The ‘conflict trap’ reduces economic growth in the shared socioeconomic pathways

Abstract

Armed conflict and economic growth are inherently coupled; armed conflict substantially reduces economic growth, while economic growth is strongly correlated with a reduction in the propensity of armed conflict. Here, we simulate the incidence of armed conflict and its effect on economic growth simultaneously along the economic pathways defined by the Shared Socioeconomic Pathways (SSPs). We argue that GDP per capita projections through the 21st century currently in use are too optimistic since they disregard the harm to growth caused by conflict. Our analysis indicates that the correction required to account for this is substantial – expected income is 25% lower on average across countries when taking conflict into account. The correction is particularly strong for the more pessimistic SSP3 and SSP4 where expected future incidence of armed conflict is high. There are strong regional patterns with countries with contemporaneous conflicts experiencing much higher conflict burdens and reduced economic growth by the end of century. The implications of this research indicate that today’s most marginalized societies will be substantially more vulnerable to the impact of climate change than indicated by existing income projections.

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Environ. Res. Lett. 18 (2023) 024028 https://doi.org/10.1088/1748-9326/acb163
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LETTER
The ‘conict trap’ reduces economic growth in the shared
socioeconomic pathways
Kristina Petrova1, Gudlaug Olafsdottir1, Håvard Hegre1,2and Elisabeth A Gilmore2,3,∗
1Department of Peace and Conflict Research, Uppsala University, Uppsala, Sweden
2Peace Research Institute Oslo, Oslo, Norway
3Department of Civil and Environmental Engineering, Carleton University, Ottawa, Canada
∗Author to whom any correspondence should be addressed.
E-mail: elisabeth.gilmore@carleton.ca
Keywords: economic growth, projections, armed conflict, shared socioeconomic pathways (SSPs), climate change
Supplementary material for this article is available online
Abstract
Armed conflict and economic growth are inherently coupled; armed conflict substantially reduces
economic growth, while economic growth is strongly correlated with a reduction in the propensity
of armed conflict. Here, we simulate the incidence of armed conflict and its effect on economic
growth simultaneously along the economic pathways defined by the shared socioeconomic
pathways (SSPs). We argue that gross domestic product per capita projections through the 21st
century currently in use are too optimistic since they disregard the harm to growth caused by
conflict. Our analysis indicates that the correction required to account for this is
substantial—expected income is 25% lower on average across countries when taking conflict into
account. The correction is particularly strong for the more pessimistic SSP3 and SSP4 where
expected future incidence of armed conflict is high. There are strong regional patterns with
countries with contemporaneous conflicts experiencing much higher conflict burdens and reduced
economic growth by the end of the century. The implications of this research indicate that today’s
most marginalized societies will be substantially more vulnerable to the impact of climate change
than indicated by existing income projections.
1. Introduction
When estimating future socioeconomic scenarios and
their implications, one of the most critical inputs
is the gross domestic product (GDP) and its rate
of growth over the long run (Christensen et al
2018). Extended end-of-century GDP projections are
important in the projection of the impacts and eco-
nomic costs of climate change (Rose et al 2017).
Reflecting the importance of GDP as an indicator of
socio-economic development, GDP projections have
been used to project energy and land use (Popp et al
2017, Riahi et al 2017), food prices (Popp et al 2017),
quality of governance (Andrijevic et al 2020), and
armed conflict (Hegre et al 2016).
Most prominent among the GDP projections in
use is the ENV-Growth model developed by Dellink
et al (2017). This model builds projections of future
economic growth, using a convergence framework
and interacting key long-run drivers of population,
total factor productivity, physical capital, employ-
ment and human capital, and energy and fossil fuel
resources (specifically oil and gas). The projections
are specified as operationalizations of each of the
five shared socioeconomic pathways (SSPs) (O’Neill
et al 2014), and cover the entire 21st century4. Other
projections have also been developed, e.g. Crespo
Cuaresma (2017), Leimbach et al (2017). These are
built on fairly similar assumptions and are highly cor-
related with the Dellink et al (2017) projections5.
