← Risk and Resilience Country Profiles
Jamaica
This UNDRR Global Risk and Resilience Metrics Country Profile brings together risk modelling, climate science and data analytics to support public sector risk-informed planning and investment.
The analysis is based on globally consistent datasets developed with partners and available through the Risk & Resilience Map Viewer Site. As not all datasets are currently available at the global scale, some datasets based on national data are presented at the end of the profile. Links to the selected national data resources are also provided.

Source: UNDRR, 2026 using UN Geospatial
Note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.
Current socio-economic context and recovery
Jamaica’s risk profile is shaped not only by hazards, but by underlying socio-economic conditions. With over half of the population living in urban areas, exposure to disasters is increasingly concentrated in cities, where infrastructure and services are under pressure.
At the same time, 38.9% of the population experiences multidimensional poverty, and specific groups — including children (22%), older persons, and people with disabilities (6.3%) — face heightened vulnerability to shocks. Early warning system coverage is moderate.

Source note: Indicator-level sources are shown in the graphic. For full metadata, including definitions, units, reference years, time coverage and source links, see the methodology.
CURRENT AND FUTURE RISK
1. Understanding current and future risk
Geographic distribution of hotspots for key hazards across the country
Jamaica’s risk landscape is strongly shaped by tropical cyclones. Their impacts are not limited to coastal areas as storms can cross the island, bringing high winds, heavy rainfall and disruption from the south and east towards northern parishes.
Flood risk is more localized but recurrent, especially in low-lying, riverine and urban areas, especially in the southern part of the country. Earthquake risk affects the whole island, with the greatest potential impacts in dense urban and economic centres such as the Kingston metropolitan area.
Drought is also a major and recurring hazard affecting food production in Jamaica. Further information on drought risk will be provided soon.

Source: UNDRR, 2026 using GEM Foundation (2026), Bloemendaal, Nadia et al (2020 STORM), JRC Global River Flood map (2024), and 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.
The first part of this analysis looks at modelled probabilistic risk from key hazards. Analysis factors in an average annual loss and probable maximum loss for the current costliest hazards.
For more information on probabilistic risk assessment and on data sources and approaches included in this analysis see the methodology section.
Average Annual Losses (AAL) by hazards
Tropical cyclones account for the largest share of the modelled AAL, at 71%, followed by earthquakes at 19%, floods at 8% and landslides at 1.7%.
Note: Drought is not included in this AAL distribution, but remains central to Jamaica’s risk profile given the importance of agriculture for livelihoods, employment and climate exposure. Further information on drought risk metrics will be provided soon.
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)
Losses from tropical cyclones are concentrated in buildings and key power and communications networks. Buildings record by far the highest estimated losses, at US$285.2 million annually. Among infrastructure sectors, telecommunications and power have the highest AAL, at US$47.8 million and US$45.0 million, respectively, followed by education infrastructure at US$35.2 million. Estimated losses are lower for ports and airports (US$5.9 million), roads and railways (US$3.9 million), water and wastewater (US$3.8 million), oil and gas (US$3.4 million), and health infrastructure (US$0.32 million).
Direct average annual losses (AAL) to infrastructure. (Second most costly hazard)
