Companies’ E-waste Estimation Based on General Equilibrium Theory Context and Random Forest Regression Algorithm in Cameroon: Case Study of SMEs Implementing ISO 14001:2015

Given the challenge of estimating or calculating quantities waste electrical and electronic equipment (WEEE) in developing countries, this article focuses on predicting WEEE generated by Cameroonian small medium enterprises (SMEs) that are engaged ISO 14001:2015 initiatives consume (EEE) to enhance their performance profitability. The methodology employed an exploratory approach involving application general equilibrium theory (GET) contextualize study generate relevant parameters for deploying random forest regression learning algorithm predictions. Machine was applied 80% samples training, while simulation conducted remaining 20% based EEE utilized over a specific period, utilization rates, repair average lifespans. results demonstrate model’s predicted values significantly close actual WEEE, evaluated using mean squared error (MSE) yielding satisfactory results. Based model, both companies stakeholders can set realistic objectives managing companies’ fostering sustainable socio-environmental practices.