conceptual simulation model for climate migration and

Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 of 22

Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org

Conceptual Simulation Model for Climate Migration

and Population Health

Rafael Reuveny

Author’s affiliation:

Professor, School of Public and Environmental Affairs, Indiana University, Bloomington,

Indiana 47405, USA

Correspondence address: [email protected]

1. Introduction

The Intergovernmental Panel on Climate

Change (IPCC, 2014) says that climate

change is already harming human and natu-

ral systems. It expects worsening impacts

worldwide in this century, including rising

sea level; more agrarian diseases and pests,

drying lands; increased water stress; dam-

age to settlements; more frequent and in-

tense weather disasters; and declining crop

yields, incomes, and food security. The

report expects that poverty will expand and

deepen in the least developed countries

(LDCs) and countries with rising inequality.

It foresees adverse health outcomes, partic-

ularly for people who are socioeconomical-

ly, institutionally, politically, or otherwise

marginalized (e.g., based on class, gender,

age, ethnicity, and culture). The size and

timing of the impacts of climate change, the

IPCC says, will vary across countries de-

pending on factors such as hazard exposure,

adaptive ability, socioeconomic develop-

ment, risk attitudes, and demographic fea-

tures, but the LDCs are the most vulnerable

Abstract

Growing literature expects that environmental degradation due to climate change (combined with

non-environmental factors) will increasingly drive migration from affected areas in this century. It

reflects psychology that views ecological decline as reducing the quality of life. Another literature

projects that climate will have adverse health outcomes worldwide, including both physical and men-

tal, but the role of this climate migration in health, particularly population health, is under-discussed.

The paper assesses and illuminates the need for greater focus and work on climate migration by con-

ceptually modeling the causal flow from environmental degradation in an origin area, to leaving this

site, to the health of migrant and native populations in the host area. The conceptual modeling con-

denses and clarifies some of the questions at stake and suggests the need for future research including

the codification and empirical testing of this or similar models. This capability is illustrated by heu-

ristically simulating the model to contribute to emerging discussions on climate migration and popu-

lation health. The article assesses the results and applies them to comment on policies seeking to

promote population health in areas poised to receive many climate migrants in this century.

RESEARCH ARTICLE



due to their limited ability to cope and

adapt.

Facing such risks, people may respond in

one of three ways, depending on how they

view them psychologically. They may do

nothing, seeing them as non-issues or ac-

cepting they cannot change them. They may

adapt in several ways, including by moving.

Or they may mitigate causes with or with-

out adjustment, deciding to take their fate in

their hand and solve the problem at its core.

Researchers have documented all these es-

sentially psychological reactions by observ-

ing what people do, but I focus on perma-

nent relocation or migration as an adapta-

tion. Though climate change will not be the

sole driver of the decision to migrate in the

future, studies say it may have a strong in-

fluence (IPCC, 2014). Stern (2007), for

example, projects 200 million climate mi-

grants by 2050, Brown (2008) more than

200 million, Myers (2009) 250 million, and

Werz and Conley (2012) up to 1 billion.

Others say these numbers are too high but

agree they will not be negligible (Boano et

al., 2009; WDR, 2012).

In 2011, the US National Institute of Health

projected climate change would cause ill

health and, noting the dearth of related stud-

ies, called for interdisciplinary work in sev-

eral medical fields and crosscutting areas

such as climate migration and modeling

(NIH, 2011). Recent studies have called for

developing dynamic simulation models for

climate change and health (Hess, 2015;

Betts, 2016) or environmental health

(Rosen, 2016). Portier et al. (2013), Lancet

Commission (2015), and Wu et al. (2016)

also called to study the impact of climate

migration on the health of everyone in the

destination, all the while noting a wide gap

in knowledge in this area. Only a few stud-

ies examine the link between climate migra-

tion and health. The IPCC, for example,

lists one work for individual health out-

comes (IPCC, 2014: 11.8.4). I discuss a few

more studies in the next section, but the

topic has not been adequately addressed,

especially for population health.

This paper seeks to contribute to ongoing

discussions of climate migration by focus-

ing on population health for newcomers and

natives in a host area. It illustrates the need

for greater focus and research in this area

by developing a general conceptual simula-

tion model for the causal flow from envi-

ronmental decline to migration to popula-

tion health. This under-discussed and chal-

lenging topic is broad enough to be served

by many approaches. I take a social science

approach. The current model does not pre-

sent empirical data, but rather elucidates

connections among significant factors and

sets the stage for condensing and clarifying

some of the research and policy questions at

stake. Once codified, it could be honed and

specialized by empirical work and provide

insights via simulations, examples of which

I give later. I illustrate this point by heuris-

tically simulating the model for storylines

assuming current climate change trends and

by using the results to comment about pub-

lic policy looking to promote population

health in areas poised to receive many cli-

mate migrants in this century.

