In the context of the worldwide attention on climate change, examining how land use relates to the carbon sink functions of regions is essential. This research innovatively utilizes the 2000–2020 land use data of Changde City, integrating the PLUS and InVEST models to analyze spatiotemporal changes and predict scenarios. It also combines the parameter geodetector and multiscale geographically weighted regression model to dissect driving factor distributions and mechanisms, capture interactions and multiscale impacts, uncover underlying laws, pioneer new paths for similar studies, and support regional ecological sustainability. The results show that from 2000–2020, forest and arable land areas declined while construction land expanded, leading to a yij1,172,200-ton carbon storage reduction in Changde City. Carbon storage decreased under natural development and arable land protection scenarios but increased in the ecological scenario. The main drivers of carbon storage in Changde City are the DEM, slope, and annual average temperature, with their interactions enhancing spatial heterogeneity. Human activities, especially in mountains and urbanizing regions, negatively impact carbon storage. This study aids in optimizing land resource allocation, improving land use efficiency, and promoting coordinated and sustainable development in Changde City’s ecological, economic, and social systems.

The Camellia oleifera fruit harvester, a specialized agricultural device, is engineered for efficient operation within the densely planted C. oleifera groves of China’s undulating terrains. Its design features a notably small footprint to navigate the constrained spaces between trees. With the enhancement of the functionality and power of the harvester, the engine compartment becomes even more congested. This, while beneficial for performance, complicates heat dissipation and reduces harvesting efficiency. In this study, experiments were initially conducted to collect temperature data from the main heat-generating components and parts susceptible to high temperatures within the harvester’s engine compartment. Subsequently, a 3D model was developed for numerical simulations, leading to the proposal of optimization schemes for the engine compartment’s structure and the validation of these schemes’ feasibility. A comparison of the experimental data, both before and after optimization, revealed a significant reduction in the surface temperatures of components within the engine compartment following optimization. As a result, the heat dissipation of the engine compartment has been greatly optimized. The harvester has demonstrated prolonged normal operation, enhancing the reliability and economy of the harvester.

Camellia oleifera harvester is a compact agricultural vehicle utilized in plantations located in China’s red soil hilly regions. To enhance its functionality and off-road performance, additional electronic devices and a more powerful powertrain system have been integrated within the engine compartment. However, the increased component density has resulted in constrained heat dissipation space, leading to critical issues including insufficient engine power, delayed control response, and reduced vibration frequency of the harvesting device. These thermal problems significantly compromise operational efficiency and pose safety hazards to operators. To address these heat dissipation challenges, this study proposes a collaborative optimization approach integrating computational fluid dynamics (CFD) simulation with an Adaptive Particle Swarm Optimization (APSO) algorithm. Initially, preliminary experiments, coupled with CFD simulations, were conducted to analyze the airflow distribution and temperature field within the engine compartment. Based on these findings, the component arrangement was reconfigured to improve thermal performance. Subsequently, an “engine compartment cover parameters–temperature” correlation model was established, and the dimensional parameters of the engine compartment cover were optimized using the APSO algorithm. Experimental results demonstrate that the optimized configuration achieves an average surface temperature reduction of approximately 17.82% for critical components, enabling prolonged stable operation and significantly enhanced operational reliability of the harvester.