were allowed to select multiple answers from seven choices. Those who reported that their reason for prioritizing alternatives with source separation of plastic waste was to improve the environmental impacts were considered to have ignored the environmental impacts that we presented in the profiles and to have instead answered the conjoint questions based on the above-mentioned bias. Respondents who met these conditions (60 of the 434 effective respondents; Table 3) were removed from the data set as unreliable respondents. Excluding these individuals left 1428 choices (86.4% of the total effective choices) for use in our analysis.
2399
Table 5 Coefficient estimates for the models of utility for the sample from which unreliable respondents were removed Model 1
Model 2
bHLLE1 bHLLE2 bHLLE3 blnHLLE
0.033 (0.124) 0.007 (0.409) 0.043 (0.118)
0.020 (0.217) 0.030 (0.086) 0.085 (0.001)
bLF1 bLF2 bLF3 blnLF
0.287 (0.001) 0.070 (0.199) 0.003 (0.490)
bPW
0.331 (0.000)
bPAY1 bPAY2 bPAY3 bPAY4 bPAY
0.054 (0.189) 0.034 (0.376) 0.380 (0.000) 0.505 (0.001)
bCO21 bCO22 bCO23 bCO20
0.118 (0.207) 0.351 (0.002) 0.318 (0.002)
0.206 (0.000) 0.297 (0.000) 0.013 (0.429) 0.153 (0.029) 0.283 (0.000)
We specified three models for the systematic component of utility, V (Table 4). First, we estimated the coefficients in Model 1, with each attribute level represented by a dummy variable, for the sample from which unreliable respondents were removed (Table 5), and performed an one-tailed t-test for each coefficient in order to determine whether the estimated value was significantly consistent with the expected direction; the implications for each coefficient are summarized in Table 6. The coefficients for PAY1 and PAY2 were positive but not significant. To examine this result, we divided the samples into two groups based on the answers to the question about additional payment for MSW management (see Table 2). That is, respondents who agreed to the payment even if there would be no environmental improvement were classified as group A, and the respondents who would agree to the payment if there was enough environmental improvement or who would disagree with the payment even if there was enough environmental improvement were classified as group B. The coefficients were then estimated separately for both groups, and the relationships between the payment amounts and the coefficient estimates for PAYd are illustrated in Fig. 2. For both groups, a linear relationship was apparent, but the relationship was strongest for group B (R2 = 0.900), and the y-intercept was close to zero. On the other hand, the coefficient estimates for PAY1 (PAY = 0.2) and PAY2 (PAY = 0.5) were positive and significant for group A. This means that respondents in group A derived utility from the act of payment. This kind of utility is known as ‘‘the purchase of moral satisfaction” (Kahneman and Knetsch, 1992) or ‘‘the warm glow of giving” (Andreoni, 1989).
0.382 (0.000)
0.343 (0.000)
0.408 (0.000)
0.427 (0.000)
0.052 (0.278) 0.236 (0.008) 0.227 (0.008) 0.309 (0.000) 0.550 (0.000)
0.572 (0.000)
a1 a2 a3 a4
1.032 (0.000) 0.331 (0.000) 0.341 (0.000) 1.085 (0.000)
1.036 (0.000) 0.328 (0.000) 0.349 (0.000) 1.094 (0.000)
1.038 (0.000) 0.330(0.000) 0.349 (0.000) 1.096 (0.000)
Log-likelihood AIC q2 (pseudo R2)
2,180.0 4,388.0 0.045
2,172.8 4,369.5 0.048
2,170.7 4,353.4 0.049
bMS
4.3. Analysis
Model 3
Note: One-tailed p-values for the t-distribution are given in parentheses. The number of observations in each model was 1428.
Table 6 Expected direction for each coefficient bHLLE1 < 0 < bHLLE2 < bHLLE3
Utility increases with decreasing LLE for one’s household
bLF1 < bLF2 < 0 < bLF3
Utility increases with an increase in the number of remaining years of landfill capacity Utility decreases when plastic waste is separated Utility has no relationship with the act of separating plastic waste Utility increases when plastic waste is separated Utility decreases with increasing additional household payments Utility increases with a reduction in CO2 emissions
bPW < 0 bPW = 0 bPW > 0 bPAY1 > bPAY2 > bPAY3 > bPAY4, bPAY < 0 bCO21 < 0 < bCO22 < bCO23
On the basis of these results, we developed Model 2, in which the relationship between the amount of the payment and the associated utility was expressed using two variables (PAY and MS). Here, MS is a variable that equals 1 in profiles with a positive additional payment and 0 in profiles with no additional payment for respondents in group A. MS always becomes 0 for respondents in group B. The
2400
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
a
Group A
Coefficient
1.0
2 R = 0.428
0.5 0.0 -0.5 -1.0
0.0
0.5
1.0
1.5
2.0
2.5
PAY
b Coefficient
Group B 1.0 0.5
2 R = 0.900
0.0 -0.5 -1.0
0.0
0.5
1.0
1.5
2.0
2.5
PAY Fig. 2. Relationship between the additional household payment and its estimated coefficient in Model 1. Range bars represent the 90% confidence interval for the coefficient estimates.
coefficient estimate for MS was positive and significant (see Table 5), which supports the hypothesis about the presence of utility from the act of payment (i.e., moral satisfaction) for group A. The relationships between the values of the environmental impacts and their coefficient estimates are illustrated in Fig. 3. The current value of each environmental impact (indicated by a white symbol) is the baseline for the dummy variables, thus its coefficient equals zero. The coefficient
a
loss of life expectancy Coefficient
0.20 0.15 0.10 0.05 0.00 -0.05 -0.10
0.5
0.4
0.3
0.2
0.1
0.0
LLE
b
estimates for HLLE (Fig. 3a) were approximated better by the logarithmic function of HLLE indicated by the solid line (R2 = 0.995) than by a linear function (R2 = 0.894). Similarly, the coefficient estimates for LF (Fig. 3b) were approximated better by the logarithmic function of LF indicated by the solid line (R2 = 0.939) than by a linear function (R2 = 0.883). Fig. 3c, which presents the coefficient estimates for CO21, can be interpreted as follows: where CO2 emissions are larger than the current level, the coefficient was not significantly different from zero (its one-tailed p-value was 0.278; Table 5). Moreover, the difference between the coefficient estimates for CO22 and CO23 was not significant (one-tailed p-value for the difference = 0.456). This relationship suggests that residents were indifferent to the level of CO2 emissions, even though they approved of CO2 reduction. Based on the results for Model 2, we established Model 3 using the following assumptions: (i) the utility associated with LLE could be expressed by a logarithmic function; (ii) the utility associated with the landfill capacity could be expressed by a logarithmic function; (iii) the utility associated with CO2 emissions could be expressed by a dummy variable (CO20), which becomes 1 in profiles where CO2 emissions are less than 800,00 t and becomes 0 in profiles where CO2 emissions are greater than or equal to 80,000 t. With regard to CO2 emissions, this model only considers whether the CO2 emissions are less than the present amount (80,000 t). As Table 5 indicates, Model 3 was the best of the three models based on Akaike information criterion (AIC), but was also a good model based on the significance of each coefficient. Therefore, Model 3 could appropriately express the preferences of the residents of Kawasaki City. The MWTP for the alleviation of each environmental impact was calculated from the estimation results in Model 3 using Eq. (9), and the results are shown in Table 7. The coefficient (and also the MWTP) for PW was positive and significant even after removing the unreliable
Coefficient
landfill capacity 0.4 0.2 0.0 -0.2 -0.4 -0.6
Table 7 MWTP values for source separation of plastic waste and the alleviation of its related environmental impacts 0.0
0.1
0.2
0.3
0.4
0.5
LF
c Coefficient
co2 emissions 0.6 0.4 0.2 0.0 -0.2 -0.4 1.2
Household MWTP (yen/ mo) (mean [90% confidence interval])
Interpretations
ln HLLE
Household WTP for a decrease in the natural logarithm of the LLE value by 1 Household WTP for an increase in the natural logarithm of the remaining years by 1 Household WTP for the implementation of source separation of plastic waste Household WTP for the satisfaction or responsibility towards contributing to the global environment
ln LF
PW 1.0
0.8
0.6
0.4
0.2
0.0
CO2 Fig. 3. Relationships between the environmental impacts and their coefficient estimates in Model 2. White symbols represent the current values for each environmental parameter. Range bars represent the 90% confidence intervals for the coefficient estimates.
CO20
483 [334– 624] 663 [434– 869] 803 [565– 1080] 724 [537– 898]
Note: At the time we collected our data, 1 USD corresponded to around 105 yen. Calculated from the estimation results for Model 3. 90% Confidence intervals were obtained by the bootstrap method (1000 replicates).
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
respondents. The estimated MWTP for source separation of plastic waste represents the respondent’s aggregate evaluation of the effects of all the potential factors related to separate waste collection, with the exception of other attributes included in the profiles. It is not necessarily persuasive that residents are willing to spend time and effort on waste separation even if there were no visible effects (i.e., the improvement of the environmental impacts presented in the profiles). A possible explanation may be that household MWTP for PW was still biased upward owing to the presence of ‘‘yea-sayers” who agreed with source separation of plastic waste regardless of their true views (see Section 2.3). The other possibility is that residents actually derived utility from positive aspects of waste separation other than the improvement of the environmental impacts that was presented in the profiles. The latter hypothesis was supported by the answers to the supplementary questions (Table 3): most respondents chose the response that the attitudes of citizens toward solid waste issues would be improved as their reason for accepting separate waste collection. In addition, some respondents suggested in their comments that they had a guilty conscience for not performing waste separation when most people in other Japanese cities were already doing so. 5. Conclusions
2401
Our results suggest that the residents of Kawasaki City derived utility from the act of separating plastic waste, and were willing to separate plastic waste at home in anticipation of its ‘‘invisible effects” such as the improvement of the attitudes of citizens toward solid waste issues, even without visible effects such as the improvement of environmental impacts. Such a desire was, when converted into a monetary value, estimated to be worth approximately 800 yen/mo (1 USD corresponded to around 105 yen) for an average household. The estimated WTPs in this paper can be applied to the CBA framework in which the benefits of separate waste collection should be evaluated based on the WTP of stakeholders. In particular, the procedure to identify the utility function that we have demonstrated in this paper ensures that the preferences of the residents for the act of waste separation and its related environmental impacts are properly considered. Still, it should be noted that the estimated WTP values were potentially affected by a non-response bias (i.e., the fact that nearly 47% of the people selected for the survey did not respond). Obviously, it will be important in the actual decision-making that all stakeholder preferences be appropriately reflected in the sample. In future research, a non-biased sample will be the key to ensuring a successful contribution of conjoint analysis to the actual decision-making process for MSW management.
In this study, we evaluated the source separation of plastic waste and the improvement of related environmental impacts as monetary values using conjoint analysis. Our analysis had several interesting characteristics: First, making good use of dummy variables in the utility models enabled us to express the detailed preferences of the respondents for various kinds of environmental impacts. It was found that the utilities associated with LLE and landfill capacity were well expressed as logarithmic functions, and that residents were indifferent to the level of CO2 emissions even though they approved of CO2 reduction. These results can be interpreted as follows:
Acknowledgements
1. As the value of LLE becomes smaller, the utility per unit reduction of LLE (i.e., the marginal utility) increases. Residents thus evaluate their own LLE based on its relative change rather than its absolute value. 2. As the number of remaining years of landfill capacity decreases, the utility per unit increase in the number of remaining years increases. That is, as the landfill capacity decreases, residents value the remaining years of landfill capacity more highly, but remain indifferent to the remaining years of landfill capacity so long as sufficient landfill capacity remains. 3. Residents were indifferent to the level of CO2 emissions, even though they approved of CO2 reduction itself. That is, the utility they associate with CO2 emissions was possibly derived from satisfaction or responsibility towards contributing to the global environment.
Andreoni, J., 1989. Giving with impure altruism: applications to charity and Ricardian equivalence. Journal of Political Economy 97 (6), 1447– 1458. Boardman, A.E., Greenberg, D.H., Vining, A.R., Weimer, D.L., 2001. Cost-Benefit Analysis: Concepts and Practice, second ed. Prentice Hall, New Jersey. Cairns, J., van der Pol, M., 2004. Repeated follow-up as a method for reducing non-trading behavior in discrete choice experiments. Social Science & Medicine 58, 2211–2218. Caplan, A.J., Grijalva, T.C., Jakus, P.M., 2002. Waste not or want not? A contingent ranking analysis of curbside waste disposal options. Ecological Economics 43, 185–197. Carlsson, F., Martinsson, P., 2001. Do hypothetical and actual marginal willingness to pay differ in choice experiments? application to the valuation of the environment. Journal of Environmental Economics and Management 41, 179–192. Hanley, N., Milne, J., 1996. Ethical beliefs and behaviour in contingent valuation surveys. Journal of Environmental Planning and Management 39 (2), 255–272.