In principle, climate migrants (and all mi-

grants for that matter) may move within and

across countries. Most authors say they will

originate mainly in LDCs and move within

their own countries, as this migration offers

the security of similar culture without legal

barriers and the excessive cost of migrating

to DCs (Brown, 2008; WDR, 2012). Others

mention these migrants may also move to

DCs (Carballo & Smith., 2008; Solana,

2008; Scheffran & Battaglini, 2011). Both



views are sensible, the latter not least since

the vast majority of the more than 700 mil-

lion potential migrants in the world, by far

mostly from LDCs, want to move to DCs

(Gallup, 2017). DCs share culture with

many LDCs due to their colonial pasts and

people from LDCs living in DCs may help

comrades back home to migrate, as many

do today. While today most people move

internally, LDC to DC migration is not

small. Currently, some 104 million docu-

mented people from LDCs live in DCs; up

to 42.6 million do so illegally1. Another 2.8

million move each year from LDCs to DCs

as temporary migrant workers or students,

and 425,000 seek asylum.2

By 2050, 900 million may join the LDCs’

workforce, while the DCs’ workforce may

fall by 75 million (UN, 2013). This gap will

likely continue to drive LDC to DC migra-

tion (Hugo, 2011; NIC, 2012). The UN

(2015) projects that the net gain in migrants

from LDCs to DCs (entry minus exit) be-

tween 2015 and 2050 will 91 million, or 2.6

million per year on average, regardless of

climate change. Against this backdrop, I

1 In 2010, 97.6 million migrants (and refu-

gees) from LDCs resided in DCs (World

Bank, 2011). In 2007-12, on average, 3.7

million migrants and refugees came to DCs

each year (OECD, 2013: Table 1.1; p. 21 for

2012), 68% of which (2.5 million) from

LDCs (OECD, Table 1.7). I get: 104.2 = 96.7

+ 3 x 2.5. The 42.6 million is from Reuveny

(2016).

2 In 2006-11, on average, 2.3 million short-

term migrant workers came each year to DCs

in 2006-11 (OECD, 2013: Table 1.5); for the

share of LDC origins, I use (56.9% + 68%)/2.

Most asylum seekers come from LDCs (Ta-

ble 1.6). In 2004-10, on average, 2.3 million

students came to DCs each year; 60% were

from LDCs (Table 1.8 and p. 36).

develop a conceptual simulation model for

climate migration as it impacts health for

the arriving and native populations in a host

area, regardless of its location. Section 2

outlines the literature. The next two parts

present the model and Section 5 simulates it

heuristically. The conclusion suggests fu-

ture research and says something about

public policy.

2. Prior Literature

The literature provides a natural starting

point to gain insight. I outline models of

actual migration, health outcomes for mi-

grants and residents in a host area, use of

health care by both, population health fac-

tors and simulation, violence and climate

change, and migrant-host violence. These

bodies of work may seem eclectic, but they

separately studied aspects of our problem. It

is too large for me to review it here thor-

oughly. I give examples of studies and re-

sults.

Migration models assume that potential

migrants behave according to the following

psychology. They choose to live in the

place maximizing their expected or per-

ceived net benefit (benefit – cost). This per-

ceived net gain is theorized to depend on

many factors, including the difficulty of

moving to a new place and the barriers fac-

ing people looking to enter this place. Em-

pirical models of actual migration find that

international, illegal, and internal migrants

move for similar reasons. The number of

migrants rises with pull factors such as in-

come, jobs, political stability, and peace

operating in the destination. It increases

when these forces decline in the sending

area, and when population size and, less so,

cumulative causation increase. It rises with

the extent of shared culture between the



origin and destination areas. Migration de-

clines when the cost of moving, entry barri-

ers to the destination and, less so, place

identity in the origin increase. See, for ex-

ample, Bratsberg (1995), Mayda (2010),

Fussell (2010), Weeks et al. (2011), Belot

& Ederveen (2012), Mendoza (2015), and

Flores et al. (2013). These models do not

include environmental forces.

A smaller subset does, controlling for those

other factors. Several studies model surveys

from some LDC, especially for internal

migration (Henry et al., 2004; Lewin et al.,

2012; Gray & Muller, 2012). These models

attempt to find how people thought about

the environmental problems when they de-

cided to move. Other studies model interna-

tional or internal migration counts that

come from official sources such as coun-

tries and international organizations

(Reuveny & Moore, 2009; Feng et al.,

2010; Marchiori et al., 2012; Liu & Shen,

2014). The findings of both types of models

show that environmental problems raise

emigration from, and reduce immigration

to, affected areas. Some survey models find

there is a threshold at which the severity of

hurdles lowers the ability to migrate. The

psychological attachment of people to plac-

es thus takes account of the state of the en-

vironment. People view a harsh environ-

ment as reducing the quality of life and so

may move away from it.

Many studies model the effect of migrant

status on health outcomes (e.g., overall

health status, physical health status, mental

health status, the occurrence of a particular

disease) based on individual sample surveys

run in a destination, typically a DC and

occasionally an LDC. In both types of

countries, the bulk of the immigrants are

coming from LDCs, or from rural areas in

the case of LDCs. Controlling for factors

like income, education, healthcare use, age,

and ethnicity, many find a healthy migrant

effect: migrants arrive healthier than na-

tives, which is usually ascribed to healthy

people self-selecting to be migrants. The

health gap is found to shrink over time. It

may turn into a deficit due to poverty, and

psychological and physical impacts of ac-

culturation stress, defined as the tension

associated with getting accustomed to a

new place, a new culture, and a new lan-

guage, and anti-immigrant host public atti-

tudes. See, for example, Rubalcava et al.

(2008), Maio & Kemp (2010), Chen (2011),

Domnich et al. (2013). Lower quality

healthcare to migrants than natives also

plays a role (Newbold, 2009; Grabovshi et

al., 2013; Frank et al., 2013). Others studies

find the healthy migrant effect does not

apply to groups such as refugees, illegal

migrants, rural-urban migrants, temporary

migrant workers, and child and pregnant

migrants. For example, see Huang &

Ledsky (2006); Hu et al. (2008), Bollini et

al., (2009), Magalhaes et al. (2011), Benach

et al. (2012), and Kiss et al. (2013).

