Constant environmental deterioration is a problem widely addressed by multiple international organizations. However, given the current economic and technological limitations, alternatives that immediately and significantly impact environmental degradation negatively affect contemporary development and lifestyle. Because of this, rather than limiting population consumption patterns or developing sophisticated and highly expensive technologies, the solution to environmental degradation lies more in the progressive transformation of production and consumption patterns. Thus, to support this change, the objective of this article is to forecast the behavior of consumption and regeneration of biologically productive land until the year 2030, using a deep neural network adjusted to Global Footprint Network data for prediction, and to provide information that favors the development of local economic strategies based on the territorial strengths and weaknesses of each continent. The most relevant findings about biocapacity and ecological footprint data are: fishing grounds have the great renewable potential in the global consumption of products and focused on the Asian region being approximately 55% of the world’s ecological footprint; grazinglands indicate an exponential growth in terms of ecological footprint, however South America and Africa have almost 55% of the distribution in the world biocapacity, being great powers in the generation of agricultural products; forest lands show a decrease in biocapacity, there is a progressive and exponential deterioration of forest resources, the highest deficit in the world is generated in Asia; cropland presents an environmental balance between biocapacity and ecological footprint; and builtland generates great impacts on development and regeneration in other lands, indicating the exponential crisis that could eventually be established by needing more and more resources from large built metropolises to replace the natural life provided by other lands.

This paper centers on the role and economic effect of the factors that affect the endogenous growth of organizations. To this end, it analyzes the case of Colombian companies, whose economy has been characterized in Latin America by maintaining a traditional economic policy and sectors that seek to maximize the opportunities offered by international markets. This study seeks to identify and analyze the factors that influence the endogenous economic growth of organizations and that allow them to be sustainable over time. For this purpose, adaptation algorithms based on fuzzy logics and supervised and unsupervised learning methods were used, generating an adaptation strategy that allows classifying and knowing the endogenous growth of companies in the Colombian context and identifying the most relevant aspects to take into account. Finally, it was found that most Colombian companies focus their business policies on strengthening human capital, followed by innovation, financial resources and, to a lesser extent, strategic alliances.

To show the potential of non-commensurable fractional-order dynamical systems in modeling epidemiological phenomena, we will adjust the parameters of a fractional generalization of the SIR model to describe the population distributions generated by SARS-CoV-2 in France and Colombia. Despite the completely different contexts of both countries, we will see how the system presented here manages to adequately model them thanks to the flexibility provided by the fractional-order differential equations. The data for Colombia were obtained from the records published by the Colombian Ministry of Information Technology and Communications from March 24 to July 10, 2020. Those for France were taken from the information published by the Ministry of Solidarity and Health from May 1 to September 6, 2020. As for the methodology implemented in this study, we conducted an exploratory analysis focused on solving the fractional SIR model by means of the fractional transformation method. In addition, the model parameters were adjusted using a sophisticated optimization method known as the Bound Optimization BY Quadratic Approximation (BOBYQA) algorithm. According to the results, the maximum error percentage for the evolution of the susceptible, infected, and recovered populations in France was 0.05%, 19%, and 6%, respectively, while that for the evolution of the susceptible, infected, and recovered populations in Colombia was 0.003%, 19%, and 38%, respectively. This was considered for data in which the disease began to spread and human intervention did not imply a substantial change in the community.

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.

The world economy and technology have been affected by globalisation through an uneven process [1,2]. The second half of the 20th century has seen increased interdependence between countries, which...