This research was supported by a Grant-in-Aid for Exploratory Research from the Japan Society for the Promotion of Science. The authors gratefully acknowledge valuable comments on an earlier version of the manuscript by Kentaro Yoshida (Tsukuba University), Takaaki Kato (Tokyo Institute of Technology), and Satoshi Ishii (The University of Tokyo). The authors are also grateful to the residents of Kawasaki City for responding to our questionnaire. References
2402
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
Hanley, N., Wright, R., Adamowicz, W., 1998. Using choice experiments to value the environment: design issues, current experience and future prospects. Environmental and Resource Economics 11 (3–4), 413–428. Johnson, F.R., Desvousges, W.H., 1997. Estimating stated preferences with rated-pair data: environmental, health, and employment effects of energy programs. Journal of Environmental Economics and Management 34, 79–99. Kahneman, D., Knetsch, J.L., 1992. Valuing public goods: the purchase of moral satisfaction. Journal of Environmental Economics and Management 22, 57–70. Kuriyama, K., Ishii, Y., 2000. Estimation of the environmental value of recycled wood wastes: a conjoint analysis study. Journal of Forest Research 5, 1–6. Lake, I.R., Bateman, I.J., Parfitt, J.P., 1996. Assessing a kerbside recycling scheme: a quantitative and willingness to pay case study. Journal of Environmental Management 46, 239–254. Mitchell, R.C., Carson, R.T., 1989. Using surveys to value public goods: the contingent valuation method. Resources for the Future. Nakatani, J., Aramaki, T., Hanaki, K., 2005. Statistical analysis for residents’ attitudes toward separate collection of plastics waste and waste collection charge: a case study of Chofu city. In: Proceedings of 33rd Annual Meeting of Environmental Systems Research, pp. 91–97 (in Japanese). Nakatani, J., Aramaki, T., Hanaki, K., 2007. Multi-Aspect impact assessment of plastics waste management: a case study of
Kawasaki city. Environmental Science 20 (3), 181–194 (in Japanese). Ohno, E. (Ed.), 2000. Business Practice of Economical Assessment of Environment, Tokyo, Keiso-Syobo (in Japanese). Rizzi, L.I., Ortuzar, J.D., 2003. Stated preference in the valuation of interurban road safety. Accident Analysis and Prevention 35, 9–22. Rouwendal, J., Blaeij, A.T., 2004. Inconsistent and Lexicographic Choices in Stated Preference Analysis. Tinbergen Institute Discussion Paper. Sakata, Y., 2007. A choice experiment of the residential preference of waste management services – the example of Kagoshima city, Japan. Waste Management 27 (5), 639–644. Scott, A., 2002. Identifying and analysing dominant preferences in discrete choice experiments: an application in health care. Journal of Economic Psychology 23, 383–398. Spash, C.L., Hanley, N., 1995. Preferences, information and biodiversity preservation. Ecological Economics 12, 191–208. Tiller, K.H., Jakus, P.M., Park, W.M., 1997. Household willingness to pay for dropoff recycling. Journal of Agricultural and Resource Economics 22 (2), 310–320. van der Pol, M., Cairns, J., 2001. Estimating time preferences for health using discrete choice experiments. Social Science & Medicine 52, 1459– 1470. Washida, T., 1999. Introduction to Environmental Assessment, Tokyo, Keiso-Syobo (in Japanese).
Waste Management 28 (2008) 2393–2402 www.elsevier.com/locate/wasman
Evaluating source separation of plastic waste using conjoint analysis Jun Nakatani *, Toshiya Aramaki, Keisuke Hanaki Department of Urban Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Accepted 25 November 2007 Available online 18 January 2008
Abstract Using conjoint analysis, we estimated the willingness to pay (WTP) of households for source separation of plastic waste and the improvement of related environmental impacts, the residents’ loss of life expectancy (LLE), the landfill capacity, and the CO2 emissions. Unreliable respondents were identified and removed from the sample based on their answers to follow-up questions. It was found that the utility associated with reducing LLE and with the landfill capacity were both well expressed by logarithmic functions, but that residents were indifferent to the level of CO2 emissions even though they approved of CO2 reduction. In addition, residents derived utility from the act of separating plastic waste, irrespective of its environmental impacts; that is, they were willing to practice the separation of plastic waste at home in anticipation of its ‘‘invisible effects”, such as the improvement of citizens’ attitudes toward solid waste issues. 2007 Elsevier Ltd. All rights reserved.
1. Introduction In recent years, the necessity for a transparent decisionmaking process has become recognized in the field of municipal solid waste (MSW) management. One of the tools used by decision-makers is cost-benefit analysis (CBA), which makes it possible to specify an alternative that maximizes the net social benefit on the basis of ‘‘potential Pareto efficiency” (see, e.g., Boardman et al., 2001). A high-quality decision-making process should address the interrelationships among environmental, social, and economic aspects. From this perspective, the social costs and benefits of the environmental, social, and economic impacts of decisions should be accounted for within the framework of CBA. Decision-making related to MSW management is often strongly related to global and site-specific environmental impacts, as well as to the amenity for residents. Separate collection of plastic waste, which has recently been implemented nationwide in Japan under the country’s Contain* Corresponding author. Present address: Department of Chemical System Engineering, School of Engineering, The University of Tokyo, Japan. Tel./fax: +81 3 5841 6876. E-mail address: [email protected] (J. Nakatani).
0956-053X/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2007.11.010
ers and Packaging Recycling Law, can be linked with various kinds of environmental improvements and impacts, and requires the participation of residents in the form of source separation (i.e., separation of plastic waste from other domestic wastes at the source of these wastes). In the framework of CBA, the benefit derived from the implementation of a specific plan is assumed to be based on the stakeholders’ willingness to pay (WTP). Therefore, in order to apply CBA to the decision-making process for MSW management, it is desirable to evaluate the decision in monetary terms based on the WTP of stakeholders. Several methods for estimating WTP for an environmental good have been developed, and numerous researchers have adopted conjoint analysis because it allows separate estimation of WTPs for the individual attributes of a multi-attribute good. The objective of the present study was to estimate the marginal WTPs (MWTPs) of residents for source separation of plastic waste, and the related environmental improvements that resulted from this decision, using conjoint analysis within the CBA framework. To perform this analysis, we chose a case study of the MSW management system of Kawasaki City (Kanagawa Prefecture, Japan), and considered the local residents’ loss of life expectancy (LLE), the landfill capacity of Kawasaki City, and CO2
2394
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
emissions to be environmental impacts associated with source separation of plastic waste. In our analysis, we considered the existence of respondents with dominant preferences and the identification of the utility function to be important factors, and the preferences of residents related to separate waste collection and the above-mentioned environmental impacts were characterized. 2. Socioeconomic evaluation 2.1. Socioeconomic evaluation of waste management Several authors have estimated the WTP of residents for waste management options using the contingent valuation (CV) method or the contingent ranking (CR) method. Lake et al. (1996) undertook a CV survey in the village of South Norfolk, UK, and estimated the WTP for maintaining a recycling system that was already operational. The benefit of the curbside recycling system was evaluated and compared with its expected cost. Tiller et al. (1997) applied CV to estimate the household WTP for a dropoff recycling option in a rural and suburban area of Tennessee, USA. Caplan et al. (2002) used the CR approach to estimate household WTP for various curbside services that enable the separation of green waste and recyclable materials from other solid wastes in Utah, USA. In each of these studies, the implementation of a specific MSW management option was presented to respondents and their responses were analyzed. Positive WTPs for the implementation of waste separation or recycling were observed in those studies, possibly because respondents expected some beneficial effects (e.g., decreased environmental impacts) of the plans. However, those studies did not account for the factors that made respondents willing to pay for those options. Most residents were probably unaware of the extent of environmental improvement that would result from the implementation of a given waste management option, and it is thus doubtful whether the resulting WTPs were meaningful in the framework of CBA. In particular, several plastic waste management options exist in practice (e.g., mechanical recycling, feedstock recycling, energy recovery, landfilling), and their environmental impacts (e.g., human health damage due to the emission of air pollutants, decreases in landfill capacity, emissions of greenhouse gases) generally differ. Therefore, in order to properly evaluate the social costs and benefits of various MSW management options, it is necessary to elicit the preferences of residents for the various environmental impacts associated with these options. Conjoint analysis has been used for the evaluation of multi-attribute goods, and it is therefore also applicable to evaluating the various environmental impacts associated with MSW management options. Sakata (2007) elicited the preferences of residents for different forms of waste management using conjoint analysis, and evaluated the monetary value of recycling rate, dioxin emissions, and the number of waste separations. However, the recycling rate
