Worthy to Lose Some Money for Better Air Quality: Applications of Bayesian Networks on the Causal Effect of Income and Air Pollution on Life Satisfaction in Switzerland

One important determinant of well-being is the environmental quality. Many countries apply environmental regulations, reforms and policies for its improvement. However, the question is how the people value the environment, including the air quality. This study examines the association between air pollution and life satisfaction using the Swiss Household Panel survey over the years 2000–2013. We follow a Bayesian network (BN) strategy to estimate the causal effect of the income and air pollution on life satisfaction. We look at five main air pollutants: the ground-level ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter of 10 micrometres (PM10). Then, we calculate the individuals’ marginal willingness to pay (MWTP) of reducing air pollution that aims to improve their life satisfaction. Beside the BN model, we take advantage of the panel structure of our data and we follow two approaches as robustness check. This includes the adapted probit fixed effects and the generalised methods of moments system. Our findings show that O3 and PM10 present the highest MWTP values ranging between $8000 and$12,000, followed by the remained air pollutants with MWTP extending between $2000 and$6500. Applying the BNs, we find that the causal effect of income on life satisfaction is substantially increased. We also show the causal effects of air pollutants remain almost the same, leading to lower values of willingness to pay

The life satisfaction approach to environmental valuation

In many countries environmental policies and regulations are implemented to improve environmental quality and thus individuals’ well-being. However, how do individuals value the environment? In this paper, we review the Life Satisfaction Approach (LSA) representing a new non-market valuation technique. The LSA builds on the recent development of subjective well-being research in economics and takes measures of reported life satisfaction as an empirical approximation to individual welfare. Micro-econometric life satisfaction functions are estimated taking into account environmental conditions along with income and other covariates. The estimated coefficients for the environmental good and income can then be used to calculate the implicit willingness-to-pay for the environmental good

How Property Markets Determine Welfare Outcomes: An Equilibrium Sorting Model Analysis of Local Environmental Interventions

This paper examines the pivotal role played by property markets in determining the magnitude and distribution of welfare changes resulting from localised environmental change. We address that issue using an equilibrium sorting model (ESM) calibrated, by way of example, to the circumstances of a road infrastructure project in the English town of Polegate. Previous ESM research has tended to assume that all households rent property from a fixed property stock. The narrative that arises from those models concerns environmental gentrification, wherein access to environmentally improved locations is appropriated by the relatively wealthy through their ability to out-compete the less wealthy in the rental property market. Our research shows that to be only part of a much more complex story. We develop a model that extends the sophistication with which ESMs replicate property market dynamics, allowing for households to choose whether to rent or purchase their home and introducing greater realism into housing supply responses to changing market conditions. Our research shows that property markets redistribute welfare gains across the population in complex ways in which tenure choice and housing supply constraints play central roles.This paper has been produced as part of a studentship jointly funded by the ESRC and Department of Transport (Ref ES/G018618/1). The research was additionally supported by the ESRC through the Social and Environmental Economic Research (SEER) project (Ref RES-060-25-0063).