Search

Abrupt air pollution accidents can endanger people's health and destroy the local ecological environment. The appropriate emergency response can minimize the harmful effects of accidents and protect people's lives and property. This paper provides an overview of the key emergency response technologies for abrupt air pollution accidents around the globe with emphasis on the major achievements that China has obtained in recent years. With decades of effort, China has made significant progress in emergency monitoring technologies and equipment, source estimation technologies, pollutant dispersion simulation technologies and others. Many effective domestic emergency monitoring instruments (e.g., portable DOAS/FT-IR systems, portable FID/PID systems, portable GC-MS systems, scanning imaging remote sensing systems, and emergency monitoring vehicles) had been developed which can meet the demands for routine emergency response activities. A monitoring layout technique combining air dispersion simulation, fuzzy comprehensive evaluation, and a post-optimality analysis was proposed to identify the optimal monitoring layout scheme under the constraints of limited monitoring resources. Multiple source estimation technologies, including the forward method and the inversion method, have been established and evaluated under various scenarios. Multi-scale dynamic pollution dispersion simulation systems with high temporal and spatial resolution were further developed. A comprehensive emergency response platform integrating database support, source estimation, monitoring schemes, fast monitoring of pollutants, pollution predictions and risk assessment was developed based on the technical idea of "source identification - model simulation - environmental monitoring" dynamic interactive feedback. It is expected that the emergency response capability for abrupt air pollution accidents will gradually improve in China. [Display omitted] [ABSTRACT FROM AUTHOR]

Emergency decision-making is vital for nations or communities because it increases emergency management's effectiveness and legitimacy, which in turn greatly minimizes environmental damage, fatalities, and economic loss. The evaluation of emergency judgements must take into account significant inaccuracy, fuzzyness, and ambiguity. While high risk and uncertainty are frequently characteristics of emergency decision-making (EDM) circumstances. The current EDM methodologies do not take into account the psychological behavior of the decision makers in addition to the various emergency situations and the various responses. In this paper, we presented a new method based on a Complex Probabilistic Hesitant Fuzzy Soft Set (CPHFSS) as represented by membership in 2-D with hesitant probability. The idea that is being put forth captures the ambiguity and takes into account emergency situations while also taking into account the psychological state of the decision-makers involved in the EDM procedure. The introduction of a new score function for the CPHFSS serves as the second objective of this paper, which is to address a number of comparability issues. We examine a novel family of hybrid operators for CPHFSS that utilize numerous independent variables as a solution to these unforeseen issues. The group decision-making strategy and EDAS technique are then suggested employing these operators. Later, an emergency decision-making situation involving a major fire in China is used to demonstrate the validity of the algorithms. • Concept of complex probabilistic hesitant fuzzy soft set is presented. • Introduce a new score function for the CPHFSS. • Group decision-making strategy and EDAS technique presented. • Emergency decision-making problems discussed in the study. • Case study related to industrial disaster emergency decision-making discuss. [ABSTRACT FROM AUTHOR]

Small modular reactors (SMRs) are rapidly developed worldwide in recent years for their potential extensive applications. China is very active in promoting SMR design and construction. However, the current emergency preparedness (EP) guidance in China is developed based on the characteristics of large water-cooled reactors, and they are only partially applicable to SMRs. In our previous work, we have fully demonstrated from a technical perspective that it is feasible for China to establish a unified EP framework for all SMR designs based on the dose and consequence of an accident spectrum. Therefore, this paper will comprehensively discuss the costs and benefits of establishing a unified EP guidance for all SMR designs in China. Uncertainty analysis is also conducted for variables with great uncertainty. Through the study in this paper, it can be concluded that developing a new unified EP regulatory guidance for all SMR designs in China is cost beneficial in most cases. In addition, it will also bring other benefits that are hard to be quantified, such as the consistency of supervision, the clarity of the EP plan, and the improved engagement of all stakeholders during SMR development. In a word, it would always be beneficial to establish a new unified EP regulatory guidance for all SMR designs in the long term. • The fact that current emrgency preparedness guidance is only partially applicable for licensing SMR. • This paper proposes an operable process of cost-benefit analysis for regulatory guidance formulation. • Quantification approach is used to discussed cost-benifit analysis in guidance development. [ABSTRACT FROM AUTHOR]

