Greenspace can promote health via diverse pathways. A common approach to assessing greenspace exposure is to estimate vegetation availability within buffers surrounding locations where people reside or spend time. However, no clear framework for informed buffer selection exists, and choices made show considerable heterogeneity, impeding evidence synthesis and causal inference. In this Personal View conducted by an interdisciplinary panel of experts, we aimed to establish a framework for informed buffer selection for epidemiological studies on greenspace. We began by reviewing available approaches for the selection of buffer types, which range from single fixed-location approaches to high-resolution mobility-based activity-space approaches, as well as different buffer sizes. We then summarised the determinants of buffer type and size selection including health outcomes and underlying mechanisms, study population, contextual factors, and data characteristics. Finally, based on these determinants, we developed recommendations for future research. Buffer type and size selection should be hypothesis driven, reflecting presumed greenspace–health mechanisms. Buffer selection should target activity-based approaches where feasible, and multiple buffer sizes should be tested. Overall, the assessment of greenspace exposure should shift from ad-hoc approaches to personalised, multiscale, and context-specific methods. We call for standardising and reporting the rationale for buffer selection to minimise bias and enhance comparability and evidence synthesis across studies.

Uneven access to greenspaces or visible greenness is an environmental justice (EJ) issue. In this paper, we use a social equity lens to develop geospatial models that measure convenient walking access to urban greenspaces such as parks and street-level green exposure en route to greenspaces. We utilized earth science, geospatial, and demographic datasets to develop two models—Greenspace Accessibility and Visible Greenness Exposure—and applied them in Camden and Jersey City, USA, two communities experiencing environmental injustices. Modeling results show that greenspace accessibility is a concern in both cities, with Jersey City experiencing more prominent disparities. We observed significant positive relationships in Camden between greenspace accessibility and two EJ variables: Black segregation and Hispanic segregation. Most streets in both cities have poor greenness exposure, although Jersey City faces higher inequality compared to Camden. We also observed significant negative relationships in Jersey City between street-level greenness exposure and low-income populations. We conclude the paper by explaining the implications of our findings for greenspace planning and policymaking.