# Perceived Economic Benefits of Reducing the Risk of Mortality from Air Pollution in France

Caroline Orset, Marco Monnier

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## To cite this version:

Caroline Orset, Marco Monnier. Perceived Economic Benefits of Reducing the Risk of Mortality from Air Pollution in France. Journal of Environmental Assessment Policy and Management, 2025, 27 (2), pp.2550002. 10.1142/S1464333225500024. hal-05097551

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# Perceived Economic Benefits of Reducing the Risk of Mortality from Air Pollution in France

Caroline Orset<fnref n="1" /> and Marco Monnier<fnref n="2" />

## Abstract

France must meet the daily and annual air quality thresholds established by the World Health Organization. Consequently, there is an urgent need to reinforce public policies to mitigate the risk of mortality associated with air pollution. The concept of the value of a statistical life (VSL) serves as a decision-support tool in the formulation of such policies. The VSL is derived from a contingent valuation survey, which assesses individuals' willingness to pay (WTP) to reduce the risk of death linked to air pollution, using a representative sample of the French population. This study's findings indicate that various factors shape the willingness to pay for risk reduction. These include perceived characteristics, such as the temporality and magnitude of pollution-related damages, the level of awareness regarding these harms, and the degree of individual exposure. Personal and behavioural factors, including age, income, place of residence, and the adoption of preventive measures, also play a significant role. The estimated average VSL ranges from €0.085 million to €0.158 million. These results highlight, first and foremost, a widespread public acceptance of measures to mitigate this risk as citizens demonstrate a readiness to contribute financially. Secondly, they emphasise the extent to which the French population recognises the economic and societal benefits of public health investments to reduce mortality caused by air pollution. The study suggests stricter air quality standards and economic incentives, such as pollution taxes and subsidies for clean technologies, to reduce mortality risks from air pollution and align government expenditures with societal priorities for health and environmental conservation.

Keywords: air pollution; contingent evaluation; public policy; value of statistical life; willingness to pay.

<footnote n="1">¹ Paris-Saclay Applied Economics (PSAE), AgroParistech, INRAe, Université Paris-Saclay, caroline.orset@agroparistech.fr, 0033189101313, Campus Agro Paris-Saclay, 22 place de l'Agronomie, 91120 Palaiseau, France. ORCID: 0000-0002-3964-5717). Corresponding author.</footnote>

<footnote n="2">² BETA, CNRS, Université de Strasbourg, Université de Lorraine, INRAE - marco.monnier@unistra.fr, 61 avenue de la Forêt-Noire, 67000 Strasbourg, France</footnote>
Introduction

Air pollution represents a significant global environmental and public health issue. The principal pollutants include fine particulate matter with diameters of less than 2.5 micrometres (PM<sub>2.5</sub>) and 10 micrometres (PM<sub>10</sub>), ozone (O<sub>3</sub>), nitrogen oxides (NO<sub>x</sub>), carbon monoxide (CO), and sulphur dioxide (SO<sub>2</sub>). Extensive international research has documented the adverse health effects of air pollution (Rasoulinezhad et al., 2020). The World Health Organization (WHO) has established indicative daily and annual thresholds to mitigate air pollution's health impacts. Despite improvements in air quality over the past two decades, France has yet to meet these targets. According to IQAir data, the average annual exposure of the French population to PM<sub>2.5</sub> in 2022 was estimated at 11.5 micrograms per cubic metre, surpassing the WHO recommendation of 5 micrograms per cubic metre. Furthermore, the national air quality forecasting platform, PREV'AIR, which operates within the French air quality monitoring and management framework, frequently reports daily exposure levels exceeding 25 micrograms per cubic metre, particularly in the Île-de-France region. In light of this ongoing challenge, France must adopt stricter policies to reduce the mortality risks associated with air pollution. However, policymakers often face the difficult task of prioritising interventions under budgetary constraints. In this context, the Value of a Statistical Life (VSL) is a valuable tool for evaluating the benefits of preserving human lives and guiding public policy decisions. It is a monetary estimate of the value people place on reducing the risk of death. It represents the amount society is willing to pay<sup>3</sup> to reduce the risk of one additional death in a population. This measure is particularly relevant to air pollution, which indiscriminately affects the entire population.

Notably, no studies have yet been published in France that estimate the VSL specifically about air pollution. Existing research has instead employed alternative monetary approaches, such as valuing one year of life to estimate the financial worth of life extension. For instance, Desaigues et al. (2011) calculate the value of one year of life to range between €25,000 and €100,000, while Chanel and Luchini (2014) estimate it at €142,000. While both approaches provide complementary insights into evaluating health-related public policies, they differ conceptually. The value of one year of life emphasises the monetary worth of extending life expectancy. In contrast, the VSL reflects the societal willingness to invest in reducing mortality risks within a given population. Our study contributes to this discourse by offering an estimate of the VSL linked explicitly to the effects of air pollution. This framework aims to enhance the evidence base for informed decision-making and prioritising public health interventions in France. The VSL represents the marginal substitution rate between income

<footnote n="3">³ In economics, willingness to pay (WTP) refers to the maximum amount of money an individual is willing to spend to obtain a good or service, or to avoid something undesirable, such as a risk or a loss. It reflects the value that a person places on a particular benefit or outcome.</footnote>
and mortality risk, capturing an individual's willingness to pay (WTP) to reduce that risk. In economics, VSL is commonly estimated using the WTP approach, typically derived from stated preference surveys. This method is favoured<fnref n="4" /> when revealed preference data—such as market-based behaviour—are unavailable to inform the analysis. To estimate WTP, survey questions can be designed in an open-ended or binary format, each approach having distinct advantages and limitations. Open-ended questions give respondents greater flexibility but may lead to confusion due to the variability of potential answers. In contrast, binary questions simplify the decision-making process by limiting choices. To elicit respondents' WTP for annual health checks to reduce mortality risks from air pollution, the study adopted a combined dichotomous-choice contingent valuation method formulated by Bishop and Heberlein (1979) and an open-ended questioning method. This approach seeks to improve respondent comprehension and minimise confusion.

This study contributes to the literature on VSL estimation by focusing on the health risks posed by air pollution, a critical public health challenge (Alberini et al., 2006; Biausque, 2012; Boiteux & Baumstark, 2001; Hammitt & Zhou, 2006; Mahmud et al., 2013). While numerous studies have estimated VSL since the 1970s, most have concentrated on labour market risks or road traffic. A context-specific VSL tailored to air pollution allows for a more precise allocation of resources by comparing the expected benefits of various environmental policies. The results of our study, which focused on France, yielded an average VSL of €0.085 million and €0.158 million. These findings emphasise individuals' significant value in managing health risks associated with air pollution, reinforcing the priority of public policies to reduce these risks.

Additionally, this study enhances the body of research on the determinants of WTP and VSL (Chilton et al., 2002; Ghanem et al., 2023; Jones-Lee & Loomes, 1995; Subramanian & Cropper, 2000; Vassanadumrongdee & Matsuoka, 2005). Using econometric models—ordinary least squares (OLS) regression and the Tobit model—we examined the relationship between WTP (and VSL) and various personal, behavioural, and perceptual characteristics across differing levels of proposed mortality risk reduction (30 per 100,000 and 60 per 100,000 annually). Our analysis reveals that WTP and VSL vary according to the scale of risk reduction and perceptual factors, including respondents' understanding of the temporality and magnitude of air pollution's effects, their awareness of the issue, and their level of exposure. Moreover, personal and behavioural characteristics, such as age, income, place of residence, and the adoption of preventive measures, significantly influence WTP

<footnote n="4">⁴ Discrete choice models (DCMs) permit the indirect inference of WTP through the observation or modelling of choices pertaining to the attributes of a given good or service. This method is the preferred approach when it is essential to understand the trade-offs between attributes or the relative importance of characteristics. The objective of this study was to ascertain the monetary value of a particular policy change. The WTP method is more appropriate for this purpose than DCMs, as it enables the direct estimation of the monetary value that individuals ascribe to the policy.</footnote>
and VSL. These results demonstrate that individuals' responses to risks are heterogeneous, enabling targeted public interventions that align with social priorities.

The structure of this article is as follows: The first section reviews the literature on WTP and VSL about air pollution reduction. The second section presents the theoretical framework. The third section elaborates on the contingent valuation methodology, survey design, and sample characteristics. The fourth section presents the results of the WTP questionnaire, as well as the estimates and the determinants of WTP derived from the econometric models (OLS and Tobit). This section also calculates VSL values and analyses the influence of the determinants on VSL. The fifth section discusses the results. The final section concludes with the policy implications of our findings.

