Climate Change,Drought,and Potential Environmental Migration Flows Under Different Policy Scenarios

Abstract

Unmitigated climate change will likely produce major problems for human populations worldwide. Although many researchers and policy-makers believe that drought may be an important “push” factor underlying migration in the future, the precise relationship between drought and migration remains unclear. This article models the potential scope of such movements for the emissions policy choices facing all nation-states today. Applying insights from climate science and computational modeling to migration research, we examine the likely surge of drought-induced migration and assess the prospects of different policy scenarios to mitigate involuntary displacement. Using an ensemble of 16 climate models in conjunction with high-resolution geospatial population data and different policy scenarios, we generate drought projections worldwide and estimate the potential for internal and international population movement due to extreme droughts through the remainder of the 21^st century. Our simulations suggest that a potential for drought-induced migration increases by approximately 200 percent under the current international policy scenario (corresponding to the current Paris Agreement targets). In contrast, total migration increases by almost 500 percent, should current international cooperation fail and should unrestricted policies toward greenhouse gas emissions prevail. We argue that despite the continued growth projections of drought-induced migration in all cases, international cooperation on climate change can substantially reduce the global potential for such migration, in contrast to unilateral policy approaches to energy demands. This article highlights the importance of modeling future environmental migrations, in order to manage the pressures and unprecedented policy challenges which are expected to dramatically increase under conditions of unmitigated climate change.

Keywords

environmental migration climate change forced migration involuntary immobility policy international cooperation drought

Introduction

From a historical perspective, migration has been a natural response to global environmental changes (McLeman and Smit 2006; McLeman 2014). From a political perspective, however, environmentally induced migration (IOM 2015), particularly migration due to extreme droughts caused by changing climate (Chen and Caldeira 2020; IPCC 2013; Prudhomme et al. 2013; Samson et al. 2011; Smirnov et al. 2016; Wanders and Wada 2015), has recently emerged as one of the most pressing issues on global political agendas (Cattaneo et al. 2019; Goldstone 2001; Martin 2010, 2014; McAdam 2012; Slaughter 2013). At the intersection of climate change and involuntary population flows, the pressure is on states to respond to the challenges of present mitigation strategies, designed to reduce the severity of future climate change, and future adaptation strategies, meant to respond and adjust to climate change.

As the topic of climate adaptation, in the form of policy responses, has increasingly engaged migration policy makers and scholars (Carling and Collins 2018; Carling and Schewel 2018; Collyer, Düvell and de Haas 2012; McAdam 2011, 2012, 2015; Schewel 2019), climate projections can better inform relevant humanitarian policies (IPCC 2014). The variety and breadth of anthropogenic (human-induced) climate change manifestations (e.g., desertification and extreme weather events) have the potential to cause significant internal and international population movements (McLeman and Smit 2006). In this article, we focus on one such manifestation—extreme drought—as a potential cause of migration. Our findings suggest that international cooperation on climate change can significantly reduce the global potential for drought-induced migration, in contrast to the unrestricted energy policies by the largest emitters of greenhouse gases, such as China, the United States, the European Union (EU), Russia, India, and Japan, should these countries choose to pursue their own short-term economic interests. We argue that climate change mitigation and adaptation initiatives that are built on international multi-level policy instruments (e.g., reducing greenhouse gas emissions, disaster displacement assistance, planned relocation, rights protections, development programs, non-agricultural skills training, etc.) are likely to be more effective in responding to the mutual pressures of drought and human migration than any set of other adaptation policies based on national or unilateral (i.e., go-it-alone) approaches.

To develop these arguments, we, first, clarify the nature of agent-based modeling (ABM) in general (Epstein 2006; Klabunde and Willekens 2016) and our computational model in particular. The behavioral assumptions that we introduce in our model are coarse: humans are essentially automata following very basic rules that we formally specify below, as well as in the computer code presented in the Online Appendix. The agents in our model do not have age, gender, resources, social capital, diaspora networks, or other characteristics, all of which undoubtedly influence migration behavior. However, these simplifying assumptions—typical for many ABMs (Epstein 2006)—allow us to explore migration decisions and behavior of more than 7 billion agents on a high-resolution world map for each month of the 21^st century. Each simulation run must be further repeated multiple times for a different set of parameters and scenarios.¹ Our model's computational complexity (and, therefore, its output) clearly limits our ability to go beyond theoretical projections. Thus, while we do not claim to offer literal results or a representation of actual future realities, our model may be seen as a tool for exploring drought-induced migration. In an effort to convey this point explicitly, we frame our key findings in relative, as opposed to absolute, terms. We do not predict that there will be a specific number of migrants for a given set of parameters and, instead, compare how the number of migrants changes, in percentage terms, between different scenarios. As we show below, this approach is robust to different modeling assumptions.

In addition to the computational complexity described above, we face a challenge of essentially unpredictable institutional change in the future. Any attempt to predict the exact number of future migrants based on historical trajectories will suffer from overfitting and especially from the assumptions that political institutions change in a predictable manner (Hitchcock and Sober 2004). We believe that such an approach would be naïve, given the idiosyncratic nature of political leadership, technological change, and all kinds of random shocks in the world, such as pandemics, wars, and ecological and technological disasters. Therefore, we do not model political institutions or alliances which may facilitate or constrain population movements across national borders. As a consequence, all results should be seen as “potential migration pressures,” instead of actual migration flows. For example, as we show below, the largest projected migration flow in the 21^st century is from India to China. Given the logistical barriers and political relations between the two countries, this outcome is unlikely in the 21^st century. Instead of modeling the actual migration, we explore how a changing climate may affect migration pressures and hypothesize that a large increase in such pressures will raise the potential for future migration.

Finally, we are not modeling future adaptation policies, such as measures that will increase households’ resilience and enable them to better adapt to a changing climate. Instead, we are looking to identify where such adaptation policies will be most needed. For example, our simulation results identify Egypt, Mali, and Turkey as the countries with the largest projected displacement due to future droughts. Consequently, we conjecture that these countries are likely to benefit from climate adaptation policies. Modeling such policies would affect the migration flows and potentially conceal the problem's urgency. It would also require an additional set of arbitrary modeling assumptions with respect to how adaptation policies will affect internal and external migration flows 80 years into the future.

The remainder of this article is organized as follows. We begin by contextualizing drought- and climate-induced migration within the context of international politics and cooperation. From there, we introduce our climatic and behavioral modelling assumptions, parameters, and limitations. We, then, present the potential migration pressures based on distinct emissions scenarios. Finally, we discuss the policy implications of our results and remind readers about the uncertainty and limitations in our model. Given the growth potential of drought-induced migration, we argue that international cooperation on climate change promises to be more effective than unilateral policy approaches to energy demands.

