key: cord-0579356-rjdaplvc
authors: Cho, Eunjung; Cho, Youngsang
title: Estimating the economic value of ultrafine particles information: A contingent valuation method
date: 2021-07-07
journal: nan
DOI: nan
sha: cf158c9cb11e428af9b31ff81ea8e64ef99e1f31
doc_id: 579356
cord_uid: rjdaplvc

Global concern regarding ultrafine particles (UFPs), which are particulate matter (PM) with a diameter of less than 100nm, is increasing. These particles-with more serious health effects than PM less than 2.5 micrometers (PM2.5)-are difficult to measure using the current methods because their characteristics are different from those of other air pollutants. Therefore, a new monitoring system is required to obtain accurate UFPs information, which will raise the financial burden of the government and people. In this study, we estimated the economic value of UFPs information by evaluating the willingness-to-pay (WTP) for the UFPs monitoring and reporting system. We used the contingent valuation method (CVM) and the one-and-one-half-bounded dichotomous choice (OOHBDC) spike model. We analyzed how the respondents' socio-economic variables, as well as their cognition level of PM, affected their WTP. Therefore, we collected WTP data of 1,040 Korean respondents through an online survey. The estimated mean WTP for building a UFPs monitoring and reporting system is KRW 6,958.55-7,222.55 (USD 6.22-6.45) per household per year. We found that people satisfied with the current air pollutant information, and generally possessing relatively greater knowledge of UFPs, have higher WTP for a UFPs monitoring and reporting system. The results can be used to establish new policies response to PM including UFPs.

Numerous epidemiological studies have been conducted on the effects of particulate matter (PM) with a diameter of less than 2.5µm (PM2.5) on human health (Dockery et al., 1993; Hart et al., 2015; Li et al., 2018) . The literature, consequently, is replete with evidence of its negative effects on human beings (Atkinson et al., 2014; Burgan et al., 2010; Cesaroni et al., 2014; Yuan et al., 2019) .

However, during recent years, general public concern-and interest-regarding ultrafine particles (UFPs), which are PM with a diameter of less than 100nm, has also increased. The characteristics of PM depend on its size (Morawska et al., 2008) , and there are four distinguishing characteristics of UFPs. First, they constitute less than 20% of the total mass concentration of particles, but more than 90% of the total number concentration of particles, compared to PM with a diameter of less than 10µm (PM10) and PM2.5 (Kittelson, 1998; Kumar et al., 2009) . Second, UFPs have a high share in direct emissions from anthropogenic sources, such as road transportation and power plants, whereas PM2.5 has a high share in secondary sources, that is, through chemical processes in the atmosphere (Kittelson, 1998; Liang et al., 2016; Morawska et al., 2008) . UFPs emitted from road transportation account for over 60% of the total air pollution, as compared to non-road transportation (19%) and domestic combustion (13%) (Kumar et al., 2014) . Third, various natural factors, such as wind direction, wind speed, and breathability, affect UFPs concentration (Buccolieri et al., 2010; Chen et al., 2016) . In urban areas, the vortex effect caused by dense traffic concentration and the temperature gradient (Kumar et al., 2008; Marini et al., 2015) , results in UFPs-emitted by vehicular fuel combustion-hanging in the atmosphere for a long time, at high concentration levels. Kumar et al. (2014) compared UFPs concentration in Asian countries, such as China and India, and European countries. They found that outdoor average UFPs concentration in Asian cities is about four-times higher than in European cities. According to them, understanding the variability of UFPs is the key to designing effective monitoring strategies and estimating the relationships between UFPs and health effects in urban areas. Fourth, UFPs have low mobility. Choi et al. (2018) built a mobile monitoring platform to measure UFPs at intervals of 90m before and after the intersection center, and found that their concentration peaks within 30m of the intersection, decreasing sharply thereafter.

