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Polish rural areas face various social, economic and ecological problems. These processes greatly affect diversification of the standard of living in rural areas. The goal of the study was to assess spatial diversification of the standard of living in rural areas in Poland. It focused on all rural gminas as well as rural parts of rural-urban gminas. In the analysis, the standard of living was evaluated with Perkal’s synthetic index, with 12 variables concerning demographic and economic issues. The time span for the analysis was the period of 2003-2013. The highest standard of living was reported for rural areas located in the vicinity of urban agglomerations. The lowest standards of living were in typical rural gminas with poorly developed service functions. These were mainly depopulated areas from the outskirts of voivodeships. The Vistula River marked the dividing line in the standards of living in rural areas of Poland.

This study aims to assess the adoption and impact of Artificial Intelligence (A.I.) tools in higher education, focusing on a private university in Latin America. Guided by the question, \"What is the impact, as perceived by university students, of using Artificial Intelligence tools on various dimensions of learning and teaching within the context of higher education?\" the study employs a rigorously validated 30-item instrument to examine five key dimensions: 1) Effectiveness use of A.I. tools, 2) Effectiveness use of ChatGPT, 3) Student's proficiency using A.I. tools, 4) Teacher's proficiency in A.I. and 5) Advanced student skills in A.I. These dimensions form a synthetic index used for comprehensive evaluation. Targeting 4,127 students from the university's schools of Engineering, Business, and Arts, the study garnered 21,449 responses, analyzed using Confirmatory Factor Analysis for validity. Findings indicate a significantly positive impact of A.I. tools on student academic experiences, including enhanced comprehension, creativity, and productivity. Importantly, the study identifies areas with low and high A.I. integration, serving as an institutional diagnostic tool. The data underscores the importance of A.I. proficiency among both educators and students, advocating for its integration as a pedagogical evolution rather than just a technological shift. This research has critical implications for data-driven decision-making in higher education, offering a robust framework for institutions aiming to navigate the complexities of A.I. implementation.

Due to the increasing amount of events and the complexity of the relationships and synergies between the stakeholders involved in the events and the environment there is a need to understand the contribution of events to the area where they are hosted. This article aims to develop an innovative impact assessment system that allows the effects of the event to be assessed from a global sustainability perspective via a multistakeholder approach and tries to solve the problems of comparability and commensurability raised by integrating economic, sociocultural and environmental impacts. This novel impact assessment method creates an ad hoc synthetic index unique for each event. The impact categories are aggregated and weighted according to the specific realities of the region. This theoretical contribution involves measuring event impacts through a multiperspective and multistakeholder analytical methodology, allowing the effects to be more efficiently assessed and compared in monetary terms.

Multidimensional crisis phenomena (financial–economic, environmental and social), plaguing the international community, especially in the last 30 years, have intensified resentment towards traditional models of growth and socio-economic development. The European Commission has placed the idea of a green economy (GE) at the heart of the Europe 2020 strategy. This paper presents an assessment of the implementation of the green economy assumptions in EU countries in 2018, taking 2010 as the base year. Using taxonomic methods, a synthetic evaluation index (GEI—Green Economy Index) was constructed based on a multi-criterion set of 27 indicators. This paper attempts to answer the following questions: How green are the European economies? What are the main challenges in this context? The average value of the index for the EU countries decreased in the studied years from 0.3423 to 0.3294, which can be interpreted as a slowdown in the greening processes. The key recommendations for the upcoming years include the improvement of energy efficiency indicators, the further increase in the share of renewable energy sources in the energy balance. Moreover, a significant problem continues to be the high percentage of the population at risk of poverty or social exclusion, as well as low CO2 and resource productivity rates.

