The rapid development of e-commerce in China in recent years has transformed China’s economic and social production landscape as well as people’s lifestyles. In addition, it has an increasing impact ...on China’s energy consumption. Based on Melitz Econometrica, 2003, 71 (6), 1695–1725, this paper introduces the energy efficiency parameters to propose a theoretical analysis model, and performs theoretical and empirical analysis of the impact of e-commerce on China’s energy consumption using panel data at the provincial level in China. This paper suggests that the development of e-commerce can significantly enhance China’s energy output efficiency, reduce energy consumption per unit of GDP, and curb the excessive growth of total energy consumption. Meanwhile, increased investment in energy conservation, technological progress, and optimized energy consumption structure can also help decrease China’s energy consumption per unit of GDP, thus decreasing environmental pollution. Finally, this paper proposes policy suggestions to lower energy consumption per unit of GDP and control the excessive growth of China’s total energy consumption. Compared with existing studies, the main contributions of this paper are as follows. First, because there are only a few studies on e-commerce and energy consumption, this paper further supplements and enriches relevant literature. Second, this paper proposes a theoretical model regarding the impact of e-commerce development on energy consumption, adding a new perspective to research. Third, there is a lack of adequate studies on the development of China’s e-commerce landscape. While using China’s e-commerce development index data at the provincial level, this paper sets up a panel data regression model and performs in-depth analysis of the impact of e-commerce development on China’s energy consumption.
Remote sensing image scene classification has been widely applied and has attracted increasing attention. Recently, convolutional neural networks (CNNs) have achieved remarkable results in scene ...classification. However, scene images have complex semantic relationships between multiscale ground objects, and the traditional stacked network structure lacks the ability to effectively extract multiscale and key features, resulting in limited feature representation capabilities. By simulating the way that humans understand and perceive images, attention mechanisms can be beneficial for quickly and accurately acquiring key features. In our study, we propose a channel-attention-based DenseNet (CAD) network for scene classification. First, the lightweight DenseNet121 is selected as the backbone for the spatial relationship between multiscale ground objects. In the spatial domain, densely connected CNN layers can extract spatial features at multiple scales and correlate with each other. Second, in the channel domain, a channel attention mechanism is introduced to strengthen the weights of the important feature channels adaptively and to suppress the secondary feature channels. Third, the cross-entropy loss function based on label smoothing is used to reduce the impact of interclass similarity upon feature representations. The proposed CAD network is evaluated on three public datasets. The experimental results demonstrate that the CAD network can achieve performance comparable to those of other state-of-the-art methods. The visualization through the Grad-CAM ++ algorithm also reflects the effectiveness of channel attention and the powerful feature representation capabilities of the CAD network.
Long-term stability remains a key issue impeding the commercialization of halide perovskite solar cells (HPVKSCs). The diffusion of molecules and ions causes irreversible degradation to photovoltaic ...device performance. Here, we demonstrate a facile strategy for producing highly stable HPVKSCs by using a thin but compact semimetal Bismuth interlayer. The Bismuth film acts as a robust permeation barrier that both insulates the perovskite from intrusion by undesirable external moisture and protects the metal electrode from iodine corrosion. The Bismuth-interlayer-based devices exhibit greatly improved stability when subjected to humidity, thermal and light stresses. The unencapsulated device retains 88% of its initial efficiency in ambient air in the dark for over 6000 h; the devices maintain 95% and 97% of their initial efficiencies after 85 °C thermal aging and light soaking in nitrogen atmosphere for 500 h, respectively. These sound stability parameters are among the best for planar structured HPVKSCs reported to date.
For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and ...object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body; (2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful; (2) FS improved classification accuracy and reduced features; (3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5) based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.
Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional ...geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.
The Three Gorges region of central western China is one of the most landslide-prone regions in the world. However, landslide detection based on field surveys and optical remote sensing and synthetic ...aperture radar (SAR) techniques remains difficult owing to the dense vegetation cover and mountain shadow. In the present study, an area of Zigui County in the Three Gorges region was selected to test the feasibility of detecting landslides by employing novel features extracted from a LiDAR-derived DTM. Additionally, two small sites—Site 1 and Site 2—were selected for training and were used to classify each other. In addition to the aspect, DTM, and slope images, the following feature sets were proposed to improve the accuracy of landslide detection: (1) the mean aspect, DTM, and slope textures based on four texture directions; (2) aspect, DTM, and slope textures based on aspect; and (3) the moving average and standard deviation (stdev) filter of aspect, DTM, and slope. By combining a feature selection method and the RF algorithm, the classification accuracy was evaluated and landslide boundaries were determined. The results can be summarized as follows. (1) The feature selection method demonstrated that the proposed features provided information useful for effective landslide identification. (2) Feature selection achieved an improvement of about 0.44% in the overall classification accuracy, with the feature set reduced by 74%, from 39 to 10; this can speed up the training of the RF model. (3) When fifty randomly selected 20% of landslide pixels (PLS) and 20% of non-landslide pixels (PNLS) (i.e., 20% of PLS and PNLS) were utilized in addition to the selected feature subsets for training, the test sets (i.e., the remaining 80% of PLS and PNLS) yielded an average overall classification accuracy of 78.24%. The cross training and classification for Site 1 and Site 2 provided overall classification accuracies of 62.65% and 64.50%, respectively. This shows that the random sampling design (which suffered some of the effects of spatial auto-correlation) and the proposed method in this present study contribute jointly to the classification accuracy. (4) Using the Canny operator to delineate landslide boundaries based on the classification results of PLS and PNLS, we obtained results consistent with the referenced landslide inventory maps. Thus, the proposed procedure, which combines LiDAR data, a feature selection method, and the RF algorithm, can identify forested landslides effectively in the Three Gorges region.
