Estimating the cost of air pollution in South East Queensland: An application of the life satisfaction non-market valuation approach

Ambrey, Christopher L.; Fleming, Christopher; Chan, Andrew

doi:10.1016/j.ecolecon.2013.11.007

Cited by 86 publications

(51 citation statements)

References 45 publications

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“…However, most studies rely on aggregated air quality data spanning a rather long period, such as one year (Welsch 2006; Luechinger 2009, 2010; Menz and Welsch 2010; Menz 2011; Chen and Shi 2013; Ambrey 2014). Consequently, the aggregated long-term air pollution used in analyses may not reflect the actual exposure at the time of interview faced by survey subjects, resulting in measurement errors.…”

Section: Introductionmentioning

confidence: 99%

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Valuing Air Quality Using Happiness Data: The Case of China

Zhang

Chen

2017

Ecological Economics

116

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This paper estimates the monetary value of cutting PM2.5, a dominant source of air pollution in China. By matching hedonic happiness in a nationally representative survey with daily air quality data according to the dates and counties of interviews in China, we are able to estimate the relationship between local concentration of particulate matter and individual happiness. By holding happiness constant, we calculate the tradeoff between the reduction in particulate matter and income, essentially a happiness-based measure of willingness-to-pay for mitigating air pollution. We find that people on average are willing to pay ¥258 ($42, or 1.8% of annual household per capita income) per year per person for a 1% reduction in PM2.5.

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Section: Introductionmentioning

confidence: 99%

“…Moreover, the literature primarily uses life satisfaction as a measure of happiness (Welsch 2006; MacKerron and Mourato 2009; Luechinger 2009, 2010; Menz and Welsch 2010; Menz 2011; Ferreira 2013; Ambrey 2014). Life satisfaction, an evaluative happiness measure, reflects one’s overall assessment of life in the long term.…”

Section: Introductionmentioning

confidence: 99%

Valuing Air Quality Using Happiness Data: The Case of China

Zhang

Chen

2017

Ecological Economics

116

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“…These requirements, however, are typically met in practice. 10 So far, the life satisfaction approach has been applied to a variety of contexts (see, for example, Welsch (2002Welsch ( , 2006Welsch ( , 2007, Rehdanz and Maddison (2008), Menz (2011), Ferreira et al (2013), or Ambrey et al (2014 for the context of air pollution; Israel and Levinson (2003) for the context of water pollution; Frijters and van Praag (1998), Rehdanz and Maddison (2005), and Maddison and Rehdanz (2011) Specifically, the studies which are most closely related to this study are van Praag and Baarsma (2005) and Rehdanz and Maddison (2008) for the context of noise pollution and Ambrey and Fleming (2011) and Kopmann and Rehdanz (2013) Kopmann and Rehdanz (2013) find that the willingness to pay for artificial land covers, such as minding, dumping, and construction sites, is negative.…”

Section: Life Satisfaction Approachmentioning

confidence: 99%

“…5 As the life satisfaction approach has already been applied to valuing other environmental externalities monetarily, including air pollution (see, for example, Welsch (2002Welsch ( , 2006Welsch ( , 2007, Rehdanz and Maddison (2008), Ambrey and Fleming (2011), Menz (2011), Ferreira et al (2013), Kopmann and Rehdanz (2013), or Ambrey et al (2014)) or noise pollution (see, for example, van Praag and Baarsma (2005) or Rehdanz and Maddison (2008)), we contribute to a steadily growing stream of literature.…”

Section: Introductionmentioning

confidence: 99%

Sowing the Wind and Reaping the Whirlwind? The Effect of Wind Turbines on Residential Well-Being

Krekel

Zerrahn

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This series presents research findings based either directly on data from the German SocioEconomic Panel study (SOEP) or using SOEP data as part of an internationally comparable data set (e.g. CNEF, ECHP, LIS, LWS, CHER/PACO). SOEP is a truly multidisciplinary household panel study covering a wide range of social and behavioral sciences: economics, sociology, psychology, survey methodology, econometrics and applied statistics, educational science, political science, public health, behavioral genetics, demography, geography, and sport science. Terms of use: Documents inThe decision to publish a submission in SOEPpapers is made by a board of editors chosen by the DIW Berlin to represent the wide range of disciplines covered by SOEP. There is no external referee process and papers are either accepted or rejected without revision. Papers appear in this series as works in progress and may also appear elsewhere. They often represent preliminary studies and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be requested from the author directly.Any opinions expressed in this series are those of the author(s) and not those of DIW Berlin.Research disseminated by DIW Berlin may include views on public policy issues, but the institute itself takes no institutional policy positions. AbstractWe investigate the effect of the physical presence of wind turbines on residential wellbeing in Germany, using panel data from the German Socio-Economic Panel (SOEP) and a unique novel panel data set on more than 20,000 wind turbines for the time period between 2000 and 2012. Using a Geographical Information System (GIS), we calculate the proximity between households and the nearest wind turbine as the most important determinant of their disamenities, e.g. visual interference into landscape aesthetics.Our unique novel panel data set on wind turbines, which was collected at the regional level, includes their exact geographical coordinates and construction dates. This allows estimating the causal effect of the physical presence of wind turbines on residential wellbeing, using a difference-in-differences design. To ensure comparability of the treatment and control group, we apply propensity-score and novel spatial matching techniques based on exogenous weather data and geographical locations of residence, respectively.We show that the construction of a wind turbine within a treatment radius of 4,000 m...

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“…Air pollution 10 countries, 199010 countries, -199710 countries, Welsch (2007 Air pollution 54 countries Brereton et al (2008) Environmental amenities Ireland Moro et al (2008) Environmental amenities Ireland Di Tella and MacCulloch (2008) Air pollution 13 countries, 1975-1997Rehdanz and Maddison (2008 Air pollution, noise Germany Smyth et al (2008) Environmental Gandelman et al (2012) Air pollution, noise Uruguay Levinson (2012) Air pollution USA Menz and Welsch (2012) Air pollution 10 countries, 1990-1997Ambrey and Fleming (2013 Ecosystem diversity Australia Air pollution European regions Environmental amenities Ireland Guardiola et al (2013) Water access Mexico Koopman and Land Ambrey et al (2014) Air pollution Australia Li et al (2014) Air pollution China…”

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confidence: 99%