Frequent occurrence of natural disasters has become one of the most serious challenges to human society. As rural communities are the forefront of disasters, enhancing the resilience of rural community is crucial to mitigate the impact of natural disasters and seek rapid recovery. This paper provides an analytical framework for changing trajectories of the resilience of rural communities after disasters. Taking the disaster area affected by the catastrophic debris flow in China as an example, we analyze the changing trajectories of rural community resilience after disaster in three villages with different damage levels from 2010 to 2020 based on the data from key person interviews, the disaster situation and development reports, village work records and farmers' questionnaires. We found that the changes of community resilience after disaster show obvious stages due to the difference in the intensity of effects of government intervention and community self-organization ability during disaster relief reconstruction period and self-recovery period. The resilience of communities with different disaster losses showed an increasing trend. However, during the disaster relief and reconstruction period, the resilience of the severely affected community was improved faster, followed by the most severely affected community, while the resilience of generally affected community was improved more slowly. During the self-recovery period, the resilience of the affected general community was improved faster than that of the other two communities. The evolution process of community resilience reflects the increasing role of self-organizational capacity in post-disaster community development, aligning with the overall global trend in rural community governance. In addition, there is a significant sequence in the improvement speed of different dimensions of community resilience at different stages after disasters. During the disaster relief and reconstruction period, the resilience of community management was improved faster and the resilience of environment was slower. During the self-recovery period, the economic resilience of community was improved faster and the social resilience was improved slower. Finally, some suggestions were put forward to strengthen natural disasters prevention and community resilience in disaster-prone areas. [Display omitted] • Provide an analytical framework for the changing trajectories of the resilience of rural communities after disaster. • Community self-organization ability play an important role in improving community resilience. • There are significant differences in resilience of communities with different disaster losses. • There is an obvious sequence in the improvement speed of different dimensions of community resilience after disaster. [ABSTRACT FROM AUTHOR]

Coastal areas in China are particularly suffering from marine disasters, as they are attacked by marine disasters frequently, suddenly, catastrophically, leading to massive economic losses, even deaths. It is, therefore, necessary and urgent to build and reinforce a robust risk reduction arrangement for marine disasters in China. Marine disaster index insurance (MDII) has been recognized as an excellent emerging instrument for marine disaster risk management in coastal areas due to its dual advantages of avoiding moral hazard and adverse selection, besides immediate compensation. However, a clear understanding of its evolution, experience, and characteristics of MDII in China is remaining deficient. Taking the review and analysis of policy, this paper traces the history of evolution and investigates China's MDII pilots since 2013. It was found that China has made great progress in piloting MDII with the support of government policies in the past decade, which can be summarized in three stages: germination stage, initial development stage, and comprehensive development stage. However, some deficiencies and challenges have been identified from the investigation, including limited coverage, insufficient demand, inadequate supply, and imperfect subsidy policies. This paper proposes the following improvement strategies: improve the market ecosystem and regulatory system for MDII, enhance the ability of MDII insurers to deal with immediate huge claims, diversify the channels and forms of subsidy policies for MDII, raise public awareness of using MDII to manage marine disaster risks, and design scientific MDII products. [ABSTRACT FROM AUTHOR]

• A chance constrained stochastic programming model is proposed. • The uncertainty, dynamism and diversity of distribution system are considered. • The level assignment method is utilized to address distribution center disruptions. • The multimodal transportation system is introduced for road network disruptions. • An evolutionary algorithm is developed for large-scale cases. This paper presents a comprehensive approach to addressing the challenges of designing a reliable emergency logistics network under the dynamic uncertainty of natural disasters. The proposed multimodal transportation strategy based on level assignments can effectively mitigate the risk of facility disruptions and network uncertainty and enhance the adaptability of the system to diverse emergency supplies. The established two-stage chance constrained stochastic programming model and improved evolutionary algorithm are powerful tools for optimizing the network design and improving its resilience. Based on the real-world case of the Ya'an earthquake in China, the impacts of various practical constraints are analyzed. Several insights into the decision-making process of emergency logistics management are provided: managers should prioritize time utility over cost utility and use multiple aircraft and helicopters, supplies should be sorted into fewer and more important categories and placed in smaller packages for efficient transportation, multimodal transportation should be considered based on geographical and traffic conditions. [ABSTRACT FROM AUTHOR]