Losses from earthquakes are concentrated primarily in buildings, with smaller but notable losses across essential infrastructure networks. Buildings record by far the highest estimated losses, at US$88.2 million annually, followed by education infrastructure at US$10.7 million. Among network infrastructure, power has the highest AAL at US$6.0 million, followed by roads and railways (US$4.8 million) and telecommunications (US$3.9 million). Estimated losses are lower for water and wastewater (US$1.7 million), ports and airports (US$0.44 million), oil and gas (US$0.31 million), and health infrastructure (US$0.10 million).
Expected economic losses in 1-in-100 year event (Probable Maximum Loss - PML)
Tropical cyclones have the highest estimated PML, at around US$2.29 billion, followed by earthquakes at around US$1.55 billion. Floods show an estimated PML of around US$339.0 million, while landslides have the lowest estimated PML among the hazards shown, at around US$83.4 million.
Direct Average Annual Losses (AAL) to public infrastructure
Tropical cyclones account for the highest estimated losses in several sectors, including telecommunications at around US$47.78 million, power at US$45.03 million, education at US$31.63 million, and ports and airports at US$5.90 million per year. Earthquake-related losses are highest for education, at around US$9.65 million, followed by power at US$6.01 million, roads and railways at US$4.66 million, and telecommunications at US$3.93 million. Flood-related losses are lower across the sectors shown, while landslide losses are reported only for roads and railways, at around US$10.04 million per year.
Direct Probable Maximum Losses (PML) to public infrastructure
Tropical cyclones show the highest estimated losses in telecommunications at around US$191.5 million, power at US$174.7 million, ports and airports at US$26.3 million, water and wastewater at US$18.3 million, and oil and gas at US$12.7 million. For roads and railways, the highest estimated loss is associated with landslides, at around US$83.4 million, followed by earthquakes at US$62.1 million. Flood-related losses are lower across the sectors shown.
Direct Average Annual Losses (AAL) to housing
Tropical cyclones record the highest estimated losses, at around US$59 million for both the low-middle and middle income classes. Earthquake losses are estimated at around US$18.1 million for the low-middle income class and US$18.3 million for the middle income class, while flood losses are lower, at around US$8.6 million for both groups. Estimated losses for the high and low income classes are lower across all hazards shown.
NATIONAL VULNERABILITY PROFILE
2. Social and economic exposure
The analysis in this section looks at key socioeconomic indicators for the country that impact its vulnerability and exposure to disasters. It also includes modelled analysis of how this may impact recovery for specific socio-economic groups to financially recover, based on UNDRR - World Bank analysis. See the methodology for details.
Recovery speed of top/bottom income groups from a 1-in-100 year event
In Jamaica, a 1-in-100-year hazard event could reduce household consumption by between 29% and 39% for the lowest income quintile, and between 37% and 56% for the highest income quintile. Wind shows the largest estimated consumption loss for the highest income households, at around 56%, while earthquakes show the largest estimated loss for the lowest income households, at around 39%.
Recovery of household consumption and asset ownership resilience after an extreme event
Poorer households take around 1.4 years to recover 50 per cent of their consumption levels after an extreme event, compared with 0.8 years for richer households. This shows that disaster impacts differ not only in the level of consumption loss, but also in the time required for households to recover.
Time to recover 50% of consumption levels after a 1-in-100 year hazard event by households income quintile

