Assessing Poverty Reduction Efforts in the European Union: A Novel Analytical Approach

This article presents a framework supported by multi‐criteria decision‐making (MCDM) techniques to monitor and assess regional progress on SDG 1 (No Poverty) within European Union (EU) countries. The analysis is based on data from 27 EU member states covering the period 2018–2022. Missing data were addressed using the average predictions derived from five machine learning methods: linear models, neural networks, support vector machines, decision trees, and Gaussian process regression. The Simultaneous Evaluation of Criteria and Alternatives (SECA) method was employed to determine the objective weights of the criteria, while the Multi‐Criteria Ranking by Alternative Trace (MCRAT) method was used to compute country performance scores. The robustness of the results was validated through sensitivity analysis. The findings reveal significant disparities in poverty reduction performance across countries, with Cyprus, the Czech Republic, Finland, and others ranking highest, while Spain, Bulgaria, and Romania appear among the lowest performers. The study offers a flexible monitoring tool to support policymakers in aligning regional progress with the SDGs. The findings of the study provide comparative insights into the effectiveness of poverty reduction policies, offering strategic decision support for policymakers. Within this framework, it contributes to a more efficient allocation of resources in economic policies and the strengthening of regional cooperation.