Figure 1shows GDP per capita projections
according to the ENV-Growth model for the
4The SSPs were developed by the climate change research com-
munity to harmonize the assumptions that modellers make in
developing projections of the costs of mitigation and adaptation
to climate change. In addition to GDP, the SSPs define alternative
bounding scenarios for variables such as population and education.
5In this article, we only discuss the Dellink et al (2017) since data
for the other projections proved difficult to obtain.
© 2023 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
Figure 1. ENV-Growth projections for five countries from Dellink et al (2017): Afghanistan, Bangladesh, the Democratic
Republic of Congo, France and Tanzania, SSP2, 2017–2100.
‘middle-of-the-road’ scenario SSP 2 for one high-
income country (France), two lower-middle income
countries (Bangladesh and Tanzania) and two low-
income countries (Afghanistan and Democratic
Republic of Congo). By 2100, the model suggests that
the income of the Democratic Republic of Congo
(DRC) has converged with France, and that the
incomes in Bangladesh and Tanzania will also con-
verge with France within only a few more decades.
Are these projections plausible? Dellink et al
(2017) emphasize that they disregard external shocks
or other non-economic factors affecting productiv-
ity or technological transfer, such as governance or
environmental damages. One such growth-inhibiting
shock is internal armed conflict. Organized polit-
ical violence is often so detrimental to a country’s
economy that it has been termed ‘development in
reverse’ (Collier et al 2003). A number of independ-
ent studies agree that the armed conflicts that his-
torically have afflicted 15%–25% of all countries at
any time, leads to an annual growth shortfall of 2%
per conflict year (Collier 1999, Gates et al 2012)6.
Afghanistan has had continuous armed conflict since
the 1970s (Pettersson and Öberg 2020). Ignoring this
constraint on Afghanistan’s future growth traject-
ory seems unrealistic. Neglecting this feedback means
that we are likely to overestimate future GDP (Buhaug
and Vestby 2019) for Afghanistan, the DRC, and
other conflict-prone countries. Previous efforts that
approximate the risk of armed conflict in the future,
as demonstrated in Hegre et al (2016) and Witmer
et al (2017), suggest that this shortcoming can be
addressed.
Both armed conflict and economic performance
can interact with climate mitigation and adaptation
efforts (Buhaug and von Uexkull 2021, Gilmore and
Buhaug 2021). Armed conflict curtails economic
6See supplemental information A-1 for a review of these studies.
activity and reduces capacity to development chal-
lenges (e.g. de Groot et al 2022) . Thus, reducing the
burdens of armed conflict is critical for addressing
financial constraints as well as the social and polit-
ical unrest that can hinder efforts to adapt to adverse
climate impacts, especially in the more vulnerable
countries. Mitigation efforts may also be affected by
armed conflict. Furthermore, by reducing institu-
tional capacity, armed conflict could constrain efforts
to coordinate mitigation of greenhouse gas emissions
from national to international levels.
To demonstrate how the dynamics of armed con-
flict and GDP interact over the long-term, we develop
in this article the first joint projections for growth
in GDP per capita and armed conflict that con-
sider the reciprocal effect of the two phenomena on
one another. We use these new GDP pathways to
adjust the ENV-Growth GDP per capita projections
for the plausible losses due to destructive armed con-
flict. To simulate armed conflict and its implications
for GDP, we develop empirical models of the onset
and duration of conflict and the effect of conflict
on GDP growth, as well as a simple model of eco-
nomic growth. We then jointly simulate these out-
comes using the forecasting approach outlined in
Hegre et al (2013,2016). We run the simulation for
each of the five SSP scenarios, and revise the ENV-
Growth model results based on the simulated preval-
ence of armed conflict.
2. Materials and methods
Given their prominence and sophistication, we take
the Dellink et al (2017) projections as our point of
departure, called Yo
it here. We estimate a country-
specific correction dit , and compute a corrected set of
projections as
Yc
it =Yo
it −dit.(1)
2

Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
Figure 2. Projections from KC and Lutz (2017). Total global population (a), and average global proportion of population with
secondary education (b). Education levels are unweighted averages of countries’ education level.
Table 1. Fixed-effects OLS results, two growth models, 1960–2016. Detailed specification and results in supplemental information A-2.