Accurate forecasting is required for the effective risk management of drought disasters. Many machine learning- and deep learning-based models have been proposed for drought forecasting, however, they cannot handle the temporal and/or spatial dependencies in the input data, causing unexpected forecasting results. In order to solve the challenging issue, in this paper we proposed the Heterogeneous Spatio-Temporal Graph (HetSPGraph), for drought forecasting. It includes three major layers: spatial aggregations including inter and intra aggregations, temporal aggregation, and a forecasting network. The main function of HetSPGraph is to learn the dynamic spatiotemporal correlations between the regions and to further predict the drought in different regions, based on which accurate drought forecasting can be achieved. Experimental forecasting results of the Standardized Precipitation Evapotranspiration Index (SPEI) in China indicated that the HetSPGraph model outperformed the traditional baseline methods including the Long Short-Term Memory model (LSTM), Convolutional Neural Network-LSTM (CNN-LSTM), Gated Recurrent Unit (GRU), Spatio-Temporal Graph Convolutional Networks (STGCN) and Geographic-Semantic-Temporal Hypergraph Convolutional Network (GST-HCN). Even for long-term forecasting (12 months), more accurate forecasting results, with the coefficient of determination R 2 higher than 0.89, can also be obtained by HetSPGraph compared to the other three models. The proposed HetSPGraph model has the potential for wider use in forecasting drought and other natural disasters. • A novel HetSPGraph approach with three layers is proposed for drought forecasting. • Dynamic spatiotemporal correlation of droughts at multi-time scales can be described by HetSPGraph. • The HetSPGraph model outperformed conventional Deep Learning models for drought forecasting. • The HetSPGraph model can keep its stable accuracy at long-term forecasting period (12 months). [ABSTRACT FROM AUTHOR]

Floods pose significant threats to farmers, and engaging in effective disaster avoidance behaviors can mitigate some of these risks for farmers in flood risk areas (FRA). However, due to farmers' bounded rationality characteristics, they often have a cognitive bias of blindly overestimating or underestimating flood risk, thus failing to perform correct disaster avoidance behaviors. Based on survey data from 180 farmers in FRA in southwest China, this study constructs an assessment method for farmers' subjective and objective flood risk and analyzes the characteristics of farmers' subjective and objective flood risk. A probit regression model explores the correlation between subjective and objective flood risk consistency and farmers' willingness to purchase natural disaster insurance (WPNDI). Results showed that of the 180 farmers surveyed, 63.89% had inconsistent subjective and objective flood risks, mainly reflected in an underestimation of objective flood risk. Farmers' subjective and objective flood risk consistency was found to be positively and significantly correlated with their WPNDI. When other conditions remain unchanged, for every unit increase in the consistency of farmers' subjective and objective flood risk, their WPNDI increased by 0.601 units on average. This paper originally proposed the hypothesis that the consistency of farmers' subjective and objective flood risk is positively and significantly correlated with their WPNDI, and proved the reliability of this hypothesis through research. These findings can provide policy references for disaster risk management and resilient disaster prevention system construction in FRA. [Display omitted] • The subjective and objective flood risk of farmers in FRA is assessed. • The correlation between farmers' subjective and objective flood risk consistency and WPNDI is analyzed by probit model. • Inconsistency of farmers' subjective and objective flood risk mainly reflected in underestimating the objective flood risk. • Farmers' subjective and objective flood risk consistency is positively and significantly correlated with their WPNDI. [ABSTRACT FROM AUTHOR]

