Journal of Applied Operational Research (2013) 5(4), 135–152
© Tadbir Operational Research Group Ltd. All rights reserved. www.tadbir.ca
ISSN 1735-8523 (Print), ISSN 1927-0089 (Online)
Willingness to pay to reduce environmental
impacts from road transportation: a case study
from the Spanish Pyrenees
Fernando Lera-López *, Javier Faulin and Mercedes Sánchez
Public University of Navarre, Navarre, Spain
Abstract. Road transportation is known to be one of the major sources of externalities worldwide. This paper performs an
ad hoc survey in the population living in the Spanish Pyrenees in Navarre, Spain to examine their willingness to pay to reduce
the two main externalities attributed to road transportation: noise and air pollution. The probability models were estimated
based on contingent valuation, and the results demonstrate that the population living in that area is willing to pay in orde r
to reduce the problems of air pollution and noise contamination; albeit, with different valuations. Moreover, this research
shows evidence of the existence of a close relationship between the air and noise pollution and the economic valuations
given by the residents.
Keywords: air pollution; contingent valuation method; noise; road transportation; willingness to pay
* Received September 2012. Accepted May 2013
Introduction
Transportation has become a strategic sector of the economy and a major contributor to social and economic progress
in recent decades due to its outstanding growth and importance in modern societies. In 2004, the transport sector
accounted for more than a quarter of the world energy consumption (International Energy Agency 2006).
Transportation is responsible for producing a variety of negative effects, such as noise, air pollution, and congestion,
which have rarely been quantified in transportation planning strategies.
A number of studies have tried to quantify the economic impact of these externalities in Europe. According to
INFRAS/IWW (2004), the external costs from transportation in 2000 nearly reached 8.5% and 9.5% of the gross
domestic product (GDP) in the European Union and Spain, respectively. Road transportation is responsible for
nearly 91% of the external costs from transport in Europe (INFRAS/IWW 2000). Concerns have focused on the
use of road transport as the primary mode of freight movement in developed countries, since it is the largest
source of freight-related CO2 emissions (Intergovernmental Panel on Climate Change 2007).
Presently, there is a general agreement regarding the need to internalize these negative externalities by treating
them as a priority when formulating infrastructure policies and logistics strategies. The European Union, for example,
has developed an infrastructure use taxation system (Directives 1999/62/EC and 2006/38/EC, “Eurovignette”)
based on the “user and polluter pays” principle (European Commission, 1999, 2006). In some exceptional cases
involving infrastructure in mountainous regions, the directives suggest the possibility of adding a mark-up to the
* Correspondence: Fernando Lera-López, Dept. of Economics,
Public University of Navarre, Los Madroños Blg. 2nd floor Campus
Arrosadia, 31006 Pamplona, Navarre, Spain.
E-mail: [email protected]
136
Journal of Applied Operational Research Vol. 5, No. 4
toll charges. For example, one directive emphasizes that “particular attention should be devoted to mountainous
regions such as the Alps or the Pyrenees” (European Commission 2006). Economic valuation of transport related
to external impacts is absolutely essential to the success of the normative proposals.
Previous research has indicated that there are two main types of traffic-related environmental impacts: air pollution
effects (Arsenio et al. 2006, Sinha and Labi 2007, Yoo et al. 2008, Lee et al. 2011) and noise effects (Navrud
2002, Tuan and Navrud 2007). Navrud (2002) published an economic assessment of these two environmental
problems using contingent valuation in a European area with high levels of air pollution and noise contamination.
The analysis of both environmental effects facilitates the design of suitable environmental policies. Thus, it
would be very interesting to distinguish the double effects of this problem. This distinction could allow a better
assignment of the economic resources to solve the environmental problems.
The aim of this paper is to calculate the willingness to pay (WTP) of residents in an area bordering the Pyrenees
in Navarre (Spain) to reduce traffic noise and air pollution from transportation. We selected the Contingent Valuation
Method (CVM) (Navrud 2002, Bjoner 2003) in order to measure the willingness to pay of those environmental
problems. Moreover, Wardman et al. (2004) and Tuan and Navrud (2007) detected similar results comparing
CVM with other alternatives such as Choice Modeling when different valuations were carried out. Finally,
Vázquez et al. (2006) considered CVM as a good method before the application of environmental policies. Additionally,
this method allows the inclusion of intangible problems in the measure definition. With this goal in mind, we selected
the five most important roads crossing the Pyrenees in Navarre as well as the 14 localities through which these
roads pass. We then surveyed 900 adult residents of the selected localities. One of the main innovative features of
our work is that it allows for an analysis of both externalities in the same resident sample. Some studies that performed
double valuations include those by Delucchi et al. (2002), Kondo et al. (2003), Rehdanz and Maddison (2008),
and Wardman and Bristow (2004).
Another novel contribution of this paper is its assessment of the relationships between the current levels of
noise and air pollution in the study area and the economic valuations given by residents to those problems. The
combined use of economic and technical tools in environmental impact valuation studies is uncommon due to the
difficulties it involves (Andersson et al. 2010, Wardman and Bristow 2004, Lambert et al. 2001).
Lastly, our work focuses on predominantly rural, small and medium-sized localities in the Spanish Western
Pyrenees where pollution seems to be producing less concern than in larger cities. Hitherto, research has mainly
focused on large (Bell et al. 2006, Wang et al. 2006, Yoo and Chae 2001, Yoo et al. 2008) and medium-size cities
(Andersson et al. 2010, Barreiro et al. 2005, Martín et al. 2006).
This article is organized into four main sections. Following this introduction (section 1), section 2 describes the
methodological considerations, including the selection of the study area, the physical and economic evaluation of
