Carbon sinks are of great significance for mitigating the greenhouse effect and climate change. However, only a few carbon sink measurement methods are suitable for small-scale research, such as at the city-region scale. Methods that can accurately distinguish the high–low gradients of forest carbon sinks within small-scale areas have not yet been established. To fill this gap, we used a tree allometric growth model—the i-Tree Eco model—and applied it to Tai’an, which is a National Forest City in China. By using indicator conversion methods, we innovatively combined the China Forest Resources Inventory Geographic Information Database with i-Tree Eco. The results showed that i-Tree Eco successfully estimated the carbon sinks provided by urban–rural forests (in 2019)—the total carbon storage in Tai’an forest was 5,828,165.90 t; the average carbon storage per hectare was 37.19 tC·ha⁻¹; the total carbon sequestration was 936,789.03 tC·yr⁻¹; and the annual carbon sequestration was, on average, 5.97 tC·ha⁻¹·yr⁻¹. Our method improved the spatial resolution of carbon sequestration and storage compared to the commonly used InVEST model, from about 350 m × 350 m to 195 m × 195 m. Compared to the traditional IPCC method, the i-Tree Eco model provided greater accuracy and timeliness in small-scale carbon sequestration measurements, eliminating the need to wait for the next forest inventory to be published. Our method yielded results that covered the entire city region and better reflected the spatial heterogeneity of carbon sinks. We conclude that the innovative application of the i-Tree Eco model to urban–rural-scale carbon sink measurements provides stronger technical support for urban green space planning, as well as data guidance, in relation to local carbon mitigation strategies.

Rural post-poverty alleviation areas are not on a solid developmental footing and therefore remain at risk of returning to poverty in the midst of rapid urbanization. Vulnerability assessment of socio-ecological systems is critical for identifying risks and enhancing resilience in rural areas transitioning out of poverty. Based on research data from 2012, 2017, and 2022 in the post-poverty alleviation areas of Hunan Province, this research establishes a Vulnerability-Scoping-Diagram (VSD) assessment framework for rural development vulnerability and Spatially-Explicit-Resilience-Vulnerability (SERV) analysis model from a socio-ecological system perspective. It comprehensively analyzes the spatial and temporal variations of the Rural Development Vulnerability Index (RDVI) in the study area. Geodetector is used to explore the main factors influencing the spatial and temporal variability of RDVI, and vulnerability type zones are classified by combining the dominant elements method. The findings indicate that: (1) The rural development vulnerability index of post-poverty alleviation areas in Hunan Province has obvious characteristics of spatial and temporal differentiation. The RDVI in western Hunan and southern Hunan is always high, while the RDVI in ChangZhuTan and Dongting Lake regions decreases year by year. (2) The RDVI of post-poverty alleviation areas in Hunan Province is determined by the three dimensions of exposure, sensitivity, and adaptability, exhibiting significant spatial and temporal variations. (3) Spatial autocorrelation analysis showed that areas with similar rural socio-ecological vulnerability in post-poverty alleviation areas of Hunan Province were significantly clustered spatially. (4) The core influencing factors of RDVI in Hunan’s post-poverty alleviation areas have shifted from natural disaster risk to multiple risk dimensions encompassing social resource load and ecological environment risk superimposition, resulting in more complex and diversified influencing factors. (5) By combining results from the RDVI assessment with the dominant elements method, the regions can be classified into multiple vulnerability type districts dominated by multiple elements or single-element dominance, leading to corresponding development suggestions. The study aims to examine the process of changes in vulnerability within rural development in post-poverty alleviation areas and its causal factors from a socio-ecological system perspective. This will provide a foundation for policy formulation to consolidate the results of post-poverty alleviation and promote the sustainable development of rural areas.

Rurality is an important indicator to reflect the development of villages and reveal internal differences in rural areas. The unbalanced development of transportation infrastructure in the current period of rapid urbanization has become one of the principal elements triggering spatial differences in rurality and changes in rural territorial characteristics. However, there are few studies on the impact of transportation infrastructure accessibility on the multidimensional characteristics of rurality from the perspective of heterogeneity. This paper analyzed the spatio-temporal characteristics of transport accessibility (TA) and the county rurality index (CRI) in Guangdong in 2005, 2010, 2015 and 2020 using an accessibility model and the rurality index and explored the clustering characteristics and interactions of TA and the CRI through exploratory spatial data analysis (ESDA) and geographic weighted regression (GWR) modeling. The findings showed that (1) TA and the CRI in Guangdong were significantly unbalanced in terms of space. The CRI showed a weakening trend in general, forming a distribution pattern of “high in the north and low in the south, high in the west and low in the east”, while TA was on the rise, maintaining a stable pattern of “high in the middle and low in the periphery”. (2) Both TA and the CRI in Guangdong had a Moran’s I value greater than 0.6 during the study period, exhibiting strong spatial agglomeration, while the two showed a significant spatial negative correlation. (3) The influence of TA on the CRI in different dimensions showed dynamic changes in stages, with TA having a higher intensity of effect on society rurality and land rurality in 2005, while on society rurality and industry rurality in 2020. (4) This paper grouped 77 counties in Guangdong into four types of policy zonings—coordination types, lagged types of accessibility, lagged types of rurality and double lagged types—and put forward corresponding development recommendations. The study conducted in this paper contributes to an in-depth understanding of the impact of transportation infrastructure development on the multidimensional characteristics of rurality and provides a basis for policy formulation for coordinated urban–rural development and sustainable rural development.