No Plan, No Aid?

This paper develops an integrated theoretical and empirical framework to examine the impact of National Adaptation Plan (NAP) adoption on adaptation finance flows, a key mechanism in global climate finance. In the theoretical approach, we build an innovative general equilibrium model that captures the dynamic interplay between recipient-side decision-making and donor strategic interactions. Our model delineates how NAP adoption simultaneously signals reduced vulnerability and enhanced institutional capacity, thereby affecting donor allocation decisions. Transitioning to the empirical analysis, we first apply a staggered difference-in-differences (DiD) approach to exploit both temporal and cross-country variations in adaptation aid. Recognizing the limitations of conventional methods in addressing high-dimensional confounders and the endogeneity of treatment, we further refine our estimation with a novel double machine learning (DML) technique. This approach leverages the Neyman-orthogonality property and cross-fitting to robustly control for model misspecification. While traditional DiD estimates initially suggested a counterintuitive decline in aid post-NAP adoption, our DML analysis reveals a significant positive effect—thereby reshaping our understanding of policy signals and donor behavior. These findings provide critical insights for policymakers and donors, suggesting that well-designed NAPs can effectively enhance a country’s capacity to attract adaptation finance.

Our analysis utilizes a staggered difference-in-differences approach to compare countries before and after NAP adoption, controlling for time-invariant and common factors. To address potential endogeneity and high-dimensional confounders, we employ a Double Machine Learning framework which leverages machine learning algorithms (e.g., LASSO, Gradient Boosting) to yield unbiased estimates.

The refined DML estimates reveal that NAP implementation is associated with a significant increase in adaptation aid, suggesting that the positive capacity signals outweigh the reduction in perceived vulnerability.

The study provides critical insights:

• For Recipient Countries: Strengthening institutional capacity and communicating clear adaptation strategies can enhance eligibility for increased funding.
• For Donors: Revising evaluation frameworks to capture both capacity improvements and shifts in vulnerability is essential for effective aid allocation.
• For Global Climate Finance: Coordinated donor strategies can mitigate the risks of aid reallocation and ensure sustained support for climate resilience initiatives.