Variable
Growth model I Growth model II
Coefficient Std. error Coefficient Std. error
Intercept 0.0072 0.014 0.0243 0.014
Conflict −0.0233 0.004 −0.0980 0.011
Log education 0.0912 0.027 0.0856 0.027
Population growth −0.5885 0.189 −0.6402 0.189
Log population −0.0104 0.008 −0.0170 0.008
Int. Population×conflict 0.0260 0.003
Country decay fixed effects Yes Yes
To quantify dit , we estimate a (separate) set of lin-
ear and logistic regression models of the relationships
between economic growth and armed conflict that
reproduce the consensus view on the empirical rela-
tionship between these. We then run two sets of simu-
lations for each of the SSPs: Ic
it, where we jointly simu-
late armed conflict and GDP growth, and Ip
it, where we
simulate GDP growth while ignoring armed conflict.
For each combination of SSP, country, and year, we
calculate the difference dit =Ip
it
−Ic
it in simulated log
GDP per capita between each pair of matched simula-
tions. We finally subtract dit from the original Dellink
et al (2017) projections.
Using the Dellink et al (2017) projections as the
base recognizes the many strengths of their approach.
By simply providing the corrections dit, we do not
remake the entire projection process, but rather illus-
trate the need to take armed conflict into account
when thinking about economic growth for the long
future.
2.1. Data
We develop our models of armed intrastate con-
flict with data from the 2017 update of the UCD-
P/PRIO Armed Conflict Dataset (Gleditsch et al
2002, Allansson et al 2017), which records conflicts
between governments and organized armed actors
with a political motivation that lead to at least 25
battle-related deaths in a year. Historic GDP per cap-
ita is derived primarily from the World Develop-
ment Indicators (World Bank 2017)—the same set of
sources as used by Dellink et al (2017).
The exogenous country-level variables in our
model are total population, population growth, and
rates of secondary education attainment, available
from IIASA (KC and Lutz 2017). Figure 2shows
observed and projected total global population under
each of the five SSPs as well as the proportion of
the population that have completed upper second-
ary education. SSP1 (sustainability; green line) and
SSP5 (conventional development; black line) have
optimistic assumptions regarding population growth
and expansion of education. SSP4 (inequality; red
line) assumes minimal expansion of education and
higher population growth, whereas SSP3 (fragmenta-
tion; purple line) have similarly pessimistic education
expansion assumptions and an even stronger popula-
tion growth. SSP2 (blue line) is a middle-of-the-road
scenario.
2.2. Short-term impact of conflict on growth
Table 1shows the results from a fixed-effects OLS
regression with difference in log GDP per capita from
one year to the other as the dependent variable. Per-
capita growth is higher the higher the education level
of a country, the lower the population growth, and,
most importantly for our purposes, when there is no
conflict7. In model I, log growth is 0.0233 lower in
years when a country experiences conflict, roughly
corresponding to 2.3% lower growth in percentage
7A detailed discussion of the covariates and the estimated coeffi-
cients are found in supplemental information A-2.
3

Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
terms. In model II, we include an interaction term
between conflict and population size to model that
armed conflict of a given size might be more severe
in smaller countries.
2.3. Simulation procedure
To generate the basis for the conflict-corrected GDP
per capita projections (Ip
it,Ic
it) we expand the ‘dynamic
simulation’ procedure used in Hegre et al (2013,
2016). Explicitly modeling the endogenous connec-
tion between conflict and growth, we simulate both
probabilities of armed intrastate conflict as well as
GDP growth per capita for each year, allowing the
simulations to inform one another. Armed conflict is
a covariate in the growth equation, and growth and
income in the conflict equation. We first estimate the
probability of conflict, and feed that into the growth
models for that year8. The procedure implies estim-
ating a set of underlying statistical models (the one in
table 1as well as a conflict model shown in table A-
3), assuming the projections for population and edu-
cation from IIASA for 2017–2100 (figure 2) are exo-
genous to conflict and growth. The models include a
set of country and region fixed effects, and we assume
these terms are exogenous. Since it would be unreal-
istic that unobserved differences between countries
will remain unchanged over many decades, we reduce
their importance in the future by letting them decay
with a half-life of 20 years as the simulations reach
into the future9.