both externalities using the CVM. Section 3 presents the main results and outcomes of the study. Finally, the
conclusion provides a description of the study limitations, and we suggest potential avenues for future research.
Methodological considerations
In this section we are going to explain the geographical scope of our problem with the selection of five routes and
fourteen localities. After this selection, we determine the levels of physical road transportation noise and pollution
nuisance experienced by each interviewee in the aforementioned localities. In a second step, we will apply the
CVM to measure the economic valuation of both externalities. Finally, we will explain the final experiment design.
Geographical scope
The geographical scope of our study is the Pyrenees, which forms a natural boundary between Spain and France.
Each day, more than 150,000 vehicles cross the Pyrenees, of which nearly 30% are heavy goods vehicles
(Observatorio Hispano-Francés de Tráfico en los Pirineos 2008). In fact, the Pyrenees is a region with a very high
density of road transportation. The busiest routes are located close to the mountains in Catalonia (La Junquera),
the Basque Country, and Navarre (Irún-Behovia), and they cut through areas of great ecological value. These
crossing points experience a high environmental impact and level of road traffic nuisance. Our study focuses on
the main international routes crossing the Pyrenees in Navarre, which are geographically located in this strategic
F Lera-López et al
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region. We considered five main routes, which all begin in Pamplona (the capital of Navarre) and end in France,
as shown in Figure 1. These routes, which pass through various towns and villages, range from less traveled main
national routes to heavy traffic highways.
After selecting the roads, the next step was choosing the localities for the noise and air pollution survey. We
began by designating a 400-meter-wide buffer zone, or influence area, on each side of the road in the affected
locations. The localities were then selected according to the following criteria: a) the importance based on the
proximity to the selected roads, b) the number of residents, making a distinction between rural and semi-urban
areas, and c) other factors, such as village morphology and land uses. The final selection comprised 14 localities,
which were almost evenly distributed between the five routes, as shown in Figure 1.
Fig. 1. Geographical scope: roads and villages
Physical noise and air pollution measurements
Afterwards, we carried out noise measurements and air pollution estimations in the selected routes in order to
determine the levels of road transportation noise and pollution nuisance suffered in each locality. The most relevant
information concerning the measurement and estimation procedures is provided in Table 1.
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138
Table 1. Noise measurements and air pollution estimations
Noise
Air Pollution
Date
Number and
duration
Time
Procedure
Equipment
Results
Parameters
considered
Software
Estimation model
Results
January 2009
2, 3 or 4, depending on villages. 30 minutes per measurement
Daytime: 10-20 h; Night: From 00 h
1.5 meters high, both in front of facade and in open areas
Sound level meter. Symphonie bicanal analyzer. Decibel Trait software.
Equivalent sound levels after external human activities abatement.
Traffic density (Government of Navarre 2007), geographical information,
strength and direction of wind, temperature and atmospheric conditions.
DISPER 4.0
Industrial Source Complex Short Term (ISCST) model based on
Environmental Protection Agency (EPA), USA
Concentrations and dispersion for CO, NOX and CXHY
We consider the traffic flow and the average traffic speeds on the five roads and municipalities to estimate pollution
level using information provided by the Government of Navarre (2007) and data gathered by the research team.
Next, the concentrations of the most important pollutants (CO, NO x and SOx) and their dispersions in the space
from the road were calculated using the program DISPER 4.0. This program was specifically created by the firm
Canarina (www.canarina.es) in 2004 to analyze the concentrations and release of gases produced by different
agents, such as road transportation, mining, forest fires, and dumps.
To estimate the levels of noise produced by road transportation, additional estimations were performed in each
of the 14 municipalities with the technical support of the firm Acustica. An average of 2 or 3 measurements were
recorded for each municipality and were taken during an entire day (day and night) at road distances of 0, 100,
and 200 meters. Those measurements were made at a height of 1.5 meters, and noise produced by sources apart
from transportation were deleted (e.g., human activities, animal noises, or churches chimes). In this manner, the
software program ‘db Trait’ calculated the noise level equivalent for each measurement point.
These measurements and estimations of noise and pollutants allowed us to define two different zones per locality,
A and B, which had significantly different noise levels and pollution concentrations. The main characteristics of
both zones are shown in Table 2. Zone A was defined as the area with people suffering noise levels above the
noise legal limit vales in residential areas in Spain (65db during the day) and with pollutant concentrations higher
than the legal limit (40 μg/m3). In general, zone A includes people living a distance between 0 and 80/100 meters
to the road, depending of the physical characteristics of the each locality (e.g. relief, height, etc.). Zone B includes
people living from 80/100 to 400 meters of distance to the highway/road and suffering lower level of noise (< 65db)
and pollutant concentrations (<40 μg/m3) than people living in zone A. People living far away of 400 m. to the
road/highway were not included into the survey/experiment.
Table 2. Characteristics of noise and air pollution zones
Distance from highway
Approximate noise levels
Pollutants concentrations (1)
(1)
Zone A
Zone B
0 to 80/100 m
> 65 dB
> 40 μg/m3
From 80/100m to 300m
< 65 dB
< 40 μg/m3
The concentrations were dependent on the placement characteristics (e.g. relief) and slightly different between villages.
The border limits between zones are not univocally defined and vary depending on the small towns and villages considered.
The final step in this study was to assess the current levels of noise and air pollution suffered by each respondent,
which were measured in decibels (dB) and μg/m3 of NOx, respectively. It should be noted that this assessment was
