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In the past decade, several efforts have been made to project armed conflict risk into the future. However all of these approaches neglected the impact of hydrological extremes, specifically drought, on potential conflicts. This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways towards 2050 including hydrological feedbacks.We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk.With our study being at the forefront of ML applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Specifically, the links between drought and conflicts are mostly region specific and not necessarily well reflected in the available data. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security under changing hydroclimatic conditions.

Research findings on the relationship between climate and conflict are diverse and contested. Here we assess the current understanding of the relationship between climate and conflict, based on the structured judgments of experts from diverse disciplines. These experts agree that climate has affected organized armed conflict within countries. However, other drivers, such as low socioeconomic development and low capabilities of the state, are judged to be substantially more influential, and the mechanisms of climate–conflict linkages remain a key uncertainty. Intensifying climate change is estimated to increase future risks of conflict. Climate has affected organized armed conflict within countries, and intensifying climate change is estimated to increase future risks of conflict, although other drivers are substantially more influential and the mechanisms of climate–conflict linkages remain uncertain.

In the past decade, several efforts have been made to project armed conflict risk into the future. One arising technique is the use of machine-learning (ML) models. In this study we explore its opportunities to project sub-national armed conflict risk for three shared socio-economic pathway (SSP) scenarios and three Representative Concentration Pathways (RCPs) by 2040-2050 in Africa, using the novel and open-source ML framework CoPro. Results are consistent with the underlying socio-economic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with RandomForest classifiers agree with comparable studies. In SSP1-RCP2.6, conflict risk is low or absent in most regions, although the Horn of Africa and parts of Kenya, Tanzania and Mozambique continue to be conflict-prone. Conflict risk intensifies in the more severe SSP3-RCP6.0 scenario, especially in central Africa and large parts of western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited but can differ locally depending on the overall sign of climate change impact and the contextual (socio-economic) factors defining the overall magnitude of those impacts. With our study being at the forefront of ML applications for conflict risk projections, we have identified various challenges for this arising scientific field. A major concern is the inconsistent data availability of observed conflict events as well as of socio-economic indicators for the various SSPs. Nevertheless, ML models such as the one presented here are a viable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

The socioeconomic impacts of changes in climate-related and hydrology-related factors are increasingly acknowledged to affect the on-set of violent conflict. Full consensus upon the general mechanisms linking these factors with conflict is, however, still limited. The absence of full understanding of the non-linearities between all components and the lack of sufficient data make it therefore hard to address violent conflict risk on the long-term. Although it is neither desirable nor feasible to make exact predictions, projections are a viable means to provide insights into potential future conflict risks and uncertainties thereof. Hence, making different projections is a legitimate way to deal with and understand these uncertainties, since the construction of diverse scenarios delivers insights into possible realizations of the future. Through machine learning techniques, we (re)assess the major drivers of conflict for the current situation in Africa, which are then applied to project the regions-at-risk following different scenarios. The model shows to accurately reproduce observed historic patterns leading to a high ROC score of 0.91. We show that socio-economic factors are most dominant when projecting conflicts over the African continent. The projections show that there is an overall reduction in conflict risk as a result of increased economic welfare that offsets the adverse impacts of climate change and hydrologic variables. It must be noted, however, that these projections are based on current relations. In case the relations of drivers and conflict change in the future, the resulting regions-at-risk may change too. By identifying the most prominent drivers, conflict risk mitigation measures can be tuned more accurately to reduce the direct and indirect consequences of climate change on the population in Africa. As new and improved data becomes available, the model can be updated for more robust projections of conflict risk in Africa under climate change.

Significance Threats to human livelihoods resulting from natural hazards are increasing due to climate change. Climate-related disasters such as floods, storms, and droughts have destroyed shelter, reduced crop yields, harmed livestock, and fueled conflict, especially in developing countries. The key finding is that UN aid in the aftermath of climate-related disasters is largely driven by humanitarian need. The UN seems able to fend off donor states’ strategic interest and allocate more aid after disasters where hazard severity is greater and need is more pressing. Based on this finding, we argue that the UN lives up to its stated principles of neutrality, impartiality, and independence in disaster aid, corroborating the legitimacy of the UN in allocating disaster aid., As the climate changes, human livelihoods will increasingly be threatened by extreme weather events. To provide adequate disaster relief, states extensively rely on multilateral institutions, in particular the United Nations (UN). However, the determinants of this multilateral disaster aid channeled through the UN are poorly understood. To fill this gap, we examine the determinants of UN disaster aid using a dataset on UN aid covering almost 2,000 climate-related disasters occurring between 2006 and 2017. We make two principal contributions. First, we add to research on disaster impacts by linking existing disaster data from the Emergency Events Database (EM-DAT) to a meteorological reanalysis. We generate a uniquely global hazard severity measure that is comparable across different climate-related disaster types, and assess and bolster measurement validity of EM-DAT climate-related disasters. Second, by combining these data with social data on aid and its correlates, we contribute to the literature on aid disbursements. We show that UN disaster aid is primarily shaped by humanitarian considerations, rather than by strategic donor interests. These results are supported by a series of regression and out-of-sample prediction analyses and appear consistent with the view that multilateral institutions are able to shield aid allocation decisions from particular state interests to ensure that aid is motivated by need.

Low level of GDP per capita is a robust and widely applied predictor of civil war. Yet, GDP is a crude macro-level indicator that masks considerable heterogeneity in economic structures, and it is less well able to explain variation in conflict risk among low-income countries. Here, we consider the merit of classic dual sector theory in improving common economic models of civil war. Two basic expectations are derived: the relative size of the traditional sector increases conflict risk via low opportunity cost and high share of immobile wealth, whereas high relative labor productivity (RLP) in the modern sector compared to the traditional sector facilitates labor mobility and wage growth, thus reducing the viability of rebellion. We evaluate these expectations via out-of-sample prediction analysis of civil conflict involvement, drawing on a unique 10-sector dataset of economic activity among 40 countries across the world since 1969. The analysis provides robust evidence that poor countries with a comparatively productive modern sector are less conflict prone than countries at similar income levels with lower RLP ratios. However, further probing into potential mechanisms producing this relationship does not provide decisive evidence in favor of any potential mechanism. We conclude that replacing GDP per capita with indicators of sector size and relative productivity improves the predictive performance of common civil war models, although more research is needed to assess the generalizability of these findings and to gain further insight into the underlying causal pathways linking relative labor productivity with reduced conflict risk.

Climate change threatens core dimensions of human security, including economic prosperity, food availability, and societal stability. In recent years, war-torn regions such as Afghanistan and Yemen have harbored severe humanitarian crises, compounded by climate-related hazards. These cases epitomize the powerful but presently incompletely appreciated links between vulnerability, conflict, and climate-related impacts. In this article, we develop a unified conceptual model of these phenomena by connecting three fields of research that traditionally have had little interaction: ( a) determinants of social vulnerability to climate change, ( b) climatic drivers of armed conflict risk, and ( c) societal impacts of armed conflict. In doing so, we demonstrate how many of the conditions that shape vulnerability to climate change also increase the likelihood of climate–conflict interactions and, furthermore, that impacts from armed conflict aggravate these conditions. The end result may be a vicious circle locking affected societies in a trap of violence, vulnerability, and climate change impacts.

Common political economy models point to rationalist motives for engaging in conflict but say little about how income shocks translate into collective violence in some cases but not in others. Grievance models, in contrast, focus on structural origins of shared frustration but offer less insight into when the deprived decide to challenge the status quo. Addressing these lacunae, we develop a theoretical model of civil conflict that predicts income loss to trigger violent mobilization primarily when the shock can be linked to pre-existing collective grievances. The conditional argument is supported by results of a comprehensive global statistical analysis of conflict involvement among ethnic groups. Consistent with theory, we find that this relationship is most powerful among recently downgraded groups, especially in the context of agricultural dependence and low local level of development, whereas political downgrading in the absence of adverse economic changes exerts less influence on ethnic conflict risk.

In this presentation, we report on a comprehensive and balanced assessment of the relationship between climate and conflict risks and its implications for future directions of research. Research fi...

Climate change will likely exacerbate droughts, increase regional water demands and affect agricultural yields. In addition, projected population growth combined with lack of ‘good’ governance is likely to enhance the negative impacts of droughts and crop failure in the future as agriculture increasingly expands onto marginal lands. There is a global concern about these trends, because crop failure, droughts, increasing pressure on suitable agricultural land and rangeland for livestock, and changes and quality of governance can also increase the risk of conflict and (organized) violence.In this presentation we explore the strength and impact of the climate-conflict trap., We use historical drought simulations and future drought projections to study the link between conflict and drought. Conflict data are taken from the Uppsala Conflict Data Program and combined with hydrological simulations from the global hydrological model PCR-GLOBWB.The results show that drought occurrence is expected to increase under all climate scenarios, with stronger impacts for the higher emission scenarios. On the other hand, at the global scale conflicts are likely to reduce as increased economic wealth compensates for the increased climate vulnerability.This work helps us to better understand the interplay between the natural hydrological system and society. To better understand unsustainable and potentially devastating pathways for the coming decades, we have the greater aim to start unravelling the complex dynamics between changes in drought, society and risk of conflicts.

Armed nonstate conflict without the direct involvement of the state government is a common phenomenon. Violence between armed gangs, rebel groups, or communal militias is an important source of instability and has gained increasing scholarly attention. In this article, we introduce a data collection on conflict issues and key actor characteristics in armed nonstate conflicts that provides new opportunities for investigating the causes, dynamics, and consequences of this form of organized violence. The data builds on and extends the Uppsala Conflict Data Program Non-State Conflict data set by introducing additional information on what the actors in the conflict are fighting over, alongside actor characteristics. It covers Africa during the time period 1989–2011. The data set distinguishes between two main categories of issues, territory and authority, in addition to a residual category of other issues. Furthermore, we specify sub-issues within these categories, such as agricultural land/water as sub-issue for territory and religious issues for other issues. As actor characteristics, the data set notes whether warring parties received military support by external actors and whether religion and the mode of livelihood were salient in the mobilization of the armed group. The article presents coding processes, key features of the data set, and point to avenues for new research based on these data.

