Rapid industrialization and urbanization have significantly accelerated carbon dioxide emissions, intensifying climate mitigation challenges. Accurate micro-scale assessment of urban carbon emissions is imperative for formulating effective reduction policies in China; however, current efforts are often constrained by a lack of high-resolution data, limiting the ability to capture fine-grained spatial heterogeneity. To address this gap, this study integrates the 1 m resolution national land cover product (SinoLC-1) with OpenStreetMap (OSM) networks and point of interest (POI) data to delineate urban functional zones in Beijing. We subsequently developed a method to estimate and spatially allocate carbon emissions at a 1 m resolution across these zones, categorized by industrial sector. Results for 2020 indicate that carbon sequestration by forests, water bodies, and grasslands totaled approximately 624,900 tons, while total emissions from cultivated land and energy consumption in built-up areas reached 107,692,300 tons. Built-up land was identified as the primary carbon source, whereas forestland and water bodies functioned as key sinks. Notably, the tertiary industry accounted for the largest share of energy-related emissions (41.80%), driven primarily by electricity and kerosene consumption. Spatially, emissions exhibited pronounced heterogeneity, with high-value clusters concentrated in the central urban core and specific suburban hubs. Cross-validation demonstrates that this functional-zone-based spatial allocation method significantly outperforms traditional nighttime light-based approaches in resolving micro-scale emission patterns. This high-resolution analysis improves the characterization of spatial variability in urban carbon cycles, offering robust data support for precision low-carbon planning and energy management.

Seasonal dynamics in wetland landscapes are closely associated with habitat availability and are likely to influence the spatial organization and diversity of waterbird communities. However, most existing studies rely on static land-cover representations or single spatial scales, limiting our ability to characterize how waterbirds respond to seasonally shifting habitats across scales. Focusing on the Qilihai Wetland in Tianjin, China, we combined high-frequency waterbird surveys from 2019–2021 with multi-temporal, season-matched Sentinel-2 imagery and the Dynamic World dataset. Partial least squares regression (PLSR) was applied across a continuous spatial gradient (100–3000 m) to quantify scale-dependent statistical associations between landscape composition and configuration derived from satellite-mapped habitat mosaics on different functional groups. Waterbird diversity exhibited pronounced seasonal contrasts. During the breeding and post-fledging period, high-diversity assemblages were stably concentrated within core wetland areas, showing limited spatial variability. In contrast, during the wintering and stopover period, community distributions became increasingly dispersed, with elevated spatial heterogeneity and interannual variability associated with habitat reorganization. The scale of effect shifted systematically between seasons. In the breeding and post-fledging period, both waterfowl and waders responded predominantly to local-scale landscape factors (<800 m), consistent with nesting requirements and microhabitat conditions. During the wintering and stopover period, however, the characteristic response scale of waterfowl expanded to 1500–2000 m, suggesting stronger associations with broader landscape context, whereas waders remained closely linked to local-scale shallow-water and mudflat connectivity (~200 m). Functional traits played a key role in structuring these scale-dependent responses, with diving behavior and tarsus length being associated with strong constraints on habitat use. Overall, our results suggest that waterbird diversity patterns emerge from the interaction between seasonal habitat dynamics, landscape structure, and functional trait filtering, underscoring the need for phenology-informed, multi-scale conservation strategies that move beyond static spatial boundaries.

Terrestrial ecosystems play a critical role in regulating atmospheric CO₂ through land–atmosphere carbon exchange. While Net Ecosystem Exchange (NEE) serves as a key integrative metric for carbon dynamics, its robust global estimation remains challenging due to profound environmental heterogeneity and nonlinear ecosystem responses. In this study, we propose a dual-track experimental framework to invert annual global terrestrial NEE at a 0.1° spatial resolution for 2000–2024. Initially, a long-term historical baseline inversion (2000–2024) was developed using explicit multi-source environmental predictors. Subsequently, to overcome the representational limitations of conventional spectral indices over complex terrains, we integrated high-dimensional remote-sensing embeddings from the AlphaEarth framework for the 2017–2024 overlapping period. This approach was designed to explicitly quantify the added value of these advanced features. Our results demonstrate that embedding features substantially enhance inversion performance, reducing prediction errors and improving spatial coherence. Adopting the standard meteorological sign convention, global terrestrial NEE remained consistently negative. Based on the 2000–2024 baseline inversion, our predicted global NEE fluctuated between −3.50 and −4.38 Pg C yr⁻¹. To validate these long-term estimates, we systematically cross-validated our results against an independent, recently published multi-network fusion dataset, which reported a comparable range of −3.11 to −3.75 Pg C yr⁻¹. This comparison demonstrates consistent interannual dynamics and corroborates the magnitude of the global terrestrial carbon sink. Spatial patterns exhibit a stable latitudinal structure, with stronger net carbon uptake in low latitudes. Interannual variability is expressed mainly as magnitude fluctuations rather than systematic spatial reorganization. Overall, this study highlights that high-dimensional Earth observation embeddings provide significant, measurable information gains for global NEE inversion without introducing new process-based assumptions, thereby offering a robust and internally consistent basis for evaluating long-term carbon dynamics.

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks.