<!-- Start of picture text --> 200 Climate change<br>impacts<br>Low<br>150 High<br>131.5<br>100<br>67.7<br>50 42.0<br>32.2<br>0<br>Pessimistic Optimistic<br> people living in extreme poverty<br>due to climate change (million, 2030)<br>Additional<br><!-- End of picture text --> Figure 5: Effect of socioeconomic development to the number of people falling into extreme poverty in 2030

# **4 Discussion and conclusion**

Building upon the methodology developed in (Hallegatte et al., 2015; Rozenberg & Hallegatte, 2015; Hallegatte & Rozenberg, 2017), this study uses household survey data from the 2020 Global Monitoring Database to update climate-driven poverty projections for 2030.

Several similarities in results compared to projections from a previous study (Hallegatte & Rozenberg, 2017) can be observed. For example, we find that health impacts are the dominant channel in most scenarios, pushing the largest number of people into extreme poverty. Impacts through increased food prices are the most uncertain ones (i.e., widest range of distribution), and could have an effect on poverty even bigger than health impacts in the most pessimistic cases.

Under the high climate change impact scenarios, we find that climate-driven increase in prevalence of diseases on average leads to 44 million people falling into extreme poverty. This projection is slightly higher than the previous projection (around 30 million people), although it is still within a similar order of magnitude.

This analysis does more than updating previous results and provides further results. For instance, we explore the dominant climate change impact channel in each region. Health impacts are particularly important in relatively richer regions (East Asia & Pacific and Latin America & Caribbean), while impacts of food prices dominate in poorer regions (South Asia and Sub-Saharan Africa). This is because the impacts through health shocks (and disasters) are concentrated on a fraction of the population and can therefore push people into poverty even if their income would be relatively high without climate change impacts. Impacts on poverty through food prices, in contrast, affect mostly people near poverty who spend a large share of their income on food. In the richest regions, even poor people are rich enough to manage the expected rise in food prices without falling into extreme poverty (which does not mean that they are not affected: increased food prices would reduce their real consumption).

Several differences compared to the previous study (Hallegatte & Rozenberg, 2017) are also observed. First, this study projects a higher climate vulnerability, in terms of increase in poverty headcount, compared with previous work. For example, under the worst case scenario (high climate change impact and pessimistic baseline), this study projects that on average there will be 131.5 million people pushed into extreme poverty whereas the previous projection was 122 million. Second, rapid and inclusive development has a slightly smaller effect, although still substantial, on reduction of climate change impacts. In the previous study, the optimistic baseline reduces climate change impacts by 86%, while this study shows that the optimistic baseline reduces the impacts by only 50%.

This discrepancy can be attributed to a different selection of the ’optimistic’ and ’pessimistic’ baseline (termed prosperity and poverty scenarios in the previous study). While the previous study uses only one representative scenario for the optimistic baseline and one for the pessimistic baseline, this study uses a set of scenarios for characterizing the two baselines. Despite the differences, the main message is still 9