With the adoption of new technologies, more risk is introduced into modern society. Important decisions about new technologies tend to be made by specialists, which can lead to a mismatch of risk perception between citizens and specialists, resulting in high social cost. Using contingent valuation methods, this paper analyzes the relationship between willingness to pay (WTP) and the factors expressed through people’s image of nuclear power plants (NPP), their perception of NPP safety, and how these can be affected by their scientific background level. Results indicate that groups with a high scientific background level tend to have low risk perception level, represented through their image and safety levels. Further, the results show that mean WTP is dependent on scientific background and image levels. It is believed that these results could help decision makers address the mismatch of trust between the public and specialists in terms of new policy.
Society becomes more complex as new technologies are invented and interact with each other [
Considering the complexity and exclusivity of the nuclear energy field, the role of its experts is essential to achieve public consensus by resolving the conflict induced by a mismatch of perception [
It has been suggested that supplying suitable information through education may solve this problem by inducing perception change [
The risk perception factors can affect willingness to pay (WTP), which refers here to the amount of money people would pay to reduce risk in nuclear power plants. WTP is a common measure of the value of goods or services to the individual in economics [
These risk perception factors have also been researched outside the nuclear field. It was found that a group of factors including benefit, public exposure, and dread, called the
This study examines how the representative risk factors affect perception level, finds a mean WTP according to levels of risk perceptions, and analyzes their relationships. Three risk perception factors are addressed regarding people’s perception of NPPs: their image of NPPs, their perception of NPP safety, and their scientific background level. According to previous studies, image and scientific background levels represent the dread and the unknown groups, respectively [
Based on the results, if a specific group needs to be considered in the calculations of external costs for NPPs, mean WTP, making up the largest portion of external costs, can be estimated by existing image or safety scores which have already been investigated for that group [
A specially designed questionnaire is necessary for the investigation of risk perception with perception factors and the CVM. General data like gender and age is first asked to respondents. Perception of NPP image and safety is then asked to verify the relationship between them and WTP. In this step, a 5-point Likert scale is applied, being the most common [
Samples are then divided into three groups according to the participants’ scientific knowledge level. Group 1 consists of people currently majoring in nuclear engineering. Group 2 is made up of current science or engineering majors. Randomly selected people from the general population in Korea make up group 3, with a variety of dwellings, income, and education. As groups 1 and 2 are made up of students, the age range is from 18 to 29 and half of them have very low incomes. As dependents, they may not consider their own income, which may be advantageous as they may judge mean WTP neutrally. However, it is hard to compare these group’s WTP with that of others’ considering the differing values they may assign to any money amount. However, it is hard to compare this group’s WTP with that of others’ considering the differing values they may assign to any money amount. For consistency in comparing the groups, group 3 samples were restricted to a similar age range. The survey method is different according to the group. For groups 1 and 2, face-to-face surveys were selected, while internet surveys were employed for group 3 to obtain data independent of the distance between the researchers and respondents. Data from various respondents with different genders, ages, dwellings, and education levels is beneficial.
For eliciting WTP, there were three questionnaires with different initial bids, each with three questions about WTP as the double-bounded dichotomous choice (DBDC) model was applied. There are several means of paying for NPP risk reduction, such as through income tax or additional electricity fees, and in this study additional electricity fees were chosen. Therefore, “How much are you willing to pay to reduce the hazards related to NPPs?” was the first question with an initial bid
Logical process of the questionnaire used for the CVM survey.
A CVM is an analytical technique used to estimate the value of nonmarket goods. Estimation data from a hypothetical market is used instead of that from a real market. Therefore, researchers need to create a hypothetical market and use specially designed questionnaires to investigate responses under several conditions [
There are several methods of CVMs, for example, bidding game, open-ended, payment card, and dichotomous choice [
Binary responses from the questionnaire can be processed by the utility difference model. Related parameters are estimated by maximum likelihood estimation (MLE). Mean WTP can be assessed according to the distribution’s characteristics [
The utility function refers to whether a respondent is willing to pay or not. It has three variables
However, this function contains some components that are unobservable to the econometric investigator, such as the factors which affect the respondent’s reasoning behind their answer. These unobservable components are separated from the direct utility function as shown in
Here,
To convert this equation using WTP and
For easy analysis, the indirect utility function can be converted to an equation using
By using this difference, mean WTP can be calculated as follows:
As seen in (
In the DBDC model, if the respondent answers “yes” when the suggested bid is
There are four kinds of answers: Yes-Yes, Yes-No, No-Yes, and No-No. The probability of each case is indicated as
The log likelihood function of WTP in the DBDC model is expressed as
Therefore,
There were 224 observations from the survey. The observations can be categorized according to the demographics of gender, education, and income, expressed in Table
Basic demographics of respondents.
