人力资本是自主创新的主体,其结构的高级化更是经济高质量发展的有效支撑。文章利用2013~2022年中国省级面板数据,以向量夹角法测度了人力资本结构高级化水平,并进一步运用Dagum基尼系数、Moran指数对其差异化及空间相关性进行了分析。...人力资本是自主创新的主体,其结构的高级化更是经济高质量发展的有效支撑。文章利用2013~2022年中国省级面板数据,以向量夹角法测度了人力资本结构高级化水平,并进一步运用Dagum基尼系数、Moran指数对其差异化及空间相关性进行了分析。研究发现:1) 中国人力资本结构高级化整体水平呈现上升趋势,但不同地区的差异性较为明显,存在由东向西过渡的局面。2) 区域内的人力资本结构高级化水平差异除中部地区外,其余地区均出现波动性增长;区域间的人力资本结构高级化东部与东北部之间差距最大,中西部之间差距最小,且差异来源主要为区域间差异。3) 中国人力资本结构高级化水平存在明显的空间相关性,存在“高–高”集聚与“低–低”集聚的现象。Human capital is the mainstay of independent innovation, and its advanced structure is an effective support for high-quality economic development. The article uses provincial panel data in China from 2013 to 2022 to measure the level of advanced human capital structure using the vector angle method, and further analyzes its differentiation and spatial correlation using Dagum Gini coefficient and Moran index. Research has found that: 1) The overall level of advanced human capital structure in China is showing an upward trend, but there are more obvious differences in different regions, with a transition from east to west. 2) The difference in the level of advanced human capital structure within the region shows fluctuating growth in all regions except for the central region;the upgrading of human capital structure between regions has the largest gap between the eastern and northeastern regions, and the smallest gap between the central and western regions, with the main source of differences being regional differences. 3) There is a clear spatial correlation in the advanced level of China’s human capital structure, with the phenomenon of “high-high” clustering and “low-low” clustering.展开更多
为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,...为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,利用各上游和本地最近的风速观测值来训练预测模型。最后,将各上游风速的当前观测值输入模型,即可得到本地的风速预测值。以偏最小二乘回归(partial least squares regression,PLSR)为主要模型,并采用线性回归(linear regression,LR)、最小二乘支持向量回归等模型进行对照。以冬季风时期的荷兰Huibertgat和天津为被预测地点,进行了PLSR、LR预测误差与模型阶数、样本容量之间关系的数值实验。研究表明,在冬季风时期,当样本容量达到一定程度后,预测误差的变化对阶数、样本容量和模型的类型均不再敏感。这表明空间相关性是一种较为可靠的超短期风速预测方法。展开更多
文摘人力资本是自主创新的主体,其结构的高级化更是经济高质量发展的有效支撑。文章利用2013~2022年中国省级面板数据,以向量夹角法测度了人力资本结构高级化水平,并进一步运用Dagum基尼系数、Moran指数对其差异化及空间相关性进行了分析。研究发现:1) 中国人力资本结构高级化整体水平呈现上升趋势,但不同地区的差异性较为明显,存在由东向西过渡的局面。2) 区域内的人力资本结构高级化水平差异除中部地区外,其余地区均出现波动性增长;区域间的人力资本结构高级化东部与东北部之间差距最大,中西部之间差距最小,且差异来源主要为区域间差异。3) 中国人力资本结构高级化水平存在明显的空间相关性,存在“高–高”集聚与“低–低”集聚的现象。Human capital is the mainstay of independent innovation, and its advanced structure is an effective support for high-quality economic development. The article uses provincial panel data in China from 2013 to 2022 to measure the level of advanced human capital structure using the vector angle method, and further analyzes its differentiation and spatial correlation using Dagum Gini coefficient and Moran index. Research has found that: 1) The overall level of advanced human capital structure in China is showing an upward trend, but there are more obvious differences in different regions, with a transition from east to west. 2) The difference in the level of advanced human capital structure within the region shows fluctuating growth in all regions except for the central region;the upgrading of human capital structure between regions has the largest gap between the eastern and northeastern regions, and the smallest gap between the central and western regions, with the main source of differences being regional differences. 3) There is a clear spatial correlation in the advanced level of China’s human capital structure, with the phenomenon of “high-high” clustering and “low-low” clustering.
文摘为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,利用各上游和本地最近的风速观测值来训练预测模型。最后,将各上游风速的当前观测值输入模型,即可得到本地的风速预测值。以偏最小二乘回归(partial least squares regression,PLSR)为主要模型,并采用线性回归(linear regression,LR)、最小二乘支持向量回归等模型进行对照。以冬季风时期的荷兰Huibertgat和天津为被预测地点,进行了PLSR、LR预测误差与模型阶数、样本容量之间关系的数值实验。研究表明,在冬季风时期,当样本容量达到一定程度后,预测误差的变化对阶数、样本容量和模型的类型均不再敏感。这表明空间相关性是一种较为可靠的超短期风速预测方法。
