The effect of risk perception on willingness to pay for reductions in the health risks posed by particulate matter 2.5: A case study of Beijing,China

Abstract

Particulate air pollution is a serious problem and has received extensive attention in Beijing. The public might amplify the health risks because of the visibility and frequency of haze, potentially leading to anxiety and panic. Public risk perception might affect individual behaviors and their willingness to pay for improving air quality. We used contingent valuation method and psychometric paradigm to investigate public risk perception and elicit willingness to pay for reductions in the health risk posed by fine particulate matter 2.5 smaller than 2.5 microns in width in Beijing, China. The logit model results showed that the level of fear and four characteristics of risk perception—familiarity, scope of impact, relevance to individual, and voluntariness—had significant effects on respondents’ payment decision. The ordinary least squares model results showed the level of anxiety, anger, and satisfaction about the current situation and four characteristics of risk perception—scope of impact, overall risk, duration of effects, and controllability—had significant effects on the amount of willingness to pay.

Keywords

Environmental economics air quality willingness to pay risk perception Beijing

Introduction

Beijing is the capital city and one of the most important financial, cultural, and educational centers of China. Since China’s reform and opening up in 1978, Beijing has experienced unprecedented growth. The annual increase in GDP between 1978 and 2015 averaged 15.57%.¹ However, this rapid economic development has also resulted in severe air pollution, including particulate matter (PM) and sulfur dioxide. Of these, fine particulate matter 2.5 smaller than 2.5 microns in width (PM_2.5) was one of the most important pollutants. The dairy PM_2.5 average concentration in Beijing from 1 January 2014 to 31 December 2016 is shown in Figure 1. Complying with the ambient air quality standard of China,² the ratio of the dairy PM_2.5 average concentration lower than 35 and 15 µg/m³ was 27.29% and 8.49%, respectively. This means that the current situation of PM_2.5 in Beijing is not optimistic.

Figure 1.

The dairy PM_2.5 average concentration in Beijing from 1 January 2014 to 31 December 2016. Source: Adapted from “Beijing Current Air Quality Index—Historical Data.” By Embassy of The United States in Beijing, www.stateair.net/web/historical/1/1.html.

PM_2.5 attracts much attention from researchers and the public because it might cause damage to human health. Recent studies have shown that chronic exposure to PM_2.5 increases the risk of respiratory and cardiovascular diseases such as flu, lung cancer, myocardial infarction, and pneumonia.^3–6 In addition, PM_2.5 might have effects on human capital accumulation and utilization because of the differences in the degree of influence caused by individual differences.⁷

In response, both the Chinese central government and the Beijing municipal government enacted policies and regulations to address the issue, for example, industrial structure changes and traffic control. But environmental governance requires long-term effort. This means that the public will be exposed to health risks posed by PM_2.5 for a long time. During this period, the visibility and frequency of haze will continue to stimulate the public, potentially leading to feeling of anxiety and panic. If the public does not receive timely guidance, they may form cognitive bias to enlarge the risk, and may make irrational decisions, such as the public rush to buy radix in China in 2003 because of the SARS incident. Therefore, it is necessary to thoroughly understand the situation and characteristics of public perception of the health risks posed by PM_2.5 to provide a reference for the formulation and implementation of risk management strategies. However, relatively few researches on the risk perception and attitude of air pollution were carried out in China. In addition, these environmental governance policies brought about considerable health benefits, but also included great opportunity costs. The public may not accept the huge costs due to the low income of Chinese residents. If they did not accept the cost, the public may have psychological resistance and not follow the policies. This would reduce the effect of policy implementation. Therefore, it was necessary to determine how important air quality was for Beijing residents, how much cost was accepted by the public, and how much benefit was realized in reducing the health risk posed by PM_2.5 to make a cost-benefit analysis.

Literature review

Estimating the benefits of reducing the health risk posed by PM_2.5

We found few studies that estimated the benefits for reducing the health risks posed by PM_2.5 but a substantial number on the benefits of improving air quality. An explicit market for clean air in Beijing did not exist, so the economic value of improving air quality needed to be assessed through nonmarket methods. Traditionally, nonmarket methods have been divided into two categories: revealed preference methods and stated preference methods. The revealed preference approach mainly includes the human capital method,^8–10 the cost of illness method,^11–13 the value of statistical life,^14,15 and the hedonic pricing method.^16–18 However, these revealed preference methods could not reveal public risk perception and attitude. The stated preference approach that included choice experiment (CE) and contingent valuation methods (CVM) could elicit public willingness to pay (WTP) for improving air quality directly. Because of our expectation that public risk perception and attitude might have a significant effect on the WTP, CVM was chosen for this study.

