This study examines the policy and technological dynamics shaping China’s road transport sector’s transition to low-carbon sustainability, focusing on battery electric vehicles (BEVs) and hydrogen fuel cell electric vehicles (HFCEVs). As the world’s second-largest carbon emitter, China faces significant challenges in reducing its fossil fuel dependency in road transport, which accounts for diverse emissions and energy security risks. The present work, using a dual tech multi-level perspective (DTMLP) framework integrating multi-level perspective (MLP) and an advocacy coalition framework (ACF), analyzes the interplay of landscape pressures (global carbon constraints), regime dynamics (policy–market interactions), and niche innovations (BEV/FCEV competition). The results reveal BEVs’ dominance in light-duty markets, achieving remarkable operational emission reductions but facing lifecycle carbon lock-ins from battery production and coal-dependent power grids. HFCEVs demonstrate potential for heavy-duty decarbonization but struggle with gray hydrogen reliance and infrastructure gaps. Policy evolution highlights shifting governance from subsidies to market-driven mechanisms, alongside regional disparities in implementation. This study proposes a three-phase roadmap: structural optimization (2025–2030), technological adaptation (2030–2045), and hydrogen–electric system integration (post-2045), emphasizing material innovation, renewable energy alignment, and multi-level governance. Our findings underscore the necessity of coordinated policy–technology synergies, grid decarbonization, and circular economy strategies, to overcome institutional inertia and achieve China’s ‘Dual Carbon’ targets. This work provides actionable insights for global sustainable transport transitions amid competing technological pathways and geopolitical resource constraints.

The road transport sector in China is confronted with the dual challenges of escalating demand and the critical need to mitigate greenhouse gas emissions. This situation necessitates urgently formulating a precise and comprehensive low-carbon development framework congruent with the objectives outlined in the Paris Agreement. By synthesizing pertinent national and regional policies, this analysis underscores the pivotal role of governmental interventions in promoting innovation and facilitating investment in low-carbon technologies. We propose a series of strategic public interventions essential for transitioning to low-carbon transportation systems within the forthcoming five-year period. These are anticipated to effectively counterbalance the anticipated rise in carbon emissions triggered by increasing demand. Furthermore, the present analysis encompasses a comparative assessment of various transportation technologies' benefits, drawbacks, and obstacles. This assessment focuses mainly on the developmental maturity of these technologies and the low-carbon potential inherent in electric and fuel-cell vehicles. Ultimately, this study delineates a strategic roadmap for establishing a resilient low-carbon transport system, contributing to the broader discourse on sustainable transportation. It also provides actionable recommendations for policymakers and stakeholders dedicated to fostering a greener future.

This paper investigates frequentist prediction methods for the Burr X distribution under Type II censored data. To address the challenges of small sample sizes and high censoring rates commonly encountered in survival analysis, we derive four point prediction methods: best unbiased prediction (BUP), maximum likelihood prediction (MLP), conditional median prediction (CMP), and median unbiased prediction (MUP). For interval prediction, we examine four approaches—the pivotal method, the Wald method, the highest conditional density (HCD) method, and the shortest-length method to construct prediction intervals. The finite-sample performance of these methods is evaluated through Monte Carlo simulations and illustrated using three real-world datasets. The results demonstrate that CMP provides the most stable point predictions, with its advantage being particularly pronounced in small samples due to the conditional median’s robustness to extreme values. For interval prediction, the pivotal method yields the most consistently reliable coverage. The shortest-length method exhibits high accuracy and efficiency. Analyses of three real datasets further validate the applicability of these methods to both complete and right-censored data.