Edited by: Girma Gezimu Gebre, Ritsumeikan University, Japan
Reviewed by: Mohammed Adem Molla, Bahir Dar University, Ethiopia; Asmiro Abeje Fikadu, Debre Tabor University, Ethiopia
This article was submitted to Climate, Ecology and People, a section of the journal Frontiers in Climate
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Climate continues to pose significant challenges to human existence. Notably, in the past decade, the focus on the role of climate on conflict and social unrest has gained traction in academic, development, and policy communities. This article examines the link between climate variability and conflict in Mali. It advances the argument that climate is a threat multiplier, in other words, climate indirectly affects conflict occurrence through numerous pathways. We take the view that maize production and household food security status sequentially mediate the relationship between climate variability and the different conflict types. First, we provide a brief review of the climate conflict pathways in Mali. Second, we employ the path analysis within the structural equation modeling technique to test the hypothesized pathways and answer the research questions. We use the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a nationally representative data from Mali merged with time and location-specific climate and the Armed Conflict Location and Event Data (ACLED) data. Results show that an increase in positive temperature anomalies when sequentially mediated by maize production and household food security status, increase the occurrence of the different conflict types. The results are robust to the use of negative precipitation anomalies (tendency toward less precipitation compared to the historical norm). Our findings highlight two key messages, first, the crucial role of climate change adaptation and mitigation strategies and interventions on influencing household food security status and thus reducing conflict occurrence. Second, that efforts to build peace and security should account for the role of climate in exacerbating the root causes of conflict.
The recent Intergovernmental Panel on Climate Change (IPCC) report identifies climate change and variability as one of the main challenges threatening human existence (IPCC,
Existing empirical studies contributing to the climate-conflict debate provided mixed findings. Some support the argument that climate change exacerbates conflict (Burke et al.,
Even with the growing consensus that there is an indirect relationship between climate and conflict, there are no generally agreed upon impact pathways, rather, the indirect relations are complex and dynamic with feedback mechanisms. In the studies supporting the hypothesis that climate is indirectly associated with the emergence and persistence of conflict, resource scarcity is the dominant discourse explaining the mechanism at play (Klomp and Bulte,
Resource scarcity discourse views climate as a driver that creates resource scarcity which in turn fuels conflict (Evans,
Another unsettled issue within the climate-conflict research is the question of which climatic events influence conflict. Largely, existing studies consider precipitation and temperature anomalies—the deviation from the historical normal precipitation and temperature. Precipitation and temperature anomalies have been shown to have different effects on conflict depending also on the type of conflict in consideration (Hsiang et al.,
This paper contributes to filling the knowledge gap and on the debate on the association between climate and conflict in at least four ways. First, we provide a contextualized impact pathways for Mali explaining the mechanisms through which climate variability may trigger to conflict. Second, we model maize production and household food security status as sequentially mediating the association between climate variability and conflict. Third, estimate path analysis (serial mediation) through the structural equation modeling (SEM) approach. Fourth, we provide a detailed analysis of the association between both temperature and precipitation variability and conflict.
Overall, we advance the argument that the relationship between climate and conflict is complex and dynamic. Specifically, we hypothesize that climate variability negatively affects maize production and this, in turn, adversely affects the household food security status which consequently may trigger different types of conflicts.
The next sections of this article are organized as follows. In Section Climate security impact pathways we briefly provide the contextual climate security pathways in Mali and the theoretical framework of the mechanisms that explain the relationship between climate variability and conflict. Section Data and methods outlines the data and methods. Section Results presents the results and discussion, and in Section Conclusions we draw conclusions placing our findings in the growing debates on climate security, climate adaptation and mitigation in fragile contexts and climate finance.