The Politics of International Migration and Climate Change

Traditionally, scholars of the classic “push-pull” migration phenomenon have focused on economic or social variables to predict demographic outcomes (Abel and Sander 2014; Bell and Charles-Edwards 2013; Massey et al. 1993; UNDESA 2019). Recent scholarship, however, has pointed to the likelihood of large-scale migration in response to future environmental and climate changes, including drought (Black et al. 2011; McLeman 2013; McLeman and Smit 2006; Reuveny 2007). There are ample reasons to focus on drought as a possible cause of migration. First, environmental migration due to drought has become increasingly salient for the general public (e.g., Glasser and Swing 2016; Jakes 2016; Lustgarten 2020) and for scholars (e.g., de Sherbinin et al. 2012; Henry, Boyle and Lambin 2003; Hugo 1996; Mortimore 1989; Reuveny 2007). Second, historically, drought has affected the largest number of people relative to other natural disasters (Guha-Sapir, Below and Hoyois 2015; Dilley 2005; Laczko and Aghazarm 2009, 5; Mishra and Singh 2010). Furthermore, in the 21^st century, droughts are projected to increase in frequency and intensity due to climate change (Dai 2013; IPCC 2014; Prudhomme et al. 2013) and are expected to affect up to two-thirds of the global population (IDMC 2019; McLeman 2019, 912).

Policy options related to climate-induced migration have remained politically elusive and controversial for several reasons. First, while “migrants” are typically differentiated from “refugees” by their motives (economic vs. political) and circumstances (voluntary vs. involuntary) in their origin countries (Adelman 2001), environmental migrants² have defied such classification (Renaud et al. 2011). More often than not, environmental migration takes place within national borders (Rigaud et al. 2018) and may stem from voluntary decisions to avoid anticipatory harm from damaging climate impacts (IPCC 2018). The heated and continuing debate over the term “environmental refugee” reflects states’ persistent reluctance to expand the definition of “refugee” enshrined in the 1951 Geneva Refugee Convention and its 1967 NY Protocol (Lahav 2016; Zimmermann, Dörschner and Machts 2011), which would extend humanitarian rights’ protections to roughly 60 million people today (UNHCR 2015). To the extent that climate-induced movements also largely take place within country boundaries, they fall outside the Refugee Convention and are, thus, largely left to the political jurisdiction of internally displaced persons frameworks (Deng 1999; McAdam 2019), national and local instruments (Warner et al. 2014), or institutional impasse (McAdam 2015).

Second, identifying a causal relationship between climate change and migration is complicated because it is difficult to assess. Assuming that a large proportion of people living in an environmentally “at-risk” zone of a low-income country will migrate neglects the multiple decisions that humans make in dealing with climate change, as well as constraining factors that can influence their migration-related decisions (de Sherbinin 2020). Isolating environmental factors from other drivers of migration is also challenging. Decisions to stay or leave are rarely based on climatic events alone, making it difficult to disentangle non-climactic from climatic factors (Black et al. 2011; IOM 2015). In most cases, these decisions are also closely linked to socioeconomic, political, demographic, and cultural factors (Laczko and Aghazarm 2009). In addition, psychological (Fawcett 1985) and personal factors, such as aspirations, resilience, and/or capacity to migrate (Carling 2002; Carling and Collins 2018; Carling and Schewel 2018; De Haas 2014), may play definitive roles in leading to or preventing mobility. Furthermore, drought-induced migration's pervasive but slow onset may make the timing of such migration or immobility unpredictable and blur the lines between voluntary or involuntary movement.

Third, climate change's directional and temporal effects on human mobility remain ambiguous: as some researchers have shown, environmental change can make migration less or more probable (Carleton and Hsiang 2016; Findley 1994; Gray and Mueller 2012; Gray 2009; Lewin, Fisher and Weber 2012; Morrissey 2013; Neumann and Hermans 2017; Ocello et al. 2015). Migration can be expensive, requiring substantial and diverse forms of capital. Yet populations experiencing climate change may also face a reduction in just such capital, making a migratory response more problematic or impossible and generating what some refer to as “immobile” (Schewel 2019) or “trapped populations” (Black and Collyer 2014; Dustmann and Okatenko 2014; Foresight 2011). Note that the term “immobile” is largely used in relationship to mobility and assumes human agency (aspirations, capacities, desires) in not migrating (Schewel 2019, 330). Ranging from forced to voluntary individual decisions, the concept is fundamentally different from the notion of “trapped populations” (Foresight 2011), which are involuntarily immobilized by exogenous constraints, such as the loss of resources at the origin as a result of climate change. In our model, “immobile” persons are unable to move because available destinations are more costly (e.g., affected by the same climate impacts such as extreme drought).

Fourth, a reduced capacity for out-migration within a population may compromise important forms of income support, notably from remittances, making staying in place no more sustainable than leaving for elsewhere. These situations compel states to embrace long-term and broad development strategies by supporting people to either move in a more “orderly” fashion or stay in place and adapt locally by investing in infrastructure, poverty reduction, new non-agricultural sectors, or social protections (Rigaud et al. 2018).

Fifth, given that migratory flows tend to respond to multiple events (Betts 2011; de Haas 2010), accurate numerical estimates of climate-induced migration are often difficult to derive. Although we know that almost half the world's migrants flow from one part of the developing world to another, otherwise known as “south-to-south” flows (UN Population Division 2015), migration is a manifold “push-pull” phenomenon which often includes further migration to optimal “pull” areas (OECD 2007). Thus, what may begin with a climate-induced “push” factor at t₁ may conclude with an economic “pull” factor at t₂. Additionally, while some portion of such flows may become international, initially, some of the flows are internal (Rigaud et al. 2018; Skeldon 2006). Therefore, some migration flows may start internally but end with international movements (Waldinger 2015). These internal-international distinctions are particularly critical for national policy-makers, due to the principle of state sovereignty, and may explain why there is no enforceable correlate system of global governance on internal migration that parallels trends in the realm of refugees or international displacement (Bell and Charles-Edwards 2013; Betts 2013; Ocello et al. 2015). Moreover, these distinctions underscore the diverse policy strategies that may be more effective in responding to such climate-induced pressures (e.g., planned relocations, relief operations, disaster risk reductions, temporary stays etc.). In working toward “multifaceted solutions to this multidimensional issue” (World Resources Institute 2014), the 2015 Sendai Framework, for example, seeks “public policies that aim at reducing displacement risks” at different stages (e.g., before, during, and after displacement) of migration and before hazards become disasters (McAdam 2016).

