(iPoster) Determinants of Bilateral Climate Aid Allocation: Analyzing OECD DAC

• In Event: Poster Session: International Political Economy II

Abstract

Introduction
This study explores the factors influencing bilateral climate aid allocation by OECD Development Assistance Committee (DAC) countries. It focuses on how donor countries’ economic interests, recipient countries’ economic needs, governance quality, and environmental vulnerability shape the distribution of mitigation and adaptation aid. By uncovering motivations behind aid flows, the research aims to propose strategies for enhancing the effectiveness of climate aid.


Significance of the Study
Climate aid addresses pressing challenges, including natural disasters and long-term low-carbon development goals, requiring international cooperation to mitigate the impacts of climate change. Despite the growing importance of climate aid, research on its determinants, motivations, and effectiveness remains insufficient. Existing studies often fail to integrate analyses of adaptation versus mitigation aid or differentiate bilateral from multilateral contributions among DAC members. This research seeks to fill this gap by investigating the unique factors driving bilateral adaptation and mitigation aid allocation, distinguishing these from general foreign aid patterns.


Literature Review and Hypotheses
The literature offers insights into donor motivations, recipient needs, and aid distribution dynamics. Neumayer (2003) highlights governance as critical for general aid allocation, while Barthel et al. (2014) explore export-driven competition and spatial dependencies. Cisneros and Ilbay-Yupa (2023) link trade to climate aid using advanced econometrics, and Moon (2022) examines green ODA with an emphasis on donor interests and international norms.
However, limitations persist. Studies like Neumayer (2003) and Barthel et al. (2014) focus on general aid, limiting their relevance to climate-specific allocations. Moon (2022) and Cisneros and Ilbay-Yupa (2023) fail to differentiate between mitigation and adaptation aid, overlooking distinct determinants. Furthermore, recipient vulnerability is often treated as a control variable rather than a primary determinant.

This study seeks to address these gaps through testing the following hypotheses:
- H1: Donor countries allocate more climate aid to recipients with significant trade ties.
- H2: Economically disadvantaged countries receive more climate aid, reflecting a prioritization of need.
- H3: High governance quality in recipient countries ensures more effective aid allocation.
- H4: Mitigation aid prioritizes trade relationships and environmental potential, while adaptation aid emphasizes recipient vulnerability.
This study posits that mitigation aid aligns with donor goals of fostering low-carbon markets, favoring recipients with high CO₂ emissions and carbon sink capacities (Halimanjaya, 2014; Yoon et al., 2023). In contrast, adaptation aid prioritizes recipients exposed to extreme weather, flooding, and sea-level rise, moderated by governance quality to ensure efficient use of resources (Betzold & Weiler, 2017; Mukherjee et al., 2022).


Methodology
This study employs dyadic-panel data for 32 DAC donor countries and their recipient countries (2002–2022). The dependent variable is bilateral climate aid, disaggregated into mitigation and adaptation aid. Independent variables include trade volume, GDP per capita, governance quality, and environmental vulnerability, alongside controls for geopolitical factors, GHG emissions, population size, and geographical distance.


Variables and Data Sources
- Dependent Variable: OECD bilateral climate change aid database.
- Independent Variables:
o Trade volume (CEPII BACI database).
o GDP per capita (World Bank).
o Governance quality (World Bank’s Worldwide Governance Indicators).
o Environmental vulnerability (ND-GAIN index).
- Control Variables:
o GHG emissions per capita (Our World in Data).
o Geopolitical factors (UN voting similarity, colonial ties, language commonality).
o Population size (World Bank).
o Distance between donor and recipient capitals (CEPII).


Statistical Techniques
Panel regression (fixed and random effects) will be used, guided by the Hausman test, alongside Tobit models to handle zero-aid flows. Lagged variables mitigate endogeneity concerns, and subgroup analyses will separately examine mitigation and adaptation aid determinants. This study provides a comprehensive framework to examine how donor and recipient characteristics influence climate aid allocation, advancing understanding of its determinants and improving the strategic distribution of resources.


Expected Contributions
This study provides a comprehensive framework to examine how donor and recipient characteristics influence climate aid allocation, advancing understanding of its determinants and improving the strategic distribution of resources.

Author

• Keun Young Oh, independent