Civil wars in the post-Cold War period concentrate in lower latitudes. We investigate the sensitivity of the strong negative latitude-conflict correlation to eight plausible mediating factors: climate, geography, age of first settlement, demography, culture, colonial legacy, political institutions, and economic development. Results indicate that institutions and especially development are the most plausible intermediate pathways. Yet latitude prevails as a relevant correlate of civil war even when controlling for all factors simultaneously. We suggest a possible causal chain from latitude via environmental conditions to socioeconomic development and conflict, although alternative pathways remain plausible and merit further examination.

This paper introduces a structured expert elicitation to develop narrative descriptions of political futures for the Shared Socioeconomic Pathways (SSPs). The SSPs are scenarios widely used to explore how alternative futures affect the challenges for mitigation and adaptation. Despite the central role of political dimensions (e.g. institutional inclusiveness, institutional effectiveness, and peace) in shaping development trajectories and the feasibility of climate action, the SSPs do not systematically incorporate political features. Political development is often non-linear and relationships between political dimensions and climate action are contested. Expert elicitation provides a transparent approach to link available empirical evidence as well as evaluate the degree of confidence and assess the conditionality of the relationships. Preliminary findings from the elicitations highlight that institutional effectiveness is a consistent differentiator of climate action. High state capacity, low corruption, and credible enforcement reduce challenges to mitigation and adaptation, while weaker institutions and armed conflict substantially increase them.

Climate action is shaped as much by politics as by technology and economics. The Shared Socioeconomic Pathways (SSPs), central to mitigation and adaptation assessments, do not yet include a quantitative representation of political development. We outline a research agenda to systematically integrate political dimensions into climate scenario modelling.

Violent conflict is a major barrier to sustainability. Does this mean that peace operations promote sustainable development? Do sustainability interventions foster peace? Surprisingly, scientific evidence in support of these assumptions remains limited. There is an urgent need for more research on synergies and trade-offs between peacebuilding and sustainability interventions, evaluated over longer time horizons and supported by high-quality data.

Concerns about adverse security implications of climate change have fostered a booming research agenda and have also gained increasing traction in international political fora, such as the United Nations Security Council (UNSC). To what extent do policy discourses and decisions reflect the scientific understanding of the problem? Here, we assess evidence on uptake of scientific research on climate change and violent conflict in high-level UNSC debates, 2007–2022. We show that UNSC member states increasingly acknowledge climate-conflict risks as real but context-dependent, consistent with recent academic literature on the topic. Even so, views remain divided among prominent Council members, blocking progress on this matter. Explicit engagement with science on climate change and conflict, which might have helped resolve disagreements, remains rare and partly selective. We highlight challenges and opportunities for the scientific community to improve the perceived relevance and accessibility of research to stakeholders and strengthen science-policy interaction.

Societal impacts from extreme climate and weather events depend not only on hazard magnitude but also on the vulnerability of the affected population. Existing research suggests that adverse socioeconomic conditions are associated with higher baseline vulnerability to many types of risk, but comparatively little attention has been paid to political drivers of vulnerability. Focusing on floods, the most frequent climate-related hazard, this article evaluates the impact of political development on flood mortality. Findings from a Bayesian predictive analysis of global flood impacts from 2000 to 2018 suggest that democracy, institutional quality, and peace reduce the predicted human cost of flooding. The effect of a breakdown of peace on predicted flood mortality is especially pronounced. These results indicate that promoting peace, justice, and strong institutions (Sustainable Development Goal 16) can help to mitigate disaster risks and support effective climate change adaptation.

What is the association between climate and conflict? Will climate change lead to an increase in armed conflict in the future? This chapter takes stock of observed trends and scientific evidence linking climatic conditions with political violence across time and space. It shows, first, that the prevalence of conflict is significantly higher in hot, dry, and tropical regions than in cooler continental climates; it demonstrates, second, that temporal fluctuations in conflict occurrence are only weakly explained by contemporaneous changes in climate; and it argues, third, that the reverse association, from armed conflict to climate change vulnerability, constitutes a major challenge to future peace and stability. Accordingly, the best way to minimise the security threat imposed by climate change is to address, and resolve the dominant causes of contemporary wars.

