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Geospatial patterns of forest fragmentation over the three traditional giant forested areas of China (Northeastern, southwestern and Southern China) were analyzed comparatively and reported based on a 250-m resolution land cover dataset. Specifically, the spatial patterns of forest fragmentation were characterized by combining geospatial metrics and forest fragmentation models. The driving forces resulting in the differences of the forest spatial patterns were also investigated. Results suggested that forests in southwest China had the highest severity of forest fragmentation, followed by south region and northeast region. The driving forces of forest fragmentation in China were primarily the giant population and improper exploitation of forests. In conclusion, the generated information in the study provided valuable insights and implications as to the fragmentation patterns and the conservation of biodiversity or genes, and the use of the chosen geospatial metrics and forest fragmentation models was quite useful for depicting forest fragmentation patterns.

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With the rapid development of China’s Internet finance industry and the continuous growth of transaction amount in recent years, a variety of financial risks have increased, especially credit risk in the financial industry. Also, the credit risk evaluation is usually made by using the application card scoring model, which has the shortcomings of strict data assumption and inability to process complex data. In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. At the same time, an Internet lending company in China is taken as a case study to compare the performance of the traditional credit card scoring model and the proposed machine learning (ML) algorithm model. The results show that ML algorithm has a very significant advantage in the field of Internet financial risk control, it has more accurate prediction results and has no particularly strict assumptions and restrictions on data, and the process of processing data is more convenient and reliable. We should increase the application of ML in the field of financial risk control. The value of this paper lies in enriching the related research of financial technology and providing a new reference for the practice of financial risk control.

Many textural measures have been developed and used for improving land cover classification accuracy, but they rarely examined the role of textures in improving the performance of forest aboveground biomass estimations. The relationship between texture and biomass is poorly understood. In this paper, SPOT5 HRG datasets were ortho-rectified and atmospherically calibrated. Then the transform of spectral features is introduced, and the extraction of textural measures based on the Gray Level Co-occurrence Matrix is also implemented in accordance with four different directions (0u, 45u, 90u and 135u) and various moving window sizes, ranging from 3 6 3 to 51 6 51. Thus, a variety of textures were generated. Combined with derived topographic features, the forest aboveground biomass estimation models for five predominant forest types in the scenic spot of the Mausoleum of Sun Yat-Sen, Nanjing, are identified and constructed, and the estimation accuracies exhibited by these models are also validated and evaluated respectively. The results indicate that: 1) Most textures are weakly correlated with forest biomass, but minority textural measures such as ME, CR and VA play a significantly effective and critical role in estimating forest biomass; 2) The textures of coniferous forest appear preferable to those of broad-leaved forest and mixed forest in representing the spatial configurations of forests; and 3) Among the topographic features including slope, aspect and elevation, aspect has the lowest correlation with the biomass of a forest in this study.

This paper studies the impact of the implementation of smart city policy (SCP) on the development of low-carbon economy (LCE) in China. For this purpose, we developed a nonconvex meta-frontier data envelopment analysis (DEA) approach to measure LCE and used the differences-in-difference (DID) analysis method in the econometric model to empirically analyze the impact of SCP on LCE, using the dataset of 230 cities from 2005 to 2018. The results show that the implementation of SCP can significantly improve the LCE of cities, and the dynamic effect test presents that the promotion of smart cities to low-carbon economy increases with time. In addition, SCP promotes the development of LCE by optimizing government functions and improving the efficiency of governance and the degree of implementation openness. But there is heterogeneity between different cities as follows: the implementation of SCP has a more significant effect on the promotion of LCE in central and western regions in China and large-scale cities and cities without strict environmental protection planning. Finally, the robustness test verifies the reliability of the experimental data again and puts forward conclusions and policy recommendations.

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