Finally, because migration is linked to several different policy sectors (e.g., development, human rights, environment, security, trade, public health), it mandates a comprehensive or holistic approach to global governance (Betts 2011, 2013). While the 2018 Global Compact for Safe, Orderly and Regular Migration identifies migration as “a multi-dimensional reality that cannot be addressed by one government policy sector alone,” the structures of global governance that are specifically related to migration remain notably weak (Keohane and Victor 2011; Lahav and Lavenex 2013; McAdam 2016, 2019). In particular, climate-change governance is based on asymmetric trade-offs (e.g., with politically difficult demands for rich countries to bear the costs for high-emission defectors) which make negotiations much more politically and structurally challenging.

The international community has constructively advanced the issues of climate adaptation and mitigation on the global agenda for promoting sustainable development, health, security, and stability (UNDESA 2015). Examples of regulatory collaboration include the UN 2015 Agenda for Sustainable Development Goals 2030 (SDG), the 2012 launch of the Nansen Initiative, the 2015–30 Sendai Framework for Disaster Risk Reduction, the 2010 Cancun Declaration, the 2015 Migration, Environment and Climate Change (MECC) Division, and the 2018 Global Compact for Safe, Orderly, and Regular Migration. These mounting initiatives, as well as the institutionalization of a policy infrastructure under the IOM's auspices (now part of the UN system), have made formidable strides toward climate change mitigation and adaptation.

Nonetheless, as the various and ongoing international attempts to negotiate climate commitments have revealed since the original 1992 UN Convention on Climate Change Framework (UNFCCC), binding progress in this area is hampered by a fragmented landscape of often-competing organizations, levels, and actors—what Keohane and Victor (2011, 7) refer to as a “regime complex.” These attempts remain limited in scope, are non-binding, and lack important signatory states (Bloom 2019). Most importantly, the intergovernmental nature of climate change mitigation and adaptation reflects the difficulties in overcoming state concerns about intrusions to national sovereignty, especially related to migration (Zolberg 1999). Thus, even as scientific consensus around disastrous climate-change levels emerges, the absence of a coherent and comprehensive institutional architecture to cope with and respond to such crises impedes further progress. Given such complexities, the task for researchers and policy-makers interested in mitigating climate-induced migration is to compare distinct emissions scenarios and to determine the relatively optimal policy approach to attenuate concerns about human mobility forecasts.

Modeling Migration in Response to Climate Change

Modeling future migration responses to climatic changes is a challenging and controversial topic requiring a major interdisciplinary effort, including input from environmental science, data science, engineering, social sciences, and migration studies. The task's urgency was captured in the landmark 2015 Paris Agreement, which urged UNFCCC expert groups, “as well as relevant organizations and expert bodies outside the Convention,” to “develop recommendations for integrated approaches to avert, minimize and address displacement related to the adverse impacts of climate change” (UNFCCC COP 2015). Working toward such goals, researchers have developed georeferenced quantitative models of environmental migration, both regional (Cunfer 2005; McLeman et al. 2010) and global (de Sherbinin et al. 2012). The latter models are especially challenging, due to knowledge gaps in the low- and middle-income countries (LMICs) where there are few reporting agencies and reliable data collection efforts (Sweeney et al. 2016).

Interdisciplinary models of future human migration tend to focus on individual countries (i.e., Burkina Faso (Kniveton, Smith and Wood 2011) and Tanzania (Smith 2014)), with modeling assumptions based on survey research conducted in those countries. Notably, the 2018 World Bank's “Groundswell” report modelled internal migration for Sub-Saharan Africa, South Asia, and Latin America (Rigaud et al. 2018) and represents one of the most impressive and ambitious studies of human displacement in the context of future climate change. To model human migration, the Groundswell report combined the use of high-resolution population distribution data, development scenarios (Shared Socioeconomic Pathways, or SSPs), climate scenarios (Representative Concentration Pathways, or RCPs), global circulation models (GCMs), water availability, and crop productivity models, as well as sea-level rise projections. Using a gravity model, the report identified and correlated geographic, socioeconomic, and demographic characteristics of a population with its spatial patterns of displacement. The model was calibrated for a subset of countries in each region over the period 1990–2010, with 1970–2010 average as the baseline (Rigaud et al. 2018, 66). The model was then applied to generate specific predictions with respect to the number of internally displaced people under three scenarios: “pessimistic” (RCP 8.5/SSP4), “more inclusive development” (RCP 8.5/SSP2), and “more climate-friendly” (RCP 2.6/SSP4). For the pessimistic scenario, the results included 86 million internal climate migrants in Sub-Saharan Africa by 2050, 40 million in South Asia, and 17 million in Latin America.

Our modeling approach has some similarities to, as well as major differences from, the Groundswell report's methodology (see Table 1). While the Groundswell report examines only internal migration for the regions in question, we model internal and international migration for all countries in the world. As we elaborate below (and in the Online Appendix), allowing cross-border migration requires a number of simplifying assumptions and forces us to discuss our results in relative, as opposed to absolute, terms (as in the Groundswell report). For example, when discussing international migration flows, we can only talk about potential migration pressures, as opposed to actual migration, since we do not know how future political responses (e.g., migration policies) will affect and potentially constrain future migration flows. Similarly, we do not rely on the absolute number of migrants, since these numbers are sensitive to the arbitrary modeling assumptions. Instead, we compare the migration potential, using percentage change for different emissions scenarios. As we show below, the percentage change projections are robust with respect to different modeling assumptions.

Table 1.

Comparison of the key Features Between the Present Model and the Groundswell Report.

	Present model	Groundswell report
Coverage	Global, all countries of the world	Regional, subregional, and subset of individual countries
Migration flows	Internal and external	Internal only
Immobility	Involuntary immobility	Not applicable
Period	2008–2100	2010–2050
Representative concentration pathways	Constant climate, RCP 4.5, RCP 8.5	RCP 2.6, RCP 8.5
Global circulation models	16 models	2 models
Climate impacts	Extreme drought (24-month SPEI < −2)	Water and crop models, sea-level rise
Baseline population distribution	LandScan (1 km spatial resolution)	CIESIN GPWv4 (1 km spatial resolution)
Population growth	United Nations medium fertility and high fertility scenarios	Shared Socioeconomic Pathway scenarios SSP2 and SSP4
Population characteristics	National identification	Urban vs rural
Population movement	Agent-based satisficing model, based on a random choice of an acceptable destination within a feasible distance area	Gravity model, based on distance, socioeconomic, demographic, and political variables
Spatial mask (movement constraints)	Water	Water, slope, elevation, protected areas
Political responses to migration	No border restrictions	Not applicable
Main sources of uncertainty	Climate and population projections, drought index (SPEI), fragile states index (FSI), agent-based model parameters	Climate and population projections, water and crop models, gravity model parameters
Migration model parameterization	Simulations for a set of key parameters and comparison of the results in relative terms	Calibration of the model based on observed population change for 1990–2000 and 2000–2010 periods
Results presentation (units)	In relative terms: percentage change and differences between scenarios	In absolute terms: the number of people displaced
Future adaptation efforts, conflict, cultural, political, institutional, or technological changes	Not implemented	Inclusive development adaptation policy

