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Armed conflicts in Ukraine and Gaza are very visible reminders of the severe negative impacts that armed conflicts have on the environment. Yet, despite knowledge of singular environmental issues and specific country contexts, the magnitude, temporal, and multiscalar impact of armed conflict on broader environmental performance has not been quantified. This knowledge gap limits the ability to design broad and targeted measures of environmental protection during armed conflict as well as rehabilitation in the post-conflict period. Here, we conduct the first global study on the environmental impacts of armed conflict. Our analysis shows that countries with an armed conflict have a significantly worse environmental performance. Armed conflict length and severity are both negatively affecting the environmental performance of countries. Even after an armed conflict ends, countries need about 20 to 30 years to recover in terms of environmental performance. This finding is particularly concerning when considering the risk of conflict recurrence due to environmental stress and bad natural resource management. Taken together, this demonstrates the urgency of measures to protect the environment during armed conflicts and in post-conflict settings.

Over the past decade, the field of conflict forecasting has undergone a remarkable metamorphosis, transforming from a series of isolated efforts with low predictive power into large, globe-spanning projects with impressive performance. However, despite this evolution, many challenges still remain. First, while we are good at predicting absolute risks, we are poor at predicting conflict dynamics (onsets, escalations, de-escalations and terminations). Second, we are over-reliant on spatio-temporal features and mechanistic models due to the nature of the event-data we use, thus excluding actor agency. Third, we do not handle either data or model uncertainty. Fourth, we are lagging behind the state-of-the-art in machine-learning. This dissertation attempts to resolve some of these salient difficulties, by contributing to six core elements of current-generation forecasting systems. First, time, by looking at the substantive effects and uncertainties of the temporal distance between data and forecast horizons. Second, space, by looking at the inherent uncertainties of high-resolution geospatial data and proposing a statistical method to address this. Third, feature space, by tackling the extreme feature sparsity in event-data and proposing a novel, deep active learning approach to mine features from existing large conflict-related text corpora. Fourth, substantive knowledge, by combining findings from the previous papers to take a fresh look at the microdynamics of conflict escalation. Fifth, the forecasting process itself, by building models that directly forecast from text, eliminating the intermediate step of manual data curation. Finally, the frontier of event-data, by looking at whether the news-media heavy way we collect violent fatal events can be extended to the collection of non-violent events. Methodologically, the dissertation introduces state-of-the art methods to the field, including the use of large language models, Gaussian processes, active learning and deep time series modelling. The six papers in the dissertation exhibit significant performance improvement, especially in forecasting dynamics.

The detrimental impacts of wars on human development are well documented across research domains, from public health to micro-economics. However, these impacts are studied in compartmentalized silos, which limits a comprehensive understanding of the consequences of conflicts, hampering our ability to effectively sustain human development. This article takes a first step in addressing this gap by reviewing the literature on conflict impacts through the lens of an inter-disciplinary theoretical framework. We review the literature on the consequences of conflicts across 9 dimensions of human development: health, schooling, livelihood and income, growth and investments, political institutions, migration and displacement, socio-psychological wellbeing and capital, water access, and food security. The study focuses on both direct and indirect impacts of violence, reviews the existing evidence on how impacts on different dimensions of societal wellbeing and development may intertwine, and suggests plausible mechanisms to explain how these connections materialize. This exercise leads to the identification of critical research gaps and reveals that systematic empirical testing of how the impacts of war spread across sectors is severely lacking. By streamlining the literature on the impacts of war across multiple domains, this review represents a first step to build a common language that can overcome disciplinary silos and achieve a deeper understanding of how the effects of war reverberate across society. This multidisciplinary understanding of conflict impacts may eventually help to reconcile divergent estimates and enable forward-looking policies that minimize the costs of war.

