DOI http://dx.doi.org/10.1590/S0102-3098201500000028
Population transitions and temperature
change in Minas Gerais, Brazil:
a multidimensional approach*
Alisson F. Barbieri**
Gilvan R. Guedes***
Kenya Noronha****
Bernardo L. Queiroz*****
Edson P. Domingues******
José Irineu R. Rigotti*******
Glaucia P. da Motta********
Flavia Chein*********
Francisco Cortezzi**********
Ulisses E. Confalonieri***********
Kenia de Souza************
Climate change will exacerbate the vulnerability of places and people around the world in the
next decades, especially in less developed regions. In this paper, we investigate future scenarios
of population vulnerability to climate change for the next 30 years in 66 regions of the state of
Minas Gerais, Brazil. Based upon the Alkire & Foster Index, we integrate simulated and projected
dimensions of population vulnerability into a Multidimensional Index, showing how scenarios
of temperature change would affect each region’s relative vulnerability in the future. Results
suggest that economic and health dimensions are the highest contributors to increases in
temperature-related vulnerability, with the poorest and agribusiness regions being the most
impacted in decades to come.
Keywords: Climate change. Population vulnerability. Population transitions. Multidimensional
index. Minas Gerais.
* Funding for this research was provided by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (Fapemig), grant
number PPM-00125-14.
** Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG, Brasil (barbieri@
cedeplar.ufmg.br).
*** Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG, Brasil (grguedes@
cedeplar.ufmg.br).
****Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG, Brasil (knoronha@
cedeplar.ufmg.br).
***** Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG, Brasil (lanza@
cedeplar.ufmg.br).
****** Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG, Brasil (epdomin@
cedeplar.ufmg.br).
******* Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG, Brasil (rigotti@
cedeplar.ufmg.br).
******** Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG, Brasil (glaucia.
possas@gmail.com).
********* Faculdade de Economia, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora-MG, Brasil (flavia.chein@ufjf.edu.br).
********** Departamento de Geografia, Université Paris IV – Sorbonne, Paris, França (fmcortezzi@gmail.com).
*********** Fundação Oswaldo Cruz (Fiocruz), Rio de Janeiro-RJ, Brasil.(pmags@ensp.fiocruz.br).
************ Centro de Desenvolvimento e Planejamento Regional (Cedeplar), Universidade Federal de Minas Gerais (UFMG),
Belo Horizonte-MG, Brasil (keniadesouza@gmail.com).
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Introduction
Research shows that climate change will exacerbate the vulnerability of places and
people around the world in the next decades, with a disproportionate impact on less
developed regions (VÖRÖSMARTY et al., 2000; FIELD et al., 2011), coastal cities, and flood
plain areas (McGRANAHAN et al., 2007). Not surprisingly, poor people – especially those
residing in rural areas – and localities or regions with weaker social and physical equipment
will carry most of the burden associated with climate change, regardless of geographical
and geological place-specific attributes (KURIAKOSE et al., 2013).
In response to the range and damage potential associated with climate shocks,
institutions like the United Nations (through the One-UN Social Protection Floor Initiative)
and World Bank (through the Social Protection and Labour strategy) have proposed to tackle
the negative effects of these changes by means of Social Protection (SP) programmes.
Mirroring the structure of inclusive development and economic resilience to financial crises
(WORLD BANK, 2008), the SP framework advocates that “countries that have SP systems
in place before a shock hits are better able to launch a more immediate and effective
response when such events occur” (KURIAKOSE et al., 2013, p. 20). The explanation is
quite simple, but powerful: impoverished areas usually have overlapping vulnerabilities,
disproportionately increasing their susceptibility to permanent loss of well-being (FÜSSEL;
KLEIN, 2006). For instance, the rural population is more reliant on climate-dependent
economic activities, while the urban poor are usually pushed to live in marginal urban areas,
where costs are low and geological risks are greater (WILSON et al., 2008). Because they
live on the edge of minimum living standards, any additional shock can trigger a sequence
of capital depletion, affecting their livelihoods and preventing them from recovering from
a lack of investment capacity in the long run (VANWEY et al., 2012).
Kuriakose et al. (2013) state that by promoting social protection initiatives, a given place
can position its population to have increased resilience to climate shocks. These initiatives,
such as cash transfer programs, a universal pension system, free basic education, and low-cost
health systems, have an a priori cushioning effect, absorbing part of the shock (resilience),
while a posteriori initiatives, such as infrastructure rebuilding and post-shock aids for the
most in need would promote equity. More structural investment efforts in education and
health would empower individuals to permanently adapt to inevitable future and uncertain
shocks in the long run, reducing risk by promoting individual capabilities (opportunities).
Two remarkable features of climate change are its short-term irreversibility and its
unpredictability. These two factors are key when dealing with population-based vulnerability.
Climate change coupled with globalization of markets drastically affect risk estimations,
virtually exposing everyone to anything (BECK, 1992; O’BRIEN; LEICHENKO, 2000). Historical,
social, environmental, and political vulnerability expose individuals to a wider variety of risks.
Media coverage of environmental hazards and disasters rapidly diffuse local events worldwide,
thus affecting local and regional perceptions. This produces and reproduces the social stigma
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attached to hazardous activities. This social amplification of risks influences the economic
profitability and employment potential of environmentally related economic activities
(PIDGEON et al., 2003). Therefore, the globalization of news related to the environment adds
another layer to the local impact of global environmental change.
More and more, risk estimation and climate-related human and material losses
are mediated by differences between perceived and objective risk (SLOVIC, 2000). This
environmental risk gap is partially explained by the inability of individuals to rationally
account for uncertain risk gradients that pose an additional challenge to precise predictions
of when, where, and how (HALLEGATTE, 2009). Under this new scenario of increasing
uncertainty related to climate shocks and variability, the promotion of social resilience
and adaptation is critical.
One of the basic recommendations of the World Bank and the UN is to target places
and populations that are vulnerable to climate impacts with a maximum level of precision
and information. Reliable, meaningful, and synthetic measures of vulnerability have been
developed recently (BOURGUIGON; CHAKRAVARTY, 2003; DEUTSCH; SILBER, 2005; ALKIRE;
FOSTER, 2011), but most of them have been applied to economic studies not directly related
to climate shocks. The literature on vulnerability to environmental change suggests that
some dimensions are key for targeting purposes: health, economy, population processes,
governance, and social protection (HELTBERG et al., 2009; KURIAKOSE et al., 2013). In
this regard, axiomatic multidimensional measures seem an appropriate choice, especially
those accounting for both the level and intensity of deprivation (ALKIRE; FOSTER, 2011). In
addition, because of the diffuse impact of climate variability and change on the economy
in the short term (and on population components in the long run), systems-like modelling
approaches are necessary to correctly tackle direct and indirect effects of changes in climate
indicators on the population over time.
Taking advantage of the socioeconomic, institutional, demographic, and biotic
heterogeneity of the state of Minas Gerais in Brazil, this paper investigates potential
scenarios of population vulnerability to climate change in all of the 66 microregions of the
state for the period 2010-40. Following an original conceptual and methodological strategy
proposed in Barbieri et al. (2010), we first investigate the multidimensional nature of
population vulnerability to climate change through four critical dimensions – demographics,
economics, health, and institutions – and their evolution over time. Ultimately, we estimate
a Multidimensional Index of Scenarios of Population Vulnerability, based on the Alkire and
Foster (AF) methodology (ALKIRE; FOSTER, 2011), in order to evaluate the contribution of
each dimension to overall vulnerability before and after projected temperature changes.
