Climate variability and conflict risk in East Africa, 1990–2009

Abstract

Recent studies concerning the possible relationship between climate trends and the risks of violent conflict have yielded contradictory results, partly because of choices of conflict measures and modeling design. In this study, we examine climate-conflict relationships using a geographically disaggregated approach. We consider the effects of climate change to be both local and national in character, and we use a conflict database that contains 16,359 individual geolocated violent events for East Africa from 1990 to 2009. Unlike previous studies that relied exclusively on political and economic controls, we analyze the many geographical factors that have been shown to be important in understanding the distribution and causes of violence while also considering yearly and country fixed effects. For our main climate indicators at gridded 1°resolution (∼100 km), wetter deviations from the precipitation norms decrease the risk of violence, whereas drier and normal periods show no effects. The relationship between temperature and conflict shows that much warmer than normal temperatures raise the risk of violence, whereas average and cooler temperatures have no effect. These precipitation and temperature effects are statistically significant but have modest influence in terms of predictive power in a model with political, economic, and physical geographic predictors. Large variations in the climate-conflict relationships are evident between the nine countries of the study region and across time periods.social instability | standard precipitation index | generalized additive modeling | negative binomial modeling | disaggregated spatial analysis

Reconciling disagreement over climate–conflict results in Africa

“…We estimate Buhaug models 5-9 simultaneously with the Burke et al model using seemingly unrelated regression (SUR) to test a null hypothesis that coefficients from the two models are the same in bottom panel. by demonstrating that Buhaug does not provide evidence that contradicts the results reported in Burke et al Notably, however, other recent analyses obtain results that largely agree with Burke et al (2)(3)(4)(5), so we think it is likely that analyses following our approach will reconcile any apparent disagreement between these other studies and Buhaug. Finally, we argue that the statistical procedures and reasoning used to obtain our conclusions are broadly applicable and should form the basis for future comparisons between statistical findings in applied research.…”

“…In their preferred specification, Burke et al (1) report that a 1°C increase in average temperature elevates the probability of civil war by 0.043, a 39% increase relative to the average rate of war during the period. Subsequent studies have obtained similar findings in modern Africa for intergroup conflict at local scales (2)(3)(4) and civil conflict at the continental scale (5), as well as at various scales elsewhere around the world (6,7). However, work by Buhaug (8, p. 16480) reports that the original findings by Burke et al "do not hold up to closer inspection."…”

Mapping the Influence of Land Use/Land Cover Changes on the Urban Heat Island Effect—A Case Study of Changchun, China

“…During the process of urbanization natural landscapes, like cropland and forests, are transformed into impervious surfaces consisting of chemical materials that effectively store short-wave radiation, such as cement, metal, and asphalt [4][5][6]. The UHI has profound effects on social, economic, and environmental problems, such as human health and well-being, mortality and risk of violence, higher energy costs, air quality, and urban runoff [7][8][9][10]. In terms of statistics, the UHI effects are found in more than 1000 cities of different sizes through all latitudes in both hemispheres, and more cities will suffer from it in the future [11].…”

Dynamic population mapping using mobile phone data

“…Modeling techniques that spatially downscale population numbers into gridded datasets continue to be refined, with basic dasymetric models increasing in sophistication, incorporating multiscale remotely sensed and geospatial data and making improvements in the type of statistical algorithms used in the modeling process (19)(20)(21). These detailed population databases have proven crucial for studies reliant on information about human population distributions, typically for calculating populations at risk for human or natural disasters (22)(23)(24), to assess vulnerabilities (7,25), or to derive health and development indicators (3,5,26,27). However, despite improvements, these data still have many limitations.…”