Côte d'Ivoire

Socioeconomic context

The country sees a rapid demographic growth with 47.8% of the population being under the age of 18. Roughly 1 in every 3 people (34.9%) live in multidimensional poverty, while 1 in 5 (20.9%) live on less than USD 3 a day.

Geographic distribution of hotspots for key hazards across the country

The main hazards that impact Cote d'Ivoire are floods and earthquakes.

Flood risk is most concentrated in the southern and coastal parts of the country, especially around Abidjan, where population, infrastructure, and economic activity are highly concentrated. Earthquake risk is lower but could have major impacts in the southern urban corridor because of the density of buildings, power assets, and telecommunications infrastructure. Landslide risk is more localized, mainly in the western and mountainous areas.

Source: UNDRR, 2026 using UNEP Grid MapX

Note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.

Average Annual Losses by hazards

Floods dominate disaster risk, accounting for over 90% of total Annual Average Losses (AAL), while earthquakes contribute a smaller but still significant share ( around 9%) and landslides contribute only a very small share of total direct losses (around 0.1%) at the national level.

Note: CDRI/GIRI AAL estimates cover modelled direct damages to buildings and infrastructure for selected hazards only. They do not yet provide comparable AAL estimates for all relevant hazards, including agricultural drought, heatwaves and wildfires. Figures should therefore be interpreted within the scope of the CDRI/GIRI methodology and not directly compared with estimates from other sources. UNDRR’s Risk and Resilience Metrics Facility aims to progressively develop methodologies to integrate these additional hazards into future comparable risk metrics.

Direct average annual losses (AAL) to infrastructure. (Costliest hazard)

Buildings account for the largest estimated losses from floods, at US$36.9 million per year, followed by power infrastructure at US$12.3 million, education assets at US$8.1 million, and telecommunications at US$6.0 million. Losses in the remaining sectors are lower, including oil and gas at US$0.68 million, roads and railways at US$0.57 million, water and wastewater at US$0.16 million, ports and airports at US$0.09 million, and health infrastructure at US$0.01 million.

Note: Information presented for the CDRI/GIRI top hazard's AAL estimation.

Most exposed infrastructure by sector (AAL) (Second most costly hazard)

Buildings account for the largest estimated losses from earthquakes, at US$2.67 million per year, followed by power infrastructure at US$1.41 million, telecommunications at US$0.84 million, and education assets at US$0.59 million. Losses in the remaining sectors are lower, including roads and railways at US$0.33 million, water and wastewater at US$0.26 million, oil and gas at US$0.09 million, ports and airports at US$0.02 million, and health infrastructure at approximately US$0.00 million.

Note: Information presented for the CDRI/GIRI second-top hazard's AAL estimation.

Expected economic losses in 1-in-100 year event (PML)

Based on CDRI/GIRI estimates, flooding represents Côte d’Ivoire’s largest modelled 1-in-100-year loss, reaching approximately US$443.4 million, followed by earthquakes at around US$79.5 million and landslides at less than US$1 million.

Direct Average Annual Losses to public infrastructure

Flood-related losses are higher than earthquake-related losses in both sectors. Education infrastructure accounts for the largest losses, with estimated annual losses of around US$5.77 million from floods and US$0.42 million from earthquakes. Estimated losses to health infrastructure are much lower, at around US$1,251 from floods and US$90 from earthquakes.

Direct Probable Maximum Losses to public infrastructure

Floods produce the largest sector-level PML estimates, particularly for power (US$86.3 million) and telecommunications (US$43.8 million). These sectors also record the highest earthquake PMLs, at US$18.1 million and US$10.7 million, respectively. Estimated losses are lower across the remaining sectors.

Direct Average Annual Losses to housing

Losses are highest for the low-middle income class, at around US$12.23 million per year. The middle income class records estimated annual losses of around US$2.46 million, while the low income class records around US$1.11 million. The high income class shows no estimated losses in this dataset.

Probable Maximum Losses of real GDP now and 2050

The results show that direct damages to physical assets have relatively limited impacts on Côte d'Ivoire’s economy. For a 1-in-100 year event, the model results present a projected decline of between -0.1 and -0.3% for floods and between -0.005 and -0.055 for earthquakes, coming from mainly privately owned homes and commercial and industrial buildings.

Chance of a disaster exceeding public financing capacity (Fiscal gap)

The PML from 1-in-100 year event flood would raise the debt ratio marginally from 56.6% of GDP to between 56.7 and 56.9%. The debt is projected to decline again after the shock because the estimated increase is small and economic growth continues to support debt reduction. If the government maintains its fiscal consolidation plans, debt could fall faster than shown in the model.

Sovereign debt due to an extreme event

The modelled 1-in-100-year loss is equivalent to around 0.53% of 2025 GDP. While this suggests that the loss is relatively limited at the aggregate macroeconomic level, it could still be fiscally relevant. The loss is equivalent to around 3.1% of annual government revenue and 2.6% of annual public expenditure. In a context where the government is projected to run a fiscal deficit of around 3.0% of GDP, such a shock could place additional pressure on available fiscal space and require budget reallocations, additional borrowing or external support.

Coastal flood impact on real GDP deviation from steady state

A major coastal flood could affect Côte d’Ivoire’s economy. Without resilience investment, real GDP is estimated to fall by up to 6% below its expected path. With resilience investment, the deviation is smaller, at around 4%, and the model shows a faster return toward the baseline.

In this scenario, the resilience investment pathway is associated with a smaller GDP deviation and faster recovery than the no-resilience pathway. Results should be interpreted within the assumptions of the simulation.

Public debt impact of a severe coastal flood scenario

A World Bank/IMF DSA simulation shows that a severe coastal flood shock could temporarily worsen Côte d’Ivoire’s debt trajectory. Under this scenario, public debt rises from around 53% to about 62% of GDP shortly after the shock, before gradually declining. This result should be read as a DSA stress scenario, not as directly comparable to the Oxford DIGNAD PML estimates, which use a different damage input, baseline and scenario design.

Coastal flood impact on total public debt (Debt Financing)

After a coastal flood, public debt would increase in both scenarios, but the outcome is very different depending on investment choices. Without resilience investment, debt could rise to around 68% of GDP and remain high over time. With resilience investment, debt would increase more slowly and stabilize at around 60% of GDP. Investing in resilience not only reduces losses, but also helps keep public debt under control in the long run.

Two approaches to funding disasters

Financing disaster losses differs significantly depending on the strategy adopted. Under a base approach, costs are split evenly between post-budget reallocations and ex-post sovereign borrowing, increasing fiscal pressure after the event. In contrast, a layered financing strategy shifts a larger share toward pre-arranged instruments, with over 60% covered through planned mechanisms, reducing reliance on costly post-disaster borrowing. The layering approach strengthens fiscal resilience by spreading risk, improving liquidity, and limiting long-term debt accumulation

Direct losses from floods on crops area: without and with protection measures

Flood losses to crop areas in Côte d’Ivoire are expected to increase significantly under future climate scenarios, but protection measures can substantially reduce these impacts. Annual flood losses to crop areas are expected to rise sharply under future climate scenarios, reaching up to USD 182 million per year under high warming. Protection measures can substantially reduce these recurring losses, especially when targeted to high-risk agricultural areas.

Source: CIMA Foundation, 2025 (unpublished paper)

Expected PML to physical assets

For a 1-in-100-year flood event, crop losses could reach up to USD 425 million under high warming. Strengthening flood protection could significantly reduce these extreme shocks, helping safeguard food production, rural livelihoods, and fiscal stability.

Source: CIMA Foundation, 2025 (unpublished paper)

Coastal flood losses and climate investment (%GDP)

According to IMF-cited World Bank estimates, the annual cost of coastal floods in Côte d’Ivoire is around 4% of GDP. Planned climate investment represents about 2.7% of GDP per year, including 1.5% for adaptation and 1.25% for mitigation. This means that even total planned climate investment remains below the estimated annual cost of coastal floods. The comparison should be read as an indication of scale, not as a one-to-one adequacy threshold: adaptation and mitigation spending do not need to equal annual losses every year to be effective. Rather, the figures highlight the importance of targeting adaptation finance toward the coastal risks, assets and locations where avoided losses could be greatest.

Adaptation benefit to poverty headcount (%)

Without adaptation, poverty reduction slows down under climate stress. Even in a high-growth pathway, poverty remains higher under climate-affected scenarios (dry-hot and wet-warm) compared to the baseline, showing that climate change can slow progress and increase inequality, especially for more vulnerable populations.

Adaptation benefit to poverty headcount (%)

With adaptation, poverty declines faster and more evenly across scenarios. The gap between climate scenarios and the baseline becomes smaller, meaning that adaptation helps protect vulnerable households and reduces inequality, supporting more inclusive growth despite climate risks.