Rapid industrialisation and urbanisation have moved contemporary civilization ahead but also deepened clashes with nature. Human society’s long-term evolution faces a number of serious problems, including the climate issue and frequent natural disasters. This research analyses the spatiotemporal evolution features of land use remote sensing data from 2005, 2010, 2015, and 2020. Under the Nature-based Solutions (NbS) idea, four scenarios are established: Business as Usual (BAU), Woodland Conservation (WLC), Arable Land Conservation (ALC), and Urban Transformation and Development (UTD). The RF–Markov–CA model is used to simulate the spatiotemporal patterns of land use for the years 2025 and 2030. Furthermore, the InVEST model is utilised to assess and forecast the spatiotemporal evolution features of carbon storage. The findings show that (1) the primary land use categories in the Three Gorges Reservoir Area (TGRA) from 2005 to 2020 are arable land and woodland. Arable land has a declining tendency, whereas woodland has an increasing–decreasing trend. (2) The WLC scenario exhibits the greatest growth in woodland and the lowest drop in grassland from 2020 to 2030, indicating a more stable ecosystem. (3) The TGRA demonstrates substantial geographic variation in carbon storage from 2005 to 2030, with a broad distribution pattern of “higher in the north, lower in the south, higher in the east, lower in the west, with the reservoir head > reservoir centre > reservoir tail”. (4) In comparison to the other three scenarios, the WLC scenario sees a slower development of construction and arable land from 2020 to 2030, whereas the ecological land area rises the highest and carbon storage increases. As a result, the WLC scenario is the TGRA’s recommended development choice. The study’s findings have substantial implications for the TGRA’s ecological preservation and management, as well as for the optimisation of ecosystem carbon cycling and the promotion of regional sustainable development.

Rapid socioeconomic growth has altered land use patterns, resulting in a surge in worldwide CO₂ emissions, triggering global climate challenges and adversely affecting human health, safety, and sustainable socioeconomic development. As a result, immediate action is required to undertake climate mitigation and adaptation strategies. This study, based on the causal logic of climate change, blue-green space patterns, and carbon emissions, uses the system dynamics (SD) model, patch-generating land use simulation (PLUS) model, and integrated valuation of ecosystem service and trade-offs (InVEST) models to simulate the evolution of blue-green space patterns and predict the spatial distribution of carbon storage in Wuhan to 2060 from 2030 under three SSP-RCP scenarios from CMIP6 and investigates their mechanisms. The findings show that across various SSP-RCP scenarios, the blue-green space patterns in Wuhan would decline over the next 30 years, with green spaces decreasing to some amount and blue spaces growing marginally. The carbon storage is also expected to decline due to the shrinking blue-green space patterns. The SSP126 scenario has the least shrinkage of blue-green spaces, resulting in a reduction of 7.18Tg in carbon storage. Under the SSP245 scenario, the expansion of non-blue-green spaces encroaches on blue-green spaces, resulting in an 8.13 Tg decrease in carbon storage. Across the SSP585 scenario, non-blue-green spaces expand the fastest, resulting in the highest loss of blue-green spaces and a considerable drop in carbon storage of 11.67 Tg. This research is extremely important for optimizing regional land use patterns, coordinating green and high-quality development in Wuhan, and assisting with the implementation of urban climate change adaptation plans.

Due to a lack of guidance in urban systems thinking, China’s rapid urbanization has intensified the interactions and coercive effects between the various urban space subsystems. As a result, “urban diseases” such as environmental pollution, frequent earthquakes, and unbalanced urban–rural development have spread. As a complex giant system, the exploration of urban resilience enhancement is critical to ensuring the joint spatial development of cities and towns. Based on the PSR model, this study screens 38 indicators in five levels of the natural-material-economic-social-intelligent regulation subsystem of the Three Gorges Reservoir Area urban giant system, and constructs a multi-source data resilience assessment framework. Likewise, it employs the Geodetector model to investigate the key factors impacting the resilience mechanism. The results demonstrate that: (1) between 2011 and 2020, the overall resilience in the Hubei section of the Three Gorges Reservoir Area increased from low to high and the coupled characterization of the “pressure-state-response” increased at different rates, with the state layer increasing the most; (2) the frequency of geological hazards, urbanization rate, and total number of early warning and monitoring of geological hazards are the key factors that contribute to changes in spatial resilience; (3) enhanced resilience is the result of the synergistic effects of different driving factors. Our model is used to assess the resilience of the urban system, assisting decision-makers in planning strategies to respond to urban system problems effectively and improve urban resilience.

