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Do Deltas Remain Attractive? Testing the Migration to Coast Hypothesis Abu, Mumuni and Codjoe, N.A. Samuel Regional Institute for Population Studies, University of Ghana Corresponding author: Mumuni Abu. P. O. Box LG 96, University of Ghana,


  1. Do Deltas Remain Attractive? Testing the Migration to Coast Hypothesis Abu, Mumuni and Codjoe, N.A. Samuel Regional Institute for Population Studies, University of Ghana Corresponding author: Mumuni Abu. P. O. Box LG 96, University of Ghana, Legon. mabu@ug.edu.gh Abstract Earlier studies have found net movement into coastal regions globally - net loss of populations in drylands and mountain areas, and net in-migration to coastal areas. This is because while deltas are at risk from environmental degradation, they tend to have large urban areas which have such economic primacy that they are always protected and act as sources for net in- migration. This paper examines whether delta regions continue to be a magnet for populations and the drift to the coast is continuing. We hypothesise that urban Districts have less or zero net out-migration, and therefore more net in-migration compared to rural districts. We do so by examining a range of deltas, including the most densely populated large deltas in the world (Ganges-Brahmputra-Meghna – Bangladesh and India) along with smaller deltas (Mahanadi and Volta) and estimating net migration from the most recent census interval. Keywords: Coastal regions, net-migration, deltas, vulnerability, climate change Introduction Coastal areas continue to host significant concentrations of people and livelihoods in spite of their high exposure to environmental hazards (McGranahan, Balk, & Anderson, 2007; Neumann et al., 2015). Earlier studies have found net movement into coastal regions globally - net loss of populations in drylands and mountain areas, and net in-migration to coastal areas ((Nicholls & Cazenave, 2010; Seto, 2011). This is because while coastal areas are at risk from

  2. environmental degradation, they tend to have large urban areas which have such economic primacy that they are always protected and act as sources for net in-migration (Seto, 2011). We hypothesise, in this paper, that coastal regions will not necessarily continue to be a magnet for populations and that the drift to the coast may not continue. We do so by examining a range of deltas, including the most densely populated large deltas in the world (Ganges-Brahmaputra- Meghna – Bangladesh and India) along with smaller deltas (Mahanadi, India and Volta, Ghana). We hold this contrary position due to the following. First, the literature on migration into deltas in Africa and Asia reveal deltas are receiving areas because of the presence of “primate cities” which gives them greater economic advantage over other areas (Nicholls & Cazenave, 2010; Seto, 2011). However, not all deltas contain major or capital cities, as shown in the study by Seto (2011), and so may not necessarily act as points of attraction for migrants. On the contrary, there may be major economic settlements which, due to their proximity to the deltas, may attract migrants from the delta. Apart from the contrary hypothesis to the predominant position of earlier studies, we make a methodological contribution by using population census data at the local level which is a departure from previous studies. These studies used population estimates which were not so accurate because they used low-resolution data which makes it impossible to accurately estimate population density and mobility at the level of local administrative units (McGranahan et al., 2007; Small & Nicholls, 2003). They were however, an improvement upon earlier estimates of coastal population dynamics which were based on figures that were “unsubstantiated” but “widely repeated statements” (Small & Nicholls, 2003, p. 584). In this paper, we use data from the two most recent population and housing censuses of Bangladesh and India (2001 & 2011) and Ghana (2000 & 2010), to estimate net migration for the GBM, Mahanadi and Volta deltas respectively. The paper commences with a synthesis on migration

  3. in deltas and a demographic analysis of the deltas with reference to their respective countries to set the context within which migration occurs. Climate Change and Migration in deltas Global climate change has dire implications for delta populations which are considered highly vulnerable to environmental dynamics as well as socioeconomic challenges, particularly in developing contexts (Nicholls & Cazenave, 2010). In the most extreme case scenario, large- scale displacement of people living in deltas is expected. Deltaic regions are among the most vulnerable types of coastal environment due to the coincidence of vulnerable physical characteristics (i.e. low elevation and high flood probability, significant land erosion and gain, dependence on fluvial inputs of water and sediment, high sensitivity to small changes such as climate) and socio-economic characteristics (i.e. high population density, high prevalence of poverty, gender inequalities, low levels of socio-economic development and lack of connectivity with the main market places). Climate change impacts could reinforce many of the baseline stresses that already pose a serious impediment to development in deltas (Agrawala et al., 2003). These include heavier and more erratic rainfall leading to increased flooding and river bank erosion; warmer average temperatures; and changing intensity of tropical cyclones with higher wind speeds and storm surges (MoEF, 2008). The anticipated sea level rise in the bay of Bengal, for instance, is expected to submerge low lying land, increasing the penetration of storm surges and increasing saline intrusion (Karim and Mimura, 2008; Khan et al., 2011). Also, extensive human activities interfere with the integrity of deltas’ naturally d ynamic water and sedimentary systems, thus increasing the risk of relative sea-level rise, inundation and erosion (Church et al., 2013; Tessler et al., 2015). These anthropogenic geophysical modifications interact with socioeconomic characteristics of populations to determine the overall risks in deltas (Tessler et al., 2015). Deltas have some of the highest population

  4. densities in the world with about 500 million, often poor residents. The adaptive strategies available to delta residents (e.g., disaster risk reduction by building shelters, or land and water use management) may also exacerbate gender inequalities, and may not be adequate to cope with pervasive, systemic, or sudden changes associated with climate change. Hence, large movements of people are often projected from deltas under climate change. A simple projection of existing trends suggests that more than 8 million people could be displaced across deltas globally by 2050, with the Ganges-Brahmaputra-Meghna (GBM), Nile and Mekong deltas having the largest estimated displacement (Ericson et al., 2006). With additional climate- induced rises in sea level, tens of millions of men and women could be displaced during the 21 st Century in the GBM and Nile deltas alone (Milliman et al., 1989; Woodroffe et al., 2006). Ericson et al. (2006) studied 12 deltas in Africa and Asia and estimated that 5.4 million people might be displaced by 2050 based on observed trends of sea-level rise and subsidence out of a global total of 8.7 million displaced people. Globally, coastal deltas are popular destinations for migrants due to the immense economic and social opportunities that are available in these areas. Historically, deltas are known to have very productive ecosystems which have attracted human settlement and agricultural activities. This has overtime transformed the global major deltas from agrarian economies into industrial and service-driven cities which continue to attract migrants (Small and Nicholls, 2003; Okonjo- Iweala and Osafo-Kwaako, 2007). Migration into deltas is driven by spatial inequalities between receiving delta areas and their sending areas. It is expected that with continuous urbanisation and a built up momentum, primate cities in these deltas will continue to attract in- migrants (De Souza et al., 2015; Seto, 2011).

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