What explains human consequences of weather-related disaster? Here, we explore how core socioeconomic, political, and security conditions shape flood-induced displacement worldwide since 2000. In-sample regression analysis shows that extreme displacement levels are more likely in contexts marked by low national income levels, nondemocratic political systems, high local economic activity, and prevalence of armed conflict. The analysis also reveals large residual differences across continents, where flood-induced displacement in the Global South often is much more widespread than direct human exposure measures would suggest. However, these factors have limited influence on our ability to accurately predict flood displacement on new data, pointing to important, hard-to-operationalize heterogeneity in flood impacts across contexts and critical data limitations. Although results are consistent with an interpretation that the sustainable development agenda is beneficial for disaster risk reduction, better data on societal consequences of natural hazards are critically needed to support evidence-based decision-making.

While migration is often conceptualized as an adaptive response to climate hazards, migration can also present severe risks to people on the move. In this paper, we attempt to operationalize the Representative Key Risks (RKR) framework of the Sixth Assessment Report of Working Group II of the Intergovernmental Panel on Climate Change (IPCC) for human mobility. First, we provide a framework for understanding how mobility risks emerge by engaging with the concept of habitability. We argue that uninhabitability occurs where the physical environment loses suitability and where there is a loss of agency in local populations. The severity of the risk from the loss of habitability is then represented by the high potential for human suffering. When climate hazards affect physical suitability and agency, the forms of migration that occur undermine human wellbeing and the right to self-determination: forced displacement, community relocation/resettlement, and involuntary immobility. Second, we show how such forms of mobility are more or less likely along different Shared Socioeconomic Pathways (SSPs). This paper asserts a central concern around human suffering to recentre scenario discourse on where, and how, adaptation, changes to development patterns, and government policies can reduce this suffering. Proactive governance at local, national, and international levels that attends to people’s adaptation and mobility needs can avert the more frequent emergence of severe risks related to mobility in a changing climate.

The rise in global displacement has inspired a wave of quantitative comparative research in recent years. While deeper systematic knowledge on contextual determinants of disaster-related mobility and associated risks is in high demand, quantitative modelling of human displacement should be exercised with care. In this commentary, I reflect on three central challenges related to the quality of available displacement statistics. Future scientific progress in this field would benefit tremendously from harmonization and validation of displacement data that separate between distinct mobility responses.

Anthropogenic climate change is commonly characterized as a threat to human security. However, the extent to which and under what conditions climate impacts and responses may produce severe risks to peace have seen less systematically assessment to date. This essay provides a conceptual discussion of what risks to peace entail and how such risks might be considered severe, acknowledging that perceptions, values, and social scale must be grappled with in the identification of severity. Informed by available empirical research, the essay then explores the conditions under which climate-related risks could become severe during this century. Three illustrative scenarios based on different assumptions about climate-driven risks and risks related to social responses to climate change serve to illustrate how alternative warming and adaptation trajectories will have distinct implications for the prospect of future peace. The essay ends by reflecting on some implications for future research needs.

The world’s population is increasingly concentrated in cities. Research on urbanization’s implications for peace and security has been hampered by a lack of comparable data on political mobilization and violence at the city level across space and through time, however. Urban Social Disorder 3.0 is a detailed event dataset covering 186 national capitals and major urban centers from 1960 to 2014. It includes 12 types of nonviolent and violent events, detailing the actors involved and their targets, start and end dates of each event, and the number of participants and deaths. We provide an overview of the main features of these data, and trends in urban social disorder across space and time. We demonstrate the utility of the dataset by analyzing the relationship between city size and the frequency of lethal disorder events. We find a positive relationship between city population and lethal urban social disorder, unlike previous studies. These new data raise promising avenues for future research on democratization; climate change and food security; and spillovers between different forms of mobilization and violence.

