Once again the media are promoting a link between climate change and human conflicts. It is obvious to anyone in their right mind that wars correlate with environmental destruction. From rioting in Watts, to the wars in Iraq, or the current chaos in Syria, there’s no doubt that fighting degrades the environment big time.
What is strange here is the notion that changes in temperatures and/or rainfall cause the conflicts in the first place. The researchers that advance this claim are few in number and are hotly disputed by many others in the field, but you would not know that from the one-sided coverage in the mass media.
Lately the fuss arises from this study: Climate, conflict, and social stability: what does the evidence say?, Hsiang, S.M. & Burke, M. Climatic Change (2014) 123: 39. doi:10.1007/s10584-013-0868-3
Hsiang and Burke (2014) examine 50 quantitative empirical studies and find a “remarkable convergence in findings” (p. 52) and “strong support for a causal association” (p. 42) between climatological changes and conflict at all scales and across all major regions of the world. A companion paper by Hsiang et al. (2013) that attempts to quantify the average effect from these studies indicates that a 1 standard deviation (σ) increase in temperature or rainfall anomaly is associated with an 11.1 % change in the risk of “intergroup conflict”.1 Assuming that future societies respond similarly to climate variability as past populations, they warn that increased rates of human conflict might represent a “large and critical impact” of climate change.
The Bigger Picture
This assertion is disputed by numerous researchers, some 26 of whom joined in a peer-reviewed comment: One effect to rule them all? A comment on climate and conflict, Buhaug, H., Nordkvelle, J., Bernauer, T. et al. Climatic Change (2014) 127: 391. doi:10.1007/s10584-014-1266-1
In contrast to Hsiang and coauthors, we find no evidence of a convergence of findings on climate variability and civil conflict. Recent studies disagree not only on the magnitude of the impact of climate variability but also on the direction of the effect. The aggregate median effect from these studies suggests that a one-standard deviation increase in temperature or loss of rainfall is associated with a 3.5 % increase in conflict risk, although the 95 % highest density area of the distribution of effects cannot exclude the possibility of large negative or positive effects. With all contemporaneous effects, the aggregate point estimate increases somewhat but remains statistically indistinguishable from zero.
To be clear, this commentary should not be taken to imply that climate has no influence on armed conflict. Rather, we argue – in line with recent scientific reviews (Adger et al. 2014; Bernauer et al. 2012; Gleditsch 2012; Klomp and Bulte 2013; Meierding 2013; Scheffran et al. 2012a,b; Theisen et al. 2013; Zografos et al. 2014) – that research to date has failed to converge on a specific and direct association between climate and violent conflict.
The Root of Climate Change Bias
The two sides have continued to publish and the issue is far from settled. Interested observers describe how serious people can disagree so frequently about such findings in climate science.
Modeling and data choices sway conclusions about climate-conflict links, Andrew M. Linke, and Frank D. W. Witmer, Institute of Behavioral Science, University of Colorado, Boulder, CO 80309-0483 here
Conclusions about the climate–conflict relationship are also contingent on the assumptions behind the respective statistical analyses. Although this simple fact is generally understood, we stress the disciplinary preferences in modeling decisions.
However, we believe that the Burke et al. finding is not a “benchmark” in the sense that it is the scientific truth or an objective reality because disciplinary-related modeling decisions, data availability and choices, and coding rules are critical in deriving robust conclusions about temperature and conflict.
After adding additional covariates (models 4 and 6), the significant temperature effect in the Burke et al. (1) model disappears, with sociopolitical variables predicting conflict more effectively than the climate variables. Furthermore, this specification provides additional insights into the between- and within-effects that vary for factors such as political exclusion and prior conflict.
Sociopolitical variables predict conflict more effectively than climate variables. It is well established that poorer countries, such as those in Africa, are more likely to experience chronic human conflicts. It is also obvious that failing states fall into armed conflicts, being unable to govern effectively due to corruption and illegitimacy.
It boggles the mind that activists promote policies to deny cheap, reliable energy for such countries, perpetuating or increasing their poverty and misery, while claiming such actions reduce the chances of conflicts in the future.
Halvard Buhaug concludes (here):
Vocal actors within policy and practice contend that environmental variability and shocks, such as drought and prolonged heat waves, drive civil wars in Africa. Recently, a widely publicized scientific article appears to substantiate this claim. This paper investigates the empirical foundation for the claimed relationship in detail. Using a host of different model specifications and alternative measures of drought, heat, and civil war, the paper concludes that climate variability is a poor predictor of armed conflict. Instead, African civil wars can be explained by generic structural and contextual conditions: prevalent ethno-political exclusion, poor national economy, and the collapse of the Cold War system.
Footnote: The Joys of Playing Climate Whack-A-Mole
Dealing with alarmist claims is like playing whack-a-mole. Every time you beat down one bogeyman, another one pops up in another field, and later the first one returns, needing to be confronted again. I have been playing Climate Whack-A-Mole for a while, and if you are interested, there are some hammers supplied below.
The alarmist methodology is repetitive, only the subject changes. First, create a computer model, purporting to be a physical or statistical representation of the real world. Then play with the parameters until fears are supported by the model outputs. Disregard or discount divergences from empirical observations. This pattern is described in more detail at Chameleon Climate Models
This post is the latest in a series here which apply reality filters to attest climate models. The first was Temperatures According to Climate Models where both hindcasting and forecasting were seen to be flawed.
Others in the Series are: