Using Text Classification with a Bayesian Correction for Estimating Overreporting in the Creditor Reporting System on Climate Adaptation Finance

There is international consensus on the need to respond to the global threat posed by climate change (Paris Accord, Article 2). Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. The 2009 Copenhagen Accord (UNFCCC, 2009) aimed to mobilize USD 100 billion by 2020. Implementation of climate change adaptation measures is one of five targets set to reach the 13th Sustainable Development Goal (SDG): "Take urgent action to combat climate change and its impacts". The Creditor Reporting System (CRS), maintained by the OECD Development Assistance Committee (DAC), monitors adaptation finance flows from OECD DAC member countries to developing countries. One of the challenges in ensuring valid reporting - or at least comparable figures - across reporting agencies is that the agreements mentioned above lack indicators. To this end, the OECD DAC established in 2009 the Rio markers on climate change adaptation (CCA). For each aid activity, donors report whether it contributes to CCA, i.e. reducing "the vulnerability of human or natural systems to the current and expected impacts of climate change, including climate variability, by maintaining or increasing resilience, through increased ability to adapt to, or absorb, climate change stresses, shocks and variability and/or by helping reduce exposure to them" (OECD DAC, 2022, p. 4). Activities are eligible for a marker if "a) the climate change adaptation objective is explicitly indicated in the activity documentation; and b) the activity contains specific measures targeting the definition above."