Abstract
Who are the groups stewarding the environment within local communities, where do they work, and who do they work with? The Stewardship Mapping and Assessment Project (STEW-MAP) survey has cataloged and mapped environmental stewardship groups in dozens of cities within the US and worldwide. The survey collects relational, network ties among respondents and their collaborators. In this study, we focus on the 2019 Baltimore, Maryland survey to better understand the relationships among environmental stewardship organizations across the city. We utilize exponential random graph models (ERGMs) to explore the factors that predict the formation of three distinct types of ties: collaboration, resource sharing, and knowledge sharing. The networks include 1,201 nodes with 2,884 total ties among them. Our results show that the network structure of each tie type is unique, but there is a shared tendency for degree distributions to be positively skewed, indicating the presence of many lower degree nodes. We also find that the main focus of these organizations and the organization type create substantial variation in their behavior; with some groups siloed, and others underutilized, one set of groups has managed to permeate all three networks: stormwater-focused groups. This study is the first to analyze this specific dataset and one of the few to use network models to analyze data collected through the STEW-MAP project. This work helps us understand the social forces shaping Baltimore’s stewardship network, while pointing to ways in which practitioners could potentially expand their reach. Overall, this work helps broaden our understanding of local environmental cooperation within a modern urban context.
Keywords
Environmental stewardship,
Collaborative governance,
Exponential random graph models (ERGMs),
Organizations
Citation
Livas, Selena M.; Locke, Dexter H.; Sonti, Nancy F. 2025. Urban environmental stewardship networks: How organizations collaborate, share resources, and exchange knowledge within Baltimore, Maryland. Social Networks. 83: 105-119. https://doi.org/10.1016/j.socnet.2025.05.002.
