Climate has contrasting direct and indirect effects on armed conflicts

Abstract

There is an active debate regarding the influence that climate has on the risk of armed conflict, which stems from challenges in assembling unbiased datasets, competing hypotheses on the mechanisms of climate influence, and the difficulty of disentangling direct and indirect climate effects. We use gridded historical non-state conflict records, satellite data, and land surface models in a structural equation modeling approach to uncover the direct and indirect effects of climate on violent conflicts in Africa and the Middle East (ME). We show that climate–conflict linkages in these regions are more complex than previously suggested, with multiple mechanisms at work. Warm temperatures and low rainfall direct effects on conflict risk were stronger than indirect effects through food and water supplies. Warming increases the risk of violence in Africa but unexpectedly decreases this risk in the ME. Furthermore, at the country level, warming decreases the risk of violence in most West African countries. Overall, we find a non-linear response of conflict to warming across countries that depends on the local temperature conditions. We further show that magnitude and sign of the effects largely depend on the scale of analysis and geographical context. These results imply that extreme caution should be exerted when attempting to explain or project local climate-conflict relationships based on a single, generalized theory.

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Environ. Res. Lett. 15 (2020) 104017 https://doi.org/10.1088/1748-9326/aba97d
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LETTER
Climate has contrasting direct and indirect effects on armed
conicts
David Helman1,2,5, Benjamin F Zaitchik1and Chris Funk3,4
1Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, United States of America
2Currently at the Department of Soil & Water Sciences, The Robert H. Smith Faculty of Agriculture, Food & Environment, Rehovot,
and Advanced School for Environmental Studies, The Hebrew University of Jerusalem, Jerusalem, Israel
3U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, South Dakota, United States of America
4Climate Hazards Center, University of California, Santa Barbara, California, United States of America
E-mail: david.helman@mail.huji.ac.il
Keywords: climate, conflict, violence, warming, Africa, Middle East, structural equation model
Supplementary material for this article is available online
Abstract
There is an active debate regarding the influence that climate has on the risk of armed conflict,
which stems from challenges in assembling unbiased datasets, competing hypotheses on the
mechanisms of climate influence, and the difficulty of disentangling direct and indirect climate
effects. We use gridded historical non-state conflict records, satellite data, and land surface models
in a structural equation modeling approach to uncover the direct and indirect effects of climate on
violent conflicts in Africa and the Middle East (ME). We show that climate–conflict linkages in
these regions are more complex than previously suggested, with multiple mechanisms at work.
Warm temperatures and low rainfall direct effects on conflict risk were stronger than indirect
effects through food and water supplies. Warming increases the risk of violence in Africa but
unexpectedly decreases this risk in the ME. Furthermore, at the country level, warming decreases
the risk of violence in most West African countries. Overall, we find a non-linear response of
conflict to warming across countries that depends on the local temperature conditions. We further
show that magnitude and sign of the effects largely depend on the scale of analysis and
geographical context. These results imply that extreme caution should be exerted when attempting
to explain or project local climate–conflict relationships based on a single, generalized theory.
1. Introduction
Although there is a suggested linkage between viol-
ent conflict and climate, the underlying mechanisms
of the link are still under debate [1,2]. One com-
monly suggested mechanism is of a climate–conflict
link through economic disruption [3,4]. Though
plausible, there is currently no robust evidence for
such a direct climate–economy–conflict nexus [5].
Instead, many studies suggest that climate-driven
depressions may lead to conflict through a combina-
tion of socioeconomic and political failures, particu-
larly in agricultural-dependent regions where people
depend directly on such resources [4]. That is, cli-
mate influences economy, which influences social and
political systems relevant to conflict.
5Author to whom any correspondence should be addressed.
It is also possible that the climate–conflict con-
nection is less direct, operating through the influence
that climate-induced changes in economy, food
security, or group interactions cascade to influ-
ence the probability of inter-group violent con-
flicts. This indirect influence is relevant to theor-
ies like the ‘engagement’ hypothesis, which claims
that when climate crisis reduces economic productiv-
ity people become more likely to engage in con-
flicts than in economic activities [6,7], or the
‘inequality’ hypothesis, which argues that conflict
may upsurge when climate crisis increases economic
inequality because of increasing efforts to redistrib-
ute assets [8], and the ‘state weakness’ hypothesis
that suggests a weakening of governmental insti-
tutions and their ability to suppress violence due
to decline in economic productivity following cli-
mate crisis [9]. All these, suggest that climate has
© 2020 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
an indirect rather than a direct effect on violent
conflicts [10].
While these hypotheses were first studied in the
context of civil wars and other state-engaged conflicts,
research in the past decade on communal, non-state
violence has also emphasized the mediated pathways
through which climate can influence conflict. This
includes the potential for harmful climate anomalies
like drought to drive conflicts in times of scarcity due
to resource competition, lowered opportunity cost, or
other mechanisms [11,12]. But it also includes the
potential for beneficial climate anomalies to increase
conflict due to rent seeking or available resources to
support violent activities during times of abundance
[13,14]. Studies have also found that climate variab-
ility in either direction can lead to increased conflict,
due to the presence of multiple mechanisms driving
conflict or to the presence of qualitatively different
categories of conflict [15,16].
