Along with the progression of globalized climate change, flooding has become a significant challenge in low-lying plain river network regions, where urban areas face increasing vulnerability to extreme climate events. This study explores climate-adaptive land use strategies by coupling blue–green infrastructure (BGI) with conventional gray infrastructure, forming blue–green–gray infrastructure (BGGI), to enhance flood resilience at localized and regional scales. By integrating nature-based solutions with engineered systems, this approach focuses on flood mitigation, environmental co-benefits, and adaptive land-use planning. Using the Minhang District in Shanghai as a case study, the research employs geospatial information system (GIS) analysis, hydrological modeling, and scenario-based assessments to evaluate the performance of BGGI systems under projected climate scenarios for the years 2030, 2050, and 2100. The results highlight that coupled BGGI systems significantly improve flood storage and retention capacity, mitigate risks, and provide ecological and social benefits. Water surface-to-catchment area ratios were optimized for primary and secondary catchment areas, with specific increases required in high-risk zones to meet future flood scenarios. Ecological zones exhibited greater adaptability, while urban and industrial areas required targeted interventions. Scenario-based modeling for 2030, 2050, and 2100 demonstrated the scalability, feasibility, and cost-effectiveness of BGI in adapting to climate-induced flooding. The findings contribute to the existing literature on urban flood management, offering a framework for climate-adaptive planning and resilience building with broader implications for sustainable urban development. This research supports the formulation of comprehensive flood management strategies that align with global sustainability objectives and urban resilience frameworks.

Restoration-oriented forest management is increasingly recognized as an important strategy for enhancing long-term carbon sequestration and rehabilitating degraded peri-urban forest landscapes. This study presents a scenario-based assessment of projected carbon sequestration trajectories under a National Reserve Forest Project implemented in peri-urban Wuhan, central China. Thirteen silvicultural models were grouped into three management pathways: intensive plantation cultivation, transformation of existing degraded stands, and tending of young and middle-aged forests. Carbon sequestration was evaluated over a 40-year assessment period (2024–2063) using a Biomass Expansion Factor-based accounting framework incorporating above- and belowground biomass, harvested wood products, and conservative baseline deductions consistent with national and provincial methodologies. The results indicate a sustained long-term increase in projected carbon sequestration despite periodic short-term declines associated with planned thinning and harvesting cycles. Transformation-oriented pathways contributed the largest cumulative project-scale sequestration and generally exhibited relatively strong area-normalized sequestration performance compared with intensive plantation and tending pathways. Intensive plantation systems displayed greater temporal fluctuation associated with shorter rotation cycles and repeated harvesting events. The analysis also highlights the importance of distinguishing between area-normalized sequestration efficiency and cumulative project-scale contribution, as models with moderate per-hectare performance generated substantial total carbon benefits because of their larger implementation area. The findings suggest that restoration-oriented management of existing degraded stands may provide a relatively stable long-term carbon-sequestration pathway in peri-urban forest systems where land availability for large-scale afforestation is constrained. The study also demonstrates the applicability of conservative scenario-based accounting frameworks for restoration-oriented forest carbon assessment and planning under data-limited conditions.