Framing the frame: Cause and effect in climate-related migration

被引:8
|
作者
Cottier, Fabien [1 ]
Flahaux, Marie -Laurence [2 ]
Ribot, Jesse [3 ]
Seager, Richard [4 ]
Ssekajja, Godfreyb [5 ]
机构
[1] Columbia Univ, Lamont Doherty Earth Observ, New York, NY 10027 USA
[2] Aix Marseille Univ, Inst Rech Dev, Lab Populat Environm Dev, Marseille, France
[3] Amer Univ, Sch Int Serv, Washington, DC USA
[4] Columbia Univ, Lamont Doherty Earth Inst, New York, NY USA
[5] Indiana Univ Bloomington, ONeill Sch Publ & Environm Affairs, Bloomington, IN USA
关键词
Climate change; Migration; Causality; Vulnerability; Risk; Disaster; Crisis; Normativity; VULNERABILITY; REGIME;
D O I
10.1016/j.worlddev.2022.106016
中图分类号
F0 [经济学]; F1 [世界各国经济概况、经济史、经济地理]; C [社会科学总论];
学科分类号
0201 ; 020105 ; 03 ; 0303 ;
摘要
Analytic frames shape the causality we identify in climate-related crises. Here we contrast examples from two primary categories of analytic frames, which we label 'Environmental-Drivers' and 'Social-Causal' to draw attention to the implications of each frame with regards to causality. We explore each frame via cases of 'climate-related' migration. The article illustrates that each analytic frame carries implicit causal assumptions that prefigure causal findings. Analysis can be done within either category of frame; yet the findings, however rigorous, remain contingent on the chosen frame and its assumptions. An Environmental-Drivers model will hold the social context as fixed and quantify the incremental damages of a measure of climate change, while a Social-Causal model will show how damages are generated by social vulnerability and its antecedents. The latter may show that a given climate event may have no effect on a secure population but lead to massive damages among the vulnerable - and thus that the damage cannot be solely attributed to the climate event. Frame choice is normative as frames prefigure causes, potential solutions, the locus of responsibility, and suggested policy interventions. The article poses the question of how a productive dialogue between these two frames can be generated and recom-mends that causal predisposition of models be made explicit so that the findings they indicate can be understood as partial to the choice of models. As causal findings imply policy options, making the assumptions explicit while exploring the directions that other models would point in, will help broaden the range of possible policy responses.(c) 2022 Elsevier Ltd. All rights reserved.
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页数:9
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