performed after the survey, since it required collection of the survey point data. The interviewees were located on
a Geographic Information System (GIS) based on their postal address, with the aid of a Web Map Service (WMS,
offering cadastral data provided by Infrastructure of Spatial Data of Navarre-IDENA (2009)), which enabled us
to determine the distance of each respondent’s home from the road.
F Lera-López et al
139
Thus, we were able to determine the noise level suffered by each respondent by linking noise measurements
and distance data. We used formulas (1) and (2), which were developed by Sinha and Labi (2007), to estimate the
decibel values affecting each survey point.
Leq  L  10 log10 d0 d 
Leq  L  20 log10 d0 d 
(1)
(2)
where Leq is the continuous level of noise to be estimated, L is the level of noise at a specific point (where the
measurements were taken), d0 is the measurement distance from the road, and d is the distance from the road to
respondent’s house. Formula (1) was used for villages with traffic flows below 3,000 vehicles per day where
noise is considered as a point source. When working with villages with traffic flows above 3,000 vehicles, we
used formula (2), where noise is considered as a linear variable. These two equations (1) and (2) have been built
using experimental statistical data obtained by Sinha and Labi (2007) from their studies in many transportation
infrastructures in the world. More details about the characteristics from the formulas are discussed in the Sinha
and Labi’s (2007) book.
DISPER 4.0 (Canarina 2004) software was used to obtain both dispersion and concentration data for each point
analyzing the air pollution. These data were then superimposed on the survey points to obtain the level of air pollution
affecting each respondent.
Economic noise and air pollution valuations
As stated in the introduction section, the contingent valuation method (CVM) was chosen to measure the impact
of road transportation externalities from roads crossing the Pyrenees. The CVM is a common technique used in
the valuation of this type of externality (Bjoner et al. 2003, Barreiro et al. 2005, Dziegielewska and Mendelsohn
2005, Navrud 2001, del Saz 2004, Tuan and Navrud 2007, Vázquez et al. 2006, Wang et al. 2006). For example,
Navrud (2002) and Bjoner et al. (2003) showed the possibilities of this methodology in this environmental context.
The high flexibility of this CVM suits a broad range of public goods and situations, hence its widespread use in
the valuation of environmental goods. It has the advantage of providing a hypothetical and direct method of estimating
the monetary value of environmental resources based on the results of public surveys (Mitchell and Carson 1989).
Vázquez et al. (2006) considered CVM as a good alternative to use before the application of environmental policies.
Additionally, it allows the inclusion of intangible problems in the measure definition (del Saz 2004). Nevertheless,
the CVM has often been criticised for its potential biases (Sudgen 2005), despite its well-known advantages (Carson
et al. 2001).
Questionnaire sections
We followed the recommendations proposed by the National Oceanic and Atmospheric Administration (NOAA)
panel published by Arrow et al. (1993) in order to design the survey questionnaire. These recommendations suggest
a longer-than-usual questionnaire to present the environmental problem to the respondents. This observation,
along with the fact that our survey involved the valuation of two types of improvements, led us to choose the personal
interview as the most suitable data collection method.
The questionnaire, which was designed to obtain a monetary valuation of the reduction of these externalities,
was subdivided into three main sections. Following Vázquez (2002) we initially included an extensive introduction
to ensure that the respondents understood the problem under consideration. Given that we were valuing two different
externalities, noise and air pollution, and that one of our aims was to compare the results obtained for each valuation,
we opted to describe the externalities to the respondents, so that they might appreciate the diverse issues involved.
In the first section of the questionnaire, the respondents were asked for their opinions on the main sources of
noise and air pollution in their area (Martín et al. 2006), the area environmental status (Aprahamian et al. 2007;
Lambert et al., 2001), the level of nuisance in the last 12 months (ISO/TS 15666 2003), and the health problems
affecting the respondents or their families arising from these externalities. Some of these introductory questions
had the ISO/TS 15666 (2003) structure, in which respondents rated on a scale of 1 to 5, making these results
comparable to those obtained in previous studies.
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The second part of the survey searches an estimation of the WTP for the noise and pollution reduction in
environmental problems. The scenario presented to respondents prior to the CVM questions was formulated to be
as realistic as possible and remind them the main assets of the problems (Kotchen and Reiling 2000, Pedroso et
al. 2007, Buckley et al. 2009). The purpose of this scenario choice was the enhancement of the credibility of the
proposal and the minimization of the misinterpretation risk which is inherent in this valuation method. The standard
dichotomous format of the survey in combination with open-ended questions was used to elicit WTP (Bateman et
al. 1999, Zoppi 2007). Similarly, the inclusion of a control question helped to identify the reasons for possible
unwillingness to pay (León 1996, Jorgensen et al. 2001). Protest responses were identified as those which the
respondents expressed opposition to the proposed scenario; that is, they either felt no responsibility for the noise
and air pollution and finding the reduction insufficient, or thought they already pay enough taxes (Dziegielewska
and Mendelsohn 2005, Ovaskainen and Kniivilä 2005).
The three bid prices selected were 15€, 30€ or 45€, and, to ensure independence, they were uniformly and randomly
distributed across interviewers (Scarpa et al. 2000). Knowing that the values obtained in previous studies show a
great value of dispersion (see Table 3) and comparability problems due to different time spans, locations,
methodologies and scenarios proposed, we consider the recommendation made by the panel of experts consulted
in the development of the pre-test. Anyway, the three bid prices finally considered in the study are in the same
range of the results shown by seven out of fourteen previous studies considered in Table 3. The payment vehicle
