Labor, land, and funds are keys to revitalizing rural areas around the world. Previous studies have focused on the impacts of funds on agricultural production, but placed little emphasis on its role in agricultural land-use transformation. Thus, this study explores the quantitative relationship between agricultural credit and farmland abandonment from the perspective of rural revitalization. Using data on 8031 households from 27 provinces obtained from China’s Labor Force Dynamics Survey (CLDS), this study uses a Tobit model to examine the quantitative impacts of informal and formal agricultural credit on farmland abandonment. The results indicate that: (1) Access to agricultural credit helps to reduce farmland abandonment. (2) Compared with formal agricultural credit (provided by institutions), informal agricultural credit (provided by family and friends) is more significant in reducing farmland abandonment. Thus, this study enhances our understanding of the relationship between agricultural credit and farmland use. It will also prompt policymakers to improve rural financial markets in order to reduce the misallocation of farmland resources, thereby improving food security and rural economies.
Financialization of non-financial corporations is an important factor affecting innovation activities. This paper calculates the optimal financialization of enterprises and the deviation of optimal financialization, divides amples into moderate and excessive financialization, then investigates the relationship between financialization and sustainable innovation in different research samples using the data of A-share manufacturing enterprises in China from 2012 to 2018. The results indicate that the deviation of optimal financialization is negatively related to the persistent innovation of enterprises. However, financialization had significantly different effects on persistent innovation in different research sample. More specifically, excessive financialization could crowd out the persistent innovation, but moderate financialization may promote the persistent innovation. This study provides a new perspective for understanding the relationship between financialization and innovation, and helps finance better serve the real economy.
The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines.
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