CVM had been widely used previously to reveal public preference and health benefits for improving air quality in different countries. There are many differences in specific research objectives (e.g., avoid episodes of illness related to the ambient concentration levels of PM in Taiwanese cities,¹⁹ premature death and chronic bronchitis attributed to PM₁₀ in nine Pearl River delta cities in China²⁰), WTP guidance technology (e.g., open-ended,²¹ single-bounded and double-bounded²²). However, These studies clearly demonstrated that most respondents are willing to pay for reducing air pollution,^21,23 and that the benefits of improvements in air quality were significant.^20,24 Furthermore, there were large differences between countries. In addition, a considerable number of these studies focused on Beijing residents. Especially after the Beijing, 2008 Olympic Games, related researches have rapidly increased. But there are huge differences in the different studies. For example, both Du and Mendelsohn and Tan and Zhao investigated the WTP of Beijing residents to maintain the improved air quality during the Olympic Games, but the mean annual WTP per household ranged between 22,000 and 24,000 China Yuan (CNY) in Du’s study²⁵ and was just 925.25 CNY in Tan’s study.²⁶ Furthermore, most of these studies focused on air quality and other pollutants, for example, PM₁₀ instead of PM_2.5. Nevertheless, large gaps were found in the value of reducing different pollutants, and the value of simultaneously reducing pollutants was not equal to the sum of these values.²⁷ Furthermore, these studies highlighted WTP for improving air quality or avoiding illness but said little about the benefits of health risk-reductions related to PM_2.5. Clearly, a better understanding of the benefits of reductions in health risks posed by PM_2.5 is needed to support the decision-making process for PM_2.5 management in Beijing.

The role of risk perception and attitude

According to the results of previous studies, a resident’s WTP for improving air quality was likely determined by various factors such as income,²⁸ education,²⁹ age,³⁰ and visits to the doctor.³¹ However, most of these studies did not involve the relationship between WTP for air quality improvement and health risk perception posed by air pollution. Nevertheless, studies in other fields, such as food safety and natural diseases, have shown that residents’ risk perception and attitude had a significant impact on WTP. For example, Lim et al.³² found that consumers’ WTP for country-of-origin labeled imported beefsteak in the United States increased if consumers’ risk perceptions from consuming beef were greater. Similarly, Angulo and Gil³³ found that the perception of beef safety was one of the main determinants of consumers’ WTP for certified beef in Spain. Zhai et al.³⁴ found that WTP might increase with the perception of flood risk, but might decrease with the perception of other risks (e.g., earthquakes) in Japan. However, the majority of these studies only investigated how great the perceived risk was, which did not comply with the psychometric paradigm when investigating public risk perception, and could not reveal the public perception of different dimensions of risk.

The public may amplify or ignore risks they experience. Thus, it is important to know about public risk perception for risk management. Risk perception has been used to describe public attitudes and intuitive judgments of risks.³⁵ Public risk perception can be better described through the characteristics of those risks. Slovic³⁶ identified the two most important dimensions of risks: dread risk that has a high score in the perceived lack of control, dread, catastrophic potential, fatal consequences, and the inequitable distribution of risks and benefits, and unknown risk that is defined at its high end by hazards judged to be unobservable, unknown, new, and delayed in the manifestation of harm. Lazo et al.³⁷ elicited perceptions of 31 risk characteristics to examine and compare perceptions held by laypersons and ecologists about risks to ecosystems. With the application of the psychometric paradigm, studies have shown that there are significant differences in risk perception between experts and laypersons^38–40 and that public risk perception was affected by many factors, such as age,⁴¹ gender, income, education,⁴² individual attitudes, and basic beliefs.⁴³ There are plenty of studies in public perception for air pollution in China in recent years. However, these studies paid more attention to public perception of local air quality⁴⁴ or tourists’ perception of haze pollution’s impacts on tourism experience,⁴⁵ not risk perception. Studies on risk perception for air pollution in China are scarce. Therefore, a study of public risk perception to PM_2.5 is urgently needed to support the risk management concept in Beijing. In the present study, we were interested in how people evaluated the characteristics of health risks posed by PM_2.5 and therefore adopted the psychometric paradigm.

Attitude is defined as an acquired or predisposed mental state regarding an object with some degree of positivity or negativity which is perceived from a social or personal stimuli in Psychology. We mainly focused on public emotional reaction caused by PM_2.5. In the face of environmental risks, the public may have different degrees of emotional response. Emotional response may eventually be reflected in behavior. For example, Böhm et al. found that prospective loss-based emotions have a significant influence on behaviors of help/prevention, and ethical emotions have a significant influence on behaviors of aggression/retaliation. Furthermore, some overreaction may lead to radical self-protection behavior and may be passed on to others.⁴⁶ In addition, much research has explored the relationship between WTP and emotion. For example, Danner et al.⁴⁷ investigated WTP for wine and founded that WTP would increase significantly because of positive emotion, for example, happy and optimistic, and would decrease significantly because of negative emotion, for example, lonely and sad. Biel et al.⁴⁸ explored the discrepancy between WTP and willingness to accept (WTA) and showed that affective responses associated with a moral reaction could account for most of the WTA–WTP discrepancy. However, few studies have focused on public emotional reactions to PM_2.5 in China. We investigated five emotional reactions of Beijing residents to understand the basic situation for better guidance and management, and explored the relationship between these emotional reactions and WTP.