In a number of repeated simulations, we draw
realizations of model coefficients based on the estim-
ated coefficients and the variance-covariance matrix
for the estimates; calculate probability distributions
for conflict and growth rates for year t0based on
the realized coefficients and the predictor variables,
and randomly draw realized conflict and growth
rates based on these. We then update the values for
the variables measuring historical experience of con-
flict and growth in the country and neighbourhood.
After drawing realized conflict and growth for a year,
we add the simulated growth to the previous year’s
logged GDP per capita to obtain a new value for
the simulated GDP per capita, and repeat for each
year in the forecast period 2017–2100, and record the
8See the supplemental information for details on the modelling
(A-2), on the simulation procedure (A-3), as well as detailed estim-
ation results (A-4).
9The chosen value for the decay of the fixed effects implies a rate
of change in countries’ fundamental social, economic and polit-
ical structures between the faster changes seen in East Asia or oil-
producing Arab countries from the 1960s to today and slower
changes observed in the structures of countries in North Africa or
Latin America. This assumption of slow convergenceis a conservat-
ive assumption. Countries that are currently poor will grow more
rapidly than under a no-convergenceassumpt ionand will therefore
have less conflict, and consequently have a smaller GDP correction.
This also avoids assuming that historical differences are permanent
(no convergence), which is also in line with Dellink et al (2017)
where convergence plays an important part of their model.
simulated outcomes for growth, GDP per capita, and
conflict. The updated conflict, growth, and GDP per
capita variables are then used when simulating the
next year’s values for these three variables. We label
the procedure a ‘dynamic simulation’ since the out-
comes we draw affect the incidence of conflict at time
steps t+2,t+3, etc10.
To even out uncertainty about model specifica-
tions, we run simulations for both sets of growth
models (table A-2) and conflict models (table A-3)
and average over the results. In 40% of the simulations
we have no region fixed effects in the conflict models,
and the remaining 60% are distributed equally over
four different region definitions11. When simulating
conflict, we assume that the underlying unexplained
conflict propensity of the past six years will remain the
same as in the 2011–16 period12. We run 100 simula-
tions for each of the clusters for each of ten imputed
datasets, totalling 5000 simulations, and take the aver-
age of the results to create our corrections.
We calculate dit =Ip
it
−Ic
it by running two pairs of
simulations of our economic growth model for the
2017–2100. In the first (Ip
it) we simply assume there
will be no conflicts anywhere, just as in Dellink et al
(2017). In the second (Ic
it), we simulate how much
growth-reducing conflict to expect over the period,
and update the growth paths of countries in which we
simulate conflict using the growth model in table A-2.
The final step in our correction procedure is to
add the difference dit to the original ENV-Growth
model projections to arrive at conflict-corrected
growth projections.
3. Results: corrected GDP per capita
projections 2017–2100
3.1. GDP per capita corrections, global level
Figure 3(a) shows the cumulative difference dit in
(unweighted) global GDP per capita between sim-
ulations where we ignore armed conflict and those
where we take them into account. The corrections
are very large—on average, countries’ GDP per cap-
ita are 20%–30% lower by the century, depending on
the SSP. The conflict trap indeed reduces economic
growth dramatically, and ignoring it is not tenable.
As we show below, for some countries the Dellink
et al (2017) end-of-century projected incomes are 4–
5 times larger than what our more plausible set of
assumptions yields.
Figure 3(b) shows the simulated proportion of
countries in conflict that causes the growth losses
10 See Hegre et al (2013,2016) for further details.
11 The results for the remaining conflict models are reported in
supplemental information A-4.
12 That is, we assume that the ‘temporal fixed effects’ for the 2011–
16 period in table A-3 and the detailed tables in supplemental
information A-4 are the ones guiding the simulations. The underly-
ing conflict propensity was higher in the last decade with data than
in the preceding five-year periods.
