Bridging the Gap: A Study of Data Gaps and Their Causes of SDG 13 in ASEAN Countries

Purpose: This study aims to analyze the missing data patterns of SDG 13 (Climate Action) indicators across ASEAN countries and to identify the structural factors contributing to such gaps. Originality: As the climate crisis intensifies, accurately assessing SDG 13 im- plementation is vital for effective policymaking and international cooperation. However, in ASEAN, structural and institutional limitations have resulted in high missing data rates, undermining the indicators’ policy relevance and utility. Methodology: To address this issue, the study uses an explanatory sequential design, first analyzing missing rates for five SDG 13 sub-indicators (13.1.1, 13.1.2, 13.1.3, 13.2.2, 13.3.1) from 2015-2024 across ten ASEAN countries, then qualitatively exploring structural causes. Descriptive statistics, heatmaps, and policy document analysis were used to examine spatiotemporal patterns and institutional contexts. Result: The results reveal that all five sub-indicators of SDG 13 show high missing rates, with 13.3.1 (96.25%), 13.2.2 (76%), 13.1.2 (71%), 13.1.3 (68.33%), and 13.1.1 (59.4%) in descending order. At the country level, Laos, Brunei, and Singapore recorded missing rates exceeding 90%, whereas the Philippines and Malaysia showed relatively lower rates of 40-50%. Qualitative analysis further identified the major causes of missing data at three levels: Government Will and Strategy (Policy Priority, National Data Collection and Monitoring Systems, and Financial Capacity), Variability of Indicators (Ambiguity in SDG Indicator Concepts and Measurements, and Differences Between Global Indicators and Localized SDG Indicators), and Data Governance System (UN Data Collection and Reporting System, and International Coordination Mechanisms). Conclusions and Implication: These findings suggest that missing data in SDG 13 is due to complex institutional and political dynamics, rather than soley technical issues. Thus, conceptual clarity and contextual adaptation in indicator design are essential. A multi-level approach is needed—strengthening statistical capacities, improving international coordination, and aligning policy goals with indicator frameworks. Closing data gaps requires advancing both data-informed policymaking and institutional support systems.