Studying the temporal and spatial variation characteristics and driving factors of carbon reserves in the middle reaches of the Yangtze River urban agglomeration is crucial for achieving sustainable development and regional ecological conservation against the backdrop of the “double carbon” plan. Based on three periods of land use data from 2000 to 2020, combined with the InVEST model(Version 3.14.2), the spatiotemporal changes in carbon storage in the urban agglomeration in the middle reaches of the Yangtze River were analyzed. The PLUS model (Version 1.3.5) was used to predict three scenarios of natural development, urban development, and eco-development in the urban agglomeration in the middle reaches of the Yangtze River in 2035 and estimate the carbon storage of the ecosystems under different scenarios, and it used optimal parameter GeoDetectors (Version 4.4.2) to reveal the driving factors affecting the spatiotemporal differentiation of carbon storage. The results show that farmland and construction land area increased and forestland area continued to decrease from 2000 to 2020. Carbon storage decreased by 1 × 10⁶ t, with forestland conversion to farmland and construction land being the main decreasing drivers. The carbon storage of natural and urban developments decreased by 0.26 × 10⁶ t and 0.32 × 10⁶ t, while it increased by 0.16 × 10⁶ under ecological development. The results of the factor detector showed that the NDVI (Normalized Difference Vegetation Index) had the highest explanatory power on the spatiotemporal variation in carbon storage (q = 0.588), followed by the slope (q = 0.454) and elevation (q = 0.391), and the explanatory power of natural environmental factors on the spatiotemporal variation in of carbon storage was dominant. The interaction detector results showed that the spatiotemporal variation in carbon storage was affected by multiple factors, the interaction intensity between each driving factor was stronger than that of a single factor, and the synergy between the NDVI and slope was the strongest, at q = 0.646.

Ginkgo biloba L. was named by Carl Linnaeus in 1771; a “living fossil” with immense medicinal and conservation value, it is a nationally first-class protected wild plant. However, many Ginkgo populations are under threat from habitat destruction, human exploitation, and over-harvesting, which have limited their numbers and range. Using an optimized MaxEnt model in R, this study analyzed Ginkgo distribution points and 22 ecological factors in China to explore the key environmental factors affecting its geographical distribution. The study also predicted the spatial distribution patterns and centroid changes of potential suitable areas under three different carbon emission pathways: current conditions, 2021–2040 (2030s), 2041–2060 (2050s), and 2061–2080 (2070s). The findings are as follows: (1) The optimal combination of model parameters (RM = 3.2, FC = LPH) reduced model complexity and overfitting and achieved very high prediction accuracy with an optimized AUC value of 0.928. (2) The key environmental factors influencing Ginkgo growth include precipitation in the driest month (20–175 mm), minimum temperature in the coldest month (−4 to 3 °C), precipitation in the hottest quarter (450–2500 mm), and a temperature seasonal variation deviation greater than 580. (3) Under the three future climate scenarios (SSP126, SSP245, and SSP585), the potential suitable habitat area for Ginkgo in China was increased, with the distribution range migrating to higher latitudes, Under the three different development models, the total suitable area followed this order: SSP126 > SSP245 > SSP585. Highly and moderately suitable areas are concentrated in the Yangtze River Basin. This study is highly significant for the ecological protection of Ginkgo, aiding in the rational planning of potential suitable areas, enhancing the monitoring of key conservation areas, and developing effective protection strategies in a timely manner.

Land-use change affects the regional spatial patterns of carbon storage. Exploring the relationship of land use with carbon storage is crucial for optimizing land-use structure and sustainable development. We explored the spatial and temporal patterns of land use and carbon-storage changes in urban agglomerations in the middle reaches of the Yangtze River for 2035 using the PLUS-InVEST model. Natural, urban, and ecological development scenarios were considered. Land-use changes comprised increases in arable and construction lands and continuous losses in forest land from 2000 to 2020. Carbon storage decreased by 1 × 10⁶ t, with forest conversion to cropland and construction land being the main decreasing drivers. The carbon storage of natural and urban developments decreased by 0.26 × 10⁶t and 0.32 × 10⁶t, while it increased by 0.16 × 10⁶ under ecological development. NDVI (0.588) had the highest explanatory power for the spatial variation of carbon storage, followed by slope (0.454) and elevation (0.391). Natural environmental factors dominate the spatial variation of carbon storage. The combination of multiple factors affected the spatial and temporal variations of carbon storage. The interaction between soil erosion and elevation, NDVI, and annual rainfall was weakened, while the that of other factors was enhanced.

Ginkgo biloba, a "living fossil" with immense medicinal and conservation value, is a nationally first-class protected wild plant. However, many Ginkgo populations are under threat from habitat destruction, human exploitation, and over-harvesting, which has limited their numbers and range. Using an optimized MaxEnt model in R, this study analyzed Ginkgo distribution points and 22 ecological factors in China to explore the key environmental factors affecting its geographical distribution. The study also predicted the spatial distribution patterns and centroid changes of potential suitable areas under three different carbon emission pathways: current conditions, 2021-2040 (2030s), 2041-2060 (2050s), and 2061-2080 (2070s). The findings are as follows: (1) The optimal model parameter combination (RM = 3.2, FC = LPH) resulted in the lowest complexity and overfitting, while achieving very high prediction accuracy. (2) The key environmental factors influencing Ginkgo growth include precipitation in the driest month (20–175 mm), minimum temperature in the coldest month (-4 to 3°C), precipitation in the hottest quarter (450–2500 mm), and a temperature seasonal variation deviation greater than 580. (3) Under the three future climate scenarios (SSP126, SSP245, and SSP585), the potential suitable habitat area for Ginkgo biloba in China is expected to increase, with the distribution range migrating to higher latitudes. This study is highly significant for the ecological protection of Ginkgo, aiding in the rational planning of potential suitable areas, enhancing the monitoring of key conservation areas, and developing effective protection strategies in a timely manner.