Characteristics | Observations |
Percentage | |
---|---|---|---|
Gender | Male | 118 | 52.68 |
Female | 106 | 47.32 | |
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|||
Education | High school | 7 | 14.07 |
College | 199 | 73.24 | |
Graduate school | 18 | 12.69 | |
|
|||
Income |
Below 500 | 123 | 54.91 |
500–1000 | 19 | 8.48 | |
1000–1500 | 31 | 13.84 | |
1500–2000 | 31 | 13.84 | |
Above 2000 | 20 | 8.93 |
For each group, three questionnaires with different bid amounts were randomly supplied to participants. Table
Designed questionnaire divided by scientific background level (group number) and bid.
Group | Questionnaire |
Bid [ |
Observations (obs.) |
Total |
---|---|---|---|---|
1 | A | 5 | 6 | 20 |
B | 10 | 10 | ||
C | 20 | 4 | ||
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||||
2 | A | 5 | 10 | 24 |
B | 10 | 3 | ||
C | 20 | 11 | ||
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3 | A | 5 | 54 | 180 |
B | 10 | 68 | ||
C | 20 | 58 |
These raw data were analyzed according to the three factors of scientific knowledge level, image level, and safety level. As the groups were already divided according to scientific knowledge level, the respondents’ perception about image and safety level is analyzed and compared in the next section.
Figures
The most frequently observed (bold) and second-most frequently observed (italic) regions.
Group 1 | Group 2 | Group 3 | |
---|---|---|---|
Image 1 | 0 | 0 | 8.89 |
Image 2 | 0 | 0 |
|
Image 3 | 5.00 |
|
|
Image 4 |
|
|
5.56 |
Image 5 |
|
16.67 | 1.67 |
(a) Image level responses from 1 = very bad image to 5 = very good image, (b) safety level responses from 1 = highly unsafe to 5 = highly safe, and (c) average levels of image and safety according to group.
Mean WTP was assessed with a DBDC model in this study, with raw data from the questionnaire processed by STATA/SE 13.1.
In this process, people who did not want to pay were excluded from the samples. There were 32 people who selected “No” for all three questions. These zero bids can be categorized into true zero bids and protest bids. True zero bids reflect the respondents’ true preference about the goods [
In this research, the most common reason why group 1 did not want to pay was that there was not enough information to decide. Therefore, it can be presumed that they make decisions very carefully, even if they have greater scientific background knowledge than the others. Group 2 believed that taxes already paid should be used to reduce the hazards of NPPs, and the zero bids from group 3 resulted from their opinion that the government and the nuclear operator made this problem on their own. It is interesting to note the minority opinions. In group 1, the minority opinion was that NPPs are fully safe. However, in groups 2 and 3, minority opinions involved distrust of the government and nuclear operators. They felt that the government would not use their taxes for proper purposes. Most of these reasons for zero bids make them protest bids rather than true bids. Therefore, as most were biased, the zero bids were removed from the final result analysis.
As expected, people who have a higher scientific background level had a low mean WTP as verified in Table
Mean WTP estimation according to scientific background level (group number).
Category | DBDC model | |
---|---|---|
|
| |
|
||
Coefficient | 1.666684 |
0.0001962 |
|
1.59 | 4.73 |
Mean WTP [ |
9.379 | |
95% confidence interval [ |
5.719~13.039 | |
|
||
Coefficient | 2.17656 |
0.0001809 |
|
3.31 | 5.40 |
Mean WTP [ |
12.629 | |
95% confidence interval [ |
8.835~16.423 | |
|
||
Coefficient | 1.750892 |
0.0001005 |
|
10.44 | 13.45 |
Mean WTP [ |
19.013 | |
95% confidence interval [ |
16.393~21.634 | |
|
||
Coefficient | 1.735719 |
0.0001116 |
|
11.84 | 16.12 |
Mean WTP [ |
17.014 | |
95% confidence interval [ |
14.975~19.054 |
Mean WTP comparison according to (a) scientific knowledge level (group number) and (b) type of scientific knowledge (group number).