Methods and materials

The data for this study were compiled from responses to a questionnaire administered in May 2014 to local residents in Beijing. The questionnaire was piloted to residents in the area surrounding the Renmin University of China and was revised according to the feedback to account for the respondents’ understanding of the choice tasks and the duration of the survey. Subsequently, the questionnaire was administered face-to-face in six districts (Dongcheng, Xicheng, Chaoyang, Fengtai, Haidian, and Shijingshan) using stratified random sampling according to the regional population size in June 2014. In total, 1322 questionnaires were recycled, and 1027 questionnaires were screened as effective, with the remainder discarded because of nonresponse to specific items (e.g., 111 nonresponses to WTP), out of which 82, 124, 293, 193, 276, and 59 were drawn from the Dongcheng, Xicheng, Chaoyang, Fengtai, Haidian, and Shijingshan districts, respectively.

The questionnaire was organized in five sections, and the structure is shown in Figure 2. The first section introduces the situation of PM_2.5 in Beijing and relates knowledge of the health risks posed by PM_2.5 to ensure that respondents have basic knowledge about PM_2.5.

Figure 2.

Questionnaire structure and measurement items.

The second section elicited responses to perceived health risks posed by PM_2.5 by asking whether respondents noticed air pollution and haze in Beijing. It subsequently explored what respondents actually thought about the health risk itself. Ten characteristics of risk that were elicited from the psychometric paradigm of Slovic’s³⁶ and Lazo et al.’s³⁷ studies, which evaluated the respondents’ risk perception. These items took many factors into accounts, such as severity, equality, and controllability, and were evaluated on a 5-point Likert scale. The details of these questions and corresponding options are presented in Table 1.

Table 1.

Characteristics scales of risk.

Serial number	Characteristic of risk	Description	Low (1)	High (5)
RP1	Familiarity	Rate how much you know about PM_2.5	Very much	Very little
RP2	Scope of impacts	Please rate, in terms of size of the area affected, the scope of the effects from PM_2.5 on society	Very little	Very big
RP3	Overall risk	Rate how “risky,” you think the risk probability of the health risk posed by PM_2.5	Very little	Very big
RP4	How destructive	Please rate, in terms of the extent of health damage, the severity from PM_2.5	No serious	Very serious
RP5	Duration of effects	Do you agree the process that PM_2.5 harm health is a long, continued process	Strong disagree	Strongly agree
RP6	Controllability	Please rate how controllable the health risk posed by PM_2.5 is in terms of people’s ability to control its impact on health	Completely controllable	Completely uncontrollable
RP7	Equitability	Please rate how equitable the health risk posed by PM_2.5 is for all the Beijing residents	Completely equitable	Complete inequality
RP8	Anthropogenic	Do you agree, the health risk posed by PM_2.5 appear due to human activities	Strongly disagree	Strongly agree
RP9	Relevance to individual	Please rate the scope of health effects from PM_2.5 on you or your family	No impact	Great impact
RP10	Voluntariness	Please rate the extent of voluntary of you to take the health risk posed by PM_2.5	Completely voluntary	Completely involuntary

The third section explored respondents’ attitudes to the health risk, which included the level of fear, anxiety, anger, satisfaction with the current situation, and optimism about the future. The evaluation was based on a 5-point scale, but the options differed slightly in format. The options for fear, anxiety, and anger were set to range from 1 = none to 5 = very much, while the options for satisfaction and optimism were opposite.

The fourth section elicited respondents’ WTP for reducing the health risk posed by PM_2.5. Prior to asking the questions, we had a brief introduction about the health risks involved to ensure that the respondents were informed about the situation. It is important that the hypothetical market scenarios used in CVM should be credible and acceptable to respondents. Because we were dealing with health risks, and health risks differ from person to person due to health conditions and exposure, we used the reduction of the annual mean concentration of PM_2.5 as the hypothetical market scenario to represent the reduction of the health risk and to account for the respondents’ understanding. The annual mean concentration of PM_2.5 in Beijing in 2013 was 90 µg/m³. Combining that statistic with the goal of 35 µg/m³ in the ambient air quality standard of China, we set a hypothetical scenario to reduce PM_2.5 by 61% and two reserved scenarios to reduce PM_2.5 by 30% and 45%. Respondents were asked about their monthly WTP for the three scenarios. How much are you willing to pay per month if the annual average concentration of PM_2.5 drops from 90 to 63 µg/m³ (50 or 35 µg/m³)? The WTP was elicited by a payment card because payment card could increase response compared with open-end and is easy to carry out compared with dichotomy formats. And WTP was divided into 24 levels for convenient choice, which ranged from 0 CNY to over 700 CNY (The WTP values of each level are shown in Table 2. The gaps between smaller levels of WTP were designed to be smaller because previous studies found that the median WTP was always small and showed that people might be more sensitive to smaller payment levels^29,49). This section ended with the zero-bid reasons offering six options.