For climate input, we use a larger number of global climate models (GCMs) than the Groundswell report (16 vs. 2). Using the GCMs, we model extreme drought projections on the basis of the Standardized Precipitation Evapotranspiration Index (SPEI) drought index (Vicente-Serrano, Beguería and López-Moreno 2009). Given the SPEI's long-term version (24 months), along with a significant threshold for an “extreme” drought (SPEI <−2), we capture negative aspects of both meteorological and hydrological droughts. Although we do not implement explicit water stress and crop productivity models, as does the Groundswell report, we note that extreme droughts are known to be associated with major water stress and crop losses (Sheffield and Wood 2012; Svoboda et al. 2002; Wilhite and Glantz 1985).

A major strength of the Groundswell report is its sophisticated gravity model underlying population movement. The model's parameters were calibrated for a subset of individual countries, which allowed the researchers to make specific numerical predictions about future internal displacement. We use a simpler version of population displacement, which has some features of the gravity model but relies on a smaller set of underlying assumptions. As discussed above, we are able to establish that our results are robust to the model assumptions if framed in terms of relative change. However, we are not able to make projections in absolute terms. We note, though, that even empirically calibrated models, such as the one in the Groundswell report, should be used with extreme caution, since (1) the world in the 21^st century may look very different from the world during the 1970–2010 period and (2) virtually all calibration efforts are vulnerable to overfitting (Hitchcock and Sober 2004).

Another difference between our model and that of the Groundswell report is our identification of “immobile persons” (Carling and Schewel 2018; Schewel 2019), defined as individuals who would migrate but are unable or unwilling to do so, due to environmental constraints and/or lack of suitable destinations. Given that drought conditions due to climate change and population density due to population growth are the only input variables that change in our model, we can conclude that any relative increase in projected “immobility” that we observe is related to climate change alone (since population density is not a mobility constraint in our model).

Finally, this analysis diverges from the Groundswell report in its policy prospects. In our model, we explore three climate scenarios: (1) constant climate fixed at 2008–2017 levels, (2) RCP 4.5, and (3) RCP 8.5 (see details in the Online Appendix), while the Groundswell report replaces the RCP 4.5 with the RCP 2.6 scenario. Using the RCP 2.6 may serve as a baseline for comparison (since this scenario is not realistic), while the RCP 4.5 is at least possible in the case of successful climate change mitigation. We use a hypothetical constant climate scenario as a baseline reference point. In this scenario, drought conditions are based on the 2008–2017 decade and do not change throughout the rest of the 21^st century. Therefore, any changes in migration under this scenario can only be attributed to population growth. Second, we use the low emissions RCP 4.5 scenario (Thomson et al. 2011) as a benchmark for successful international cooperation on climate change, consistent with the Paris Agreement (UNFCCC COP 2015). Under this scenario, migration may still increase, due to a combination of population growth and greenhouse gas emissions already accumulated in the atmosphere. Finally, similar to the Groundswell report, we focus on the high-emissions RCP 8.5 scenario (Riahi et al. 2011). This unilateral “business-as-usual” scenario is characterized by states’ failure to either agree to or fulfill international greenhouse gas limiting agreements. Since the high-emissions scenario corresponds to the largest increase in the global temperature by the end of the 21^st century (IPCC 2013), migration and, possibly, involuntary immobility are expected to increase the most in this case. Broadly speaking, we concur with others (Foresight 2011; McAdam 2016) that climate-induced displacement is inevitable, but we hypothesize that without international adoption of austere energy policies, migration pressures will increase exponentially.

Behavioral Modeling Overview

In this section, we provide a broad outline of our modeling approach (for technical description of modeling assumptions, see the Online Appendix). Substantively, we agree with migration researchers that the drivers of human mobility tend to be both complex and erratic (Boucher and Gest 2018; De Haas, Castles and Miller 2014; de Haas 2010; Fussell, Hunter and Gray 2014; IOM 2015; Neumann and Hermans 2017). However, for simplicity's sake, we have to make a number of assumptions. One assumption underlying our model's formal structure is that some people living in a given area and confronting an extreme drought will have a positive probability of moving if they have a capacity to escape the drought (Carling and Schewel 2018; Henry, Boyle and Lambin 2003; Mortimore 1989). Thus, extreme drought may generate a positive “push” factor for out-migration, but such migratory attempts are not certain and are likely to be invariably constrained. For our behavioral model, we assume the following:

Consistent with models of development (Martin and Taylor 1996; Tapinos and Delaunay 2000), the likelihood of adequate resources in one's home area for addressing drought-related problems will be higher in more-developed countries than in less-developed ones (Burkett 2012; de Haas 2010).

Receiving (or destination) nations’ attributes, policies, and borders (McLeman 2019), in the case of international migration, will vary in ways that make those states more (or less) attractive as alternatives to staying at the origin (Brochmann and Hammar 1999; Henry et al. 2004; Zolberg 1989).

Potential migrants are boundedly³ rational decision-makers who will not pay the costs associated with a move to a potential “target” location that is also suffering from extreme drought.

Given that migration is significantly based on geographic proximity (Abel and Sander 2014; Cohen et al. 2008; Greenwood and McDowell 1982; Kim and Cohen 2010; Mayda 2010; OECD, 2007; UNDESA 2019), migrants are estimated to travel up to some finite distance toward specific destinations (Afifi 2011; Jülich 2011).

Affected migrants will continue their search until a suitable destination is found. If no such destination is suitable, the potential migrant becomes either “trapped” or “immobile” and unable or unwilling to overcome migration's costs (Carling and Schewel 2018; Schewel 2019). Involuntary immobility is more likely if drought conditions affect a large land area.

Figure 1 outlines our population movement algorithm based on the above assumptions. Clearly, no formal model of a complex system can incorporate all parameters that are likely to affect predictions, and this reality is certainly true for our model. Importantly, our outlined propositions apply only to migration provoked by drought per se, not to migration prompted by other circumstances, including other consequences of climate change. Further, we are not modeling endogenous political responses to migration that might increase or reduce migration flows, including conflict and restrictions on migration by potential receiving countries (McLeman 2019; Missirian and Schlenker 2017). The role of national borders acting as constraints on agents’ migratory decision is especially crucial in an international system organized around the principle of state sovereignty (Lahav and Lavenex 2013; Zolberg 1989). As the migrant flows out of the Middle East and North Africa region between 2014 and 2016 have demonstrated, migrant populations can provoke populist protest within potential receiving countries, responses that can have significant consequences for migrants’ ease of movement or, indeed, their ability to move at all (Dennison and Geddes 2019). For example, India has already constructed a substantial wall along its border with Bangladesh, analogous to the fences erected between some EU member-states (e.g., Hungary, UK, Slovenia, Greece) in response to 2015 refugee crises (Greaves and Faunce 2017; Schain 2019). In all cases, the effects of state policies have important impacts on potential migrant decisions and future flows (Bhagwati 2003; Czaika and De Haas 2013; McLeman 2019), but we do not model them here.