Many models compare health care use rates

by migrants and natives using surveys, con-

trolling for health and other factors. Most

find a lower use for migrants (Regidor et

al., 2009; Pylypchuk & Hudson, 2009;

Magalhaes et al., 2011; Fan et al., 2013).

Some find migrants have a higher use rate

of emergency rooms for primary care

(Buron et al., 2008) or no gap for public

health care (Pylypchuk & Hudson,

Newbold, 2009; Wadswarth, 2013).

We saw above that migrant health falls over

time. The results for the lower health care

can thus reflect barriers to healthcare ac-

cess. In DCs, studies find various obstacles

to migrant access, including language, user

fees, discrimination and cultural insensitivi-



ty in the health care system, perceived bias,

feelings of being insulted and of hopeless-

ness, problems in obtaining insurance, and

long delays in health care centers for the

uninsured. Illegal immigrants face all these

barriers, as well as fear of being detected by

healthcare personnel, reported, and deport-

ed. See the studies by Magalhaes et al.

(2011), Maio & Kemp (2010), Edge &

Newbold (2013), Rechel et al. (2014), and

Frank et al. (2013). We again see that psy-

chological factors (fear, insult, hopeless-

ness, perceived discrimination, cultural in-

sensitivity) play a role.

Another factor in health care use is legal

eligibility. Laws naturally change across

countries. In general, landed (legal) mi-

grants, including refugees, have the most

entitlement to access. Temporary migrant

workers and asylum seekers have less ac-

cess than legal migrants, and illegal mi-

grants have almost no access. See the arti-

cles by Magalhaes et al. (2011), Rechel et

al. (2014), Zimmerman et al. (2011),

Fortuny & Chaudry (2011), and Bustamante

& Wees (2012). In LDCs, migrants often

have no access to healthcare (WHO, 2010,

HDR, 2009). Internal migrants have full

access, except when access is tied to resi-

dence areas, as in China (Mou et al., 2013),

Vietnam (UN–Vietnam, 2011), and Russia

(Ionikan & Rakovskaya, 2012).

The health and healthcare models noted

above and others like them use the individ-

ual as a unit of analysis. Other studies mod-

el population health for a country or a re-

gion. Though they do not look at migration,

they are relevant here, given the paper’s

focus on population health. Studies examine

population health measures such as mortali-

ty, disability-adjusted life years, or rate and

burden of symptoms for some disease. Con-

trolling for demographic factors, they find

population health rises with access to and

quality of healthcare, environmental health

services (e.g., waste removal, sanitation),

social capital, income, education, democra-

cy, and welfare programs, and declines with

harmful behaviors, joblessness, and envi-

ronmental degradation. See, for example,

Baltagi et al. (2012), Kim & Saada (2013),

Muntaner et al. (2013), WHO (2014), Park

et al. (2015), and Friis (2018).

Emerging research uses simulation models

for studying population health when exper-

iments are not workable, especially for pro-

jecting needs and delivery of healthcare and

environmental health services, forecasting

effects of practices, assessing existing abili-

ties to address crises, predicting the spread

of infectious diseases, and evaluating the

cost-effectiveness of delivery systems.

Since population health issues are multifac-

eted, these models use many variables, in-

cluding healthcare features, epidemiological

factors, public health policies, upstream

health causes, health risks, socioeconomic

forces, and environmental aspects. Their

development typically begins with a con-

ceptual model, followed by equations, pro-

gramming, choice of parameters and trajec-

tories for exogenous variables, and tests

discussed in Section 4 (Kopec et al., 2013;

Okhmatovskaia et al., 2012; Levy, 2014).

Another emerging strand reviews outcomes

for rural-urban migrants in LDCs, migrants

from LDCs residing in DCs, refugees, peo-

ple displaced by disasters, and people

moved by governments to make way for

development projects. It assumes these out-

comes will resemble, in turn, those obtained

for people planning migration in anticipa-

tion of, or in reaction to, climate change

impacts, people displaced by weather disas-

ters, and people relocated by governments

to reduce exposure to climate change. Re-



views of the currently observed outcomes

suggest that affluent areas may face more

exposure to communicable diseases carried

by migrants from deprived regions. People

relocated or displaced may see new infec-

tions, stress, trauma, and poverty, but those

who plan their migration may fare relatively

better (Carballo & Smith., 2008; Sherbinin

et al., 2011; McMichael et al., 2012; Licker

& Oppenheimer, 2013).

Finally, two nascent research strands link

migration and climate change to armed vio-

lence, respectively, which can naturally

impact health. One stand associates the ar-

rival of newcomers with violent events in a

host area, controlling for other factors. Re-

sults show refugee flow from nearby states

(Salehyan & Gleditsch, 2006) and internal

migration (Fearon & Latin, 2011) raise the

risk of civil war in a host area. Movement

linked to an ecological decline in an area of

origin (among other factors) increases the

risk of riots, revolt, and warfare in a desti-

nation area (Reuveny, 2007, 2008;

Bhavnani & Lacina, 2015). Interstate im-

migration raises the risk of clashes with

natives (Reuveny; Dancygier & Laitin,

2014), terror attacks (Choi & Salehyan,

2013) and coups (Gebremedhin &

Mavisakalyan, 2013). In a second strand,

many link climate change impacts to rising

risk of armed violence (NIC, 2012; IPCC,

2014), while others say they can also cata-

lyze acts of violence (CNA, 2014; DOD,

2014). Some writers downplay the risk of

disorder, arguing the extent of climate

change is unclear, and say people can re-

solve problems, that the current migration is

mostly peaceful, and sociopolitical integra-

tion of migrants can ease tensions

(Salehyan, 2008; Theisen et al., 2013).

