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As there are many studies on the predictors of corporate social responsibility (CSR) reporting, the influences of foreign direct investments (FDIs) knowledge spillovers (KSs) on CSR reporting in developing countries remain unclear. This paper introduces the passive‐learning KSs concept to explain whether, why, and how domestic firms learn CSR knowledge from their foreign counterparts. First, we investigate the relationship between foreign ownership and the quality of CSR reporting (CSRQ). Second, we test the moderating effect of three types of direct institutional pressures: the green development policy, the mandatory CSR reporting requirement, and CSR regulations issued by the State‐owned Assets Supervision and Administration Commission. Third, we examine the moderating effect of board interlocks to explore the indirect impacts of institutional pressures. We run regressions on a sample of Chinese A‐share listed companies and the hypotheses are generally supported. This paper contributes to the literature on CSR reporting and FDIs KSs by adopting the passive‐learning KSs theory to explain CSR KSs in developing countries. [ABSTRACT FROM AUTHOR]

Using atmospheric observational data from 1998 to 2013, station rainfall data, TRMM (Tropical Rainfall Measuring Mission) data, as well as annual statistics for the plateau vortex and shear line, the joint activity features of sustained departure plateau vortexes (SDPVs) and southwest vortexes (SWVs) are analyzed. Some new and useful observational facts and understanding are obtained about the joint activities of the two types of vortex. The results show that: (1) The joint active period of the two vortexes is from May to August, and mostly in June and July. (2) The SDPVs of the partnership mainly originate near Zaduo, while the SWVs come from Jiulong. (3) Most of the two vortexes move in almost the same direction, moving eastward together with the low trough. The SDPVs mainly act in the area to the north of the Yangtze River, while the SWVs are situated across the Yangtze River valley. (4) The joint activity of the two vortexes often produces sustained regional heavy rainfall to the south of the Yellow River, influencing wide areas of China, and even as far as the Korean Peninsula, Japan and Vietnam. (5) Most of the two vortexes are baroclinic or cold vortexes, and they both become strengthened in terms of their joint activity. (6) When the two vortexes move over the sea, their central pressure descends and their rainfall increases, especially for SWVs. (7) The two vortexes might spin over the same area simultaneously when there are tropical cyclones in the eastern and southern seas of China, or move southward together if a tropical cyclone appears near Hainan Island. [ABSTRACT FROM AUTHOR]

In the meteorology of Global Navigation Satellite System, the weighted mean temperature (Tm) is a key parameter in the process of converting the zenith wetness delay into precipitable water vapor, and it plays an important role in water vapor monitoring. In this research, two deep learning algorithms, namely, recurrent neural network (RNN) and long short-term memory neural network (LSTM), were used to build a high-precision weighted mean temperature model for China using their excellent time series memory capability. The model needs site location information and measured surface temperature to predict the weighted mean temperature. We used data from 118 stations in and around China provided by the Integrated Global Radiosonde Archive from 2010 to 2015 to train the model and data from 2016 for model testing. The root mean square error (RMSE) of the RNN_Tm and LSTM_Tm models were 3.01 K and 2.89 K, respectively. Compared with the values calculated by the empirical GPT3 model, the accuracy was improved by 31.1% (RNN_Tm) and 33.9% (LSTM_Tm). In addition, we selected another 10 evenly distributed stations in China and used the constructed model to test the prediction capability of the weighted mean temperature from 2010 to 2016. The RMSE values were 2.95 K and 2.86 K, which proved that the model also exhibits high generalization in non-modeling sites in China. In general, the RNN_Tm and LSTM_Tm models have a good performance in weighted mean temperature prediction. [ABSTRACT FROM AUTHOR]