Using Data to Safeguard Vulnerable Communities from Climate Risks

Granular data helps create tailored policies to improve infrastructure and effectively allocate assistance in high flood-risk areas. Photo credit: ADB.

Share on:           

Published:

Analysis of geographically granular data is essential to identify vulnerable areas and optimize resource distribution.

Introduction

The severity of extreme weather events has significantly increased over time. A United Nations study showed that from 2000 to 2019, the world experienced 7,348 major disasters caused by natural hazards, resulting in 1.23 million fatalities—up from the 1.19 million deaths recorded between 1980 and 1999. In addition, economic losses from these events reached $2.97 trillion during the same period, a twofold increase compared to two decades earlier.

Floods, among the top climate-related risks, severely impact people’s livelihood and hinder economic prospects, often reversing years of progress in poverty reduction. The risk of more severe flooding could further increase and affect millions of people. An Intergovernmental Panel on Climate Change report forecasts sea levels to rise by up to 1.1 meters by 2100.

Asia and the Pacific are particularly vulnerable. In 2023, the region topped the list of the world’s most affected by weather, climate and water-related disasters, with floods and storms causing the greatest number of deaths and economic damage.

The “Data for Climate Action” theme of the Key Indicators for Asia and the Pacific 2024—Asian Development Bank’s (ADB) flagship statistical publication—underscores the importance of utilizing geographically granular data (data broken down by geographical area or unit) to address where poverty intersects with high climate vulnerability, including flooding risks. The data is crucial for policymakers to formulate actions to safeguard Asia and the Pacific’s most vulnerable population from climate change impacts.

Visualizing Poverty and Flood 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.

Addressing Statistical Gaps

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.

Strengthening Statistical Systems

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.

Arturo Martinez, Jr.
Senior Statistician, Economic Research and Development Impact Department, Asian Development Bank

Art Martinez works on Sustainable Development Goals indicator compilation, particularly poverty statistics and big data analytics. Prior to joining ADB, he was a research fellow at the University of Queensland where he also got his doctorate in Social Statistics.

Yating Ru
Economist, Economic Research and Development Impact Department, Asian Development Bank

Yating Ru works on interdisciplinary approaches that harness geospatial big data, data science tools, and economic methods to tackle development challenges related to poverty, food insecurity, and climate change. She earned her master’s degree in Regional Planning and PhD in Regional Science from Cornell University. Prior to joining ADB, she worked at the International Food Policy Research Institute in Washington, DC.

Follow Yating Ru on

Dennis Dizon
Consultant, Asian Development Bank

Dennis Dizon is an economics and statistics specialist at the Asian Development Bank. He is part of the team compiling various indicators for the ADB Key Indicators for the Asia and the Pacific report. Prior to joining ADB, he earned his bachelor’s degree in Geography and master’s degree in Statistics at the University of the Philippines-Diliman, and a master’s degree in Cartography at the Technical University of Munich.

Oshean Lee Garonita
Consultant, Asian Development Bank

Oshean Lee Garonita is a geospatial data scientist with the ADB team that helps enhance partners’ use of geospatial data, such as road quality and poverty data, for better policy making. Before joining ADB, he was an environmental planner and a geospatial technologist who worked across various sectors in government and the private sector. He holds a bachelor’s degree in Human Ecology from the University of the Philippines Los Baños and a graduate diploma in Urban and Regional Planning from UP Diliman.

Follow Oshean Lee Garonita on

Christian Leny Hernandez
Consultant, Asian Development Bank

Leny Hernandez is a monitoring and evaluation specialist who helped prepare ADB’s Key Indicators for Asia and the Pacific and the implementation of the pilot initiative on job skills survey. Prior to joining ADB, she worked at the National Economic and Development Authority and United Nations Development Programme. Leny holds a bachelor's degree in Human Ecology from the University of the Philippines Los Baños and a master’s on Development Studies from the International Institute of Social Studies in the Netherlands.

Raymond Adofina
Consultant, Asian Development Bank

Raymond Adofina is an economics and statistics specialist, working on compiling and analyzing data for the Key Indicators for Asia and the Pacific. His experience includes managing and visualizing economic and statistical data, and he has contributed to similar efforts at UNICEF Philippines. He holds a bachelor’s degree in Business Economics from the University of Santo Tomas.

Asian Development Bank (ADB)

The Asian Development Bank is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members—49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance.

Follow Asian Development Bank (ADB) on
Leave your question or comment in the section below:
Disclaimer

The views expressed on this website are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area.