3. Motivation and Basic Design

Section 2 suggests that predicting popula-

tion health for climate migrant and host

populations is complicated, not least since it

involves many forces, including environ-

mental decline, environmental health,

health care use, healthcare quality, barriers

to accessing healthcare, socioeconomic and

demographic factors, and, possibly, vio-

lence. These forces are, to some extent,

stochastic, introducing uncertainty. Studies

statistically model them one at a time, either

as a dependent or an independent variable,

but this suggests they evolve as a complex

dynamic system or in response to one an-

other and other factors. Studying our sys-

tem in controlled experiments would consti-

tute the first best approach, but this is not

possible. Alternatively, a simulation model

can project trajectories of endogenous vari-

ables under various assumed policies and

scenarios for exogenous factors, accounting

for uncertainty, interdependencies, nonline-

arities, delayed effects, and feedbacks

(Fone et al., 2004; Levy, 2014; Smith et al.,

2014). This section motivates such a model

for this paper and explains its basic design.

A simulation model can usefully address

planning and policy questions related to

climate migration and population health.

For example, it can forecast the effects of a

particular flow or stock of climate migrants

on the host and migrant population health

over time. It can assess whether existing

healthcare services and access eligibilities

suffice to sustain a certain population health

level and whether there are specific lever-

age points in the system for which minor

changes would create substantial beneficial

effects. The model can make similar projec-

tions for environmental health. Also useful

is the model’s ability to outline population

health effects based on origin and destina-



tion features (e.g., income, internal, interna-

tional), types of climate movers (migrants,

displaced by disasters, moved by a govern-

ment), and types of armed violence and

climate impacts.

The development of simulation models typ-

ically begins with a conceptual design. This

stage specifies links between endogenous

variables (rely on each other for causal in-

puts) in response to exogenous variables

(evolve on their own). Conceptual simula-

tion models are usually displayed graphical-

ly using arrows with – and + signs to show

the direction of effects (Carpiano & Daley,

2006). The next section takes this approach

for a model at the population level of analy-

sis for the migrant and host people, respec-

tively.

Population health measures typically

change gradually over time, suggesting us-

ing periods a few months to a few years, as

I do here (Upraising et al., 2011). Our mod-

el simplifies by including one origin-

destination pair and by treating migrants as

one entity. A more elaborated model could

consist of several origins and destinations

and migrant subsets (e.g., international,

internal, illegal, age-stratified, ethnically

grouped). I will discuss extensions in the

last section. Some exogenous variables may

be endogenous themselves, but for reasons

of practicality and usefulness, one cannot

model all possibilities in this regard.

The endogenous forces here reflect our goal

and the literature. Migration and population

health are naturally endogenous. Section 2

outlined that healthcare, environmental

health, and immigration affect health;

health and being a migrant impacts health

care use; migration and environmental deg-

radation affect violence, and degradation

and acts of violence affect movement. All

these forces influence population size,

which affects needed services, the environ-

ment, and violence. Meanwhile, violence

harms the environment, health, and

healthcare services, and raises the need for

healthcare (Altare & Guha-Sapir, 2013).

Accordingly, I model migration flow, and

population, violence, healthcare services,

environmental health services, and popula-

tion health by origin and destination as en-

dogenous variables. The other factors dis-

cussed in Section 2 (e.g., wage, socioeco-

nomic development, ethnic composition,

environmental degradation) are modeled as

exogenous in both sites, evolving according

to pre-defined scenarios (not unlike the

IPCC scenarios for projecting climate

change). The modeled levels of some of the

exogenous variables can change somewhat

at runtime around their given levels depend-

ing on computations engaged by the user,

making the model flexible for any number

of specific questions. These variables then,

are not entirely exogenous in the model, but

the model does not capture determinants of

their time.

Many of the variables are multifaceted, so I

define them as indices, by the site. For ex-

ample, Healthcare services (HCS) include

inputs such as primary, emergency, and

dental care, and Environmental health ser-

vices (EHS) waste removal, food safety,

sanitation, and pollution control. Violence

includes crime, clash, terrorist act, civil

war, and war and so on. For health, the In-

stitute of Medicine (2011) suggests an in-

dex summing up health problems weighted

by their severity, for society at a given time;

the model defines population health as a

stock variable rising when this cluster falls.

The exogenous environmental degradation

index includes climate change impacts, en-

vironmental health issues (e.g., waste, pol-



lution), declining resources, and factors like

urban heat island (cities are warmer than

rural areas since urban surfaces release

more heat than natural, and denser setups

hinder heat dissipation). To the extent that

climate migrants will move to cities (as

most migrants do today), degradation in the

destination may disproportionally reflect

floods (many cities are in low-lying coastal

and river areas), urban heat island and heat

waves, and environmental health problems.

The model does not require building indi-

ces; it interprets some user-provided inputs

as indices and requires users to read outputs

as such. Using it to say something about

reality may need index creation; see Section

5. The next section describes the conceptual

model.

4. Conceptual Simulation Model

The computations evolve period by period.

The user sets initial levels for some of the

endogenous variables and defines scenarios

for the exogenous variables. The user also

decides whether to include violence as a

variable and chooses whether to engage

processes that change some of the exoge-

nous variables around their scenario levels.