•The aspect, DTM, and slope textures based on aspect direction are newly introduced.•Feature selection enhances the classification accuracy and reduces the feature set.•Forested landslides can be detected using a LiDAR DTM and supervised classification.
Stability issues and high material cost constitute the biggest obstacles of a perovskite solar cell (PVSC), hampering its sustainable development. Herein, we demonstrate that, after suitable surface ...modification, the low-cost cerium oxide (CeO x ) nanocrystals can be well dispersed in both polar and nonpolar solvents and easily processed into high-quality electron transport layers (ETLs). The inverted PVSC with the configuration of “NiMgLiO/MAPbI3/6,6-phenyl-C61-butyric acid methyl ester (PCBM)/CeO x ” has achieved a high efficiency up to 18.7%. Especially, the corresponding devices without encapsulation can almost keep their initial PCEs in 30% humidity-controlled air in the dark for 30 days and also show no sign of degradation after continuous light soaking and maximum power point tracking for 200 h in a N2 atmosphere. These results have been proved to be associated with the dual functions achieved by the PCBM/CeO x bilayer ETLs in both efficient electron extraction and good chemical shielding. Furthermore, an all inorganic interfacial layer based PVSC with the configuration of “NiMgLiO/MAPbI3/CeO x ” has also achieved a promising efficiency of 16.7%, reflecting the potential to fabricate efficient PVSCs with extremely low cost.
Deep convolutional neural networks have become an indispensable method in remote sensing image scene classification because of their powerful feature extraction capabilities. However, the ability of ...the models to extract multiscale features and global features on surface objects of complex scenes is currently insufficient. We propose a framework based on global context spatial attention (GCSA) and densely connected convolutional networks to extract multiscale global scene features, called GCSANet. The mixup operation is used to enhance the spatial mixed data of remote sensing images, and the discrete sample space is rendered continuous to improve the smoothness in the neighborhood of the data space. The characteristics of multiscale surface objects are extracted, and their internal dense connection is strengthened by the densely connected backbone network. GCSA is introduced into the densely connected backbone network to encode the context information of the remote sensing scene image into the local features. Experiments were performed on four remote sensing scene datasets to evaluate the performance of GCSANet. The GCSANet achieved the highest classification precision on AID and NWPU datasets and the second-best performance on the UC Merced dataset, indicating the GCSANet can effectively extract the global features of remote sensing images. In addition, the GCSANet presents the highest classification accuracy on the constructed mountain image scene dataset. These results reveal that the GCSANet can effectively extract multiscale global scene features on complex remote sensing scenes. The source codes of this method can be foundin https://github.com/ShubingOuyangcug/GCSANet .
Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using ...high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.
Remote sensing image scene classification plays an important role in remote sensing image interpretation. Deep learning brings prosperity to the research in this field, and numerous deep learning ...models are proposed in order to improve the performance of scene classification. However, images of different remote sensing scenes vary a lot, showing similar or diverse textures and simple or complex contents. Using a fixed convolutional neural network framework to classify scene images is performance-limited and not practice-flexible. To address this issue, in this article, we propose the SEMSDNet (multiscale dense networks with squeeze and excitation attention). The framework multiscale dense convolutional network (MSDNet) with multiple classifiers and dense connections can automatically transform between a small network and a deep network according to the complexity of test samples and the limitation of computational resources. Moreover, in order to extract more effective features, the squeeze-and-excitation (SE) attention mechanism is introduced into the framework to process the features of various scenes self-adaptively. In addition, considering the limited computing resources, we impose two settings with computational constraints at the test time: budgeted batch classification, which is a fixed computational budget setting for sample classification, and anytime prediction, which forces the network to output a prediction at any given point-in-time. Experimental results on several public datasets show that the proposed SEMSDNet method is superior to the state-of-the-art methods on both performance and efficiency. Experiments also reveal its capability to treat samples of different classification difficulties with uneven resource allocation and flexible network architecture, showing its potentials in practical applications.
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