The recent Russia-Ukraine war has reignited the world's biggest energy crisis in decades. As an important pillar of energy security, system resilience has attracted much attention. Previous research has focused on conception, composition and measurement. Few captured the dynamic features of resilience process of energy systems. In this paper, an input-output nested system dynamics (IO-SD) model is proposed to study the resilience of natural gas systems in a crisis. As a special feature, the model captures both inter-sectoral linkages and dynamic transmission of the crisis. The study highlighted and analyzed the role of emergency measures such as the release of stockpiles and emergency supplies. Later, the resilience performance of China gas system in 2017-crisis was examined in various aspects, e.g., economic loss and efficiency of emergency measures. We find that strategic storage could be beneficial to the resilience of NG systems. But expanding the storage size in isolation is not enough. Two key measures to enhance the resilience of the system are: (1) increasing the flexibility and operational efficiency of emergency facilities, e.g., storage releasing capability and gasification efficiency; and (2) building efficient infrastructure and services, enhancing connectivity of storage infrastructure and supply networks, and removing bottlenecks in regional interconnection. • Resilience plays an increasing important role in securing natural gas security. • The proposed resilience framework considers both restoring and economic performance. • The IO-SD model captures dynamic feature and intersection relations of gas system. • Enhance storage releasing rate can improve China gas system resilience effectively. • Strengthen connectivity of infrastructure can improve system resilience effectively. [ABSTRACT FROM AUTHOR]

• Built the storm surge disaster dataset with 50 indicators and 132 events. • Scientific data preprocessing methods ensure the reliability of the model. • Rank the importance of 50 indicators; hazard indicators are more important. • The indicator screening renders model simplification and workload reduction. • Satisfy machine learning models of storm surge disaster loss estimation are obtained. Storm surge is the most severe marine disaster in China, affecting the whole coastal area. Estimating storm surge disaster loss (SSDL) is significant to disaster prevention, sustainability and decision-making. Taking 11 provincial administrative regions in the coastal areas of China as the study area, this paper estimated SSDL grades based on four machine learning (ML) algorithms. A total of 132 pieces of official open-source data of storm surge disasters were collected and divided into a cross-validation set (CV set) and a test set. First, a comprehensive indicator system was constructed from three perspectives, covering the hazard (16) of disaster-causing factors, the vulnerability (22) and resilience (12) of disaster-bearing bodies, including 50 indicators. A few data preprocessing methods are implemented to improve the model performance such as normalization, SMOTE, etc. Then, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic model tree (LMT), and K-star were applied to construct the estimation model of SSDL grades. Principal component analysis (PCA) and recursive feature elimination (RFE) are adopted for an intelligent screening of the indicators. Finally, the models' performance is compared through Precision, Recall, F1 score and Kappa metrics. The results show that scientific and efficient data preparation is a strong guarantee for the reliability and stability of the models. RFE is verified more suitable for indicator selection in this paper compared with PCA. The importance ranking of RFE enhances the interpretability of the ML model, which shows that the hazard indicator is the most important, the vulnerability indicator is the second, and the resilience indicator is the least. The 27-indicator K-star model, with advantages of accurate estimation, strong generalization, and less workload, is the optimal SSDL estimation model. The number of input indicators of the optimal SSDL estimation model is 27, its CV Precision, Recall, F1 score, and Kappa are 0.838, 0.832, 0.827, and 0.776, and its Precision, Recall, F1 score, and Kappa for test set are 0.819, 0.786, 0.781, and 0.714, respectively. This paper provides a scientific basis for the government's decision-making and risk management, and it can be used as a typical demonstration case of SSDL research. [ABSTRACT FROM AUTHOR]