The endogenous flow variables are given

value by algebraic equations each period

and the stock variables by equations of mo-

tion. Given space limits, I discuss here only

a few of the exogenous effects. The model

is presented using two figures, one for each

of its parts. The design differentiates be-

tween migrant and host populations in the

destination, but the graphical presentation

combines them for improved ease of under-

standing.

Figure 1 shows the Environment and Mo-

bility part or module. Boxes are variables,

arrows causal effects, + and – the direction

of impact, +/– nonlinear effects, and indica-

tors with no sign effects whose signs are set

based on logic valued at runtime. Origin

Degradation and Destination Degradation

are stock indices of problems.

Origin/Destination Violence are flow indi-

ces of events.

Origin/Destination Population are stocks of

residents. The Origin and Destination Popu-

lation Health denote the health stocks de-

fined in Section 3. Migrant Flow indicates

the number of migrants per period. Needed

EHS and Provided EHS are flow indices of

environmental health services required and

received in the destination, defined on the

same metric. EHS Quality varies from one,

denoting 100% success at fixing problems

in one period (perfect), to zero (no effect).

Origin Degradation follows a scenario

whose levels can be attuned in runtime to

rise with Origin Violence and Population.

Origin Population Health follows a scenario

whose levels can be adjusted to fall as

Origin Violence and Degradation rise.

Origin Population rises due to its natural

birth rate and when Origin Population

Health rises (indicating fewer health prob-

lems). It falls due to its natural death rate

and Migrant Flow, and when Origin Vio-

lence and Degradation rise. The natural

birth and death rates are parameters.

Origin Violence follows a probabilistic pro-

cess in which its likelihood and intensity (if

realized) rise with Origin Degradation,

Origin Population, the prior probability of

violence, and the previously attained Origin

Violence.



Migrant Flow increases with Origin Popula-

tion and the Host and Migrant Populations

(Destination Population in Figure 1). It falls

as Destination Violence and Degradation

and Origin Population Health rise and rises

with the Host/Migrant Population Health,

showing people’s preference for peace,

healthy society, and environmental quality.

Migrant Flow increases with Origin Vio-

lence and Degradation until they reach their

respective turning point levels, above which

their effects reverse. At the outset of vio-

lence or degradation, the desire to leave due

to these forces is enough to overcome diffi-

culties in moving due to the injury, death,

and financial distress they cause. Once the

two factors grow large enough, these im-

pediments to departure dwarf the desire to

migrate.

In the destination, Migrant Population rises

due to its birth rate and Migrant Flow and if

Migrant Population Health rises. It falls due

to its death rate, more degradation or vio-

lence, and if Migrant Population Health

falls below a critical level.

Host Population is modeled similarly with-

out Migrant Flow. The migrant and host

population health levels come from the

module presented in Figure 2.

Destination Degradation follows a scenario

whose levels can be attuned to rise in

runtime with Destination Violence,

Host/Migrant Population, and Host/Migrant

Needed EHS (showing a decline in envi-

ronmental health). This variable can be at-

tuned in runtime to fall as Host/Migrant

Provided EHS and Host/Migrant EHS

Quality rise.

Host/Migrant Needed EHS, Host/Migrant

Provided EHS, and Host/Migrant EHS

Quality come from Figure 2.



Destination Violence is set by a probabilis-

tic process whose likelihood and intensity

(if realized) rise with Migrant Population,

Host Population, Destination Degradation,

and occurrence of violence in the prior pe-

riod, as well as when Migrant Flow rises,

suggesting that high inflow is less condu-

cive to a peaceful adjustment.

Figure 2 shows the Migration and Health

part of the model. Needed/Provided HCS

are flow indices of healthcare types. HCS

Capacity is the maximum HCS flow the

health care system can deliver with its cur-

rent resources in the destination (e.g., hos-

pital beds, doctors, medicines), all meas-

ured on the same metric. HCS Barriers var-

ies from one, denoting no barriers to access

HCS in the destination, to zero for no ac-

cess. EHS Capacity and EHS Barriers are

similarly defined. HCS Quality varies from

one (perfect) to zero (completely ineffec-

tive). Health Change is a change in Destina-

tion Population Health, per period, where

both variables represent all aspects of

health, both psychological and physical.

Other variables are as defined above.

Migrant Needed HCS falls as Migrant Pop-

ulation Health and Origin Population

Health rise, as healthier people need a lower

level of HCS, and rises with Migrant Popu-

lation and Destination Violence/Degra-

dation.



Migrant Needed EHS rises with Destination

Degradation and Violence and Migrant

Population.

Host Needed HCS/EHS are set likewise (excluding Origin Population Health).

HCS Capacity, Migrant HCS Quality and

HCS Barriers, and the associated EHS and the Host variables, follow scenarios.

Destination Violence can be engaged to

reduce the capacity and quality at runtime,

as its occurrence may harm providers and

facilities.

For the Migrant Provided HCS, if HCS Ca-

pacity is larger than or equal to the sum of

Migrant Needed HCS and Host Need HCS,

both adjusted for barriers (Migrant Needed

HCS x Migrant HCS Barriers + Host Need-

ed HCS x Host HCS Barriers), groups get

their barriers-modified needs. That is, Mi-

grant (or Host) Provided HCS equals Mi-

grant (or Host) Needed HCS x Migrant (or

Host) HCS Barriers.3 Else, capacity is in-

sufficient, and the Migrant (or Host) Pro-

vided HCS follows a capacity allocation

scenario.

