To combat climate change and meet energy conservation and emission reduction goals, the building sector must adopt low-carbon technologies and low-carbon management methods. To systematically explore existing research areas and track future research trends of carbon emission in the construction stage (CECS), this study conducts a bibliometric and content analysis of CECS studies. 563 relevant publications published between 2000 and 2022 are examined and analyzed using data from the Web of Science (WoS) core collection database. The findings reveal that studies of CECS have evolved through three stages: preliminary exploratory period, stable development period, and rapid development period. In addition, the literature co-citation network and content analysis classify the 13 found co-citation clusters into four knowledge domains: sources definition, data statistics, assessment methods, and carbon reduction strategies. Finally, a knowledge map of CECS studies is presented, outlining significant aspects of research, existing gaps in knowledge, and directions for future study. This work will make it easier for academics and professionals to pinpoint promising areas of study, fill in knowledge gaps, and broaden the scope of existing research on CECS.

The use of safety helmets in industrial settings is crucial for preventing head injuries. However, traditional helmet detection methods often struggle with complex and dynamic environments. To address this challenge, we propose YOLOv8s-SNC, an improved YOLOv8 algorithm for robust helmet detection in industrial scenarios. The proposed method introduces the SPD-Conv module to preserve feature details, the SEResNeXt detection head to enhance feature representation, and the C2f-CA module to improve the model’s ability to capture key information, particularly for small and dense targets. Additionally, a dedicated small object detection layer is integrated to improve detection accuracy for small targets. Experimental results demonstrate the effectiveness of YOLOv8s-SNC. When compared to the original YOLOv8, the enhanced algorithm shows a 2.6% improvement in precision (P), a 7.6% increase in recall (R), a 6.5% enhancement in mAP_0.5, and a 4.1% improvement in mean average precision (mAP). This study contributes a novel solution for industrial safety helmet detection, enhancing worker safety and efficiency.