The modelling strategy applied in this study requires a dialogue across multiple
disciplines, in order to understand the relationship between distinct factors shaping
population vulnerability and how they are affected by climate change. Although provisional,
we believe that our empirical strategy provides useful insights into the potential
consequences of climate change on population vulnerability, thus facilitating the targeting
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of regions experiencing greater health and economic sensitivity to climate shocks. It
should be noted that we use past and future temperature as the sole proxy for climate
change. This methodological option will reflect the clustering of impacted regions where
temperature-dependent activities are concentrated, as in less urbanized or agricultureoriented regions. Other climate change markers, such as floods, droughts, and landslides
could provide better insights on vulnerability to climate change where urbanization is more
intense. Data availability for such markers and the inability to project disasters, however,
led our analysis to focus on temperature change only. Despite its limitations in addressing
overall spatial vulnerability to climate events, our empirical exercise sheds light on how
not only vulnerability, but also climate change is a multidimensional concept that may
render different expected impacts depending on how it is proxied and where it happens.
We conclude the study by discussing the challenges that policymakers must face in terms
of fostering an adaptive capacity in following decades.
The next section discusses population transitions in Brazil and their connection to
climate-related vulnerability. Next, we describe the geographic area of this study. This is
followed by a discussion of how each of the four dimensions of population vulnerability
was modelled. Finally, we conclude by presenting scenarios of population vulnerability to
climate shock, integrating the results into a synthetic measure.
Population transitions and vulnerability to climate change
The concept of “vulnerability” in this paper refers, in a narrow sense, to the lack of
capacity of a given population to prevent, mitigate, or attenuate adverse consequences
caused by exposition to hazards, and its relative lack of capacity to respond to them
(CUTTER, 1996). Confalonieri and Barata (2011) also discuss this concept based upon the
idea that “vulnerability” corresponds to the way in which a given population is exposed
to a hazard, its sensitivity, and its response to this hazard. In this sense, the “hazard”
results from a combination of three attributes of the population: exposition, sensitivity,
and adaptive capacity.
Since vulnerability levels and intensity are determined by several factors (such as
economic, demographic, health, and institutional issues), we define a multidimensional
perspective that identifies and combines various relevant elements to explain future
vulnerability scenarios under different climate shocks. We frame this discussion in terms
of how the unfolding demographic, urban, and health transitions, and their association
with stages of socioeconomic development in Brazil may create future vulnerabilities, or
reproduce and amplify existing ones.
The association between population transitions and environmental change is defined
by the evolution of development stages in a society. Strictu sensu, transitions unfold when
a society progresses along the stages of modernization. In the narrative of population
transitions, modernization refers largely to the process of urbanization associated with
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the transformation of an agrarian society into one wherein services and industry constitute
the core of the productive sector. In this regard, each stage of modernization is linked to
structural transformations in demographic, epidemiologic and spatial features which are
themselves related, in specific ways, to modifications in the environment.
In this regard, the demographic transition aims to characterize the implications of
changes in vital events – mortality and fertility – on population growth, size, and age
structure. An agrarian society evolves from a low population growth characterized by
homeostatic equilibrium (with high mortality and fertility) to a modern, post-industrial
society, again characterized by low growth and homeostatic equilibrium (however, with low
fertility and mortality levels). Rapid population growth occurs between these two extremes
when mortality decreases at a pace faster than fertility (LEE, 2003, 2011).
In addition to population growth, the demographic transition has two other dramatic
features: the concentration of population in urban areas, as predicted by modernization
theories, and population aging. Indeed, the final stage of the demographic transition
implies below-replacement fertility linked to a predominantly older and urban population
(LEE, 2003, 2011).
The steady reduction of Total Fertility Rates (TFR) in Brazil during coming decades
will not only reduce population growth, but will also produce an older age structure and
transformations in the country’s spatial makeup, as urban concentration continues.
Population aging will increase substantially around 2030, challenging the public funds
that cover costs of pension benefits and health demands (QUEIROZ; TURRA, 2010). This
scenario does not differ from that in our study site.
Many studies have focused on the impacts of a changing age structure on consumption,
carbon emissions, and mitigation strategies (DYSON, 2006; ZAGHENI, 2009, 2011; O’NEIL
et al., 2010). Few, however, have addressed the challenging question of how a changing
age structure may pose additional stress on the ability of a population to respond to
environmental change and, more specifically, to climate change (for exceptions, see
JIANG; HARDEE, 2001; McLEMAN, 2010). High demographic dependency ratios (due to
both very young and very old age structures) would be sensitive to climate change for
different reasons. Places with high young dependency ratios would be more affected by
climate change through its higher need for consumption (HEDENUS et al., 2014). Since
temperature change is likely to affect agricultural areas more intensively, and since these
areas have higher young dependency ratios, we would expect increased temperature-related
vulnerability to rise in these places. Places with high old dependency ratios would also
be affected by climate change by means of increased need for health care, given that the
elderly are less prepared to deal with episodes of heat wave, temperature variation, and
climate-related hazards, such as floods, droughts and landslides (HAJAT et al., 2014). The
effect of the dependency ratio on vulnerability may vary depending on the type of social
protection needed. Thus, a multidimensional analysis will have to explicitly incorporate
both types of dependency situations in order to correctly tackle vulnerability.
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It is also expected that increasing population mobility in the next decades will continue
to favour growth in urban centres, particularly in larger metropolitan areas. Modernization
implicitly brings a process of labour reallocation from low-productivity (rural) to highproductivity (urban-based) sectors facilitated by the spread of more efficient communication
and transportation systems, as well as by labour allocation to infrastructure development
(roads, damns, etc.). The profound transformations in rural and urban spaces are thus a
major driver of increasing mobility and urban concentration as development unfolds. In
this regard, according to his Theory of Mobility Transition, Zelinsky (1971) suggests that
the advancement of socioeconomic development will be related to an increasingly complex
mobility pattern, fostering a more intense and complex process of urban occupation.1 This
will demand an adaptive capacity, which translates into heavy investments in adequate
urban planning and infrastructure, such as housing quality, water and sanitation access,
and institutional arrangements that include the elaboration of municipal contingency plans
and Civil Defence Coordination.
Overall, we hypothesize that the combination of population ageing, with its implication
for public health and pension systems, coupled with the lack of infrastructure in urban areas
may result in the reproduction, persistence or amplification of vulnerability in the future.
This incomplete urban transition is particularly challenging when additional pressures from
a changing environment that require larger public budgeting for infrastructure preparedness
come into play. We also speculate that other factors may increase resilience and promote an
adaptive capacity when faced with future scenarios of climate change, such as higher levels
of formal education, increased coverage of public transfers to the most in need, and basic
infrastructure expansion. Education, in particular, increases resilience to climate change
through a variety of underlying mechanisms, such as by improving individuals’ willingness to
adopt climate robust technological innovations (DERESSA et al., 2009; DESSAI; HULME, 2007).
A key assumption regarding population transitions is that they will be engendered
by the advancement of the “modernization process” and the achievement of an overall
increase in societal welfare levels and inequality reduction. In the Brazilian case, a
potential contradiction may arise. Different from developed countries, population aging
here has not been sufficient to trigger a complete epidemiological transition. The country
experiences an increasing prevalence of chronic diseases, while still facing high rates of
communicable diseases, such as dengue, malaria, cholera, and schistosomiasis (OMRAN,
2005). This dual burden implies that the advancement of the demographic transition has
not been coupled with the necessary investments to reduce historical poverty, inequality,
and a lack of access to water and sanitation by a large share of the population (PRATA,
1992; SCHRAMM et al., 2004).
1 However, it is not clear in which situations increasing mobility into urban areas due to climate impacts may be an effective
adaptive response to reduce vulnerability. While some examples in the literature suggest the importance of distinguishing
migration status as an adaptation mechanism, its impacts on vulnerability and adaptive capacity have not been sufficiently
discussed in order to isolate the effects of other social, economic, and climate change processes (McLEMAN; SMIT, 2006).
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The incomplete epidemiological transition in Brazil may represent an additional source
of stress as climate change comes into play, since it works as a force that holds back the
transition of a population still affected by infectious diseases. Vulnerability can increase
even further as rural-urban migration, induced by climate-related effects on agro-pastoral
areas, translates into the growth of shantytowns characterized by poor sanitation and
poverty. The opposite can hold as well. Barbieri et al. (2010) show that out-migration from
the Northeast of Brazil will likely increase with climate change, reflecting increased flows
of environmental refugees. In this case, as suggested by Tacoli (2009), environmentallyrelated out-migration could decrease vulnerability as people move out from environmentally
affected areas. The persistence of old vulnerabilities may thus interact with future
vulnerabilities associated with the population characteristics emerging from incomplete
demographic, mobility, urban, and epidemiological transitions.
Study area: the state of Minas Gerais, Brazil
Minas Gerais is a good example of how inequality and heterogeneity of adaptive
capacity can pose important challenges in dealing with vulnerability to climate change.
The state comprises 853 municipalities, organized into 66 administrative units known
as “microregions” (hereafter named “regions” for the sake of simplicity), and hosts four
distinct biomes: Caatinga (a semi-arid biome), Campo Rupestre de Altitute (high altitude
grasslands), Cerrado (a Savannah-like vegetation), and the Atlantic Forest (tropical forest).
This biotic heterogeneity is unique in Brazil. Figure 1 shows a map of Minas Gerais indicating
its administrative divisions and biomes.2
Minas Gerais has historically been one of the most important states in Brazil in both
economic and demographic aspects. In 2010, it was ranked second in population among the
26 Brazilian States (19,5595,309 inhabitants), third in Gross Domestic Product (accounting
for 10% of the Brazilian GDP), and fourth in area (586,528 Square Kilometres). The state
capital is Belo Horizonte, with 2,357,444 inhabitants.
The choice of Minas Gerais for this study is deliberate. Like Brazil, the state is
characterized by large socioeconomic and regional inequalities. On the one hand, the North
and Northeast regions of the state are the poorest areas, showing indicators equivalent to
African standards. On the other, the South and East host a more dynamic economy, with
2 A biome-driven analysis would better capture how vulnerability on demographic, economic, health, and institutional
dimensions would be impacted by increases in temperature over time. However, most data used in the current article
refer to the microregion level, using traditional political boundaries. Political boundaries were used instead of biome for
the estimation of indicators because temperature impact was simulated on the health and economic dimensions only,
as discussed in the methods section. To estimate all indicators based on the overlay of biome and political boundaries,
some spatial interpolation would be required, since some microregions are in transitional biome areas. The estimation
of indicators based on the biome-political boundaries dyad would also require a longer projection period in order for
temperature impact to be effective on any institutional and demographic indicator. Finally, the biome-specific indicators
would be more sensitive if other climate proxies were addressed, including precipitation, humidity and types of disasters.
The estimated spatial distribution and concentration of the vulnerability index, as well as the spatial correlation between
dimensions and the biomes are beyond the scope of this article, and will be object of future research.
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high levels of educational and health indicators. This context provides us with interesting
possibilities in testing the impacts of climate change within diverse socioeconomic,
demographic, and environmental scenarios.
FIGURE 1
Municipalities, regions, and their biomes
State of Minas Gerais
Source: Instituto Brasileiro de Geografia e Estatística – IBGE; Centro de Desenvolvimento e Planejamento Regional – Cedeplar,
Universidade Federal de Minas Gerais – UFMG.
Temperature change in Minas Gerais
We use historical and projected data on the average annual temperature as a proxy
for climate change. Data comes from IPCC’s A1B1 scenario, a regional ETA model, that
contains average annual temperatures for South America in grids of 40 km X 40 km. Data
were built from the global HadCM3 model, containing temperature values at two meters
in the area delimited by the following co-ordinates: LAT N = 5,0o, LAT S = -35,0o, LON E =
-35,0o and LON W = -75,0o.
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We used two time slices in the ETA model: 1960-90 and 2010-40, and estimated
annual temperatures in K (Kelvin).3 Thus, we created a dataset with annual temperature
estimates for each municipality in Brazil, particularly in the state of Minas Gerais, using
an imputation of the average temperature in the points with the municipality area or, when
this municipality did not have any point in its territory, the point closest to its centroid.
Figure 2 shows the distribution of the average temperature across municipalities in Minas
Gerais in two periods (1960-90 and 2010-40) and the difference between the maximum
and minimum temperatures observed between 2010 and 2040 for each municipality. The
Northwest and West regions (Triângulo Mineiro) host municipalities with the highest average
temperatures and the highest amplitudes. We observe an overall temperature increase
state-wise, as assumed by the A1B1 scenario.
FIGURE 2
Average temperature in Kelvin (1960-1990 and 2010-2040) and average range (2010-2040)
State of Minas Gerais
Average
Temperature
< 291.2
291.2 to 292.7
292.7 to 293.8
293.8 to 295.3
> 295.3
2010 to 2040
1960 to 1990
Max - Min
1.9 to 2.2
2.2 to 2.4
2.4 to 2.6
2.6 to 2.8
2010 to 2040
2.8 to 3.2
56 0
28 0
0
56 0 K m
Source: Historical and Projected Climate Data. Centro de Previsão do Tempo e Estudos Climáticos/Instituto Nacional de Pesquisas
Espaciais – CPTEC/Inpe.
3 The HadCM3 model was run many times, with varying parameters for the atmospheric processes. Models considering
low, medium, and high climatic sensitivity in the year 2099 were also compared to create a plausible range of temperature
projections under the A1B emission scenario. Among these runs, we used the original, with no change in the parameters
(version CNTRL). Subset models for 4 timeslices (1960-1990, 2010-2040, 2040-2070, and 2070-2099), from version CNTRL –
Eta model domain, were used in this study. The key climate variable was temperature measured in Kelvin (K). The database
with georeferrenced points from South America allowed us to obtain the average annual temperature for each municipality,
by each year by timeslice. This was generated from imputing the average point temperature within the municipality area, or
the temperature point closest to the municipality centroid when no point was available within the municipality boundary.
No information was given by INPE documents on why there is a gap between 1960-1990 and 2010-2040 on temperature.
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Economic dimension
Because of the diffuse and system-like impact of climate change on the economy,
we use a computable general equilibrium model to isolate the direct impacts of a temperature
increase from 2010 to 2040 on selected economic indicators. We first estimate the influence
of a change in the temperature on the labour supply and agricultural productivity. Two
scenarios were defined: the baseline scenario (without considering climate impacts) and
the A1B1 scenario. The baseline scenario is defined as considering the average historical
temperatures recorded between 1960 and 1990. The A1B1 scenario considers projections
of temperature for the following years: 2010, 2015, 2020, 2025, 2035, and 2040.
