Forecasting biocapacity and ecological footprint at a worldwide level to 2030 using neural networks

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; grazing lands 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 built land 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.

• Autores: Moros Ochoa, María Andreína; Castro Nieto, Gilmer Yovani; Quintero Español, Anderson; Llorente Portillo, Carolina
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2022
• Institución: Colegio de Estudios Superiores de Administración
• Repositorio: Repositorio CESA
• Idioma: eng
• OAI Identifier: oai:repository.cesa.edu.co:10726/5034
• Acceso en línea: http://hdl.handle.net/10726/5034; https://doi.org/10.3390/ su141710691
• Palabra clave: Biocapacity; Ecological footprint; Sustainable business models; Neural networks
• Rights: openAccess
• License: Abierto (Texto Completo)