bound one retained for our central scenario that relies on Hallegatte et al. (2013) and the alternative higher bound one that uses the findings from Englhardt et al. (2019).

In total, as shown in Table 3, aggregate land values increase in both the CCP and OCP settings. For our central scenario using lower bound damage estimates, aggregate land values increase by 0.10% and 0.79% respectively. With our central baseline calibration parameters,<sup>12</sup> this translates into net land value creations of US $0.38 and $2.94 billion respectively. Using higher bound damage estimates, the increases in land values are much higher, +0.48% and +4.57% in the CCP and OCP settings respectively, amounting to US $1.93 and $16.95 billion net land value creation from resilience investments. The large range of these figures shows the importance of the choice of modeling closed or open cities but also of the choice of the flood vulnerability curves. The closed city assumption is more realistic in the short term, but some degree of in-migration due to reduced flood risks, consistent with the open city assumption, could happen in the longer term. In comparison, the flood mitigation works are estimated at US $338 million.<sup>13</sup> This means that the lower bound land value appreciation estimation alone would nearly be able to cover the cost of the resilience investments.

The flood protection investments not only increase aggregate land values, opening up the possibility for land value capture, but are unsurprisingly also welfare improving. In the CCP setting, household utility increases by 0.08% and 0.48% for lower and higher bound flood damage estimates, whereas in the OCP setting the investments trigger an in-migration of approximately 28,000 and 165,000 households (or a 0.68% and 4.01% population increase).

||**CC**<br>|**P**<br>|**OC**<br>|**P**<br>|
|---|---|---|---|---|
||**Lower bound**<br>**damage estimates**<br>**from Hallegatte et**<br>**al.(2013)**|**Higher bound**<br>**damage estimates**<br>**from Englhardt et**<br>**al.(2019)**|**Lower bound**<br>**damage estimates**<br>**from Hallegatte et**<br>**al.(2013)**|**Higher bound**<br>**damage estimates**<br>**from Englhardt et**<br>**al.(2019)**|
|Aggregate land<br>value change|$379 million|$1,928.7 million|$2,935.8 million|$16,949.3 million|
|Aggregate land<br>value variation|+0.10%|+0.52%|+0.79%|+4.57%|
|Household<br>utilityvariation|+0.08%|+0.48%|0%|0%|
|Population<br>change|+/- 0 households|+/- 0 households|+27,991<br>households|+164,742<br>households|
|Population<br>variation|0%|0%|+0.68%|+4.01%|

12 A large sensitivity analysis, using techniques adopted for Decision Making under Uncertainty, is performed on most calibrated parameters of the model to explore the robustness of our results as well as which parameters drive our results. This analysis is documented in section 6. It is shown that parameter , which intervenes in the construction cost function and the real interest rate have strong impacts on our results and that land value creation ranges from approximately US$ 6 thousand to US$ 4.5 billion depending on their values (excluding 3 of 3,000 runs bb that return negative values). ii 13 see the project appraisal document of the project: https://documents.banquemondiale.org/fr/publication/documentsreports/documentdetail/287961468328119648/argentina-flood-risk-management-support-project-for-theautonomous-city-of-buenos-aires.

19