was a compulsory annual tax for the next five years on the entire population, making all residents of Navarre net
contributors to the abatement.
Table 3. Mean WTP in previously published CVM surveys
Author
Year
Location
Issue
Vainio
Yoo and Chae
2001 Sweden
2001 S. Korea
Noise
Air
E (WTP)
[nom. €]
81
15.50
Navrud
2001
Sweden
Air
17.90
Lambert et al.
2001
France
Noise
73
Bjoner et al
2003 Denmark Noise
Air
Wardman and Bristow 2004
UK
Noise
135
115.20
106.70
16
Dziegielewska and
Mendelsohn
2005
Barreiro et al.
2005
Spain
Noise
20
26–29
Vázquez et al.
2006
Spain
Air
48
Wang et al.
Martín et al.
Desaigues et al
2006
2006
2007
China
Spain
France
Air
Noise
Air
14.30
7.20
29.4
Poland
Air
Scenario
WTP to noise reduction
WTP to improve air quality
WTP to avoid one extra day per year of worse
health due to air pollution
WTP to implement a program to reduce noise
nuisance
WTP to reduce noise reduction
WTP for a 50% reduction in air pollution
WTP for a 50% reduction in noise level
25% reduction in the number of people affected
by air pollution
Same for a 50% reduction
Annual payment to reduce noise annoyance
WTP to reduce the number of people affected by
diverse symptoms through an improvement in air
quality
WTP for a 50% reduction in air contaminants
WTP to reduce noise nuisance
WTP for a risk reduction
Source: Own elaboration
* Values translated to Euros from original currency at exchange rates at time of calculation
The third section of the questionnaire presents a list of classification questions about environmental issues and
the main statistical characteristics of the respondents. For example, the respondents were asked whether they had
carried out any work or change at home and, and whether the reasons to do so were connected with noise or air
pollution (Barreiro et al. 2005, Martin et al. 2006, Wardman and Bristow 2004). A question about pro-environmental
attitudes was included using the scale proposed by Castanedo (1995) to allow us to assess the respondents’ interest
in the pollution issue, their views on government intervention, and their attitudes towards the problem solution.
Finally, socio-economic data on the respondents, including gender, age, level of education, household size, labor
F Lera-López et al
141
situation, and income were also collected. These variables generally reveal the reasons for differences in valuation
between groups of respondents when determining the WTP level in the valuation process (Rehdanz and
Maddison 2008, Tuan and Navrud 2007, Wang et al. 2006). Table 4 shows the distribution of the socio-economic
variables in the sample.
Table 4. Socio-demographic characteristics of the selected sample.
Characteristics
Sex
Male
Female
Age
<35 y
35-54 y
55-70 y
Habitat
Urban
Rural
Income level
Modest
Medium
Higher
Education level
Illiterate
Primary School
High School
University
% Sample
% Population
47%
53%
49%
51%
34%
41%
25%
31%
43%
26%
19%
81%
58%
42%
41%
52%
7%
35%
57%
8%
4%
37%
40%
19%
2%
19%
53%
26%
Final experimental design
After the methodological analysis of the physical and economical evaluation of noise and pollution levels, we are
going to explain how both measures have been combined into the applied experiment design. For the noise reduction
scenarios, the survey design proposed one reduction in zone B and two reductions in zone A, since it had a higher
initial level of noise. The reductions were proposed both in terms of the number of decibels abated and in percentage
terms. A 17% abatement, from 60 to 50 dB, was suggested for zone B. The sample in the zone A was divided into
14% and 29% reductions, from an initial 70 to 60 and 50 dB, respectively. Given the well known difficulty of reducing
noise levels to 50 dB, we established this value as our abatement limit.
A relevant and novel feature of our study is that the respondents were asked for listening a recorded noise sample
based on the noise level affecting their dwellings, before they know the survey scenarios. Once those scenarios were
presented, the respondents were asked to listen to a second noise sample and an additional track simulating the
proposed abatement after application of the intended policy measure. Additionally, we proposed two different air
pollution reductions based on estimations of initial levels using computer models. The pollution abatement proposals
were for 25% or 50% reductions from their initial values in zone A and only a 25% reduction in zone B. Furthermore,
we decided to propose the air quality improvement as a public welfare increase, knowing the disagreement in the
literature concerning the direct effects of air pollutants (Carlsson and Johansson-Stenman 2000, Dziegielewska
and Mendelsohn 2005).
Kahneman and Knetsch (1992) modeled health impact as a global problem in order to increase the percentage
of positive WTP responses arising from the sense of “moral satisfaction” or “altruistic behavior” that respondents
obtain from contributing to the general welfare. Hence, respondents were first asked to contribute to reduce the
number of people mildly affected by air pollution and later asked to contribute to reduce the number severely affected
people. The mildly affected were described as the population suffering from coughing, irritation of the eyes, and
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142
respiratory problems that might trigger allergic responses. Approximately 160,000 people in Navarre are affected
by such ailments according to the Spanish Society of Allergology and Clinical Immunology (2009), so a 25% (or
50%) reduction in air pollution would benefit 40,000 (or 80,000) people. The severely affected were described as
the population suffering from violent coughing or acute respiratory failure that might lead to asthma or pneumonia.
According to the Navarre Allergic and Asthmatic Association (2009), approximately 60,000 people in Navarre
show severe symptoms, and the proposed 25% (or 50%) reduction in air pollution would benefit 15,000 (or 30,000)
people. Methodologically speaking, this procedure is similar to Dziegielewska and Mendelsohn’s (2005) technique.
To sum up, the alternative scenarios for noise and air pollution reductions and the different bid prices are
shown in Table 5. We have considered four different scenarios, considering simultaneously three reductions in
noise level and two reductions in pollutant concentrations in zones A and B. As we use three different bid prices
(15, 30 and 45€) for each scenario, finally we have constructed twelve different questionnaires, with a balanced
distribution of the sample.
As noise and air pollution are presented as personal and global problems, respectively, the study does not make
a direct comparison of both valuations; however, we could perform an indirect comparison in relative terms because