The effects of the type of scientific knowledge, general or NPP-specific, were found by combining two groups and comparing with the remaining group. By combining groups 1 and 2 and comparing it with group 3, the effect of general scientific knowledge can be analyzed. On the other hand, if groups 2 and 3 are combined, the effect of scientific knowledge about NPPs can be analyzed. Similar to the previous results, the more scientific knowledge the respondents had, the lower the amount of money they wanted to pay, irrespective of the type of knowledge. However, as shown in Figure
In the case of image level, as shown in Figure
(a) Mean WTP of each image level and its trend line with coefficient of determination (
However, the results of image levels 1 and 2 are out of the regression range. This is believed to be due to the small sample size, as there were smaller samples of levels 1 and 2 than those of levels 4 and 5. Moreover, there were unusually large differences between the respondents’ bids in levels 1 and 2. For image 1, many respondents wanted to pay only a small amount of money (a quarter of the initial bid) while image 2 respondents wanted to pay a large amount of money (double the initial bid). Such variation can happen on a small scale on account of each person’s different preferences. These differences can be calibrated by a model with large sample sizes.
To find the best fit regression of the function between the image levels and mean WTP, various regression functions were applied, as shown in Table
Various regression lines of WTP and coefficient of determination (
Type | Image level | Safety level | ||
---|---|---|---|---|
Function |
|
Function |
| |
Exponential |
|
0.6214 |
|
0.0004 |
Linear |
|
0.567 |
|
0.0216 |
Logarithm |
|
0.3591 |
|
0.0023 |
Power |
|
0.4019 |
|
0.0049 |
The mean WTP by safety level is shown in Figure
As with the image level, the best fit regression function analysis is described in Table
In this research, the factors which can affect WTP regarding NPP hazard reduction were investigated and the relationship between them and mean WTP was verified by using a specially designed questionnaire. Image level, safety level, and scientific background level were selected as the factors, with survey data analyzed by the CVM-DBDC model and processed by statistical software.
As a result, the people who have more scientific background knowledge tended to have a good image about NPPs and thought that they are safe. Coincidently, their WTP was lower than the others’. In this analysis, the type of knowledge had a small effect on mean WTP. When mean WTP was analyzed according to image level, the trend was inversely proportional. That is, the people who have a better image on NPPs showed a lower WTP. It is natural to interpret this result by considering that the people who have a low image level might feel that they are being threatened by NPPs, so their WTP to reduce risk tends to be higher.
It is notable that in this research most of the zero bids were protest bids, and the reason for two-thirds of the zero bids was that the government and nuclear operator need to reduce NPP hazard by using paid taxes without outside help. Along with the minority opinions, this reason leads to the belief that mistrust of the government and the nuclear operator influenced why people chose “No” for the WTP questions. This may imply that the public’s distrust on authority might affect their preferences. If policy makers want to use WTP to assess the external costs of NPPs, this misbelief needs to be considered as one of the factors that can affect cost estimation.
Following this study’s results, mean WTP can be estimated by using image and scientific background levels from existing data. In the case of residents near an NPP, there are many data which indicate their perception about NPPs. When compensation needs to be appropriated for them, mean WTP from their image level can be used as a reference value and the compensation standard can be subdivided. This study shows that for NPP operators and builders, external costs can be reduced by supplying specific knowledge about NPPs.
This study can be improved in three ways. First of all, the effects of age can also be analyzed if the age range of target respondents is enlarged. In this research, as groups 1 and 2 were made up of students, group 3 needed to be restricted to a similar age range. Data from specialists and graduates are required to enlarge research targets from the 20s age range to all ages. For example, in future studies, if group 1 is composed of people working in research institutes instead of nuclear engineering students and group 2 is engineering graduates, then the analysis of all age groups will be possible. In addition, as researchers have more knowledge than students, they would be more proper for group 1. Secondly, this study utilized three out of nine elements to analyze the relation between risk perception and WTP. In future studies, other factors which affect risk perception need to be considered to discover the overall relationship between them and mean WTP. Finally, a bigger sample size is recommended for further studies to obtain clear trends in image level and to find the relationship between safety perception and mean WTP. A large sample size will also reduce the confidence interval of each level. Because of the broad confidence interval of image levels 1 and 2 and safety level 5, the coefficient of determination was low and it was hard to find a fitting function.
The authors declare that they have no competing interests.
This work was supported by the “Valuation and Socioeconomic Validity Analysis of Nuclear Power Plants In Low Carbon Energy Development Era” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted by the Ministry of Trade, Industry & Energy of the Republic of Korea. 20131520000040.