Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
175
ASSESSING MONETARY VALUE OF THE NUISANCE
CAUSED BY AN MSW LANDFILL
Joone Joy1
, George K Varghese2
1
(Assistant Professor in Sree Narayana Gurukulam College of Engineering, Kadayiruppu, Ernakulam)
2
(Assistant Professor in National Institute of Technology, Calicut)
ABSTRACT
This paper describes the application of Conjoint Analysis for allocating a monetary value to the nuisance
suffered by a community living near an MSW landfill. The willingness to pay (WTP) of the community to avoid the
problems they face because of the landfill is considered as the money value they attach to their sufferings. Conjoint
analysis is a method that elicits public preferences indirectly by creating a hypothetical situation before the respondents
and answering questionnaires and analysis of the respondent's data. The approach is illustrated by applying it to an
existing landfill site at Njeliyanparamba in Kozhikode district in Kerala (India). This case study demonstrates that the
method can be applied easily and can be used in the overall cost calculation of a landfill, which includes the external cost
in addition to the direct cost.
Key Words: Conjoint Analysis, External Cost of Landfill, MSW Landfill, Willingness to Pay.
1. INTRODUCTION
The most common means of disposing municipal solid waste is burial in a sanitary landfill. Garbage disposal
has become an increasingly serious problem in urban, densely populated areas, where the main reasons for concern are
dwindling landfill space and the environmental problems experienced with existing, old landfills, such as contamination
of groundwater, odours and aesthetic deterioration of the environment. Municipal solid waste landfills are notorious for
having adverse impacts on those within their sphere of influence during the active life of the landfill (the time that wastes
are received by the landfill). This situation leads to a justified NIMBY (“not in my backyard”) attitude on the part of the
public (Stephen Hirshfeld, 1989) and hence the idea of an “external” cost associated with a landfill. The external cost of
the landfill should ideally be the monetary value of the nuisance caused by the landfill to the surrounding community and
to anyone who is affected adversely by the landfill.
Willingness to pay (WTP) generally refers to the value of a material to a person as what they are willing to pay,
sacrifice or exchange for it. WTP is the maximum monetary amount that an individual would pay to obtain a good. For
the purpose of this study, the willingness to pay (WTP) of the community to avoid the problems they face because of the
landfill is considered as the money value they attach to their sufferings. People may be ready to pay some amount
depending on their purchasing power to alleviate the pollution effects by landfill in their surroundings. This amount that
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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
176
they are willing to pay is found out and aggregated over the affected households to arrive at the external cost of the
landfill.
Direct measurement of WTP for a particular commodity is possible by asking people how much they are ready
to pay for getting that. When it comes to a situation under current study, people may not be able to give a clear answer.
Sometimes people may not understand the importance of the matter and may not respond in a meaningful way. So, rather
than directly asking them the WTP, a hypothetical situation is created before the people with a number of alternatives
regarding the attributes so that they are able to select an option. From the ranking obtained for the various options, by a
statistical analysis, the monetary value that an individual attaches for a particular option can be calculated. This method
is called Conjoint Analysis (Harry Telser, 2002).
2. CONJOINT ANALYSIS TECHNIQUE
Conjoint analysis is a survey procedure that is used to derive the values of particular attributes of goods or
services. Information is collected about individuals' choices between different goods that vary in terms of their attributes
or service levels. With this information, it is possible to derive values for each particular attribute or service. If price is
included as an attribute in the choice scenarios, values can be derived in monetary terms (Green, 1971).
Conjoint analysis assumes that consumers evaluate the value or utility of a product (real or hypothetical) by
combining the separate amounts of utility provided by each attribute. An experimental design is used to analyse this
behaviour. The procedure involves asking respondents to provide their overall evaluations of a set of hypothetical
products that combine the possible attributes of that product at various levels. A consumer is asked to compare different
products attribute combinations and rank them. Respondents are to indicate the combination they most prefer, the second
most preferred, etc. Conjoint analysis is applied to categorical variables, which reflect different features or characteristics
of products.
3. DESIGN OF A CONJOINT ANALYSIS STUDY
The various steps in the design of a conjoint experiment (Vinaytosh Mishra(2006) and its analysis
are given below
1) The first step is the selection of the attributes (factors) related to the actual issue (parameter we want to
determine) that define the good to be valued. The attributes should be selected on the basis of what the goal of
the valuation exercise is, prior beliefs of the researcher, and evidence from focus groups. For valuation, one of
the attributes must be the “price” of the commodity or the cost to the respondent of the program delivering a
change in the provision of a public good. Attributes can be quantitative or qualitative in nature.
2) The next step is to select the levels of the attributes. The levels of the attributes should be selected so as to be
reasonable and realistic, or else the respondent may reject the scenario and/or the choice exercise.
3) One has to fix and be careful about the sample size when choosing attributes and levels. The sample size should
be large enough to accommodate all of the possible combinations of attributes and levels of the attributes
4) Once the experimental design is created, the researcher needs to construct the choice sets. The choice sets may
consist of two or more alternatives, depending on how simple one wishes to keep the choice tasks.
5) After this, respondants are asked to rank the selected combination according to their preferences from the most
preferred to the least preferred.
6) The rankings thus obtained from the whole sample size, is then analysed to get the utility of each attribute.
7) Since one of the attribute is a monetary value, its utility can be compared with the utilities of other attributes to
obtain their corresponding monetary values.
4. CASE STUDY
The site for processing and disposal of the solid waste from Calicut corporation is located at Njeliyanparamba
which is about about 8km from the city centre on the National Highway 17 (NH 17). A waste treatment and processing
plant is situated near the landfill site. The whole landfill site along with the waste treatment and processing plant spreads
over an area of 7.41 Ha.
The landfill at Njeliyanparamba has been in the focus of the state for quite some time because of the protests of
the people living in the nearby communities against it and the alleged ground water pollution because of it. The people
living in the vicinity has many sad stories to tell including the social isolation they face. Most of the families, being not
so well to do, can’t think of leaving the place and getting settled elsewhere without assistance from the government.

Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
177
5. CONJOINT ANALYSIS TO ASSESS WTP
5.1 Identifying the attributes of the study
Evidence shows that people tend to simplify choices among complex options so as to reduce the cognitive strain
and information overload. They do this by focusing on a few attributes in their decision. Thus, as a first step in the
conjoint analysis it is necessary to determine which attributes to study.
Several alternative methods exist for identifying the attributes relevant to consumers in forming their
preferences. Based on a pilot survey done among the people regarding their preferences for a residential plot, the
following four attributes were identified as the most important ones (listed in alphabetic order):
1) Contamination ambiance
2) Drinking water source
3) Plot access
4) Proximity to facilities
As we need to get the willingness to pay value in monetary terms, a monetary attribute was introduced in the
choices, termed monthly savings. Different levels were also proposed for these attributes based on the conditions
prevailing in that region. They are given below.
a) Drinking Water Source – own water, corporation water
b) Proximity to facilities – with in 3km, within 5km, within 10km.
c) Contamination ambiance – clean site, next to landfill site
d) Plot access – pedestrians only, Light Motor Vehicle (LMV), Heavy Motor Vehicle(HMV)
e) Savings per month – Rs 1000, Nil, Rs( -5000).
The rationale behind the monetary values given as choice is as follows. Assume that a family residing near the
Njeliyanparamba landfill site wants to buy a new land after selling their current plot. It is assumed that a minimum of
5cents of land is required for building a house. If they are selling their current property (5 cent) having a price of
Rs.50000.00/ cent, they will get Rs 2.5 lakhs. If they are buying a 5 cent plot having a price of Rs.1.5 lakhs per cent, they
will have to pay Rs. 7.5 lakhs. They have to get an additional amount of Rs.5 lakhs. It is assumed that the additional fund
is burrowed from a bank at an interest of 12% per annum. This means, they have to pay Rs. 5000 per month as interest.
Since for the responder it is an expense, it is given a negative sign (considering the savings of the family). If they are
buying a 5 cent plot having a price of Rs. 25000 per cent, they will have to pay Rs 1.25 lakhs. They will get a balance
amount of Rs. 1.25 lakhs after the purchase of new land. Assume that they will deposit this excess amount in a bank at an
interest of 8% per annum. They will get Rs. 1000 as interest every month. This can be taken as savings per month and is
given a positive sign. If they are buying a 5 cent plot having a price of Rs. 50000/ cent (same as the price of their current
holding), they will have to pay Rs 2.5 lakhs. They can buy it by using the full money they got after selling their current
land. There will not be any savings or expenditure. So the savings per month is nil.