Household consumption, GRP (Gross Regional Product), and employment are
endogenous variables estimated by a computable general equilibrium (CGE) model called
IMAGEM-MG (Integrated MultiRegional General Equilibrium Model for Minas Gerais). The
IMAGEM-MG was calibrated for 2005, in accordance with the sector industry and product
commodity classifications in IBGE’s (Brazilian Statistical Bureau) input-output matrix.4 The
recursive dynamics mechanisms allow us to make explicitly temporal use of the CGE model,
wherein the endogenous variables are adjusted throughout the analysed period, after an
initial shock or series of shocks.
Climate change’s impact on agricultural output is the difference between the expected
agricultural output per hectare, considering the historical climate conditions and the
expected agricultural output per hectare in the new climate conditions. The average climate
change impacts on agricultural productivity in each region of Minas Gerais were considered
as productivity shocks in a general equilibrium model. The model considers workers who
migrate from the most affected regions to the least affected regions, guided by real wage
differentials (equation 1). These real wage differentials come from the model of impacts of
climate change on the economy. Inter-regional labour migration or labour supply percentage
change (ld) adjusts due to the difference between regional real wage (wd) and national real
wage (w), calculated from 2010 Brazilian Demographic Census data. The elasticity parameter
(σd) measures the labour supply response to real wage differentials.
(1)
ld = σd (wd - w)
Our simulations are based on the assumption that all municipalities face the same
capital return and do not present systematic technological differences. Another underlying
assumption is the absence of technological responses to the new climate conditions. The
results are predicted using the imputation of climate forecasts on the current technological
status in agriculture.
4 This is divided into 18 sectors and 18 commodities. There are five final demand components (household consumption,
government consumption, investment, exports, and inventory), three primary factors (capital, land, and labour), two marginal
sectors (trade and transport), and imports by product commodity for each of the 18 sectors. The model has 67 endogenous
regions, 66 in Minas Gerais and one as the “rest of Brazil”.
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According to our results, on average, there is no sizeable impact on the economic
factors. However, the analysis by region shows important results. The most important
regional economic impact is related to agriculture and labour shocks, but also depends on
the economic structure and inter-regional linkages. The 20 most impacted regions in terms
of percentage change in GRP and employment are, in general, regions with weak economic
activities, concentrated in the Northern part of the state (one of its poorer regions). The
exceptions are Uberlandia and Uberaba (accounting for almost 12% of the state’s economic
production). These regions are part of the “Triângulo Mineiro” (together with Ituiutaba and
Frutal). They are located in the west of the state and are significant cattle and agriculture
producers that also serve as the most important terrestrial transportation hubs in the
country. Although these 20 regions account for 84% of the total GRP losses in the state,
they represent only 21% of the economy. It is worth observing that in the two most affected
regions, Januária and Unaí, their share on climate change impact is almost 20 times their
share on the economy. This indicates that some small regions in the state are the most
vulnerable to climate change impacts. The Southern part of the state is more industrialized
and less dependent on agriculture, thus showing lesser economic vulnerability.
Health dimension
In order to evaluate the relationship between climate change and health, reliable time
series on both health and climate would be required. Due to data limitations on changes
in health over time, we estimate the impact of temperature change using a semilog OLS
(Ordinary Least Squares) regression on cross-section data considering the 5,563 Brazilian
municipalities identified in the administrative political division of 2010.
To simulate the average health of the population, different indicators were used as
dependent variables in our models (prevalence of leptospirosis, rate of hospitalization
for childhood diarrhea, rate of hospitalization for cardiovascular diseases, rate of
hospitalization for respiratory diseases, rate of hospitalization for infectious diseases, and
prevalence of dengue). Only three, however, showed statistically significant relationships
with temperature (p-value>0.001): prevalence of dengue (BRASIL, 2005-2010a), rate of
hospitalization for infectious diseases (BRASIL, 2005-2010b), and rate of hospitalization
for respiratory diseases (BRASIL, 2005-2010b). Since the prevalence of health events is
low, especially in small areas, all indicators were calculated considering the average of
events from 2005 to 2010.
The key independent variable, which will be used to simulate a climate shock, is the
average historical temperature recorded between 1960 and 1990 for each municipality. The
models control for the following socioeconomic and demographic characteristics: GDP per
capita (SUFRAMA, 2009), proportion of individuals who are illiterate or did not complete
elementary school (IBGE, 2010), unemployment rate (IBGE, 2010), proportion of women
(IBGE, 2010), proportion of individuals aged 60 years and over (IBGE, 2010), population size
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(IBGE, 2010), and dummy variables for each geographic region of Brazil (North, Northeast,
South, Southeast, and Centre West).
Based on the regression results, we estimated the predicted average morbidity rates
by each region of Minas Gerais for the base year (2010). Different from the economic
dimension, all betas (parameters) estimated, as well as the levels of independent variables,
were held constant from 2010 to 2040, including the average temperature. This represents
our baseline scenario, where all levels and effects are held constant throughout the 30-year
window. To simulate a climate shock and isolate its effect from concurrent ones, we estimate
counterfactual average morbidity rates by replacing the average historical temperatures
with the projections of temperature. The other control variables and estimated parameters
were held constant.
Our results suggest that a temperature change from 2010 to 2040 could increase the
morbidity rates by a considerable amount (Table 1). For example, if we compare the rate
of dengue in the baseline scenario of 0.5 to the same rate after the temperature change
in the same year (2040), this rate would increase to 1.1. This is a 130% increase due to
the change in temperature alone. The hospitalization rates for infectious and respiratory
diseases could increase by 12% and 3%, respectively, for the same year. If we had taken into
account precipitation patterns in our analyses, the combined (individual plus interactive)
impact of both climate change proxies would likely have been more pronounced. Even in
the absence of precipitation data (due to a lack of information available at the time of our
analysis), the sole impact of temperature change on dengue is remarkable.5 The higher
increase in the rate of dengue due to the change in the temperature is related to the higher
effect (coefficient) estimated by the econometric model especially compared to the other
two health indicators. While the estimated effect of temperature on the rate of dengue is
equal to 0.30, its effect on rates of hospitalization for infectious and respiratory diseases
is equal to 0.06 and 0.02, respectively. In addition, the variance of projected temperature
by year across municipalities is higher for 2020 and 2040, the last two periods considered.
The largest temperature impact is estimated to be in dengue prevalence, followed by
infectious diseases. The increase in dengue and infectious diseases is mostly concentrated
in the west and northwest portions of the state. These are areas with the highest registered
and projected temperatures (as suggested by Figure 2), and hold a strong spatial overlapping
pattern with the climate related economic impacts. If health had been considered in the
economic model, it is likely that the effect of climate change on economic indicators would
have been even stronger, since these areas are also the ones with the highest loss in GRP.
Similarly, if the impact of the climate on economic indicators had been mapped for the
5 We also estimated the standardized parameters (based on the standard deviation of each independent variable). By
standardizing our independent variables, each parameter can be compared by their magnitude. The results (not shown,
but available upon request) suggest that the effect of temperature change is still strongest for dengue and infectious
diseases, and among the strongest for respiratory disease, yielding additional empirical support for the importance of
climate shocks on population health.
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health model, we would probably have captured an even stronger climate-related effect
on morbidity rates.
TABLE 1
Base and impact scenarios of indicators in the health dimension
State of Minas Gerais – 2010-2040
Scenarios
Average prevalence rate
of dengue (per 1,000
inhabs.)
Indicators
Average prevalence rate
of respiratory diseases
(per 1,000 inhabs.)
Average prevalence rate
of infectious diseases
(per 1,000 inhabs.)
0.5
0.5
0.5
0.5
7.0
7.0
7.0
7.0
3.9
3.9
3.9
3.9
0.7
0.8
1.1
1.2
7.1
7.2
7.2
7.3
4.0
4.1
4.3
4.3
Base scenario
2010
2020
2030
2040
Impact scenario (A1B1)
2010
2020
2030
2040
Source: IBGE (Demographic Census 2010); Brazil/Ministério da Saúde (SIH/SUS, 2005-2010b); Centro de Previsão do Tempo e
Estudos Climáticos/Instituto Nacional de Pesquisas Espaciais – CPTEC/Inpe.
Note: Authors’ calculations based on secondary data sources.
Demographic dimension
The demographic dimension encompasses three components over time: age
composition (0 to 14, 15 to 64, and 65 or more years old), households with access to
proper sanitation, and expected years of education. We used the Demographic (Total)
Dependency Ratio as a synthetic indicator of age structure. This refers to the ratio between
the population below 15 years old plus the population above 64 years old, divided by the
population between 15 and 64 years old. Population scenarios for 2010-40 and by age
group for Brazilian states were elaborated from a standard population projection method
known as the cohort component method (and assuming 2010 as the baseline). We used
data from previous Brazilian population censuses (1970, 1980, 1991, 2000, and 2010),
recent National Household Surveys (PNAD, IBGE 2000-08), and birth and death statistics
since 1980. Population estimates were disaggregated by regions using a combination of
two population projection methods for small areas: the Apportionment Method, and the
Method of Cohort Relations (DUCHESNE, 1989).6
The indicator “Expected Years of Education” refers to the mean number of years of
education that a cohort of children will attain until a certain age. The indicator was built
6 The Apportionment Method consists in projecting a population of a smaller area (for example, municipalities) and
considering its relative contribution to the growth of a larger area (for example, states), while the Method of Cohort Relations
consists in following the age and sex structure of the population in the smaller area and ensuring its consistency with the
structure of the larger area.
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upon data on school attendance per year of education and by age (both from the 2010
Brazilian Census). We first estimated the mean years of study weighted by the population
at each age (7 to 18 years old), which corresponds to the 12-year period of basic education
in Brazil. Ideally, each cohort of single ages should contribute with one year of education
at each calendar year, when the successive sum of the contribution of each cohort would
represent the total expected years of education until that age. Formally:
12
A x = ∑ Ai , x
i =1
Pop xi
Pop x
(2)
where:
A x = average years of education of a cohort of age x;
Ai,x= year of education i attended by a student of age x, considering i = 1, 2, … , 12;
Pop xi = population of age x attending the year of education i;
Popx = population of age x.
The ratio between A x and the years of education relative to that age Ax represents the
contribution of the cohort to the expected educational attainment at the end of the period
of basic education in Brazil. This ratio would equal one when each student enters the
educational system at the correct age, she/he does not fail a given year, and does not leave
school. Thus, the sum of the ratios corresponding to each age x represents the expected
years of education of a synthetic cohort:
18
Ax
A0 = ∑
x=7 Ax
(3)
To project the number of expected years from 2010 to 2040 for each region, we use
the average linear growth rate per age from the modal age contribution to the expected
education to the lowest age contribution. This average growth rate was multiplied by five
(for each five years of the projection), and equation (3) was re-calculated, conditioned on
the restriction that it must reach a maximum of 12 years of expected education sometime in
the future. Imagine that, at age 12, a student contributes to, on average, 0.9 years until she
completes 13 years of age. Assume that another student, with eight years of age, contributes
to only 0.6 years of education until she is nine years old. Thus, if we assume a linear growth
rate, the educational level should grow by 0.06 for each single age between age eight and
12. Thus, five years from now, an eight year old student should be contributing to 0.6 +
0.06*5 = 0.9 years of education. After a couple more years, a typical eight year old student
would achieve one year of expected education from eight to nine years of age, reaching
the maximum contribution allowed by a single-age synthetic cohort (RIGOTTI et al., 2013).
Finally, we estimate the growth rate in the implementation of households with proper
sanitation services from 1981 to 2010 and use this rate to project forward the coverage of
the service. For the regions that had coverage above 85% in 2010, we applied the rates until
they reached 100%. For the other regions we applied the observed growth rate to forecast
until 2040 (REZENDE et al., 2007; SAINI et al., 2011; NERI et.al, 2009).
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Our estimates do not take into account feedbacks between demographic factors and
potential climate impacts. In order to project the demographic indicators, we make two
assumptions: a) that there are no significant mortality or migration shocks from climate
change which could substantially influence population stock and composition by age;
and b) that the expansion of sanitation services and educational attainment follow a
trajectory independent from climate scenarios. Both assumptions seem reasonable since
our time window is relatively short for observing significant changes in fertility patterns
and sanitation policies. Mortality could theoretically be impacted by temperature change,
even in a short time window, and this would make our first assumption somewhat fragile.
However, our regression estimates of temperature change on mortality indicators show no
significant effect when historical data are used.7
Table 2 shows estimates of demographic components over the period 2010-2040.
Population projections reflect the advancement of the demographic transition, with below
replacement fertility contributing to an increasing proportion of the population over 64 years
of age (from 8.1% in 2010 to 18.4% in 2040). The Total Dependency Ratio decreases from
44.9% in 2010 to 42.5% in 2020, reflecting the relative increase of the active population.
From 2030 on, the ratio starts to increase due to population aging.
While an older age structure represents a potentially higher vulnerability to climate
shocks, more expected years of education (from 9.2 in 2010 to 11.7 in 2040) might increase
the adaptive capacity of the population. Furthermore, our results show that the percentage
of households receiving basic sanitation services in Minas Gerais will increase from 75% in
2010 to 92% in 2040, minimizing a potential spread of sanitation-related diseases under
temperature stress. However, we still observe strong regional differences: regions located
in the Northern part of the state have lower coverage and slower coverage growth rates than
in more developed regions from the Southern parts of the state (Figure 3). It is worth noting
that the type of projected sanitation coverage was based on a deterministic projection.
However, we acknowledge that this is a policy choice and not an easily projectable dynamic.
TABLE 2
Estimates of projected indicators in the demographic dimensions
State of Minas Gerais – 2010-2040
Indicators
Population 0-14 y.o. (%)
Population 15-64 y.o. (%)
Population 64+ y.o. (%)
Dependency ratio (%)
Expected years of education
Sanitation (% of households)
2010
22.9
69.0
8.1
44.9
9.2
71.4
Base scenario
2020
2030
19.5
17.6
70.1
68.5
10.4
13.9
42.5
45.9
10.4
11.2
78.7
87.2
2040
16.1
65.6
18.4
52.5
11.6
92.8
Source: Based on: IBGE (Demographic Censuses of 1980, 1991, 2000, 2010, Estimativas de População 2015); Centro de Previsão
do Tempo e Estudos Climáticos/Instituto Nacional de Pesquisas Espaciais – CPTEC/Inpe.
7 Mortality indicators used in our regressions: infant mortality, child mortality, and elderly mortality (results not shown,
but available upon request).