Source: UNDRR based on GAR 2025 and World Bank, 2025
CLIMATE AND FINANCIAL STABILITY
3. Economic and financial instability risk
This section uses the IMF DIGNAD methodology to explore the impact of Jamaica's major disasters on the wider economy and specifically GDP losses and public debt. These results assume a 1-in-100 year event. See methodology for details.
Probable Maximum Losses of real GDP now and 2050
The overall results indicate that the direct damages from disasters pose significant risks to Jamaica’s economic growth, especially higher return period, more severe scenarios from cyclone and earthquake events. Through probabilistic damages, the real GDP growth is projected to reduce further, and in some cases with significant declines, with a different range depending on the disaster type. The PML impacts on growth range between -0.1 and -0.4% for floods, -0.6 and -1.3% for cyclones wind, -0.5 and -1.6% for cyclones storm surge, and -0.2 and -2.0% for earthquakes. The primary sources of physical damages stem from commercial, industrial, and residential buildings, as well as power- and telecommunication-related critical infrastructure.
Chance of a disaster exceeding public financing capacity (Fiscal gap)
Jamaica has demonstrated strong fiscal discipline and sustained public debt reduction in recent years. However, Oxford DIGNAD modelling shows that severe disaster scenarios can still place upward pressure on debt trajectories. Under the model baseline, public debt starts at around 90% of GDP in 2022. A 1-in-100-year flood scenario raises the debt ratio only moderately, while cyclone wind, cyclone storm surge and earthquake scenarios produce larger increases, with the earthquake scenario reaching the highest trajectory at around 92.8% of GDP. This represents an increase of up to around 2.8 percentage points relative to the model baseline.
Sovereign debt due to an extreme event
Tropical cyclone has the largest relative scale across the indicators shown, equivalent to 10.3% of Jamaica’s 2025 GDP, 34.2% of annual government revenue, 30.4% of annual public expenditure and 15.2% of gross public debt. Earthquake is the second largest hazard-specific PML, followed by flood and landslide.
A loss of this scale could create significant pressure on fiscal space, debt management and post-disaster financing needs, particularly if a large share of losses required public support or reconstruction financing. The comparison should be read as a scale indicator, not as a direct estimate of how much public debt would increase after the event.
RESILIENCE OPTIONS
4. Current resilience investment
This section draws on information compiled in UNDRR's Finance Observatory. It provides a snap-shot of current investment in risk reduction and resilience building, and the extent of anticipatory and resilience finance available based on publicly available data.
For more information on the criteria and scaling used for these financial indicators, see the methodology for details.
The costed DRR financing strategy is reported at US$1.35 million per year. Pre-arranged financing includes a US$150 million catastrophe bond and US$285 million in contingent credit. Average humanitarian relief expenditure over 2020–2024 was US$4.2 million per year, equivalent to 6.5% of total ODA.
5. Potential for loss and volatility reduction
This section draws on a series of country-specific diagnostic studies and policy assessments for the country. Together, they explore the potential to reduce disaster losses and economic volatility, and illustrate how resilience measures can change fiscal, economic, social and environmental outcomes.
Unlike the globally comparable metrics presented earlier in the profile, these studies use different models, assumptions, reference years and data sources, and may incorporate national calibrated information. As we build out the dataset through the Facility in the interim, the data below is based on national data and detail hypothesis.Their results should therefore be interpreted within the scope of each individual study and should not be directly compared with one another or with the global metrics.
For further information on the methodology, assumptions and sources used in each analysis, see the methodology page and the source links beneath each chart.
Bank solvency / capital adequacy considering Average Annual Losses from top hazards
For both securities dealers and deposit-taking institutions, this indicates that a major shock could lead to reduced credit supply, as financial institutions adjust to higher risk and tighter conditions. This highlights a key transmission channel through which disasters can affect the wider economy, by constraining access to finance during recovery.
A severe disaster shock would significantly affect financial sector balance sheets. The increase in capital adequacy ratios following a 1-in-100-year event reflects a contraction in lending and risk-weighted assets, rather than an improvement in financial strength.
Disaster losses to assets and consumption
Disaster risk in Jamaica is significantly larger when measured in terms of well-being rather than physical assets. While expected losses to assets represent around 1.5% of GDP, the impact on household consumption rises to over 2.5% of GDP, reflecting how disasters disproportionately affect incomes and livelihoods. In the modelled resilience scenario, expected asset losses decline only slightly (from 1.46% to 1.39% of GDP), whereas well-being losses fall much more markedly (from 2.58% to 2.01% of GDP). This illustrates that strengthening socioeconomic resilience primarily reduces the welfare consequences of disasters, even when physical asset losses change relatively little.
Household consumption resilience after a severe shock
Under the modelled severe shock scenario, households retain 57% of their pre-disaster consumption level under current conditions. In the resilience scenario, households retain 69% of their pre-disaster consumption. This means that the modelled consumption loss is reduced from 43% to 31%, a reduction of approximately 28%, illustrating how socioeconomic resilience can lessen the welfare impacts of disasters.
Avoided annual flood damage from mangroves
Without mangroves, annual damages are estimated at around US$136 million. With existing mangroves, estimated annual damages fall to around US$103 million. The difference represents approximately US$33 million in avoided annual flood damage.
Assets protected by mangroves under extreme flood scenarios
Coastal ecosystems provide substantial protection against extreme events. In 2013, Jamaica had around 9,800 hectares of mangroves, mostly located along the south coast. Avoided losses by these are estimated at around US$386 million for a 1-in-100-year event and up to US$2.4 billion for a more extreme 1-in-500-year event.

Source: World Bank, 2019
Links to the National Government DRR analysis
The Government of Jamaica is developing its geospatial analysis platform which is not yet publicly accessible. More information on Jamaica's DRR efforts are accessible on the government's website.
Disclamer
The results presented are based on probabilistic risk modelling and forward-looking climate and economic projections. Average Annual Loss (AAL) and Probable Maximum Loss (PML) estimates reflect expected losses over long time horizons rather than specific events or years. As with all model-based analyses, results depend on assumptions related to hazard frequency and intensity, exposure, vulnerability, and future socioeconomic pathways.These findings should be interpreted as indicative of relative risk patterns and potential magnitudes, rather than precise forecasts. Methodologies to expand coverage of hazards such as droughts, heatwaves and wildfires are currently under development.