# 1 Literature review

Recent research by Ghanem et al. (2023) demonstrated that Egyptian households were willing to support policies to halve air pollution financially. The study, conducted in Greater Cairo and Alexandria, surveyed 1,051 households and found that 73% favoured an air quality improvement strategy. The estimated average WTP for a 50% reduction in PM<sub>2.5</sub> pollution ranged between 12.03 and 17.2 euros per month.<fnref n="5" /> Similar to earlier studies by Jones-Lee and Loomes (1995), Subramanian and Cropper (2000), Chilton et al. (2002), and Vassanadumrongdee and Matsuoka (2005), this research highlights the influence of socio-economic factors (age, income, employment status, presence of children, and geographic location) and health-related considerations (health status, risk perception, protective measures, and information and awareness) on WTP for reducing mortality risks from air pollution. Specifically, Ghanem et al. (2023) found that higher-income individuals, those living in more polluted areas, and those informed about air pollution's health impacts were more likely to pay for improvements in air quality. Our study aims to estimate the WTP in France to reduce the risk of mortality from air pollution, thereby gauging public interest in addressing this pressing health issue. It also examines the impact of previously identified influencing factors while introducing three novel considerations: long-term damage caused by air pollution and the intergenerational effects of pollution reduction for current and future generations. By incorporating these factors, we aim to assess whether individuals internalise the externalities associated with current pollution that produces delayed effects. Additionally, we explore solidarity and intergenerational priorities by evaluating whether current generations are willing to pay to protect future generations and to what extent individuals prioritise immediate personal benefits (their health) over long-term benefits for

<footnote n="5">⁵ We convert all monetary values into euros in 2019.</footnote>
others. This approach provides critical insights for designing equitable and sustainable environmental policies that reduce air pollution-related mortality risks.

Empirical studies estimating VSL in the context of air pollution remain relatively rare. Notable examples include Vassanadumrongdee and Matsuoka (2005), Hammitt and Zhou (2006), and Ghanem et al. (2023), which respectively focused on Bangkok, China, and Egypt. Vassanadumrongdee and Matsuoka (2005) estimated a VSL between €1.60 million to €1.97 million, Hammitt and Zhou (2006) reported a range of €0.01 million and €0.24 million, while Ghanem et al. (2023) identified a range of €0.22 million to €0.40 million. In France, existing VSL studies have not explicitly addressed air pollution. For instance, using a compensated human capital approach, the Boiteux and Baumstark report (2001) calculated a VSL of €0.79 million for road risks. Alberini et al. (2006) estimated VSL across France, the United Kingdom, and Italy without specifying the sources of risk, with averages ranging from €1.25 million to €2.09 million for France, €0.91 million to €1.36 million for the United Kingdom and €1.36 million to €1.69 million for Italy. Biausque's (2012) meta-analysis of OECD countries proposed a VSL range of €2.18 million to €4.35 million for traffic, health, and environmental risks, with a reference VSL of €3 million established for OECD countries, including France. The variation in VSL estimates across studies of air pollution-related mortality risks can be attributed to differences in methodologies, socio-economic contexts, and risk perceptions across countries. By estimating the VSL in France, our study critically compares VSL values reported in other developed and developing nations. This enables a nuanced understanding of cross-country differences, facilitating the development and evaluation of tailored air pollution control policies that account for these disparities.

## 2 Theoretical framework

Willingness to pay (WTP) is based primarily on utility theory (von Neumann-Morgenstern, 1944), which suggests that individuals seek to maximise their satisfaction (or utility). WTP reflects the subjective value that an individual attributes to a good or an improvement (for example, a reduction in risk or a better quality of life) based on the utility they derive from it. WTP is often used to evaluate risk or uncertainty choices, such as health. Willingness to pay is crucial in assessing public goods, such as air quality. Since these goods are not traded on the market, WTP is used to measure their economic value, which can be used to justify economic and political decisions.

According to the literature (Chilton et al., 2002; Ghanem et al., 2023; Jones-Lee & Loomes, 1995; Subramanian & Cropper, 2000; Vassanadumrongdee & Matsuoka, 2005), the WTP is influenced by
several interrelated variables: economic factors, risk perception, behavioural factors and socio-demographic characteristics. Based on these works, we hypothesise that:

* **H1:** Perceived severity of air pollution positively impacts WTP.

* **H2:** Higher awareness of air pollution risks leads to a greater perceived risk reduction benefit, increasing WTP.

* **H3:** Awareness of the health impacts of air pollution positively impacts WTP.

* **H4:** Age negatively affects WTP due to shorter perceived life expectancy.

* **H5:** Income positively correlates with WTP for mortality risk reduction due to air pollution.

* **H6:** Urban residents may have higher WTP due to exposure levels.

* **H7:** Those with pre-existing conditions may show higher WTP due to direct vulnerability. In addition, our intuition leads us to propose two other assumptions:

* **H8:** Lower discount rates (preference for future benefits) increase WTP.

* **H9:** Altruism leads to higher WTP for societal risk reductions.

Employing both OLS and Tobit models to analyse the WTP to reduce mortality risk due to air pollution allows for a more comprehensive understanding, addressing different aspects of the dataset and potential biases. Indeed, OLS is a widely used econometric method for estimating linear relationships between dependent and independent variables. In addition, Tobit models are designed to handle censored dependent variables. Some respondents may report zero WTP in WTP studies, either because they are unwilling to pay or because of budget constraints, making the data left-censored.

Either a sample comprising N observations and K explanatory variables, the OLS model can be written as follows:

$$Y = \alpha + Z\beta + u, \quad (1)$$

where Y is the dependent variable, represented as a column vector of dimension N, α is the model constant, also as a column vector of dimension N, Z is the matrix of explanatory variables of dimension N×K, β is the vector of coefficients associated with explanatory variables of dimension K, and u is the vector of residues (error terms) of dimension N.

However, to account for null responses (i.e., a WTP equal to zero), the WTP has been left-censored to zero, which justifies using a Tobit model. This model examines the impact of null responses on VSL estimates. The Tobit model is specified as follows:

$$Z_i^* = W_i'\theta + v_i, \text{ with } Z_i = \begin{cases} Z_i^* & \text{if } Z_i^* > 0; \\ 0 & \text{if } Z_i^* \le 0. \end{cases} \quad (2)$$
The index i = 1, ..., N corresponds to individuals, $Z_i^*$ is an unobserved latent variable, $Z_i$ is the observed dependent variable, $W_i$ is a vector of the explanatory variables of dimension K, $\theta$ is a vector of coefficients of dimension K, and $v_i$ is the error term, assumed to follow a normal distribution $v_i \sim N(0, \sigma^2)$.

The Statistical Value of Life (VSL) is based on the theory of decision-making under uncertainty (Kahneman & Tversky, 1979), which examines how individuals evaluate choices where outcomes are uncertain. The VSL is, by definition, the marginal substitution rate between wealth risk and mortality risk. As it would be unproductive to ask respondents about their marginal substitution rate, we asked about their willingness to pay (WTP) for a marginal reduction in mortality risk. The VSL is then calculated as the ratio between the maximum amount a person is willing to pay for a risk reduction that maintains their utility and the level of risk reduction:

$$VSL = \frac{WTP}{\Delta p} \text{ with } \Delta p \text{ indicating the level of risk reduction.} \quad (3)$$

# 3 Methodology: Contingent evaluation model

## 3.1 Survey design

This article aims to estimate the statistical value of a life (VSL) associated with air pollution in France and identify the key determinants influencing this estimate. The VSL represents the monetary value attributed to a reduction in mortality risk, effectively preventing a statistical death. Given the absence of specific data on individual mitigation measures to address mortality risks linked to air pollution, a questionnaire survey was conducted to bridge this data gap.

The survey began with a brief introductory statement: «This study is being conducted by university researchers for purely academic purposes, with no connection to marketing research. It focuses on the risks of air pollution in France and its consequences for human health." This introduction aimed to minimise potential framing and anchoring biases that could otherwise influence respondents' answers.<fnref n="6" /> The survey was structured into eight distinct stages, each addressing various aspects of health, risk perception, and mortality risks related to air pollution.

<footnote n="6">⁶ The framing effect is a cognitive bias in which people choose options depending on whether the options are presented with a positive (gain) or harmful (loss) connotation. Anchoring bias occurs when people rely too much on pre-existing information or the first information they find when making decisions.</footnote>
In the **initial stage**, participants were asked about their general health status and medical history, explicitly focusing on air pollution-related pathologies, including asthma, chronic cough, cancer, hypertension, and cardiovascular diseases. This approach aligns with methodological recommendations by Hammitt and Zhou (2006) and Vassanadumrongdee and Matsuoka (2005). Additionally, respondents provided information on their overall health (good or poor), frequency of annual health check-ups, and tobacco consumption habits.