Figure 1.

Population movement model. There are five distinct steps in the population movement algorithm. Model details, including the complete computer code in Python, can be found in the Online Appendix.

A future extension of the behavioral model may include social networks, with migrants choosing their destinations based on diasporic kinship and shared culture (Banerjee 1983; Ryan 2011; Shain 2007). Pre-existing networks (Taylor 1986) and social capital (Massey et al. 1999) are undoubtedly cost effective and risk reducing for displaced populations and, thus, influence the direction of migration flows. Nonetheless, we are hesitant to include a social network variable in our initial predictive model. Using historical migration flows as a deterministic path for migrants for the next 80 years may be too restrictive and hide interesting developments in the distant future. For example, the political relationships among countries may change dramatically in the next 20, 40, 60, or 80 years, and any such changes may no less dramatically affect existing migration networks. Since we do not model actual migration and, instead, focus on “potential migration pressures,” exploring all possible directions that future migrants may consider, given the enormous uncertainty about the future political climate, socioeconomic, and technological developments, appears more fruitful.

Uncertainty in the Behavioral Model

To address uncertainty about future greenhouse gas emissions, we use the standard set of emissions scenarios (Moss et al. 2010) and diverse climate predictions, as generated by an ensemble of 16 such models (Taylor, Stouffer and Meehl 2011). We emphasize that the observational data that would permit defendable calibration of our model are scarce. For that purpose, we would need historical monthly migration data for each cell of the global grid and unambiguous evidence that observed migration in such past circumstances was, in fact, due to droughts. Such data based on past events, however, are inherently limited in projecting future scenarios, given the dynamic nature of demographic, economic, political, technological, and other variables in the context of a rapidly changing geopolitical world order (Weiner and Russell 2001). Instead of projecting the absolute numbers of migrants, we focus on the relative numbers (percentage differences between scenarios), which are much less sensitive to simulation parameters (see the Online Appendix for the relevant comparisons).

Figure 2 shows the distribution of simulation outcomes under different climate models and the crucial behavioral model parameter—the maximum migration probability, α (see the Online Appendix for a more detailed version of the figure which identifies the individual climate models). Each circle in the figure represents the ratio, at the end of the 21st century, of migration predicted under the RCP 8.5 emissions scenario to migration predicted under the RCP 4.5 scenario. The figure describes uncertainty both among climate models (on the vertical axis) and associated with different behavioral model assumptions (on the horizontal axis).

Figure 2.

Distribution of simulation outcomes for the ensemble of 16 climate models, 10 different values for the maximum probability of migration and two emissions scenarios. The ratio between the annual, global number of migrants under RCP 8.5 and RCP 4.5 is based on mean monthly values for 2081–2100. In the box-and-whisker plots (Tukey 1977), the “hinges”—separated by the median—correspond to the first and third quartiles; the “whiskers” extend from the hinges to the highest value that is within 1.5 times the interquartile range; the end of the whiskers are outliers.

Consistent with the climate science literature (IPCC 2013), there is a substantial range of uncertainty in the predictions reported here among the different climate models. The large discrepancies among such models can be substantially explained by different modeling choices at the regional level. For example, some models predict droughts in densely populated areas (with, therefore, substantial movement out of those areas), while others predict such droughts in less populated areas (with many fewer such people moving).

In contrast, the uncertainty along the horizontal axis is much smaller. Different values of the maximum migration probability do not affect our substantive results when these results are presented in relative terms. In this respect, Figure 2 shows that the median predicted incidence of migration under RCP 8.5 is approximately twice that under RCP 4.5, regardless of the simulation parameter value we employ. We note that a higher probability of migration leads to a slightly lower (and marginally decreasing) relative difference between the business-as-usual RCP 8.5 and the optimistic RCP 4.5 (see the Online Appendix for a detailed mathematical analysis of this phenomenon).

Results

Changes in Global Migration and Immobility

All our results, at the global and national levels, are reported for three distinct climate scenarios (described above): (1) constant climate, (2) low emissions RCP 4.5, and (3) high emissions RCP 8.5. Table 2 shows the changes in global migration at the end of the 21^st century (2081–2100) relative to the present (2008–2017). As emphasized above, our findings are presented in relative terms, since this approach is less affected by our choice of the behavioral model's simulation parameters.

Table 2.

Changes in Global Migration and Migration Immobility at the end of the Century (2081–2100) Relative to the Present (2008–2017).

	Global Change: 2081–2100 monthly mean values relative to 2008–2017 monthly mean values
	Migration			Migration immobility
Climate Model	Constant Climate	RCP 4.5	RCP 8.5	Constant Climate	RCP 4.5	RCP 8.5
ACCESS1-0	108.9%	317.9%	1332.2%	9.7%	145.2%	1244.5%
BCC-CSM1.1	8.5%	88.7%	600.7%	4.8%	105.7%	419.4%
BNU-ESM	73.1%	183.9%	189.3%	27.2%	193.0%	290.3%
CCSM4	45.3%	352.1%	591.8%	14.7%	168.1%	1136.1%
CESM1	−1.8%	190.9%	86.6%	14.7%	156.0%	315.8%
CMCC-CM	30.5%	116.2%	1023.9%	8.1%	42.9%	807.1%
CNRM-CM5	12.8%	−14.1%	−32.8%	0.3%	−20.3%	14.8%
CSIRO-Mk3	46.4%	382.9%	364.4%	20.0%	261.2%	535.9%
EC-EARTH	53.6%	530.4%	416.6%	35.6%	191.8%	534.2%
FGOALS-g2	64.7%	39.7%	517.8%	24.4%	47.7%	651.7%
GFDL-CM3	16.4%	364.1%	121.0%	21.0%	558.6%	307.4%
IPSL-CM5A	7.2%	181.7%	1189.4%	12.6%	234.8%	1475.1%
MIROC5	31.6%	72.9%	381.7%	24.3%	137.4%	366.7%
MPI-ESM	50.2%	161.1%	333.3%	20.8%	286.3%	246.0%
MRI-CGCM3	65.8%	86.1%	162.9%	17.1%	34.3%	105.4%
NorESM1-M	42.3%	168.1%	360.0%	16.3%	268.4%	640.0%
Mean	41.0%	201.4%	477.4%	17.0%	175.7%	568.2%
Median	43.8%	174.9%	373.1%	16.7%	162.1%	476.8%
Min	−1.8%	−14.1%	−32.8%	0.3%	−20.3%	14.8%
Max	108.9%	530.4%	1332.2%	35.6%	558.6%	1475.1%

The numbers are based on the difference between the mean monthly values for the respective periods. For the simulations in the table, the maximum migration propensity is set to 0.001 (see Figure 2).