Port based transportations are key elements governing the operation of global maritime logistics. In China, the southeast coastline suffers from typhoons frequently every year and causes many disruptions to port operations and thus economic losses. Quantitative evaluation of coastal ports' economic losses induced by typhoons is quite demanding and can serve as guidance for port operation planning and disaster prevention. In this paper, a framework based on a hybrid wind numerical model for assessing the direct and indirect economic losses of coastline caused by typhoon-induced extreme wind is proposed. The estimation of port disruptions and their durations is realized based on the typhoon hybrid wind numerical model. For the direct economic losses, this model considers losses to the port based on the loading/unloading operations of containers. The disruption days and economic losses of 31 ports along China's coastline are selected. These include ports from the Bohai Rim Port Cluster, Yangtze River Delta Port Cluster, Southeastern Coastal Port Cluster, Pearl River Delta Port Cluster, and Southwestern Coastal Port Cluster. The analysis takes the record of typhoons from 2006 to 2020 for this investigation. The influence of time-varying variables on economic losses, such as economic growth rate, has been considered. For the indirect economic losses, the input-output models of 11 different provinces (or municipalities) are employed to analyze each sector. Features of adopting a climate model in the economic evaluations are discussed. [ABSTRACT FROM AUTHOR]

• Develop a complete problem-solving approach for group decision making. • Orient complex large-scale groups and time-sensitive emergency decision scenarios. • Design a feasible mechanism of group consensus strategies under time pressure. • Propose feedback recommendation strategies with a subgroup identification method. • A real case study of China's fight against the COVID-19 epidemic. Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards. [ABSTRACT FROM AUTHOR]

• A new method for evaluating the failure of roof and floor strata under dynamic load impact is proposed. • The Energy Variation Coefficient (CV E) and Two Spatial Factors (S and α) are introduced to study the energy difference and aggregation characteristics of microseismic events respectively. • The vulnerability index model is established based on the principal component - entropy method, and the risk zoning map is determined by ArcGIS. • The different microseismic response characteristics of roof and floor are distinguished, which can be used for targeted disaster prevention and control. The disaster induced by dynamic loads in the coal mining process seriously affects the safe production of coal mines. A new method for failure evaluation of roof and floor strata under dynamic load impact is proposed in this paper. The key roof strata breaking and periodic weighting experienced by the 9212 working face of the Dongpang Coal Mine in China while passing through the abandoned roadway is taken as the research background. Firstly, the research stages are divided through theoretical analysis and microseismic monitoring, and then the plane, profile distribution and spatial position relationship of microseismic events in different stages are analyzed. After that, the energy variation coefficient ( CV E) and two spatial factors (S and α) are introduced to analyse the difference in the migration characteristics and damage degree of roof and floor strata under dynamic load impact. Then, the comprehensive weight is determined by principal component analysis and entropy weight method, and the failure evaluation model is established based on vulnerability index method; Finally, the risk grade zoning map of three stages is drawn by using ArcGIS technology. The research shows that the angle factor α can better describe the development direction of roof and floor fissures. The area factor S combined with the energy variation coefficient CV E can explain the development speed of fissures and the damage degree of coal and rock mass. According to the failure evaluation model, the corresponding risk degree of roof and floor in different stages can be comprehensively evaluated. The research results can be used as a reference for targeted disaster prevention and control in similar situations, such as large-area roof collapse and floor water inrush. [ABSTRACT FROM AUTHOR]

Rapid urbanization and exacerbated natural hazards, for example, floods, have been the big challenges for the sustainable development of many countries. Understanding the coupling coordination between urbanization and flood is important for promoting the sustainable development. However, there are few quantitative studies on the interaction between these two in China which is experiencing accelerated urbanization and climate change. In this paper, we developed an improved coupling coordination degree (CCD) model, applied Moran's I and projection pursuit model based on genetic algorithm to analyze the spatiotemporal distribution and spatial clustering effects of coupling coordination in urbanization–flood disasters system under the context of China's Five-Year Plan for National Economic and Social Development. Our results showed an upward trend of the comprehensive level of urbanization subsystem and a decreasing first and rising later trend of flood disaster subsystem in China from 2001 to 2018. There was a U-shaped relationship between the two subsystems during the research period. From 2001 to 2018, provinces with the moderate and slight uncoordinated development decreased by 9.68% and 16.13%, respectively, while provinces with the barely coordinated development increased by 16.21%. Spatially, there was a remarkable spatial dependency on CCD. The CCD between two subsystems in Northwestern China was higher than that in Southeastern China. Due to the effects of geographic position, rainfall, human activities, and economic development, the low-low (L-L) clusters concentrated in Southeastern China, and the high-high (H–H) clusters appeared in Northwestern China. Our results show that the improved CCD model, along with Moran's I analysis, can provide theoretical basis for policy makers to formulate reasonable measures for promoting the coupling coordination between urbanization and flood disasters. • Spatiotemporal distribution and dynamic evolution of nexus between urbanization and flood disasters were investigated. • Improved CCD model, Moran's I and projection pursuit model based on genetic algorithm were developed. • There was a U-shaped relationship in comprehensive level between urbanization and flood disasters. • CCD between two subsystems in Northwestern China was higher than that in Southeastern China. • There was a remarkable spatial clustering effects in CCD. [ABSTRACT FROM AUTHOR]