The model sets the Migrant and Host Pro-vided EHS using similar logic.

Migrant Population Health equals the sum

of its prior level and two other terms. The

first, Migrant Health Change, is the expres-

sion Migrant Provided HCS x Migrant HCS

Quality ‒ Migrant Needed HCS; the setting

of Migrant Provided HCS indicates it is

either zero (for free access and perfect ser-

3 The idea behind the expression Needed

HCS x HCS Barriers (by group) is this. HCS

Barriers is in the range 0 to 1. If HCS Barriers

= 1, all the group’s needs take part in figuring

out if the total need tops the capacity. If HCS

Barriers = 0, a group cannot access HCS, so

the model ignores its needed HCS. If 0 <

HCS Barriers < 1, the model considers a part

of the demand.

vices) or negative (for all combinations of

barriers and quality). The second term

comes from changes in exogenous varia-

bles, by the group, which can be greater

than zero (e.g., representing exercise) or smaller than zero (e.g., capturing smoking).

The model sets the Host Population Health

using a similar algorithm.

5. Heuristic Simulations

The computation requires computer pro-

gramming and setting parameters and exog-

enous variables. Testing uses the following

steps, though they may not always be feasi-

ble. First, check the face validity of equa-

tions and outputs. Second, verify input-

output consistency. Third, check the con-

sistency among all outputs in response to an

input. Fourth, validate the reasonableness of

outputs for different parameters and exoge-

nous values. Fifth, check compatibility with

other simulations. Sixth, check compatibil-

ity with empirical data. Seventh, evaluate

the desirability of outputs from implement-

ing model-based decisions. See, for exam-

ple, Kopec et al. (2013), Okhmatovskaia et

al. (2012) and Levy (2014).

The first four steps of the testing are work-

able here. The fifth activity is feasible to the

extent that similar models exist. Stage six is

achievable to the degree that empirical data

exist for situations like those simulated. The

seventh step is feasible only if controlled

trialing is. We can say the conceptual de-

sign of the model in this article has some

face validity, as it reflects the literature.

Testing needs a computerized simulation.

However, the current model captures and

clarifies some of the issues at stake, so we

can try to gain insight by applying the mod-el in heuristic (or qualitative) simulation.

Applying the simulation to real-life situa-

tions requires linking variables and parame-

ters to practical measures and coming up



with relevant storylines. We cannot observe

index variables, but we can use proxies to

represent them. Alternatively, we can com-

pute indices from their lower-level factors

(e.g., as weighted sums). The second possi-

bility is not easy; our population health in-

dex, for example, requires collecting data

and finding weights. The storyline is a set

of assumptions defining numerical parame-

ters, scenarios for the trajectories of the

exogenous variables, and initial values for

endogenous variables that require them. In

general, this set is chosen to suit research

needs. This section applies the model in

heuristic simulation to illustrate some of the

insights it can give and distill some issues

for future research. In this approach, we

would need to make assumptions to resolve

competing effects in equations as we go

(which a codified model would determine

numerically).

Simple Storyline without Violence

This storyline depicts a simple case of mi-

gration from a poor origin to an affluent

destination, without violence. We present

its parameters, scenarios, and initial values.

Parameters

1. The origin and migrant populations

have the same birth and death rate (as they belong to the same group).

2. The origin and migrant birth rates

are higher than their respective

death rates, in line with observed

population growth patterns.

3. The birth rate of the host population is also larger than its death rate.

4. The net birth rate (birth rate – death

rate) in the origin area is higher than

that in the destination, in line with

data from poorer and wealthier re-gions, respectively.

5. The processes adjusting exogenous

variables in runtime (Section 4) are

activated.

Scenarios for exogenous variables

1. The economic features do not

change, and the destination’s econ-

omy is doing better than the origin’s

economy (e.g., the standard of liv-ing, wages, jobs).

2. Origin Degradation follows four

phases.

a. It grows slowly.

b. It rises faster and climbs

above its turning point effect

on migration.

c. It falls fast to the level ob-

tained had the slow growth

continued in phase b.

d. It grows as in step a.

This scenario depicts weather disas-

ters occurring on top of more gradu-

al degradation due to climate change

(e.g., sea level rising with a hurri-

cane making landfall and dissolving.

3. Destination Degradation does not

change and is lower than the initial

Origin Degradation.

4. Origin Population Health is constant

and smaller than the initial Host

Population Health.

5. There are no HCS/EHS Mi-grant/Host Barriers.

6. Migrant/Host HCS/EHS Quality is

perfect.

7. The HCS and EHS Capacities do

not change, which is a sensible in

the short run and, depending on

technical and financial abilities,

even longer, as it takes time to add



capacity-builders (e.g., doctors,

hospitals, labs, sewage pipes, waste

treatment).

8. Other factors are fixed and similar in both sites.

Initial values for endogenous variables

1. Migrant Population is positive (mi-grants live in the destination).

2. Host Population is larger than Mi-

grant Population (usually the case).

3. Origin Population is larger than

Host Population (depicting some-

thing like migration from Mexico to

the State of Texas or from rural

China to Beijing).

4. Migrant Population Health equals

the Host Population Health (as there

are no barriers to accessing perfect

HCS and EHS), and both are much

greater than Origin Population Health at the start of its scenario.

5. Destination Degradation is smaller

than the initial scenario level for Origin Degradation.

