Green Infrastructure, particularly public greenspaces such as urban parks, play an important role in urban environments. Recognising the barriers to equitable access and the need for improving participation in greenspace governance in view of increasingly restricted local authority budgets and consequent challenges around greenspace provision and management, enhancing public engagement in greenspace practices and encouraging environmental stewardship can help address some of the challenges of greenspace governance (Martin et al., 2020; Mathers, Dempsey & Molin, 2015). Recent literature has referred to theories of place indicating the potential of citizen science in fostering environmentally-responsible behaviours, such as voluntary stewardship and involvement in national/local environmental policy concerns (Day et al.,2022; Haklay,2013; Toomey et al.,2020). We identify a gap in research as to whether participation in citizen science projects can enhance connections between people and place and encourage better community participation in the stewardship of parks and urban greenspaces. This research adopts a Living Lab approach to utilise the community’s local intelligence in developing digital experiments in the pilot site using a Nature Data Probe toolkit and seeks to explore its potential for enhancing nature-connectedness by revealing hidden nature. We describe an action research method working with 126 participants from a secondary school close to a large urban park in Plymouth, UK. The findings suggest a deeper awareness of the natural environment was created. Analysis of qualitative data gathered around awareness/consciousness of natural environment revealed participants were more observant of hidden nature post-intervention, with an increase in the number of participants and the specific and descriptive responses identifying nature. In summary, we discuss the implications of research outcomes as a pathway to increasing participation in greenspace governance.
Ashita Gupta; Katharine Willis
University of Plymouth
ID Abstract: 422