Enabling effective urban green space stewardship through planning: A qualitative comparative analysis in Southwest England
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https://datadryad.org/dataset/doi:10.5061/dryad.x69p8czz3
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资源简介:
Amid increasing urbanisation and biodiversity decline, ‘effective
stewardship’ of urban green space (UGS) is a complex but critical
nature-based solution for long-term environmental, social, and economic
gain. In this study, we investigate which social and ecological conditions
can enable effective stewardship in UGS sites, aiming to assist Local
Governments in future-proof UGS planning. Through 138 on-site interviews,
project meetings, online research, and biodiversity and landscape
appraisals, we scored the likelihood of ongoing effective stewardship at
25 UGS sites across Cornwall (England). These sites had been enhanced for
people and wildlife through a local government-led project. We created
this score by combining measures of inclusive volunteer activity at the
site after completion of works (IND), local government maintenance budget
for the site (BUD), biodiversity change at each site (BIO), and local
people’s perception of site enhancement works (POS). Through Qualitative
Comparative Analysis (QCA), we found five social/ecological conditions
influenced the likelihood of ongoing effective stewardship after project
end: pre-existing neighbourhood capacity (NCAP), pre-existing landscape
quality (LQ), relations between residents and local government bodies
(REL), change in sense-of-place from before to after enhancement works
(SOP) and financial cost of enhancement works (FIN). This
dataset brings together and summarises the anonymised qualitative
and quantitative data used to inform QCA calibration and scoring (final
scores are given in Appendices 9.1 and 9.2 of the published article). The
aim of publishing these data is to encourage greater transparency of a
predominantly qualitative process and provide an example of how this can
be done. A careful balance has been sought between transparency and
confidentiality needs.
提供机构:
Dryad
创建时间:
2025-11-03



