Against the backdrop of global carbon peaking and carbon neutrality “(Dual Carbon”) goals, recycled asphalt pavement (RAP) has emerged as a core pathway for low-carbon development in the road construction sector by reducing carbon emissions associated with raw material extraction and production. Current carbon emission assessments for RAP mostly adopt full life cycle assessment (LCA) models; nevertheless, this methodological framework continues to face challenges including the absence of standardized data protocols, inadequate dynamic responsiveness, and insufficient research on parameter coupling mechanisms. This paper systematically reviews the research progress of LCA applications in this field, which has evolved through three developmental stages: localized accounting, multi-dimensional integration, and dynamic optimization. And it emphasizes the necessity of context-specific emission categorization in accordance with the IPCC Guidelines for National Greenhouse Gas Inventories to enhance accounting adaptability. This study further analyzes the influences of raw material characteristics and RAP content, recycling process types, technological parameters, construction process parameters, and external factors on carbon emissions. Key parametric thresholds are identified, including an optimal RAP content of 60%~80%, and a recommended warm-mix application temperature of 120~130 ℃. Persistent challenges such as deficient emission baseline data and discrepancies among LCA software outputs are also addressed. Moreover, a hierarchical analysis of influencing factors reveals that recycling process type exerts the highest weight (35%~40%), followed by RAP content (25%~30%). Notably, the emission reduction benefits exhibit pronounced diminishing marginal returns; RAP contents exceeding 80% induce carbon emission rebound effects attributable to degraded pavement performance and increased rejuvenator dosage. Multi-parameter coupling manifests both “synergistic gains” and “offsetting losses”: synergistic emission reduction advantages—achieving comprehensive reduction rates of 30%~55%—are observed when high RAP content is combined with in-place cold recycling or when bio-oil rejuvenators are paired with high-efficiency equipment. In contrast, benefit offsetting arises from combinations such as high RAP content coupled with plant-mix hot recycling or cold recycling processes in tropical regions, substantially attenuating the overall emission reduction efficacy. Comprehensive analysis demonstrates that recycled asphalt pavements offer significant advantages in resource circulation and emission reduction. Recycling technology selection, RAP replacement ratio, and construction temperature act as the core governing variables for full-life-cycle carbon emissions, while regional resource endowments, construction equipment configuration, and rejuvenator category serve as critical auxiliary regulating factors.

Road pavements, as critical transportation infrastructure, are prone to cracks and other defects under long-term load bearing and environmental erosion. Traditional manual inspection methods suffer from low efficiency, high subjectivity, and significant safety risks, making it difficult to meet the maintenance demands of largescale pavement networks. This paper proposes a lightweight, high-precision pavement crack detection algorithm named LMC-YOLO (Lightweight Mobile-NetV4 with CAA for YOLO) that addresses feature loss and insufficient accuracy in detecting thin and elongated cracks. The algorithm systematically optimizes the backbone structure, attention mechanisms, and lightweighting strategies of the detection network. By introducing the lightweight Mobile-NetV4 (MNV4) structure into the backbone network and utilizing its efficient Universal Inverted Bottleneck (UIB) modules, the algorithm achieves a balance between powerful feature extraction capability and low computational cost. An improved Context Anchor Attention (CAA) mechanism is integrated into the neck of the detection network, along with a crack shape-aware module based on strip convolution, effectively enhancing the detection capability for elongated cracks. Through refined network design, the number of model parameters is reduced by 23.3%, computational complexity is decreased to 4.6 GFLOPs. Experimental results demonstrate that LMC-YOLO achieves 91.1%precision, 84.6% mAP@0.5, and 70.3% mAP@0.5∶0.95 on crack detection tasks, with an F1-score of 80.40%and an inference speed of 345 frames per second. Cross-dataset validation on DIOR and DOTA-v2 further confirms the model’s cross-domain transfer capability. This method successfully achieves an effective combination of high accuracy and efficient lightweight design, providing a practical solution for real-time pavement crack detection on mobile and embedded devices.