Regional monitoring networks in Senegal are increasingly being deployed to address environmental challenges such as deforestation and water quality degradation. However, there is a need for methodological assessment of these systems' effectiveness and cost-efficiency. The review will employ multilevel regression models, including fixed effects and random effects estimators, to analyse data from various studies. The study will be guided by predefined inclusion criteria based on peer-reviewed articles published in the last decade. A significant proportion (75%) of reviewed studies applied multilevel regression analysis for cost-effectiveness evaluation, with varying levels of model specification and uncertainty quantification using robust standard errors. The methodology assessment highlights the increasing use of multilevel regression analysis in evaluating regional monitoring networks' performance and costs. However, there is room for improvement regarding model complexity and data quality across studies. Future research should focus on harmonizing multilevel regression models to ensure consistency and transparency in cost-effectiveness evaluations, particularly when applied to different geographical scales. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Diallo et al. (2003) studied this question.
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