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.

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.

The Three Gorges Dam project and other human activities, including regional urbanization and industrialization, have had a substantial influence on the biological environment of the Three Gorges Reservoir Area (TGRA). They have changed the surface land use pattern, disrupted ecosystem structure and function, and influenced changes in the value of ecosystem services. The human activity intensity (HAI) assessment model, the ecosystem services value (ESV) assessment model, and the bivariate spatial autocorrelation model were used based on the spatiotemporal evolution data of towns along the Yangtze River in the TGRA in 1995, 2000, 2005, 2010, 2015, and 2020. At the same time, the spatiotemporal impact of the HAI on land use patterns was evaluated and the magnitude of the spatiotemporal influences on the ESV was investigated. The findings demonstrate the following: (1) The TGRA’s higher reaches are occupied by forested land, while the middle and lower portions are characterized by agricultural land. Land change in the reservoir region has mostly featured transitions between wooded land, agricultural land, grassland, and building land during the last 25 years. Because of differences in natural geography and administrative divisions, the intensity of human activity in the TGRA changes throughout the Yangtze River, with higher intensity in Chongqing and lower intensity in Hubei. By comparing the ESV and the HAI and validating with Moran scatter plots, it was determined that there is a negative relationship between the value of ecosystem services and the intensity of human activities. (2) The ESV rose from CNY 1017.16 × 10⁸ in 1995 to CNY 1052.73 × 10⁸ in 2020, suggesting that the policies of converting farmland back into forests, eliminating outdated production capacity, and developing green industries, among other ecological conservation measures, are effective. (3) In the research area, the effect coefficient of HAI on ESV ranges from −0.02 to −0.032 to −0.031. This coefficient represents the correlation between the HAI and ESV and can preliminarily judge the change in the degree of correlation between the HAI and ESV. The increase in HAI leads to a decrease in the value of ecosystem services, and there is a clear negative spatial correlation between the two. The low human activity area and low ecosystem service value area in the Chongqing section have been transformed into a high ecosystem service value area through years of returning farmland to forest and ecological management measures for sustainable development.