Applying Social Learning to Climate Communications—Visualising ‘People Like Me’ in Air Pollution and Climate Change Data
1.1. Moving Away from ‘Where’ and ‘What’ to ‘Who’ and ‘Why’
1.1.1. Social Identity Theory
- Social categorisation—we categorise people (including ourselves) to understand the social environment. We define appropriate behaviour by reference to the norms of the groups to which we belong. For instance, car drivers may categorise themselves as different from cyclists, or parents with pushchairs may feel different from young commuter pedestrians.
- Social identification—in this stage, we adopt the identity and norms of the group we have categorised ourselves as to which we belong. For instance, male cyclists commuting to work may start to wear lycra or use performance monitoring gadgets.
- Social comparison—once we have categorized and identified ourselves as part of a group, we then tend to compare that group with other groups. For instance, car drivers may mock cyclists in the rain or compare their speed and comfort in a car to that of travelling by bike.
- Social identity theory indicates that these ‘social badges’ play out whether we are in front of others or by ourselves. They are used to identify and connect with people or societies we deem to be like ourselves (in-groups—people like me), and also to stereotype and disparage people or cultures which we deem to be different from us (out-groups). This need to relate to others generates unconscious biases resulting in judgement towards the non-dominant ‘out-groups’ in society.
1.1.2. Social Cognitive Theory
- Vicarious experiences (i.e., comparisons of capability to others, modelling and observing)—a woman deciding whether to cycle will be influenced by whether other women cycle; if this is considered a ’normal’ thing for women to do, then other women will likely join in. In contrast, if women are observed to receive negative feedback from male cyclists or aggressive drivers, then it will put other women off cycling.
- Mastery or performance accomplishments (i.e., experiences of relevant success)—a beginner female cyclist will be more likely to continue cycling if they have a positive experience cycling on main roads; they will then have a memory to recall about their ability to cycle alongside cars.
- Verbal persuasions (positive feedback from peers and supervisors, coaching)—to continue cycling, the female cyclist would need to receive direct positive feedback on this activity. In contrast, negative feedback would reduce self-efficacy and put the woman off cycling.
- Emotional arousal—both vicarious (indirect) and mastery (direct) experiences can influence our emotional states. To improve self-efficacy for an activity, we need to experience positive emotional responses. Therefore, the woman would need to feel that she is capable and confident at cycling and that other people approve or admire her behaviour.
1.1.3. Overton Window of Political Possibility
1.2. Seeing People in the Data
2. Materials and Methods
- How can we better represent citizens in emissions data so that modelling indicates recognisable daily practices?
- How can citizen preferences for future policies be modelled to show potential changes in emissions?
- How can we apply social learning to communicating these models to citizens?
2.1. Emissions Source Apportionment by Demographics and Motive
- Establish a noded network of the city. OpenStreetMap (OSM) (https://www.openstreetmap.org/ accessed on 4 March 2021) holds all details necessary for traffic assignment including road type (residential, regional, highway) number of lanes, directions, speed limits, etc. A MATLAB script converted the OSM map to a simple network.
- Generate transport demand from land-use information to an origin-destination table. A generalised approach focussed on peak travel demand that allows different data sources for land-use. OSM in itself is a potential source, but full land-use information was determined by UrbanAtlas (https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-urban-atlas accessed on 4 March 2021).
- Assign demand on the network to generate traffic at link level. Generic assumptions were made for trip distance and flexible assumptions on preferences for different types of road (insofar as they are available), and these were assigned to the map by linking to Travel Survey data from TravelWest in 2015  and the U.K. National Travel Survey in 2018 .
- Calibrate the traffic demand with a limited amount of counting points. The traffic generation from surveys is highly uncertain and needs to be scaled in such a way that the resulting traffic demand at link level corresponds to measurements. These data were calibrated with counting information from Bristol City Council.
- Multiply the traffic demand with common emission factors. The emission factors are derived from the publicly available COPERT V  methodology, taking into account the fleet composition (age, fuel type, Euro standards, etc.) at country level.
- The individual scale:
- Income group (3 groups);
- Age group (5 groups);
- Gender (male/female);
- Car ownership (0, 1, more).
- The trip scale:
- Transport mode (bicycle, bus, car, motor, taxi, train, walk);
- Trip motive (business, commute, education, leisure, other escort, personal business, shopping, other);
- Time of day (morning, midday, evening, night) as well as day type (weekday, weekend).
2.2. Citizen Engagement to Model Future Policy Outcomes
2.2.1. Citizen Engagement for Policy Preferences
2.2.2. Ratification of Emissions Scenarios
2.3. Communicating Behaviour to Salient Social Groupings
3.2. Citizen-Centred Source Apportionment of Baseline NOx Emissions
3.2.1. Kilometres Travelled
3.2.2. The role of Gender and Age
3.2.3. The Influence of Socio-Economic Factors (Income and Car Ownership)
3.3. Future Behaviour Change
4.1. Policymaking in Bristol
4.2. Communications for Social Cognition—Seeing ‘People Like Me’
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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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
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/su13063406Chicago/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