Using Data to Safeguard Vulnerable Communities from Climate Risks

Analysis of data on poverty and flood risk assists policymakers in developing targeted strategies and allocating resources to reduce the poor’s vulnerability to severe flooding.

For example, the graph below outlines the level of riverine or coastal flood risk faced by poor communities across the Philippines and Thailand—two countries whose populations face an escalating threat from more extreme weather events driven by climate change.

The report shows that about 12.7 million Filipinos living below the national poverty line reside in regions with medium-to-high risk of river and coastal flooding. In contrast, Thailand has a smaller population of poor people (around 400,000) in such high-risk areas, with only the northern and northeast regions experiencing lower flood risks.

Figure 1: Intersection of Poverty and Flood Risk in the Philippines and Thailand

Note: National poverty incidence for the Philippines was 16.6% in 2018, while for Thailand was 7.8% in 2017. The specified years were chosen based on the availability of granular spatial data for poverty estimates. More recent poverty estimates for the Philippines and Thailand were only available at lesser spatial resolution or higher geographic aggregation levels. High coastal flood risk areas are areas tagged as medium-to-high, high, or extremely high in the coastal flood risk classification label provided in the source data.
Sources: Asian Development Bank analysis using various data sources. For water risk data: Aqueduct. Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators. For poverty incidence per 3-kilometer area data: Asian Development Bank. Mapping Poverty through Data Integration and Artificial Intelligence: A Special Supplement of the Key Indicators for Asia and the Pacific. For nation poverty incidence data: Philippine Statistics Authority and Thailand National Statistics Office.

Meanwhile, the maps below integrate satellite-based poverty data with geographically granular data on the risk of river or coastal flooding in the Philippines and Thailand reveal:

• In the Philippines, 59.6% of the analyzed land faced both high poverty levels and medium-to-high risk of river flooding, and 51% faced both poverty and medium-to-high risk of coastal flooding. Regions with the highest overlap were Visayas and Mindanao.
• In Thailand, 33% of the analyzed land area had high poverty rates and medium-to-high risk of river flooding, predominantly in the Northern and Western regions. Only 3.7% of the area showed an intersection of poverty and a medium-to-high risk of coastal flooding.

Figure 2: Intersection of Poverty and Flood Risk in the Philippines and Thailand

Note: National poverty incidence for the Philippines was 16.6% in 2018, while for Thailand was 7.8% in 2017. The specified years were chosen based on the availability of granular spatial data for poverty estimates. More recent poverty estimates for the Philippines and Thailand were only available at lesser spatial resolution or higher geographic aggregation levels. High coastal flood risk areas are areas tagged as medium-to-high, high, or extremely high in the coastal flood risk classification label provided in the source data.
This map was produced by the cartography team of the Asian Development Bank. The boundaries, colors, denominations, and any other information on this map do not imply on the part of the Asian Development Bank, any judgment on the legal status of any territory, or any other endorsement or acceptance of such boundaries, colors, denominations, or information.
Sources: Asian Development Bank analysis using various data sources. For water risk data: Aqueduct. Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators. For poverty incidence per 3-kilometer area data: Asian Development Bank. Mapping Poverty through Data Integration and Artificial Intelligence: A Special Supplement of the Key Indicators for Asia and the Pacific. For nation poverty incidence data: Philippine Statistics Authority and Thailand National Statistics Office.

Analyzing geographically granular data is essential for pinpointing vulnerable areas and optimizing resource distribution. Detailed data helps create tailored policies to enhance infrastructure and allocate financial assistance in high flood risk areas. It also promotes community participation by encouraging local involvement in formulating flood defense measures.

Granular data also aids in long-term planning by evaluating the effectiveness of existing programs and guiding development projects to mitigate future flood risks. By providing a better understanding of climate change vulnerability, detailed geographical data enhances resource optimization—a common challenge in many developing countries.

However, there are statistical gaps on climate vulnerability information systems that create blind spots for policymakers.

ADB’s survey from 29 national statistics offices revealed major concerns about geographic granularity of data for various core aspects of climate change vulnerability. Data granularity for buildings and infrastructure vulnerable to climate change and other vulnerability topics were rated “fair” at best. Only the topic of vulnerable population received a granularity rating higher than fair.

Figure 3: Rating of Geographic Granularity of Data for Specified “Vulnerability” Topics

Note: The height of each bar represents the number of respondents who answered “fair”, “insufficiently disaggregated” or “no response” when asked to compare the level of geographic granularity of indicators on climate change vulnerability.
Source: Asian Development Bank analysis using data from the bank’s 2024 Climate Change Data Granularity and Statistical Capacity Building Survey.

Another challenge is the complex and constantly evolving concept of “vulnerability” to climate change. While various frameworks and interpretations can be applied—ranging from focusing solely on exposure to hazards to incorporating elements of sensitivity to hazard exposure and adaptive capacity—the challenge lies in capturing the myriad of environmental, economic, social, and political factors that can influence climate change vulnerability.

The Global Set of Climate Change Statistics and Indicators, which provides a common statistical framework to streamline international reporting, reflects difficulties in standardizing vulnerability measures. Almost all statistical indicators with the term “vulnerable” (e.g., vulnerable species, vulnerable or fragile ecosystems, infrastructure vulnerable to climate change) are classified as lacking consistent definition and methodologically sound compilation procedure, and/or not having even economy-level data available.

National statistical systems are challenged by a lack of uniform definitions and methodologies to compile data on climate change vulnerability. For example, only 25% of vulnerability indicators in the Asia and the Pacific have available data, 38% in Africa.

Figure 4: Availability of Data on Climate Change Vulnerability

Note: Number of respondents per region: Africa = 12; Americas = 18; Asia-Pacific = 22; Europe = 24. Recreated from the results of the Global Consultation for the Global Set. Figures for Asia and the Pacific were the sum of weighted percentages from Asia and Oceania regions.
Source: United Nations, Economic and Social Council. Background Document to the Report to the Secretary-General on Climate Change Statistics: Global Consultation on the Global Set. 27 January.

It is crucial to enhance the capacity of national statistical systems to provide data-driven and actionable insights.

Among the reasons for the statistical challenges cited in the ADB survey included insufficient technical staffing, limited financial resources, methodological and technical difficulties, poor coordination with stakeholders, and lack of prioritization of climate change data.

There are several ways to address these issues:

• Develop a comprehensive national statistics plan to guide data collection, analysis, and dissemination, aligned with the national development agenda. This ensures the availability of high-quality, timely data for informed policymaking and to monitor development progress. Putting together the plan would also show the root causes of data gaps and allow national and international statistical bodies to take the necessary actions to correct these.
• Implement a dedicated climate change statistics program to prioritize essential indicators. This would identify existing data, promote data-sharing to avoid duplication, and enhance coordination among stakeholders, which would improve resource and expertise utilization.
• Invest in human resources to build a skilled workforce that can drive the creation of robust, reliable, and relevant climate statistics, contributing to policy decisions and sustainable development.
• Foster collaboration among governments, international bodies, academia, and the private sector to establish a unified standard for data management to facilitate coordination and data sharing, both within and between economies.

Asian Development Bank. 2024. Key Indicators for Asia and the Pacific 2024: Data for Climate Action.