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Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
Figure 3. Simulation results, global unweighted averages, 2017–2100. SSP1 (green), SSP2 (blue), SSP3 (pink), SSP4 (red), SSP5
(black).
requiring this correction. The projected global pro-
portion of countries in conflict is roughly in line
with earlier studies using the same basic setup (Hegre
et al 2016)13. The simulations for SSP1 and SSP5 sug-
gest a clear decline in conflict from current levels, to
less than 15% of all countries at the end of the cen-
tury. This decline is driven by the moderate popula-
tion growth and robust expansion of education under
these scenarios (see figure 2). Conversely, the fore-
casts for SSP3 and 4 suggest an increasing incidence
of conflict (per country, if not per capita), to about
25% of all countries in 2100. This increase is driven
by high population growth and a slow expansion of
education levels.
In any year in the future, then, 10%–25% of all
countries, depending on the SSP, will have an ongo-
ing armed conflict. Our estimates (table A-2) suggest
that every year we simulate that a country is in con-
flict, the country has a growth rate that is on average
2.3% lower than if a similar country avoids conflict.
Over the 84 years of simulation, these growth losses
accumulate, especially in the SSPs where projected
conflict levels are high. In the low-conflict SSP5, the
unweighted average GDP per capita is more than 30%
lower in 2100 than what conflict-ignorant projec-
tions indicate. For the high-conflict SSP3 and SSP4
13 Since we are using more elaborate and credible projections for
education (KC and Lutz 2017) than Hegre et al (2016), the fore-
casts for SSP 3 and 4 are somewhat more optimistic than the pre-
vious study. The forecasts for SSP 1, 2, and 5, on the other hand,
are relatively more pessimistic, since we here include the corrected
growth projections in the conflict forecasts.
scenarios, unweighted average GDP per capita is more
than 35% lower. In these scenarios, the low under-
lying economic growth rate compounds the effect
in a conflict trap. The high population growth and
low education levels suppress income, thus increasing
expected conflict levels, and further decrease growth
rates. Figure 3(c) shows the end-of-century income
correction for all the countries included as a func-
tion of the end-of-century predicted conflict probab-
ility. Countries like the Scandinavian countries have
incomes that are unaffected by conflict. Countries
like Bangladesh (BNG), Tanzania (TZ), Afghanistan
(AFG), and the DRC are predicted to have conflict in
2100 in 40%–60% of the simulations, and our estim-
ated corrections range between −40% and −65%.
Figure 3(d) shows the corrected and uncorrected
ENV-Growth projections for the five SSPs. The ori-
ginal projections (again as unweighted global aver-
ages) are shown as dotted lines. Compared to the pro-
jected increases in income over the next 80 years, our
20%–30% corrections are not large. As we discuss
below, correcting for armed conflict are probably not
sufficient to obtain truly plausible growth estimates
over such a long forecasting horizon, but are clearly a
step in the right direction.
3.2. Region- and country-level results
The cumulative size of the correction in GDP per
capita differs greatly between countries and regions.
Figure 4illustrates the divergence between countries
for the middle-of-the-road scenario (SSP2). The fur-
ther toward the red end of the scale, the larger the
5

Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
Figure 4. Cumulative correction in SSP2 for GDP per capita by 2100, by country.
Figure 5. Dellink et al (2017), projections (dashed lines) and corrected projections (solid lines), (2017–2100), by region and by
SSP. Top left: East Africa, top right: West Africa, bottom left: South Asia, bottom right: Latin America.
correction. For countries with hardly any correction
to their growth projections, the colour is purple. The
corrections are greatest for countries our model sug-
gests have a high future risk of conflict. This risk is
high for countries with a recent, extended conflict his-
tory, as well as large and poor countries. Mali, Niger,
Afghanistan and Ethiopia tick off many of these
boxes, and the simulated effect of conflict on future
economic growth over time is large. Their future
income levels compared to a peaceful counterfactual
are much more reduced than, for instance, Iceland or
New Zealand that have a very low risk of future con-
flict. Some countries with no recent conflict history,
such as France, Germany and the US, also see decrease
in GDP per capita as a result of projected conflict.
Armed conflict has recently affected a few large, high-
income countries, for example the Basque and North-
ern Ireland conflicts. Spillovers from neighbouring
countries with conflict risks and strong population
growth along some SSPs means that intermittent con-
flict could affect growth in Western countries in the
future.