all data were obtained from the same sample. Twelve different types of questionnaires were designed to randomly
survey the study population, which was stratified by locality size. The survey was developed selecting 900 residents in
14 localities which are situated on or near the border highways in the Pyrenees in Navarre, and was conducted
between February and March in 2009. The sampling error was 3.1%, and the confidence level was 95%.
Table 5. Different scenarios proposed and the number of people surveyed.
Contamination zone
Noise reduction
Pollution reduction
Initial bid (€)
Type of questionnaire
Nº of questionnaires
A
70 - 50
50%
15
30
45
1
2
3
100 100 100
B
70 - 60
25%
50%
15 30 45 15 30 45
4 5 6 7 8 9
50 50 50 50 50 50
60 - 50
25%
15
30
45
10
11
12
100 100 100
Results and discussion
The results section has been organized into the following subsections. Firstly, the generic results are presented,
including the average valuations obtained to estimate the total WTP. The second part presents the relationship between
the real pollution level in the selected municipalities and the residents’ WTP to reduce the environmental problems.
WTP per reduction of noise and air pollution
In the valuation of a non-market commodity, individuals who decide to pay for negative externality abatement
are deemed to form part of the commodity market considered (Casado et al. 2004). The answering rate has been
high (80 %) because the survey has been developed in a personal way. People who did not want to answer the
survey were replaced by others having similar socio-demographic characteristics, until reaching the number of
900 people surveyed. Table 6 shows the general results obtained from the total sample, and from the sample excluding
the protest zero, according to different environmental problems. The results showed a high proportion of respondents
not wanting to pay the proposed bid price for improve the noise level or air quality condition. The situation is better
when it is employed the excluding protest responses sample, because there are a high proportion of zero responses
classified as protest. But, in all situations it is possible to observe the negative and significant statistically relationship
between the increase of bid prices and the decrease of willingness to pay. These results are coherent with the habitual
studies of CVM methodology.
Moreover, the protest responses were removed from the final analysis according to the classification described
in the previous section. It is noteworthy that a non-negligible portion of the sample is not willing to pay to mitigate
the externalities (greater than 60% in the whole sample and around 40% in the sample without protest zeros); albeit,
this situation is common in this geographic context (Casado et al. 2004).
F Lera-López et al
143
Table 6. Ratios of the willingness to pay for each bid price
Whole Sample
Bid Price (€)
Total Sample
15 €
30 €
45 €
Noise (899)
No
Yes
72.3% 27.7%**
74.3%
25.7%
80.9%
19.1%
75.9%
24.1%
(682)
(217)
Air Pollution
Mildly affected (899)
Severely affected (899)
No
Yes
No
Yes
68%
32%**
67.7%
32.3%**
66.3%
33.7%
66.3%
33.7%
76.6%
23.4%
75.6%
24.4%
70.3%
29.7%
69.9%
30.1%
(632)
(267)
(628)
(271)
Excluding protest responses
Noise (465)
15 € 50.9%
49.1%*
30 € 49.0%
51.0%
Bid Price (€)
45 € 60.7%
39.3%
53.3%
46.7%
Total Sample
(248)
(217)
Note: ** p < 0.05, * p < 0.10.
Air Pollution
Mildly affected (495)
Severely affected (500)
44.2%
55.8%**
43.3%
56.7%**
39.2%
60.8%
39.9%
60.1%
55.4%
44.6%
54.7%
45.3%
46.1%
53.9%
45.8%
54.2%
(228)
(267)
(229)
(271)
These results led us to use the spike model proposed by Kriström (1997) because it is the most common methodology
to tackle with problems related to zero responses in contingent valuation. We have defined all variables in the
problem in the same way than Kriström (1997) in order to follow his analysis. Other studies facing the same
problem have also used the spike model to estimate WTP (del Saz and García, 2001; García and Riera, 2003;
Casado et al. 2004; Scarpa and Willis, 2006; Broberg and Brännlund, 2008; Duran and Vázquez, 2009; Haltia et
al., 2009; Hanley et al. 2009; García de la Fuente et al. 2010; Glenk and Fischer, 2010; Jou et al., 2012; Jou and
Wang, 2012; Lee, 2012; Ramajo and del Saz, 2012; del Saz and Guaita-Pradas, 2013). Only the real zeros were
incorporated, and the spike model assigned them a probability different from zero. Previously there were estimated
other models (as classical Hanemann models) considered ‘whole sample’ and the results obtained were coherent
with the values obtained with ‘excluding protest responses’. It has been detected the negative effect of ‘bid price’
on WTP. It is true that the mean of WTP obtained when protest responses are removed will be higher than assigned to
whole sample. Concretely and following Kriström 1997 and Jou et al. (2012), the spike model assumes that the
individual’s utility function can be written as:
U ( Y , X ,Q )  V ( Y , X ,Q )  
(3)
where Y is income, X is a vector of socio-economic characteristics, Q is a vector of the asset value and  is a
random disturbance term with zero expected value. When residents are WTP for reduce the noise level or for improve
the quality of air, it means that they prefer the new state ( V1 ) over the current state ( V0 ) (meanwhile their asset
value will alter from the current asset ( Q0 ) to the new asset ( Q1 ). Thus, the individual’s utility can be written as:
V1 ( Y  A1 X 1Q1 )  1  V0 ( Y , X ,Q0 )   0
(4)
When the resident is WTP for a reduction of noise level or for improve the quality of air, their income will be
reduced by A1 , though they still prefer the new utility V1 . We assume an income equal to Y in the initial stage,
V0 (Y0  Y ) , and  0 and  1 are random terms with an independent and identical (iid) Gumbel distribution. Thus,
the probability function that a given residents pays amount A1 in the new state can be derived as:
Journal of Applied Operational Research Vol. 5, No. 4
144
V ( Y  A1 X 1Q1 )  V ( Y , X ,Q0 )   0  1 ; Pr( Yes )  Pr( V (*)   )  F ( V (*))
(5)
Where V (.) indicates the difference between the utilities of the new state and the current state. Moreover, if
the bid A1 offered in the questionnaire is smaller than the willingness to pay value (willingness to pay  A1 ), it
means that the resident will pay the amount A1 in the new state can be derived as:
Pr( Yes )  Pr( willingness to pay  A1 )  1  G( A1 )  F ( V (*))
(6)
Where G( A1 ) is the cumulative distribution (c.d.f.) of the respondent who is not willingness to pay the amount
A1 . The domain of the cumulative distribution function can them be expressed as:
0, A1  0



