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Sicheng Li
College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China

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Journal article
Published: 18 June 2020 in Sustainability
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We investigate the influence of corporate social responsibility (CSR) on the maturity mismatch of investment and financing from the perspective of both polluting and non-polluting companies. The results reveal that CSR performance can aggravate the maturity mismatch of investment and financing; and the effect can be more serious in the polluting companies. At the same time, we find that CSR makes companies obtain more short-term debt. What is more, polluting companies perform more environmental responsibilities in the form of long-term investments than non-polluting companies. These phenomena exacerbate the maturity mismatch of investment and financing; and this effect is only significant when polluting companies choose CSR mandatory disclosure. The impact of CSR on the maturity mismatch of investment and financing is more apparent in companies with lower value and at smaller scales. We show that companies should not only perform their CSR to maintain a balanced economic and ecological development, but also pay attention to the aggravation of the maturity mismatch of investment and financing.

ACS Style

Xiaolan Bao; Qiaosheng Luo; Sicheng Li; M. James C. Crabbe; Xiaoguang Yue. Corporate Social Responsibility and Maturity Mismatch of Investment and Financing: Evidence from Polluting and Non-Polluting Companies. Sustainability 2020, 12, 4972 .

AMA Style

Xiaolan Bao, Qiaosheng Luo, Sicheng Li, M. James C. Crabbe, Xiaoguang Yue. Corporate Social Responsibility and Maturity Mismatch of Investment and Financing: Evidence from Polluting and Non-Polluting Companies. Sustainability. 2020; 12 (12):4972.

Chicago/Turabian Style

Xiaolan Bao; Qiaosheng Luo; Sicheng Li; M. James C. Crabbe; Xiaoguang Yue. 2020. "Corporate Social Responsibility and Maturity Mismatch of Investment and Financing: Evidence from Polluting and Non-Polluting Companies." Sustainability 12, no. 12: 4972.

Journal article
Published: 27 June 2019 in Sustainability
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In order to investigate the factors influencing the sustainable guarantee network and its differences in different spatial and temporal scales, logistic regression algorithm is used to analyze the data of listed companies in 31 provinces, municipalities and autonomous regions in China from 2008 to 2017 (excluding Hong Kong, Macau and Taiwan). The study finds that, overall, companies with better profitability, poor solvency, poor operational capability and higher levels of economic development are more likely to join the guarantee network. On the temporal scale, solvency and regional economic development exert increasing higher impact on the companies’ accession to the guarantee network, and operational capacity has increasingly smaller impact. On the spatial scale, the less close link between company executives and companies in the western region suggests higher possibility to join the guarantee network. The predictive accuracy test results of the logistic regression algorithm show that the training model of the western sample enterprises has the highest prediction accuracy when predicting enterprise behavior of joining the guarantee network, while the accuracy is the lowest in the central region. When forecasting enterprises’ failure to join the guarantee network, the training model of the central sample enterprise has the highest accuracy, while the accuracy is the lowest in the eastern region. This paper discusses the internal and external factors influencing the guarantee network risk from the perspective of spatial and temporal differences of the guarantee network, and discriminates the prediction accuracy of the training model, which means certain guiding significance for listed company management, bank and government to identify and control the guarantee network risk.

ACS Style

Han He; Sicheng Li; Lin Hu; Nelson Duarte; Otilia Manta; Xiao-Guang Yue. Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm. Sustainability 2019, 11, 3525 .

AMA Style

Han He, Sicheng Li, Lin Hu, Nelson Duarte, Otilia Manta, Xiao-Guang Yue. Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm. Sustainability. 2019; 11 (13):3525.

Chicago/Turabian Style

Han He; Sicheng Li; Lin Hu; Nelson Duarte; Otilia Manta; Xiao-Guang Yue. 2019. "Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm." Sustainability 11, no. 13: 3525.

Journal article
Published: 01 January 2019 in Neural Computing and Applications
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This paper discusses the applicability of Kalman filter in 3D dynamic monitoring of environmental cost. By selecting Kalman filtering algorithm which is suitable for dynamic environmental cost monitoring, the three-dimensional state-space model of environmental cost and the three-dimensional observation system were established based on the analysis and test of the three-dimensional dynamic data of environmental cost. In addition, by analyzing the algorithm of 3D dynamic monitoring model of environmental cost, a three-dimensional state-space monitoring model of environmental cost based on Kalman filter was constructed. Finally, empirical research study of the cement manufacturing enterprise of Ezhou city of Hubei province was carried out.

ACS Style

Shuai Liu; Sicheng Li. Three-dimensional dynamic monitoring of environmental cost based on state-space model. Neural Computing and Applications 2019, 31, 8337 -8350.

AMA Style

Shuai Liu, Sicheng Li. Three-dimensional dynamic monitoring of environmental cost based on state-space model. Neural Computing and Applications. 2019; 31 (12):8337-8350.

Chicago/Turabian Style

Shuai Liu; Sicheng Li. 2019. "Three-dimensional dynamic monitoring of environmental cost based on state-space model." Neural Computing and Applications 31, no. 12: 8337-8350.