Inspecting the GDP per capita corrections by
region further illustrates important differences.
Figure 5shows the corrected and uncorrected ENV-
Growth projections for the five SSPs for four regions.
The correction is substantial in East Africa (top left)
for all the five scenarios, reflecting a high frequency of
simulated conflicts in this region. For SSP4, the cor-
rected average GDP per capita in 2100 is under half of
6

Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
Figure 6. Historical observations and projections, individual countries.
the Dellink et al (2017), original, and the correction
is almost as large for the other SSPs. The adjustments
are less marked in West Africa (top right), which
historically has been more peaceful than its Eastern
neighbours. Likewise, the correction is even smaller in
Latin America (bottom right), a region where armed
conflict is approaching obsolescence14. In South Asia,
on the other hand, our correction is again substantial.
Several countries in the region, e.g. India, Pakistan,
14 Note that the organized criminal violence in the region mostly
falls outside our definition of armed conflict (Allansson et al 2017).
and Myanmar, have had virtually continuous conflict
since independence. Our models suggest that this
will continue for several decades, given their relative
poverty levels and conflict history.
Disaggregating further down to the country level
we return to the illustrative cases shown in figure 1,
to further understand how country-level differences
in input data shape the outcomes.
Figure 6shows corrected and uncorrected GDP
per capita and conflict for Afghanistan, the DRC,
Bangladesh, and Tanzania. The left column shows the
ENV-Growth (Dellink et al 2017) projections as well
as these projections with our correction, for each SSP.
7

Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
The right column shows the conflict projections for
each SSP.
All four countries are all low- or lower-middle
income and have a high projected probability of
armed conflict. The corrections for Afghanistan and
the DRC have considerable face validity. Afghanistan,
for example, has been continuously at war for forty
years and may conceivably continue to be so for many
decades. If the war takes off 2% annually from the
real growth potential of the country, the loss easily
accumulates to in excess of 80% loss over a century.
The conflict predictions for Bangladesh and Tanzania
are pulled up by their large population sizes and low
income levels. Toward the end of the century, all these
countries are forecasted to see more than 25 deaths in
75% of the years under the pessimistic scenarios SSP 3
and 4. Under the optimistic scenarios, the simulations
yield conflict in about half of the years. These forecasts
seem high, but recall that the population in 2100 is
projected to exceed 200million in both Tanzania and
DRC in SSP 3 and 4, and well over 100million even
in the low-population growth scenario. With a fixed
threshold of 25 deaths in the definition of armed con-
flict, population size is a major predictor of conflict
(Raleigh and Hegre 2009).
The income corrections for these countries are
substantial. In Afghanistan under the pessimistic
scenario of SSP4, Dellink et al (2017) projects an
increase in GDP per capita to about 10 000 dollars by
the end of the century. In the same scenario the pro-
jected income with our correction is slightly below
the current levels of 2000, only a fifth of the ori-
ginal projection. In this scenario, our model predicts
a high probability of continued conflict, given strong
population growth and little expansion of education,
and as such produces severely depressed growth rates.
For the more optimistic scenario SSP5, where Dellink
et al (2017) projects Afghanistan’s income to reach
the implausible value of 140 000 dollars, our correc-
tion still suggests a value about 75% lower. In this
scenario, with lower population growth rates, expan-
sions of education and thus conditions that facilitate
conflict mitigation in the future, the estimated prob-
ability of conflict is far lower than in SSP4. How-
ever, our projections still take into account that the
risk of conflict remains high at first, and this risk is
likely to continue to shape the economic trajectory of
the country for decades to come. The growth projec-
tions for Afghanistan and DRC with our correction
are more realistic than the original for SSP1 and SSP5,
but still likely to be overly optimistic. As the history of
these countries suggest, there are also other sources of
growth failures. The GDP per capita of the DRC, for
instance, fell steadily from 1970 to 1995, for instance,
despite the low levels of conflict in that period.