For the past three decades, Mali has experienced an increase in competitive pressures over the access to and use of natural resources by different livelihood groups. These groups are often associated with specific ethnic groups, leading to overlaps between conflict lines. For instance, in northeast Mali, there are considerable tensions between Tuareg and Fulani pastoralist communities over the control of pasture lands and sources of water for their livestock (Nagarajan,
Climate change and variability in Mali continues to impact negatively climate-sensitive livelihoods, including agriculture, livestock, and fishing, reducing their production and productivity (Nagarajan,
In this context, the climate crisis has the potential to exacerbate the competition over the access to and use of available resources through its impact on natural resource availability and environmental conditions. In Mali's conflict-affected context, the increasing competition may continue to reduce levels of social cohesion, further increasing the risks that conflicts will be sustained or (re)emerge between and amongst different socio-professional and ethnic groups (Raineri,
Farmer-herder conflicts have increased in the last decade due to various factors, including the expansion of farming into livestock corridors and the mobility of herders induced by the violent conflict and droughts (Ibrahim and Zapata,
Harsh climate conditions with more severe dry seasons force pastoralists to move toward the Niger Delta in search of pasture. This becomes a real problem when animals arrive before the crops have been harvested as they damage crops, impacting farmers' livelihoods and increasing the risk of food insecurity and conflict (Ibrahim and Zapata,
The debate around climate-conflict nexus has gained traction since 2007 when climate change was reframed as a national and international security issue as opposed to being understood as purely an environmental shock (Brzoska,
Whereas our research is rooted in the second strand of conceptualization, we nonetheless test the direct association between climate variability and conflict. Studies that have estimated the direct association between climate variability and conflict often stem from the intersection of psychology and economics disciplines. For instance, supporting this line of conceptualization (Anderson et al.,
To advance the second strand of conceptualization that the association between climate variability and conflict is mediated by some factors, we reflect on the impact pathways. In general, the pathway through which climate variability may influence conflict are numerous, complex and context specific. According to Sakaguchi et al. (
In another study, Martin-Shields and Stojetz (
In sub-Saharan Africa, Jun (
Finally, to our knowledge, limited effort has been directed to unravel the association between climate and conflict through both food production and food (in)security “closing the loop”.
In this study we attempt to close this loop by modeling both maize production and food security status as mediators. The choice of maize yield is based on the importance of maize production to household food and livelihood security in Mali. Maize was widely adopted by farmers in the late 1970s following the great droughts during that decade as a crop diversification strategy aimed at addressing national chronic food shortages as well as ensuring food security (Diallo,
In this study, we present food production (maize) and food insecurity as the mechanism through which climate influence conflict. To put our impact pathway into perspective, we postulate that climate variability (as measured by both precipitation and temperature anomalies) has a direct effect on maize production, and this in turn has a direct effect on household food security status, consequently influencing conflict. Given the above, we test the following hypotheses:
We test these hypotheses through a process called serial/chain mediation analysis in structural equation modeling technique—where the influence of the independent vari-able flows through multiple mediators before impacting the outcome variable (Collier,
Conceptual model indicating the pathways linking climate variability to conflict.
The data used to answer the research questions is based on rich nationally representative household data from Mali which is administered by the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) of the World Bank. We use the pooled data of the two waves of Mali LSMS-ISA (2014/15 and 2017/18). We use the pooled data since it is documented that it was not possible to track households between the two waves, thus it is recommended that the data should be considered a cross-sectional survey
Maize yield is derived from the agricultural production section calculated by the sum of harvested maize production (kgs) in the two waves of data, this approach has been used previously (Caruso et al.,
The conflict variables were derived from the Armed Conflict Location and Event Data Project (ACLED). ACLED is geo-Referenced event dataset collected and coded to tract the conflict and violence occurrence globally. It aims to capture the modes, frequency and intensity of political violence and conflicts as they occurs (Raleigh et al.,
The climate data used were derived from the Climate Hazards Group InfraRed Precipitation with Station? data (CHIRPS) which contains information on maximum and minimum temperature and precipitation (Funk et al.,
where
TA denotes temperature anomalies and PA precipitation anomalies.
For the conflict variables, we consider the number of the different forms of conflicts that were reported 12 months after the survey period. Following our conceptual model logic and mediated hypothesis, if maize production affects household food security, then it is not logical that climate variability in time
In this study we investigate the empirical associations between climate variability (as measured by the temperature and precipitation anomalies) maize production, household food insecurity and conflict. Given the complexity of the associations, we employ the structural equation modeling (SEM) approach which has previously used to unravel complex relationships such as the association between climate and conflict through different pathways (Helman et al.,
To estimate the structural model and test the mediation effects James and Brett (
The control variables were included in the structural model and regressed on the dependent variables (types of conflict). Descriptive statistics of the variables used in the analyses can be found in
Selection of results of the econometric model showing the direct effects of climate, maize production, food insecurity and conflict, without control variables (col. 2) and with control variables (col. 3). The last column shows whether the results support the hypotheses of our theoretical background presented in section 3.