The direct influence of climate on individual
tendency toward violence may also play a role.
Warming, for example, has been shown to enhance
violence through a direct psychological mechanism
[the General Aggression Model—GAM] by mak-
ing people uncomfortable and irritated [17]. Altern-
atively, warming may enhance violence in cooler
environments because warm, more favorable weather
conditions lead to increased activity and interac-
tion between people [Routine Activity Theory—
RAT], which may lead to more opportunities for
conflict [18].
To assess climate impacts on violence and uncover
whether the underlying mechanisms are direct, indir-
ect, or a combination of both, ‘non-climatic’ effects
must be isolated. Some studies do this by pooling
data across locations and applying statistical mod-
els that control for non-climatic factors explicitly.
The climate influence is then examined through its
partial effect on violence [19,20]. Other researchers
argue that controlling for non-climatic factors expli-
citly can absorb most of the climatic impact and,
therefore, may result in an underestimation of the cli-
mate effect [21]. For this reason, it is argued, pooling
analysis across sites is misleading, and climate effects
should be studied by comparing each place with itself
in time rather than with other places. Studies using
this site self-comparison approach have reached more
conclusive results regarding climate impacts on viol-
ence than cross-sectional studies using explicit con-
trols [21,22]. The problem with this self-comparison
approach, however, is that it cannot identify under-
lying ‘universal’ mechanisms because the analysis is
conducted location-by-location rather than across
locations [23].
To some extent, the contrasting results published
in the literature are a reflection of that disagree-
ment [24], with this inconsistency leading to cri-
ticism of climate–conflict research. Some research-
ers have claimed that the link between climate and
conflict is unsupported by the evidence [25]. Fur-
thermore, researchers have been accused of bias in
their approach to the problem [26,27]. Yet, most
experts do believe that climate has a significant effect
on human conflicts [28], though the generality of the
links and the underlying mechanisms are yet to be
established.
Here we use a powerful assemblage of disaggreg-
ated data (table S1 (available online at stacks.iop.
org/ERL/15/104017/mmedia)), which includes the
Uppsala Conflict Data Program (UCDP) conflict
dataset [29] as well as climatic [temperature and rain-
fall anomalies] and non-climatic [anomalies in water
availability, infant mortality rates, agricultural yield,
and economic welfare] datasets derived from satel-
lites and land surface models to test generalizability of
climate–conflict relationships from national to con-
tinental scale. To leverage the strengths of the two
approaches—the site self-comparison and the use of
explicit controls in a cross-sectional analysis—and
explore general mechanisms, we make use of struc-
tural equation modeling [SEM] [30] in which non-
climatic factors are explicitly controlled while dir-
ect and indirect effects of climate—through the non-
climatic factors—are quantified in order to uncover
the underlying mechanisms.
We choose to focus on non-state conflicts rather
than civil wars because small-scale conflicts are likely
to be more sensitive to environmental and climatic
changes [19,28]. Also, we focus on Africa and the
Middle East [ME] because these two regions exper-
ienced a large number of armed conflicts in the last
three decades. Finally, we hypothesize that compar-
ing these two ethnically and culturally distinct, but
yet geographically close regions may reveal contrast-
ing mechanisms.
2. Data and methods
2.1. Armed conflict dataset
2.1.1. The UCDP geolocated violent conflict dataset
We used the most updated Georeferenced Event Data-
set [GED] Global version 18.1 (2017) of the UCDP
[29] for location-specific information on armed con-
flicts in Africa and the ME. The GED.v18.1 is UCDP’s
most disaggregated data set, covering individual
events of organized violence as phenomena of lethal
violence occurring at a given time and place. Events
are sufficiently fine-grained to be geo-coded down to
the level of individual villages, with temporal dur-
ations disaggregated to single, individual days [31].
Conflicts used here are ‘non-state’ conflicts, defined
by UCDP as ‘the use of armed force between two
organized armed groups, neither of which is the gov-
ernment of a state, which results in at least 25 battle-
related deaths in a year’ [31]. Information on specific
conflict is freely available at [www.ucdp.uu.se], and
questions regarding the definitions used by UCDP
as well as the content of the dataset can be directed
2

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
to that site. In the GED dataset, each conflict has a
unique identifier [conflict ID], while the start date is
recorded as precisely as possible with the level of pre-
cision for day, month and year indicated alongside
[‘Startprec’ variable in GED.v18.1].
For our analysis we used conflicts indicated with a
‘Startprec’ level of at least five meaning that ‘Day and
month are assigned, year is precisely coded; day and
month are set as precisely as possible’. A violent event
was defined as a coded event, which is unique in terms
of starting and ends dates, and is not a continuation or
part of a previous event. All events were first binned at
a spatial resolution of 0.5◦×0.5◦for African and ME
regions by summing the total number of events per
grid per year. Events were assigned to a specific year
by indicated starting date. A layer of violent events by
0.5◦per year was produced alongside another layer
with the sum of events for the entire period of 1990–
2017 . Because we look for effects on the risk of viol-
ent conflict outbreak, each layer was converted into
a binary layer in which each grid was assign a value
of 1 for grids that experienced violence during this
year, or 0 for grids that did not experience violence.