is generally less likely to fully represent the environmental impacts, since different forms of recycling will have different degrees and types of environmental impacts. In addition, the dioxin problem might not necessarily illustrate the full spectrum of environmental impacts associated with MSW management. Therefore, the economic evaluation of the recycling rate itself, and probably that of dioxin emissions, may be inappropriate in the framework of CBA. To date, no other studies seem to have fully evaluated the environmental impacts associated with waste management. 2.2. Conjoint analysis In the field of environmental economics, CV has traditionally been used to evaluate environmental goods. An individual’s WTP can be elicited using CV, but the CV method cannot distinguish the individual values of the attributes of a multi-attribute object. Therefore, many researchers have recently used conjoint analysis to independently estimate an individual’s MWTP for each attribute. In addition, conjoint analysis has advantages over CV, such as built-in tests of scope sensitivity (Hanley et al., 1998). Conjoint analysis takes several different forms: full-profile rating, the CR method, choice experiments, and pair-wise choice experiments (Washida, 1999; Ohno, 2000). Choice experiments and pair-wise choice experiments are being used with increasing frequency. The advantage of pair-wise choice experiments over choice experiments is that a large number of attributes can be evaluated with a relatively light burden on the respondents because attributes which have the same level between the two alternatives in a choice set do not have to be presented to respondents. In the present study, we used pair-wise choice experiments. Briefly, individuals were asked which of two alternatives they preferred using a rating scale with the lowest value for one alternative (referred to as ‘‘the left side”) and the highest value for the other alternative (referred to as ‘‘the right side”). For example, a rating scale might range from 1 to 5, where 1 indicates a strong preference for the left-side alternative, 5 indicates a strong preference for the right-side alternative. Each alternative consists of multiple attributes. If one of the attributes can be assigned a monetary price, then it is possible to calculate the MWTPs of respondents for each attribute (i.e., the marginal rates of substitution between each attribute and the monetary attribute) based on their responses. 2.3. Dominant preferences In recent years, some researchers have discussed the issue of dominant preferences in the context of choice experiments (e.g., Cairns and van der Pol, 2004). A dominant preference, often called ‘‘non-trading behavior”, is a special case of a ‘‘lexicographic preference” (Scott, 2002). According to the theory of a lexicographic ordering, a deci-
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
sion-maker first considers the best alternatives with respect to the first-ranked (most important) attribute, and then, paying attention exclusively to these alternatives, considers the best of these alternatives with respect to the secondranked attribute, and so on until all attributes have been considered. Thus, a respondent with a dominant preference always chooses the alternative that optimizes one particular attribute. The difference between a dominant preference and a lexicographic preference is that an individual with a dominant preference is willing to trade other attributes when the levels of the dominant (first-ranked) attribute are equal. Clearly, this may lead to problems in pair-wise choice experiments. There is, as yet, no general agreement as to how to deal with responses that are consistent with dominant preferences, and the appropriate treatment of these responses depends on why individuals were not willing to trade (van der Pol and Cairns, 2001; Cairns and van der Pol, 2004). Several researchers who have studied CV and choice experiments have discussed the influence of lexicographic choices on estimates of research parameters (e.g., Spash and Hanley, 1995; Hanley and Milne, 1996; Carlsson and Martinsson, 2001; Rizzi and Ortuzar, 2003; Rouwendal and Blaeij, 2004). Lexicographic choices in stated-preference methods can be explained in three ways: (i) as being a simplified way (a rule of thumb) of answering questions, (ii) as indicating that the levels of attributes are not sufficiently differentiated to allow such respondents to make any trade-offs, or (iii) as genuinely lexicographic preferences. These three explanations are also valid for dominant preferences (Cairns and van der Pol, 2004). Individuals whose responses are consistent with explanations (ii) or (iii) might be regarded as merely having strong preferences. On the other hand, responses of type (i) would be the explanation for decision-makers who use decision heuristics to simplify their choices. According to Scott (2002), factors that influence the existence of dominant preferences include an individual’s past experiences as well as the complexity of the conjoint questions. It is likely that past experiences create a stereotyped bias in favor of a specific attribute. In addition, ‘‘yea-saying”, which is defined as the tendency of some respondents to agree with an interviewer’s request regardless of their true views in the context of CV (Mitchell and Carson, 1989), can obviously lead to type (i) dominance responses. Such responses should be excluded from the analysis in order to gain a more accurate evaluation. 3. Survey design 3.1. Study subjects Kawasaki City is an industrial city and a residential suburb of Tokyo located on the southern side of the Tokyo metropolitan area. Its population was 1,307,089 as of 1 February 2005. Overall environmental quality in the city was once regarded as poor. In particular, ‘‘Kawasaki Asth-
2395
ma” was a major public health issue during the 1960s. Household wastes in Kawasaki City are sorted at the source into combustibles, recyclable wastes (i.e., aluminum and steel cans, glass bottles, PET bottles, metals, and batteries), and bulky waste. Plastic waste is collected along with the combustibles, and combustible waste is incinerated at four municipal facilities. Incineration ash is then disposed of at the Ukishima landfill site on the city’s waterfront, which is the city’s only landfill site. An environmental impact assessment study of Kawasaki’s MSW management system (Nakatani et al., 2007) indicated that the following impact categories were related to the implementation of separate collection of plastic waste: emissions of greenhouse gases (leading to global warming), fossil fuel consumption (leading to the depletion of fossil resources), damage to human health due to exposure to air pollutants, and a shortage of landfill space. Preliminary surveys of resident attitudes toward separate waste collection in Kawasaki City and Tokyo (Nakatani et al., 2005) found that citizens recognized CO2 emissions, exposure to air pollutants, and landfill capacity as environmental impacts related to this policy. Based on these impact categories, we therefore used conjoint analysis in the present study to evaluate three factors: the loss of life expectancy (LLE) due to exposure to air pollutants, used to represent the damage to human health; the number of years remaining before the landfill capacity was used up; and CO2 emissions, which account for most of the emissions of greenhouse gases. 3.2. Profiles and choice sets Each alternative in a choice set was characterized using five attributes: loss of life expectancy (LLE), the landfill capacity, the amount of CO2 emissions, whether or not source separation of plastic waste was implemented, and the additional household payment. Although the use of designated trash bags is the most common means of funding waste collection in Japan, it was considered that using the price of the trash bags in the profiles could not allow respondents to estimate their expenses. Therefore, we used fixed monthly payments per household as a monetary attribute in the profiles. The levels for each of these five attributes are presented in Table 1. The values of environmental impacts were based on their actual values in 2001 in Kawasaki City (Nakatani et al., 2007). The amounts of the additional payments were determined from the results of pilot surveys conducted before the present study, and bore no relationship to the actual costs of implementing particular MSW management options such as separate waste collection, but respondents were asked to assume that the additional payment would be used exclusively for MSW management. We selected 25 profiles from 640 possible combinations of the attribute levels using the orthogonal main effect design tool provided by the SPSS Conjoint software. In addition, we established a status quo alternative in which
2396
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
provides an example of the description of a choice set. Respondents chose a value for each question to represent their choice between the left-side alternative and the rightside alternative. In the questionnaire, the instruction ‘‘consider the LLE for all individuals in your household” was given. Then the estimated MWTP for the reduction of LLE was defined as the value of avoiding loss of life in the respondent’s household. Ten types of questionnaires were created, and four pairs (conjoint questions) were included in each questionnaire.
