The ClairCity process consists of three key phases which have been implemented across the six case studies following adaptation of the approach for each city/region.
The first phase involved the establishment of the baseline evidence in each city/region. The project created an innovative practice-activity data analysis approach that was integrated into the data modelling chain. This allowed the project to source apportion pollution by mode (cars, HGVs, taxi), motive (commuting, leisure, shopping) and demographics (age, gender, income). This approach created data that allowed a shift in the public debate towards recognising the role of people in the generation of air pollution and new evidence for local authorities to recalibrate their actions. The baseline policy reports and the review of social science in air quality management identified that high level strategies and civic society often champion the need for a more citizen-centred approach. The implementation of this is often lagging behind due to a lack of data, technical capabilities and a techno-centric approach to interventions.
Phase two involved citizen and stakeholder engagement to raise awareness of the challenges and work together to co-design solutions. The Citizen Delphi, Schools Activities, GreenAnts App and Mutual Learning Workshops gave citizens a sense of ownership and a platform to describe their personal future visions. We asked citizens how they currently behave, what barriers to they perceive to behaviour change and how they want their cities to look like in the future. Common concerns across case studies included reliability (public transport, renewable energy), affordability (trains, electric vehicles, solar energy), accessibility and flexibility (access to public transport) and road safety (related to cycling). The Skylines Game crowd-sourced the public perception of different policy options. It achieved >4,200 unique players and >11,000 unique plays across our case studies. Analysis illustrated that people prioritise environment and health outcomes over economy and personal satisfaction when choosing interventions, and that males choose more technological interventions than females. This evidence is useful for local authorities to ensure demographics, motives and behaviours are considered when policies are designed, calibrated, communicated and implemented.
Once consensus on future interventions for each case study city was achieved, the third phase quantified the citizen-led scenarios and created bespoke city policy packages. Common across all case studies was the realisation that there is no silver-bullet solution but citizens were demanding more ambitious interventions in terms of scale and timelines. These scenarios were quantified to determine their impact on carbon emissions, air pollutant emissions, concentrations and health. The approach showed that it can achieve 24-95%, 4-17% and 2-54% health risk benefits due to reduction in NO2, PM10 and PM2.5 respectively by 2025 depending on the ambition of the scenarios. These results were presented to the cities in bespoke ClairCity Policy Packages.
Using the quantification data (emissions, concentrations and socio-economic data), environmental justice challenges were explored in each case study. The approach illustrated where locations of high air pollution and low socio-economic status or demographic differences exist, providing recommendations for cities to take a people-centred approach to air quality management. To support other cities that may wish to replicate our approach, the project developed a good practice guidance to generate practice-activity data, a database for all cities with a population of >50,000 and a ‘Peoples Report’ which synthesised the social science observations. Finally, the project created a suite of infographics and visuals as advocacy packages that community groups can utilise for engagement and dissemination.