Although we had information on violence for 1990–
2017, we used only layers for years 1992–2012 in the
SEM analysis because this was the period in which we
had a complete data set of climate and non-climate
variables (see below). We included Syria in our ana-
lysis, but excluded the years after 2010 because of the
poor information on violent events during the period
of the Syrian civil war [31,32].
2.2. Climate data
We used temperature and rainfall data sets, described
below, to seek direct and indirect effects of tem-
perature and rainfall anomalies on non-state con-
flicts. Direct effects of climate could be only at the
time of occurrence, so relationships were analyzed
for the same year (present-year violence). However,
indirect effects—through food, water, and economic
welfare—may occur at a certain time-lag. Because
linking climate anomalies indirectly to conflicts at too
long time-lag periods may be problematic (because of
the uncertainty that such climatic changes are really
related to conflicts many years after), we looked only
for links with a one-year time lag (next-year violence).
2.2.1. Temperature anomaly
We used monthly maximum temperatures from
the newly derived Climate Hazards center InfraRed
Temperature with Stations [CHIRTS] dataset [33].
CHIRTS provides monthly 2 m maximum air tem-
peratures at a high spatial resolution of 0.05◦and
a quasi-global coverage [60◦S–70◦N] from 1983 to
2016. Temperature estimates are derived using a com-
bination of thermal imagery from a constellation of
geostationary satellites, a high-resolution climatology
from the Climate Hazards Center’s Tmax climatology,
and in situ monthly 2 m Tmax air temperature obser-
vations obtained from the Berkeley Earth and Global
Telecommunication System [GTS]. We used the tem-
perature estimates from CHIRTS because these were
shown to be suitable for monitoring temperature
anomalies and extremes in data-sparse regions like
Africa and the ME [33]. The high spatial resolution
temperature estimates were averaged over 0.5◦×0.5◦
for the period of the analysis [1992–2012], and the
yearly anomaly was calculated per grid as z-score [the
long-term mean annual temperature was subtracted
from the specific year mean temperature and divided
by the standard deviation].
2.2.2. Rainfall anomaly
For rainfall anomaly, we used the Climate Hazards
center Infrared Precipitation with Stations [CHIRPS]
dataset, available at a high spatial resolution of 0.05◦
[34]. This product is a quasi-global precipitation
product with daily to seasonal time scales and a
1981 to near real-time period of record. CHIRPS
uses three main types of information: (1) global
0.05◦rainfall climatologies, (2) time-varying grids of
satellite-based rainfall estimates, and (3) in situ rain-
fall observations. CHIRPS is built on ‘smart’ inter-
polation techniques and high resolution, long period
of record estimates based on infrared cold cloud
duration [CCD] observations as well as on satellite
information, used to represent ungauged locations.
CHIRPS is quite reliable in regions like Africa and
the ME where most rainfall products fail to accurately
represent the high temporal and spatial variability in
rainfall [35] due to the sparse gauge network in this
region [36].
We used CHIRPS monthly rainfall sums [from
January to December] to assess the annual rainfall
anomaly for 1992–2012, calculated as z-scores [the
long-term mean annual rainfall subtracted from spe-
cific year rainfall sum, divided by the standard devi-
ation]. Each year a z-score map was produced while
pixels were aggregated to the spatial resolution of the
analysis [0.5◦×0.5◦]. Annual rainfall is not a com-
prehensive proxy for conflict-relevant rainfall variab-
ility, but it offers a practical, objective measure that
can be applied consistently across our diverse study
domain.
2.3. Non-climate data
2.3.1. Infant mortality rate
As a proxy of socioeconomic development, we used
information on infant mortality rate [IMR] from
the Global Subnational Infant Mortality Rates, Ver-
sion 1 [GSIMR.v1] [37]. The GSIMR.v1 dataset is
produced by the Columbia University Center for
International Earth Science Information Network
[CIESIN] at a high spatial resolution of 5 km and is
freely available for download as a raster data layer
from [http://www.ciesin.columbia.edu/povmap].
The GSIMR.v1 consists of IMR estimates for the
3

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
year 2000, which was collected from vital registration
data, surveys and models or estimated using repor-
ted live births and infant deaths data. Though our
analysis spans the period of 1992–2012, we assume
that the 2000 GSIMR.v1 is, in average, representat-
ive of the entire period following previous studies
[38]. The IMR is calculated as the number of deaths
of infants of less than one year old divided by the
number of live births and multiplied by 1000. We
preferred using the IMR as a proxy of poverty and
socioeconomic status instead of using other variables
because measures like gross domestic product [GDP]
or population living on less than one U.S. dollar per
day, are difficult to obtain at sub-national levels, par-
ticularly for the regions of this study. Moreover, using
IMR has several advantages over other socioeconomic
metrics. For example, IMR is a highly standardized
measure compared to other measures, which means
that it can be used to compare between countries
with different economic systems better than GDP, for
example [38]. Also, IMR is less likely to be influenced
by skewed wealth distribution. And, information on
IMR is available for�90% or more of the population
in medium- and low-income countries. The original
5 km IMR data layer was binned at the spatial resol-
ution of 0.5◦×0.5◦, which is the resolution of the
analysis and used as a static map layer.