Similar to PM10 and PM2.5 studies, there are studies on health effects of UFPs exposure. These particles are too small for the human nose and bronchioles to effectively filter out, resulting in their deep absorption into the alveoli or the membranes (HEI, 2013) .

Thus emanate numerous respiratory diseases, and the smaller the particle size, the greater the risk (Penttinen et al., 2001) . According to Chen et al. (2016) , about 50% of all PM deposited on the alveoli is of the size of 20nm, and 10-20% from 100nm to 2.5µm. Stafoggia et al. (2017) analyzed the relationship between short-term exposure to UFPs concentration and mortality in eight European countries. The results showed a 0.35% increase in non-accidental mortality as the number of UFPs increased by 10,000 particles/m 3 . Liu et al. (2018) estimated the relationship between maximum blood pressure, minimum blood pressure, high sensitivity-C-reactive protein, and UFPs concentrations for 100 non-smokers in Taiwan.

Consequently, when the UFPs concentration increased by 0.97µg/m 3 , maximum blood pressure, minimum blood pressure, and high sensitivity-C-reactive protein increased by 6.3%, 5.6%, and 8.5%, respectively-higher than the effects of PM10 and PM2.5. In Korea, Song et al. (2011) analyzed the effect of UFPs concentration on respiratory system functions in 41 children aged 8-12 years with atopic dermatitis. They found a 3.1% increase in itch symptom score in children with atopic dermatitis as the number concentrations of UFPs increased by the interquartile range. Although a few studies suggest the high potential of adverse health effects due to UFPs exposure, more studies are required to prove the same with greater reliability (Ohlwein et al., 2019; Schraufnagel, 2020) .

The current PM response policies and measurement methods cannot properly reflect the characteristics of UFPs; therefore, they need to be managed separately from existing air pollutants. Consequently, several developed countries have included UFPs in their inventory of fine PM (Lewis et al., 2018) . However, several other countries, including Korea, do not have in place adequate standards and regulatory limits for UFPs. Therefore, establishment of a future policy on air pollutants, including UFPs, necessitates a wide range of UFPs studies to collect scientific evidence. Additionally, prevention of potential UFPs risks requires disclosure of information to the public so as to facilitate voluntary avoidance of UFPs exposure. To collect and disseminate this information, a new UFPs monitoring and reporting system-different from traditional systems-is required, which will raise the financial burden on the government and people. Moreover, research on public perception of UFPs and the economic value of its information remains insufficient.

This study has two main aims: to analyze the economic value of UFPs information by using the contingent valuation method (CVM) to estimate the willingness-to-pay (WTP) for building a UFPs monitoring and reporting system; and, to derive policy implications for the government to respond to UFPs. The remainder of this paper is organized as follows:

Section 2 explains the model and data used in this study. Section 3 presents estimation results and discusses implications. Section 4 provides the conclusions and limitations of this study.

This study used the CVM to estimate the WTP for building a UFPs monitoring and reporting system, to evaluate the economic value of UFPs information. The CVM has been widely used for economic valuation of non-market goods, especially environmental goods, and consumer perception analysis (Bateman and Langford, 1997; Han et al., 2011; Mwebaze et al., 2018) .

To estimate the WTP for non-market goods using the CVM, it is important to design a questionnaire that facilitates respondents' comprehension of issues and obtains their WTP information. Generally, payment card, open-ended, and dichotomous choice (DC) formats are employed to elicit respondents' WTP data through the CVM. In the DC format, pre-set bids are presented to the respondents with a "yes" or "no" choice, and econometric analysis-the probit or logit model-is used to estimate the WTP. We designed the questionnaire using the DC format because of its following strengths: easy to answer, low biases, and low likelihood of estimating unreliable WTP (Hanemann, 1984; Oerlemans et al., 2016) .

