## 2 Materials and Methods
### Construction of Indicator System
For example, based on [PERSON]'s research, the \"GDP growth rate\" is retained instead of the \"GNP growth rate\" because it is more closely related to national competitiveness; finally, regional adaptation is made, and the indicators need to reflect the unique challenges of French-speaking African countries, such as incorporating \"population electricity use rate\" to address the problem of insufficient infrastructure. The 30 indicators finally determined balance, comprehensiveness, and feasibility. The selected indicators are all supported by theory and empirical evidence. The core competitiveness index \"R&D expenditure as a percentage of GDP\" (X6) is based on productivity growth, which is a key driver of competitiveness [44]; the basic competitiveness index \"Railway density (km per thousand square kilometers)\" (X15) is selected as an alternative indicator of logistics efficiency due to its wider data availability [45]; the auxiliary competitiveness index \"Government effectiveness index\" (X28) is based on [PERSON] et al. ([PERSON] et al. ), The number of indicators selected is consistent with the regional research in this study. Related research shows that the competitiveness assessment of developing regions usually uses 25-35 indicators to adapt to data limitations [46]. Evaluation of Factors Affecting Competitiveness of French-Speaking African Countries Based on Adaptive LASSO Algorithm  
The core reason for choosing the adaptive LASSO algorithm is that it can specifically address the issues of high-dimensional data and multicollinearity in competitiveness assessment, and it significantly outperforms other alternative algorithms in terms of methodological characteristics [47]. The traditional LASSO algorithm imposes equal penalties on all variable coefficients, which may lead to the incorrect elimination of important variables with small initial coefficients and is not suitable for the scenario in competitiveness research where there are significant differences in variable importance [48]. Nonlinear machine learning algorithms, although capable of capturing complex relationships, have a \"black-box\" characteristic in their model mechanism, making it difficult to clearly explain the specific impact of each factor on competitiveness and thus not conducive to the precise formulation of subsequent policy recommendations [49]. Principal Component Analysis (PCA) simplifies the data structure by reducing dimensions and combining multiple indicators into composite components [50]. However, it masks the economic meaning of individual indicators and cannot directly identify the specific factors that play a key role in competitiveness. In contrast, the adaptive LASSO algorithm assigns differentiated weights to regression coefficients based on their initial estimates [51]. It imposes smaller penalties on important variables to retain their impact and larger penalties on less important variables to drive their coefficients towards zero. This not only enables precise screening of high-dimensional variables but also preserves the economic meaning of the original indicators and provides interpretable coefficient results. It perfectly meets the research's needs for accuracy in variable selection, interpretability of results, and relevance to policymaking, and thus becomes the optimal choice. French-speaking African countries occupy a unique position in the global economic landscape, but their competitiveness development faces challenges such as prominent regional heterogeneity and complex correlations between multi-dimensional indicators. In-depth exploration of the factors affecting their competitiveness is of key significance for identifying development bottlenecks and formulating differentiated policies. The current research involves 30 high-dimensional indicators such as economic level, scientific and technological strength, and infrastructure. Traditional linear models are difficult to achieve accurate variable screening while dealing with multicollinearity.