The aim of this study is to analyze the relationship between sustainability and tourism competitiveness and potential differences in these parameters between geographical regions. The Travel and Tourism Competitiveness Index (TTCI) of the World Economic Forum is most commonly used to measure tourism competitiveness, however, this index has been criticized by some academics. We propose a synthetic indicator (Imα) using the multicriteria double reference point method, which can measure tourism competitiveness more accurately by applying different degrees of substitutability among pillars. The Sustainable Development Index (SDG Index) frames the implementation of Sustainable Development Goals and was used to analyze sustainability data. The new tourism competitiveness index (Imα) was obtained at both the global and regional level. It is important to note that some countries have a different ranking in the regional and global tourism competitiveness indexes, which shows a different behaviors among regions. The relationship between sustainability and tourism competitiveness is positive in all the analyses performed, though it is stronger when calculated without allowing substitutability, especially when considering the regional index. These trends show the value of this regional study of tourism competitiveness, because in addition to helping managers develop strategies to improve tourism competitiveness, it allows them to know the effect that these strategies will have on sustainability.

This article aims to conceptualize and assess social cohesion in Mexico, taking into account their multilevel and multidimensional nature. To measure social cohesion levels and dimensions, we used the Social Welfare Survey (N = 2871) for the 16 municipalities in Mexico City. Then, we employed the DP2 distance method to construct a social cohesion index. In addition, we define social cohesion degrees (very high, high, medium, and low) to explore the relevance of each indicator. The findings suggest that the objective and subjective indicators at the meso‐level has the highest contribution, regardless of the territory or degrees of cohesion. This finding suggests that neighborhoods are well organized for solving everyday problems. Meanwhile, the willingness to help both friends and members of one’s network was not a priority, which can be explained by the suspicion toward other people and the prevalent economic conditions.

This paper aims to map the degree of implementation of the 15-min city model in a medium-sized city like Seville and analyze the demographic, economic, and structural characteristics that affect the varying degree of implementation of the model. To this end, facility density was estimated from 15-min walking isochrones for each census tract, and a synthetic index was calculated from the coefficients obtained for each type of facility using a Geographically Weighted Regression (GWR) model that takes into account the spatial variation in infrastructure availability. A second GWR model was used to study the spatial impact of several demographic, socio-economic and structural variables on the calculated synthetic index. The main results show residential neighborhoods with greater accessibility and infrastructure diversity have a higher degree of compliance with the 15-min city model, while the city’s most marginalized and vulnerable neighborhoods have a negative index. It also highlights the fact that the processes of touristification and gentrification of the city’s historic center contribute to a lack of compliance with the model. These findings provide an empirical basis for designing urban policies aimed at reducing the territorial gap and towards equity in access to basic services.

This article aims to conceptualize and assess social cohesion in Mexico, taking into account their multilevel and multidimensional nature. To measure social cohesion levels and dimensions, we used the Social Welfare Survey (N = 2871) for the 16 municipalities in Mexico City. Then, we employed the DP2 distance method to construct a social cohesion index. In addition, we define social cohesion degrees (very high, high, medium, and low) to explore the relevance of each indicator. The findings suggest that the objective and subjective indicators at the meso-level has the highest contribution, regardless of the territory or degrees of cohesion. This finding suggests that neighborhoods are well organized for solving everyday problems. Meanwhile, the willingness to help both friends and members of one’s network was not a priority, which can be explained by the suspicion toward other people and the prevalent economic conditions.