By the end of the 21^st century, global migration is projected to increase by an average of about 41 percent under the constant climate scenario (with all that increase attributed to population growth), 201 percent under RCP 4.5, and 477 percent under RCP 8.5 (the median climate model increases are, respectively, 43.8, 174.9, and 373.1 percent). As expected, the individual models’ results have large variance, a fact underscoring the importance of using an ensemble of climate models. Despite such variance, the broad global pattern is categorically clear: While population growth is predicted to contribute to an increased number of environmental migrants, the models suggest that droughts caused by anthropogenic climate change will most certainly be the primary cause of human displacement, especially under the RCP 8.5 scenario, which is consistent with other recent studies on future migration pressures (Xu et al. 2020).

Given the greenhouse gas emissions accumulated in the atmosphere during the past 200 years of industrialization, we still observe a substantial increase in the intensity and duration of extreme droughts in the 21^st century, even if international efforts toward mitigating greenhouse gas emissions are successful (RCP 4.5). However, should those efforts fail (as envisioned in the RCP 8.5 scenario), the global number of migrants would approximately double. Whether through large-scale Paris-type international cooperation, unforeseen (and rapid) technological innovation, or unilateral or bilateral action by a few of the major emitting nations, mitigation of such greenhouse gas emissions is important to alleviate any destabilization associated with the potential numbers of future migration.

Table 2 also presents equivalent data for immobile persons. Under the constant climate model, the number of immobile persons (i.e., motivated to migrate by exposure to extreme droughts but unable to do so, due to the absence of suitable destinations) is projected to increase only by about 17 percent by the end of the 21^st century. This estimate contrasts to 175.7 percent under the low emissions scenario and 568.2 percent under the high emissions scenario (the median climate model increases are, respectively, 16.7, 162.1, and 476.8 percent). Since climate is the only variable that changes between the three scenarios, the dramatic increase in immobility can only be attributed to more frequent and intense droughts likely to be associated with humans’ failure to significantly reduce greenhouse gas emissions. We emphasize that under the RCP 8.5, we will see an increase in both migration and immobile populations. In other words, greater immobility does not imply less migration.

Changes in Migration at the National Level

At the end of the 21^st century (2081–2100), Nigeria and Egypt are projected to have the largest monthly human displacement (internal and international combined) if the world's countries fail to cut greenhouse gas emissions (Table 3). The high/low emissions scenarios ratio of 1.82 for Nigeria is just below 1.97 for the world as a whole. On the other hand, the ratio of 3.33 for Egypt indicates that climate change plays a bigger role in predicting displacement for that country than does population growth.

Table 3.

Total Displacement at the State Level.

		RCP 8.5	RCP 8.5	RCP 8.5	RCP 8.5 /RCP 4.5	RCP 8.5 /RCP 4.5
		2081–2100Share(mean)	2081–2100Share(low)	2081–2100Share(high)	2041–2060	2081–2100
	WORLD	100.00%			1.27	1.97
1	Nigeria	7.82%	3.29%	12.35%	1.75	1.82
2	Egypt	5.17%	3.38%	6.96%	3.19	3.33
3	China	4.88%	0.98%	8.77%	1.64	1.85
4	Turkey	4.53%	3.32%	5.74%	1.95	3.95
5	Algeria	4.15%	3.47%	4.83%	1.34	2.60
6	Mexico	3.78%	2.55%	5.01%	1.42	2.39
7	Morocco	3.46%	2.96%	3.95%	1.27	3.08
8	Venezuela	3.19%	2.17%	4.21%	1.72	2.10
9	Indonesia	3.16%	0.95%	5.36%	1.10	1.95
10	India	2.98%	1.26%	4.69%	0.91	0.72
11	Syria	2.83%	2.20%	3.46%	2.35	3.28
12	Senegal	2.62%	1.68%	3.57%	2.07	3.72
13	Iraq	2.59%	1.59%	3.59%	1.26	2.06
14	Brazil	2.24%	1.55%	2.93%	1.41	1.96
15	Pakistan	2.18%	1.12%	3.25%	0.79	0.95
16	Guatemala	2.15%	1.19%	3.10%	2.08	2.72
17	Mali	2.12%	1.21%	3.03%	1.86	3.17
18	D.R. Congo	1.90%	1.11%	2.70%	0.53	1.64
19	Madagascar	1.85%	1.19%	2.52%	1.24	1.81
20	Iran	1.71%	1.13%	2.29%	1.07	2.32

Top 20 countries shown ranked by the share of the world's total displacement. The percentages/ratios are based on the mean monthly values from the ensemble of 16 climate models. The low and high values are based on SciPy Bayesian confidence intervals with alpha = 0.8.

Migration is projected to be notably high among three groups of countries: (a) African nations, (b) Middle Eastern nations (notably, Turkey, Syria, and Iraq), and (c) Central/Latin America (particularly Venezuela, Guatemala, Haiti, and Colombia). These groups follow the general patterns identified above for Nigeria and Egypt. These results are noteworthy, since these nations are already suffering from a confluence of environmental, political, economic, demographic, and security challenges (Carlsen and Bruggemann 2017). Additional challenges brought by future droughts, projected for the second half of the 21^st century, have the potential to destabilize these countries even further.

India and Pakistan are the only countries in Table 3 where the number of migrants under RCP 8.5 is projected to be smaller than under RCP 4.5 (during the period 2081–2100). The RCP 8.5/RCP 4.5 ratio for India is 0.72, indicating that there are fewer predicted drought-based migrants there under the high emissions scenario RCP 8.5. This result appears to reflect the fact that a majority of climate models project a substantial increase in precipitation for India, which is evident in the high SPEI values and consistent with the existing literature on future flood risks (Hirabayashi et al. 2013). We speculate that the total number of environmental migrants in India may, nevertheless, be large if other environmental disasters, such as floods, also lead to population displacement. In this article, however, we do not model the impact of such other climate-change related processes on migratory patterns.

