Ecological sensitivity is an essential indicator for measuring the degree of ecological fragility of a region, and traditional highway routing ignores the ecological benefits of regional nature, which causes irreversible impacts on the natural environment. Based on the AHP method, this paper carries out a comprehensive assessment of the ecological sensitivity of the K0+000~K56+438 section of the Guanzhuang Tourist Highway in the Zhangjiajie Wuling Mountain Area from 2000 to 2020, and it utilizes the spatial autocorrelation analysis method to reveal the pattern of its spatial and temporal changes in ecological sensitivity. The following results were obtained. (1) The ecological condition of the study area is gradually improving, and the areas with higher sensitivity are mainly distributed in the northeast and southwest of the study area. (2) The ecological sensitivity of the study area has strong spatial autocorrelation, but the autocorrelation has been reduced in recent years, and the distribution of the ecological sensitivity has been gradually discretized. (3) There are problems of high ecological risk and construction difficulty in the traditional highway route selection scheme of the A line. The optimized route selection scheme, the K line, can effectively avoid highly sensitive areas by adjusting the route and bridge settings. Finally, the article proposes engineering construction suitability and ecological restoration strategies for different road sections regarding vegetation cover, landscape risk, and geological hazards. This study establishes a set of comprehensive technical chains for tourism highway routing in ecologically fragile areas based on the spatial and temporal evolution of sensitivity, which provides new ideas for traditional highway routing and is of great significance for maintaining the balance and stability of ecosystems within the road area.

As sensitive human-environment systems, lake-intensive regions are critical governance areas for advancing global low-carbon development. Rapid economic growth has intensified the imbalance between economic carbon sources and ecological carbon sinks in these regions. However, methods for measuring territorial space carbon budgets tailored to “production–living–ecological” functions are underdeveloped, and the mechanisms driving carbon imbalance risks remain unclear. To address these issues, this study develops a spatial measurement model for “carbon sources-carbon sinks” in the Dongting Lake region. Using exploratory spatiotemporal data analysis, this study identifies grid-scale variation patterns in carbon budgets. Finally, using the logarithmic mean Divisia index (LMDI) decomposition model, this study examines the driving mechanisms of carbon budgets from a territorial space perspective. The results indicate the following: (1) The territorial space of the Dongting Lake region follows a pattern where “ecological spaces surround production spaces, with living spaces interspersed among water network spaces”. Between 2005 and 2020, functional transitions primarily occurred between agricultural production spaces and forest or water ecological spaces. (2) The study area’s territorial space carbon budgets increased annually, though the growth rate slowed. Construction land was the most significant carbon emission source in territorial space. Spatially, carbon budgets exhibit a radial pattern, with high values concentrated in plains near water bodies, gradually decreasing inland. Spatiotemporal differentiation followed a north–south development trend along the water system axis. High-High clusters were concentrated in municipal areas with dense water networks. In contrast, Low-Low clusters appeared in peripheral mountainous regions to the west, east, and south. (3) Land-use efficiency had the most potent inhibitory effect on carbon budgets, cumulatively reducing carbon emissions by 1.37 × 10⁸ tC. Economic development had the strongest positive effect, adding 1.31 × 10⁸ tC in carbon emissions. Therefore, the Dongting Lake region should promote intensive land use, adjust industrial structures, and develop a green ecological economy to achieve sustainable carbon source–sink management.