G( A1 )   P , A1  0

 F ( A ), A  0
1
1


(7)
We can further derive the expected willingness to pay as:








E( willingness to pay)  (1 - G(A1 )dA  ( G( A1 )dA  ( F ( V (*))dA  ( 1  F ( V (*))dA
0
0
0
(8)
0
Where p belongs to (0,1) and F ( A1 ) is a continuous and increasing function such that F ( A1  0)  p and
lim A  F(A1 )  1. The maximum likelihood function for the sample is then given as:
1
N
N
N
i
i
i
L   M iWi ln( 1  G( A1 ))   M i ( 1  Wi ) ln( G( A1 )  G( 0 ))   ( 1  M i ) ln( G( 0 ))
(9)
Where M indicates whether the willingness to pay is greater than 0 or not, if residents rejects a series of bids it
will generate a willingness to pay smaller than 0. Another W is defined by whether the willingness to pay is
greater than the bid A1 or not, as Eqs. (10) and (11) calculate respectively:
1, willingn ess to pay  0 
M 

0, otherwise

1, willing ness to pay  A1 
W 

0, otherwise

(10)
(11)
Without a loss of generality, we assume that the utility function is a linear utility function, and consider only
2
the effect of income Y . The utility function (Eq.(3)) can thus be rewritten as:
V ( A, X ,Q )   j  A1 , j  0,1
(12)
The difference between the utilities of the new state and the current state can therefore be expressed as:
V (*)  1   0  A1    A1
(13)
We then assume G( A1 ) has the form of a logistical function, meaning that F (V (*)) can be shown as:
F Lera-López et al
F ( V (*)) 
145
1
1  exp(   A1 )
(14)
Eq. (7) can be further expressed as:
0,

1
G( A1 )  1  exp(  )

1
1  exp(   A1 )
A1  0 

A1  0 

A1  0
(15)
Where  is the marginal utility of improving noise level or the quality of air conditions after adopting the proposed
reductions.  is the marginal utility to pay for the achieved improvement. We can derive the amount than residents
are WTP as:



0
0
0
E( willingnes s to pay)   (1 - G(A1 ))dA1   ( G( A1 ))dA1   (

exp(   A1 )
dA1 
1  exp(   A1 )

1
lim A1  (  ln1  exp(   A1 )  ln1  exp(  )

(16)
The expected price residents are WTP can be derived as A1   and shown as:
E( willingnes s to pay) 
1
ln1  exp(  )