2.3.2. Distance to border
Distance from/to political borders was assessed using
a geographical information system and a shapefile
layer of the political borders of African and the ME
countries. The minimal distance from each grid cell
to the nearest border was recorded and used in the
SEM analysis. Because this information is static [i.e.
it does not change during the period of analysis] the
same value was used in all years.
2.3.3. Agricultural dependence
To assess agricultural dependence as share of cropland
area in a 0.5◦grid cell, we used the Climate Change
Initiative [CCI] of the European Space Agency [ESA]
Land Cover product. The ESA CCI product is an
annual global land cover time series from 1992 to
2015 [now available also for 2016 to 2018], available
at an unprecedent high spatial resolution of 300 m
(https://www.esa-landcover-cci.org/?q =node/175).
This unique dataset was produced by reprocessing
and interpretation of daily surface reflectance of five
different satellite missions. It uses the full archive of
the MEdium-spectral Resolution Imaging Spectro-
meter (MERIS) [2003–2012], with 15 spectral bands
and 300 m spatial resolution and the 1 km time series
from AVHRR [1992–1999], SPOT-VGT [1999–2013]
and PROBA-V [2014 and 2015]. The baseline was
established through MERIS data and use of machine
learning and unsupervised algorithms [39].
The advantage of this product over other products
that are derived from several observation systems is
that it maintains a good consistency over time. This
is done by confirming changes observed in earlier
and later MERIS era satellites via back- and forward-
checking through the 10 year MERIS base-line Land
Cover (LC) maps. The ESA CCI LC product was
evaluated with a global independent validation data-
set according to international standards, testing the
accuracy of both LC classes and LC change in time
[39]. It was also found accurate through a compar-
ison using country-level information provided by the
Food and Agriculture Organization of the United
Nations [FAO-STAT] in several countries [40].
We used the 1992–2012 ESA CCI LC maps to clas-
sify pixels into agricultural versus non-agricultural
classes. More specifically, LC classes #10, 20, 30, and
40, which include also mosaics of croplands and nat-
ural vegetation, were designated as agricultural pixels
while others were assigned as non-agricultural pixels.
We then aggregated the 300 m pixels into the coarser
resolution of 0.5◦[resolution of analysis] and calcu-
lated the total share of agricultural area in each 0.5◦
grid cell [as the percentage of total area]. These estim-
ates were used to examine influence of agricultural
dependence [larger crop share of area equals higher
agricultural dependency [38]] on violence risk as well
as to derive yearly change in agricultural yield produc-
tion [see next sub-section].
2.3.4. Yield production
To quantify changes in agricultural yield produc-
tion, we used NASA’s VIPPHEN EVI2 satellite
product [41]. The VIPPHEN EVI2 data product
is provided globally at 0.05◦[�5600 m] spatial res-
olution and contains 26 Science Datasets [SDS],
including phenological metrics such as the start,
peak, and end of season as well as the maximum,
average, and background calculated EVI2 (https://
lpdaac.usgs.gov/products/vipphen_evi2v004/). It is
currently the longest and most consistent satellite-
based global vegetation phenology product available.
VIPPHEN SDS are based on the daily VIP product
series and are calculated using a 3 year moving win-
dow average to eliminate noise.
The modified 2-band enhanced vegetation index
[EVI2] is highly correlated with the commonly-used
EVI [42], which was found to be useful for tracking
changes related to vegetation dynamics [43] as well
as gross primary productivity [44]. EVI2 differs from
the traditional EVI by its use of two bands, the red and
near infrared, instead of the use of three bands, which
also includes the blue band in the index calculation.
The integral over the growing season of EVI2 [EVIGSI;
figure S1] was used here as a proxy of agricultural
yield production. Growing season integrals of veget-
ation indices are usually well correlated with biomass
of green tissues, particularly in annual vegetation sys-
tems [45–47], and as such may serve as a good proxy
of crop yield production [48]. EVIGSI was derived per
year for agricultural pixels with > 50% of agricultural
4

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
area cover [estimated from the ESA CCI LC 300 m
product]. Pixels with < 50% of agricultural area cover
were discarded from the analysis in order to remove
influences of non-agricultural vegetation systems on
EVIGSI.
Because agricultural fields differ in crop type and
different crop types may have similar EVIGSI values,
we used the relative anomaly of EVIGSI as a proxy of
relative anomaly in local yield production instead of
the absolute EVIGSI value. In order to assess the valid-
ity of this approach, we compared yearly anomalies of
national yield production, derived from the food and
agriculture data provided by the Food and Agricul-
ture Organization of the United Nations [FAO-STAT
[49]], with country-level EVIGSI anomalies (z-scores)
for the period of analysis [1990–2012; see supplement-
ary material and figures S2 to S5]. Yield is provided
in FAO-STAT as hectograms per hectare [hg/ha] for
cereals, citrus fruit, coarse grain, fibre crops, oil-
crops, pulses, roots and tubers, treenuts, vegetables
and fruits (www.fao.org/faostat/en/#data/QC). The
total annual yield and the long-term mean annual
yield [1990–2012] from FAO-STAT were calculated
to derive the relative anomaly in percentages of the
long-term average yield [%]. The same procedure
was applied for the calculation of the EVIGSI annual
anomaly [as percentages of the mean EVIGSI].