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FIGURE 3
Baseline and projected demographic indicators at microregion level
State of Minas Gerais – 2010-2040
Expected Education (2010)
Expected Education (2040)
2010 (Jenks)
2040 (Jenks)
8.4 to 8.7
10.6 to 11.4
8.7 to 9.0
11.4 to 11.6
9.0 to 9.1
11.6 to 11.7
9.1 to 9.5
11.7 to 11.8
9.5 to 9.9
11.8 to 12.0
Dependency Ratio (2010)
Dependency Ratio (2040)
2010 (Jenks)
2040 (Jenks)
0.40 to 0.43
0.42 to 0.43
0.43 to 0.45
0.43 to 0.45
0.45 to 0.48
0.45 to 0.48
0.48 to 0.52
0.48 to 0.52
0.52 to 0.62
0.52 to 0.59
Sewage Coverage (%) (2010)
Sewage Coverage (%) (2040)
2010 (Jenks)
360
180
0
360 km
2040 (Jenks)
11.4 to 33.8
11.4 to 33.8
33.8 to 58.9
33.8 to 58.9
58.9 to 73.7
58.9 to 73.7
73.7 to 84.4
73.7 to 84.4
84.4 to 95.4
84.4 to 100.0
Source: IBGE (Demographic Censuses, 1980, 1991, 2000, 2010, Estimativas de População 2015).
Note: Authors’ calculations based on secondary data.
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Institutional dimension
We use data from the State System of Information on Sanitation8 on the proportion of
municipalities in a region having a municipal contingency plan to manage hazards. Due to
the high uncertainty levels in forecasting the supply of public services, we simply assume
that the present (2010) public capacity to respond to hazards will be reproduced throughout
the 2010-40 period. Government transfers are calculated as the number of beneficiaries
of transfers (public pension, social assistance, rural and old age pension for the poor) to
the number of working age individuals paying taxes. We call this the “government transfer
support ratio”, estimated as the population projections weighted by the percentage of
beneficiaries and percentage of taxpayers. We assume that the age profile of beneficiaries
and taxpayers will remain constant until 2040, thus the support ratio is mainly affected by
changes in population age structure for each region of the state and by the institutional
base in 2010. This procedure is similar to that used in other studies (MILLER et al., 2010;
QUEIROZ; FIGOLI, 2011).
Our results show that, on average, only 19.9% of the municipalities within regions had
municipal contingency plans to manage hazards. Regarding the reliance on government
transfers, the support ratio could fall from 1.6 workers per beneficiary in 2010 to 1.4 workers
per beneficiary in 2040. The municipalities in the less developed Northern regions of the
state have a smaller support ratio than the more developed ones, despite the older age
structure of the population in the Southern region. In relation to Bolsa Família (one of the
largest social programs in Brazil), transfers in some Northern regions in 2010 are about 5%
of the local GRP. For example, this is the case in Januária, Araçuai, Salinas, and Janaúba,
whereas the percentage in the Southern regions of the state is around 1% of the GRP or less.
Integrating the dimensions: the Multidimensional Vulnerability Index (MVI)
The four dimensions discussed above were combined into a single multidimensional
vulnerability index (MVI) known as the Alkire-Foster methodology (ALKIRE; FOSTER, 2011).
For each decennial projected year (2010, 2020, 2030, and 2040) we calculated the MVI
before and after the climate impact. In addition, we calculated the percentage contribution
of each dimension to the total vulnerability measured for each decennial year in order to
analyse how evolving scenarios of temperature change modifies the relative contribution of
each dimension of population vulnerability over time. Table 3 summarizes the dimensions
and indicators used, in addition to the cut-off level below which each region is considered
as being deprived for each indicator. The same weights are assigned to each indicator in the
construction of the MVI, since we have no a priori theoretical evidence as to the importance
of each dimension on projected vulnerability to climate related impacts.
8 Data available from the João Pinheiro Foundation: http://seis.fjp.mg.gov.br.
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TABLE 3
Dimensions, indicators, indicator cut-offs, and weighting scheme
Dimension (d) Indicator (I)
Economic
% variation in GRP
% variation in employment
% variation in family consumption
Demographic Total dependency ratio
Expected education
% households with proper sanitation
Health
Average prevalence of dengue
Average prevalence of respiratory diseases
Average prevalence of infectious diseases
Institutional
Government transfer support ratio
% of municipalities with risk management plan
Vulnerability cut-off (Zl)
Locational quotient <=1
Locational quotient <=1
Locational quotient <=1
= state average
1st quartile
= state average
= 75% of cdf (base: 2010)
= 75% of cdf (base: 2010)
= 75% of cdf (base: 2010)
= state average
= state average
Weight
1/11
1/11
1/11
1/11
1/11
1/11
1/11
1/11
1/11
1/11
1/11
The MVI (M0) is defined by the interaction of two components: a multidimensional
deprivation level (H) and a multidimensional deprivation intensity (A). We start by defining
the number of simultaneous dimensions of deprivation that will characterize a region
as vulnerable. Following Alkire and Foster (2011), we estimate different proportions of
indicators in which each region is classified as deprived in order to analyse where the curve
of the adjusted headcount (M0) ratio is stable. We used temporal dominance analysis (not
shown here) to define the 50% point as the vulnerability cut-off since it represents the point
at which there is a disturbance in the vulnerability trend among regions. To calculate the
cut-off point, k, we estimate the different ratios c/d, where:
c = number of indicators censored (deprivation dummy)
d = total number of indicators.
Thus, k varies from 0% to 100%. If we call ci the deprivation vector - or the number of
indicators a region i is deprived, k equal to 50% would represent ci>=50%; that is, a region must
be deprived in at least 50% of the indicators to be considered multidimensionally deprived.
Up to this point, there is relatively small variability in the number of vulnerable
regions (between 35% and 50%), and after this mark there is a sharp decline (to around
20% of vulnerable regions at the 60% cut-off). The deprivation level (H) thus represents
the proportion of regions multidimensionally deprived (in at least 50% of the indicators).
The second step is the estimation of the breadth of vulnerability experienced by each
poor region. This point is crucial in order to adjust the multidimensional vulnerability level
(H) to its intensity (A). It represents the share of possible deprivations experienced by a
multidimensionally vulnerable region. The MVI is, therefore, M0 (y,z) = H x A, also known
as the Adjusted Headcount Ratio. It measures the proportion of regions that are classified
as simultaneously deprived in at least 50% of the indicators weighted by its intensity.
Our estimates for the vulnerability index show that vulnerability level (H) and intensity
(A) increase due to climate impacts. At the 50% cut-off point, the proportion of vulnerable
regions (H) increases substantially after the climate shock in 2040 – from 0.55 to 0.71
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(compared to 0.53 to 0.55 in 2010). The intensity of vulnerability and the Adjusted
Headcount Ratio also present substantial increases in 2040 compared to 2010. While the
average intensity-adjusted multidimensional vulnerability (M0) increases from 0.35 to
0.36 in 2010 – a 3.1% change, in 2040 it increases from 0.35 to 0.48 – a 39.8% change.
Table 4 shows the climate impacts on the MVI, both overall and for each indicator. Results
show that vulnerability after climate impacts increases for all indicators. Health indicators –
particularly dengue rates – would present the greatest impact on regions, with vulnerability due
to climate impacts increasing to around 190% in 2040. Some economic indicators also show a
substantial increase in susceptibility due to the climate impact. Vulnerability due to a decline
in GRP after a climate shock, for instance, would increase from 9% in 2010 to 38% in 2040.
