Publication: Environmental Reliance, Climate Exposure, and Vulnerability: A Cross-Section Analysis of Structural and Stochastic Poverty
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Date
2015-11
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2015-11
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Abstract
This paper analyzes environmental reliance, poverty, and climate vulnerability among more than 7,300 households in forest adjacent communities in 24 developing countries. The data are from the detailed, quarterly income recording done by the Poverty Environment Network project. Observed income is combined with predicted income (based on householdsā assets and other characteristics) to create four categories of households: income and asset poor (structurally poor), income rich and asset poor (stochastically non-poor), income poor and asset rich (stochastically poor), and income and asset rich (structurally non-poor). The income and asset poor generate 29 percent of their income from environmental resources, more than the other three categories. The income poor are more exposed to extreme and variable climate conditions. They tend to live in dryer (and hotter) villages in the dry forest zones, in wetter villages in the wet zones, and experience larger rainfall fluctuations. Among the self-reported income-generating responses to income shocks, extracting more environmental resources ranks second to seeking wage labor. Given high reliance on forest and other environmental resources, a concerning finding is that, in the Africa subsample (dominated by dry forests), the rate of forest loss is more than four times higher for the income asset poor compared with the income asset rich. Special attention should be given to the poorest households in dry areas, predominantly in Africa. They are (already) exposed to more extreme climate conditions, they suffer the highest forest loss, and the forest benefits are at risk in global warming scenarios.
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āAngelsen, Arild; Dokken, Therese. 2015. Environmental Reliance, Climate Exposure, and Vulnerability: A Cross-Section Analysis of Structural and Stochastic Poverty. Policy Research Working Paper;No. 7474. Ā© World Bank. http://hdl.handle.net/10986/23444 License: CC BY 3.0 IGO.ā
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