2.3.5. Satellite night-time lights as a proxy of economic
welfare
We used night-time lights intensity from the Defense
Meteorological Satellite Program [DMSP [50]] to
estimate grid-based economic welfare status and
dynamics in Africa and the ME. This night-time light
product dates back to 1992 and is considered to be
well correlated with GDP, built-up area, energy con-
sumption, poverty, and other socioeconomic welfare
variables [51–54]. We used the DMSP yearly average
stable night-time lights intensity product at a spa-
tial resolution of 30 arcsec [�1 km] for 1992–2012 to
calculate the percentage area of light per pixel [Lit-
Area]. Method was followed by that described in
[55]. In short, light intensity in DSMP is given as a
digital number [DN] from 0 to 100 for each pixel.
A DN threshold value is then used to assign each
pixel with a binary 1/0 for presence/absence of light.
The threshold of DN > 31 was used following [55].
The total LitArea per 0.5◦grid—i.e. the sum of the
squared kilometers of light in a 0.5◦grid cell—was
derived by aggregating pixels with values to the spatial
resolution of the analysis. The total number of square
kilometers was then converted into square meters and
divided by the population density in the same grid cell
to derive the relative LitArea [R-LitArea].
We divided the LitArea by the population dens-
ity because places with denser populations are expec-
ted to have higher LitArea, which will not neces-
sarily indicate a higher economic welfare status
but may just reflect a larger build-up area. By
dividing the LitArea by the population density,
we thus normalize for such an effect, remaining
with a relative measure of economic welfare. We
used the WorldPop dataset [www.worldpop.org.uk]
for grid-based information on population density.
This dataset uses an ensemble learning method for
classification, combining 30 m Landsat Enhanced
Thematic Mapper (ETM) satellite imagery for high-
resolution mapping of settlements and gazetteer
population numbers to produce gridded popula-
tion density maps at high spatial resolutions [56].
Yearly population maps for Africa and the ME
are available from 2000 to date [downloaded from:
https://www.worldpop.org/project/categories?id =3]
at the same resolution of the DMSP dataset [1 km ×
1 km]. We used simple linear interpolation to derive
population density for 1992–1999, and aggregated the
original resolution to the coarse spatial resolution of
the analysis [0.5◦×0.5◦]. R-LitArea was derived per
0.5◦grid cell as the ratio between LitArea and popula-
tion density. Finally, R-LitArea z-score was calculated
to get yearly economic welfare anomaly.
2.3.6. Grid-based water resources information from
land surface models
Gridded estimates of soil moisture and hydrolo-
gical fluxes, along with river network estimates of
streamflow, were generated using the NASA Land
Information System [LIS] [57] software frameworks.
In this implementation, LIS was implemented using
the Noah-MultiParameterization [Noah-MP] [58]
Land Surface Model and the Hydrological Modeling
and Analysis Platform [HyMAP] [59] river routers.
All simulations were performed using meteorolo-
gical forcing data drawn from the NASA Modern
Era Reanalysis for Research and Applications, v2
[MERRA-2] [60], with the exception of precipitation,
which came from the Climate Hazards InfraRed Pre-
cipitation with Stations, v2 [CHIRPSv2] [34] dataset.
Simulations were performed at 0.1◦horizontal res-
olution with a timestep of 30 min. A 30 year spin-
up was performed to equilibrate model soil moisture
states, and the simulation was then run from 1990–
2018. In this application, Noah-MP was used with
four soil moisture layers [thicknesses of 0.1, 0.3, 0.6
and 1.0 m, descending from the surface] and a simple
unconfined aquifer. Soil moisture and surface runoff
were aggregated to the spatial resolution of the ana-
lysis and the z-score of each 0.5◦grid cell was calcu-
lated to derive the inter-annual anomaly.
3. Assessing direct and indirect causal
effects
The SEM approach was used because it allows one
to evaluate direct and indirect effects of climate and
non-climate factors on violence risk, as well as to
quantify relationships among factors. In that sense,
SEM has an advantage over univariate regression
5

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
approaches, such as general additive models (GAM)
and general linear models (GLM), because it can be
used to evaluate direct effects while controlling for
joint effects. For example, it provides a way to eval-
uate the direct effect of yield on conflict risk while
controlling for the joint effects of climate variables
on yield and conflict. The ability of SEM to quantify
direct and indirect relationships makes it particularly
suited for confirming causal relationships based on a
priori hypotheses.
Our SEM was developed based on a conceptual
model designed to test a priori hypothesis that relates
climate to food and water security, economic welfare
and—directly and indirectly—to conflict risk [6–9].
