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Sub-Saharan Africa has suffered frequent outbreaks of armed conflict since the end of the Cold War. Although several efforts have been made to understand the underlying causes of armed conflict and establish an early warning mechanism, there is still a lack of a comprehensive assessment approach to model the incidence risk of armed conflict well. Based on a large database of armed conflict events and related spatial datasets covering the period 2000-2019, this study uses a boosted regression tree (BRT) approach to model the spatiotemporal distribution of armed conflict risk in sub-Saharan Africa. Evaluation of accuracy indicates that the simulated models obtain high performance with an area under the receiver operator characteristic curve (ROC-AUC) mean value of 0.937 and an area under the precision recall curves (PR-AUC) mean value of 0.891. The result of the relative contribution indicates that the background context factors (i.e., social welfare and the political system) are the main driving factors of armed conflict risk, with a mean relative contribution of 92.599%. By comparison, the climate change-related variables have relatively little effect on armed conflict risk, accounting for only 7.401% of the total. These results provide novel insight into modelling the incidence risk of armed conflict, which may help implement interventions to prevent and minimize the harm of armed conflict., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Xie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000-2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict., (© 2022. The Author(s).)

The demand for energy plants is foreseen to grow as worldwide energy and climate policies promote the use of bioenergy for climate change mitigation. To avoid competing with food production, it's critical to assess future changes in marginal land availability for energy plant development. Using a machine learning method, boosted regression tree, this study modeled potential marginal land resources suitable for cassava under current and different climate change scenarios, based on cassava occurrence records and environmental covariates. The findings revealed that, currently, over 80% of the 1357.24 Mha of available marginal land for cassava cultivation is distributed in Africa and South America. Under three climate change scenarios, by 2030, worldwide suitable marginal land resources were predicted to grow by 39.71Mha, 66.21 Mha, and 39.31Mha for the RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively; by 2050, the potential marginal land suitable for cassava will increase by 38.98Mha, 83.02 Mha, and 55.43Mha, respectively; by 2080, the global marginal land resources were estimated to rise by 40.82 Mha, 99.74 Mha, and 21.87 Mha from now, respectively. Our results highlight the impacts of climate change on potential marginal land resources of cassava across worldwide, which provide the basis for assessing bioenergy potential in the future. [ABSTRACT FROM AUTHOR]

Scrub typhus is a climate‐sensitive and life‐threatening vector‐borne disease that poses a growing public health threat. Although the climate‐epidemic associations of many vector‐borne diseases have been studied for decades, the impacts of climate on scrub typhus remain poorly understood, especially in the context of global warming. Here we incorporate Chinese national surveillance data on scrub typhus from 2010 to 2019 into a climate‐driven generalized additive mixed model to explain the spatiotemporal dynamics of this disease and predict how it may be affected by climate change under various representative concentration pathways (RCPs) for three future time periods (the 2030s, 2050s, and 2080s). Our results demonstrate that temperature, precipitation, and relative humidity play key roles in driving the seasonal epidemic of scrub typhus in mainland China with a 2‐month lag. Our findings show that the change of projected spatiotemporal dynamics of scrub typhus will be heterogeneous and will depend on specific combinations of regional climate conditions in future climate scenarios. Our results contribute to a better understanding of spatiotemporal dynamics of scrub typhus, which can help public health authorities refine their prevention and control measures to reduce the risks resulting from climate change. [ABSTRACT FROM AUTHOR]

Bioenergy from cassava is a promising alternative energy source for both energy supply and the mitigation of greenhouse gases. However, major global trends, such as climate change and competing landuse patterns, pose substantial risks to the sustainable development of bioenergy. The main purpose of this study was to assess the sustainable development of bioenergy from cassava, considering landuse change and climate change with a biogeochemical process model within the “water-energy-food” nexus framework. The results showed that the land resources that were suitable for the development of cassava bioenergy have continuously decreased in China since 1990. At the same time, the climate has also undergone significant changes, with temperature showing an increasing trend, and precipitation showing a decreasing trend. With the influences of both landuse change and climate change, the total bioenergy of cassava showed a downward trend. In China, the potential bioenergy production for the year 1990, 2000, and 2010 was 6075 PJ, 5974 PJ, and 4399 PJ, respectively. Compared to 1990, the bioenergy production in 2010 decreased by 1676.40 million GJ, which equals 57 million tons of standard coal. In addition, the water footprint of bioenergy from cassava was discussed. After considering changes to landuse, climate, and water footprint, it was concluded that Guangxi was the most suitable place to develop cassava bioenergy, followed by Fujian, Guangdong, and Yunnan. [ABSTRACT FROM AUTHOR]