Fuzzy classification of countries according to their sustainable economic potential: application of the Solow-Swan and Green Solow models.

The pollution and the progressive depletion of resources are global problems that affect all humanity, but their approach is affected by the different sociopolitical and economic conditions present in the different regions. Thus, the objective of this work is to implement and analyze a methodological structure based on the Solow/Green-Solow models that facilitates the identification and organization of countries according to their economic capacity and the polluting emissions associated with their development, with the purpose of promoting the application of more appropriate and effective solutions, considering the diversity of challenges faced by different economies. To achieve this objective, a preliminary guide is proposed to compare and group viable strategies in different territories, evaluating them according to the parameters established by the Solow-Swan and Green Solow models. For the recognition of the parameters, the Levenberg-Marquardt algorithm has been used, the result of which is classified using the fuzzy artificial intelligence method known as c-means, which provides a category for the different countries. The key findings of this article highlight how the particularities of countries are influenced not only by their geographic location, but also by their level of income and the calculated elasticity related to product exports, together with their ecological potential. It also challenges the widely held belief that enrichment is linked to increased carbon emissions.