Table 2.

WTP levels and corresponding payments.

Level	1	2	3	4	5	6	7	8
Payment^a	0	0.1–2	2.1–4	4.1–6	6.1–8	8.1–10	10.1–15	15.1–20
Level	9	10	11	12	13	14	15	16
Payment^a	20.1–25	25.1–30	31–40	41–50	51–60	61–70	71–80	81–90
Level	17	18	19	20	21	22	23	24
Payment^a	91–100	101–200	201–300	301–400	401–500	501–600	601–700	>700

WTP: willingness to pay.

^aPayment unit is CNY per month.

The final section was devoted to standard demographic questions on gender, age, education, monthly income, and occupation, the last four of which were divided into five categories according to the practical situation of Beijing.

Results and discussion

Date reliability

Cronbach’s alpha (Cronbach’s α) was used for reliability testing. According to the results, the entire questionnaire had relatively good reliability (Cronbach’s α = 0.70). Perception of the health risk had relatively lower reliability (Cronbach’s α = 0.72), except in regard to demographic attributes, and so also could be accepted.

Respondent’s WTP

Mean and median were used to reflect the respondents’ WTP. To facilitate the statistics, we used the mean of each WTP level to represent the corresponding level (the level over 700 was replaced by 750), and the monthly WTP is presented in Table 3. The proportion that respondents were willing to pay for risk reduction ranged from 79.9% to 83.1%. The mean of WTP for reducing the annual average concentration of PM_2.5 was 71.60, 85.66, and 94.31 CNY/month, respectively for reductions of 30%, 45%, and 60%. Associating these results with the total population (i.e., the permanent population was 21.15 million and the proportion aged between 18 and 70 was approximately 76.67% in Beijing in 2013 (Beijing Statistical Yearbook 2014), the total benefits of risk reduction ranged from 13.93 to 18.35 billion CNY per annum. The result is necessary to compare with the results of other related studies to ensure the certainty of the result because of the hypothetical market scenario of CVM. The proportion that respondents were willing to pay for risk reduction and the mean of WTP shown in the present paper was much higher than the results discovered by previous studies many years ago. Yao⁵⁰ found that 12.5% were not willing to pay and the mean of WTP was 70.47 CNY/month in Tianjin in 2015 for air improvement. Tan and Zhao²⁶ found that the mean of WTP in Beijing and Shanghai are 925, 426, and 263 CNY/year, respectively.²⁶ These findings are closed to our finding, while some are not able to confirm.^21,25 For example, Sun et al.²¹ showed that nearly 90% of the respondents are willing to pay for reducing air pollution, and the mean of WTP is 382.6 CNY/year, which is much less than our finding. These may be due to regional differences and different hypothetical scenarios. Furthermore, this result demonstrated that the economic value of reducing PM_2.5 was very significant. Unfortunately, we did not include any specific questions relating to the respondent’s motivation to pay for reductions in PM_2.5. Although respondents were informed about the characteristics of risk and introduced to the health risks posed by PM_2.5 before eliciting WTP, they also considered other motivations, such as traveling and appreciating the landscape. In addition, the mean of WTP only accounted for 2.07% to 2.75% of annual income, which was much lower than for other countries, for example, 4.2% in Sofia, Bulgaria in 1999.⁵¹ Furthermore, the median of WTP still was at a low level. These results implied that the WTP of Beijing residents remained low overall. Furthermore, we were able to draw inferences from monthly WTP. First, the respondents’ WTP increased with increasing percentage of decrease in the annual average concentration of PM_2.5. Second, the mean of WTP was much higher than the median due to high levels of WTP. Furthermore, we made simple statistics according to demographic attributes to better understand the characteristics of WTP and found that: (a) the mean of WTP of men was higher than of women; (b) the mean of WTP generally increased with age, education level and monthly income; and (c) students showed the lowest mean of WTP, but the highest proportion of positive WTP. Respondents working in national units showed the highest mean of WTP. We will further analyze the determinants of WTP later, so we do not test whether the difference is statistically significant in this part.

Table 3.

Monthly WTP.