This setup simplifies things for ease of un-

derstanding and to make a point below. In

reality, environmental recovery after ex-

treme weather is often partial and slow,

access to HCS and access to EHS face bar-

riers, HCS and EHS are imperfect, and the

exogenous forces change over time and

place. I relax some these assumptions later to allow for more nuanced analysis.

Let us turn to the heuristic simulation. As-

suming the effect of birth is larger than the

total population-reducing impact in Figure

1, Origin Population rises in the simulated

timeframe (as occurs in less developed are-

as). Origin Degradation increases as de-

scribed earlier, reducing Origin Population

Health below its scenario level in each

point in time. The net effect on Migrant

Flow is assumed to be positive in the time-

frame (capturing, for example, rural-urban

migration in LDCs or LDC to DC migra-

tion). The Migrant Flow peaks just before

Origin Degradation rises above its turning

point in phase b of its scenario. Migrant

Population increases due to Migrant Flow,

and Migrant Population and Host Popula-

tion grow due to their net birth. Destination

Degradation increases with both popula-

tions and works to reduce their size. Styl-

ized facts suggest the positive effects out-

weigh the negative, so Migrant Population

and Host Population rise at least for a

while; notably, the longevity of this effect

depends on what happens to their health measures.

As we enter Figure 2 in the early periods,

Destination Degradation and Migrant Popu-

lation have risen from their previous levels.

As a result, since other exogenous variables

do not change, and, for migrants, since

Origin Population Health declines, Migrant

Needed HCS/EHS and Host Needed

HCS/EHS rise. The barriers-adjusted needs

for both services equal the requirements

themselves, as Migrant/Host HCS/EHS Barriers are all set to one (no barriers).

There are now four possibilities.

Both of the Total needs are less than or

equal to their capacities. The provided

HCS/EHS are set at the needed HCS/EHS

levels, per group, respectively. The Migrant

Health Change and the Host Health Change

are zero (as HCS/EHS quality is perfect and

the exogenous health factors do not

change), so Migrant Host Population and Host Population Health do not change.

The total Needed EHS top their capacity,

and the entire Needed HCS do not. The

provided EHS follow the EHS capacity

allocation scenario and are too low for at

least one groups, so Degradation Destina-

tion rises (e.g., overrun sewage, piling



waste). As a result, the total needed HCS

increases. The total HCS provision equals

the overall need, so the Migrant and Host

changes are zero, and the related population

health indices do not change.

The Needed HCS grow above their capacity

level and Needed EHS do not: HCS provi-

sion follows its capacity allocation scenario.

Some needs go unmet, so population health

falls for at least one of the two groups. As a

result, the total needed HCS rise, and popu-

lation health continues to decline for at least one group.

The Needed HCS and Needed EHS top their

capacity levels: Here, there are again four

possibilities. The Needed EHS may first

surpass the capacity, the Needed HCS may

exceed its provision-capacity first, or both

needs may simultaneously top their provi-

sion-capacities, but the gist is similar. When

a total requirement for services exceeds its

associated capacity, some people see their

needs go unmet. Unmet Needed EHS raises

environmental degradation for at least one

of the groups, which further increases

Needed HCS and EHS. Unmet Needed

HCS reduces population health for at least one of the groups.

The needed services are likeliest to first

surpass capacity in or after phase (B) of the

origin’s degradation scenario. Hereafter, the

fall in population health hastens its decline.

In effect, the migration influx pushes the

society beyond the tipping point for health.

Unlike a natural system crossing an irre-

versible ecological tipping point, however,

the community recovers. Once Migrant

Population Health and Host Population

Health (depending on the capacity alloca-

tion scenario) fall below critical thresholds,

their death rates rise. Ultimately, either

population or both begin to fall, working to

reduce Migrant Flow. These declines lessen

the total needed HCS below HCS Capacity,

and the society returns to health normalcy, albeit with fewer people.

Adding Violence to the Simple Storyline

The occurrence of violence is likelier in the

area of origin than in the destination area

since the origin’s degradation, and popula-

tion, grow faster than in the destination and

since the sending area is relatively more

impoverished. Not shown in Figures 1 and

2, the standard of living and job prospects

fall with the likelihood, and the realized

level, of violence, the former since the oc-

currence of violence reduces will to invest

economically (more risk), and the latter due to the associated damages.

As Origin Degradation and Origin Popula-

tion increase, the likelihood of Origin Vio-

lence and, if realized, intensity rise, particu-

larly in phase (B) of the degradation scenar-

io. If violence occurs in a period, its likeli-

hood rises in the next period, Origin Popu-

lation Health falls more than under peace,

and the standard of living and job prospects

decline. Origin Degradation now grows also

due to violence. The Origin Population

grows, assuming its net birth effect still

exceeds the impact of the population-

reducing factors, though less than under

peace due to violence-induced death and

injury. Migrant Flow tops the one under the

peace storyline, indicating that people leave

due to the rising tension, violence, and the

related reductions in the standard of living,

population health, and job prospects, in line

with empirical observation. As a result,

Migrant Population now rises faster. The

arriving migrants in the current case are less

healthy than those arriving in the peace case

because the Origin Population Health is lower due to the violence.

The likelihood of violence and realized vio-

lence in the destination rise with Migrant

Flow. We assume they are smaller than

their counterparts in the sending area. The

reasons for this assumption are that the

standard of living in the destination is high-

er than that in the sending area, and the deg-

radation and population growth rates are



lower than their sending area counterparts.