The **second stage** aimed to identify the risks participants deemed most concerning. Respondents ranked eight risks according to their perceived severity: terrorism, road accidents, food hazards, climate change, air pollution, addictive behaviours (alcohol, drugs, and smoking), cancer, and AIDS. These risks were drawn from the 2018 Barometer of the Institut de Radioprotection et de Sûreté Nucléaire (IRSN), which highlights prevalent concerns among the French public. The objective was to gauge participants' attitudes toward various risks and differentiate the responses of those who ranked air pollution as one of their top three concerns from those who did not.

In the **third stage**, respondents answered binary questions (agree/disagree) regarding their perceptions of risk characteristics, inspired by Vassanadumrongdee and Matsuoka (2005). These questions examined the perceived severity of air pollution risks, personal and public exposure, the immediacy of pollution effects, and participants' knowledge of air pollution's causes and impacts.

At the beginning of the **fourth stage**, respondents were presented with detailed information on the concepts of risk, mortality, and air pollution's health impacts. They were informed that the mortality rate attributable to air pollution in France is approximately 80 per 100,000 inhabitants, equating to 80 deaths annually per 100,000 people due to air pollution. To enhance understanding, a comparative summary of various mortality rates in France was provided (as summarised in Table 1). This contextual information was intended to familiarise respondents with mortality risks, particularly those linked to air pollution. It ensures that participants have sufficient background knowledge and clarity to respond meaningfully to subsequent questions.

<table><caption>Table 1: Cause and mortality rate. Source: Santé publique France.</caption><thead><tr><td>Cause of mortality</td><td>Mortality rate</td></tr></thead><tbody><tr><td>Tobacco</td><td>100 per 100,000</td></tr><tr><td>Alcohol</td><td>51 per 100,000</td></tr><tr><td>Suicide</td><td>21 per 100,000</td></tr><tr><td>Car accident</td><td>8 per 100,000</td></tr><tr><td>Brain tumour</td><td>5 per 100,000</td></tr><tr><td>Murder</td><td>1 per 100,000</td></tr></tbody></table>
To evaluate respondents' willingness to pay (WTP) for reducing mortality risks from air pollution, they were presented with a hypothetical scenario: "Suppose there is a technological advance in health check-up, accompanied by a campaign promoting annual health check-up to reduce the risk of premature mortality associated with air pollution. This annual health check-up would effectively detect any deficiencies in your respiratory system, with no side effects and no pain during the process." Participants were encouraged to carefully consider their responses within the context of their personal financial constraints, aiming to minimise the hypothetical bias often associated with stated preference surveys. The survey introduced two distinct options for annual health check-ups, each offering a reduction in the risk of mortality attributable to air pollution: Health Check-up A, which would reduce the risk of death from 80 to $\tau_A$ per 100,000 inhabitants; Health Check-up B, which would reduce the risk of death from 80 to $\tau_B$ per 100,000 inhabitants. A scaling diagram (see Figure 1) was included in the survey to enhance participants' understanding of the proposed risk reductions. This diagram provided a clear and intuitive visual representation of the varying levels of risk reduction associated with Health Checks up A and B. Visual aids of this kind are crucial in stated preference surveys to ensure respondents grasp abstract concepts such as mortality risk changes. This structured approach facilitated a more informed and realistic assessment of WTP, allowing us to obtain meaningful data on how individuals value interventions to mitigate mortality risks from air pollution.

<figure><img src="image_3.png" /><figcaption>Figure 1: Scale diagram representing the risk reduction of 30 per 100,000 people.</figcaption></figure>

Source: Santé publique France.

The study adopted a combined dichotomous-choice contingent valuation and open-ended questioning method to elicit respondents' willingness to pay (WTP) for annual health check-ups. This two-step approach enabled participants to understand the issue better and provide more precise
responses. Initially, the single dichotomous-choice method was employed. Respondents were asked
whether they would pay a specific amount for the proposed health check-up. For participants who
accepted the suggested amount, their WTP was assumed to be greater than or equal to the specific
amount, with the upper limit set as infinite. For those who declined, their WTP was presumed to be
less than the specific amount, with the lower limit set at zero. Following the dichotomous-choice
question, an open-ended query asked respondents to specify their maximum WTP to reduce
mortality risk. The amount stated had to align with the initial dichotomous-choice response: If the
proposed amount was accepted, the WTP needed to be greater than or equal to the specific amount.
If rejected, the WTP had to be less than the specific amount. To explore the sensitivity of WTP to the
extent of risk reduction, respondents were presented with two separate scenarios for annual
mortality risk reductions: 30 per 100,000 inhabitants and 60 per 100,000 inhabitants. The
corresponding health check-ups (A and B) aimed to reduce mortality risks from 80 to τA and τB,
respectively. Three distinct bid values X —€25, €50, and €100—were tested to mitigate anchoring
biases. These amounts were chosen based on real-world reference costs for medical consultations in
France: A consultation with a social security-approved doctor costs €25; a consultation with a non-
approved doctor costs €50; and a consultation with a specialist costs €100.

In the **fifth stage**, respondents' risk preferences were evaluated using the methodology developed by Holt and Laury (2002). Participants were presented with a series of binary choices between two hypothetical lotteries: Lottery A: Exhibiting lower variability in pay-outs, representing a less risky option; Lottery B: Featuring higher variability in pay-outs, representing a riskier choice. This task was repeated ten times, with no monetary stakes for participation. The number of times participants selected Lottery A served as an indicator of their risk aversion. Consistent with the Holt and Laury (2002) framework, individuals who consistently opted for Lottery A were classified as more risk-averse. Table 2 outlines the architecture of the lottery game employed to classify respondents' levels of risk aversion. Risk-neutral respondents typically select Lottery A four times. Highly risk-averse individuals are expected to choose Lottery A at least eight times. This classification provides a nuanced understanding of risk attitudes, enabling a calculation of the relative range of risk aversion observed across the sample. This approach enhances the precision of the assessment, yielding robust insights into respondents' behavioural responses to uncertainty.
<table><caption>Table 2: Decisions on the ten-pair lottery at risk and classification of risk aversion based on lottery choices. Source: Holt and Laury (2002).</caption><thead><tr><th rowspan="2">Decision</th><th colspan="4">Lottery A</th><th colspan="4">Lottery B</th></tr><tr><th>Chance to win</th><th>Gains</th><th>Chance to win</th><th>Gains</th><th>Chance to win</th><th>Gains</th><th>Chance to win</th><th>Gains</th></tr></thead><tbody><tr><td>1</td><td>10%</td><td>€7</td><td>90%</td><td>€5</td><td>10%</td><td>€13</td><td>90%</td><td>€0</td></tr><tr><td>2</td><td>20%</td><td>€7</td><td>80%</td><td>€5</td><td>20%</td><td>€13</td><td>80%</td><td>€0</td></tr><tr><td>3</td><td>30%</td><td>€7</td><td>70%</td><td>€5</td><td>30%</td><td>€13</td><td>70%</td><td>€0</td></tr><tr><td>4</td><td>40%</td><td>€7</td><td>60%</td><td>€5</td><td>40%</td><td>€13</td><td>60%</td><td>€0</td></tr><tr><td>5</td><td>50%</td><td>€7</td><td>50%</td><td>€5</td><td>50%</td><td>€13</td><td>50%</td><td>€0</td></tr><tr><td>6</td><td>60%</td><td>€7</td><td>40%</td><td>€5</td><td>60%</td><td>€13</td><td>40%</td><td>€0</td></tr><tr><td>7</td><td>70%</td><td>€7</td><td>30%</td><td>€5</td><td>70%</td><td>€13</td><td>30%</td><td>€0</td></tr><tr><td>8</td><td>80%</td><td>€7</td><td>20%</td><td>€5</td><td>80%</td><td>€13</td><td>20%</td><td>€0</td></tr><tr><td>9</td><td>90%</td><td>€7</td><td>10%</td><td>€5</td><td>90%</td><td>€13</td><td>10%</td><td>€0</td></tr><tr><td>10</td><td>100%</td><td>€7</td><td>0%</td><td>€5</td><td>100%</td><td>€13</td><td>0%</td><td>€0</td></tr></tbody></table>