TABLE 4
Percentage change in vulnerability (overall and censored by indicator) before and after climate impact
(vulnerability cutoff = 50%)
State of Minas Gerais – 2010-2040
Indicators
Economic dimension
Average annual gross regional product
(% variation in the 5-year period)
Average annual employment
(% variation in the 5-year period)
Average annual household consumption
(% variation in the 5-year period)
Demographic dimension
Total dependency ratio
Expected education
% of households with proper sanitation
Health dimension
Average prevalence rate of dengue
Average prevalence rate of respiratory diseases
Average prevalence rate of infectious diseases
Institutional dimension
Government transfers support ratio
Proportion of municipalities with risk management plan
Total
2010
2020
2030
2040
9.4
14.3
25.9
38.2
-3.0
13.3
10.3
14.3
3.0
20.0
27.6
34.3
0.0
7.1
4.2
4.0
21.4
16.7
13.3
0.0
11.1
28.6
21.1
18.8
20.0
0.0
6.7
88.9
42.9
50.0
122.2
40.0
60.0
190.9
46.7
93.8
-4.0
4.8
3.1
4.5
26.3
21.5
9.1
31.6
25.9
29.2
28.0
39.8
Source: Based on: IBGE (Estimativas de População 2015); Brazil/Ministério da Saúde (SIH/SUS, 2005-2010b); Centro de Previsão
do Tempo e Estudos Climáticos/Instituto Nacional de Pesquisas Espaciais – CPTEC/Inpe.
In order to determine the relative contribution of each indicator to overall multidimensional
vulnerability before and after the climate impact, we decompose M0 by indicator and estimate
the proportion of M0 due to each indicator. As shown before, economic factors are the main
contributor to overall vulnerability in 2010 (baseline and after climate impact), followed by
demographic factors. In 2040, economic factors would still unambiguously be the major
contributors to overall vulnerability in Minas Gerais, before and after the climate impact. Health
indicators, while contributing the least (among dimensions) to overall vulnerability in 2010,
could displace the demographic factors as the second most important components in 2040 if
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the climate impact occurred. Overall, while institutional and demographic dimensions would
maintain their contribution to vulnerability as approximately stable over time, the opposite
would be observed for the economic and health dimensions. This is also a consequence of our
modelling strategy: while in the first case, vulnerability reflects a trend in the demographic
and institutional scenarios, in the second case this susceptibility also reflects a climate impact
scenario, acting synergistically to increase the vulnerability of regions.
Finally, we analyse the spatial distribution of vulnerable regions before and after climate
impacts between 2010 and 2040 (Figure 4). We use the measure of vulnerability intensity
built from the multidimensional index (with zero indicating no vulnerability at all, and one
indicating extreme vulnerability). Besides the heterogeneous intensity of vulnerability
across regions, an overall vulnerability increase after the climate impact can be seen at
each period and over time. In 2040, some regions remain deeply vulnerable (particularly
in the North and Northeast), while others become vulnerable in 2040, after the climate
impact (in the West/Northwest and South).
FIGURE 4
Spatial distribution of vulnerability intensity for regions
State of Minas Gerais – 2010-2040
2010 Base
impact scenario
2010 Base scenario
0,000000
0,000000
0,000001 - 0,545455
0,000001 - 0,545455
0,545456 - 0,636364
0,545456 - 0,636364
0,636365 - 0,727273
> 0,727263
0,636365 - 0,727273
0
75
150
300 km
0
75
150
300 km
0
75
150
300 km
2010 Climate
impact scenario
2010 Base scenario
0,000000
0,000000
0,000001 - 0,545455
0,000001 - 0,545455
0,545456 - 0,636364
0,545456 - 0,636364
0,636365 - 0,727273
> 0,727263
> 0,727263
0
75
150
300 km
0,636365 - 0,727273
> 0,727263
Source: Based on: IBGE (Estimativas de População 2015); Brazil/Ministério da Saúde (SIH/SUS, 2005-2010b); Centro de Previsão
do Tempo e Estudos Climáticos/Instituto Nacional de Pesquisas Espaciais – CPTEC/Inpe.
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Discussion and concluding remarks
Climate change has brought new challenges to the management of risks and reduction
of vulnerability around the world. Scientific consensus on temperature and rainfall variability
increases in the next decades have prompted a series of multilateral efforts to negotiate
mitigation initiatives, such as the reduction in greenhouse gas emissions and subsidies
for pro-environmental consumption. Most of these international agreements, however,
have proved less than effective in promoting actual and timely change (DIETZ et al., 2003;
WALKER et al., 2009).
Multi-country aid and political organizations, such as the UN and World Bank, have
shifted their approach away from mitigation towards resilience strategies. This new focus on
how to deal with the social impacts of climate change has a Social Protection (SP) paradigm
as a central piece in creating adaptation in the short run and resilience in the long run.
The main objective of climate-responsive SP is to minimize the vulnerability of people and
places and to increase equity in the defence capacity (KURIAKOSE et al., 2013). Although a
long list of desired institutional design features must be met by many of these frameworks,
two of them are of particular interest for the type of analysis performed in this study: (1)
the development of targeting schemes, such as a social profiling of vulnerability and the
geographic distribution of climate related vulnerability; and (2) investments in livelihoods
and social infrastructures that build resilience.
This article investigates the spatial distribution and temporal evolution of population
vulnerability to climate change in 66 regions in the state of Minas Gerais, Brazil. By focusing
on multiple key dimensions of population-based vulnerability – such as health, economy,
institutional capacity, social protection, and demography – we were able to estimate when,
where, and how vulnerability to climate shocks would evolve in the next decades, given
the unfolding demographic, epidemiological, and urban transitions, and the underlying
development scenarios in Brazil.
Minas Gerais is a particularly interesting setting for understanding how climate change
would influence population vulnerability because it hosts a unique portfolio of geophysical
areas, as well as high levels of social heterogeneity, mirroring the biotic and socioeconomic
structure of Brazil.
Our simulated results suggest that while the largest proportion of overall vulnerability
would be explained by the climate impact on economic indicators, health would be the
dimension with the highest contribution increase over time after a climate shock. The
expressive increase in dengue, infectious, and respiratory diseases due to climate change
may act in a synergic way with the persistence of an incomplete urban and epidemiological
transition to define future vulnerability scenarios. This has important implications for
public health initiatives regarding, for example, better water and sanitation systems, and
the control of vectors such as the Aedes Egipty (transmitter of dengue) which reproduces
at higher temperatures and where infrastructure is precarious. Morin et al (2013) suggest
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that the climate impact on dengue transmission is quite complex. However the authors
argue that precipitation and temperature would provide adequate conditions for immature
habitats, fostering vector development, survival and reproduction. Ultimately, the increase
in vector reproduction would lead to higher probabilities of dengue transmission.
The variability in demographic scenarios due to climate impacts were not considered
here, yet it is unlikely that these feedbacks are significant in the short run, since they may
first affect the economic and health indicators. As suggested by our findings, climateinduced deprivation would likely be more diffuse 30 years from now, reaching areas where
vulnerability would not exist if a climate shock had been avoided. In addition, as predicted
by the SP systems approach, areas with the lowest levels of social (government transfers)
and institutional (contingency plan for natural hazards) protection are also the places most
affected by climate-related impacts on health and the economy.