Rapid incremental urbanization in China has resulted in an incomplete modern infrastructure system and multiple point-like flaws. This is due to a lack of funding and poor scientific construction concepts and procedures. This also contributes to the infrastructure system’s low disaster-adapted resilience and insufficient coupling coordination of production-oriented and service-oriented infrastructure subsystems. Based on the “Robustness-Rapidity-Redundancy-Resourcefulness-Durability” (4R-D) frameworks, this study screens 53 indicators across three tiers of “production-oriented, service-oriented, intelligent” infrastructure subsystems to establish a modern infrastructure resilience evaluation system. We examined the overall infrastructure resilience and coupling coordination development among subsystems in the Three Gorges Reservoir Area (TGRA) from 2009 to 2020 using a coupling coordination degree model (CCDM). Grey relational analysis (GRA) was used to analyze the significant control aspects of infrastructure resilience and coupling coordination degree based on grey system theory. The findings show the following: (1) at the macro level the overall resilience, resilience of each subsystem, and coupling coordination among subsystems in the research region show an upward trend from 2009 to 2020, with the rise from 2018 to 2020 being the most significant; (2) at the micro level, from 2010 to 2013, there was no obvious spatial divergence and from 2014 to 2020, driven by the radiation of the two major urban agglomerations, the resilience and coupling coordination of Yiling and Wanzhou both show a trend of more substantial increase, while the rest of the counties have a small increase; and (3) at the meso level, seven factors have a more significant impact on the coupled and coordinated development of urban infrastructure than other indicators, including urbanization rate, average annual rainfall, the number of health technicians per 10,000 people, and the percentage of GDP in the tertiary industrial sector.

Land Use/Land Cover (LULC) is one of the most significant human variables influencing the efficiency of Ecosystem Services (ESs) in terrestrial ecosystems. Theoretical and technical assistance for regional sustainable land use planning and management, as well as ecosystem conservation and restoration, is provided by investigating the influence of changes in the LULC pattern on the efficiency of ESs. This research focuses on the interactions between socioeconomic activities and natural ecological processes in the Three Gorges Reservoir Area (TGRA). We use LULC data from the TGRA for the years 1990, 2000, 2010, and 2020. The study includes the analysis and calculation of the spatiotemporal evolution features of the current LULC pattern and the efficiency of ESs, including their spatiotemporal distribution. Considering the TGRA’s national development orientation and guidance, three potential LULC patterns are constructed under various develop-ment scenarios. To calculate the efficiency of ESs, the GeoSOS-FLUS future LULC simulation model is linked, and several methodologies such as INVEST, RUSLE, and CASA are used. The goal is to investigate the influence of future changes in LULC patterns on ESs efficiency. The findings show the following: (1) From 1990 to 2020, the values of water conservation services in the TGRA decreased and subsequently increased. High-value areas are primarily located in the reservoir’s centre and eastern sections, whereas low-value areas are mostly found in the western section. Soil conservation service values initially declined and later climbed. The TGRA’s carbon storage services have in-creased yearly, from 552.64 g/m² in 2000 to 615.92 g/m² in 2020. (2) In the ecological protection scenario, carbon storage and soil erosion increased compared to the ecosystem services in 2020. The ecological system service benefits are greater when compared to the natural development scenario. (3) The four ESs show positive spatial correlations across all three scenarios, and local spatial au-tocorrelation analysis findings demonstrate that carbon storage, water yield, and habitat quality have comparable spatial distributions across all three scenarios. To some extent, high-value areas for water conservation, soil retention, carbon storage, and habitat quality overlap.