The DC format is divided into the single-bounded dichotomous choice (SBDC) that asks a single bid (Bishop and Heberlein, 1979) , and the double-bounded dichotomous choice (DBDC) that asks once more for double or half of the first bid, depending on the respondent's first answer (Hanemann, 1985) . The SBDC has a relatively low non-response rate and is convenient because it asks each respondent only one bid; however, it has the disadvantage of being statistically inefficient. The DBDC, in contrast, has high efficiency, but one disadvantage: the first and second bids are not independent, which may cause bias. To overcome SBDC's inefficiency and DBDC's bias, Cooper et al. (2002) suggested the oneand-one-half-bounded dichotomous choice (OOHBDC), in which either the lower or upper bid is randomly given to each respondent as an initial bid. If respondents answer "no" to the lower bid or "yes" to the upper bid, the survey ends. However, if they answer "yes" to the lower bid, then the upper bid is presented as the second question, and if they answer "no" to the upper bid, then the lower bid is presented as the second question. This study used the OOHBDC method to reduce SBDC's inefficiency and DBDC's bias. In the OOHBDC method, if respondents answer "no" to the lower bid or "no-no" to the upper bid, then their WTP may be zero or between zero and the lower bid. It is important to identify whether the respondent's WTP is actually zero or between zero and the lower bid, so as to estimate their accurate WTP; we used the spike model to solve this problem (Kriström, 1997) . The OOHBDC spike model asks an additional question if respondents answer "no" to the lower bid or "no-no" to the upper bid: "Are you willing to pay at least KRW 1 to build the UFPs monitoring and reporting system?"

This study applied the utility difference model suggested by Hanemann (1984) to estimate the WTP using the OOHBDC spike model. Each respondent has the indirect utility function ( , ; ) u j m S , where j is the status of the UFPs monitoring and reporting system: when j is equal to 1, it means the UFPs monitoring and reporting system is presented, otherwise j is 0. m is the respondent's income, and S is the vector of respondent's socio-economic and cognition characteristics. The indirect utility function can be expressed as the observable deterministic part, ( , ; ) v j m S , and unobservable stochastic part, j ε , as follows:

where j ε is an independently and identically distributed (i.i.d.) variable with a zero mean.

When the respondent answers "yes" to the question "Are you willing to pay A to build the UFPs monitoring and reporting system?," to maximize his/her utility, the probability of answering "yes" is expressed as follows:

where η is the difference of error terms, 0 1 ε ε − , and ( ) F η ⋅ is the cumulative distribution function (CDF) of η . Meanwhile, if the WTP (denoted as W ) of the respondent is greater than or equal to A , the respondent will answer "yes," otherwise "no." The probability that respondent answers "yes" can also be expressed as follows:

When we consider equations (2) and (3) together, we can

We assume that i A is an initial bid presented to the respondent i , and L i A and U i A represent lower and upper initial bids, respectively. There are eight possible outcomes in the OOHBDC spike model: 

and 1 ( " "), 

where the indicator function ( ) I ⋅ has a value of 1 if the proposition is true; otherwise, it is 0.

Using eight indicator functions, the log-likelihood function for the OOHBDC spike model is expressed as follows:

Assuming that the respondent's WTP has a logistic CDF, the spike model of ( )

Here, the spike is defined as

and the mean WTP is calculated as

We designed the survey to estimate the WTP for building a UFPs monitoring and reporting system. Initially, we planned a face-to-face survey; however, it was difficult to meet the respondents owing to COVID-19. Therefore, the survey was conducted online. At the beginning of the questionnaire, we explained the definition and characteristics of UFPs, and the differences between UFPs and PM2.5. Thereafter, we described the kinds of information that the UFPs monitoring and reporting system will provide to the public: (1) one-hour average data, presented online in real-time;

(2) monthly/annual reports; and (3) predicted UFPs concentration data and warning alert issuance in case of high concentration. We also explained how to use UFPs information, based on policy suggestions of previous studies (Choi et al., 2018; Choi et al., 2012; Hu et al., 2009; Lewis et al., 2018) .

