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.

Land use change has direct human impacts and profoundly alters the structure and function of terrestrial ecosystems. Numerous studies have explored land use change dynamics in the context of socio-economics, often overlooking the influence of climate change on the potential suitability of land use. To address this gap, we propose an integrated framework combining CLUE-S and MaxEnt models to analyze how land use in Tai’an City, Shandong Province, China, responds to future socio-economic and climate change scenarios. The CLUE-S model, based on land demand, and the MaxEnt model, based on suitability assessment, can effectively explore the trends of land change under the influence of human activities and global warming. This study maps the spatial distributions of land use under socio-economic development and four climate change pathways. Overall, the AUC values of the CLUE-S model were all greater than 0.7, and those of the MaxEnt model were all greater than 0.5, indicating that the results of both are relatively reliable. Our study reveals that, within the baseline development (BL) scenario, cultivated land, forest land, grassland, and unused land are projected to decrease between 2020 and 2040. Conversely, the expansion of water bodies and built land will keep growing. In addition, climate change is expected to enhance the suitability of cultivated land between 2020 and 2040, while reducing that of forest land, grassland, unused land, and built land, with only minimal effects on water bodies. Finally, our framework projected that the most widespread priority areas are cultivated land, followed by forest, grassland, water, built land, and unused land. These priority areas are largely determined by human activities, while the influence of climate change is relatively small. Our research framework has broad applicability to the other regions. Considering the MaxEnt model within the framework is beneficial for excluding unsuitable distribution areas of land use types in the CLUE-S model, which will provide new insights for the sustainable use of land resources.