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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]

• 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]

• 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]