Autonomous driving paper index
Boosting the Yellow River Basin’s Green Efficiency through Sustainable Silk Production
One-line summary
This study analyzes the green economic efficiency (GEE) of the silk industry in China’s Yellow River Basin using panel data from 2014 to 2024 across nine provinces.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This study analyzes the green economic efficiency (GEE) of the silk industry in China’s Yellow River Basin using panel data from 2014 to 2024 across nine provinces. A resource–environment– economy evaluation framework integrating the super-efficiency SBM model and Tobit regression is employed to investigate regional performance and driving mechanisms. The results show that basin-wide GEE increased at an average annual rate of 2.7%, with technological progress contributing more substantially than technical efficiency improvement. Pronounced regional heterogeneity is observed, where downstream provinces achieve higher efficiency through intelligent manufacturing and cleaner production technologies, whereas upstream regions remain constrained by ecological vulnerability and insufficient environmental investment. Tobit regression further identifies technological innovation and environmental expenditure as significant positive determinants of GEE, while excessive industrial specialization negatively affects efficiency. The proposed zoned governance strategy provides differentiated pathways for coordinated ecological and industrial development. Beyond promoting sustainable transformation in the traditional silk industry, the established evaluation framework offers methodological insights for resource-efficient manufacturing systems that support environmentally sustainable infrastructure and intelligent industrial environments relevant to advanced electromagnetic engineering applications, including sensor-enabled monitoring and smart production networks.
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