It was then applied on a 0.5◦grid basis in a time-
for-space model design for 1992–2012 (see supple-
mentary material). The SEM model was applied for
Africa, the ME, and both regions together, as well
as for each country separately. To enable comparison
between datasets with different normal distributions,
we used the relative anomaly—quantified as a stand-
ard score [z-score]—instead of the absolute values of
the climate and non-climate factors. The control vari-
ables [IMR, agricultural dependence and distance to
border], on the other hand, were maintained with
their absolute values in order to quantify the abso-
lute influence of these factors on the climate-conflict
relationships. The conflict data were converted to a
binary dataset, with 0 for non-conflict and 1 for con-
flict years/grids. The results of the SEM are presented
as standardized effects indicating the magnitude and
sign of effect.
4. Results and discussion
Non-state armed conflicts in the last three decades
were not restricted to certain climatic conditions in
Africa and the ME but rather occupied the entire cli-
matic domain (figure 1(A) to (B)). Consistent with
previous studies, violent conflicts are mostly found in
agriculture-dependent areas [38], low socioeconomic
areas [61], and close to political borders [19] in both
regions (figures 1(C) to (E)).
Non-state conflict grid cells also have higher than
average rainfall (figure 1(F)) on account of the fact
that population and agricultural activities are lim-
ited in arid regions. However, the association between
violence and agricultural dependence was about four-
fold greater in the ME (table 1), in spite of the lar-
ger average agricultural area in Africa [14% compared
to 11% for the ME] (figure 1(C)), likely because of
lower mean annual rainfall and therefore greater agri-
cultural vulnerability to drought and water scarcity
(figure 1(F)).
4.1. Contrasting climate effects in Africa and the
Middle East
When applying the SEM to both regions together
[general model] (figure 2(B)), yield and economic
welfare had the strongest effect on present-year viol-
ence risk. Increases in yield and welfare reduced the
chance of violence in both present and following year,
while warming increased the risk and rain decreased
this risk.
While these results are in accordance to previ-
ously reported by others [19,21,38], unexpected
complex climate-conflict links were revealed when
SEMs were applied to each region separately (fig-
ures 2(C) and (D)). Warming increased the risk of
violence in Africa (figure 2(C))—similar to the gen-
eral model—but unexpectedly decreased this risk in
the ME (figure 2(D)). There was no effect of rain and
yield on conflict risk in Africa and no effect of wel-
fare in the ME. But there was a weak, though signi-
ficant (P< 0.05), indirect negative effect of rain on
the risk of conflicts in Africa (table 1), which was,
surprisingly, through the effect of water availability
on welfare and not through yield (figure 2(C)). This
may be in part because satellite-based estimates of
yield have limited skill in some conflict-prone African
regions (figures S3 and S4), but could also be due to
a more complex link between rainfall, yield, and viol-
ence than that drawn by our model. In all models, the
risk of violence was greater in places where conflict
already occurred in the previous year (figures 2(B)
to (D)), which likely indicates the roles of political
instability and historic background on such conflicts.
4.2. A non-linear response of conflict to warming
To examine the generality of the contrasting effects,
we further applied the SEMs per country. After
analyzing the countries with enough conflicts to pro-
duce a statistically significant model (table S4), we
found that not only ME countries, but also some
African countries—particularly West African—had a
negative, direct temperature effect on violence risk
(figure 3(A)). This negative direct temperature effect
was in spite of the fact that some of these coun-
tries are warmer than those showing a positive effect
[e.g. East African countries], which would theoretic-
ally make them more vulnerable to heat-induced viol-
ence [23]. In general, the country data show a non-
linear relationship between the warming effect—i.e.
the standardized direct effect of temperature anomaly
on conflict risk—and the mean temperature condi-
tions across countries, with a peak response at around
32◦C (figure 3(C)). Countries with lower and higher
mean annual temperatures (MAT) tend to exhibit
lower effects of temperature anomalies on the risk of
violence, with even negative effects in some cases.
An example of the latter is Sierra Leone and
Liberia, which had the strongest effects, with a stand-
ardized negative effect of −0.43 and −0.39, respect-
ively (figure 3(A)). These two countries are charac-
terized by extremely warm and humid conditions,
with high temperatures and large amounts of rain-
fall year-round (�3000 mm y−1). Contrary to the
6

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
Figure 1. Armed conflicts in Africa and the Middle East, and associated factors. (A) Log number of armed conflicts by 0.5◦
grids for 1990–2017. (B) Mean annual rainfall and temperature binned by log number of conflicts. Gray line in (B) marks the
region’s climatic domain (95th quantile of all grids). Boxplots show median, 1st and 3rd quantiles of (C) relative agricultural area,
(D) infant mortality rate (IMR), (E) distance to border, and (F) mean annual rainfall for violent (red), non-violent (blue) and all
(violent +non-violent) grids. Violent grids are significantly different from non-violent grids in (C) to (F) at P< 0.001.
Table 1. Direct, indirect and total standardized effects of rain, temperature and yield anomalies on risk of violence, with and without
explicit controls (marked in italic). High infant mortality rate (IMR) means low socioeconomic status. Positive (negative) relationships
are shown in regular (bold) font.