(17)
Spike value can be defined as following the equation by setting A1  0.
Spike 
1
1  exp(  )
(18)
Thus, Table 7 displays the general values obtained of the spike model estimation in the total sample, and for
the different noise and air pollution level situations. These results give in the whole sample a mean WTP of €8.22
for noise reduction and a WTP of €9.31 and €9.56 to reduce air pollution for the mildly and severely affected
populations, respectively. The mean WTP for noise reduction is similar to that obtained by Martin et al. (2006),
as shown in Table 3, but it was notably lower than in other studies. For air pollution, previous reports demonstrate
payments between €5 and €10 higher than in our study (Table 3).
Several reasons may explain the differences between the present and previous results. The region of Navarre,
where the study was conducted, is the third one from the end, among Spanish regions which publicly are concerned
about environmental issues and only 3.4% of the population has ever volunteered for environmental activities.
Moreover, noise pollution affects 11.7% of the Spanish population (Spanish National Statistics Institute 2009),
but only 6.6% of Navarre households. Additionally, we expect lower WTP in analyzing rural and semi-urban areas.
Moreover, our results show that the mean WTP for noise abatement is considerably lower than for air quality
improvement. This finding is in line with the empirical evidence in Wardman and Bristow (2005), who reported
variations in air quality ranked higher than variations in noise. In addition, in our study, the severely affected
population, described as people showing the most severe symptoms, elicited a higher WTP than expected. These
results were comparable to those of Dziegielewska and Mendelsohn (2005), Navrud (2001), and Vázquez (2002),
who showed that severe symptoms were associated with higher payments.
Journal of Applied Operational Research Vol. 5, No. 4
146
Table 7. Comparison of estimated spike models. All samples, per zones and per reduction
Air pollution
Model
Noise
Mildly affected
Severely affected
0.377
9.31
0.501
0.104***
495
470.63
941.26 (0.000)
0.384
9.56
0.471
0.100***
500
483.2
966.41 (0.000)
0.370
10.305
0.531
0.096***
352
340.79
681.59 (0.000)
0.392
6.886
0.437
0.135***
143
126.32
252.65 (0.000)
0.370
10.837
0.528
0.091***
352
346.95
693.91 (0.000)
0.412
6.563
0.354
0.134***
148
131.19
262.38 (0.000)
0.357
11.241
0.587
0.091***
262
256.60
513.20 (0.000)
0.397
7.136
0.414
0.129***
233
208.72
417.44 (0.000)
0.359
11.941
0.577
0.086***
261
261.51
523.03 (0.000)
0.408
6.948
0.370
0.128***
239
214.13
428.26 (0.000)
ALL SAMPLES
Spike value
Spike mean
0.424
8.22
0.305
0.104***
465
436.63
873.26 (0.000)
α
β (BID)
Spike
Observations
Log likelihood
Likelihood ratio (prob.)
PER ZONE
Spike value
Spike mean
A
Spike
α
β (BID)
Observations
Log likelihood
Likelihood ratio (prob.)
Spike value
Spike mean
B
Spike
α
β (BID)
Observations
Log likelihood
Likelihood ratio (prob.)
0.408
9.096
0.368
0.098***
325
310.52
621.04 (0.000)
0.459
6.188
0.162
0.125***
140
123.83
247.66 (0.000)
PER REDUCTION
Spike value
Spike mean
α
Spike
HIGHER
β (BID)
[29% Noise; 50% Air]
Observations
Log likelihood
Likelihood ratio (prob.)
Spike value
Spike mean
α
Spike
LOWER
β (BID)
[17% Noise; 25% Air]
Observations
Log likelihood
Likelihood ratio (prob.)
Note: ***p < 0.01; **p < 0.05; *p < 0.10
0.324
12.781
0.732
0.088***
156
157.28
314.56 (0.000)
0.473
5.822
0.105
0.128***
309
267.10
534.21 (0.000)
Additionally, other result included in Tables 7 and 8 is the comparison of the values obtained not only across
the three different valuations, but also across respondents exposed to different noise and air pollution levels and
across different reduction proposals for both externalities in zones A and B. For that reason, some different models
(Spike, Logit and Probit) have been estimated. Starting with the spike value, the probability of WTP being equal
F Lera-López et al
147
to zero is lower in zone A for all three valuations, although the differences are not very great (40, 37, and 37%
versus 46, 39, and 41%). As initially expected, these results suggest that people exposed to higher levels of pollution
and noise are more willing to pay to reduce it. The mean WTP displayed a similar effect, since it had a smaller
value in zone B, where the pollution and noise levels were lower, in all three cases. Wardman and Bristow (2004)
also observed that people are prepared to pay more for a proportionate improvement in noise and air quality when
applied to poor or very poor current conditions.
Analysis of the different reduction proposals revealed large differences. As expected, higher abatements were
associated with a greater number of people willing to pay and a higher mean WTP value, as previously reported
by Dziegielewska and Mendelsohn (2005) and Kondo et al. (2003). Remarkable differences were observed between
higher and lower reductions in noise; the mean WTP for the higher reduction is twice than for the lower reduction.
It is noteworthy that the respondents had listened a track comparing the noise level before and after being informed
of the proposed reduction.
Impact of physical measures of environmental problems on WTP
Using the measurements of noise levels and pollutant concentrations calculated in this study (see Table 2), we
constructed Logit and Probit models to evaluate the influence of the current levels of noise and air pollution to
verify whether there was any relationship between the physical situation and the economic valuation. Other authors,
such as Barreiro et al. (2005) and Martín et al. (2006), observed that when real noise levels of surveyed areas
were added into the analysis, a strong relationship was observed between the noise nuisance levels and their economic
valuations. The chosen variables were dB for noise and NOX (measured in μg/m3) for pollutant concentrations.
NOX is the main pollutant produced by road traffic, and it is also the main regulated air pollutant. Table 8 shows
the results of these analyses for the all sample and for zones A and B. The dependent variable is a binary variable,
where 1 is assigned to the respondents who are willing to pay a positive amount, and a 0 is assigned otherwise.
Table 8. Logit models for initial bids and actual levels of pollution
All Sample
Air pollution
α
β1 (BID)
Β2 (Db/NO2)
Log likelihood
Noise
-2.308**
-0.013*
0.048***
-630.94
Mildly affected
0.206
-0.015**
0.038***
-670.93
Severely affected
0.187
-0.016**
0.043***
-674.62
Zone A
Air pollution
α
β1 (BID)
Β2 (Db/NO2)
Log likelihood
Noise
-1.477
-0.01
0.033*
-446.71
Mildly affected
0.32
-0.019**
0.038**
-474.68
Severely affected
0.297
-0.017*
0.039**
-473.29
Zone B
Air pollution
Noise
α
-2.638
β1 (BID)
-0.022
Β2 (Db/NO2)
0.056
Log likelihood
-161.64
Note: ***p<0.01; **p<0.05; *p<0.10.
Mildly affected
-0.167
-0.004
0.051
-195.5
Severely affected
-0.008
-0.013
0.052
-201.19
148
Journal of Applied Operational Research Vol. 5, No. 4
Table 8 shows that the WTP depends on the pollution level suffered by the respondent in the three considered
environmental problems. So that, the real variables, dB and NOX, are positive and significant in all three valuations.
Despite being very small in the case of air pollution, these data show that the greater the nuisance is, the more the
respondents are willing to pay. The various models were always statistically significant. This result shows that the
actual level of exposure strongly determines the WTP, as highlighted in the aforementioned studies. Our results for
noise are consistent with previous studies (Barreiro et al. 2005, Martín et al. 2006). We were unable to make
comparisons in relation to air pollution, however, since we only found one other paper that analyzed real pollutant
measurements (Wardman and Bristow 2004).
Similarly, Table 8 shows the estimated Logit models in an analysis per zone. Furthermore, the results indicated
that the decibel parameter appears non-significant, except for the case where respondents located in zone A are
exposed to a higher level of noise (> 65 dB). This result demonstrates that the actual nuisance only influences
valuation when it reaches very high levels. As suggested earlier, listening the audio track may have heightened