We believe that our empirical simulations and spatial profiling of impacts may
be useful as an exercise to provide insights about potential consequences of climate
change on population vulnerability. Despite the large degree of uncertainty inherent to
socioeconomic and demographic scenarios, and the assumption of a constant technology
in the agriculture and health sectors, our results suggest that the multifaceted nature of
vulnerability requires adaptation strategies that include the development of long-term
policies able to tackle the incomplete population transitions. Once again, social and
institutional building capacity might be one of the most solid and politically feasible
courses of action to promote resilience and minimize adverse consequences of climate
shocks in the decades to come.
To be effective, meaningful and sustainable, policies should acknowledge differences
between the short and long term, as well as the long-lasting impacts of climate variations,
such as direct impacts to the economy. In this sense, Hultman and Bozmoski (2006) suggest
that three factors would help reduce vulnerability while facilitating adaptation: a) the
decentralization of decision-making authority, moving towards more disaggregated (local
and regional) levels; b) the expansion of protection mechanisms against environmental
degradation; and c) the transfer or diversification of the risk over time, space, and different
institutions. Some of these features were incorporated into our analysis; others were omitted
due to data limitations. In this study, we were able to include the impact of differential wage
rates on labour migration for the 66 regions being examined. However, examples of feedback
still to be explored are: i) the impact of age-selective out-migration, as measured by the
economic model, on increasing dependency ratios; and, ii) feedback between increasing
morbidity and hospitalization rates and their consequences on labour productivity and,
consequently, on the performance of economic indicators.
It is also worth noting that our model provides limited analysis on how climate impacts
health. Because we did not have projected data for all variables used in the health model, the
final impact was derived from a simple counter-factual analysis keeping all the independent
variables steady held at the base scenario. It is impossible to anticipate the direction and
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magnitude of the bias introduced by our methodological strategy since feedback effects
among the variables, health and climate included, could go in any direction.
Our simulations are a first step towards understanding and explicitly modelling
population-based heterogeneity, but further refinements in the future will allow us to
incorporate estimates at the municipality level, particularly in regions characterized
by higher internal heterogeneity in terms of vulnerability. These are the political and
administrative units that design and implement most adaptive policies, since they require
the knowledge of a conjunction of local political, social, economic, institutional, and
environmental specificities. Thus, multi-level analysis seems to be a promising venue by
which vulnerability mapping should be constructed and monitored.
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About the authors
Alisson Flávio Barbieri has a Ph.D. in City and Regional Planning, University of North Carolina
at Chapel Hill (2005). Associate Professor of Demography and Core Faculty of the Center for
Development and Regional Planning (CEDEPLAR) – Federal University of Minas Gerais.
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Gilvan Ramalho Guedes has a Ph.D. in Demography, Federal University of Minas Gerais (2010).
Associate Professor of Demography and Core Faculty of the Center for Development and
Regional Planning (CEDEPLAR). Head – Socioenvironmental Sciences – Federal University
of Minas Gerais.
Kenya V. M. S. Noronha has a Ph.D. in Economics, Federal University of Minas Gerais (2005).
Associate Professor of Economics and Core Faculty of the Center for Development and Regional
Planning (CEDEPLAR) – Federal University of Minas Gerais.
Bernardo Lanza Queiroz has a Ph.D. in Demography - University of California at Berkeley (2005).
Associate Professor of Demography and Core Faculty of the Center for Development and
Regional Planning (CEDEPLAR). Head – Actuarial Sciences – Federal University of Minas Gerais.
Edson Domingues has a Ph.D. in Economics – São Paulo University (2002). Associate Professor of
Economics and Core Faculty of the Center for Development and Regional Planning (CEDEPLAR).
Head – Department of Economics – Federal University of Minas Gerais.
José Irineu Rigotti has a Ph.D. in Demography – Federal University of Minas Gerais (1999).
Associate Professor of Demography and Core Faculty of the Center for Development and
Regional Planning (CEDEPLAR). Chair – Graduate Program in Demography – Federal University
of Minas Gerais.
Gláucia P. da Costa has a Ph.D. candidate in Economics – Federal University of Minas Gerais.
Visiting scholar at the Regional Economics Applications Laboratory (REAL) – University of
Illinois at Urbana-Champaign (EUA).
Flávia Chein has a Ph.D. in Economics – Federal University of Minas Gerais. Associate Professor
of Economics – Federal University of Juiz de Fora.
Fransisco Cortezzi is a Ph.D. candidate in Geography – Université Paris IV – Sorbonne.
Ulisses E. Confalonieri has a Ph.D. in Public Health and Epidemiology – Rural Federal University
of Rio de Janeiro. Full professor (retired) – Fluminense Federal University.
Kênia de Souza has a Ph.D. in Economics – Federal University of Minas Gerais (2015). Adjunct
Professor – Center for Development and Regional Planning (CEDEPLAR) – Federal University
of Minas Gerais.
Contact address
Alisson Flávio Barbieri
Faculdade de Ciências Econômicas – Universidade Federal de Minas Gerais
Av. Antônio Carlos, 6627 – Pampulha
30270-901 – Belo Horizonte-MG, Brazil
Resumo
Transições populacionais e mudança de temperatura em Minas Gerais, Brasil: uma
abordagem multidimensional
As mudanças climáticas exacerbarão, nas próximas décadas, a vulnerabilidade de populações,
lugares e pessoas ao redor do mundo, especialmente nos países em desenvolvimento. Este
artigo investiga cenários hipotéticos futuros de vulnerabilidade às mudanças climáticas para
os próximos 30 anos, em 66 microrregiões do Estado de Minas Gerais. Com base no Índice
de Alkire e Foster, são integradas as dimensões simuladas e projetadas de vulnerabilidade
populacional em um indicador multidimensional, o qual mostra como cenários de mudanças
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de temperatura afetariam a vulnerabilidade relativa de cada região no futuro. Os resultados
sugerem que as dimensões econômica e de saúde são as maiores contribuintes para o aumento
da vulnerabilidade relacionada às alterações na temperatura média, com as regiões mais pobres
e voltadas ao agronegócio constituindo as mais afetadas em cenários futuros.
Palavras-chave: Mudança climática. Vulnerabilidade populacional. Transições populacionais.
Índice multidimensional. Minas Gerais.
Resumen
Transiciones poblacionales y cambio de temperatura en Minas Gerais, Brasil: un abordaje
multidimensional
En las próximas décadas, el cambio climático exacerbará la vulnerabilidad de las poblaciones
alrededor del mundo, especialmente en los países en desarrollo. En este artículo se investigan
escenarios hipotéticos futuros de vulnerabilidad frente al cambio climático para los próximos
30 años en 66 microrregiones del estado de Minas Gerais. Sobre la base del Índice de Alkire y
Foster, se integran en un indicador multidimensional las dimensiones simuladas y proyectadas
de vulnerabilidad poblacional, mostrando cómo los escenarios de cambios de temperatura
afectarían la vulnerabilidad relativa de cada región en el futuro. Los resultados sugieren
que las dimensiones económica y de salud son las que más contribuyen en el aumento de la
vulnerabilidad relacionada con las alteraciones de la temperatura media, y que las regiones más
pobres y volcadas al agronegocio constituirán las más afectadas en estos escenarios futuros.
Palabras clave: Cambio climático. Vulnerabilidad poblacional. Transiciones poblacionales. Índice
multidimensional. Minas Gerais.
Received for publication in 31/07/2015
Recommended for publication in 18/10/2015
Accepted for publication in 12/11/2015
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