The impacts of environmental change on UK internal migration

Fielding, Anthony

doi:10.1016/j.gloenvcha.2011.08.003

Cited by 21 publications

(16 citation statements)

References 35 publications

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“…The destination choice itself is dependent on the reason that drives the individual to migrate (Findlay 2011). One might conclude that short or long-run hazard vulnerability would be major considerations, but Black et al (2011) andFielding (2011) emphasize the primacy of socioeconomic over environmental variables in current migration decisions, though on the basis of only anecdotal information.…”

Section: Conceptual Underpinningsmentioning

confidence: 99%

The Effect of Disasters on Migration Destinations: Evidence from Hurricane Katrina

et al. 2018

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While post-disaster migration can move vulnerable populations from dangerous regions to relatively safe ones, little is known about decisions that migrants use to select new homes. We develop an econometric model of migrant flows to examine the characteristics of the destinations that attracted migrants leaving the New Orleans area following Hurricane Katrina in 2005 relative to migration behaviors in other years. We find an increased flow of migrants to large, nearby counties with a mixed effect of economic variables on migration. We find that counties that had experienced fewer disasters received a greater proportion of total migrants in 2005, but there was an overall increase in migration flow to disaster-prone regions as well.

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Section: Conceptual Underpinningsmentioning

confidence: 99%

The Effect of Disasters on Migration Destinations: Evidence from Hurricane Katrina

et al. 2018

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“…Migration from rural to urban areas and from less developed to developed areas is an inherent part of the economic development process (Parrado and Kandel, ; Villarreal and Hamilton, ; Liu and Shen, ; Lomax et al ., ; Brauw et al ., ). This phenomenon is defined as ‘internal migration’ internationally (Fielding, ; Stillwell and Dennett, ), with many studies being carried out since the 1940s (e.g. Heberle's theory of push–pull population migration).…”

Section: Introductionmentioning

confidence: 99%

The spatio‐temporal change of China's net floating population at county scale from 2000 to 2010

Qiao

Liu

et al. 2016

Asia Pacific Viewpoint

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China's total floating population exceeded 245 million at the end of 2013 and is the largest mobile population in the world. This paper explores the spatial distribution pattern and change characteristics of the net floating population at county scale from 2000 to 2010 using census data. The results show the following: (1) the net floating population increased by 120 million over 10 years, and the mobility of the population had enhanced in this period; (2) the floating population migrated to the three economic zones and provincial capital cities, while the traditional agricultural regions with dense populations experienced continuous outflow of population (which altered spatial agglomeration patterns); (3) eastern China has a high level of natural environment suitability and was a major destination area, but more pressures were placed on the carrying capacity of big cities; and (4) western China, with a relatively weak carrying capacity for resources and environment, was a net outflow region in 2010 – although five provinces had small‐scale net inflow because of western‐style development strategies. This study is offered as a contribution to understanding the features of China's floating population and will be useful for those shaping policies that guide population migration.

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“…Findlay also highlights the policy challenges posed by immobility. Seto (2011), Parnell and Walawege (2011), de Haas (2011) and Fielding (2011 review the drivers of migration in specific geographic circumstances, focusing on mega-delta cities in Asia and Africa, sub-Saharan cities, the Mediterranean and internal migration within the UK respectively. In each case, patterns and characteristics of migration are determined by specific interactions between different drivers, but with some important differences.…”

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confidence: 99%