the respondents’ perception in the case of the noise reduction. Thus, that model is valid only for the case of zone A.
The air pollution results were very similar for the mild and severe exposure levels. Both actual air pollution
parameters associated were highly significant for the different zones. Their positive signs indicate that the level
of air pollution influenced the respondents’ WTP, with higher levels driving the WTP towards positive values.
Since the interviewees were valuing public improvement, these results suggest that people under greater exposure
to the nuisance were more aware of the problem, and thus, they were willing to pay more.
Conclusions and study limitations
This paper analyzes the results of a contingent valuation survey, which was carried out in the Western Pyrenees
between Spain and France, in an attempt to calculate the environmental impacts of road transportation in the region.
The main contributions of this research are as follows. Firstly, this study performs a joint analysis of noise and air
pollution from the road traffic in the area, making comparisons between both externalities. As far as we know,
this is the first study considering both externalities at the same time. These comparisons were indirect because
these externalities were measured by different tools of contingent valuation. In this way, air pollution was addressed
in global terms and the valuation exercise was subdivided by impact levels (mild and severe). Noise levels were
addressed in personal terms, and the evaluation exercise was based on the influence of recorded noise samples.
Secondly, this study added noise level measurements and air pollution estimations into the analysis to examine
the relationship between physical and economic valuations. Not many studies developed physical estimations of
noise and pollutant concentrations estimations into the analysis of the WTP. Then, the report lets us to analyze
impacts in different zones according to noise and air quality levels to test for variation Thirdly, the geographical
scope of the study was semi-urban/rural areas where traffic flow may be lighter than in urban areas, but the
environmental impact is higher because it is concentrated in regions with high environmental quality. Traditionally,
WTP studies have been developed in urban areas where concern and awareness about environmental impacts are
higher than in rural areas.
Taking the results as a whole, we observed that the number of people stating a zero response was considerably
high. Accordingly, we used the spike model to account for some of these responses. In line with other studies, the
results highlight the fact that air pollution abatements are more appreciated than noise reductions. This could
highlight the relevance of altruistic behavior in the analysis of the environmental externalities. Similarly, the
WTP was higher for a proposed reduction in the number of people severely affected by air pollution than for a
proposed reduction in the number of mildly affected people. This result confirms to us the coherence in the
respondent behavior shown in the different scenarios.
We tested whether differently impacted zones and different proposed abatement levels were relevant to the
respondents’ WTP. We found that both variables were relevant in the valuation, since a higher nuisance level
elicited higher a WTP, as did some higher proposed abatement levels. In addition, we separately analyzed the
proposed reduction and the level of impact. Interestingly, we observed that the respondents’ WTP was more
strongly influenced by the proposed reduction than by the level of nuisance suffered.
These results may have major implications for policy decisions. For example, this situation could be a starting
point to differentiate between the effects of noise and air pollution damage to implement alternative solutions.
Moreover, any environmental policy focused on increasing social and individual concern over environmental
F Lera-López et al
149
problems caused by road transportation will positively impact the WTP to reduce air and noise pollution. Consequently,
it is very important to develop effective communication policies that engender greater individual and social concern
over environmental problems. In particular, social concern should be emphasized in the case of the air quality
improvements where altruistic behavior seems to be relevant.
Likewise, these results show the environmental impact analysis relevance before location or transport alternatives
selections. Moreover, considering an Operational Research point of view, most of the results here obtained could
be implemented as estimated cost to design green routes to do distribution activities in transportation companies.
In fact, one of the most important contributions of this paper to the application of Operational Research in optimizing
routes is the design of a new methodology to make environmental costs estimations. Without that kind of methodology
is really difficult to know the suitability of new routes from the environmental scope.
Also, with the adoption of the European Directive 2011/76/EC, European countries have the option to apply
charges and tolls to road traffic to manage pollution and noise produced by trucks and cars. Some countries have
developed time-based vignettes (Sweden, Denmark) while others have adopted distance-based tolls (Germany,
Austria). In these cases, the experience has shown reduction in environmental emissions. Also, the Directive considers
in particular the situation of mountain areas suffering potential and significant environmental damage, with possible
application to the Pyrenees in the future. Unfortunately, this Directive has not been yet applied in Spain. Recently,
the Spanish government developed a specific tax system to penalise the purchase of cars causing high levels of
pollution, along with some policies to incentive the acquisition of electric cars. Thus, our contribution could help
regional and national authorities in Spain to consider more carefully the negative impact of the environmental
externalities in the population welfare.
Besides, we analyzed whether there was any relationship between the current levels of noise and air pollution
and the respondents’ valuations. The results were consistent with expectations, since the variable measuring the
level of noise in decibels and the level of air pollution in μg/m3 of NOX was always highly significant in the estimated
models. Additionally, the results demonstrated that only high levels of noise had an impact on personal valuation,
whereas any level of air pollution had a significant influence on the personal evaluations.
Finally, this paper has three main implications. Firstly, the strong relationship between the environmental pressure
and the economic valuation shows that the economic valuation method employed is suitable for measuring the
associated externalities. Secondly, the difficulty in making simultaneous economic measures of environmental
externalities is clearly highlighted by the fact that their influence varies across the population. Thirdly, it is possible
to quantify the benefits of addressing environmental impacts, but an aggregate assessment of the different problems
would be useful to verify the cost-effectiveness of possible solutions.
In subsequent studies, it would be worth considering non-urban or semi-urban areas to test for variations between
these regions and highly polluted urban areas. Future research should therefore be oriented towards identifying
the reasons for detected differences and generating comparative reports on the effects of environmental externalities
in rural and urban areas. Moreover, an examination of the influences of different methodological approaches on
the comparative results would enhance future research efforts.
Acknowledgements. We acknowledge the financial support of the Spanish Ministry of Science (Projects TRA2006-2009-10639 and
TRA2010-21644-C03-01), the Working Community of the Pyrenees (Research Network VERTEVALLEE- IIQ13172.RI1-CTP09-R2) and
the Government of Navarre (Research Network "Sustainable TransMET").
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