In this survey, we selected an additional payment of income tax as the payment vehicle for building the UFPs monitoring and reporting system. Additionally, we explained to the respondents that they would have to pay over the next five years, considering 90% of PM2.5 monitoring stations in Korea are installed for five-year terms. 1 Consequently, we presented the question: "Are you willing to pay an additional KRW [bid] each year for the next five years from the income tax paid by your household to build the UFPs monitoring and reporting system?"

We conducted a pilot survey of 455 respondents to determine the initial bid sets to be presented to respondents in the actual survey. Based on the results of the pilot survey, we excluded the upper and lower 5% responses to remove the bias, considering them as outliers.

Then, we determined 10 sets of initial bids for the actual survey: KRW (1,000; 2,000), (2,000;

3,000), (3,000; 4,000), (4,000; 5,000), (5,000; 7,000), (7,000; 9,000), (9,000; 11,000), (11,000;

14,000), (14,000; 17,000), (17,000, 20,000). 2 The survey was conducted by Gallop Korea, a specialized market research company, from February 4-9, 2021. Applying stratified sampling, the company selected 1,042 respondents between 20-69 years of age, and completed the online survey. We excluded two respondents who did not answer the additional questions, and used a total of 1,040 respondents in the analysis.

The characteristics of the respondents in the context of the total Korean population are described in Table 1 . The proportions of sex, age, region, and average monthly income per household are similar between survey respondents and the total population. However, the education level of the respondents is relatively higher than the general population-this could be due to the inclusion of internet savvy individuals in the survey, who generally have a high level of education (Ünver, 2014) . We compared the average monthly expense for anti-dust products with another survey sample of Min (2019) in Table 1 . The proportion of respondents who spend over KRW 50,000 was lower, but the level of spending under KRW 50,000 was higher. This reflected the increase in the sale of face mask-a representative anti-dust product-due to COVID-19 after 2019 (Wu et al., 2020) . probably a protest response to the survey. In this survey, we presented an additional question to zero WTP respondents to ascertain the reason for their zero WTP, so we could distinguish the protest WTP respondents. Table 4 shows the reasons mentioned for zero WTP. We can consider the respondents who responded with "Not enough information to judge" as protest WTP respondents (Tolunay and Başsüllü, 2015) . Previous studies suggested two methods for treating protest WTP respondents. First, these respondents were excluded from the CVM analysis because their responses did not reflect their actual preferences (Jorgensen and Syme, 2000; Meyerhoff and Liebe, 2010). Second, they were included in the data set because their exclusion from the analysis, could have overestimated the mean WTP (Fonta et al., 2010; Strazzera et al., 2003) . In this study, there were 38 (3.65%) protest WTP respondents, and we included their WTP as zero in the basic analysis to avoid the overestimation bias; we also presented the estimated WTP, excluding the protest WTP respondents for comparison.

Insert [Table 4 ] about here Additionally, we conducted the Monte Carlo simulation to calculate the 95% and 99%

confidence intervals of the estimated WTP (Krinsky and Robb, 1986; Lee and Cho, 2020) .

Based on the estimated coefficients and their variance-covariance matrix, we generated 5,000

replications and calculated the mean WTP. Thereafter, we omitted 2.5% and 0.5% at both ends to obtain the 95% and 99% confidence intervals, respectively. The last two rows of To the best of our knowledge, there are no studies on the economic value of UFPs information, but a few studies have estimated the economic value of air quality improvement. Table 6 shows previous studies that used the CVM to estimate the WTP for air quality improvement. The different scenarios used in previous studies to explain specific methods for air quality improvement include strengthening policies (Kim et al., 2018; Wang and Zhang, 2009 ), reducing air pollutants (Akhtar et al., 2017; Wang et al., 2006) , and reducing the risk of mortality due to air pollution (Istamto et al., 2014; Lee et al., 2011; Ligus, 2018; Sun et al., 2016; Vlachokostas et al., 2011) . policy enhancement, such as tightening the overall regulation of the PM2.5 sources, expanding the concentration monitoring station, and improving the management of deteriorated diesel vehicles. They used an annual income tax increase as the payment vehicle, and found that the mean WTP for the enforcement of the PM2.5 concentration reduction policy was USD 4.97 (USD 5.09 in 2020) per household per year. Wang and Zhang (2009) estimated the WTP for improving air quality through enforcing a strict national air quality standard in Jinan-a Chinese city with the poorest air quality. They asked respondents how much they were willing to pay voluntarily and found that the WTP was CNY 100 (USD 22.29 in 2020) per person per year.

