The evaluation of landslide susceptibility is of great significance in the prevention and management of geological hazards. The accuracy of the landslide susceptibility prediction model based on machine learning is significantly higher than that of traditional expert knowledge and the conventional mathematical statistics model. The correct and reasonable selection of non-landslide samples in the machine learning model greatly improves the prediction accuracy and reliability of the regional landslide susceptibility model. Focusing on the problem of selecting non-landslide samples in the machine learning model for landslide susceptibility evaluation, this paper proposes a landslide susceptibility evaluation method based on the combination of an information model and machine learning in traditional mathematical statistics. First, the influence factors for landslide susceptibility evaluation are screened by the correlation analysis method. Second, the information value model is used to delimit areas with low and relatively low landslide susceptibility, and non-landslide points are randomly selected. Third, a landslide susceptibility evaluation method combined with IV-ML, such as logistic regression (IV-LR), random forest (IV-RF), support vector machine (IV-SVM), and artificial neural network (IV-ANN), is established. Finally, the landslide susceptibility factors in the Dabie Mountain area of Anhui Province are analyzed, and the accuracy of the landslide susceptibility evaluation results using the IV-LR, IV-RF, IV-SVM, and IV-ANN and LR, RF, SVM, and ANN methods are compared. The accuracy is evaluated by examining the ACC, AUC, and kappa values of the model. The results indicate that the evaluation effect of the IV-ML models (IV-LR, IV-RF, IV-SVM, IV-ANN) on landslide susceptibility is significantly higher than that of the ML models (LR, RF, SVM, ANN).

A structural landform is defined by its surface morphology, controlled by tectonics, lithology (arrangement and resistance), and folded structures, and demonstrated by the characteristics and relationships between geological and geomorphic elements. It is very important to use geographic information system (GIS) technology to accurately describe and express elements of structural landforms and their relationships. In this study, a GIS approach for expressing structural landforms, based on “forms–elements–relationships”, was developed. The contributions of this paper are as follows: (1) Combined with the surface morphological characteristics, the structural landforms were abstracted into geological and geomorphic elements, and the characteristics and relationships of these elements were analyzed. (2) The elements of structural landforms and their relationships were abstracted into spatial objects and topological relationships. The spatial objects of the structural landform were designed based on the types and characteristics of structural landform elements. The topological relationships were developed based on the definition of the structural landform morphotype. (3) The structural landform markup language (SLML) method of “forms–elements–relationships” was created. (4) Two typical structural landforms, namely, Qixia Mountain and Gaoli Mountain, were used as examples to verify the feasibility and effectiveness of the GIS approach for expressing structural landforms. This paper describes and expresses the “forms–elements–relationships” of structural landforms from the perspective of GIS, which is expected to promote the joint development of structural geomorphology and GIS.

随着国际社会对气候变化问题重要性和紧迫性认识的不断加深，如何开展国际合作，共同减缓气候变化已成为全球重要议题。由于温室气体排放存在全球负外部性，各国均存在一定的“搭便车”倾向，对促进不同国家合作减排背后的理论基础——国际环境协议的研究也在不断深入。近年来，随着全球温度增加，气候突变的可能性变高，进一步加剧了全球气候治理的难度。本文建立了同质国家参与气候合作的静态博弈模型，引入了气候临界点来刻画气候系统的不确定性，进而考察国家参与气候合作的收益变化以及合作行为。研究发现排放量阈值低但气候临界点带来的损失较高时，气候临界点会促进国家合作。而如果排放量阈值低且气候临界点带来的损失足够高时，气候临界点的威胁可能会减弱国家间的合作；而更高水平的合作总是能降低气候越过临界点的可能性，所以应当寻求更高的合作水平来减少各国的“搭便车”行为，降低气候临界点出现的风险。

新型电力系统建设是我国建设新能源体系的重要内容。针对电力系统转型规划问题，本文构建了区域电力系统规划转型模型Switch_lvliang，通过对比加入碳中和约束、热电联产技术以及工业侧需求响应后的四种情景，深入分析吕梁2025-2035年的电力系统转型路径。结果发现：1）热电联产技术有助于提高能源利用效率，工业侧需求响应则能优化能源分配，多种策略的综合应用将更有利于实现吕梁地区电力系统的低碳、高效和可持续发展；2）在未来的电力转型过程中，热电联产和工业侧需求响应技术的协同应用有望成为重要的降本增效手段，其中，加入热电联产技术后，电力系统转型成本相较于不加该技术下降12.48%，年度项目总固定成本同比降低40.39%；引入工业侧需求响应后，总成本相较于不加入需求响应的情景下降2.11%，为实现碳中和目标提供支持；3）不同情景下能源调度各有侧重。碳中和约束促使可再生资源调度占比提升，减少对煤炭依赖；热电联产技术提高系统效率与调度灵活性，降低总能源调度量与成本；工业侧需求响应实现工业与电力系统协同调度，优化能源时空分配，提高能源利用效率，减少总发电量需求。