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'Fuel riots' are a distinct type of energy-related conflict. We provide the first fuel riots database and explore their social, economic and environmental drivers. The analysis demonstrates links between fuel riots and high international crude oil prices in countries characterised by weak state capacity, deficient governance, fuel scarcity and poor economic performance. We suggest a potential causal pathway for fuel riots: when international fuel prices spike, net fuel-importing countries bear higher costs. If these countries are politically unstable and their government inefficient, the likelihood of fuel riots is high. Wealthier countries can absorb price increases and maintain subsidies, as opposed to poorer societies where fuel riots are more likely. Our findings demonstrate the role of state capacity and socio-economic conditions in enabling conflict, and will inform policy by identifying fertile ground for fuel riots, i.e. societies likely to be affected by increases in fuel prices due to scarcity and climate action (such as carbon taxes). We propose that policies which better control international prices and action designed to reduce political instability in vulnerable countries are key to preventing fuel riots. Fuel subsidy reform must anticipate popular opposition and mitigate the impact upon vulnerable populations in order to reduce the likelihood of instability and minimise hardship.

Due to negative consequences of climate change for agriculture and food production shocks affecting different areas of the world, the past two decades saw the conditions of global food security increasingly worsen. This has resulted in negative consequences for the world economy, partly causing international food price spikes and social upheavals. In this paper we present statistical findings along with a preliminary version of an original agent-based model called the Dawe Global Security Model that simulates the global food market and the political fragility of countries. The model simulates the effects of food insecurity on international food prices and how these, coupled with national political fragility and international food trade can, in turn, increase the probability of food riots in countries. The agents in the model are the 213 countries of the world whose characteristics reflect empirical data and the international trade of food is also simulated based on real trade partnerships and data. The model has been informed, calibrated and validated using real data and the results of these procedures are presented in the paper. To further test the model we also present the model's forecasts for the near future in terms of food prices and incidence of food riots. The Dawe Global Security Model can be used to test scenarios on the evolution of shocks to global food production and analyse consequences for food riots. Further developments of the model can include national responses to food crises to investigate how countries can influence the spread of global food crises.

The impact of resources on social unrest is of increasing interest to political leaders, business and civil society. Recent events have highlighted that (lack of) access to critical resources, including food, energy and water, can, in certain circumstances, lead to violent demonstrations. In this paper, we assess a number of political fragility indices to see whether they are good indicators of propensity to food riots. We found that the most accurate is the Political Instability and Absence of Violence Indicator of the Worldwide Governance Indicators by the World Bank. We compute a likelihood of experiencing a food riot for each quartile of this index. We found that the self-sufficiency of food does not seem to affect the likelihood of the occurrence of food riots, but that the level of political stability of a country does have a role. In addition, we identify a monthly and annual threshold for the Food and Agriculture Organisation Food Price Index, above which food riots in fragile states are more likely to occur.

The impact of resources on social unrest is of increasing interest to politicalleaders, business and civil society. Recent events have highlighted that (lack of) access tocritical resources, including food, energy and water, can, in certain circumstances, lead toviolent demonstrations. In this paper, we assess a number of political fragility indices tosee whether they are good indicators of propensity to food riots. We found that the mostaccurate is the Political Instability and Absence of Violence Indicator of the WorldwideGovernance Indicators by the World Bank. We compute a likelihood of experiencing afood riot for each quartile of this index. We found that the self-sufficiency of food does notseem to affect the likelihood of the occurrence of food riots, but that the level of politicalstability of a country does have a role. In addition, we identify a monthly and annualthreshold for the Food and Agriculture Organisation Food Price Index, above which foodriots in fragile states are more likely to occur.

Between 2005 and 2018, 41 countries had at least one riot directly associated with popular demand for fuel. We make use of a new international dataset on fuel riots to explore the effects of fuel prices and price regimes on fuel riots. In line with prior expectations, we find that large domestic fuel price shocks - often linked to international price shocks - are a key driver of riots. In addition, we report a novel result: fuel riots are closely associated with domestic price regimes. Countries that maintain fixed price regimes - notably net energy exporters - tend to have large fuel subsidies. When such subsidies become unsustainable, domestic price adjustments are large, often leading to riots.

This open access book brings together a set of original studies that use cutting-edge computational methods to investigate conflict at various geographic scales and degrees of intensity and violence. Methodologically, this book covers a variety of computational approaches from text mining and machine learning to agent-based modelling and social network analysis. Empirical cases range from migration policy framing in North America and street protests in Iran to violence against civilians in Congo and food riots world-wide. Supplementary materials in the book include a comprehensive list of the datasets on conflict and dissent, as well as resources to online repositories where the annotated code and data of individual chapters can be found and where (agent-based) models can be re-produced and altered. These materials are a valuable resource for those wishing to retrace and learn from the analyses described in this volume and adapt and apply them to their own research interests. By bringing together novel research through an international team of scholars from a range of disciplines, Computational Conflict Research pioneers and maps this emerging field. The book will appeal to students, scholars, and anyone interested in the prospects of using computational social sciences to advance our understanding of conflict dynamics.

