With the intensification of global climate change, the discerning identification of carbon emission drivers and the accurate prediction of carbon emissions have emerged as critical components in addressing this urgent issue. This paper collected carbon emission data from Chinese provinces from 1997 to 2021. Machine learning algorithms were applied to identify province characteristics and determine the influence of provincial development types and their drivers. Analysis indicated that technology and energy consumption had the greatest impact on low-carbon potential provinces (LCPPs), economic growth hub provinces (EGHPs), sustainable growth provinces (SGPs), low-carbon technology-driven provinces (LCTDPs), and high-carbon-dependent provinces (HCDPs). Furthermore, a predictive framework incorporating a grey model (GM) alongside a tree-structured parzen estimator (TPE)-optimized support vector regression (SVR) model was employed to forecast carbon emissions for the forthcoming decade. Findings demonstrated that this approach provided substantial improvements in prediction accuracy. Based on these studies, this paper utilized a combination of SHapley Additive exPlanation (SHAP) and political, economic, social, and technological analysis—strengths, weaknesses, opportunities, and threats (PEST-SWOTs) analysis methods to propose customized carbon emission reduction suggestions for the five types of provincial development, such as promoting low-carbon technology, promoting the transformation of the energy structure, and optimizing the industrial structure.

With the rapid development of e-commerce, the logistics industry faces multiple challenges, including high delivery costs, long delivery times, and a shortage of delivery personnel. Truck–drone collaborative delivery combines the high load capacity of trucks with the flexibility and speed of drones, offering an innovative and practical solution. This paper proposes the Truck–Drone Collaborative Delivery Routing Problem (TDCRPTW) and develops a multi-objective optimization model that minimizes delivery costs and maximizes time reliability under capacity and time window constraints in multi-truck, multi-drone scenarios. To solve the model, an innovative two-stage solution strategy that combines the adaptive k-means++ clustering algorithm with temperature-controlled memory simulated annealing (TCMSA) is proposed. The experimental results demonstrate that the proposed model reduces delivery costs by 10% to 50% and reduces delivery time by 15% to 40%, showcasing the superiority of the truck–drone collaborative delivery model. Moreover, the proposed algorithm demonstrates outstanding performance and reliability across multiple dimensions. Therefore, the proposed approach provides an efficient solution to the truck–drone collaborative delivery problem and offers valuable insights for enhancing the efficiency and reliability of e-commerce logistics systems.