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Article

Applying Social Learning to Climate Communications—Visualising ‘People Like Me’ in Air Pollution and Climate Change Data

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Science Communication Unit, University of the West of England, Bristol BS16 1QY, UK
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Air Quality Resource Management Centre, University of the West of England, Bristol BS16 1QY, UK
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Transport & Mobility Leuven, 3010 Leuven, Belgium
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Independent Researcher, SPA Sustainability, 2334 Leiden, The Netherlands
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Cycling UK, Surrey GU2 9JX, UK
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Author to whom correspondence should be addressed.
Academic Editor: Ricardo García Mira
Sustainability 2021, 13(6), 3406; https://doi.org/10.3390/su13063406
Received: 29 January 2021 / Revised: 10 March 2021 / Accepted: 15 March 2021 / Published: 19 March 2021
Technological approaches to carbon emission and air pollution data modelling consider where the issues are located and what is creating emissions. This paper argues that more focus should be paid to people—the drivers of vehicles or households burning fossil fuels (‘Who’) and the reasons for doing so at those times (‘Why’). We applied insights from social psychology (social identity theory and social cognitive theory) to better understand and communicate how people’s everyday activities are a cause of climate change and air pollution. A new method for citizen-focused source apportionment modelling and communication was developed in the ClairCity project and applied to travel data from Bristol, U.K. This approach enables understanding of the human dimension of vehicle use to improve policymaking, accounting for demographics (gender or age groups), socio-economic factors (income/car ownership) and motives for specific behaviours (e.g., commuting to work, leisure, shopping, etc.). Tailored communications for segmented in-groups were trialled, aiming to connect with group lived experiences and day-to-day behaviours. This citizen-centred approach aims to make groups more aware that ‘people like me’ create emissions, and equally, ‘people like me’ can take action to reduce emissions. View Full-Text
Keywords: social identity theory; social cognitive theory; self-efficacy; climate change; carbon emissions; air pollution; climate communications social identity theory; social cognitive theory; self-efficacy; climate change; carbon emissions; air pollution; climate communications
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MDPI and ACS Style

Fogg-Rogers, L.; Hayes, E.; Vanherle, K.; Pápics, P.I.; Chatterton, T.; Barnes, J.; Slingerland, S.; Boushel, C.; Laggan, S.; Longhurst, J. Applying Social Learning to Climate Communications—Visualising ‘People Like Me’ in Air Pollution and Climate Change Data. Sustainability 2021, 13, 3406. https://doi.org/10.3390/su13063406

AMA Style

Fogg-Rogers L, Hayes E, Vanherle K, Pápics PI, Chatterton T, Barnes J, Slingerland S, Boushel C, Laggan S, Longhurst J. Applying Social Learning to Climate Communications—Visualising ‘People Like Me’ in Air Pollution and Climate Change Data. Sustainability. 2021; 13(6):3406. https://doi.org/10.3390/su13063406

Chicago/Turabian Style

Fogg-Rogers, Laura, Enda Hayes, Kris Vanherle, Péter I. Pápics, Tim Chatterton, Jo Barnes, Stephan Slingerland, Corra Boushel, Sophie Laggan, and James Longhurst. 2021. "Applying Social Learning to Climate Communications—Visualising ‘People Like Me’ in Air Pollution and Climate Change Data" Sustainability 13, no. 6: 3406. https://doi.org/10.3390/su13063406

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