Table 1 Levels of the five attributes in the profiles
Level 1 Level 2 Level 3 Level 4 Level 5
Loss of life expectancya (min/ person)
Landfill capacity (yr)
Collection of plastic waste
45
10
Mixed
30
20
15 5 –
CO2 emissions (1000 t/ yr)
Additional payment per household (yen/mo)b 0
100
Separate
200
80
30
–
500
50
40
–
1000
10
–
–
2000
3.3. Outline of the survey
–
Before asking a respondent to fill in the questionnaire, we explained the environmental impacts associated with the MSW management in Kawasaki City and its current status as follows: the LLE due to exposure to air pollutants was approximately 30 min per person, Kawasaki City had approximately 30 yr of landfill capacity remaining, and annual CO2 emissions were approximately 80,000 t. In addition, we asked respondents to assume that separate waste collection or an additional payment would not necessarily improve the environmental impacts in the conjoint questions in order to avoid the stereotyped bias for them. We then presented the questions concerning their acceptance of source separation of plastic waste and an additional payment for MSW management, followed by the four conjoint questions. Immediately after the conjoint questions, we presented two follow-up questions (described in Section 4.2) that would allow us to eliminate unreliable responses. We randomly selected 1000 residents of Kawasaki City from the telephone directory. The survey was delivered by
Note: Bold letters indicate the present environmental impacts that were explained to respondents based on an environmental impact assessment study of Kawasaki’s MSW management system (Nakatani et al., 2007). a Calculated on the assumption that the term of exposure to air pollutants is 30 years. b At the time we collected our data, 1 USD corresponded to around 105 yen.
all of the attributes were set at the current level. We excluded nine of these profiles that were obviously unrealistic, leaving only 17 profiles, for fear that respondents would be biased against the conjoint questions. We designed 20 pairs of profiles so that only three attributelevel differences were presented to the respondents at a time, in order to lighten their decision burden. The respondents were instructed to assume that the other two attributes had the same levels between the left-side and right-side alternatives, and therefore, that the differences between the two alternatives equaled zero. Fig. 1
Which option would you prefer ? Select one choice. * Consider the loss of life expectancy for all individuals in your household.
Option 1
CO2 emissions
100000 t/year
0.37% of the total in Kawasaki City
Strongly Option 2
15 minutes /person
Option 2
Loss of life expectancy
No Difference
this case …
Option 1
Impacts in
Separate
Strongly Option 1
Collection of plastics waste
Option 2 Collection of plastic waste Impacts in
Mixed this case …
Loss of life expectancy
30 minutes /person
CO2 emissions
80000 t/year
0.30% of the total in Kawasaki City
> In both options, additional household payment are not imposed. > In both options, the remaining years of landfill capacity is 30 years.
Fig. 1. An example of a conjoint question.
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402 Table 2 Respondents’ acceptance of source separation of plastic waste and of an additional payment for MSW management
Agree even if there are no environmental improvements Agree if there are enough environmental improvements Disagree even if there are enough environmental improvements No answer Total
Source separation of plastic waste
Additional household payment
95 (21.9%)
74 (17.1%)
322 (74.2%)
262 (60.4%)
8 (1.8%)
98 (22.6%)
9 (2.1%) 434 (100.0%)
0 (0.0%) 434 (100.0%)
postal mail accompanied by gifts (ballpoint pens) on 24 March 2005, and we had received 531 completed questionnaires by 27 April (a 53.1% response rate). Individuals who did not report the number of persons in their household were regarded as ineffective respondents and were eliminated from the data set. In addition, 56 questionnaires were protest responses that we judged inappropriate for the analysis. As a result, 1652 of a total of 2124 conjoint questions in the completed questionnaires (77.8%) were used for the analysis. The answers to the questions on source separation and the additional payment are presented in Table 2. On the whole, separate waste collection was found to be more acceptable than the additional payment. 4. Models and analysis 4.1. Models The rating for pairs of alternatives can be modeled using a random utility framework (Johnson and Desvousges, 1997; Kuriyama and Ishii, 2000). First, the individual utility function can be denoted as U ij ¼ V ij þ eij ;
ð1Þ
where Uij indicates the total utility derived from alternative j in the ith pair, Vij indicates the systematic component of utility, eij is a random (unobservable) component, and j = L or R denotes the left-side or right-side alternative, respectively, in each pair. The difference in utility between alternatives R and L, DU RL (=UiR UiL), is i ¼ V iR V iL þ ei ¼ DV RL þ ei ; DU RL i i
ð2Þ
where ei = eiR eiL is the error term that captures the effect of unobservable factors. When the utility is specified as a simple linear function of the attributes, Vij = b0 xij, the utility difference is DU RL ¼ b0 ðxiR xiL Þ þ ei ¼ b0 DxRL þ ei ; i i
ð3Þ
where xij is a vector representing the attributes of alternative j in the ith pair, and b is a vector representing the attribute parameters.
2397
is not observed directly. Instead, the discrete ratDU RL i ing data related to the utility difference, yi, is observed. Then, the probability that rating s (=1, 2, . . . , 5) is observed in the ith pair, Pr (yi = s ), is expressed as 6 as P is ¼ Pr as1 6 DU RL i ¼ Pr as1 DV RL 6 ei 6 as DV RL ; ð4Þ i i where the as values are threshold parameters that demarcate the ranges of the difference in utility corresponding to rating s and s + 1 (a0 = 1, a5 = 1). Assuming that the error term, ei, in Eq. (2) is distributed as a standard normal distribution, the probability is P is ¼ U as DV RL U as1 DV RL ; ð5Þ i i where r is the cumulative standard normal distribution function. This model is known as the ordered probit model, which was proposed by Johnson and Desvousges (1997). The attribute parameters and the threshold parameters are estimated using a maximum-likelihood model. The log-likelihood function is XX ln L ¼ d is ln P is ; ð6Þ i
s
where dis is a dummy variable for the rating in the ith pair (for rating s, dis = 1; for any other rating, dis = 0). The systematic component of the utility can be assumed, for example, to be X V ¼ b0 x ¼ bk xk þ bp xp ; ð7Þ k
where xk is the attribute of the profile, xp is the monetary attribute of the profile, and bk and bp are coefficients for xk and xp, respectively. Eq. (7) can then be differentiated to produce X @V @V dxk þ dxp ¼ dV : ð8Þ @x @x k p k When the utility is fixed at the present level (dV = 0) and all attributes other than x1 are also fixed at the present level (dxk = 0; "k6¼1) in Eq. (8), the MWTP for attribute x1 is described by dxp @V @V b MWTP1 ¼ ¼ ¼ 1: ð9Þ @x1 @xp dx1 bp 4.2. Unreliable respondents In Japan, separate waste collection is usually considered by residents to be a good practice because they believe it will improve the environment. Some respondents may have answered the conjoint questions exclusively on the basis of the belief (i.e., based on a stereotyped bias) that source separation of plastic waste should be implemented because it is necessary to improve the environmental impacts of waste management. Although such a belief may be partially accurate in the real world, respondents were asked to assume that the levels of environmental
2398
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
impacts were independent of whether waste collection was separate or mixed in the conjoint analysis. Those responses are consistent with explanation (i) for the dominant prefer-
ences that was described in Section 2.3, and therefore indicate that the responses were inappropriate for use in our analysis.