4. Conclusion
This work improves the understanding of links
between economic development and civil conflict as
well as produces forecasts of future conflict burdens
that are consistent with widely used climate change
scenarios. We successfully model the effect of the
conflict trap on economic growth over the course
of the 21st century, providing a first indication of
how the ENV-Growth projections of GDP per cap-
ita can be corrected for the effect of armed conflict.
Globally, our corrected projections are close to 25%
lower than the original at the end of the century for
the most optimistic Shared Socioeconomic Pathways,
and more than 30% lower in the least optimistic ones.
Thus, the ENV-Growth model (Dellink et al 2017)
clearly over-estimates future growth in conflict-prone
countries.
As the ENV-Growth model underlies much of the
existing climate change research, these proposed cor-
rections may have substantial implications for current
estimates of future adaptation and mitigation efforts.
The correction is largest for currently poor and vul-
nerable countries with a conflict history, and suggests
that the resources these societies will have available for
adapting to climate change and other challenges are
much lower than assumed in studies that rely on cur-
rently available projections. These revised GDP pro-
jections that include armed conflict also have implic-
ations for understanding the costs of and capacity for
mitigation efforts. As armed conflict has also been
shown to lead directly to armed conflict in neigh-
bouring countries, these spillover economic effects
may even have adverse effects on an international level
(e.g. providing cover for terrorist activities). Thus,
more importantly, the increase in conflict and result-
ant institutional instability can increase challenges to
the attainment of global agreements and capacity for
climate mitigation policy.
Accounting for the risk of armed conflict is only
one among several issues that remain to be addressed
in economic growth projections. There are several
other governance failures that are less violent but
equally growth-inhibiting, as we noted for the case
of DRC above, and also exemplified by Zimbabwe
and Venezuela. For long-term growth projections to
be realistic, further research should also take broader
governance failures into account. Also, armed con-
flict and other governance failures are likely to affect
other core inputs to growth models. Persistent con-
flicts affect population health, migration patterns,
and undermine education. All of these, in turn, alter
the likely future growth paths of countries. While
these effects will be concentrated in the countries
where the conflict occurs, these effects may also be
8

Environ. Res. Lett. 18 (2023) 024028 K Petrova et al
experienced regionally in countries that share bor-
ders and more generally, through changes in trade
and other political spillovers.
Data availability statement
The data that support the findings of this study
are openly available at the following URL/DOI:
https://www.prio.org/publications/13330.
Acknowledgment
This material is based upon work supported in part
by the U.S. Army Research Laboratory and the U.S.
Army Research Office via the Minerva Initiative
under Grant No. W911NF-13-1-0307, the MISTRA
Geopolitics programme, Riksbankens Jubileums-
fond programme Societies at Risk, and the European
Research Council Project H2020-ERC-2015-AdG
694640 (ViEWS). The simulations were performed
on resources provided by the Swedish National Infra-
structure for Computing (SNIC) at Uppsala Mul-
tidisciplinary Center for Advanced Computational
Science (UPPMAX). The authors would like to thank
Frederick Hoyles for developing the simulation pro-
gram, Remco Jansen for work on the cluster regions,
Maxine Ria Leis and Hannah Frank for help with
the data, and Chandler Williams for helpful com-
ments. For more information on the ViEWS project
see https://viewsforecasting.org.
Conflict of interest
The authors have no conflicts of interest to declare.
Ethics statement
All authors have seen and agreed with the contents
of the manuscript and there is no financial interest to
report.
CRediT statement
Kristina Petrova: Validation, formal analysis and the-
ory, data curation, writing, and visualization. Gud-
laug Olafsdottir: Formal analysis and theory, data
curation, writing, and visualization. Håvard Hegre:
Conceptualization, methodology, validation, formal
analysis and theory, writing, project administration,
and funding acquisition. Elisabeth Gilmore: Concep-
tualization, formal analysis and theory, writing, and
funding acquisition.
ORCID iDs
Kristina Petrova https://orcid.org/0000-0003-
4484-4179
Gudlaug Olafsdottir https://orcid.org/0000-0002-
0833-8765
Håvard Hegre https://orcid.org/0000-0002-5076-
0994
Elisabeth A Gilmore https://orcid.org/0000-0002-
9037-6751
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