Changes in Immobile Populations at the National Level

Table 4 compares predicted numbers of immobile persons under RCP 4.5 and RCP 8.5—first globally and, then, for 30 countries ranked by their estimated numbers of such individuals under RCP 8.5 at the end of the century. We identify several notable predictions, emphasizing that our primary concern is with differences between the two emissions scenarios over time, rather than with the numbers of migrants per se. First, while the global migration estimates are consistently higher under RCP 8.5 than under RCP 4.5, the difference in these terms becomes notably greater toward the end of the 21^st century. If the inability to escape from extreme drought by migrating somewhere does, in fact, imply an increased probability of significant social and political instability “in place,” such instability appears particularly likely by the end of the 21^st century.

Table 4.

Total Migration Immobility at the State Level.

		RCP 8.5	RCP 8.5	RCP 8.5	RCP 8.5 /RCP 4.5	RCP 8.5 /RCP 4.5
		2081–2100share	2041–60 /PresentRatio	2081–2100 /PresentRatio	2041–2060	2081–2100
	WORLD	100%	2.14	5.91	1.26	2.48
1	Turkey	7.6%	4.84	14.94	1.99	3.97
2	Mexico	5.7%	5.30	12.94	1.49	2.79
3	Morocco	5.1%	7.58	26.62	1.32	3.14
4	Algeria	4.4%	5.58	15.99	1.36	2.79
5	Brazil	4.3%	3.19	7.25	1.28	1.87
6	Mali	3.9%	65.22	260.65	2.04	3.14
7	China	3.9%	0.71	1.50	1.79	1.99
8	Zambia	2.6%	3.59	17.24	0.88	2.24
9	Venezuela	2.5%	5.20	14.65	1.86	2.60
10	Zimbabwe	2.4%	1.45	7.57	0.59	3.09
11	Guinea	2.3%	5.23	23.43	1.37	3.74
12	Indonesia	2.2%	0.98	2.79	1.35	2.47
13	Mozambique	2.0%	2.30	6.35	0.76	1.51
14	Senegal	2.0%	9.29	32.66	1.92	3.43
15	Madagascar	1.9%	2.35	6.44	1.00	1.50
16	D.R. Congo	1.8%	2.04	4.69	0.59	1.41
17	Syria	1.8%	4.26	14.10	2.25	3.50
18	Angola	1.8%	2.55	9.63	1.46	2.91
19	Italy	1.6%	3.11	12.54	0.82	4.50
20	South Africa	1.6%	1.56	3.92	1.51	2.77

Top 20 countries shown ranked by the share of the world's total displacement. The percentages/ratios are based on the mean monthly values from the ensemble of 16 climate models.

The growth of trapped or immobile populations decreases migration's potential to serve as a “safety valve” for vulnerable states facing drought. As we have emphasized, the data in Table 4 do not let us specify how—or, indeed, whether—immobility might translate into social and political conflict, but they do support the conclusion that the possibility of such a result will be greater, should climate change mitigation fail.

Internal Displacement and International Migratory Flows Distinguished

Table 5 reports the cumulative incidence of drought-based internal migration under RCP 8.5 and RCP 4.5. Unsurprisingly, there is an obvious relationship between countries’ population size and the number of people displaced. More importantly, given our primary focus on predicted migration under different emissions scenarios, Table 5 also reports the difference between RCP 8.5 and RCP 4.5, which may be interpreted as the cost of failure of international cooperation such as the Paris Agreement. In all but three countries (India, Chad, and Myanmar), predicted internal displacement is substantially higher under the high emissions scenario.

Table 5.

Top 20 Internal Migration Flows Under RCP 8.5 During 2008–2100 (in Thousands).

Rank	Country	RCP 8.5	RCP 4.5	RCP 8.5—RCP 4.5 difference
1	China	10,675.4	6845.3	56.0%
2	India	5897.4	6649.5	−11.3%
3	Indonesia	4419.5	2895.0	52.7%
4	Mexico	4048.3	2251.6	79.8%
5	Brazil	3615.7	2082.0	73.7%
6	Algeria	2415.2	1361.0	77.5%
7	Egypt	1585.5	586.7	170.2%
8	Madagascar	1455.4	798.1	82.4%
9	Venezuela	1051.2	648.1	62.2%
10	South Africa	1042.6	585.5	78.1%
11	Nigeria	851.2	402.1	111.7%
12	Philippines	706.6	604.1	17.0%
13	Iran	631.9	382.4	65.2%
14	Pakistan	617.2	616.4	0.1%
15	DR Congo	567.9	475.4	19.5%
16	Colombia	507.6	212.7	138.6%
17	Morocco	466.3	214.5	117.4%
18	United States	412.9	230.7	79.0%
19	Turkey	411.9	169.6	142.9%
20	Mozambique	362.6	231.9	56.4%

The numbers are based on the mean value from the ensemble of 16 climate models and 0.001 as the maximum migration probability.

Similarly, Table 6 shows the cumulative flows of international drought-based migration between 2008 and 2100 under the high and low emissions scenarios. Similar to internal displacement, international migration flows are predicted to be larger if the Paris Agreement fails. Given the anomalous case of India's internal flows discussed above, it is particularly noteworthy that two of these four anomalies involve India as the starting point: one with the flow ending in China (the largest predicted external flow worldwide throughout the century) and the other ending in Pakistan (a smaller flow but still a substantial one).

Table 6.

Top 20 External Migration Flows Under RCP 8.5 During 2008–2100 (in Thousands).

Rank	Origin	Destination	RCP 8.5	RCP 4.5	RCP 8.5—RCP 4.5 difference
1	India	China	2350.0	2574.3	−8.7%
2	Venezuela	Colombia	2115.9	1104.5	91.6%
3	Nigeria	Niger	1900.9	1232.4	54.2%
4	Pakistan	India	1840.7	2147.6	−14.3%
5	Morocco	Algeria	1653.5	760.4	117.5%
6	Egypt	Turkey	1588.4	638.4	148.8%
7	Algeria	France	1508.5	923.2	63.4%
8	Nigeria	Gabon	1487.2	547.2	171.8%
9	Morocco	Spain	1378.6	710.1	94.1%
10	Nigeria	Cameroon	1361.8	972.2	40.1%
11	Mexico	United States	1318.0	762.1	72.9%
12	Guatemala	Mexico	1303.3	506.0	157.6%
13	China	Vietnam	1131.4	708.9	59.6%
14	Turkey	Ukraine	1117.0	464.2	140.6%
15	Algeria	Italy	974.7	532.0	83.2%
16	Egypt	Libya	967.5	343.6	181.6%
17	Iraq	Saudi Arabia	953.5	602.2	58.3%
18	China	Philippines	880.0	524.9	67.7%
19	Nigeria	Mali	869.8	542.2	60.4%
20	Syria	Saudi Arabia	866.1	380.3	127.7%

The numbers are based on the mean value from the ensemble of 16 climate models and 0.001 as the maximum migration probability.