Little research has been done on projecting long-term conflict risks. Such projections are currently neither included in the development of socioeconomic scenarios or climate change impact assessments nor part of global agenda-setting policy processes. In contrast, in other fields of inquiry, long-term projections and scenario studies are established and relevant for both strategical agenda-setting and applied policies. Although making projections of armed conflict risk in response to climate change is surrounded by uncertainty, there are good reasons to further develop such scenario-based projections. In this perspective article we discuss why quantifying implications of climate change for future armed conflict risk is inherently uncertain, but necessary for shaping sustainable future policy agendas. We argue that both quantitative and qualitative projections can have a purpose in future climate change impact assessments and put out the challenges this poses for future research.

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.

Recent research suggests that climate variability and change significantly affect forced migration, within and across borders. Yet, migration is also informed by a range of non-climatic factors, and current assessments are impeded by a poor understanding of the relative importance of these determinants. Here, we evaluate the eligibility of climatic conditions relative to economic, political, and contextual factors for predicting bilateral asylum migration to the European Union-form of forced migration that has been causally linked to climate variability. Results from a machine-learning prediction framework reveal that drought and temperature anomalies are weak predictors of asylum migration, challenging simplistic notions of climate-driven refugee flows. Instead, core contextual characteristics shape latent migration potential whereas political violence and repression are the most powerful predictors of time-varying migration flows. Future asylum migration flows are likely to respond much more to political changes in vulnerable societies than to climate change. Adverse climatic conditions are commonly reported to shape asylum migration, but their effect relative to other drivers is unknown. Here the authors compare climatic, economic, and political factors as predictors of future asylum flows to the EU and find that war and repression are the most important factors.

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.

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.

Climate policies will need to incentivize transformative societal changes if they are to achieve emission reductions consistent with 1.5 degrees C temperature targets. To contribute to efforts for aligning climate policy with broader societal goals, specifically those related to sustainable development, we identify the effects of climate mitigation policy on aspects of socioeconomic development that are known determinants of conflict and evaluate the plausibility and importance of potential pathways to armed conflict and political violence. Conditional on preexisting societal tensions and socioeconomic vulnerabilities, we isolate effects on economic performance, income and livelihood, food and energy prices, and land tenure as most likely to increase conflict risks. Climate policy designs may be critical to moderate these risks as different designs can promote more favorable societal outcomes such as equity and inclusion. Coupling research with careful monitoring and evaluation of the intermediate societal effects at early stages of policy implementation will be a critical part of learning and moderating potential conflict risks. Importantly, better characterizing the future conflict risks under climate policy allows for a more comprehensive comparison to the conflict risk if mitigation is not implemented and graver climate damages are experienced. This article is categorized under: The Carbon Economy and Climate Mitigation > Benefits of Mitigation

The study of security implications of climate change has developed rapidly from a nascent area of academic inquiry into an important and thriving research field that traverses epistemological and disciplinary boundaries. Here, we take stock of scientific progress by benchmarking the latest decade of empirical research against seven core research priorities collectively emphasized in 35 recent literature reviews. On the basis of this evaluation, we discuss key contributions of this special issue. Overall, we find that the research community has made important strides in specifying and evaluating plausible indirect causal pathways between climatic conditions and a wide set of conflict-related outcomes and the scope conditions that shape this relationship. Contributions to this special issue push the research frontier further along these lines. Jointly, they demonstrate significant climate impacts on social unrest in urban settings; they point to the complexity of the climate–migration–unrest link; they identify how agricultural production patterns shape conflict risk; they investigate understudied outcomes in relation to climate change, such as interstate claims and individual trust; and they discuss the relevance of this research for user groups across academia and beyond. We find that the long-term implications of gradual climate change and conflict potential of policy responses are important remaining research gaps that should guide future research.

This article presents a new open source extension to the Emergency Events Database (EM-DAT) that allows researchers, for the first time, to explore and make use of subnational, geocoded data on major disasters triggered by natural hazards. The Geocoded Disasters (GDIS) dataset provides spatial geometry in the form of GIS polygons and centroid latitude and longitude coordinates for each administrative entity listed as a disaster location in the EM-DAT database. In total, GDIS contains spatial information on 39,953 locations for 9,924 unique disasters occurring worldwide between 1960 and 2018. The dataset facilitates connecting the EM-DAT database to other geographic data sources on the subnational level to enable rigorous empirical analyses of disaster determinants and impacts.