for poorer households. Having a relatively higher income per capita to begin with, countries in East Asia & Pacific as well as in Latin America & Caribbean are therefore less vulnerable to increase in food prices.

Table 3: Impacts of individual channel in each region under the high climate impact scenarios

||Agri<br>revenues|Labor<br>produc-<br>tivity|Food<br>prices|Disasters|Health|Dominant<br>channel|
|---|---|---|---|---|---|---|
|EAP|-0.05|0.30|1.67|1.32|5.65|Health|
|LAC|-0.02|0.07|0.23|1.38|4.73|Health|
|SAS|-0.30|2.60|17.90|12.31|17.85|Food prices|
|SSA|-1.73|4.48|35.79|2.75|10.12|Food prices|

Additional poverty headcount in richer regions, in this case East Asia & Pacific and Latin America & Caribbean, is more influenced by the increasing prevalence of diseases. Households in these regions are rich enough to absorb shocks from increase in food prices, which affect everybody in a relatively similar way. But they are still vulnerable to health shocks (and disasters) that tend to affect a small and concentrated fraction of the population but with impacts that can be massive for a given household.

In addition, it is also important to note that some regions see one channel dominating all others, while others are affected by multiple issues equally. In Sub-Saharan Africa, our results suggest that the absolute priority should be food security, and to a lesser extent health. In South Asia, on the other hand, the impact of food prices, disasters, and health shocks are almost equal, suggesting the need for a multi-prong approach.

# **3.4 Socioeconomic development and climate vulnerability**

We now look at how different assumptions regarding socioeconomic development (i.e., the baselines) affect climate change vulnerability in terms of people pushed into extreme poverty.

To do so, we select — in each country — two sets of scenarios from the 1000 baseline scenarios (i.e., scenarios of socioeconomic and demographic changes, but without climate change). The first set of scenarios is termed the _pessimistic_ baselines. Here, we select the 100 baseline scenarios with the highest poverty rates. These baseline scenarios have global poverty rates between 14% and 15.5%. The second set of scenarios, the _optimistic_ baselines, consists of the 100 scenarios with the lowest poverty rates. The _optimistic_ baseline has global poverty rates in the 2.8% and 3.8% range. The _optimistic_ baseline can be seen as a future with rapid and inclusive socioeconomic development, which can be achieved among others through pro-poor policies (they are aligned with the World Bank objective of reducing extreme poverty below 3% of the global population by 2030).

Climate change vulnerability in terms of people falling into extreme poverty is recalculated for the two sets of baselines (see Figure 5). In the worst-case combination — with pessimistic baselines and high climate change impacts — climate change impacts make more than 130 million people fall in poverty by 2030.

Within the high climate change impact scenarios, rapid and inclusive development (i.e., the optimistic baseline) can reduce climate change vulnerability by almost 50% (from 131.5 million people on average to 67.7 million people on average). Although smaller, a noticeable effect of rapid and inclusive development is still evident under the low climate change scenarios, with almost 25% reduction in vulnerability (from 42 million to 32.2 million people). These results confirm previous findings which show that rapid development is an effective strategy to reduce climate vulnerability and is a strong determinant of a society’s adaptive capacity (Hallegatte & Rozenberg, 2017; Leichenko & Silva, 2014; Eakin, Lemos, & Nelson, 2014; Barnes et al., 2020).

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