Table 3 Attitudes of respondents toward the conjoint questions Main question: How did you answer the conjoint questions?
Number of responses 17 77 45 132 132 8 411
(1) Prioritized alternatives with mixed collection of plastic waste (2) Prioritized alternatives with source separation of plastic waste (3) Prioritized alternatives with the least additional household payment (4) Prioritized alternatives with the best improvement of the environmental impacts (5) Accounted for source separation of plastic waste, additional household payment, and the environmental impacts (6) Other Total Supplementary question: Why did you prioritize alternatives with source separation of plastic waste? (respondents were allowed to choose more than one answer) (1) Hope to improve the environmental impacts (2) Attitudes of citizens toward solid waste issues would be improved (3) Satisfaction would be derived from the act of separating plastic waste (4) Willing to obey current trends (5) Willing to obey any rule imposed by the city (6) No reason given (7) Other Total respondents = 77
Number of responses 60 26 7 16 12 0 2
Table 4 Models for the systematic component of utility Model 1
V ¼
3 P
bHLLEd HLLEd þ
d¼1
Model 2
V ¼
3 P d¼1
3 P
bLFd LFd þ bPW PW þ
d¼1
bHLLEd HLLEd þ
3 P
4 P
bPAYd PAYd þ
d¼1
bLFd LFd þ bPW PW þ bPAY PAY þ
d¼1
3 P
bCO2d CO2d
d¼1 3 P
bCO2d CO2d þ bMS MS
d¼1
Model 3
V = blnHLLE ln HLLE + bln LF ln LF + bPWPW + bPAYPAY + bCO20CO20 + bMSMS
HLLE1 HLLE2 HLLE3
Dummy variable (=n for 45 min of LLE per person, otherwise = 0) Dummy variable (=n for 15 min of LLE per person, otherwise = 0) Dummy variable (=n for 5 min of LLE per person, otherwise = 0)
HLLE
Loss of life expectancy in one’s household a[100 min/household]
LF1 LF2 LF3
Dummy variable (=1 for 10 remaining yr of landfill capacity; otherwise = 0) Dummy variable (=1 for 20 remaining yr of landfill capacity; otherwise = 0) Dummy variable (=1 for 40 remaining yr of landfill capacity; otherwise = 0)
LF
Remaining years of landfill capacity [100 yr]
PW PAY1 PAY2 PAY3 PAY4
Collection of plastic waste (separate = 1, mixed = 0) Dummy variable (=1 for 200 yen/mo of additional household payments, otherwise = 0) Dummy variable (=1 for 500 yen/mo of additional household payments, otherwise = 0) Dummy variable (=1 for 1000 yen/mo of additional household payments, otherwise = 0) Dummy variable (=1 for 2000 yen/mo of additional household payments, otherwise = 0)
PAY
Amount of additional household payment [1000 yen/mo]
CO21 CO22 CO23
Dummy variable (=1 for 100,000 t/yr of CO2 emissions, otherwise = 0) Dummy variable (=1 for 50,000 t/yr of CO2 emissions, otherwise = 0) Dummy variable (=1 for 10,000 t/yr of CO2 emissions, otherwise = 0)
CO20
Dummy variable (=1 for less than 80,000 t/yr of CO2 emissions, otherwise = 0)
MS
Dummy variable (=1 for positive additional payments, otherwise = 0)
Note: n denotes the number of persons in the household. a: Loss of life expectancy per person multiplied by n. At the time we collected our data, 1 USD corresponded to around 105 yen.
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
In this paper, as typical decision-makers following explanation (i), respondents who might answer the conjoint questions based on the stereotyped bias for source separation of plastic waste were identified and removed from the sample based on their responses to the two follow-up questions (a main one and a supplementary one; see Table 3). The main question made respondents select one of six possible answers, and those who prioritized separation of plastic waste were asked to answer a supplementary question, in which they were allowed to select multiple answers from seven choices. Those who reported that their reason for prioritizing alternatives with source separation of plastic waste was to improve the environmental impacts were considered to have ignored the environmental impacts that we presented in the profiles and to have instead answered the conjoint questions based on the above-mentioned bias. Respondents who met these conditions (60 of the 434 effective respondents; Table 3) were removed from the data set as unreliable respondents. Excluding these individuals left 1428 choices (86.4% of the total effective choices) for use in our analysis.
2399
Table 5 Coefficient estimates for the models of utility for the sample from which unreliable respondents were removed Model 1
Model 2
bHLLE1 bHLLE2 bHLLE3 blnHLLE
0.033 (0.124) 0.007 (0.409) 0.043 (0.118)
0.020 (0.217) 0.030 (0.086) 0.085 (0.001)
bLF1 bLF2 bLF3 blnLF
0.287 (0.001) 0.070 (0.199) 0.003 (0.490)
bPW
0.331 (0.000)
bPAY1 bPAY2 bPAY3 bPAY4 bPAY
0.054 (0.189) 0.034 (0.376) 0.380 (0.000) 0.505 (0.001)
bCO21 bCO22 bCO23 bCO20
0.118 (0.207) 0.351 (0.002) 0.318 (0.002)
0.206 (0.000) 0.297 (0.000) 0.013 (0.429) 0.153 (0.029) 0.283 (0.000)
We specified three models for the systematic component of utility, V (Table 4). First, we estimated the coefficients in Model 1, with each attribute level represented by a dummy variable, for the sample from which unreliable respondents were removed (Table 5), and performed an one-tailed t-test for each coefficient in order to determine whether the estimated value was significantly consistent with the expected direction; the implications for each coefficient are summarized in Table 6. The coefficients for PAY1 and PAY2 were positive but not significant. To examine this result, we divided the samples into two groups based on the answers to the question about additional payment for MSW management (see Table 2). That is, respondents who agreed to the payment even if there would be no environmental improvement were classified as group A, and the respondents who would agree to the payment if there was enough environmental improvement or who would disagree with the payment even if there was enough environmental improvement were classified as group B. The coefficients were then estimated separately for both groups, and the relationships between the payment amounts and the coefficient estimates for PAYd are illustrated in Fig. 2. For both groups, a linear relationship was apparent, but the relationship was strongest for group B (R2 = 0.900), and the y-intercept was close to zero. On the other hand, the coefficient estimates for PAY1 (PAY = 0.2) and PAY2 (PAY = 0.5) were positive and significant for group A. This means that respondents in group A derived utility from the act of payment. This kind of utility is known as ‘‘the purchase of moral satisfaction” (Kahneman and Knetsch, 1992) or ‘‘the warm glow of giving” (Andreoni, 1989).
