Temperature seasonality and violent conflict

Abstract

Current climate change research suggests that certain seasonal weather patterns will be extended and others attenuated as global temperature increases. This is important because seasonal temperature change affects both the scarcity of resources during certain times of the year and the overall mobility of people living in countries that have seasonality. Consequently, these seasonal changes have implications for the onset of violent conflict, particularly as it relates to distinguishing when, where, and how it is most likely to occur. This article evaluates the relationship between monthly temperature changes, civil war onset, and various, less-organized conflict events, offering theoretical expectations for how seasonal changes and climate aberrations are related to an increased risk of violence. The results show that prolonged periods of stable, warm weather are consistently associated with an increased risk of civil war onset and non-state conflict. These findings are best explained through the strategic viability mechanism of temperature change, which allows actors to resolve their collective action problems that are often associated with poor weather conditions, while simultaneously increasing their strategic and behavioral incentives for engaging in violent conflict. Warm weather generates more resources for rebel looting and permits predictability for coordinating troop movements and strategy development. These findings are particularly salient in areas of the world affected by strong seasonality, where prolonged extensions of warm weather conditions would be regarded as both peculiar and attractive for participating in violent action. Although these findings are notable, even under the most extreme climate change scenarios, the substantive effects for these relationships are comparatively minor relative to other well-known intrastate conflict covariates.

Climate Variability and Transnational Migration: A Dyadic Analysis

“…For the temperature and precipitation data, I follow Landis [24] and use the data from NOAA's NCEP/NCAR Reanalysis Monthly Means Dataset 1948-2011 (in degrees Celsius) and monthly precipitation data (mm/month) from the Global Precipitation Climatology Project Version 2.2, respectively. Following Landis [24], I aggregated these data at the country level and employ measures of temperature and precipitation shocks. A temperature/precipitation shock is a substantial (or extreme) deviation from a "normal" climate pattern, i.e., the standardized temperature/precipitation deviation (see [25]).…”

“…For example, countries affected by low levels of precipitation suffer from various extreme conditions, including droughts. This "can cause disruptions in economic and social systems" [24] (p. 606) and [26], especially in vulnerable societies with low adaptability measures. Specifically, as described in detail in Landis [24] (p. 608), measures of temperature and precipitation shock use the monthly deviation from a country's long-term monthly mean.…”

“…These data provide surface or near surface air temperatures (at a 0.995 sigma level) with spatial coverage of a 2.5 × 2.5-degree longitude native resolution (144 × 72). Specifically, as described in detail in Landis [24] (p. 608), the temperature shock measure "uses the monthly deviation from a country's long-term monthly mean, indicated by (X itz − X it-bar )/a it where X itz is the mean temperature of country i in month t in year z, X it-bar is the panel mean of country i's long-term monthly (t-bar) mean temperature for the period 1948-2011, and a it is the standard deviation of that panel." According to Landis [24], this approach is adopted from Hendrix and Salehyan's [25] (pp.…”

“…Specifically, as described in detail in Landis [24] (p. 608), the temperature shock measure "uses the monthly deviation from a country's long-term monthly mean, indicated by (X itz − X it-bar )/a it where X itz is the mean temperature of country i in month t in year z, X it-bar is the panel mean of country i's long-term monthly (t-bar) mean temperature for the period 1948-2011, and a it is the standard deviation of that panel." According to Landis [24], this approach is adopted from Hendrix and Salehyan's [25] (pp. 40-41) measure of rainfall deviation, as the latter study argues that deviations from the panel mean are an optimal operationalization of the "eco-shock" mechanism.…”

Climate Change and Crime Revisited: An Exploration of Monthly Temperature Anomalies and UCR Crime Data

“…We acknowledge that climate change may alter criminal behavior indirectly outside of rising temperatures, and not through a direct effect of heat on mood and impulse control, among other factors. Increasing societal tensions and conflict, internal displacement, economic decline, and reduced agricultural output are all factors that may influence crime levels and have been shown to be altered by climate (Agnew, 2012;Dell, Jones, & Olken, 2014;Hsiang & Burke, 2014;Hsiang et al, 2013;Landis, 2014;Mares, 2010). Such factors are more prominent in developing nations that are less resilient to climate change and less likely to impact developed nations such as the United States (Mares, 2010).…”