Access to greenspace is crucial for human health and well-being, aligning with the UN's 11th Sustainable Development Goal. However, global data on population exposure to greenspace has been lacking, leading to uncertainties about progress towards this goal. Existing research often simplifies the relationship between greenspace supply (total or per capita) and actual exposure, neglecting accessibility and temporal variations due to phenology. While studies have documented greenspace inequality within specific cities or regions, a global-scale, spatially explicit analysis comparing Global North and South cities is needed to understand the extent and drivers of this disparity. This study addresses this gap by employing high-resolution data and a novel methodology to assess greenspace exposure at multiple scales, examining both levels of exposure and the inequality of that exposure across different administrative units and seasons. The research questions focus on (1) global differences in greenspace exposure across various scales, (2) differences in exposure levels and inequality among cities, and (3) seasonal effects on exposure and inequality.

The literature emphasizes the importance of greenspace for ecosystem services and human health. However, current metrics for greenspace often focus on provision (total or per capita) rather than actual human exposure, leading to an ecological fallacy. Studies from various countries demonstrate significant disparities in greenspace access, highlighting the need for improved measurement and equitable distribution. Existing research, however, is limited in geographical scope and often employs inconsistent methodologies. This necessitates a global-scale analysis with standardized methods to accurately compare greenspace exposure across cities and regions. Existing studies often focus on accessibility measures, but fail to account for the actual amount of greenspace exposed to individuals, and they often miss smaller, yet potentially important, greenspace elements such as street plantings. This study directly addresses these shortcomings through its fine-resolution data and integrated analysis of population and greenspace.

The study utilized fine-resolution (10m for greenspace, 100m for population) global data for 2020 to quantify greenspace exposure. The Global Administrative Unit Layers (GAULs) were used to delineate spatial units at country, state, and county levels. A dataset of 1028 large cities (>100 km²) globally was used for city-level analysis. The WorldPop dataset provided population distribution, while the European Space Agency's WorldCover provided greenspace data. A population-weighted exposure model was applied to calculate greenspace exposure, considering various buffer distances (100m, 500m, 1000m, 1500m) for sensitivity analysis. The Gini index was used to measure greenspace exposure inequality. Statistical analysis included multiple linear regression to identify drivers of greenspace inequality, using covariates such as geographic, topographic, climate, landscape, and socioeconomic factors. Seasonal variations were analyzed using time-series Sentinel-2 data, comparing greenspace exposure and inequality across spring, summer, autumn, and winter. Validation of the greenspace metrics was performed using Sentinel-2 imagery classifications and comparisons with the WorldCover data, using four key metrics including overall accuracy, precision, recall, and the F1 score. The coefficient of spatial variation (csv) and coefficient of spatiotemporal variation (cstv) were used to analyze spatial and spatiotemporal variability of greenspace exposure.

The multi-scale analysis revealed substantial spatial heterogeneity in greenspace exposure. At the country level, a significant portion of countries had low human exposure to greenspace (<50%). Similar patterns were observed at the state and county levels. The comparison of greenspace exposure index (population-weighted) with physical greenspace coverage revealed a global "overestimation" problem when considering only greenspace coverage, as the exposure index consistently showed lower values. The city-level analysis revealed a stark contrast between Global North and South cities. Global North cities had a mean greenspace exposure of 45.84%, significantly higher than the 14.39% in Global South cities. The Gini index for greenspace exposure inequality was almost twice as high in Global South cities (0.47) compared to Global North cities (0.24). Continent-wise, Asia had the lowest greenspace exposure (13.49%), while North America had the highest (53.45%). Seasonal analysis showed that greenspace exposure inequality varied across seasons, with the most significant difference between summer and winter (R² = 0.13), attributable to vegetation phenology. Statistical modeling indicated that geographic location (latitude), climate (precipitation, vapor pressure deficit), and landscape factors (greenspace coverage rate, edge density) significantly influenced greenspace exposure inequality. Greenspace landscape (provision and configuration) accounted for 79% of the variance in inequality, with greenspace coverage contributing 21.93% and edge density 3.68%. Combined, greenspace coverage and edge density explained 53.42% of the variance in inequality.

The findings highlight the significant disparity in greenspace exposure between Global North and South cities. This disparity is not solely due to differences in overall greenspace provision, but also influenced by the spatial distribution of greenspace relative to population. The higher inequality in Global South cities suggests a need for targeted interventions to improve both the quantity and accessibility of greenspace. The strong correlation between greenspace landscape and inequality underscores the importance of considering both the amount and spatial arrangement of greenspace in urban planning. Seasonal variations in greenspace exposure highlight the need for consideration of phenology in assessing green space benefits and in managing greenspace to improve access year-round. The study suggests that the longer history of formal greenspace planning, higher municipal revenue and more mature community feedback systems may contribute to higher levels of greenspace provision and more equitable access in cities of the Global North compared to Global South cities. The results offer important insights for informing policies and strategies aimed at enhancing greenspace access and mitigating environmental inequalities.

This study provides compelling evidence of the contrasting patterns of greenspace exposure and inequality between Global North and South cities. The findings underscore the need for equitable greening policies and strategies that consider both the quantity and spatial distribution of greenspace. Future research could integrate human mobility data for a more dynamic assessment of exposure, incorporate demographic factors to identify vulnerable populations, and analyze long-term temporal trends in greenspace exposure and inequality.

The study's reliance on static population and greenspace data may not fully capture dynamic interactions between people and greenspace throughout the day. The analysis does not account for differences in population subgroups or the varying health benefits of different greenspace types. The focus on 2020 data limits the ability to assess long-term trends and the impacts of socioeconomic and climatic changes. Further research is needed to address these limitations and provide a more comprehensive understanding of human-greenspace interactions.