The other attributes like drinking water supply, Proximity to the facilities like education, transportation etc, Plot
access, contamination ambiance etc are given various levels based on the prevailing and realistic conditions in the study
area. These different levels are specified by different weightages or ratings. These weightages are provided to the various
levels of the attribute based on the importance and nature of the attribute.
5.2 Identification of study population
The criterion for the population size determination for the study is the depreciation in property value around the
landfill site. The areas where there is a drastic reduction in the property values due to landfill existence are identified. It
is found that there is drastic reduction of property values in a 1km radius stretch around the landfill. The houses within
this radius are taken as the population for the survey to collect the data for the conjoint analysis. There are nearly 210
houses within this radius. Site plan showing the location of houses around the landfill included in the survey is given in
Fig.1.
5.3 Development of the conjoint experiment
Once the list of attributes was finalised, a conjoint experiment was designed to understand how the target
individuals integrate the attributes. The process involved forming all possible combinations of the attributes and the
associated levels of each and asking respondents to rank them. The attribute levels were selected to conform to actual
levels encountered in a real site to make the conjoint experiment as realistic as possible. The attribute levels are given a
specific weightage (or rating). The various weightage are shown in Table.1.

Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
178
Figure.1: Site plan showing the location of houses (dots) around the landfill site included in the survey (Grey colour
indicates the landfill site and associated facilities)
Using the chosen set of attributes and attribute levels, 108 combinations can be made (2 x 3 x 2 x 3 x 3). But
such a large number of choices are not recommended for the analysis. People will not give proper ranking when the
number choices available to them are very large. So from the different combinations available, ten were selected for the
final survey. The more realistic ones were the ones retained.
The attributes and their levels along with the respective weightage are entered in the SPSS software. All
preliminary data supporting the attribute levels and their coding/weightage are also entered. Then fractional factorial
design is done so as to select the realistic combinations. In SPSS software, Orthoplan design will provide the set of
combinations from which the realistic combinations can be selected. As a result of Orthoplan design, 16 realistic
combinations are generated. Out of this 10 most desirable and practical combination of attribute levels that are
meaningful in the context of the prevailing conditions in the study area were selected. They are represented in a tabular
form and are given in Table 2. This can be represented in the coding format also replacing the levels with their
coding/weightage.
Table 1: Attributes levels selected along with their codings
Attributes Levels
Coding/
Weightage
Drinking water source
Corporation water 1
Own water 2
Proximity to facilities
Within 10km 1
Within 5km 2
Within 3km 3
Contamination ambiance
clean site 1
next to landfill site -1
Plot access
pedestrians only 1
LMV 2
HMV 3
Savings per month
-Rs 5000 -5
Nil 0
Rs 1000 1
5.4 Data collection
A survey was conducted among the residents of the area as part of the conjoint analysis. The people were
supplied with a sheet containing the different combinations of the attribute levels mentioned in the previous section.
There is a column in the sheet to record the ratings given according to the preference of the responder. The format of the

Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
179
economic survey sheet is shown in Table.2. During the survey, the respondents were asked to rank the combinations
according to their preferences from fist to the last one (from 1 to 10). There are 210 families in the study area, out of
which, 136 families participated in the survey (65% of the total). The remaining 35% (about 74 families), didn’t
participate in the survey.
5.5 Analysis of the data
Once the survey is over and the ratings (preferences of people) from the population were collected, the data is
fed to the software for analysis. The analysis yielded the utility of each attribute levels. The utilities can easily be
converted in monetary terms, as one of the attribute is a monetary value.
Table 2: The format of the economic survey sheet
Table 3: Output of conjoint analysis
Sl No Attributes Level Utility Std error
1
Drinking water source Own water 1.113 0.058
Corporation water -1.113 0.058
2 Proximity to facilities Within 3km 0.902 0.0114
Within 5km 0.602 0.0079
Within 10km 0.301 0.005
3 Contamination ambiance Clean site 1.112 0.073
Next to landfill site -1.112 0.073
4 Plot access Pedestrians only 1.139 0.067
LMV 2.278 0.126
HMV 3.417 0.285
5 Savings per month Rs 1000 0.254 0.0046
Nil 0 0.000
-Rs 5000 -1.269 0.077
The output obtained from the analysis shown in Table 3. Table 4 gives the monetary value attached to each
attribute obtained by Conjoint Analysis. Among the different values obtained, the monetary value attached to the
contamination ambience is important for this study. It can be taken as the average amount each family is willing to pay
for avoiding the difficulties because of a contaminated site in their vicinity, or in this case, to avoid the problems caused
by the landfill in their vicinity.
