Identification of crucial regions in need of ecological conservation and restoration based on ecological security patterns is of utmost importance for ecological restoration across national land space with regard to China’s promotion of ecological civilization. Using Changchun, the capital of northeast China, as an illustration, the study chooses ecological sources based on the importance of ecosystem services, builds an ecological security pattern using circuit theory, and organizes critical regions for ecological conservation and restoration. The findings reveal that the 20 ecological sources chosen based on ecosystem services are more concentrated on the eastern side of the city, whereas the western side of the city has a smaller overall area; 41 ecological corridors show a network distribution, among which the southeast is relatively densely distributed; 31 ecological pinch points and 15 ecological barrier points are also identified. Prioritized restoration zones, prioritized protection zones, key conservation zones, and general conservation zones were the four different types of ecological restoration regions identified by the study. Each district’s prioritized restoration zones in the main metropolitan area are larger than the others; Gongzhuling’s priority protection zones are the largest, and Yushu’s natural substrate is the best. According to the grading, targeted solutions are suggested, offering helpful advice for the improvement of ecological patterns and ecological restoration of the aforementioned national areas.

Clarifying the relationship between carbon storage and ecological risks is critical to ensuring regional sustainable development. Land use changes caused by land use policy invariably result in substantial changes in carbon storage and ecological risks. The link between carbon storage and ecological risks in green space is still unknown, even though green space is an essential ecological function carrier. According to the Blackland Conservation Utilization (BCU) policy document and natural exploitation (NP) status, this study compared and projected the carbon storage and landscape ecological risk characteristics of green space in Heilongjiang Province (HLJP) for 2030. It also quantitatively assessed the interactions and synergistic changes of the two variables in terms of coupled coordination relationships, quantitative correlations, and spatial correlations. The results demonstrated the following: (1) the green space evolution of HJLP under the BCU scenario is significantly more drastic than under the NP scenario; (2) In 2020–2030, the NP scenario’s evolution of green space results in the ecosystem losing 323.51 × 10⁶ t of carbon storage, compared to the BCU scenario’s loss of just 216.07 × 10⁶ t. The BCU policy will increase the agglomeration of high-risk ranges in the northeast and southwest will but decrease the overall landscape ecological risk level of green space; (3) BCU policy will prevent the system’s orderly development and benign coupling, but it will increase the interdependence between carbon storage and landscape ecological risks in green space; (4) Green space exchange and loss will result in the simultaneous rise or decrease in both variables. The magnitude of carbon storage increase owing to green space expansion tends to increase simultaneously with the magnitude of landscape ecological risk reduction. To a certain extent, the HLJP black land conservation and utilization policy can improve carbon storage and ensure ecological security, and the matching of dominant regions with the status of the landscape evolutionary process can support future carbon-neutral actions.

Cultural ecosystem services (CESs) are the non-material benefits people derive from ecosystems and are important for human well-being. Most research has focused on individual CES supply–demand relationships, with little systematic study of the overall CES structure, interactions, and mechanisms in metropolitan areas. This study takes the Nanjing Metropolitan Area as a case study, integrating multi-source geospatial data and employing the MaxEnt model, self-organizing maps (SOMs), Spearman correlation analysis, and the Optimal Parameters-based Geographical Detector (OPGD). It analyzes supply–demand matching, trade-offs, synergies, and drivers for four CES categories: aesthetic (AE), recreational entertainment (RE), knowledge education (KE), and cultural diversity (CD). The main findings are as follows: (1) CES supply and demand are spatially zoned: the core area has surplus supply, secondary centers are balanced, and the periphery has both weak supply and demand. (2) Three supply–demand bundles have distinct synergy and trade-off patterns: Bundle 1 primarily exhibits strong synergy between AE and CD; Bundle 2 shows a weak trade-off relationship; and Bundle 3 forms a synergy centered on AE. (3) The explanatory power of driving factors exhibits pronounced spatial heterogeneity: Bundle 1 is dominated by non-quantifiable social factors; Bundle 2 features dual synergistic drivers of population and transportation; and Bundle 3 demonstrates synergistic effects driven by facilities and economic factors. Overall, this study contributes an integrated metropolitan-scale framework that connects CES supply–demand mismatch patterns with bundle typologies, interaction structures, and bundle-specific drivers. The results provide an operational basis for targeted planning and coordinated ecological–cultural governance in the Nanjing Metropolitan Area and offer a transferable reference for other metropolitan regions.

Balancing urban nighttime development with biodiversity conservation requires a clear understanding of how artificial light at night (ALAN) affects wildlife over time. However, long-term, fine-scale quantitative assessments remain scarce. Here, we analyzed bird diversity and ALAN in Shanghai from 2000 to 2024 at a 1 km resolution by integrating bird observation records with satellite-derived nighttime light data. We quantified the interaction between bird diversity and ALAN using a coupling coordination degree model (CCDM) and identified key drivers with GeoDetector. Our results show that bird diversity increased in 16% of the study area, though spatially fragmented, while ALAN intensified and expanded outward from the urban core, affecting 4.6% of the area. Areas with moderate or higher coordination (CCD > 0.5) nearly doubled, primarily in urban–suburban transition zones. Urban land use, road density, and vegetation cover (NDVI) were the dominant drivers, with NDVI-related interactions significantly enhancing explanatory power. These findings provide the first long-term, spatially explicit assessment of ALAN–bird diversity interactions in Shanghai, offering quantitative guidance for zoning-based lighting management, green space planning, and biodiversity-friendly urban development.