The Paris Agreement, ratified in 2015, pledged to reduce greenhouse gas emissions within a Global Carbon Budget that limits the global temperature increase to less than 2 °C. With the Roadmap 2050 mitigation measures, the European Union has a target to reduce emissions by 80% of their 1990 value by 2050 but without giving an estimation or a maximum ceiling for the total amount of cumulative greenhouse gases emissions over that period. Thus, the impact of the EU regulations on global warming remains unestimated. The aim and the novelty of this study are to develop a set of potential European emissions trajectories, within the Global Carbon Budget and at the same time satisfying the Roadmap 2050 goals. The result of the study highlights the urgency to reinforce mitigation measures for Europe as soon as possible because any delay in policy implementation risks the Roadmap 2050 mitigation package being insufficient to achieve the objectives of the Paris treaty ; This work was supported by the MEDEAS project, funded by the European Union's Horizon 2020 research and innovation program under grant agreement No. 691287. The opinion expressed in the present work are those of the authors' only and are not to be attributed to any organisation of the European Union ; Peer reviewed

The Paris Agreement, ratified in 2015, pledged to reduce greenhouse gas emissions within a Global Carbon Budget that limits the global temperature increase to less than 2 °C. With the Roadmap 2050 mitigation measures, the European Union has a target to reduce emissions by 80% of their 1990 value by 2050 but without giving an estimation or a maximum ceiling for the total amount of cumulative greenhouse gases emissions over that period. Thus, the impact of the EU regulations on global warming remains unestimated. The aim and the novelty of this study are to develop a set of potential European emissions trajectories, within the Global Carbon Budget and at the same time satisfying the Roadmap 2050 goals. The result of the study highlights the urgency to reinforce mitigation measures for Europe as soon as possible because any delay in policy implementation risks the Roadmap 2050 mitigation package being insufficient to achieve the objectives of the Paris treaty.

Agent-based modelling (ABM) simulates Social-Ecological-Systems (SESs) based on the decision-making and actions of individual actors or actor groups, their interactions with each other, and with ecosystems. Many ABM studies have focused at the scale of villages, rural landscapes, towns or cities. When considering a geographical, spatially-explicit domain, current ABM architecture is generally not easily translatable to a regional or global context, nor does it acknowledge SESs interactions across scales sufficiently; the model extent is usually determined by pragmatic considerations, which may well cut across dynamical boundaries. With a few exceptions, the internal structure of governments is not included when representing them as agents. This is partly due to the lack of theory about how to represent such as actors, and because they are not static over the time-scales typical for social changes to have significant effects. Moreover, the relevant scale of analysis is often not known a priori, being dynamically determined, and may itself vary with time and circumstances. There is a need for ABM to cross the gap between micro-scale actors and larger-scale environmental, infrastructural and political systems in a way that allows realistic spatial and temporal phenomena to emerge; this is vital for models to be useful for policy analysis in an era when global crises can be triggered by small numbers of micro-level actors. We aim with this thought-piece to suggest conceptual avenues for implementing ABM to simulate SESs across scales, and for using big data from social surveys, remote sensing or other sources for this purpose

Agent-based modelling (ABM) simulates Social-Ecological-Systems (SESs) based on the decision-making and actions of individual actors or actor groups, their interactions with each other, and with ecosystems. Many ABM studies have focused at the scale of villages, rural landscapes, towns or cities. When considering a geographical, spatially-explicit domain, current ABM architecture is generally not easily translatable to a regional or global context, nor does it acknowledge SESs interactions across scales sufficiently; the model extent is usually determined by pragmatic considerations, which may well cut across dynamical boundaries. With a few exceptions, the internal structure of governments is not included when representing them as agents. This is partly due to the lack of theory about how to represent such as actors, and because they are not static over the time-scales typical for social changes to have significant effects. Moreover, the relevant scale of analysis is often not known a priori, being dynamically determined, and may itself vary with time and circumstances. There is a need for ABM to cross the gap between micro-scale actors and larger-scale environmental, infrastructural and political systems in a way that allows realistic spatial and temporal phenomena to emerge; this is vital for models to be useful for policy analysis in an era when global crises can be triggered by small numbers of micro-level actors. We aim with this thought-piece to suggest conceptual avenues for implementing ABM to simulate SESs across scales, and for using big data from social surveys, remote sensing or other sources for this purpose.

This paper reviews different approaches to modelling the energy transition towards a zero carbon economy. It identifies a number of limitations in current approaches such as a lack of consideration of out-of-equilibrium situations (like an energy transition) and non-linear feedbacks. To tackle those issues, the new open source integrated assessment model pymedeas is introduced, which allows the exploration of the design and planning of appropriate strategies and policies for decarbonizing the energy sector at World and EU level. The main novelty of the new open-source model is that it addresses the energy transition by considering biophysical limits, availability of raw materials, and climate change impacts. This paper showcases the model capabilities through several simulation experiments to explore alternative pathways for the renewable transition. In the selected scenarios of this work scenarios of this work, future shortage of 1 fossil fuels is found to be the most influential factor of the simulations system evolution. Changes in efficiency and climate change damages are also important determinants influencing model outcomes.