Things now unravel faster than under

peace, especially if the immigration contin-

ues. The occurrence of violence in the des-

tination increases the needed HCS and EHS

and reduces the EHS and HCS capacities

and qualities due to damages, so the total

required HCS or total required EHS hit the

maximum provisions sooner. Depending on

the capacity allocation scenario, the migrant

or host population health fall sooner below

the threshold under which the death rate

rises and, joined by the violence, bring

about an earlier decline in the two popula-

tions.

Adding Partially Accessible and Imper-

fect services to the Simple Storyline

A more realistic storyline with less than

fully accessible and perfect services, keep-

ing other things equal, may exhibit an even

faster decline in population health for the

migrant and host populations than in the

above two cases. Rasing the HCS capacity

above the largest total needed HCS can help

resolve this problem in the model, but this

takes time. In the meantime, the society

suffers until things return to health normal-

cy following population decline.

6. Conclusion

This paper develops a conceptual simula-

tion model of the causal flow from an eco-

logical decline in an origin area to migra-

tion to population health in a host area to

examine. It centers on the need and provi-

sion of HCS and EHS, policies defining

access to services, and the possibility of

violence. The work then simulates the mod-

el heuristically for three types of scenarios.

It would be interesting to compare the re-

sults to those obtained by other models of

this type. The literature has not offered

work in this area, and so this comparison is still not workable.

Though all models are imprecise, this one

sacrifices some realism for the sake of clari-

ty. Future research may extend it. For ex-

ample, one could take account of demo-

graphic differences (e.g., gender) and types

of newcomers (e.g., internal, international,

illegal, temporary workers, displaced by

disasters, relocated by a state). One may

add sites and have the distance traveled for

migration decline when health falls (as

moving is not easy). Other extensions may

include adding sites, health and environ-

mental problems worsening or improving

on their own, services taking effect with

delays and uncertainty, groups infecting

each other, and delays in building capacity.

Extensions could address specific ques-

tions, creating insights that may be useful

for policymakers and future research. What

we have here already illustrated potential

perils for population health in the case of

large-scale climate migration under con-

servative parameters. Problems can arise in

the current model even in a peaceful world

in which the HCS and EHS are perfect and accessing them is free.

The capacity is a vital issue. Even the best

HCS and EHS can only provide so much

care to the people relying on them at any

given point in time and raising their capaci-

ties to provide more care is an involved and

costly endeavor. Societies receiving many

climate migrants could face difficult choic-

es. Since the HCS or EHS capacities may

not suffice to meet the needs of everyone, at

least in the short run of a few years, alloca-

tion policies would need to determine

whose needs must go unmet.

The possibility suggested by the model of a

need to chose whose health care to priori-

tize is unsettling. There are ways to mitigate

this risk. For example, proactively raising

population health would delay any potential

capacity overrun and create a higher base-

line should this occur. One way to apply

this policy is to provide HCS to everyone,

regardless of pay, but this raises pressure on



the existing capacity, causing new prob-

lems. Building up the HCS and EHS ca-

pacities is maybe a panacea but requires

more resources like providers, hospitals,

sewage pipes, and waste treatment facili-

ties, which are costly and cannot be put in

place overnight. Success would require be-

ginning projects in advance of observed

needs and keeping capacities at levels suffi-

cient to meet projected rather than the cur-

rent requirements. Society should maintain

its untapped capacity structures in a state of

readiness; health care and environmental

health resources, much like standing ar-

mies, depreciate over time. Allocating cli-

mate migrants across sites could reduce

pressure on any one place; a model with

many destinations could suggest an alloca-

tion that achieves a certain level of popula-tion health across them.

Another option is to relieve climate change

impacts in origins. For example, we know

how to build seawalls against rising sea

level, strengthen structures facing weather

disasters, and reduce dependence on the

environment for livelihood through diversi-

fying incomes to fields demanding fewer

natural resources, but these schemes are

costly and technical, and many of the most

exposed regions are also poor. External aid

could meet some preparedness needs and

help promote economic development in

impoverished areas, provided they are large

enough, giving potential climate migrants

more reasons to stay in their current place.

With the ongoing fossil fuel-based energy

paradigm, however, economic development

in the more impoverished regions would

likely intensify climate change unless ac-

companied by a reduction of carbon emis-sions in the more affluent areas.

Applying such policies requires national

and subnational governments to shift means

from other goals (e.g., acquire more arms,

increase income) to aid, so wealthier socie-

ties might be tempted to focus on keeping

climate migrants out by reinforcing current

entry barriers to immigration. This ap-

proach can keep immigration in check in

the short run, but it risks tearing the social

fabric of societies who use it, for it inher-

ently necessitates a policy of ignoring hu-man suffering.

These adaptations and others are feasible,

but their consideration and application sig-

nal an acceptance of climate change as a

force majeure. Policies are most effective if

they attack problems at their core, other

things being the same. Just as we seek to

cure diseases, rather than live with their

symptoms, so should we be working to stop

climate change, not adapt to its myriad ad-

verse effects. Only when diseases are incur-

able or when the impact of treatment is

worse than the disease do we solely try to

alleviate symptoms. The cure for climate

change – mitigation of greenhouse gas

emissions – is understood and readily im-

plementable and nowhere near as costly as

the expected impacts of the problem itself;

it may be painful but in no way fatal. In-

formed observers know that mitigation is

ultimately superior to adaptation. That this

knowledge has not catalyzed a significant

policy shift toward reduction of carbon

emissions suggests a failure of our systems

of governance, a severe shortcoming that demands examination.

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