Action toward strengthened disaster risk reduction (DRR) ideally builds from evidence-based policymaking to inform decisions and priorities. This is a guiding principle for the Sendai Framework for Disaster Risk Reduction (SFDRR), which outlines priorities for action to reduce disaster risk. However, some of these practical guidelines conceal oversimplified or unsubstantiated claims and assumptions, what we refer to as ‘truisms’, which, if not properly addressed, may jeopardize the long-term goal to reduce disaster risks. Thus far, much DRR research has focused on ways to bridge the gap between science and practice while devoting less attention to the premises that shape the understanding of DRR issues. In this article, written in the spirit of a perspective piece on the state of the DRR field, we utilize the SFDRR as an illustrative case to identify and interrogate ten selected truisms, from across the social and natural sciences, that have been prevalent in shaping DRR research and practice. The ten truisms concern forecasting, loss, conflict, migration, the local level, collaboration, social capital, prevention, policy change, and risk awareness. We discuss central claims associated with each truism, relate those claims to insights in recent DRR scholarship, and end with suggestions for developing the field through advances in conceptualization, measurement, and causal inference.

The potential links between climate and conflict are well studied, yet disagreement about the specific mechanisms and their significance for societies persists. Here, we build on assessment of the relationship between climate and organized armed conflict to define crosscutting priorities for future directions of research. They include (1) deepening insight into climate‐conflict linkages and conditions under which they manifest, (2) ambitiously integrating research designs, (3) systematically exploring future risks and response options, responsive to ongoing decision‐making, and (4) evaluating the effectiveness of interventions to manage climate‐conflict links. The implications of this expanding scientific domain unfold in real time., Key Points Disagreement about the relationship between climate and the risk of violent conflict persists, limiting societal management of the risksFuture directions for climate‐conflict research include deepening insight on what the links are, when they matter, and how they manifestCompelling opportunities necessitate integrating research designs, responsive to ongoing decision‐making and potential responses

To date, the research community has failed to reach a consensus on the nature and significance of the relationship between climate variability and armed conflict. We argue that progress has been hampered by insufficient attention paid to the context in which droughts and other climatic extremes may increase the risk of violent mobilization. Addressing this shortcoming, this study presents an actor-oriented analysis of the drought-conflict relationship, focusing specifically on politically relevant ethnic groups and their sensitivity to growing-season drought under various political and socioeconomic contexts. To this end, we draw on new conflict event data that cover Asia and Africa, 1989-2014, updated spatial ethnic settlement data, and remote sensing data on agricultural land use. Our procedure allows quantifying, for each ethnic group, drought conditions during the growing season of the locally dominant crop. A comprehensive set of multilevel mixed effects models that account for the groups' livelihood, economic, and political vulnerabilities reveals that a drought under most conditions has little effect on the short-term risk that a group challenges the state by military means. However, for agriculturally dependent groups as well as politically excluded groups in very poor countries, a local drought is found to increase the likelihood of sustained violence. We interpret this as evidence of the reciprocal relationship between drought and conflict, whereby each phenomenon makes a group more vulnerable to the other.

Research findings on the relationship between climate and conflict are diverse and contested. Here we assess the current understanding of the relationship between climate and conflict, based on the structured judgments of experts from diverse disciplines. These experts agree that climate has affected organized armed conflict within countries. However, other drivers, such as low socioeconomic development and low capabilities of the state, are judged to be substantially more influential, and the mechanisms of climate-conflict linkages remain a key uncertainty. Intensifying climate change is estimated to increase future risks of conflict.

With climate change projections indicating a likely future increase in extreme weather phenomena, it is an urgent matter to assess the effect of drought on civil conflict. However, studies of this ...

The mounting evidence for climate change has put the security implications of increased climate variability high on the agenda of policymakers. However, several years of research have produced no consensus regarding whether climate variability increases the risk of armed conflict. Many have suggested that instead of outright civil war, climate variability is likely to heighten the risk of communal conflict. In particular, erratic rainfall, which reduces the availability of water and arable land, could create incentives for violent attacks against other communities to secure access to scarce resources. Yet, whether groups resort to violence in the face of environmentally induced hardship is likely to depend on the availability of alternative coping mechanisms, for example through market transfers or state accommodation. This suggests that the effect of rainfall anomalies on communal conflict will be stronger in the presence of economic and political marginalization. We evaluate these arguments statistically, utilizing a disaggregated dataset combining rainfall data with geo-referenced events data on the occurrence of communal conflict in Sub-Saharan Africa between 1990 and 2008. Our results suggest that large negative deviations in rainfall from the historical norm are associated with a higher risk of communal conflict. There is some evidence that the effect of rainfall shortages on the risk of communal conflict is amplified in regions inhabited by politically excluded ethno-political groups.