<table><thead><tr><th>Number of Safe Choices</th><th>Range of Relative Risk Aversion for<br>U(x) = <sup>x<sup>1-r</sup></sup>&frasl;<sub>1-r</sub></th><th>Risk preference classification</th></tr></thead><tbody><tr><td>0-1</td><td>r &lt; -0.95</td><td>highly risk-loving</td></tr><tr><td>2</td><td>-0.95 &lt; r &lt; -0.49</td><td>very risk-loving</td></tr><tr><td>3</td><td>-0.49 &lt; r &lt; -0.15</td><td>risk-loving</td></tr><tr><td>4</td><td>-0.15 &lt; r &lt; 0.15</td><td>risk-neutral</td></tr><tr><td>5</td><td>0.15 &lt; r &lt; 0.41</td><td>slightly risk averse</td></tr><tr><td>6</td><td>0.41 &lt; r &lt; 0.68</td><td>risk averse</td></tr><tr><td>7</td><td>0.68 &lt; r &lt; 0.97</td><td>very risk averse</td></tr><tr><td>8</td><td>0.97 &lt; r &lt; 1.37</td><td>highly risk averse</td></tr><tr><td>9-10</td><td>1.37 &lt; r</td><td>stay in bed</td></tr></tbody></table>

The **sixth stage** of the survey involved a series of binary questions to capture respondents' attitudes towards air quality and intergenerational altruism. Participants were invited to respond with a binary choice ("yes" or "no") to questions addressing two main themes: perception of air quality, which included their evaluation of current air quality, their belief in the effectiveness of individual efforts to mitigate air pollution, and their perception of public authorities' investment in air pollution research; and intergenerational altruism, which examined their willingness to make personal efforts to reduce pollution for their own benefit, their commitment to protecting the well-being of loved ones, and their dedication to preserving the environment for both present and future generations. Focusing on perception and altruistic tendencies allows for a comprehensive understanding of respondents' motivations and priorities, particularly in addressing environmental and public health challenges.

Before advancing to the final stage, the seventh stage implemented a verification mechanism designed to ensure that respondents comprehensively understood the core concepts addressed in the questionnaire, including fundamental notions such as risk and mortality. Participants were required to confirm their comprehension of these concepts, reducing the likelihood of arbitrary or
biased responses from misunderstanding. This precautionary measure was intended to enhance the reliability and validity of the data collected.

In the **eighth** and final **stage**, the survey collected information on respondents' socio-economic characteristics, including gender, age, income levels, and geographical location.

## 3.2 Survey implementation and sampling

The survey was administered online via Panelabs, a French company specialising in marketing consultancy. Conducted during June and July 2019, the survey targeted a sample of 322 French residents. A pilot test was conducted before the broader questionnaire dissemination to ensure its clarity and simplicity, which are critical factors in maximizing participant engagement and response rates. From the initial sample, seven respondents indicated difficulty in understanding the questionnaire. These participants were excluded from the analysis to ensure data accuracy and validity. Consequently, the final dataset comprised 315 respondents.

Table 3 summarises the socio-economic characteristics of the final sample. To assess its representativeness, essential variables, such as gender, age, income, and geographical distribution, were compared with corresponding data from INSEE (National Institute of Statistics and Economic Studies) for 2016. This comparison ensures the sample reliably reflects the broader French population, allowing for more generalisable findings. By carefully vetting respondents and ensuring representativeness, this methodology supports the robustness of the results. It enhances their relevance for informing policy decisions about France's air pollution and public health.
<table><caption>Table 3: Comparison of panel and INSEE 2016 data and socio-economic variables. 315 respondents.</caption><thead><tr><th>Description</th><th>Survey</th><th>INSEE 2016</th><th>Chi² test (p-value)</th></tr></thead><tbody><tr><td><em>Gender</em></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>53.3</td><td>51.6</td><td rowspan="2">0.539</td></tr><tr><td>Male</td><td>46.7</td><td>48.4</td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td></tr><tr><td>18-24</td><td>29.2</td><td>30.0</td><td rowspan="3">0.797</td></tr><tr><td>25-59</td><td>46.7</td><td>44.8</td></tr><tr><td>60 or more</td><td>24.1</td><td>25.2</td></tr><tr><td><em>Income</em></td><td></td><td></td><td></td></tr><tr><td>Less than 1000</td><td>10.8</td><td>11.9</td><td rowspan="5">0.917</td></tr><tr><td>1000-1499</td><td>20.0</td><td>20.4</td></tr><tr><td>1500-2499</td><td>19.7</td><td>20.4</td></tr><tr><td>2500-4499</td><td>33.0</td><td>32.3</td></tr><tr><td>4500 and more</td><td>16.5</td><td>15.0</td></tr><tr><td><em>Geographical area</em></td><td></td><td></td><td></td></tr><tr><td>Ile de France</td><td>20.0</td><td>18.8</td><td rowspan="3">0.248</td></tr><tr><td>North</td><td>45.7</td><td>42.3</td></tr><tr><td>South</td><td>34.3</td><td>38.9</td></tr></tbody></table>

A chi-square test was employed to evaluate the representativeness of the survey sample compared to the French population using INSEE 2016 data. The results, detailed in Table 3, indicate a notable similarity between the socio-demographic characteristics of the survey sample and the INSEE data. Variables such as gender, age, income, and geographical distribution closely matched, demonstrating that the sample represents the French population.

Respondents were randomly assigned an initial price (bid value) to test for potential order and size effects. The values tested were $X \in \{\€25, \€50, \€100\}$. In addition, participants were presented with mortality risk reductions associated with health checks-up A and B: $\tau_A, \tau_B \in \{20,50\}$ per 100,000, representing annual mortality risk reductions of 60 per 100,000 and 30 per 100,000, respectively. These values were selected based on the established methodologies of Chanel and Luchini (2014) and Krupnick et al. (2002) to ensure their appropriateness for analysis. The rationale for choosing these levels lies in their clarity and relevance, avoiding confusion that might arise with smaller reductions, as previously noted by Pidgeon and Beattie (1998) and Hammitt and Graham (1999). The survey sample was divided into six sub-groups based on combinations of price levels and mortality risk reductions. Table 4 provides a comprehensive summary of these sub-groups.
<table><caption>Table 4: Sub-samples based on initial bid value and mortality risk after receiving health annual check-ups, A and B. 315 respondents.</caption><thead><tr><th>Sub-sample</th><th>τ<sub>A</sub></th><th>τ<sub>B</sub></th><th>X</th><th>Number of respondents</th></tr></thead><tbody><tr><td>1</td><td>50</td><td>20</td><td>€25</td><td>52</td></tr><tr><td>2</td><td>50</td><td>20</td><td>€50</td><td>53</td></tr><tr><td>3</td><td>50</td><td>20</td><td>€100</td><td>49</td></tr><tr><td>4</td><td>20</td><td>50</td><td>€25</td><td>53</td></tr><tr><td>5</td><td>20</td><td>50</td><td>€50</td><td>52</td></tr><tr><td>6</td><td>20</td><td>50</td><td>€100</td><td>56</td></tr></tbody></table>

# 4 Results

## 4.1 WTP stated according to mortality risk reduction

Table 5 presents the distribution of respondents by their stated WTP, as well as the median, mean and standard error of WTP responses.

<table><caption>Table 5: WTP responses. a) Distribution of 315 respondents. b) Median, average and standard error.</caption><thead><tr><th rowspan="2">WTP in euro</th><th colspan="2">30 per 100,000 annual risk reduction</th><th colspan="2">60 per 100,000 annual risk reduction</th></tr><tr><th>Frequency</th><th>Percentage (%)</th><th>Frequency</th><th>Percentage (%)</th></tr></thead><tbody><tr><td>0</td><td>42</td><td>13.33</td><td>33</td><td>10.47</td></tr><tr><td>1-24</td><td>47</td><td>14.92</td><td>41</td><td>13.02</td></tr><tr><td>25-49</td><td>83</td><td>26.35</td><td>79</td><td>25.08</td></tr><tr><td>50-99</td><td>92</td><td>29.21</td><td>99</td><td>31.43</td></tr><tr><td>100 et plus</td><td>51</td><td>16.19</td><td>63</td><td>20.00</td></tr></tbody></table>

(a)

<table><thead><tr><th></th><th>30 per 100,000 annual risk reduction</th><th>60 per 100,000 annual risk reduction</th></tr></thead><tbody><tr><td>Median</td><td>35.00€</td><td>50.00€</td></tr><tr><td>Mean</td><td>47.47€</td><td>53.75€</td></tr><tr><td>Standard error</td><td>45.30€</td><td>54.83€</td></tr></tbody></table>

(b)

A total of 42 respondents (13.33%) and 33 respondents (10.47%) indicated a reluctance to pay, which was considered to be a true WTP zero. These responses were not classified as protest responses, as they involved a stated zero price, despite the respondents admitting that no mutual insurance or social security could cover the cost in question. The distribution of WTP within the sample shows an asymmetry, with a median lower than the average of 10 euros. In addition, it is observed that the
greater the reduction in mortality risk, the higher the maximum amount respondents are willing to pay, a result consistent with theoretical predictions (Drèze, 1962; Jones-Lee, 1974; Weinstein et al., 1980) and empirical findings (Vassanadumrongdee & Matsuoka, 2005) in the literature. Hultkrantz and Svensson (2012) posit that, in theory, the willingness to pay (WTP) is expected to grow almost proportionally to the extent of the risk reduction. However, Hammitt and Graham (1999) do not empirically validate this hypothesis. Our results also demonstrate that the WTP is not proportional to risk reduction and, like the conclusions of Corso et al. (2001), has limited sensitivity.