Precip. 3 months negative anomalies → Total conflicts | −0.015 | 0.448 | −0.066 |
0.341 | Not supported |
Precip. 3 months negative anomalies → Violence against civilians | −0.236 |
0.153 | −0.201 |
0.123 | Not supported |
Precip. 3 months negative anomalies → Riots | −0.015 | 0.053 | −0.155 |
0.038 | Not supported |
Precip. 3 months negative anomalies → Protests | 0.202 |
0.208 | 0.041 |
0.129 | Supported |
Precip. 3 months negative anomalies → Remote violence | −0.091 |
0.063 | −0.017 | 0.056 | |
Precip. 3 months negative anomalies → Battles | −0.054 |
0.125 | 0.017 | 0.104 | |
Precip. 3 months negative anomalies → Maize production | −0.076 |
0.084 | −0.073 |
0.120 | Supported |
Temp. 3 months positive anomalies → Total conflicts | 0.078 |
0.314 | 0.008 | 0.239 | |
Temp. 3 months positive anomalies → Violence against civilians | 0.195 |
0.107 | 0.050 |
0.086 | Supported |
Temp. 3 months positive anomalies → Riots | 0.369 |
0.037 | 0.171 |
0.027 | Supported |
Temp. 3 months positive anomalies → Protests | 0.091 |
0.145 | 0.017 |
0.091 | Supported |
Temp. 3 months positive anomalies → Remote violence | −0.111 |
0.044 | −0.029 |
0.039 | Not supported |
Temp. 3 months positive anomalies → Battles | −0.124 |
0.087 | −0.033 |
0.073 | Not supported |
Temp. 3 months positive anomalies → Maize production | −0.073 |
0.120 | −0.076 |
0.084 | Supported |
Maize production → Food insecurity | −0.076 |
0.001 | −0.072 |
0.001 | Supported |
Food insecurity → Total conflicts | 0.147 |
0.699 | 0.080 |
0.530 | Supported |
Food insecurity → Violence against civilians | 0.165 |
0.240 | 0.068 |
0.191 | Supported |
Food insecurity → Riots | 0.026 |
0.083 | 0.067 |
0.059 | Supported |
Food insecurity → Protests | −0.014 | 0.322 | 0.050 |
0.201 | Supported |
Food insecurity → Remote violence | 0.177 |
0.098 | 0.058 |
0.087 | Supported |
Food insecurity → Battles | 0.188 |
0.195 | 0.047 |
0.162 | Supported |
*p < 0.10,
p < 0.05,
p < 0.001.
S.E, standard errors.
For brevity we present the full direct effects results only for the key variables that we hypothesized and provide the full results in
With respect to the direct effects of 3 months positive temperature anomalies on the number of different conflict types, our results are mixed. Some hypotheses are supported indicating that increase in 3 months positive temperature anomalies increases the number of the different conflict types (H1). Specifically, one standard deviation increase in 3 months positive temperature anomalies increases violence against civilian, riots and protests, however, it reduces number of remote violence and battles. The supported hypotheses are consistent with the findings within the General Aggression Model which state that higher temperatures trigger human aggression (DeWall et al.,
With respect to the hypothesis that maize production is negatively associated with food insecurity (H3), our results support this hypothesis. This implies that increase in maize production reduces household food insecurity status. This corroborates with the findings that maize yield is crucial for household food security in Mali (Diallo et al.,
Our results also support the hypotheses that household food insecurity increase number of conflict types (H4), indicating that increase in food insecurity by one standard deviation result to an increase in total conflicts by 0.08 standard deviations; increase in violence against civilian by 0.068 standard deviations; increase in riots by 0.067 standard deviations; increase in protests by 0.050 standard deviations; increase in remote violence and battles by 0.058 and 0.047 standard deviations respectively. This is in line with previous studies that have found that household food security status is one of the mechanisms that triggers conflict (Koren and Bagozzi,
In the next step, we performed serial mediation analysis (indirect effects) while accounting for the control variables. This tests the hypothesis that maize production and household food security status sequentially mediate the association between climate variability (both temperature and precipitation anomalies) and the conflict types (H5). We rely on the parameter estimates for the path from temperature and precipitation anomalies to the conflict types via maize production and household food security status sequentially (see
Results of the serial mediation analysis showing indirect effects of climate on conflict via maize production and food insecurity. The table reports the results of the model without (col. 2) and with (col. 3) control variables. Column 4 reports whether the results support the hypotheses of our theoretical framework presented in section 3. The last column shows whether the mediation is full (i.e. direct effects are not significant) or partial (i.e. direct effects are significant).
