## 5 Conclusions
### Research Discussions  
To verify the robustness and generalizability of the proposed method, this study conducted a systematic comparison with a variety of alternative methods. In terms of variable selection, adaptive LASSO effectively reduces redundant variables compared to traditional LASSO, achieves accurate variable elimination compared to ridge regression, avoids the omission of core factors compared to random forest, and has a better model-fitting effect, which fully reflects its advantages in dealing with high-dimensional data and multicollinearity problems. In terms of multi-criteria decision-making methods, the comparison between TOPSIS and modern MCDM methods shows that it is more adaptable to the scarcity and heterogeneity of data in French-speaking African countries. Supplementary tests conducted by Python's pymcdm library show that the results of TOPSIS and SPOTIS are highly consistent and can better capture regional differences. The multi-tool benchmark test on the www.make-decision.it (accessed on 17 May 2025) platform further verifies that the competitiveness ranking based on TOPSIS is consistent with regional economic reality, which enhances the reliability of spatial pattern analysis. This study clarifies its own position in the existing academic system and fills in many research gaps. Existing research on African competitiveness focuses on the whole or a single country and pays insufficient attention to French-speaking Africa, a group with unique language, institutional, and economic connections. This study uses an integrated algorithm to systematically evaluate it for the first time. In terms of methodology, the combination of TOPSIS and adaptive LASSO is innovative, overcoming the limitations of a single method, not only achieving the ranking of comprehensive competitiveness but also accurately identifying key influencing factors, providing a new tool for regional economic analysis. At the same time, this study verifies the applicability of adaptive LASSO in data-scarce areas and the advantages of TOPSIS over modern MCDM methods, providing a methodological reference for related research on developing economies. The conclusions of this study provide specific suggestions for policymakers and China-Africa cooperation under the background of the \"Belt and Road Initiative\". Priority should be given to increasing R&D investment and the proportion of high-tech exports to strengthen innovation-driven growth, strengthen regional financial coordination, and enhance risk resistance through mechanisms such as the establishment of a regional foreign exchange reserve pool; promote China-Africa cooperation to build technology transfer centers and logistics corridors, make up for infrastructure and talent shortcomings, and help improve competitiveness. These strategies will help French-speaking African countries break through development bottlenecks and achieve sustainable development. However, this study also has certain limitations. Incomplete statistical data of some countries may affect the accuracy of indicators. In the future, non-traditional data such as satellite images and mobile phone data can be integrated to supplement official statistics. Static cross-sectional models are difficult to capture the long-term evolution of competitiveness, and panel data models or time series prediction methods need to be introduced to track time series changes. The method can be further expanded through multi-scenario simulation and integration of Bayesian adaptive LASSO to solve the problem of variable collinearity. In addition, future research can explore the impact of colonial heritage and the de-Frenchification process on competitiveness and improve the evaluation indicator system with more regional characteristics.