CC1: Joint impact assessment
CC lead: Håvard Hegre
This CC will coordinate the impact assessments in all the outcome-specific WPs, ensuring consistency in terms of methods, data, and definitions of outcome metrics and units of analysis, and binding the work together utilizing the risk framework. To the extent possible, the CC will cast outcomes from the WPs as estimates of the causal effect of conflict of a given magnitude on the outcome. We will take into account how outcomes relate to each other and depend on each other. The conflict impact will be measured as a probability distribution over deviations from levels that are normal to the units of analysis they refer to, looking to the work in CC3 that will also estimate these counter-factual trends. The output of the estimates in this CC will be fed into the early-warning system developed in CC4.
CC2: Estimating exposure by location, age, and gender
CC lead: Magnus Öberg
This CC will work with the WPs and CC1 to identify the distribution of armed conflict impacts across social groups, age groups, and gender. To accurately model how many people are affected by violence we combine geographical information on population densities  with geo-coded events data measuring the intensity and location of different types of violence . Using geographic information systems (GIS) we generate measures of how many people are affected by what type of violence and at what intensity in a given location or country for a given period of time. We then combine this with specific variables from CC3 that describe populations’ vulnerability to the impact of violence on different outcome variables (e.g. economic conditions, health conditions, age and gender structure, education levels, water access etc). We will test and evaluate various estimates of distance. Presumably, different types of violence and events with different intensities have differently sized impacts on the different outcome variables (e.g. on people’s health, economy, decisions to flee, etc) depending on the characteristics of the affected population and over time. The models of exposure will feed into the work of all the CCs.
CC lead: Paola Vesco
The most severe humanitarian crises globally are found in places exposed to a combination of human and natural induced hazards , such as severe food crises in conflict-ridden South Sudan and Northern Nigeria. The combination of social and natural events can give rise to a cascade of temporally or spatially dependent risks whose consequences cannot be predicted by observing each of these events separately. However, the effects of these compound events tend to be studied in isolation, thus potentially underestimating the risks . Likewise, knowledge of effective interventions to break the destructive interactions between natural and conflict-related hazards is limited .
This CC will adopt the innovative risk assessment method to estimate the risks of ‘compound events’ . We will observe the processes that cause adverse humanitarian impacts as inter-related and interdependent, building a bridge between social and climate science to provide a better understanding of these complex events.
The CC will estimate the underlying vulnerability component of our risk model, in the form of the statistical modeling of interactive impact, and as a composite indicator of vulnerability, to first assess conflict-induced vulnerability to adverse events. We will use the UCDP-GED conflict data, real-time surface weather data to construct agro-climatic indicators (ECWMF), novel data on the geographic location of climate-related disasters based on the widely-used EM-DAT database , food security indicators to measure the severity of disaster impact , as well as meteorological data giving exogenous indicators of disaster severity [cf. 8]. We will consider immediate effects as well as the legacy of terminated conflict activities.
CC4: Early-warning system
CC lead: Håvard Hegre
This CC will set up an early-warning system for conflict-driven humanitarian disasters to direct attention to ongoing and impending humanitarian disasters. Simulations of various conflict scenarios through the early-warning system are useful to display their effects and the likely benefits of their prevention. Finally, we will use the system as a focal point for the work in the various work packages, helping to coordinate the research and ensure that they feed into a common framework. The system will provide assessments of the risk of high levels of conflict-driven impact over the next three years, for the selected outcomes at the country level as well as for a 0.5x0.5 decimal degrees geographical grid as defined by PRIO-GRID. The system will build on the infrastructure and procedures in ViEWS, developed since 2017 with funding from the ERC . The existing ViEWS system provides an excellent point of departure for the hazard component of the impact assessment. The programme will expand ViEWS by setting it up to also produce forecasts of the number of fatalities from conflict as defined by the UCDP. The main part of the armed conflict impact forecasts will combine the ViEWS forecasts with the estimates of impacts given realized conflict developed in CC1, applying the exposure model in CC2 and the underlying vulnerabilities estimated in CC3 to produce probabilistic estimates of humanitarian disasters/‘conflict impact’.
CC5: Costs of conflict and optimal intervention
CC lead: Hannes Mueller
This WP will provide global and country- specific estimates of the costs of conflict to promote preventive action. It will use the impacts of conflict developed in the outcome-specific WPs as a basis for a dynamic costing model. These will be fed into the methodology developed in Mueller  conducted for World Bank Group & United Nations , and gradually adapted to the forecasting model developed in CC4. The long-term impact of conflict outbreaks may thereby be estimated not only from the effects of contemporaneous violence levels but also from the possible escalations and future outbreaks that follow. Such a complete, forward-looking approach that takes into account future costs will allow us to calculate what the optimal intervention regime is. Prevention could be costly because it means expenditure before conflict breaks out, but it has strong dynamic benefits if it prevents countries from falling into the conflict trap .
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