Temp. 3 months positive anomalies → Maize production → Food insecurity → Total conflicts | 0.021 |
0.006 | 0.011 |
0.003 | Supported | Full mediation |
Temp. 3 months positive anomalies → Maize production → Food insecurity → Violence against civilians | 0.008 |
0.002 | 0.003 |
0.001 | Supported | Partial mediation |
Temp. 3 months positive anomalies → Maize production → Food insecurity → Riots | 0.000 |
0.000 | 0.001 |
0.000 | Supported | Partial mediation |
Temp. 3 months positive anomalies → Maize production → Food insecurity → Protests | −0.001 | 0.001 | 0.003 |
0.001 | Supported | Partial mediation |
Temp. 3 months positive anomalies → Maize production → Food insecurity → Remote violence | 0.004 |
0.001 | 0.001 |
0.000 | Supported | Partial mediation |
Temp. 3 months positive anomalies → Maize production → Food insecurity → Battles | 0.008 |
0.002 | 0.002 |
0.001 | Supported | Partial mediation |
Precip. 3 months negative anomalies → Maize production → Food insecurity → Total conflicts | 0.030 |
0.008 | 0.015 |
0.004 | Supported | Partial mediation |
Precip. 3 months negative anomalies → Maize production → Food insecurity → Violence against civilians | 0.012 |
0.003 | 0.005 |
0.001 | Supported | Partial mediation |
Precip. 3 months negative anomalies → Maize production → Food insecurity → Riots | 0.001 |
0.000 | 0.002 |
0.000 | Supported | Partial mediation |
Precip. 3 months negative anomalies → Maize production → Food insecurity → Protests | −0.001 | 0.001 | 0.004 |
0.001 | Supported | Partial mediation |
Precip. 3 months negative anomalies → Maize production → Food insecurity → Remote violence | 0.005 |
0.001 | 0.002 |
0.001 | Supported | Full mediation |
Precip. 3 months negative anomalies → Maize production → Food insecurity → Battles | 0.011 |
0.003 | 0.003 |
0.001 | Supported | Full mediation |
*p < 0.10,
p < 0.05,
p < 0.001.
S.E, standard errors.
Mediation effects using the bootstrap method with 5,000 samples at 95% CI.
Specifically, the results indicate that the mediated effect of 3 months positive temperature anomalies on total conflicts is 0.011, on violence against civilians is 0.003, on riots is 0.001, on protests is 0.003, on remote violence is 0.001, and on battles is 0.002. These, imply that, an increase in 3 months positive temperature anomalies by 1 standard deviation increases the total conflicts by 0.011 standard deviations, increases violence against civilians by 0.003 standard deviations, increases riots by 0.001 standard deviations, increases protests by 0.003 standard deviations, increases remote violence by 0.001 standard deviations, and increases battles by 0.002 standard deviations.
With respect to precipitation, the results indicate that overall, there is a positive association between the 3 months negative precipitation anomalies and the number of conflict types mediated by maize production and household food security status sequentially. Specifically, the mediated effect of 3 months negative precipitation anomalies on total conflicts is 0.015, on violence against civilians is 0.005, on riots is 0.002, on protests is 0.004, on remote violence is 0.002, and on battles is 0.003. These, imply that, increase 3 months negative precipitation anomalies increase the total conflicts by 0.015 standard deviations, increase violence against civilians by 0.005 standard deviations, increase riots by 0.002 standard deviations, increase protests by 0.004 standard deviations, increase remote violence by 0.002 standard deviations, and increase battles by 0.003 standard deviations.
In general, all the hypotheses are supported, suggesting that maize production and the household food security status sequentially mediate the association between temperature and precipitation anomalies, and the conflict types. In other words, maize production and household food security status are some of the mechanisms through which climate variability exacerbate conflict. In terms of the type of mediation, we find partial mediation in all mediated paths except the mediated path from 3 months positive temperature anomalies to total conflicts, the mediated path from 3 months negative precipitation anomalies to remote violence and to battles which have a full mediation.
Mediated paths showing partial mediation imply that the direct paths are significant. On the one hand, this suggests that the variations in the conflict variables are explained both by the mediated paths and the direct paths. On the other hand, full mediation is where the direct path is insignificant suggesting that the variation in conflict variable is fully explained by the mediated path. While these results have policy implications, we caution that they need to be interpreted with care, this is because the scope of this paper is on one pathway (climate variability to conflict via maize production and household food security status), thus before making policy recommendations or designing interventions to reduce conflicts there is need to take into account the complexity of other pathways at play.
The world is significantly less peaceful now than it was 15 years ago. The 2021 Global Peace Index report shows that the average level of global peacefulness deteriorated for the ninth time in 13 years in 2020. Climate variability and change also accelerate this negative trend by multiplying socioeconomic risks and insecurities, such as food insecurity, forced migration, displacement, and inequality, among others, which are ultimately the root causes of instability, tensions, and conflict. Recent estimates report that approximately 971 million people live in areas with high or very high climate exposure, and of this number, 41 per cent resides in countries marked by low levels of peacefulness.
Despite growing recognition of the potential of climate to amplify existing conflict dynamics or even create new ones, robust, scientific evidence that climate is a “threat multiplier” is lacking. This is reflected in the policy agenda of many fragile countries, where climate security is not acknowledged and therefore risks associated with the nexus are not accounted for in either peacebuilding efforts or climate resilience interventions. More policy relevant research is needed on
Our study contributes to fill this gap providing answers to the
Our findings reveal that climate is a threat multiplier, this is consistent with previous studies that have found that climate indirectly leads to increased conflict occurrence (Fjelde,
Acknowledging the role of climate as threat multiplier has important implications for both peace peacebuilding efforts. Current peace and security interventions do not adequately address the change, variability, and impact of climate on socioeconomic risks that can lead to conflict. There is, therefore, a need to correct this imbalance. And this is particularly important not only for those countries where climate and fragility already intersect but also for many
This is even more important if we think that when it comes to climate action, existing strategies are unlikely to capture the wide range of context-dependent security risks that can arise from climate impacts. While an increasing number of climate interventions, investments, policies, and programmes target fragile and conflict-affected countries, these activities are often blind and less responsive to the context in which they operate. This can lead to the unintended consequences of reinforcing structural and contextual drivers of conflict. Indeed, several examples exist of conflict-insensitive adaptation measures that have increased conflict potential by damaging economic prospects, undermining political stability, and amplifying inequality and grievances.
Therefore, to reduce the potentially harmful effect of climate action and ensure that it positively impacts people and communities, there is a need to design and implement climate investments, policy, and programmes in a climate security sensitive manner. Climate security sensitivity can indeed unveil the potential peace contributing impact of climate measures, thereby addressing the root causes of conflict, and fostering societal levels of peace.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
GP: conceptualization, methodology, data analysis, writing, and supervision. DK: conceptualization, methodology, data analysis and curation, and writing. IM-L: conceptualization and writing—review and editing. VV and AB: methodology, data analysis, and curation. PL: review and editing. All authors contributed to the article and approved the submitted version.
This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details, please visit
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The views expressed in this paper are those of the authors and do not necessarily reflect the views of the funder or the authors' institution. The usual disclaimer applies.
We thank our reviewers for the constructive comments and we are grateful to the Climate Security team at Alliance Bioversity-CIAT for the support throughout the process.
The Supplementary Material for this article can be found online at:
1Further documentation of the Mali LSMS-ISA can be found here: