# 6 Conclusions

## 6.1 Main findings

The purpose of this report has been to shed light on two main research questions. First, to what extent are rural households in developing countries, especially the most vulnerable, reliant on environmental extraction from natural ecosystems for their livelihoods (RQ1)? And second, what effects does climate variability have on livelihood vulnerability of the rural poor (RQ2)? In other words, RQ1 gives us a static snapshot of livelihood dependency from our large pantropical PEN sample in the years 2005-10, whereas RQ2 looks more into the dynamics between a variable climate and livelihoods.

As for RQ1, there are important arguments for taking the environment seriously as an income provider. A previous meta-study on environmental incomes (Vedeld et al. 2004) inspired our first hypothesis, namely that environmental extractive incomes would make up at least one-fifth of total household income. In fact, this extraction of wild resources (from natural forests, bushlands, fallows, etc.) provides on average as much income (27% -- of which forests 20%, non-forest 7%) as crops (26%) across our smallholder sample. While there are important site-specific differences, e.g. with respect to forest availability and resource quality, all World Bank Group (WBG) regions but South Asia exceed the 20% environmental income threshold. Among our universe of smallholders, the poorest/ most vulnerable households rely relatively more on environmental incomes, though in absolute terms they generate (and consume) less of them than the better-off households. This also has implications for our subsequent climate-related analysis (RQ2): any role played by the environmental sector carries a strong weight on household income.

If, in a bit simplified way, we were to compare the current situation to a hypothetical world without extractive incomes, then one out of seven households would be falling below the global 1.25 USD poverty line through the loss of their environmental incomes. Thanks to their fairly equal distribution, environmental incomes also contribute to improved equality in local economies. Again, if hypothetically there was no access to extractive resources, the Gini coefficient would be higher: depending on the region of analysis, between 4% and 7% of total income would have to be redistributed to restore baseline inequality levels.

As for RQ2 -- perhaps somewhat surprisingly, given the large income weight and the pattern found in numerous case studies -- environmental extraction was only for one out of 12 households (8%) the primary safety net in response to a crop, livestock, or other asset-related shock that they had stated to have been hit by over the last 12 months. Typically other responses, such as reduced consumption, running down assets, drawing on local social capital, or finding (additional) wage employment, were more important responses. Our second hypothesis of this study, regarding the alleged importance of the environment as a household safety net, did thus find little empirical support. While the contribution to current household incomes was weighty (Hypothesis 1), the importance as emergency income was less crucial (Hypothesis 2). The environmental safety-net share was higher though for households who are particularly capital-poor (cropland, education), live in Asia, and are already specialized more in forest extraction for their regular incomes.

This leads us to a more explicit scrutiny of the climate data for our sites, and their influence on household incomes (Hypothesis 3). In our analysis of climate impacts on livelihoods, we thus distinguished between 35