Due to the national nutrition crisis and the adjustment of planting structure, millet has once again become a widely planted cash crop in Northern China. However, as the climate warms in recent years, drought has occurred frequently and repeatedly in Northern China, which has seriously threatened millet production safety in this area. Therefore, it is urgent to clarify the hazard of millet drought disaster and provide reference for the formulation of millet disaster prevention and reduction measures. In this paper, combined with the water demand of millet in each growth period, Standardized Precipitation Requirement Index (SPRI) was selected to identify and quantify the drought disaster of millet. Considering the impact of different degrees of drought in different growth periods, a hazard assessment model is constructed to evaluate the hazard of millet drought disaster in Northern China under the background of climate change. The results show that: (1) SPRI can well characterize the drought of millet in Northern China. (2) In the context of climate change, millet drought in Northern China shows a trend of increasing intensity and decreasing frequency, and it is possible that millet drought will be even more severe in the future. (3) Combined with the hazard assessment model, millet drought in Northern China will still be dominated by middle hazard in the future, but the areas with high hazard will gradually increase, mainly distributed in northeast China, central and western Inner Mongolia and northern Gansu. The research results can be of great practical significance for the adjustment of agricultural production structure in the future, especially for the sustainable development of agriculture and the formulation of measures to prevent and resist agricultural disasters. [Display omitted] • We apply SPRI to identify millet drought by considering actual water demand. • Future changes of millet drought evaluated by CMIP6-GCM models. • Hazard assessment model was presented combined with disaster loss coefficient. • The areas with high hazard of millet drought will gradually increase in future. [ABSTRACT FROM AUTHOR]

• We address a less studied topic: states' cultural response to disasters. • We propose a perspective based on the cultural sociology of the state and cultural theories of trauma, theodicy, and performance. • States' cultural response is " performance ": states project their definitions of situation of disasters and construct their positive self-images when interacting with the publics. • The efficacy of their performance depends on how effectively states' address the key components of the " nomos " or " meaning structure " about catastrophes and suffering and how this performance is influenced by their political structures. • Our theoretical perspective is illustrated in a comparative study of the 2008 Sichuan earthquake in China and the 2005 Hurricane Katrina in the US. • The findings also speak to an enduring debate over whether democratic or authoritarian regimes perform better in disaster responses. The sociology of disaster has paid scant attention to states' cultural response—states' effort to use meaningful narratives and symbolic actions to address issues about citizens' suffering and death to enhance their legitimacy and secure citizens' support. Our paper starts to address this gap by asking: How do states culturally respond to massive disasters? How effective are their responses? What can explain the efficacy of their cultural responses? We propose a perspective based on the cultural sociology of the state and cultural theories of trauma, theodicy, and performance. We illustrate this perspective in a comparative study of the 2008 Sichuan earthquake in China and the 2005 Hurricane Katrina in the United States. We argue that the efficacy of their cultural responses depended on whether and how effectively they addressed the key components of the meaning structure of disasters through compassionate reaction to citizens' suffering and death, convincing accounts of states' accountability, and cogent narratives about the long-term consequences of disasters on citizens. Both states struggled to address these issues at various points in their respective disasters, and their cultural responses were shaped by their political structures. The findings also speak to an enduring debate over whether democratic or authoritarian regimes perform better in disaster responses. We eschew the exclusive focus of the debate on administrative responses and its simplistic correlation between regime type and responses. [ABSTRACT FROM AUTHOR]