From Table 3,
The average WTP of a family to avoid contamination ambience = Rs. 52535.4/ year
Cumulative WTP for the community = 210×52535.4
= Rs 11032434.00
Thus, the community is ready to pay Rs. 110.3 lakhs/ year for avoiding the contamination ambience.
Sl.
No.
Drinking
Water Source
Proximity to
facilities
Contamination
Ambiance
Plot Access
Savings/
Month
Rating
1 Own Water Within 3 Km Clean Site HMV Rs. 1000
2 Own Water Within 5 Km Clean Site HMV Rs. 1000
3
Corporation
Water
Within 3 Km
Next to Landfill
site
HMV Rs. 1000
4 Own Water Within 3 Km Clean Site HMV -Rs.5000
5
Corporation
Water
Within 3 Km Clean Site HMV -Rs.5000
6 Own Water Within 10 Km Clean Site
Pedestrian
Only
Rs. 1000
7
Corporation
Water
Within 5 Km
Next to Landfill
site
LMV Nil
8 Own Water Within 10 Km Clean Site LMV Rs. 1000
9 Own Water Within 3 Km Clean Site LMV Nil
10
Corporation
Water
Within 10 Km
Next to Landfill
site
Pedestrian
Only
-Rs.5000

Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
180
Table 4: Conversion of utility terms in to monetary terms
Attributes Level Utility
C / utility of
Rs.1000
Money in terms
of 1000
Cost per
annum
Drinking
water supply
own water 1.113 4.381889764 4381.889764 52582.6771
Corporation water -1.113 -4.381889764 -4381.889764 -52582.6771
Proximity to
facilities
within 3km 0.902 3.551181102 3551.181102 42614.17323
within 5km 0.602 2.37007874 2370.07874 28440.94488
within 10km 0.302 1.188976378 1188.976378 14267.71654
Contamination
ambiance
clean site 1.112 4.377952756 4377.952756 52535.4330
next to
contaminated site -1.112 -4.377952756 -4377.952756 -52535.4330
Plot access
Pedestrians only 1.139 4.484251969 4484.251969 53811.02362
LMV 2.278 8.968503937 8968.503937 107622.0472
HMV 3.417 13.45275591 13452.75591 161433.0709
Savings per
month
-5000 -1.27 -5 -5000 -60000
0 0 0 0 0
1000 0.254 1 1000 12000
Using the principle of perpetuity, we can convert it in to a equivalent capital cost.
The equivalent capital cost assuming 8% discount rate is = Rs 13.8 crores
Thus the equivalent capital cost for WTP value is obtained to be Rs 138 millions.
6. CONCLUSION
While talking about the cost of a landfill, one is often inclined to consider the direct cost alone leaving aside the
so called social cost associated with it. The authors had calculated the direct cost of same landfill under study to be
nearly 640 million, which includes the land cost, operation and maintenance, etc. The amount obtained as the external
cost (WTP) is well over 20% of this. This means that a failure to consider the social impacts of a landfill while doing its
appraisal may result in decisions that are not in the better interests of the people. The method explained here tells how the
social cost can be calculated for an existing landfill. The method can be used for a proper evaluation of various
alternatives when it comes to finding out the most suitable location for a landfill.
7. REFERENCES
[1] Stephen Hirshfeld, P. Aarne Vesilindt and Eric I. Past, Assessing The True Cost Of Landfills, Journal of Waste
Management & Research, Vol.10,1992, 471-484.
[2] Vinaytosh Mishra, Marketing victory-conjoint analysis using SPSS, 2006, 44 -69.
[3] Gustafsson, A., Herrmann, A., Huber, F., Conjoint Measurement: Methods and Applications,. Springer,
Heidelberg, 2006.
[4] Harry Telser and Peter Zweifel, Measuring willingness-to-pay for risk reduction: an application of conjoint
analysis, Health Economic Research report, Vol.11, 2002, 129-139.
[5] http://www.marketingiq.com/members/rd/conjointAnalysisTutorial accessed on 04/04/2008.
[6] http://www.conjointanalysis.net/CANet/CALinks.html accessed on 15/04/2008.
[7] Nitish Puri and Deepak Soni, “Utilization of Bentonite-Silt Mixtures as Seepage Barriers in Liner Systems of
Engineered Landfills”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 2,
2013, pp. 346 - 352, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.