regions are likely to receive less and the central and eastern regions more precipitation during the 21st century (Niang et al. 2014).

The paper focuses on income derived from natural environments, which are likely to be exposed to higher stress. A key uncertainty concerns the strength of direct CO2 effects on photosynthesis and transpiration (Scholes et al. 2014, 307). In general, “tropical species, which experienced low inter-and intra-annual climate variability, have evolved within narrow thermal limits, and are already near their upper thermal limits” (page 301). The IPCC AR5 further notes that “to our knowledge nothing has been published for hunting or collection of wild foods other than capture fisheries” (Porter et al. 2014, 494).

It can be argued that for the short term, production based on harvesting of _stocks_ of biomass, such as timber and fuelwood, are less sensitive to changes in climate parameters compared to production which is based on annual increments ( _flows_ ) in resource based systems such as harvesting of wild food and crop production (e.g., Nøstbakken and Conrad 2007).<sup>5</sup> About 60% of the forest income among the households in our sample are woodfuel (fuelwood and charcoal) or structural and fiber products (Angelsen et al. 2014).

In addition to the direct production (yield) effects of climate change, prices are also likely to change through general equilibrium effects. Rural households that are net food buyers will lose from higher food prices. However, there are various channels through which rural households could benefit, and these can partly offset or even outweigh the income losses from declining yields and the expenditure increase from higher agricultural commodity prices. Net-producers should benefit through higher profits, agricultural laborers could benefit from increasing rural wages, and farmers can make long-term adjustments in production to benefit from higher prices.

In terms of implications for livelihood and poverty, the IPCC AR5 concludes that “climate change will exacerbate multidimensional poverty in most developing countries” (Olsson et al. 2014, 797). While some of the overall trends, such as higher yields for some crops in some areas, might be positive, higher climate variability will increase the frequency and severity of shocks that poor households face. Households may be able to cope with a shock during one year, for example, by selling liquid assets, but multi-year shocks will deplete their buffers. The livelihood impact also depend on their capacity to adapt, e.g., by modifying the cropping seasons or planting crops or crop varieties more suitable to the changed rainfall and temperature patterns.

# **2.3 Income fluctuations and poverty categories**

The activities rural households in developing countries engage in depends on the assets they possess and the relative returns to these assets. The returns to and availability of assets vary from year to year, causing total household income to fluctuate over time. Crop yield responds to weather conditions, and market prices change from year to year. Some households are exposed to idiosyncratic (household specific) shocks such as illness, or covariate (common) shocks such as yield-reducing drought or an economic recession that reduce wage income. In a study from Ethiopia, Dercon and Krishnan (2000) found that one third of the households identified as poor in the first year in a two-year panel data set were different from the households identified as poor the second year.

While _poverty dynamics_ is widely recognized and has become a key concept in the poverty literature (e.g., Baulch and Hoddinott 2000), cross-sectional studies of poverty-environment

> 5 See also discussion of this point in Noack et al. (2015).

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