Sample	Variable	30%^a	45%^a	60%^a
Total	Zero-bid proportion (%)	20.1	17.4	16.9
	Mean of WTP (CNY/month)	71.60	85.66	94.31
	Median of WTP (CNY/month)	12.50	17.50	22.50
Positive WTP	Mean of WTP (CNY/month)	89.57	103.74	113.55
Positive WTP	Median of WTP (CNY/month)	22.50	35.00	45.00

WTP: willingness to pay.

^aPercentage of decrease in the annual average concentration of PM_2.5.

The zero-bid proportion in the three scenarios was 20.6%, 17.9%, and 17.4%, which decreased with increasing percentage of decrease in the annual average concentration of PM_2.5. Table 4 is the statistic of the six zero-bid reasons. Although there are many people with zero-bid, most of these people (94.3%) agreed that the health effects posed by PM_2.5 are high. The main reason for zero-bid is not because of income limits, but because they believe that they should not be responsible for the cost.Those who indicated that the government should take the responsibility comprised 59.2%, and 35.6% indicated that the enterprises and individuals that caused the pollution should be responsible for the cost. Furthermore, 28.2% worried that their donation could not achieve the aim of reducing PM_2.5. In other words, they might not trust in the government because of government failure to protect the environment. In addition, 9.2% selected the option of other reasons.

Table 4.

Zero-bid reasons.

Reasons	Proportion (%)
The health effects posed by PM_2.5 were small	5.7
Income is too low to afford the cost	24.1
Enterprises and individuals that caused the pollution should take the responsibility	35.6
The government should take the responsibility	59.2
Donations could not achieve the aim of reducing PM_2.5	28.2
Other reasons	9.2

Risk perception and risk attitude

To understand the status quo of risk perception, we analyzed the 10 characteristics of risk and the mean that represented the situation of risk perception as a whole, marked as RP. Table 5 listed the statistics of risk perception and risk attitude. The result showed that Options 4 and 5 of most characteristics of risk accounted for the majority of respondents. The public did not know much about PM_2.5 (mean of “familiarity” was 3.07). However, the perception of the health risk posed by PM_2.5 was very high (mean of “scope of impact,” “overall risk,” “how destructive,” and “duration of effects” ranged from 3.96 to 4.16) due to reports of relevant information and frequent hazy weather. It raised the problem that the public might amplify the risk because of being unknown about PM_2.5. Furthermore, respondents thought the health risks were difficult to control. It might be caused by the government failure in environment management. In addition, the standard deviation of “equitability” was highest. It indicated that people showed a big difference in “equitability.” Some people thought that the health risks posed by PM_2.5 had to be borne by everyone, so they gave a small option to the question of “equitability.” On the other hand, some people believed that the health risks varied for people with different health conditions.

Table 5.

Risk perception and risk attitude.

Serial number	Characteristic of risk perception and attitude	Average	SD
RP1	Familiarity	3.07	0.92
RP2	Scope of impacts	4.02	0.75
RP3	Overall risk	4.08	0.80
RP4	How destructive	3.96	0.82
RP5	Duration of effects	4.16	0.82
RP6	Controllability	3.60	0.81
RP7	Equitability	3.05	1.12
RP8	Anthropogenic	3.82	0.93
RP9	Relevance to individual	3.63	0.91
RP10	Voluntariness	3.86	1.00
RP	the mean of 10 characteristics	3.73	0.48
RA1	Fear	3.26	0.88
RA2	Anxiety	3.32	0.93
RA3	Anger	3.53	0.86
RA4	Satisfaction with the current situation	3.97	0.80
RA5	Optimism about the future	3.63	0.85

SD: standard deviation.

In addition, we used RP as representative of risk perception to analyze the differences among different characterized respondents by rank sum test. Before processing the data, we needed to run a normality test to determine the analytical method. The result of the Kolmogorov–Smirnov normal test indicated that RP did not conform to the normal distribution (p < 0.01). Therefore, we selected the nonparametric estimation methods to test whether there is a significant difference of RP among different groups. Furthermore, Mann–Whitney U-test was usually used when there are only two groups, and Kruskal–Wallis test was used when there are more than two groups. So Mann–Whitney U-test was used to analyze gender differences, and Kruskal–Wallis test was used to analyze differences among other demographic attributes. The results are presented in Table 6.

Table 6.

Variance of public risk perception.

