Urban parks play a vital role in mitigating the urban heat island effect and enhancing urban climate resilience. However, quantitative characterization of park cooling effects and the synergistic mechanisms among multiple factors remains limited. Focusing on the central urban area of Nanjing, a typical high-density subtropical city, this study analyzes Landsat 8/9 imagery from 2022 to 2025. The inflection point method was used to quantify three core indicators—cooling intensity, cooling distance, and cooling efficiency—while Pearson correlation analysis was applied to identify key drivers and examine synergistic relationships. The results show that (1) urban parks exhibit a “central aggregation–peripheral diffusion” pattern, which corresponds to pronounced spatial variability in the thermal environment; (2) park cooling effects display strong spatiotemporal heterogeneity, with notable interannual fluctuations in cooling intensity and a relatively stable cooling distance of approximately 400–500 m; and (3) cooling performance is primarily governed by tri-factor synergy among park size, vegetation characteristics, and surrounding urban environmental conditions. Park size largely determines the cooling extent, whereas underlying surface properties and building density regulate or constrain cooling. These findings clarify quantitative patterns and composite drivers of park cooling in high-density cities and provide evidence to support climate-adaptive green space planning and urban heat mitigation strategies.

Rapid urbanization has heightened concerns over ecological degradation. This study analyzes spatiotemporal dynamics of land use in Jiangsu Province from 2000 to 2020 and integrates the intPLUS model to simulate and project land-use patterns for 2030–2050. Habitat quality was assessed with the InVEST model, and key driving factors were identified using the GeoDetector method. The results show that (1) from 2000 to 2020, cultivated land, forest, and grassland decreased markedly by 10.50%, 4.38%, and 35.55%, respectively, whereas built-up land and water bodies increased by 46.70% and 8.92%. (2) Projections for 2030–2050 indicate that land-use change will generally follow the 2000–2020 trajectory but with reduced land-use intensity, a slower expansion of built-up land, and relatively minor changes in ecological land. (3) Habitat quality declined overall during 2000–2020: areas of high habitat quality decreased by 1024.29 km², while low-quality areas increased by 6386.78 km². Spatially, habitat quality exhibited a pattern of “higher in the central region and lower in the south and north,” with relatively low values in southern and northern Jiangsu and higher values in central Jiangsu. By 2050, habitat quality is expected to improve gradually. (4) Nighttime light intensity and elevation exerted strong effects on habitat quality changes, with vegetation cover identified as the dominant driver. Among factor interactions, the interaction between nighttime light intensity and elevation showed the greatest explanatory power.

Extreme precipitation and waves both drive coastal hazards along the American Mediterranean Sea, yet whether the two have changed in coordinated ways over recent decades remains unclear. We analyzed 43 years (1981–2023) of coastal precipitation and wave climate across five sub-regions, using CHIRPS v2.0 daily rainfall at 2656 coastal land pixels and ERA5 wave reanalysis at 1159 nearshore ocean pixels. Five ETCCDI precipitation indices and five wave metrics were computed; their trends were estimated with the Mann–Kendall test and Sen’s slope, and their uncertainty was quantified by bootstrap resampling. Coastal precipitation shows a “fewer but more intense” pattern in parts of the basin: along the Mexican Gulf and Central American coasts, heavy-precipitation frequency (R10mm) declines by about 1.4 to 2.6 days/decade while the most extreme events intensify, with one-day maxima rising by about 6 mm/decade and five-day maxima by 7 to 10 mm/decade; trends in annual total precipitation are comparatively uncertain. Wave trends are generally positive, with significant wave height rising most rapidly off Central America (about 0.021 to 0.027 m/decade) and peak wave period lengthening most along the South American Caribbean coast. To test whether these changes are spatially linked, we paired neighboring land and ocean pixels and applied pairwise trend correlations, sub-regional time-series analysis, and joint clustering. Across the basin, coastal segments with stronger increases in extreme rainfall tend to coincide with segments of rising wave height. Among the examined index pairs, the largest positive correlation was observed between the trends in short-duration precipitation extremes (Rx1day and Rx5day) and the 90th-percentile wave height, although the association was modest ($\rho \approx 0.28$). Joint clustering suggests that concurrent positive trends are more frequently represented along the southwestern Gulf of Mexico and the Caribbean coast of Central America. These results indicate that the principal drivers of compound coastal flooding may not be evolving independently across this basin, and they support considering precipitation and wave climate change jointly in regional coastal hazard assessment.

Regional carbon balance emerges from the complex interplay between land-use dynamics, spatial economic activities, and ecological processes as a socio-ecological system cannot be captured by any single analytical lens. This study develops an integrated assessment workflow that links three components: (i) coefficient-based carbon emission and sequestration accounting by land-use type; (ii) intra-provincial spatial-interaction analysis operationalized through two complementary tools—the Ecological Support Coefficient (ESC) and Economic Contribution Coefficient (ECC), which characterize the local economy–ecology relationship within each city, and a gravity-based emission-linkage model that uses GDP, population, emissions, and inter-city distance to characterize the network structure of inter-city emission attraction; and (iii) the intPLUS model, which combines random-forest-derived transition probabilities with patch-generation rules to simulate multi-scenario land-use trajectories. The framework is applied to Jiangsu Province, China, across 13 prefecture-level cities, using 1995–2020 historical data and three 2030 scenarios. Model performance is validated against observed 2020 land use, with an overall Kappa coefficient of 0.82. The results reveal a stable “high-south–low-north” gradient in emissions, a contrasting “high-ECC/low-ESC” versus “low-ECC/high-ESC” combining pattern across southern and northern Jiangsu, and a hierarchical core–periphery emission-linkage network anchored on the southern metropolitan cluster. Scenario projections for 2030 show clear divergence in provincial carbon budgets, with emissions of $\mathrm{12,326.59}\times {10}^4$ t, $\mathrm{11,745.26}\times {10}^4$ t, and $\mathrm{13,243.42}\times {10}^4$ t under natural development, ecological protection, and economic development, respectively, and corresponding sequestration of $-87.10\times {10}^4$ t, $-100.29\times {10}^4$ t, and $-85.61\times {10}^4$ t. Rather than treating carbon accounting and land-use simulation in isolation, this workflow bridges the analytical gap between physical land-use transitions and socioeconomic spatial emission spillovers, translating structural interactions into actionable spatial planning strategies. Relying on widely available data, the framework demonstrates strong methodological transferability for comparable subnational systems, provided that local parameters and sink coefficients are properly recalibrated.

With accelerated urbanization and the pursuit of the “dual carbon” goals, shared bicycles have re-emerged as a green travel option. This study focuses on the central urban area of Nanjing and develops a carbon emissions reduction (CER) estimation model for shared bicycles. By analyzing spatio-temporal dimensions, it systematically assesses carbon reduction benefits and highlights the synergy with metro-connected travel. Key findings are as follows: (1) shared bicycles primarily support short-distance commuting, with a daily cycling pattern exhibiting a bi-modal distribution and a pronounced peak period demand; (2) cycling trips concentrate in densely populated and commercially vibrant zones, with a spatial pattern of central aggregation and multi-point diffusion; (3) each kilometer cycled by a shared bicycle reduces carbon emissions by about 96.19 g, with daily reductions of around 42.72 t and annual reductions up to 15,591.04 t; (4) the CER benefits of bicycle–metro integration are especially pronounced, contributing nearly 45.00% during peak periods; and (5) factors such as travel mode shifts, metro station layouts, and the development of electric vehicles continue to influence the CER benefits of shared bicycles. This work provides scientific evidence to inform urban green travel policies and transportation infrastructure optimization in cities.