Without controls With controls
Predictor Direct Indirect Total Direct Indirect Total
General model Rain n.s. −0.003
∗∗
−0.006
∗∗
−0.009
∗∗
−0.003
∗∗
−0.012
∗∗
Temperature 0.011
∗∗
−0.003
∗∗
0.008
∗∗
0.011
∗∗
−0.004
∗∗
0.008
∗∗
Yield −0.009
∗∗
−0.003
∗∗
−0.013
∗∗
−0.013
∗∗
−0.003
∗∗
−0.016
∗∗
Agricultural area – – – 0.110
∗∗
– –
IMR – – – 0.017
∗∗
– –
Distance to border – – – −0.033
∗∗
– –
Africa Rain n.s. n.s. n.s. n.s. −0.001
∗
n.s.
Temperature 0.020
∗∗ ∗
n.s. 0.020
∗∗ ∗
0.019
∗∗
n.s. 0.019
∗∗
Yield n.s. −0.002
∗∗ ∗
n.s. n.s. −0.002
∗∗
n.s.
Agricultural area – – – 0.073
∗∗
– –
IMR – – – 0.023
∗∗
– –
Distance to border – – – −0.030
∗∗
– –
ME Rain −0.015
∗∗
−0.009
∗∗ ∗
−0.025
∗∗ ∗
−0.023
∗∗
−0.012
∗∗
−0.036
∗∗
Temperature −0.026
∗∗ ∗
−0.011
∗∗ ∗
−0.037
∗∗ ∗
−0.015
∗∗
−0.014
∗∗
−0.028
∗∗
Yield −0.048
∗∗ ∗
n.s. −0.047
∗∗ ∗
−0.060
∗∗
n.s. −0.058
∗∗
Agricultural area – – – 0.264
∗∗
– –
IMR – – – 0.083
∗∗
– –
Distance to border – – – n.s. – –
n.s. not significant; ∗P< 0.05; ∗∗ P< 0.01; ∗∗∗ P< 0.001
GAM, which suggests that uncomfortable environ-
mental conditions increase violent perceptions [3],
in this case uncomfortable extreme weather con-
ditions [extra warming in already warm, humid
countries] seemed to decrease the risk of viol-
ence.
Some experimental studies suggested that phys-
ical aggression may have a rather complex, curvilinear
7

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
Figure 2. Structural equation models showing causal effects on conflict risk. (A) The conceptual model. Models were applied to
(B) Africa and Middle East together (general model), and to (C) Africa and (D) Middle East separately. Factors not affecting
present-year violence are colored gray. Numbers alongside arrows indicate the standardized direct effects, with the color of the
arrow indicating its sign (black for positive; red for negative) and width indicating its importance in the model. Constructs in our
SEM are indicated by ovals while indicators are shown as rectangles. Only significant effects at P< 0.05 are shown.
response to heat [62–64]. Aggression was shown
to increase with temperature rise, but decrease
at excessive heat in several experimental settings,
particularly when other negative-affect-producing
factors are present [64]. The explanation given
for this is that it is the urgent ‘need’ to escape
or minimize discomfort that overcomes tenden-
cies to aggressive behavior [65]. Taking this to
Sierra Leone and Liberia, a further increase in tem-
perature resulting in extremely unpleasant condi-
tions might have increased discomfort and reduced
the level of engaging in violence through such
‘escape’ mechanism. In this context, the RAT—
suggesting that people interact more under pleas-
ant conditions, which lead to more opportunities
for violence—may be another possible explanatory
mechanism [18].
The positive and negative temperature effects in
Yemen and Turkey (figure 3(B)) suggest that GAM
might be the primary mechanism in the ME rather
than the RAT. The contrasting sign effect may be
explained by a relaxation mechanism in which a
decrease in unpleasant conditions—being warming
in a cold area [Turkey] or cooling in a warm area
[Yemen]—reduces the chance of violence [66]. This
does not necessarily contradict the abovementioned
‘escape’ theory because warm and humid conditions
in both Turkey and Yemen are more tolerable than in
Sierra Leone and Liberia (figures 3(A), (B)).
4.3. Climate effects in the context of geography and
ethnicity
To further show how complex this climate–conflict
link may be, we focus on two cases—Algeria and
Mali. Algeria is the largest country in Africa, with
an economy relying heavily on energy exports.