The anomaly involving India's internal migratory patterns can now be extended. Not only is India's internal migration greater under the low emissions scenario, but that same irregularity involves drought-based migrant flows between and among India, Pakistan, and China more generally.

While the expectation of more end-of-century migrant flows under RCP 8.5 than under RCP 4.5 is generally observed for most regions (except India), the Mediterranean Basin, including the Eastern Mediterranean and North Africa, provides a notable pattern of international migration flows. The area has, of course, been affected by migration and refugee movements during the past several years (Hoerling et al. 2012). Climate scientists concur that the area will be particularly hard hit by drought by the end of the 21^st century (Dai 2013; Orlowsky and Seneviratne 2013; Prudhomme et al. 2013), and our findings suggest that related migratory pressures will only increase.

Conclusion

Applying contributions from climate science to a simple model of drought-based migratory decision-making, this article has gauged the impact of future anthropogenic drought on human mobility. With an eye toward policy remedies and governing prospects, it offers valuable insights about drought-related human displacement and the potential effects of different choices facing policy-makers. In particular, our distinction between migratory pressures expected under the high emissions RCP 8.5 scenario and the more optimistic RCP 4.5 has allowed us to emphasize the exponential importance of climate change mitigation efforts, such as the Paris Agreement (UNFCCC COP 2015).

Our findings on migratory responses to extreme drought under the RCP 4.5 and RCP 8.5 scenarios underscore the potential importance of international cooperation to mitigate climate change impacts. With the notable exception of India (and, perhaps, the India-Pakistan-China migratory nexus), migratory pressures in response to extreme drought will be unequivocally and substantially stronger should international mitigation efforts fail. Our model suggests that both migration and immobility will be more pronounced under the high RCP 8.5 emissions scenario than under the RCP 4.5 one.

A major source of uncertainty in our findings is the simplicity of our behavioral model. We recognize that such a model cannot fully capture the multitude of factors driving and constraining human migration and that particular instances of migration happen in unique economic and political circumstances (McLeman 2013). Most notably, our initial and highly simple model neglects the empirical realities of a state-centric political world organized along mutually exclusive sovereign states. Nevertheless, the inextricable link between climate change and human mobility goes beyond national boundaries, as do civil strife, sustainable development, and “new security” threats more broadly. We hope that subsequent models more fully incorporate such considerations and thereby facilitate a continuing and constructive dialogue between climate science and social and political theory (Zolberg 1989).

Population movements are multi-causal and multi-staged; therefore, good governance requires multi-level policy planning and international coordination. New evidence linking temperature rises to increased asylum applications throughout EU countries (Missirian and Schlenker 2017), for example, reinforces the critical role of state policies in affecting migration flows, and vice versa (McLeman 2019). Along similar lines, European political responses to refugee flows out of the Middle East or Africa suggest that a major future constraint on climate-based migratory pressures will be protectionist and populist responses to migratory pressures. These responses seem likely to intensify as the number of receiving countries potentially affected by all kinds of climate changes increases.

For reasons noted above, we have not included climate changes beyond drought (notably, ocean-level increases) or human responses to droughts beyond migration in our model. Nevertheless, our speculation about the possible link between involuntary immobility and social disruption in countries where significant migratory pressures exist seems worthy of further study. Additional future modeling assumptions may include border restrictions (McLeman 2019) and other institutional constraints on international migration.

Projecting political outlooks and trajectories based on present conditions may be speculative, but subsequent development of our model can be further improved with detailed policy metrics or large-N analyses. The extensive cross-national policy data sets recently compiled by migration researchers through fine-grained index building (e.g. IMPIC, IMPALA, DEMIG, MIPEX) offer a promising addition to future model building.⁴ These policy indices offer a disaggregated database of state policies toward migration longitudinally and cross-nationally and, thus, may help reduce the uncertainties in modelling migration decisions.

Another limitation of the current model is an absence of any possible adaptation policies, whether basic economic development, drought-resistant agriculture, political adaptation, or effective social welfare policies. Likewise, our model ignores the possibility that the international community may engender reciprocity amid divergent asymmetric national interests by linking migration and refugee protections (Betts 2011) to climate-change policies or to other issue areas, such as development, security, and trade (Lahav and Lavenex 2013, 760). Such policies will, no doubt, have a major impact on involuntary displacement, likely increasing adaptive capacity and, therefore, reducing the need to migrate (Rigaud et al. 2018).

There are two reasons, though, that we do not model adaptive responses to climate-induced displacement. First, since our focus is on future potential migration pressures, as opposed to actual migration, we do not want to mask any such challenges by assuming that they will be resolved. Second, future political and technological adaptation is inherently unpredictable and would require a set of prescient assumptions. By focusing on “potential pressures,” we avoid the need to make those assumptions.

The findings presented here offer important insight into critical questions facing policy-makers. To what extent can the international community reduce pressures on climate change by agreeing to lower their greenhouse gas emissions or relieve pressure on the fastest-warming countries by allowing migrants to move north across their borders and provide them orderly protection, as urged by the Global Compacts? Can nation-states embrace both long-term and short-term solutions toward adaptation and mitigation or afford to abandon both options? As politicians may ultimately discover, in the absence of climate-change cooperation, effective policy will rely on cooperative instruments like the Global Compacts to provide human rights and protections for orderly migration and security for their publics. Beyond the human toll, the prospective cost of international cooperation on greenhouse gas emissions should be weighed against the potential number of displaced persons. This calculation will depend on whether policy-makers, as well as social scientists, appreciate the challenges associated with potential environmental migration due to anthropogenic climate change.

Supplemental Material

sj-docx-1-mrx-10.1177_01979183221079850 - Supplemental material for Climate Change, Drought, and Potential Environmental Migration Flows Under Different Policy Scenarios

Supplemental material, sj-docx-1-mrx-10.1177_01979183221079850 for Climate Change, Drought, and Potential Environmental Migration Flows Under Different Policy Scenarios by Oleg Smirnov, Gallya Lahav, John Orbell, Minghua Zhang and Tingyin Xiao in International Migration Review

Footnotes

Acknowledgments

This paper is dedicated to John Orbell (Deceased October 20, 2018). The project is funded by the National Science Foundation, “Cyber-Enabled Discovery and Innovation” collaborative awards #0940822 and #0940744. We are grateful to Jamie Winders and the anonymous reviewers of the IMR for all their support and insights.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Science Foundation, (grant number #0940822 and #0940744).

ORCID iDs

Oleg Smirnov

Gallya Lahav

Tingyin Xiao

Supplemental Material

Supplemental material for this article is available online.

Notes

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