## 4.2 Factors influencing the stated WTP

First, we used the Ordinary Least Square (OLS) regression model (see equation 1). However, to account for null responses (i.e., a WTP equal to zero), the WTP has been left-censored to zero, which justifies using a Tobit model (see equation 2). In both regression models (OLS and Tobit), the dependent variable is the WTP stated by respondents based on mortality risk reductions, and the included explanatory variables are detailed in Table 6. The explanatory variables *Bid value 100, 4500 and more* and *South* were taken as reference variables.
<table><caption>Table 6: Independent variables. 315 respondents.</caption><thead><tr><th>Variables</th><th>Definition</th><th>Frequency for value is equal to 1 in (%) or Average (in years)</th></tr></thead><tbody><tr><td colspan="3"><strong>Perceived characteristics</strong></td></tr><tr><td>Controllability</td><td>Dummy variable is equal to 1 if the respondent believes that his actions can prevent him from being negatively affected by air pollution, and 0 otherwise.</td><td>74.9</td></tr><tr><td>Expert knowledge</td><td>Dummy variable is 1 if the respondent states that there is sufficient research on the causes and consequences of air pollution, and 0 in other cases.</td><td>31.8</td></tr><tr><td>Immediacy</td><td>Dummy variable is equal to 1 if the respondent postulates that air pollution can cause tangible damage in the immediate future, and 0 in other cases.</td><td>78.1</td></tr><tr><td>Long-term</td><td>Dummy variable is equal to 1 if the respondent postulates that air pollution can cause long-term damage, and 0 in other cases.</td><td>96.6</td></tr><tr><td>Personal knowledge</td><td>Dummy variable is 1 if the respondent knows the main causes of air pollution, and 0 in other cases.</td><td>81</td></tr><tr><td>Personal exposure</td><td>Dummy variable is equal to 1 if the respondent considers air pollution a potential risk to themselves and their family, and 0 in other cases.</td><td>95.6</td></tr><tr><td>Poor air quality perception</td><td>Dummy variable is equal to 1 if the respondent perceives that around him the air quality is bad, and 0 otherwise.</td><td>45.7</td></tr><tr><td>Public exposure</td><td>Dummy variable is equal to 1 if the respondent postulates that air pollution may have a negative impact on the general public, and 0 in other cases.</td><td>96.2</td></tr><tr><td>Severity</td><td>Dummy variable is equal to 1 if the respondent thinks that air pollution-related diseases can be fatal, and 0 if not.</td><td>86.7</td></tr><tr><td colspan="3"><strong>Personal characteristics</strong></td></tr><tr><td>Age</td><td>Quantitative variable is equal to the age of the respondent.</td><td>Average: 41.6</td></tr><tr><td>Gender</td><td>Dummy variable is equal to 1 if the respondent is a man, and 0 if the respondent is not.</td><td>46.7</td></tr><tr><td>Less than 1000</td><td>Dummy variable is equal to 1 if the monthly income of the respondent's household is less than 1,000 euros, and 0 in other cases.</td><td>10.8</td></tr><tr><td>1000-1499</td><td>Dummy variable is equal to 1 if the respondent's monthly household income is in the range of €1,000 and €1,499, and 0 in other cases.</td><td>20</td></tr><tr><td>1500-2499</td><td>Dummy variable is equal to 1 if the monthly income of the respondent's household is between 1,500 and 2,499 euros, and 0 in other cases.</td><td>19.7</td></tr><tr><td>2500-4499</td><td>Dummy variable is equal to 1 if the monthly income of the respondent's household is between 2,500 and 4,499 euros, and 0 in other cases.</td><td>33</td></tr><tr><td>4500 and more</td><td>Dummy variable is equal to 1 if the monthly net income of the respondent's household exceeds 4,500 euros, and 0 in other cases.</td><td>16.5</td></tr><tr><td>Ile de France</td><td>Dummy variable is equal to 1 if the respondent lives in the Île-de-France region, and 0 in other cases.</td><td>20</td></tr><tr><td>North</td><td>Dummy variable is equal to 1 if the respondent lives in the northern region of France, and 0 in other cases.</td><td>45.7</td></tr><tr><td>South</td><td>Dummy variable is equal to 1 if the respondent lives in the southern region of France, and 0 in other cases.</td><td>34.3</td></tr><tr><td>Air pollution concerns</td><td>Dummy variable is equal to 1 if the respondent identified air pollution as one of the three main concerns among those proposed, and 0 in the other cases.</td><td>41.6</td></tr><tr><td>Current health</td><td>Dummy variable is equal to 1 if the respondent reports a feeling of general well-being and physical health, and 0 in other cases.</td><td>88.9</td></tr><tr><td>Medical history</td><td>Dummy variable is 1 if the respondent has received a medical diagnosis indicating that he or she has been affected by a disease caused by air pollution, and 0 otherwise.</td><td>62.5</td></tr><tr><td>Risk aversion</td><td>Dummy variable is equal to 1 if the respondent preferred the least risky choice more than four times, and 0 otherwise.</td><td>30.8</td></tr><tr><td colspan="3"><strong>Behavioural characteristics</strong></td></tr><tr><td>Annual check-up</td><td>Dummy variable is equal to 1 if the respondent undergoes a health examination at least once a year, and 0 in other cases.</td><td>61</td></tr><tr><td>Current preference</td><td>Dummy variable is equal to 1 if the respondent is trying to reduce air pollution currently, and 0 in other cases.</td><td>79.4</td></tr><tr><td>Current smoker</td><td>Dummy variable is equal to 1 if the respondent is currently a smoker, and 0 if not.</td><td>21.3</td></tr><tr><td>Future preference</td><td>Dummy variable is equal to 1 if the respondent strives to reduce air pollution for future generations, and 0 in other cases.</td><td>79.7</td></tr><tr><td>Voluntariness</td><td>Dummy variable is equal to 1 if the respondent strives to reduce air pollution, both for himself and for his relatives, and 0 in other cases.</td><td>80.6</td></tr><tr><td colspan="3"><strong>Bids values</strong></td></tr><tr><td>Bid value 25</td><td>Dummy variable is equal to 1 if the proposed initial cost for Health Checks A and B is €25, and 0 if not.</td><td>33.4</td></tr><tr><td>Bid value 50</td><td>Dummy variable is equal to 1 if the proposed initial cost for Health Checks A and B is €50, and 0 if not.</td><td>33.3</td></tr><tr><td>Bid value 100</td><td>Dummy variable is equal to 1 if the proposed initial cost for Health Checks A and B is €100, and 0 if not.</td><td>33.3</td></tr></tbody></table>
Table 7 presents the results of the WTP estimates from the OLS and Tobit models.

<table><caption>Table 7: WTP estimates. 315 respondents</caption><thead><tr><th rowspan="3"></th><th colspan="4">Willingness to pay for 30 per 100,000 annual risk</th><th colspan="4">Willingness to pay for 60 per 100,000 annual risk</th></tr><tr><th colspan="2">OLS Model</th><th colspan="2">Tobit Model</th><th colspan="2">OLS Model</th><th colspan="2">Tobit Model</th></tr><tr><th>Coefficient</th><th>Standard error</th><th>Coefficient</th><th>Standard error</th><th>Coefficient</th><th>Standard error</th><th>Coefficient</th><th>Standard error</th></tr></thead><tbody><tr><td>Intercept</td><td><strong>51.3769**</strong></td><td>21.7559</td><td>35.6837</td><td>24.8679</td><td><strong>57.5339**</strong></td><td>26.466</td><td>32.0842</td><td>29.6478</td></tr><tr><td>Controllability</td><td>6.3627</td><td>5.8026</td><td>7.6465</td><td>6.2622</td><td>6.1839</td><td>7.0589</td><td>8.2859</td><td>7.4222</td></tr><tr><td>Expert knowledge</td><td>-2.0971</td><td>5.6449</td><td>-4.5366</td><td>6.0984</td><td>-3.1447</td><td>6.8671</td><td>-5.2777</td><td>7.1882</td></tr><tr><td>Immediacy</td><td>-6.2492</td><td>6.9019</td><td>-9.905</td><td>7.4522</td><td>4.3977</td><td>8.3961</td><td>1.5156</td><td>8.7601</td></tr><tr><td>Long-term</td><td><strong>35.7829**</strong></td><td>18.1666</td><td><strong>44.1043**</strong></td><td>19.7329</td><td>18.8913</td><td>22.0995</td><td>30.6139</td><td>23.4961</td></tr><tr><td>Personal exposure</td><td>21.1377</td><td>30.8907</td><td>58.5039</td><td>42.1858</td><td>4.9715</td><td>37.5783</td><td>1.6216</td><td>38.7536</td></tr><tr><td>Personal knowledge</td><td>4.5247</td><td>6.1825</td><td>6.4861</td><td>6.7005</td><td>10.9373</td><td>7.521</td><td><strong>13.6245*</strong></td><td>7.9364</td></tr><tr><td>Poor air quality perception</td><td>-3.4395</td><td>5.6112</td><td>-4.1539</td><td>6.0067</td><td>-5.9562</td><td>6.8259</td><td>-6.69</td><td>7.1085</td></tr><tr><td>Public exposure</td><td>-47.2209</td><td>35.277</td><td><strong>-82.9506*</strong></td><td>46.18</td><td>-9.3791</td><td>42.9143</td><td>-0.396</td><td>44.4178</td></tr><tr><td>Severity</td><td><strong>15.8197*</strong></td><td>8.5952</td><td><strong>21.5463**</strong></td><td>9.41286</td><td>7.3475</td><td>10.4561</td><td>10.7038</td><td>10.9957</td></tr><tr><td>Age</td><td>-0.1846</td><td>0.1529</td><td><strong>-0.2759*</strong></td><td>0.1654</td><td>-0.2631</td><td>0.186</td><td><strong>-0.3462*</strong></td><td>0.1955</td></tr><tr><td>Gender</td><td>-0.2787</td><td>5.4756</td><td>0.0604</td><td>5.9307</td><td>6.3314</td><td>6.6611</td><td>5.8274</td><td>6.9758</td></tr><tr><td>Less than 1000</td><td>-4.1255</td><td>10.1046</td><td>-6.1917</td><td>10.8294</td><td>-13.6407</td><td>12.2921</td><td>-15.6125</td><td>12.8346</td></tr><tr><td>1000-1499</td><td><strong>-14.1671*</strong></td><td>7.981</td><td><strong>-17.3000**</strong></td><td>8.593</td><td><strong>-23.8348**</strong></td><td>9.7089</td><td><strong>-27.6112***</strong></td><td>10.1804</td></tr><tr><td>1500-2499</td><td><strong>-16.0462**</strong></td><td>8.0612</td><td><strong>-17.9210**</strong></td><td>8.7116</td><td><strong>-24.5241**</strong></td><td>9.8065</td><td><strong>-25.2940**</strong></td><td>10.2858</td></tr><tr><td>2500-4499</td><td>-7.079</td><td>7.38</td><td>-6.3051</td><td>7.8963</td><td>-10.8311</td><td>8.9778</td><td>-9.3698</td><td>9.3392</td></tr><tr><td>Ile de France</td><td><strong>13.1718*</strong></td><td>7.0253</td><td><strong>12.3978*</strong></td><td>7.5228</td><td><strong>14.9082*</strong></td><td>8.5463</td><td>14.3753</td><td>8.8909</td></tr><tr><td>North</td><td>1.1542</td><td>5.3829</td><td>-0.7917</td><td>5.8236</td><td>-2.6079</td><td>6.5483</td><td>-3.8695</td><td>6.8508</td></tr><tr><td>Air pollution concerns</td><td>1.3124</td><td>5.056</td><td>2.5917</td><td>5.404</td><td>-0.2061</td><td>6.1506</td><td>1.5656</td><td>6.3989</td></tr><tr><td>Current health</td><td>1.9854</td><td>8.1049</td><td>5.1011</td><td>8.8331</td><td>5.582</td><td>9.8596</td><td>8.844</td><td>10.4183</td></tr><tr><td>Medical history</td><td>-6.1076</td><td>5.1758</td><td>-5.9612</td><td>5.5583</td><td>-8.6491</td><td>6.2963</td><td>-8.5334</td><td>6.579</td></tr><tr><td>Risk aversion</td><td>-1.6851</td><td>5.3827</td><td>-0.0378</td><td>5.7577</td><td>-0.3295</td><td>6.5481</td><td>0.6373</td><td>6.8158</td></tr><tr><td>Annual check-up</td><td><strong>11.1648**</strong></td><td>5.1715</td><td><strong>14.6499***</strong></td><td>5.5769</td><td><strong>12.3445*</strong></td><td>6.2911</td><td><strong>16.5920**</strong></td><td>6.6150</td></tr><tr><td>Current preference</td><td>12.895</td><td>16.0323</td><td>16.9586</td><td>17.3728</td><td>17.4189</td><td>19.5032</td><td>25.8502</td><td>20.4903</td></tr><tr><td>Current smoker</td><td>2.84557</td><td>6.0432</td><td>3.2674</td><td>6.5772</td><td>3.8227</td><td>7.3515</td><td>5.9344</td><td>7.7407</td></tr><tr><td>Future preference</td><td>-13.9205</td><td>12.6839</td><td>-14.8734</td><td>13.6526</td><td>-11.0707</td><td>15.4299</td><td>-11.3807</td><td>16.2167</td></tr><tr><td>Voluntariness</td><td>2.9576</td><td>13.4676</td><td>0.7129</td><td>14.9916</td><td>-2.2194</td><td>16.3833</td><td>-8.5586</td><td>17.5091</td></tr><tr><td>Bid value 25</td><td><strong>-44.1596***</strong></td><td>5.7812</td><td><strong>-49.9995***</strong></td><td>6.2429</td><td><strong>-51.3571***</strong></td><td>7.0328</td><td><strong>-55.9773***</strong></td><td>7.3478</td></tr><tr><td>Bid value 50</td><td><strong>-21.2106***</strong></td><td>5.8611</td><td><strong>-22.7005***</strong></td><td>6.2452</td><td><strong>-30.7431***</strong></td><td>7.13</td><td><strong>-31.9537***</strong></td><td>7.4045</td></tr><tr><td>Observations</td><td colspan="2">315</td><td colspan="2">315</td><td colspan="2">315</td><td colspan="2">315</td></tr><tr><td>R<sup>2</sup></td><td colspan="2">0.2573</td><td colspan="2">0.2496</td><td colspan="2">0.2496</td><td colspan="2">0.2496</td></tr><tr><td>Adjusted R<sup>2</sup></td><td colspan="2">0.1846</td><td colspan="2">0.1761</td><td colspan="2">0.1761</td><td colspan="2">0.1761</td></tr><tr><td>Log-likelihood</td><td colspan="2">-1600.857</td><td colspan="2">-1451.234</td><td colspan="2">-1662.588</td><td colspan="2">-1537.456</td></tr><tr><td>P-value</td><td colspan="2">P-value (F)<0.0001</td><td colspan="2">P-value (χ<sup>2</sup>)<0.0001</td><td colspan="2">P-value (F)<0.0001</td><td colspan="2">P-value (χ<sup>2</sup>)<0.0001</td></tr></tbody></table>

Note: * p<0.1; ** p<0.05; *** p<0.01.

Table 7 demonstrates that the sign and magnitude of the significant results remain consistent across all models, reinforcing their robustness. It further indicates that respondents who perceive themselves as knowledgeable about the causes of air pollution display a higher willingness to pay (WTP) to reduce the risk of mortality associated with air pollution. This relationship may be attributed to the role of knowledge in fostering a better understanding of the health risks posed by air pollution, including the link between pollution and mortality. Such understanding enhances awareness of the issue and motivates individuals to act, including contributing financially, to mitigate pollution-related mortality risks.
Moreover, Table 7 highlights that respondents who believe air pollution harms society exhibit a lower WTP than others. One possible explanation is that recognising the collective nature of pollution's impact leads to the belief that addressing these risks is a societal responsibility. Consequently, individuals may conclude that mitigation measures should be funded through public resources or collective initiatives rather than personal contributions. This perspective can foster a "free-rider effect," where individuals defer responsibility, expecting others—especially those more directly affected or the polluters themselves—to bear the financial burden. Additionally, a sense of individual inefficacy, where one feels their contribution would have a negligible impact, might discourage financial participation.

Conversely, Table 7 shows that respondents who believe air pollution can be fatal have a higher WTP to reduce mortality risks. Several factors can explain this. Firstly, recognising the lethality of air pollution emphasises the urgency and importance of addressing it to protect one's health and that of others. Secondly, contributing financially to reducing pollution-related mortality risks provides individuals with a sense of actively safeguarding themselves and their loved ones. Lastly, an awareness of the collective benefit of these measures may further strengthen their willingness to contribute.

Table 7 also reveals that respondents who believe air pollution has long-term effects exhibit a stronger WTP to reduce mortality risks. This may be explained by the realisation that air pollution poses progressive and enduring health threats, motivating individuals to take proactive measures to mitigate these risks before they become irreversible. Concerns about one's future and subsequent generations exposed to these risks over extended periods may also play a role. Additionally, respondents may view their financial contribution today as an investment to prevent significantly higher costs in the future.

Table 7 shows that WTP's ability to reduce mortality risks from air pollution decreases with age. This decline may result from older individuals perceiving their life expectancy as more limited, reducing their incentive to invest in long-term initiatives such as pollution reduction. Older respondents may also feel they are not the primary beneficiaries of such measures, which are seen as more beneficial for younger people or future generations. Furthermore, older individuals may adopt a more fatalistic attitude, accepting mortality risks as inevitable, which could dampen their motivation to contribute financially.

Additionally, Table 7 highlights that WTP increases with income. This relationship reflects the link between WTP and disposable income, as higher-income individuals have more financial resources to allocate to non-essential expenses, such as reducing environmental risks. For these individuals, the proportion of their income required to contribute to such initiatives is smaller, making such expenditures more accessible. Higher-income individuals may also feel a greater sense of social
responsibility or altruism, recognising their ability to make a meaningful difference by contributing
to societal or environmental causes.

Table 7 also demonstrates that respondents living in Île-de-France have a higher WTP to reduce pollution-related mortality risks than those in other regions. This could be due to greater exposure to intense pollution in Île-de-France, driven by heavy traffic, industrial activity, and a dense population. Such exposure heightens perceptions of health risks, motivating individuals to contribute financially to pollution reduction. Moreover, frequent exposure to air quality alerts, such as pollution peaks and traffic restrictions, and the immediate effects of pollution (e.g., poor visibility, unpleasant odours, respiratory issues) likely reinforce residents' awareness of the problem and willingness to act.

Table 7 also shows that respondents who undergo annual health checks exhibit a higher WTP. Regular health checks may identify early signs of diseases linked to air pollution, such as respiratory or cardiovascular conditions, raising awareness of pollution's health impacts and associated future costs. This awareness may increase their motivation to contribute financially to reduce these risks. Additionally, taking a proactive approach to health through regular check-ups may indicate a predisposition to invest in preventive measures, including reducing environmental risks, to avoid worsening existing conditions or developing new health issues.

Finally, Table 7 shows that higher initial prevention costs are associated with a greater WTP to reduce pollution-related mortality risks. Respondents may anticipate that costly prevention measures suggest even higher potential costs for medical care and reduced quality of life resulting from pollution-related illnesses. This realisation makes prevention a more attractive and cost-effective option, encouraging individuals to invest in upstream actions to minimise exposure and avoid future expenses.

## 4.3 VSL estimates to reduce the risk of deaths from air pollution and influencing factors

As defined in equation 3, the VSL is then calculated as the ratio between the maximum amount a person is willing to pay at most for a risk reduction that maintains its constant usefulness and the level of risk reduction. Table 8 presents the VSL estimates derived from the calculations mentioned above by the WTP and the respondents' statements.
<table><caption>Table 8: VSL estimates.</caption><thead><tr><th></th><th>VSL from median WTP,<br>Million Euros</th><th>VSL from mean WTP,<br>Million Euros</th><th>Standard error of VSL,<br>Million Euros</th></tr></thead><tbody><tr><td colspan="4" style="text-align: center;">30 per 100,000 annual risk reduction</td></tr><tr><td>Stated choice</td><td>0.117</td><td>0.158</td><td>0.151</td></tr><tr><td>Estimated with the model (1)</td><td>0.156</td><td>0.158</td><td>0.077</td></tr><tr><td>Estimated with the model (2)</td><td>0.144</td><td>0.148</td><td>0.086</td></tr><tr><td colspan="4" style="text-align: center;">60 per 100,000 annual risk reduction</td></tr><tr><td>Stated choice</td><td>0.083</td><td>0.090</td><td>0.091</td></tr><tr><td>Estimated with the model (3)</td><td>0.086</td><td>0.090</td><td>0.046</td></tr><tr><td>Estimated with the model (4)</td><td>0.081</td><td>0.085</td><td>0.050</td></tr></tbody></table>

All the values are remarkably similar, thus underlining the robustness of the results. Since the VSL is calculated as the ratio between the willingness to pay and the magnitude of the risk reduction, the factors influencing the WTP similarly affect the VSL. Our results are consistent with previous studies. As Hammitt & Liu (2004) and Andersson (2007), we show a decrease in the VSL with age. In addition, we corroborate empirical studies such as those of Blaeij et al. (2003), Persson et al. (2001) and Vassanadumrongdee & Matsuoka (2005), which suggest a decrease in VSL if the risk is reduced. This shows that respondents could accurately understand the magnitude of the risk reduction presented. Furthermore, we confirm the existence of a wealth effect, which is well documented in the empirical literature (de Blaeij et al., 2003; Liu et al., 1997; Mrozek & Taylor, 2003). This is attributed to the fact that when income rises, individuals have more disposable income to allocate towards goods and services, including non-market goods like health and environmental quality (Viscusi & Aldy, 2003).

# 5 Discussion

Our findings reveal that respondents demonstrate a significant willingness to pay to reduce mortality risks associated with air pollution. This result holds considerable implications for public policy in France. It highlights that citizens assign economic value to living in a cleaner environment, justifying public investment in emission reduction initiatives. These results should encourage the French government and the European Commission to intensify efforts in sectors identified as significant contributors to air pollution, such as transport, industry, and residential heating. Strategies include fostering the development of environmentally friendly technologies and promoting sustainable alternatives. Moreover, improving air quality for citizens could be facilitated through more ambitious policies, such as the adoption and enforcement of stricter environmental standards (particularly for fine particulate matter and nitrogen dioxide), the introduction of targeted environmental taxes (on fossil fuels or industrial emissions), and the provision of subsidies or tax incentives to promote electric vehicles and renewable energy sources (Rasoulinezhad, 2020). These measures would align
with broader efforts to accelerate the energy transition, meet societal expectations, and maximise public health and environmental benefits.

This study also emphasises the potential of incorporating public participation and stakeholder engagement to complement economic analyses, fostering more inclusive and effective policy decisions. While economic analyses provide a valuable foundation for policy decisions, incorporating public participation and stakeholder engagement can enhance the effectiveness and inclusivity of these measures. Engaging citizens, community leaders, and industry stakeholders in the policy design and implementation phases ensures that diverse perspectives are considered, increasing public trust and adherence to interventions. Collaborative initiatives such as town hall meetings, participatory budgeting, and citizen advisory panels can offer nuanced insights into local needs and priorities, complementing the economic findings of studies like ours.

Our findings are broadly aligned with those of the existing literature. Factors such as an individual's level of knowledge about the causes of air pollution (Vassanadumrongdee & Matsuoka, 2005; Ghanem et al., 2023), the perceived severity of pollution (Chilton et al., 2002; Jones-Lee & Loomes, 1995; Subramanian & Cropper, 2000), age (Hammitt & Liu, Furthermore, studies have indicated that other factors, such as income (Ghanem et al., 2023), the level of pollution in one's residential area (Ghanem et al., 2023), the completion of an annual health check (Hammitt & Zhou, 2006), and the initial cost of the health check-up (Andersson, 2007), also have a positive correlation with WTP. These factors emphasise the importance of combining environmental education, health prevention, and targeted actions aimed at the most vulnerable populations to maximise environmental policies' effectiveness and public acceptance. Increasing citizens' knowledge and awareness of the health impacts of air pollution provides a strong foundation for implementing ambitious risk-reduction measures.

However, some results diverge from previous studies. Notably, the coefficient associated with public exposure is significantly negative, contradicting findings by Chilton et al. (2002), Jones-Lee and Loomes (1995), and Subramanian and Cropper (2000). This discrepancy can be interpreted through the lens of the "tragedy of the commons" theory, which explains how shared resources, such as air, are susceptible to overuse or degradation when individuals prioritise personal interests over collective well-being. Air pollution serves as a negative externality, with its costs borne by society while polluters often escape direct responsibility. This dynamic can encourage "free-rider" behaviour, where individuals rely on others to finance environmental improvements while refraining from contributing. Consequently, this phenomenon may lead to an undervaluation of WTP for pollution abatement policies. Addressing this challenge requires robust government intervention, such as implementing taxes on pollutant emissions (e.g., carbon taxes) or establishing strict environmental standards (Qiu et al., 2024). Such measures compel polluters to internalise the societal costs of their
activities while increasing individuals' perceived value of clean air. Additionally, raising public awareness of the health impacts of air pollution on individuals and their loved ones could help mitigate this free-rider tendency.

Our results also show that awareness of the long-term damage caused by air pollution significantly increases WTP to reduce mortality risks. This positive impact emphasises the importance of adopting future-oriented economic and political approaches. Such approaches advocate for sustainable public investments that address citizens' expectations while maximising benefits for both present and future generations.

The VSL derived from our study (€0.085-€0.158 million) is in line with those observed in countries like China (€0.01-€0.24 million), Thailand (€1.60-€1.97 million), and Egypt (€0.22-€0.40 million). It is comparable to estimates from Central and Eastern European countries such as Poland, the Czech Republic, and Greece (€0.5-€1.5 million; Biausque, 2012; Dekker et al., 2011). However, it is significantly lower than the French benchmark (€3 million), as well as estimates from Western European countries like Germany, the United Kingdom, the Netherlands, and Sweden (€2-€4 million; Alberini et al., 2006; Biausque, 2012) and the United States (€10.59 million in 2019, according to the US Department of Transportation).

These differences are mainly due to methodological differences in estimating the VSL. In France, other Western European countries and the United States, VSL is typically derived from empirical studies using willingness-to-pay surveys and labour market data, particularly risk premiums in the labour market, which often yield higher estimates. In contrast, our study, similar to those in China, Thailand and Egypt, focuses on the sensitivity of individuals to air pollution. Socio-economic factors also contribute to these differences. Countries with a higher per capita income, such as the United States, the United Kingdom or Sweden, tend to report higher VSL values. Higher income levels allow for a higher willingness to pay for risk reduction than countries with transition economies and lower income levels, such as China, Thailand, Egypt or Central and Eastern European countries (e.g. Poland, Czech Republic and Greece). In France, the strong social safety net and publicly funded healthcare system reduce individuals' reliance on private spending to mitigate risk. Although French incomes are comparable to those in high-income countries, the robust public health system may reduce the willingness of individuals to pay for risk reduction measures, unlike in countries where access to health care is more dependent on private funding. Collective priorities in France may also downplay the perceived need for individual contributions, whereas other countries emphasise personal investment in preventive measures.
# 6 Conclusion

The willingness to pay and the value of a statistical life for risk reduction are valuable tools for understanding individual preferences and behaviours when faced with environmental and health risks. They provide critical insights for public decision-makers, enabling them to design policies that align with citizens' priorities while addressing economic considerations. This study assesses WTP and VSL for reducing the risk of mortality due to air pollution in France, revealing that respondents attach considerable value to mitigating these risks. By quantifying the perceived benefits of reduced mortality, our findings justify the costs of public policies to improve air quality and support investment in pollution abatement measures and environmental regulation.

Public acceptance of such measures can guide the implementation of economic instruments to incentivise pollution reduction. Policymakers can use our findings to advocate for more stringent air quality standards that align with WHO-recommended thresholds for PM<sub>2.5</sub> and other pollutants. These standards can be implemented effectively using monitoring frameworks, such as PREV'AIR, which provide comprehensive data for assessing compliance and progress over time. However, adopting stricter standards entails carefully evaluating associated costs and benefits. On the cost side, industries will need to invest in pollution control technologies, such as scrubbers and filters, to meet the new standards, and governments will face increased administrative expenses to monitor compliance and enforce regulations. Additionally, high-emission sectors like manufacturing, transportation, and energy may experience short-term economic disruptions as they adapt to the new requirements. However, the benefits are significant and far-reaching. Implementing more stringent standards is expected to result in significant health improvements, including a reduction in the prevalence of respiratory and cardiovascular diseases and a consequent reduction in healthcare costs. Enhanced air quality is predicted to enhance workforce productivity and overall well-being. Furthermore, implementing these measures will yield environmental benefits, including improved ecosystem health, reduced acid rain, and greater biodiversity. Over the long term, these measures are projected to generate substantial economic savings by mitigating infrastructure and agriculture damage caused by pollution. While the initial costs are non-negligible, the long-term economic and societal benefits outweigh these challenges. A phased implementation approach and targeted support for affected industries can ensure a smooth transition, making stricter air quality standards economically feasible and highly impactful.

Another way of action could be taxes on harmful pollutants, such as fine particulate matter and nitrogen dioxide, which could encourage industries (Aung et al., 2017; Yang et al., 2024) and individuals to adopt cleaner technologies and practices (Yoshino et al., 2021). Regions like Île-de-France, where exposure is higher, should be prioritised for interventions. This could involve localised
measures such as emission caps, enhanced public transport systems, increased urban greenery (parks, green roofs, and walls), and integrating pollution-absorbing designs in urban planning. Nevertheless, to avoid upfront infrastructure costs and ensure equity in access to green transport options, extending low-emission zones and restricting access to polluting vehicles could further encourage the use of greener modes of transport.

Financial mechanisms, such as a dividend distribution policy, can play a pivotal role in reducing greenhouse gas emissions by reallocating resources towards cleaner technologies and sustainable practices (Rasoulinezhad & Saboori, 2018). Targeted financial incentives, including subsidies for green technologies such as particulate filters, clean heating systems, and low-emission vehicles, have the potential to support the behavioural changes needed to transition to a low-carbon economy successfully. However, implementing such subsidies necessitates a comprehensive understanding of their costs and benefits. From a cost perspective, governments must allocate substantial direct subsidies to support clean energy initiatives (e.g., wind and solar power) and technologies that reduce pollution. These expenditures may incur opportunity costs, potentially diverting resources from other essential public services. Nevertheless, the benefits are extensive. Subsidies drive market transformation by accelerating the adoption of cleaner technologies and reducing reliance on fossil fuels. Furthermore, providing subsidies has been demonstrated to stimulate innovation and job creation in green technology sectors, thus contributing to economic growth. For consumers, the long-term benefits of subsidies are evident in the form of savings derived from improved energy efficiency and lower costs for renewable energy sources. In terms of feasibility, the sustainability of subsidies is ensured through the utilisation of pollution taxes or carbon pricing mechanisms, thereby ensuring that polluters rather than taxpayers shoulder the financial burden. Collaboration with private investors has been shown to reduce government expenditure further and amplify these initiatives' impact. Financial incentives for clean technologies provide a practical and economically viable pathway toward achieving significant environmental and societal benefits by fostering market shifts, supporting innovation, and reducing emissions.

Our results also support health and social policies such as increasing allocations for preventive health measures (annual respiratory health checks) and providing financial support for clean energy and green technologies to reduce the economic burden on low-income households, who may exhibit lower WTP but face disproportionate risks from air pollution. Raising awareness about the health risks of air pollution and the collective benefits of reduction strategies by launching campaigns and collaborating with healthcare providers and NGOs for outreach in ensuring accessibility and affordability of health services, especially in underserved areas, could mobilise individuals to participate in air quality improving, sustaining long-term engagement and ensuring inclusivity.

Our study has limitations common to contingent valuation methods, the hypothetical nature of the
scenarios presented in the survey, such as introducing a novel annual health check-up for air pollution-related mortality risk reduction, may have led to hypothetical bias. Respondents could overstate or understate their willingness to pay due to the absence of actual financial consequences or a clear understanding of the described benefits. This discrepancy between stated intentions and real-world behaviours is a common limitation in contingent valuation methods. To minimise this bias, we designed explicit, plausible scenarios informed by feedback from a pilot study and encouraged participants to envision realistic situations. Additionally, while relatively modest, our sample size of 315 respondents is representative in terms of gender, age, income, and geographic distribution. The sample size ensures a 95% confidence level and a 5% margin of error for our statistical analysis.

While the study provides critical insights, there are areas where further research could strengthen its applicability and broaden its scope. For example, although representative, expanding the sample size and geographic coverage (different regions of France, in particular urban and rural areas, or different countries with different levels of pollution, access to healthcare and socio-economic conditions in France) could refine the robustness of the conclusions. In addition, incorporating additional control variables (such as trust in institutions managing air pollution) could also promote more comprehensive control, thereby enhancing the robustness of our findings. Finally, a promising direction for future research is to investigate the impact of participatory mechanisms on the effectiveness and public acceptance of air pollution control policies. Specifically, such research could examine how integrating public participation and stakeholder engagement into policy design and implementation influences policy relevance, local acceptance and measurable improvements in air quality. Comparative studies of participatory approaches in different cities or regions could provide valuable insights into their effectiveness. In addition, research into the socio-economic and regional factors that determine the success of these mechanisms could help identify best practices for fostering trust, inclusiveness and collective ownership of air quality interventions. This line of inquiry would advance our understanding of the role of public engagement in environmental governance and provide actionable guidance for policymakers seeking to develop sustainable and broadly supported solutions to air pollution problems.
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