• We propose a new forest prediction model and use actual deforestation data for optimization and verification. • Use the XGBoost method to reconstruct deforestation intensity data. • Deforestation hotspots in China are mainly concentrated in the southern and southeastern regions. • Our model provides certain reference information for forest disaster prevention and forest management. Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China. [ABSTRACT FROM AUTHOR]

In this paper, we utilize network theory to model and study the coupled high-speed rail–aviation network (HSR–AN) in China. The HSR–AN depicting the transport services is modelled as a transfer network in P-space (TNP) in this research. The basic topology properties of the TNP and the corresponding critical components (e.g., nodes and areas) are analysed. As one type of spatial networks coupled two transportation networks, TNP of HSR–AN, counterintuitively, exhibits small-world properties which can be attributed by offering service connecting long-distance cities without transfer and complementary relationship between two transport modes. Additionally, an analysis of vital node (area) in vulnerability environment indicates that the importance of network components critically depends on the economic development level, population density, and geographical features in the corresponding city. Besides, the failure of high-speed rail station generates worse effect than that on airport in HSR–ANs. The findings are beneficial for enhancing the efficiency of HSR–ANs and developing emergency response plans for transport managers. [ABSTRACT FROM AUTHOR]

Upon the occurrence of a disaster, emergency supplies should arrive at disaster areas with different severities in the shortest time. Therefore, it is of pivotal importance to plan and schedule appropriate processes. This paper first elaborates on the principle of ordered arrival of emergency supplies. Then, it establishes a multi-objective multi-constraint emergency material scheduling (EMS) model based on the principle of ordered arrival. Subsequently, a hybrid ant colony optimization (HACO) is proposed to solve EMS. The computational results for 15 multi-depot benchmark instances are reported and the average error between HACO and the best known solution (BKS) is only 0.16% Then, real geological disasters from Hubei province in China are examined as a case study. Experimental results show that HACO performs better than non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective ant colony algorithm (MOACO) in the case study and benchmark instances. • Study a large-scale real landslide disaster. • Establish a multi-objective multi-constraint EMS model. • Propose a HACO to solve the EMS model. • The HACO is tested on benchmarks and disaster network. [ABSTRACT FROM AUTHOR]

• The characteristics of residents' livelihood capital, evacuation and relocation willingness were analyzed. • The correlations between residents' livelihood capital, evacuation and relocation willingness were explored. • Livelihood capital is an important influence on residents' willingness to relocate. Evacuation and relocation are the most effective ways for residents to avoid disasters, and can ensure the property and safety of life. Therefore, it is of great significance for regional disaster risk management systems to explore the driving factors of residents' willingness to evacuate and relocate from earthquake-stricken areas. However, few scholars have quantitatively explored the relationships between residents' livelihood capital and their evacuation and relocation willingness, with consideration of residents' livelihood sustainability. According to the research objectives, stratified sampling and equal probability random sampling were used to determine the sample farmers, and the survey data of 327 households in Wenchuan and Lushan earthquake-stricken areas were obtained. This paper analyzed the characteristics of residents' livelihood capital, evacuation and relocation willingness, and used ordinary least squares (OLS) to explore correlations between residents' livelihood capital, evacuation and relocation willingness. The results show that: (1) the livelihood capital of residents is mainly comprised of human capital and social capital. Facing the threat of earthquake disaster, 40.98% and 28.75% of residents would be strongly willing to evacuate or relocate, respectively. (2) Livelihood capital is an important influence on residents' willingness to relocate; the higher the scores of natural capital, the weaker the willingness of residents to relocate. From the perspective of rural households' sustainable livelihoods, this study deepens our understanding of evacuation and relocation behaviors and decision-making by residents of the most earthquake-stricken areas. Thus, it provides useful information for regional disaster risk management. [ABSTRACT FROM AUTHOR]