基于5W2H和ESCRI法在企业安全管理中的应用分析

为了系统的分析事故发生的原因,本文在基础工业工程的方法之上,通过5W2H提问技术对G企业的安全事故从类型、发生地点、时间、责任人、造成损失方面进行分层提问、回答,弄清楚问题的现状。然后再运用ESCRI法对安全事故管理流程进行取消、简化、合并、重排、新增来改善和探讨改进的可能性。丰富并发展了安全管理的方法,并具取得了很好的效果。     

Impact of foreign direct investment on greenhouse gas emissions in agriculture of developing countries

This research analyses the impact of foreign direct investment on greenhouse gas emissions in the agriculture sector of developing countries. Panel data from 63 developing countries for the period 2005 to 2014 was used to estimate a dynamic econometric model by applying a system‐generalised method of moments. The empirical results indicate a positive impact of foreign direct investment in agriculture on the carbon dioxide equivalent emission intensity in developing countries. The results provide weak support for the pollution havens hypothesis and imply the importance of coordination between foreign direct investment and environmental policies.     

Factors driving embodied carbon in international trade: a multiregional input–output gravity model

ABSTRACT

Concerns about the effects and consequences of climate change have notably increased in recent decades. Despite large advances in the understanding of this phenomenon, further research into the determinants of gas emissions is necessary, to shed light on the responsibilities of producers and consumers, and their potential contribution to mitigation strategies. This paper studies the trajectories and determinants of carbon embodied in world trade during a period of 15 years. Our methodology relies on a multiregional input–output model, environmentally extended. Drawing on data from the World Input–Output Database, we estimate embodied emissions in bilateral flows. Then, we assess the determinants of CO₂ emissions embodied in trade, combining input–output modelling with trade gravity panel data analysis. This paper offers a methodological approach that explains and quantifies the underlying factors of carbon trade, integrating the production and consumption perspectives and considering the geographical, structural and institutional context of countries.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

我国M2现状及未来趋势浅析

对我国当前M2快速增长的原因,从经济发展、居民储蓄和外汇储备等三个方面进行深入分析,并在此基础上对未来发展趋势进行预测分析。     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

铬掺杂对硅酸铁锂电学性能影响的计算机仿真研究

硅酸铁锂是一种锂离子电子的正极材料,由于其晶胞在理论上可允许可逆脱嵌两个锂原子,使得其理论比容量有巨大的提升空间,加上原料易得、无污染及成本低的优势,受到了人们的重视。正极材料掺杂被认为是改善其导电性能的有效途径。论文通过运用基于第一性原理方法的计算机仿真技术,对铬掺杂硅酸铁锂的结构和导电性能的影响进行了计算机仿真研究,结果表明未掺杂的硅酸铁锂其带隙快读为2.44eV,掺杂铬之后硅酸铁锂带隙减小为2.31eV,表明铬掺杂可提高硅酸铁锂的导电性能。     

多传感器长途客车智能新风系统设计研究

本研究设计了一种结合二氧化碳传感器、PM2.5粉尘传感器、TVOC传感器来实现智能更新长途客车封闭空间内污浊空气的长途客车车载新风系统。本系统由各项传感器实时采集车厢内CO2、PM2.5、TVOC等数据上传至单片机,单片机判断各项数据是否超过设定阈值,若超过设定阈值,新风系统自动工作改变进、排风量;反之,新风系统不工作,实现智能控制。实验证明,本系统具有明显地改善长途客车内空气质量的效果,能够更好地保护人体健康。     

The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature – the pricing of Airbnb listings – by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) increases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially.