In the face of decades of unsustainable development that has led to significant depletion of resources and environmental imbalances, the need for advanced methods to understand and mitigate adverse environmental effects has never been more critical. This study introduces an innovative approach using Artificial Neural Networks (ANN) to predict the biocapacity and ecological footprint, focusing on the forest land indicator in Latin America and the Caribbean up to 2030, aligning with the Sustainable Development Goals (SDGs). Utilizing the Python programming language and leveraging the TensorFlow library for its robustness in handling complex datasets, we designed a neural network model that underwent thirty thousand iterations to identify the optimal processing time, approximately five minutes per dataset. Our analysis includes 57 annual records across 128 countries, highlighting the region’s rich natural resources. The findings underscore the critical importance of developing sustainable business models that responsibly harness these resources, offering stakeholders fresh opportunities to engage in sustainable development practices actively. Moreover, the study serves as a vital roadmap for other developing regions aspiring to enhance their environmental sustainability strategies and climate change mitigation efforts. By accurately predicting biocapacity and ecological footprints, this research not only aids in the strategic planning of sustainable development but also sets a precedent for applying artificial intelligence in environmental science, offering a novel approach for policymakers and business practitioners alike in Latin America and the Caribbean. These findings provide a practical guide for policymakers and business practitioners to develop sustainable business models and enhance environmental sustainability strategies.

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.

Contiene: Capitulo 1. Turismo sostenible como herramienta de reduccion de la pobreza: ensenanzas para los paises de America Latina y el Caribe / Jenny Paola Danna-Buitrago, Remi Stellian, Andres Fernando Garzon Garzon, David Velandia Ayala -- Capitulo 2. Del turismo de masas al turismo sostenible: hacia un enfoque integrativo del turismo comunitario / Jenny Paola Danna-Buitrago, Rosalia Burgos Doria, Alvaro Luis Mercado Suarez -- Capitulo 3. Contribucion del turismo comunitario integrativo en el desarrollo sostenible de las comunidades: algunos casos de exito / Jenny Paola Danna-Buitrago, Rosalia Burgos Doria, Laura Fabiola Alvarez, Maria Andreina Moros Ochoa -- Capitulo 4. Turismo en Colombia durante el conflicto armado y oportunidades para la implementacion del turismo comunitario integrativo en el posconflicto / Melva Ines Gomez-Caicedo, Adriana Milena Gasca Cardozo, Pedro Nel Paez Perez -- Capitulo 5. La innovacion en la calidad del servicio como elemento de sostenibilidad en el sector turistico. Caso de estudio en hoteles de Bogota / Maria Andreina Moros Ochoa, Gilmer Yovanni Castro Nieto, Mercedes Gaitan Angulo, Pedro Nel Paez Perez

The increasing use of online hospitality platforms provides firsthand information about clients preferences, which are essential to improve hotel services and increase the quality of service perception. Customer reviews can be used to automatically extract the most relevant aspects of the quality of service for hospitality clientele. This paper proposes a framework for the assessment of the quality of service in the hospitality sector based on the exploitation of customer reviews through natural language processing and machine learning methods. The proposed framework automatically discovers the quality of service aspects relevant to hotel customers. Hotel reviews from Bogotá and Madrid are automatically scrapped from Booking.com. Semantic information is inferred through Latent Dirichlet Allocation and FastText, which allow representing text reviews as vectors. A dimensionality reduction technique is applied to visualise and interpret large amounts of customer reviews. Visualisations of the most important quality of service aspects are generated, allowing to qualitatively and quantitatively assess the quality of service. Results show that it is possible to automatically extract the main quality of service aspects perceived by customers from large customer review datasets. These findings could be used by hospitality managers to understand clients better and to improve the quality of service.

While prior research has looked at big data's role in strengthening the environmental justice movement, scholars rarely examine the contexts, mechanisms and processes associated with the use of big data in monitoring and deterring environmental offenders, especially in the Global South. As such, this research aims to substitute for this academic gap through the use of multiple case studies of environmental offenders' engagement in illegal deforestation, as well as legal deforestation followed by fire. Specifically, we have chosen four cases from three economies in the Global South: Indonesia, Peru and Brazil. We demonstrate how the data utilized by environmental activists in these four cases qualify as true forms of big data, as they have searched and aggregated data from various sources and employed them to achieve their goals. The article shows how big data from various sources, mainly from satellite imagery, can help discern the true extent of environmental destruction caused by various offenders and present convincing evidence. The article also discusses how a rich satellite imagery archive is suitable for analyzing chronological events in order to establish a cause-effect chain. In all of the cases studied, such evidentiary provisions have been used by environmental activists to oblige policy makers to take necessary actions to counter environmental offenses.