The article is devoted to the development of a methodical approach to modelling a synthetic indicator of the competitiveness of agricultural enterprises using the tools of neural networks.The authors used general scientific and special research methods, such as monographic, logical-theoretical, statistical and economic-mathematical, visualization, system analysis, taxonomy and neural network modelling, generalization, logical abstraction and conclusion generation. The study was based on materials from the State Statistics Service of Ukraine, scientific developments of foreign and domestic scientists on the defined topic, and financial statements of the agricultural enterprises of Vinnytsia region LLC «Ahrokompleks «Zelena dolyna», PJSC «Dashkivtsi», LLC «Selyshchanske», PE «Dary sadiv», PE «Fortuna» the main type of economic activity of which according to Classification of economic activities 01.11 – cultivation of cereals (except rice), legumes and oilseeds. The article develops and presents a non-classical approach to the assessment of the competitiveness of agricultural enterprises has been developed, which is based on the principles of neural network modelling. It allows to obtain a well-founded quantitative indicator, which can be easily interpreted into a linguistic evaluation on a three-level scale of competitiveness and used for comparison, monitoring and making sound decisions on improving the competitiveness of agricultural enterprises.The non-classical approach complements traditional methods of competitiveness assessment, expanding their capabilities and eliminating certain limitations. The use of neural network modelling in competitiveness assessment allows to take into account complex and non-linear relationships between different factors and indicators, which contributes to an increase in the objectivity and accuracy of competitiveness assessment, which in turn allows enterprises to make better decisions and improve their strategies to achieve success in the market.The results of the study can be used to support strategic decision-making in the agricultural sector, identify priority development directions, and improve the competitive strategies of enterprises and the functioning of business processes.

Introduction/Objectives: Poverty is a multidimensional phenomenon that affects various aspects of well-being across territories. This study aims to develop a Synthetic Municipal Poverty Index (SMPI) for the state of Querétaro, with the objective of assessing socioeconomic disparities among municipalities and prioritizing areas for public policy intervention. Methodology: A quantitative approach with a deductive, non-experimental design was employed. Socioeconomic data from 16 municipalities in Querétaro were collected from official sources such as INEGI and CONEVAL. The SMPI was constructed using Principal Component Analysis (PCA), a technique applied to determine the weights of selected indicators. Index validation was carried out through hierarchical clustering and the XGBoost machine learning model, ensuring the robustness and accuracy of the index. Results: The findings reveal significant disparities in poverty levels across the analyzed municipalities. Income and food-related sub-indices emerged as the main determinants of poverty. Validation using the XGBoost model demonstrated high predictive performance of the SMPI, reinforcing its value as an analytical tool. Conclusions: The SMPI serves as a reliable instrument for measuring multidimensional poverty at the municipal level. Its application supports the identification of priority areas and contributes to the design of more targeted and effective public policies. This index provides a solid framework for evidence-based decision-making and the development of comprehensive strategies to reduce poverty at the territorial level. RESUMEN: Introducción/Objetivo: La pobreza es un fenómeno multidimensional que impacta diversos aspectos del bienestar en los territorios. Este estudio tiene como propósito desarrollar un Índice Sintético de Pobreza Municipal (ISPM) para el estado de Querétaro, con el fin de evaluar las desigualdades socioeconómicas entre municipios y priorizar áreas de intervención en materia de política pública. Metodología: Se adoptó un enfoque cuantitativo, con un diseño deductivo y no experimental. Se recopilaron datos socioeconómicos de 16 municipios del estado de Querétaro, provenientes de fuentes oficiales como el INEGI y el CONEVAL. La construcción del ISPM se realizó mediante Análisis de Componentes Principales (ACP), técnica utilizada para asignar ponderaciones a los indicadores seleccionados. La validación del índice se llevó a cabo mediante análisis de conglomerados jerárquicos (clustering) y el modelo de aprendizaje automático XGBoost, lo que permitió asegurar su robustez y precisión. Resultados: Los resultados revelan marcadas disparidades en los niveles de pobreza entre los municipios analizados. Los subíndices relacionados con ingresos y alimentación emergieron como los principales determinantes de la pobreza. La validación con el modelo XGBoost mostró una alta capacidad predictiva del ISPM, lo cual respalda su utilidad como herramienta analítica. Conclusiones: El ISPM constituye un instrumento confiable para medir la pobreza multidimensional a escala municipal. Su aplicación facilita la identificación de áreas prioritarias y contribuye al diseño de políticas públicas más focalizadas y efectivas. Este índice ofrece un marco sólido para la toma de decisiones basadas en evidencia y para la formulación de estrategias integrales orientadas a la reducción de la pobreza en el territorio.