Enabling effective urban green space stewardship through planning: A qualitative comparative analysis in Southwest England

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 Compa..., , # Enabling effective urban green space stewardship through planning: A qualitative comparative analysis in Southwest England

Dataset DOI: [10.5061/dryad.x69p8czz3](https://doi.org/10.5061/dryad.x69p8czz3)

## Description of the data and file structure

This dataset consists of a CSV spreadsheet (Combined__summarised_scoring_data_for_achive_21.10.25.csv) to supplement Appendices 9.1 and 9.2 of the published manuscript (final QCA scores). The 25 UGS sites are listed in column A. Columns B to L summarise anonymised qualitative and quantitative data on constituent factors used to score the output 'likelihood of ongoing effective stewardship' (IND, BUD, BIO, and POS). See below for acronym definitions. Columns M to O summarise anonymised qualitative and quantitative data for NCAP, columns P to W summarise data for LQ, columns X to Z summarise data for REL, columns AA to AF summarise data for SOP, and columns AG to AI summarise data for FIN.

138 on-site interviews were undertaken by the l...,