0.382 (0.000)
0.343 (0.000)
0.408 (0.000)
0.427 (0.000)
0.052 (0.278) 0.236 (0.008) 0.227 (0.008) 0.309 (0.000) 0.550 (0.000)
0.572 (0.000)
a1 a2 a3 a4
1.032 (0.000) 0.331 (0.000) 0.341 (0.000) 1.085 (0.000)
1.036 (0.000) 0.328 (0.000) 0.349 (0.000) 1.094 (0.000)
1.038 (0.000) 0.330(0.000) 0.349 (0.000) 1.096 (0.000)
Log-likelihood AIC q2 (pseudo R2)
2,180.0 4,388.0 0.045
2,172.8 4,369.5 0.048
2,170.7 4,353.4 0.049
bMS
4.3. Analysis
Model 3
Note: One-tailed p-values for the t-distribution are given in parentheses. The number of observations in each model was 1428.
Table 6 Expected direction for each coefficient bHLLE1 < 0 < bHLLE2 < bHLLE3
Utility increases with decreasing LLE for one’s household
bLF1 < bLF2 < 0 < bLF3
Utility increases with an increase in the number of remaining years of landfill capacity Utility decreases when plastic waste is separated Utility has no relationship with the act of separating plastic waste Utility increases when plastic waste is separated Utility decreases with increasing additional household payments Utility increases with a reduction in CO2 emissions
bPW < 0 bPW = 0 bPW > 0 bPAY1 > bPAY2 > bPAY3 > bPAY4, bPAY < 0 bCO21 < 0 < bCO22 < bCO23
On the basis of these results, we developed Model 2, in which the relationship between the amount of the payment and the associated utility was expressed using two variables (PAY and MS). Here, MS is a variable that equals 1 in profiles with a positive additional payment and 0 in profiles with no additional payment for respondents in group A. MS always becomes 0 for respondents in group B. The
2400
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
a
Group A
Coefficient
1.0
2 R = 0.428
0.5 0.0 -0.5 -1.0
0.0
0.5
1.0
1.5
2.0
2.5
PAY
b Coefficient
Group B 1.0 0.5
2 R = 0.900
0.0 -0.5 -1.0
0.0
0.5
1.0
1.5
2.0
2.5
PAY Fig. 2. Relationship between the additional household payment and its estimated coefficient in Model 1. Range bars represent the 90% confidence interval for the coefficient estimates.
coefficient estimate for MS was positive and significant (see Table 5), which supports the hypothesis about the presence of utility from the act of payment (i.e., moral satisfaction) for group A. The relationships between the values of the environmental impacts and their coefficient estimates are illustrated in Fig. 3. The current value of each environmental impact (indicated by a white symbol) is the baseline for the dummy variables, thus its coefficient equals zero. The coefficient
a
loss of life expectancy Coefficient
0.20 0.15 0.10 0.05 0.00 -0.05 -0.10
0.5
0.4
0.3
0.2
0.1
0.0
LLE
b
estimates for HLLE (Fig. 3a) were approximated better by the logarithmic function of HLLE indicated by the solid line (R2 = 0.995) than by a linear function (R2 = 0.894). Similarly, the coefficient estimates for LF (Fig. 3b) were approximated better by the logarithmic function of LF indicated by the solid line (R2 = 0.939) than by a linear function (R2 = 0.883). Fig. 3c, which presents the coefficient estimates for CO21, can be interpreted as follows: where CO2 emissions are larger than the current level, the coefficient was not significantly different from zero (its one-tailed p-value was 0.278; Table 5). Moreover, the difference between the coefficient estimates for CO22 and CO23 was not significant (one-tailed p-value for the difference = 0.456). This relationship suggests that residents were indifferent to the level of CO2 emissions, even though they approved of CO2 reduction. Based on the results for Model 2, we established Model 3 using the following assumptions: (i) the utility associated with LLE could be expressed by a logarithmic function; (ii) the utility associated with the landfill capacity could be expressed by a logarithmic function; (iii) the utility associated with CO2 emissions could be expressed by a dummy variable (CO20), which becomes 1 in profiles where CO2 emissions are less than 800,00 t and becomes 0 in profiles where CO2 emissions are greater than or equal to 80,000 t. With regard to CO2 emissions, this model only considers whether the CO2 emissions are less than the present amount (80,000 t). As Table 5 indicates, Model 3 was the best of the three models based on Akaike information criterion (AIC), but was also a good model based on the significance of each coefficient. Therefore, Model 3 could appropriately express the preferences of the residents of Kawasaki City. The MWTP for the alleviation of each environmental impact was calculated from the estimation results in Model 3 using Eq. (9), and the results are shown in Table 7. The coefficient (and also the MWTP) for PW was positive and significant even after removing the unreliable
Coefficient
landfill capacity 0.4 0.2 0.0 -0.2 -0.4 -0.6
Table 7 MWTP values for source separation of plastic waste and the alleviation of its related environmental impacts 0.0
0.1
0.2
0.3
0.4
0.5
LF
c Coefficient
co2 emissions 0.6 0.4 0.2 0.0 -0.2 -0.4 1.2
Household MWTP (yen/ mo) (mean [90% confidence interval])
Interpretations
ln HLLE
Household WTP for a decrease in the natural logarithm of the LLE value by 1 Household WTP for an increase in the natural logarithm of the remaining years by 1 Household WTP for the implementation of source separation of plastic waste Household WTP for the satisfaction or responsibility towards contributing to the global environment
ln LF
PW 1.0
0.8
0.6
0.4
0.2
0.0
CO2 Fig. 3. Relationships between the environmental impacts and their coefficient estimates in Model 2. White symbols represent the current values for each environmental parameter. Range bars represent the 90% confidence intervals for the coefficient estimates.
CO20
483 [334– 624] 663 [434– 869] 803 [565– 1080] 724 [537– 898]
Note: At the time we collected our data, 1 USD corresponded to around 105 yen. Calculated from the estimation results for Model 3. 90% Confidence intervals were obtained by the bootstrap method (1000 replicates).