Attribute	Groups	Percent	RP^a	p Value	Attribute	Groups	Percent	RP^a	p Value
Gender	Male	48.3	3.7	0.03**	Household disposable income	0–1000	21.2	3.63	0.00***
Gender	Female	51.7	3.75	0.03**		1000–4000	25.9	3.63
Age	18–24	41.6	3.64	0.00***		4000–9000	29.4	3.69
	25–34	37.8	3.81			9000–15,000	14.6	3.93
	35–44	13.2	3.7			>15,000	8.9	4
	45–54	5	3.85		Occupation	Student	31.8	3.67	0.11
	55–70	2.4	3.77			National units	20.9	3.75
Education	Junior middle school or below	4.2	3.42	0.00***		Private business or enterprise	38.4	3.76
	High school	10.5	3.64			Nonprofit organization	0.9	3.57
	Junior college	12.3	3.74			Others	8	3.76
	Bachelor degree	56.6	3.72
	Postgraduate or above	16.5	3.86

Note. Household disposable income unit is in CNY a month. *, **, and *** denote significance at 10%, 5%, and 1% level, respectively.

^aMean of RP.

It was apparent that public perception of the health risk posed by PM_2.5 varied greatly across the sample, which reflected underlying differences in related knowledge. The rank sum test shows that some of the differences in risk perception can be explained by the sociodemographic characteristics of the respondents. (a) Many studies have demonstrated that the risk perception of women was higher.^52–54 That might be caused by the difference of the sensitivity to health issue. Female might pay more attention to the health of themselves and their family. (b) The result showed that risk perception among ages had significant differences, with risk perception between ages the 18 and 24 the lowest.⁵⁵ In addition, in running the Kruskal–Wallis test, we excluded the 18–24 age group, and the results showed that there was not a significant difference among the other four groups (p = 0.17). In other words, the difference in the age results came entirely from the 18 to 24 age cohort. People aged between 18 and 24 years old are too young to pay attention to health, and put a lot of energy into jobs. (c) The effect of income on risk perception remains controversial and require further study.^56,57 However, the effect of education level on risk perception was confirmed in some studies.⁵⁸ The result showed that risk perception increased with increasing education level and monthly income. People with higher income could have more energy and time for living and health issues. (d) Risk perception of different occupations did not show significant differences.

Furthermore, the result showed that dissatisfaction with the current situation is the highest among the five emotional reactions.Those who indicated that they are very dissatisfied with the situation of PM_2.5 comprised 25.9%, and 49.0% indicated that they are dissatisfied. Secondly, most of the residents were not optimistic about the future; 12.1% were very pessimistic about the future. Only 0.7% indicated that they are very optimistic. Fear and anxiety are relatively lower. Just 5.9% and 8.1% indicated that they are very scared and very anxious. Although residents are dissatisfied with the current situation and not optimistic, they did not have a strong sense of fear and anxiety.

Determinants of respondent’s WTP

Respondents’ WTP might be influenced by their perception of the health risk, attitudes to the risk, and demographic attributes. Three regression models should be built for three scenarios. However, the respondents’ WTP for different scenarios might have some correlation. The Pearson correlation result shows that all the correlations were higher than 0.90 and significant at the 1% level (two-tailed). Thus, we only used one scenario in which the annual average concentration of PM_2.5 decreased 60%. Respondent’s gender was set as a categorical variable with values of 0 (male) and 1 (female). Occupation was set as dummy variable. The option of other occupations was set as reference groups. Age, educational level, income, perception of the health risk, and attitudes were included in the regression according to the actual scores given by the respondents, which scaled from 1 to 5. We used a multiple stepwise regression methods because of the strong correlation among the explanatory variables. EViews 6 was used to analyze the model.

In addition, WTP can be divided into two parts to analyze. First was the payment decision, that is, whether to pay for the reduction of the annual average concentration of PM_2.5. Second was the amount decision, that is, how much to pay for the reduction of the annual average concentration of PM_2.5. We analyzed the determinants of the two decisions. The determinants of the payment decision were analyzed by a logit model. The respondents’ WTP was transformed to 0 and 1. If a respondent’s WTP was equal to 0, the WTP remained the same, and positive WTP was transformed to 1. The determinants of the amount decision were analyzed by an ordinary least squares (OLS) model. The OLS model only included the respondents with a positive WTP, and WTP was transformed to a logarithmic scale, which might have been able to reduce the problem of non-normality and heteroscedasticity.⁵⁹ The estimated results of two models are presented in Table 7.

Table 7.

Estimates of the respondents’ WTP.

Payment decision		Amount decision
Variable	Coefficient	Variable	Coefficient
C	−0.40 (0.61)	C	−2.03*** (0.35)
Familiarity	−0.26*** (0.10)	Scope of impacts	0.22*** (0.08)
Scope of impacts	0.31*** (0.12)	Overall risk	0.14* (0.08)
Relevance to individual	0.31*** (0.11)	Duration of effects	0.12* (0.06)
Voluntariness	−0.21** (0.10)	Controllability	0.14** (0.06)
Level of fear	0.32*** (0.11)	Level of anxiety	0.13** (0.06)
Student	0.96*** (0.21)	Level of anger	0.12* (0.06)
		Level of satisfaction to thecurrent situation	0.16** (0.06)
		Gender	−0.22** (0.09)
		Income	0.64*** (0.05)
		Student	0.50*** (0.15)
		Private business or enterprise	−0.22** (0.11)

Note. WTP: willingness to pay. *, ** and *** denote significance at 10%, 5%, and 1% level, respectively.

Because we adopted a stepwise regression method, the estimated results of two models showed a great difference. The results of the payment decision model showed the four characteristics of risk perception—familiarity, scope of impacts, relevance to individual, and voluntariness—have significant effects on respondents’ payment decision. Combined with the option setting, we found that all four characteristic of risk perception have a significantly positive relation with WTP. People who know much about PM_2.5, think it has a great impact on society, and are willing to shoulder responsibility, were more willing to pay for the reduction of PM_2.5. People who strongly feared the health risk posed by PM_2.5 and students were also more willing to pay for reduction.

Compared with the results of payment decision, more factors influence the amount decision, and there were large differences in the impacts of influencing factors. Four characteristics of risk perception showed significant effect—scope of impact, overall risk, duration of effects, and controllability. Scope of impacts, overall risk, and duration of effects were observed to have a positive relationship with the amount of WTP. By contrast, controllability showed a negative effect. For respondents’ attitude, the level of anxiety and anger had significant positive effects on the amount of WTP, but the level of satisfaction with the current situation showed negative influence. For demographic attribute, men and people with higher incomes showed higher WTP. With the option of other occupations taken as reference groups, students showed higher WTP, and people who worked in private business or enterprise showed lower WTP.

Although the validity of the result of WTP had previously been tested by comparison with the results of other related studies, it was further confirmed by the relationship between WTP and income.⁴⁴ The significant influence of income on WTP was confirmed in most studies.^21,49 The results of the payment decision model did not show that income have a significant influence on the payment decision. It might be caused by widespread concern of smog issue. Most residents are becoming increasingly aware of the importance of improving air quality.⁶⁰ Therefore, income did not reveal significant influence on payment decision. However, income showed a significant influence on the amount decision (p < 0.01), which also can prove the validity of this study. People with higher income could spend more time on other aspects rather than working, so they showed higher WTP due to higher environmental awareness and fewer budgetary limitations. Furthermore, studies on the effect of the demographic attributes on WTP were substantial. The influence of gender on WTP was not completely confirmed. Some studies showed significant influence,⁶¹ while others were not able to confirm.^62,63 The amount of WTP of males was observed to be significantly higher than females in this paper. The reason might be that females are more sensitive than males about money. The influence of age and education on WTP was also not completely confirmed.^28,29,64 Although statistics showed that WTP also generally increased with age and increasing education level, the final result did not show significant effects. In addition, students showed the lowest mean of WTP and highest proportion of positive WTP. The reason might be that students belong to a group of better education but low income. Thus, the government needs to continue its efforts to improve education, especially on public environmental awareness, and to improve income levels.

The result showed that the determinants of WTP to reduce health risk posed by PM_2.5 are complex. Clearly, in addition to individual sociodemographic and economic factors, two other factors, risk perception and risk attitude, were also emphasized and showed significant effect on WTP in this paper. The effect of risk perception on WTP has been supported in many studies. Hunter et al.⁶⁵ found that the respondents’ WTP was strongly dependent on perceptions toward health risks posed by toxic cyanobacterial blooms. Zhai et al.³⁴ found that perception of flood risk may increase WTP for protection against floods, while perception of other risks may decrease it. Our study showed that people who knew more about PM_2.5 were willing to pay more, which indicates the importance of related knowledge. People who perceived greater influence of PM_2.5 on society were more willing to pay and showed higher WTP. People who perceived greater influence on themselves also were more willing to pay and those perceived a longer duration of effect and greater probability would result in a higher WTP, which are in line with Yang et al.’s⁵⁹ findings. Longer duration of effect, greater probability, and greater uncontrollability engender a strong sense of crisis about the environment. Respondents wanted to avoid the risk posed by PM_2.5 and showed higher WTP for reduction of the risk, which agreed with Pratt and Zeckhauser’s⁶⁶ findings.

In addition, some studies took emotion into consideration to analyze the effect of emotion on the decision-making. However, the result is not completely confirmed. For example, López-Mosquera and Sánchez⁶⁷ investigated visitors’ WTP to two suburban natural areas in Spain and found that the significant effect of positive emotions on WTP was confirmed only for the San Pedro Park, and was rejected for the Grajera Park. The effect of negative emotions on WTP is not significant for the San Pedro Park and the Grajera Park. Freimuth and Hovick found that worry did not increase health protective action for the high-worry and high-action risk and positively increased health protective action for the high-worry but low-action risk.⁶⁸ We found that anxiety, anger, and dissatisfaction showed a significantly positive effect on WTP. In addition, people who strongly feared the risk posed by PM_2.5 were more willing to pay, which did not agree with Yang’s finding that people who strongly feared the risk of greenhouse gasses showed lower WTP. The different results might be caused by the different research objects. Compared with the risk of greenhouse gasses, the risks posed by PM_2.5 were more easily perceived by people because of long exposure to foggy weather. Respondents feared the consequence of the risk and responded with higher WTP.