Insert [Table 6 ] about here

The Obtaining accurate information about UFPs is necessary to conduct studies on their emission sources, characteristics, and health effects. Therefore, we estimated the economic value of UFPs information using the CVM, and the scenario was building a new UFPs monitoring and reporting system.

The mean WTP was estimated as KRW 6,958.55-7,222.55 (USD 6.22-6.45) per

household per year, applying three models. We identified that the cognitive level regarding PM rather than income had a positive effect on the WTP. In other words, people who are already experiencing inconveniences from high PM, such as increased expenditure on antidust products and deteriorating health, desire UFPs information. People perform various antidust activities, such as wearing face masks, refraining from outdoor activities, or using air purifiers, to avoid exposure when PM concentration is high (Cho and Kim, 2019; Noonan, 2014; Saberian et al., 2017; Wells et al., 2012) . They use real-time PM information to decide upon such behaviors. Consequently, they believe that accurate information is important to prevent potential risks from PM exposure, and therefore, their WTP for UFPs monitoring and reporting system is high. Additionally, we found that the more reliable the PM information provided by the government, the higher the WTP for a UFPs monitoring and reporting system.

Thus, it is important to accurately measure PM concentration and provide reliable PM information to increase public acceptance of UFPs monitoring and reporting systems.

Furthermore, we identified that the higher the level of perception of UFPs, the higher the WTP. Most of our respondents had a low perception of UFPs, but it is expected that the need for UFPs information will increase in future as public perception of UFPs increases.

Therefore, policymakers should continuously monitor the public's perception of UFPs, and allocate budgets and establish policies so that the UFPs monitoring and reporting system can be implemented at an appropriate time.

This study is meaningful as the first study to estimate the economic value of UFPs information. However, it also has several limitations. First, the survey was online, which means only the internet savvy could participate. As shown in Table 1 , the distribution of sex, age, and income for the respondents was similar to that of the total population, but the respondents' education level was slightly higher than that of the general population. This could be because the internet-literate generally have a relatively higher education level. In general, the WTP among the highly educated is high (Kim et al., 2018; Wang and Zhang, 2009; Wang et al., 2006) . We employed an online survey because of COVID-19, but a faceto-face survey should be conducted in future to overcome this limitation and to obtain results that are more accurate. Second, we cannot compare the estimated results of this study with previous studies because to the best of our knowledge, there are no similar studies on the subject and scenarios. Given that concerns about UFPs are increasing, more studies are required to estimate their potential risks and social costs. Third, our respondents had generally low perception of UFPs. We found that respondents with pre-survey knowledge of UFPs placed a higher value on UFPs information. Therefore, there is a possibility that the WTP will change as the perception of UFPs changes in future; therefore, a follow-up research is imperative.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 

Bid amount (KRW) Upper bid is given as an initial bid Lower bid is given as an initial bid Lower Upper "yes" "no-yes" "no-no-yes" "no-no-no" Total "yes-yes" "yes-no" "no-yes" "no-no" Total 

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a Sex, age, and region are for the 20-69 years old population in 2020 (Source: MOIS, 2021); Education level is based on

Average monthly income per household is for the entire population in

Average monthly expense for anti-dust products is a survey sample of 1

Notes: Values in bracket are t-statistics except Wald statistic

* Statistically significant at the 5% level. ** Statistically significant at the 1% level