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
respondents. The estimated MWTP for source separation of plastic waste represents the respondent’s aggregate evaluation of the effects of all the potential factors related to separate waste collection, with the exception of other attributes included in the profiles. It is not necessarily persuasive that residents are willing to spend time and effort on waste separation even if there were no visible effects (i.e., the improvement of the environmental impacts presented in the profiles). A possible explanation may be that household MWTP for PW was still biased upward owing to the presence of ‘‘yea-sayers” who agreed with source separation of plastic waste regardless of their true views (see Section 2.3). The other possibility is that residents actually derived utility from positive aspects of waste separation other than the improvement of the environmental impacts that was presented in the profiles. The latter hypothesis was supported by the answers to the supplementary questions (Table 3): most respondents chose the response that the attitudes of citizens toward solid waste issues would be improved as their reason for accepting separate waste collection. In addition, some respondents suggested in their comments that they had a guilty conscience for not performing waste separation when most people in other Japanese cities were already doing so. 5. Conclusions
2401
Our results suggest that the residents of Kawasaki City derived utility from the act of separating plastic waste, and were willing to separate plastic waste at home in anticipation of its ‘‘invisible effects” such as the improvement of the attitudes of citizens toward solid waste issues, even without visible effects such as the improvement of environmental impacts. Such a desire was, when converted into a monetary value, estimated to be worth approximately 800 yen/mo (1 USD corresponded to around 105 yen) for an average household. The estimated WTPs in this paper can be applied to the CBA framework in which the benefits of separate waste collection should be evaluated based on the WTP of stakeholders. In particular, the procedure to identify the utility function that we have demonstrated in this paper ensures that the preferences of the residents for the act of waste separation and its related environmental impacts are properly considered. Still, it should be noted that the estimated WTP values were potentially affected by a non-response bias (i.e., the fact that nearly 47% of the people selected for the survey did not respond). Obviously, it will be important in the actual decision-making that all stakeholder preferences be appropriately reflected in the sample. In future research, a non-biased sample will be the key to ensuring a successful contribution of conjoint analysis to the actual decision-making process for MSW management.
In this study, we evaluated the source separation of plastic waste and the improvement of related environmental impacts as monetary values using conjoint analysis. Our analysis had several interesting characteristics: First, making good use of dummy variables in the utility models enabled us to express the detailed preferences of the respondents for various kinds of environmental impacts. It was found that the utilities associated with LLE and landfill capacity were well expressed as logarithmic functions, and that residents were indifferent to the level of CO2 emissions even though they approved of CO2 reduction. These results can be interpreted as follows:
Acknowledgements
1. As the value of LLE becomes smaller, the utility per unit reduction of LLE (i.e., the marginal utility) increases. Residents thus evaluate their own LLE based on its relative change rather than its absolute value. 2. As the number of remaining years of landfill capacity decreases, the utility per unit increase in the number of remaining years increases. That is, as the landfill capacity decreases, residents value the remaining years of landfill capacity more highly, but remain indifferent to the remaining years of landfill capacity so long as sufficient landfill capacity remains. 3. Residents were indifferent to the level of CO2 emissions, even though they approved of CO2 reduction itself. That is, the utility they associate with CO2 emissions was possibly derived from satisfaction or responsibility towards contributing to the global environment.
Andreoni, J., 1989. Giving with impure altruism: applications to charity and Ricardian equivalence. Journal of Political Economy 97 (6), 1447– 1458. Boardman, A.E., Greenberg, D.H., Vining, A.R., Weimer, D.L., 2001. Cost-Benefit Analysis: Concepts and Practice, second ed. Prentice Hall, New Jersey. Cairns, J., van der Pol, M., 2004. Repeated follow-up as a method for reducing non-trading behavior in discrete choice experiments. Social Science & Medicine 58, 2211–2218. Caplan, A.J., Grijalva, T.C., Jakus, P.M., 2002. Waste not or want not? A contingent ranking analysis of curbside waste disposal options. Ecological Economics 43, 185–197. Carlsson, F., Martinsson, P., 2001. Do hypothetical and actual marginal willingness to pay differ in choice experiments? application to the valuation of the environment. Journal of Environmental Economics and Management 41, 179–192. Hanley, N., Milne, J., 1996. Ethical beliefs and behaviour in contingent valuation surveys. Journal of Environmental Planning and Management 39 (2), 255–272.
This research was supported by a Grant-in-Aid for Exploratory Research from the Japan Society for the Promotion of Science. The authors gratefully acknowledge valuable comments on an earlier version of the manuscript by Kentaro Yoshida (Tsukuba University), Takaaki Kato (Tokyo Institute of Technology), and Satoshi Ishii (The University of Tokyo). The authors are also grateful to the residents of Kawasaki City for responding to our questionnaire. References
2402
J. Nakatani et al. / Waste Management 28 (2008) 2393–2402
Hanley, N., Wright, R., Adamowicz, W., 1998. Using choice experiments to value the environment: design issues, current experience and future prospects. Environmental and Resource Economics 11 (3–4), 413–428. Johnson, F.R., Desvousges, W.H., 1997. Estimating stated preferences with rated-pair data: environmental, health, and employment effects of energy programs. Journal of Environmental Economics and Management 34, 79–99. Kahneman, D., Knetsch, J.L., 1992. Valuing public goods: the purchase of moral satisfaction. Journal of Environmental Economics and Management 22, 57–70. Kuriyama, K., Ishii, Y., 2000. Estimation of the environmental value of recycled wood wastes: a conjoint analysis study. Journal of Forest Research 5, 1–6. Lake, I.R., Bateman, I.J., Parfitt, J.P., 1996. Assessing a kerbside recycling scheme: a quantitative and willingness to pay case study. Journal of Environmental Management 46, 239–254. Mitchell, R.C., Carson, R.T., 1989. Using surveys to value public goods: the contingent valuation method. Resources for the Future. Nakatani, J., Aramaki, T., Hanaki, K., 2005. Statistical analysis for residents’ attitudes toward separate collection of plastics waste and waste collection charge: a case study of Chofu city. In: Proceedings of 33rd Annual Meeting of Environmental Systems Research, pp. 91–97 (in Japanese). Nakatani, J., Aramaki, T., Hanaki, K., 2007. Multi-Aspect impact assessment of plastics waste management: a case study of
Kawasaki city. Environmental Science 20 (3), 181–194 (in Japanese). Ohno, E. (Ed.), 2000. Business Practice of Economical Assessment of Environment, Tokyo, Keiso-Syobo (in Japanese). Rizzi, L.I., Ortuzar, J.D., 2003. Stated preference in the valuation of interurban road safety. Accident Analysis and Prevention 35, 9–22. Rouwendal, J., Blaeij, A.T., 2004. Inconsistent and Lexicographic Choices in Stated Preference Analysis. Tinbergen Institute Discussion Paper. Sakata, Y., 2007. A choice experiment of the residential preference of waste management services – the example of Kagoshima city, Japan. Waste Management 27 (5), 639–644. Scott, A., 2002. Identifying and analysing dominant preferences in discrete choice experiments: an application in health care. Journal of Economic Psychology 23, 383–398. Spash, C.L., Hanley, N., 1995. Preferences, information and biodiversity preservation. Ecological Economics 12, 191–208. Tiller, K.H., Jakus, P.M., Park, W.M., 1997. Household willingness to pay for dropoff recycling. Journal of Agricultural and Resource Economics 22 (2), 310–320. van der Pol, M., Cairns, J., 2001. Estimating time preferences for health using discrete choice experiments. Social Science & Medicine 52, 1459– 1470. Washida, T., 1999. Introduction to Environmental Assessment, Tokyo, Keiso-Syobo (in Japanese).