Conclusions, policy implications, and limitations of the study

This study investigated Beijing residents’ risk perception of the health risk posed by PM_2.5 and WTP for risk reduction and explored the relationship between risk perception and WTP. The result showed that the level of fear and four characteristics of risk perception—familiarity, scope of impact, relevance to individual, and voluntariness—had significant effects on respondents’ payment decisions. The level of anxiety, anger, and satisfaction about the current situation and four characteristics of risk perception—scope of impact, overall risk, duration of effects, and controllability—showed significant effects on the amount decisions. Furthermore, we found that the risk perception levels of women were higher than with those of men. Risk perception of people aged 18–24 was significantly lower than for other age groups. Risk perception increased with education levels and monthly income.

The findings of this study have significant implications for the risk management and governance policies on PM_2.5. Firstly, our study proved that efforts to reduce the health risks posed by PM_2.5 would provide significant nonmarket benefits to society. We estimate the total benefits of risk reduction to be ranged from 13.93 to 18.35 billion CNY per annum when aggregated over the local population. Clearly, such welfare estimates should be taken into explicit consideration when assessing costs and benefits of programs for the restoration of air quality.

Secondly, regarding the determinants of the WTP, risk perception has significant effects on the WTP, so interventions to promote risk awareness are especially needed. The inadequate knowledge and information may lead to the biased risk perception of the public, which means people tend to amplify the subjective risk and perform irrational behavior. It is necessary for the government to build public emergency information platform and release authoritative information about the air quality to the public timely and accurately, which can help the bolster people’s faith in their governments and guide the public correctly form the risk perception. What’s more, the analysis showed that different characterized respondents might have different risk perception. So the government should have a deep knowledge of the diverse concerns of the public and make a distinction among different groups. Further, risk perception and WTP might undergo a significant change due to special events, such as media frenzy. It is important to update public risk perception and WTP, particularly before the policy debates.

In addition, emotions such as anxiety, anger, and dissatisfaction also showed a significantly positive effect on WTP, which reveals that the understanding of environmental issues of the public largely depends on the negative emotions rather than scientific knowledge. This requires the government to devote more attention to people’s emotional reactions and guide the public to get a correct understanding of the current environmental conditions through positive information reported by media, universities, and NGOs. Such education and publicity measures will lead the public to look into the positive effects of environmental protection and gradually enhance the environmental awareness, which will contribute to realize better social benefits.

This paper also has some limitations. First, specific questions relating to the respondent’s motivation to pay for reductions PM_2.5 were excluded in the questionnaire. Second, perceptions of 31 risk characteristics were elicited in Jeffrey’s study. While only nine characteristics of the health risk posed by PM_2.5 were investigated in this study. Many other factors should be considered in future studies to obtain a comprehensive understanding of risk perception. Furthermore, a comparison of risk perception between expert and layperson in China is needed. In addition, risk perception and WTP may have a significant change due to some special events, for example, media frenzy. It’s important to update public risk perception and WTP, particularly before the real-life policy debates.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the major Project of the National Social Science Foundation of China (NSSFC): Research on compensation standard for marine ecological damage and system design (fund no. 16ZDA049).

Zhihua Xu received his PhD degrees in Population, resources and Environmental Economics at Renmin University of China, Beijing, China, in 2017. He is a Lecturer in economics at Ocean University of China, Qingdao, China. His main teaching course are urban economics, sampling method and ecological economics. His research areas include environmental impact assessment, environmental behavior and payment for ecosystem services.

Jingzhu Shan is a PhD student at School Of Economics , Ocean university of China, Qingdao, China. Her research interests include payment for ecosystem services and environmental impact assessment.

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The effect of risk perception on willingness to pay for reductions in the health risks posed by particulate matter 2.5: A case study of Beijing,China

Abstract

Keywords

Introduction

Literature review

Estimating the benefits of reducing the health risk posed by PM2.5

The role of risk perception and attitude

Methods and materials

Results and discussion

Date reliability

Respondent’s WTP

Risk perception and risk attitude

Determinants of respondent’s WTP

Conclusions, policy implications, and limitations of the study

Footnotes

Declaration of conflicting interests

Funding

References

Estimating the benefits of reducing the health risk posed by PM_2.5