This research contributes to an understanding of the relationship between climate change, economic impacts and migration. We model the long-term relationship (up to 45 years of projection) between demographic dynamics—particularly migration—driven by changes in the performance of the economy due to climate changes in the Northeast region of Brazil. The region is of particular relevance to the study of climate change impacts given its large human population (28% of Brazil’s population) and high levels of impoverishment, having an extensive semi-dry area which will be severely impacted by growing temperatures. Ultimately, the integrated model generates state- and municipal-level migration scenarios based on climate change impacts on the primary economic sectors and their articulations with other sectors. Results suggest that the predicted climate changes will impact severely the agriculture sector in the region, acting as a potential migration push factor to other regions in the country. Finally, we discuss how the increased vulnerability of some groups, particularly migrants, can be factored into Brazilian public policy and planning.
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The A2 and B2 regional scenarios were the two available from INPE at the time of this study.
See also McGranahan et al. (2007) for a study of the impact of sea level rise on migration from urban areas.
Adamo (2008) reviews estimates of potential population displacements due to climate change impacts (particularly sea level rise) focusing on less comprehensive methods and found that several estimates usually reflect populations at risk as surrogates for population displacements. As a result, these studies suggest a great variation in estimates, depending on the methods and data used.
See further discussion on Füssel (2007).
TFR is the average number of sons and daughters a woman in a given population will have during her reproductive ages (usually defined as being 15–49 years of age). The underlying assumption is that the TFR refers to a woman who completes the reproductive age, or in other words, who survives from age 15 to 49.
Recent trends of higher longevity and lower fertility will lead to a process of population ageing in the Northeast: the population 65 years of age or more in 2050 will be 19%, against 6% in 2000; and the proportion of the population below 15 years of age will decrease from 33% in 2000 to 16% in 2050 (CEDEPLAR 2007).
See further details on the projection of demographic components in CEDEPLAR (2007).
The Apportionment Method consists in projecting a population of a smaller area (e.g. municipalities) and considering its relative contribution to the growth of a larger area (e.g. states), while the Method of Cohort Relations consists in following the age and sex structure of the population in the smaller area and assuring its consistency with the structure of the larger area.
The model closure represents considerations about the operating hypothesis of the model, associated with the hypothetical time horizon of the simulations, related to the amount of time needed for the change in endogenous variables towards a new equilibrium. We assume that these changes take 4 years, so we can relate the simulations with the macroeconomic scenario and land shocks on a 4-year basis. Take, for instance, the adjustment in the markets for primary production factors, labor and capital. The closures can be for the short-run and the long-run. The main difference between the two closures relates to the adjustment in the capital stock and in the mobility of labor. In the short run, the capital stock is given, whereas in the long run capital and labor can move between sectors and between regions. On CGE model closures, see Dixon et al. (1982).
For a detailed discussion of agricultural scenarios by EMBRAPA, see Pinto and Assad (2008).
This result is robust and significant, and consistent with other studies for Brazil using different data and methodologies (see, e.g. Lima 1995).
The concept of ‘Net Migration’ in this article and its estimation in Eqs. 1–4 is not equivalent to the usual concept in the literature, as the difference between in-migrants and out-migrants in between two points in time. In the same way, the term ‘Net Migration’ in Eqs. 5 and 6 does not reflect a typical measure of ‘Net Migration Rate’ in the literature given the peculiarity of the numerator (the NM) in this article.
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The authors thank the United Kingdom Embassy in Brazil for supporting the execution of this research project, which was funded by the Global Opportunities Fund, the United Kingdom Ministry of Foreign Affairs and had the institutional support of CEDEPLAR/UFMG and FIOCRUZ. Thanks also are due to INPE for the data on climate scenarios and EMBRAPA for providing the agricultural scenarios. The authors, however, take full responsibility for the results and interpretation described here.
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Barbieri, A.F., Domingues, E., Queiroz, B.L. et al. Climate change and population migration in Brazil’s Northeast: scenarios for 2025–2050. Popul Environ 31, 344–370 (2010). https://doi.org/10.1007/s11111-010-0105-1