Though Algeria’s government has promoted agricul-
tural development, yield is highly unstable due to cli-
mate variability [67]. This instability is likely to pro-
mote violence, particularly in agriculture-dependent
areas as shown from our results (figure 3(D)). The
contrasting indirect [negative] and direct [positive]
effects of temperature in Algeria are likely due to
a positive temperature effect on yield and a direct
adverse influence of heat, which may be explained by
8

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
Figure 3. Contrasting effects of temperatures on conflict risk. Direct (close symbols) and indirect (open symbols) standardized
effects of temperature on the risk of conflict from SEMs in (A) African and (B) Middle Eastern countries. Mean annual
temperature and rainfall over 1992–2012 are shown. Effects with bars not crossing the vertical line in (A) and (B) are considered
significant at P< 0.05. (C) The standardized direct effect of temperature vs. local temperature conditions expressed as the mean
annual temperature [MAT]. Each symbol in (C) is a single country (Sierra Leone and Liberia are excluded for clarity), with the
size indicating the average temperature change [�T] for the period of analysis and the line in depicting the nonparametric
regression with corresponding confidence interval. (D) The standardized direct (black) and indirect (white) effects of
temperature, rain, and yield on conflict risk in Algeria and Mali. Inserts in (D) show the effects of agricultural dependence and
infant mortality rate (IMR) in Algeria and Mali. Asterisks denote significant effects at P< 0.05.
the GAM. In contrast, the influence of yield on viol-
ence risk was positive and significantly smaller in Mali
[50% smaller than in Algeria]. This is in spite of the
fact that Mali’s economy is more centered on agricul-
ture than Algeria [68]. Moreover, the positive yield
effect was limited to the northern part of Mali, which
is less agricultural than its southern [and central] part
(insert in figure 3(D)).
Putting this in context, we know that most con-
flicts in Mali during the period of analysis were
intra-state conflicts between the government and the
Tuareg nomadic inhabitants living in the northern
part of the country. Because our analysis is limited
to small-scale conflicts, the non-state, inter-group
aspects of the Tuareg conflict, which occur primar-
ily in the northern, less agricultural part of the coun-
try, is well noted (figure 1(A)) [68]. The Tuaregs are
primarily pastoral and as such continuously compete
for scarce resources between pastoral groups and with
the few crop farmers and settled villagers in the north
[68]. Tuareg conflict is believed to be an example of
a resource conflict driven by climatic changes [69]
and the positive effect of yield on violence risk in our
SEM is likely a reflection of this struggle, with periods
of increased yield in the northern region being a
potential driver of ethnic tension and inter-group
violence.
These contrasting complex links in Algeria and
Mali imply that the climate–violence linkage should
be investigated in the context of historical, geograph-
ical and ethnical backgrounds of each location rather
than as a general cross-sectional analysis. Such an
approach can shed light on contrasting effects of
climate.
5. Concluding remarks
Our findings reveal previously unreported effects of
climate on risk of conflict outbreak. More specifically,
contrasting effects of temperature were detected at a
regional scale and in numerous countries in Africa
and the ME. Importantly, temperature and rainfall
direct effects on conflict risk seem to be stronger than
any indirect effect through resources such as water
availability and agricultural production (figure 3and
table 1). This could mean one of three things: that
climate affects violence mostly through psychological
and/or interactive mechanisms [e.g. GAM and RAT
9

Environ. Res. Lett. 15 (2020) 104017 D Helman et al
[17,18]]; that indirect effects depend on aspects of
climate variability that we have not considered [70];
or that underlying mechanisms in which resource
scarcity or conflict-relevant abundance patterns affect
violence are more complex than those modeled by our
SEMs.
As in previous studies [21], the use of explicit
controls affected the strength of the climate effect in
our SEMs [i.e. the difference between total and direct
effects in table 1], in our case by up to 46%. This was
important enough to expose indirect rain effects in
Africa (table 1). It is important to note that the SEMs,
although statistically significant (table S4), confirm-
ing the validity of the a priori hypothesis, had very
little predictive power [with 1st and 3rd quantiles
being 1.6% and 11%, respectively, across countries]
(table S5). This means that although our SEMs did
confirm impacts of climate on armed conflicts by
effectively quantifying its direct and indirect effects,
these effects were relatively small compared to unob-
served factors like political, ethnic and likely other
unaccounted socioeconomic factors. These were only
partly considered in our analysis, due to the difficulty
to account for such factors at a grid-cell level, in the
form of next-year violence risk, which was shown to
be greatly affected by present year violence (explain-
ing between 10% and 16% of the variance, at the con-
tinental level; figures 2(B) to (D)).
Our results demonstrate that no single pro-
posed climate–conflict mechanism can alone explain
the empirical patterns that underlie the climate–
conflict linkage across contrasting regions or coun-
tries, and that this linkage is more complex than
some analyses have previously suggested [21]. We
conclude that extreme caution should be exercised
when attempting to explain or project local climate–
violence relationships on the basis of a single, gen-
eralized theory. Large-scale cross-sectional studies
can be useful for identifying general associations
and trends, but an appropriately scaled and struc-
tured analysis is required to explain and, potentially,
address climate–violence risk factors in geographic
context.
Acknowledgments
Authors thank C Helman for helping to organize
the data for the analyses, A Mussery for helping
to organize the SEMs results in the SM, and two
anonymous reviewers for insightful comments. D
Helman was a USA-Israel Fulbright Post-Doctoral
Fellow at Johns Hopkins University 2018/19. C Funk
is supported by the U.S. Geological Survey Drivers of
Drought program and the U.S. Agency for Interna-
tional Development’s Famine Early Warning Systems
Network.
Data availability statement
The data that support the findings of this study are
available upon reasonable request from the authors.
ORCID iD
David Helman https://orcid.org/0000-0003-0571-
8161
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