https://orcid.org/0000-0002-2792-2537
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Abstract
This paper looks at three contaminated communities in southern Europe facing pollution from industrial and mining activity and analyses forms of avoidance behaviour, using both economic and sociological approaches. Based on a quantitative household survey, we show that avoidance behaviour is mainly explained by residential location and socio-economic characteristics. Pollution perception is not statistically correlated to most avoidance behaviour. From in-depth qualitative interviews, we learn more about people’s risk perception and whether and why people adopt avoidance behaviour, including discovering some inventive solutions. To conclude, our results cast doubt on the efficacy of current public advisory communications.
Acknowledgements
All ethical standards concerning data collection and analysis were respected. The authors wish to thank several interns, namely Rodrigo Azevedo, George Butler and Sergio Martin Fernandez, and one PhD student, Marcos Barainca, who helped investigate the case studies. The authors are also particularly grateful to our colleague Teresa Melo, who introduced us to the case study area in Estarreja.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are openly available in ‘Portail Data INRAE’ at https://data.inrae.fr/dataverse/root, and more precisely at: https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/YUFRUW
Notes
1 We use the terms ‘avoidance strategy’, ‘avoidance behaviour’, ‘coping practice’, ‘precautionary practice’ and ‘averting behaviour’ interchangeably in accordance with different strands of the literature.
2 Since Love Canal and Woburn (Brown, Citation1997), people have begun to produce the knowledge necessary to assess the health consequences of their exposure to industrial pollution (Brown, Citation2007; Lerner, Citation2010). Some authors have described cases, where corporations and authorities tended to minimise risks (Masterson-Allen & Brown, Citation1990), others have focused on environmental inequalities (Walker et al., Citation2005) and still others have illustrated the stages that lead from the identification of a problem to the formal complaint (Cable & Shriver, Citation1995; Holifield, Citation2009).
3 Due to precariousness (Auyero & Swistun, Citation2008), institutional and political constraints (Kroll-Smith et al., Citation2002; Zavestoski et al., Citation2002) or community and place attachment (Mah, Citation2009; Phillimore & Bell, Citation2013).
4 They showed that those suffering from asthma, hay fever or lung disease, for example, are more likely to take action to avoid air pollution.
5 Each food item is independent and is analysed in separate regressions. in the Appendix reports the survey questions asked to respondents for the outcome variables. In the survey, water consumption was collected in a categorical way (water tap, bottled water, or well water) and food consumption was collected in an ordinal way using four ordinal categories (less than 25%, between 25 and 50%, between 50 and 75%, and more than 75% of the total food consumption). We systematically transform these variables in binary-response indicators for more consistency among eating and drinking outcomes, in addition to make the interpretation of results simpler. We also tested ordinal logit estimates using the four ordinal categories of each food item as outcome indicator. As the results were similar, we only report linear probability estimations based on OLS.
6 The wealth index is the sum of any of the following assets that are owned by the household: principal home (homeownership), second home, car, air conditioning system, computer, mobile phone and financial assets. The wealthiest households have a score of 7 while the most deprived have a score of 0.
7 Here and in the following regressions, we dropped all observations with at least one missing variable.
8 In alternative estimates using household income in PPP as indicator of material resources (not reported here), we also observed that household income increases the consumptions of wild food, local food and bottle water.
9 Other measures of economic status based on household income in PPP were also tested. Moreover, we tested various model specifications (with and without control variables related to sociodemographic factors such as having a foreign origin, having a garden, housing size, and education).
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Notes on contributors
Pierre Levasseur
Dr. Pierre Levasseur is a research fellow at INRAE Paris, at UMR SADAPT. He holds a PhD in economics from the University of Bordeaux. His research areas are development economics, food and health economics and econometrics.
Katrin Erdlenbruch
Dr. Katrin Erdlenbruch is a research director at INRAE Montpellier, at UMR CEE-M. She holds a PhD in economics from the University of Montpellier. Her research areas are environmental and natural resource economics with a special focus on the study of natural and environmental risks.
Christelle Gramaglia
Dr. Christelle Gramaglia is a research fellow at INRAE Montpellier, at UMR G-EAU. She holds a PhD in sociology from Ecole des Mines de Paris. She works on scientific and technical controversies and the study of environmental and health risks in polluted territories.
Sofia Bento
Dr. Sofia Bento is Associate Professor at ISEG in Lisboa. She holds a PhD in sociology from Ecole des Mines de Paris. She is especially interested in analyses about knowledge and instituitions as well participation and public engagement.
Lúcia Fernandes
Dr. Lúcia Fernandes is research fellow at the Center for Social Studies, Coimbra. She holds a PhD in sociology from University of Coimbra. She deals with collaborative research about environmental justice, citizen mobilisation and environmental health.
Pedro Baños Páez
Dr. Pedro Baños Páez is Professor at the University of Murcia. He holds a PhD in sociology from the University of Murcia. His research focuses on environmental issues related to mining sites.