This study advances the field of conflict forecasting by using text-based actor embeddings with transformer models to predict dynamic changes in violent conflict patterns at the actor level. More specifically, we combine newswire texts with structured conflict event data and leverage recent advances in Natural Language Processing (NLP) techniques to forecast escalations and de-escalations among conflicting actors, such as governments, militias, separatist movements, and terrorists. This new approach accurately and promptly captures the inherently volatile patterns of violent conflicts, which existing methods have not been able to achieve. To create this framework, we began by curating and annotating a vast international newswire corpus, leveraging hand-labeled event data from the Uppsala Conflict Data Program. By using this hybrid dataset,  our models can incorporate the textual context of news sources along with the precision and detail of structured event data. This combination enables us to make both dynamic and granular predictions about conflict developments. We validate our approach through rigorous back-testing against historical events, demonstrating superior out-of-sample predictive power. We find that our approach is quite effective in identifying and predicting phases of conflict escalation and de-escalation, surpassing the capabilities of traditional models. By focusing on actor interactions, our explicit goal is to provide actionable insights to policymakers, humanitarian organizations, and peacekeeping operations in order to enable targeted and effective intervention strategies.

Socio-hydrology strives to incorporate 'the social' into the understanding of hydrological processes, aiming to enrich the analysis of water systems by considering human interactions. While there is a broader interest in integrating socio-political processes into hydrology, our paper specifically emphasizes the significant contributions of peace and conflict research to understanding the complex social dynamics surrounding water. We conduct a brief review of key literature on interstate water sharing, international norms on water, and domestic water disputes, drawing extensively from empirical studies within peace and conflict research—a field with a rich tradition of examining the interplay of water systems and social dynamics. Building on this foundation, we propose ways to weave insights from peace research, especially environmental peacebuilding, into the realm of socio-hydrology. We also highlight the crucial role of power, politics, and social factors in shaping water-related interactions and conflicts. By fostering a dialogue between socio-hydrology and peace and conflict research, we advocate for a more nuanced understanding of water management and governance. This interdisciplinary approach, we argue, is essential for promoting sustainable and equitable water use, and for addressing the challenges posed by water-related conflicts in a rapidly changing global context.

The proliferation of large-scale, geographically disaggregated data on armed conflicts, protests, and similar events has opened new avenues of research, but has also introduced significant data quality challenges. A notable yet often overlooked issue involves observations with “known geographic imprecision” (KGI), where event locations are unknown and instead arbitrarily assigned by dataset authors. Although this issue is widely recognized and accounts for up to a quarter of observations in datasets like UCDP GED, it is rarely addressed by users. This paper presents a stochastic method derived from the multiple-imputation literature, employing spatio-temporal Gaussian processes and leveraging latent actor-conflict features in the data to enhance location accuracy. Extensive Monte-Carlo simulations demonstrate that this approach substantially enhances the accuracy of these observations and improves predictive performance beyond the state-of-the-art when applied out-of-sample. Additionally, an adapted version of the UCDP GED dataset that employs this new procedure is provided, showcasing the practical application and benefits of the methodology.

High-resolution event data on armed conflict and related processes have revolutionized the study of political contention. However, most datasets of this type only collect spatio-temporal and conflict intensity data at that level of detail. Information on dynamics, such as targets, tactics, and purposes, is rarely collected due to the substantial effort of collecting data. This study proposes an inexpensive, high-performance approach to increase the feature richness of such datasets by leveraging active learning -- an iterative process of improving a machine learning model based on guided human input at each step of the learning process. Active learning is employed to then fine-tune (train in steps) a large, encoder-only language model fitted to the rich corpus of textual data underlying such datasets. This allows for the extraction of features related to conflict dynamics, such as electoral violence and attacks on religious targets. The approach achieves a performance comparable to the human (gold-standard) coding, while reducing the necessary human annotation by as much as 99 percent.

What explains conflict escalation during civil war? This article explores whether provocative attacks on religious sites and public transport constitute a precursor to a surge of violence. One argument pertains that the symbolic and doctrinal importance of places of worship means that attacks on these will affect individuals and the community emotionally and thereby increase the risk of escalation. However, it can also be suggested that the everyday societal importance of a public space is similar for religious sites and public transport hubs. We test these arguments using novel new global event data on these forms of selective targeting for 1989-2015, and find that the risk of conflict escalation increase in the aftermath of either attacks on places of worship or public transport, suggesting that community behavior is more affected to disruptions of societal everyday life than to the importance of symbols.