论文标题
使用贝叶斯校正的文本分类，以估计气候适应融资的债权人报告系统中过度报告
Using Text Classification with a Bayesian Correction for Estimating Overreporting in the Creditor Reporting System on Climate Adaptation Finance
论文作者
论文摘要
发展资金对于资助气候变化适应至关重要，因此是国际气候政策的重要组成部分。但是，缺乏共同的报告实践使得很难评估此类资金的数量和分配。研究质疑报告数字的信誉，表明改编融资实际上低于已发表的数字所建议的。声称与气候变化适应相关的项目比目标变化更大，称为“过度报告”。为了估计在大型数据集中过度报告的现实速率，我们提出了一种基于最先进的文本分类的方法。迄今为止，对信誉的评估依赖于小型的手动评估样本。我们使用此类示例数据集以$ 89.81 \％\ pm 0.83 \％$（十倍的交叉验证）的精度训练分类器，并推断到较大的数据集以识别过度报告。此外，我们提出了一种包含较小，更高质量数据的证据的方法，以使用贝叶斯定理纠正预测速率。这使得对不同注释方案的比较可以估计气候变化适应性过度的程度。我们的结果支持发现，表明$ 32.03 \％$的广泛报告，可靠的间隔为$ [19.81 \％; 48.34 \％] $。
Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. % However, the absence of a common reporting practice makes it difficult to assess the amount and distribution of such funds. Research has questioned the credibility of reported figures, indicating that adaptation financing is in fact lower than published figures suggest. Projects claiming a greater relevance to climate change adaptation than they target are referred to as "overreported". To estimate realistic rates of overreporting in large data sets over times, we propose an approach based on state-of-the-art text classification. To date, assessments of credibility have relied on small, manually evaluated samples. We use such a sample data set to train a classifier with an accuracy of $89.81\% \pm 0.83\%$ (tenfold cross-validation) and extrapolate to larger data sets to identify overreporting. Additionally, we propose a method that incorporates evidence of smaller, higher-quality data to correct predicted rates using Bayes' theorem. This enables a comparison of different annotation schemes to estimate the degree of overreporting in climate change adaptation. Our results support findings that indicate extensive overreporting of $32.03\%$ with a credible interval of $[19.81\%;48.34\%]$.
