Understanding how green spaces foster sense of place is critical for sustainable urban planning and human well-being, yet no comprehensive synthesis has integrated findings across the fragmented literature spanning multiple disciplines. This systematic review analyzed 497 empirical studies examining green space-place attachment relationships, following PRISMA guidelines across three major databases through June 2025. Beyond documenting the field’s rapid growth—from 10 annual publications pre-2010 to over 50 by 2021—this review reveals critical patterns and gaps with implications for theory and practice. While the term ‘place attachment’ was most frequently used (45% of studies), the field employs diverse terminology often without clear definitional boundaries. Only 18% comprehensively addressed the Person-Process-Place tripartite model, with process dimensions particularly neglected. This theoretical incompleteness limits the understanding of how attachments form and evolve. Geographic analysis exposed severe disparities: 78% of studies originated from high-income countries, with Africa (2.4%) and South America (3.6%) critically underrepresented, raising questions about the applicability of current theories beyond Western contexts. Urban settings dominated (49.5%), potentially overlooking rural and indigenous perspectives essential for comprehensive understanding. Methodologically, studies demonstrated sophistication through strategic deployment of quantitative (60%), qualitative (15%), and mixed methods (25%). Key thematic areas, residence duration, restorative benefits, and pro-environmental behaviors, showed promise, yet environmental justice remained underexplored despite its critical importance. This synthesis advances the field by identifying specific pathways for progress: expanding geographic representation to develop culturally inclusive theories, employing longitudinal designs to capture attachment formation processes, developing validated cross-cultural measures, and centering environmental justice in green space planning. These findings provide essential guidance for creating equitable green spaces that foster meaningful human-nature connections across diverse global contexts.

This study introduces a novel multi-scale framework for analyzing transportation-related greenhouse gas emissions across Illinois from a landscape infrastructure perspective. By integrating county-level, census tract, and high-resolution (30 × 30 m) data of road, rail, and aviation emissions, the research reveals distinct landscape corridor effects where major transportation routes create linear zones of elevated rural emissions connecting urban centers. While urban areas demonstrate higher total emissions, rural regions show higher tract-level and per capita emission intensities along transportation corridors. The extensive available land in rural areas, particularly along these high-emission corridors, presents significant opportunities for strategic green infrastructure deployment and mitigation. The study establishes a technical foundation for data-driven green infrastructure planning by identifying optimal locations for landscape interventions based on emission patterns. This research advances the integration of transportation and landscape planning by providing actionable insights for policymakers and designers working to mitigate climate impacts through strategic green infrastructure and nature-based solutions.

Climate change intensifies natural disasters, requiring enhanced understanding of urban resilience mechanisms. While green infrastructure’s disaster mitigation role has been established through engineering studies, empirical evidence linking green infrastructure quality to post-disaster economic adaptation remains limited. This study examines whether community-level green infrastructure quality correlates with post-disaster economic adaptation following Hurricane Laura’s August 2020 landfall. [Methods] Using a natural experiment design, we analyzed 247 Census Block Groups in two coastal Texas communities (Galveston and Port Arthur) experiencing differential disaster severity. We employed ordinary least squares regression with SafeGraph foot traffic data to measure economic recovery and satellite-derived Normalized Difference Vegetation Index (NDVI) to measure green infrastructure quality. Results demonstrate that green infrastructure quality significantly correlates with post-disaster adaptation (β = 1.27, p < 0.001), independent of socioeconomic characteristics. The NDVI–severity interaction proved non-significant, indicating consistent associations across impact contexts. These findings suggest that green infrastructure supports resilience universally rather than only in moderate-risk areas. From an environmental justice perspective, equitable distribution may reduce disaster-related inequalities, supporting “bouncing forward” adaptation trajectories.

Equitable urban heat adaptation requires knowing not only where socially vulnerable neighborhoods are hotter, but also whether disparities are widening, which dimensions of vulnerability drive change, and whether the hottest places are warming fastest. We link Landsat-derived summer land surface temperature (LST) to a principal-component-based social vulnerability index for 2,130 Chicago census block groups from 2010 to 2024. Spatially corrected regressions, block-group fixed-effects models, and geographically weighted regression distinguish inequities in heat levels, trajectories, and local heat–vulnerability coupling. The results reveal distinct and partly divergent geographies of thermal inequity. Persistent between-neighborhood heat disparities are most strongly associated with Hispanic share and linguistic isolation. A 10-percentage-point increase in Hispanic share is associated with 0.34 °C higher LST, while a one-standard-deviation increase in the Hispanic and linguistic-isolation component is associated with 0.97 °C higher LST. This spatially pervasive gradient persisted but did not widen. Warming trajectories, however, follow housing tenure and older age rather than ethnicity or income. A 10-percentage-point increase in renter share is associated with 0.14 °C per decade of cooling relative to the citywide trend, whereas owner-occupied areas with higher older-adult shares gained heat. This pattern withstands spatial-confounding correction, spatial-error modeling, alternative scene-selection rules, and sensor checks and is not explained by greening. At the same time, the within-block-group association between composite vulnerability and LST nearly tripled from 2014 to 2024. These findings demonstrate that heat levels, warming trajectories, and intensifying heat–vulnerability coupling have different social drivers. Snapshot composite indices may therefore misdiagnose risk and misdirect adaptation resources.

Cities worldwide aim to simultaneously achieve environmental protection, social equity, and economic vitality. These seemingly simple goals, however, require evaluating sustainability trade-offs across hundreds of instances, projects and places – a cognitive limitation traditionally mistaken for physical impossibility. In this paper, we present a multi-objective AI framework that analyzes hundreds of census tracts and variable configurations across three competing objectives to achieve maximum performance (in all three) simultaneously (+ 90%). The solution sets of such a large and complex array have been computationally invisible to conventional analysis. Using the city of Chicago as our test case (801 census blocks, 59 configurations) we found 22 uniquely optimal solutions that cluster along century-old green space corridors established by Burnham's original 1909 Plan. Interestingly, these solutions are found in the wealthier north and northwest parts of city with none in the highly disinvested south and west. This pattern demonstrates how historic infrastructure decisions can create path dependencies that may be difficult to overcome. Our AI framework reveals critical thresholds that appear to enable solution success, including critical open space access and population densities. The work provides an evidence-based approach for climate-responsive urban design and planning decisions. The framework supports an emerging urban cognitive ecosystem approach that uses data and AI to create intelligent, adaptive ecosystems that can learn and proactively deliver services, transforming planning from pattern recognition to revealing systemic and historic inequities.

Species Distribution Modeling (SDM) is central to biodiversity assessment, yet its diagnostic outputs rarely translate into spatially explicit conservation strategies. We present an AI-driven Geodesign framework that embeds Maximum Entropy (MaxEnt) modeling in an iterative refinement cycle to produce phased conservation priorities. Applied to Illinois, we compiled 826,989 occurrence records for 10,029 species across 11 taxonomic classes (fungi, invertebrates, vertebrates, and plants) modeling class-level habitat suitability against 27 environmental predictors at 600 m resolution. Iterative refinement improved mean apparent Area Under the Curve (AUC) from 0.684 to 0.764 (mean True Skill Statistic 0.439). Comparison with 542 Critical Trends Assessment Program (CTAP) field sites showed statistically significant but modest concordance between the modeled plant component and observed plant richness (Spearman ρ = 0.275, p  < 0.001). Protected area covers only 4.42% of state lands, misaligned with modeled conservation priorities along the Mississippi River corridor and Shawnee Hills. Four conservation tiers quantify protection gaps: the most conservative (95th percentile, 7.25% of area) requires 1.6-fold expansion, while meeting 30×30 goals (75th percentile, 36.25%) demands an additional 4.64 million hectares. A Biodiversity Efficiency Index showed deciduous forests support 120-fold greater composite habitat suitability per hectare than barren land. The framework provides a reproducible workflow with quantitative benchmarks aligned with 30×30 and sustainable development goals.